bmj open2 of unstable angina and non-st-elevation myocardial infarction 3 josien engel, msc* 1,...

For peer review only

A cross-sectional multicenter study of cardiac risk score use in the management of unstable angina and non-ST-

elevation myocardial infarction

Journal: BMJ Open

Manuscript ID: bmjopen-2015-008523

Article Type: Research

Date Submitted by the Author: 16-Apr-2015

Complete List of Authors: Engel, Josien; EMGO Institute for Health and Care Research / VU University Medical Center, Public and Occupational Health Van der Wulp, Ineke; EMGO Institute for Health and Care Research / Vu

University Medical Center, Public and Occupational Health de Bruijne, Martine; EMGO Institute, VU University Medical Center, Amsterdam, the Netherlands, Dept. of Public and Occupational Health Wagner, Cordula; NIVEL, Netherlands Institute for Health Services Research, ; EMGO Institute, VU University Medical Center, Amsterdam, the Netherlands, Dept. of Public and Occupational Health

<b>Primary Subject Heading</b>:

Health services research

Secondary Subject Heading: Cardiovascular medicine, Evidence based practice, Health services research

Keywords: acute coronary syndromes, risk assessment, guideline adherence, predictors, chart review

For peer review only - http://bmjopen.bmj.com/site/about/guidelines.xhtml

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A cross-sectional multicenter study of cardiac risk score use in the management 1

of unstable angina and non-ST-elevation myocardial infarction 2

3

Josien Engel, MSc*1

, Ineke van der Wulp, PhD1

, Martine de Bruijne, PhD1

, Cordula Wagner, 4

PhD 1,2

5

6

1

EMGO Institute for Health and Care Research, Department of Public and Occupational 7

Health, VU University Medical Center, Amsterdam, the Netherlands 8

2

NIVEL, Netherlands Institute for Health Services Research, Utrecht, the Netherlands 9

10

* Corresponding Author: 11

Josien Engel; EMGO Institute for Health and Care Research, Department of Public and 12

Occupational Health, VU University Medical Center; Van der Boechorststraat 7, 1081 BT 13

Amsterdam, The Netherlands; Tel: +31 20 4445452; E-Mail: [email protected] 14

15

Key words: acute coronary syndromes, risk assessment, guideline adherence, predictors, 16

chart review 17

18

Word count: main text: 2822 words, abstract: 285 words 19

Number of figures/tables: 6 20

21

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STRENGTHS AND LIMITATIONS 22

• This multicenter study is the first to provide thorough insight in the extent of cardiac 23

risk score use in clinical practice by reviewing patient charts in thirteen hospital 24

throughout the Netherlands. 25

• Generalized Linear Mixed Models were used to study hospital- and patient-related 26

factors associated with lower or higher cardiac risk score use. 27

• The results can serve as a basis for future improvement initiatives to enhance cardiac 28

risk score use in practice, by tailoring strategies to patient groups in which risk scores 29

are less often documented; 30

• It was not possible to study associations between the extent of cardiac risk score use 31

and the occurrence of adverse patient outcomes (e.g. death) due to practical 32

constraints; 33

• Using retrospective review of medical charts as the underlying method of data 34

collection made it impossible to determine the actual influence of cardiac risk score 35

use on clinical decision making of health care providers involved in the management 36

of patients with unstable angina or non-ST-elevation Myocardial Infarction. 37

38

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ABSTRACT 39

40

Background: Quantitative risk assessment in unstable angina (UA) and non-ST-elevation 41

myocardial infarction (NSTEMI), by using cardiac risk scores, is recommended in 42

international guidelines. However, a gap between recommended care and actual practice 43

exists, as these instruments seem underused in practice. Prior to creating future improvement 44

initiatives to increase cardiac risk score use, knowledge about the extent of this gap and 45

associated factors is necessary. 46

Methods: A retrospective chart review of UA and NSTEMI patients, discharged in 2012, in 47

thirteen hospitals throughout the Netherlands was conducted. Generalized Linear Mixed 48

Models were used to study the factors associated with cardiac risk score use. 49

Results: A total of 1788 charts were eligible for analysis. In 57% (n=1019) of the charts, 50

physicians documented the use of a cardiac risk score. Substantial variation between hospitals 51

was observed (16.7% to 87%), although this variation could not be explained by the presence 52

of on-site revascularization facilities or a hospitals’ teaching status. Obese patients (OR=1.49; 53

CI 95%= 1.03–2.15) and former smokers (OR=1.56; CI 95%= 1.15–2.11) were more likely to 54

have a cardiac risk score documented. Risk scores were less likely being used among patients 55

diagnosed with unstable angina (OR=0.60; CI 95%= 0.46–0.77), in-hospital resuscitation 56

(OR=0.23; CI 95%= 0.09–0.64), in-hospital heart failure (OR=0.46; CI 95%= 0.27–0.76) or 57

tachycardia (OR=0.45; CI 95%= 0.26–0.75). 58

Conclusions: Despite recommendations in cardiac guidelines, the use of cardiac risk scores 59

has not been fully implemented in Dutch practice. A substantial number of patients did not 60

have a cardiac risk score documented in their chart. Strategies to improve cardiac risk score 61

use should pay special attention to patient groups in which risk scores were less often 62

documented, as these patients may currently be undertreated. 63

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BACKGROUND 64

65

In the past decade mortality rates in acute coronary syndromes, including unstable angina 66

(UA) and non-ST-elevation myocardial infarction (NSTEMI), decreased significantly due to 67

substantial improvements in treatment possibilities.[1, 2] Despite these advancements, these 68

conditions still account for a large part of the annual deaths worldwide and are expected to 69

account for the largest disease burden worldwide by 2020.[3, 4] Part of these deaths may be 70

prevented, as it has previously been reported that a substantial number of patients were not 71

treated according to the current standards of care.[5-7] Patients with diabetes mellitus, renal 72

insufficiency, signs of heart failure and patients aged 75 years or older, were often neglected 73

guideline recommended care.[5] On the other hand, patients presenting to academic hospitals 74

and to hospitals with revascularisation facilities on-site (e.g. percutaneous coronary 75

intervention (PCI) or coronary artery bypass grafting (CABG)), were more often treated in 76

accordance to the guidelines.[6,8] Patients diagnosed with UA or NSTEMI can be treated 77

with medication or invasive procedures such as PCI or CABG. According to international 78

cardiac guidelines the decision to treat such patients with one or the other may be made on the 79

basis of a quantitative assessment of the patient’s risk of re-infarction or death.[9-11] To assist 80

clinicians in identifying patients at high risk of adverse cardiac events that would benefit most 81

from invasive therapies, several instruments have been developed.[9-11] The GRACE (Global 82

registry of Acute Coronary Events),[12, 13] TIMI (Thrombolysis in Myocardial 83

Infarction),[14] FRISC (fast revascularization in instability in coronary disease),[15] 84

PURSUIT (Platelet glycoprotein IIb/IIIa in Unstable angina: Receptor Suppression Using 85

Integrilin),[16] and HEART risk scores [17] are examples of validated cardiac risk scoring 86

instruments. In estimating risk, these instruments incorporate and combine several diagnostic 87

elements including a patients’ history, biomarkers and electrocardiogram findings, and can be 88

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used in the emergency department or coronary care unit. The predictive validity of these 89

instruments was reported to be good.[15, 18, 19] Previous research found that cardiac risk 90

scores were effective in identifying patients at high risk for cardiac events.[20, 21] However, 91

it has been suggested that cardiac risk scores are not routinely used in clinical practice, which 92

possibly contributed to the perpetuating treatment risk paradox.[20, 22, 23] In this paradox 93

patients with low risk of adverse cardiac events, opposite to cardiac guideline 94

recommendations, were more likely to receive invasive cardiac treatment compared to high 95

risk patients.[5, 24-29] To improve physicians’ risk estimation on the one hand and efficient 96

use of resources on the other, the present study aimed to determine the extent of cardiac risk 97

score use in Dutch clinical practice and to study factors associated with lower or higher 98

cardiac risk score use. 99

100

METHODS 101

102

This study concerns a cross-sectional multicenter study. A detailed description of the study 103

protocol has been published previously.[30] The study protocol was reviewed and approved 104

by the medical ethical committee of the VU University Medical Center Amsterdam. Where 105

required, approval from hospitals’ local ethics board were obtained. 106

107

Setting 108

Thirteen hospitals in the Netherlands participated in this study. By a multistage random 109

sampling procedure, initially 12 hospitals were selected from a pool of 40 hospitals that 110

participated in a nationwide quality improvement program. The program comprised several 111

themes, including the theme ‘Optimal care for Acute Coronary Syndromes’ which, among 112

other things, aimed to increase the application of cardiac risk scores in clinical practice.[31] 113

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Additionally, one hospital was selected to obtain optimal diversity in on-site revascularization 114

facilities and teaching status. The final sample consisted of two university hospitals, seven 115

tertiary teaching hospitals, and four general hospitals. Bed capacity in the hospitals varied 116

between 200 and 1200 beds. 117

118

Data collection 119

The primary study outcome was the extent to which cardiac risk scores were used in the 120

management of UA and NSTEMI patients. Data were collected monthly by means of 121

retrospective chart review. 122

Potentially eligible charts were selected from the hospitals’ billing system based on 123

diagnostic-related group codes for UA and NSTEMI. All patients discharged in 2012, 18 124

years or older, with a diagnosis of UA or NSTEMI (as confirmed in the discharge letter) were 125

considered for inclusion. Charts of patients who were transferred in from another hospital 126

were excluded, as these patients were initially treated elsewhere and therefore the necessary 127

data could not be obtained. In addition, charts of patients who provided insufficient 128

information regarding the discharge diagnosis, who were hospitalized for an elective 129

procedure, or who had an underlying illness or condition, other than a coronary stenosis, 130

causing UA or NSTEMI (e.g. anemia) were excluded. 131

Charts of patients were selected per month in chronological order of discharge until the 132

screening capacity of the chart abstractors was reached. Charts of potentially eligible patients 133

were manually reviewed to confirm a discharge diagnosis of UA or NSTEMI. In case a 134

patient’s final discharge diagnosis was unclear, a physician of the cardiology department was 135

consulted. The following patient-related and hospital-related information was registered on 136

standardized data extraction forms, including the following: demographic characteristics, 137

cardiac history, presence of cardiac risk factors, presenting symptoms, biochemical and 138

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electrocardiographic findings and treatment practices. In addition, information regarding 139

cardiac risk score use was registered, including the use of a validated risk score (yes/no), date 140

of application, type of risk score used and risk score outcome and classification. Besides 141

patient related information, the following hospital factors were registered: teaching status 142

(yes/no) and the presence of on-site revascularization facilities. 143

The data were entered into a database using fixed entry fields (BLAISE version 4.7, Statistics 144

Netherlands) and data reliability checks were conducted. To ensure reliable data extraction, 145

more than 5% (103/1933) of the charts were screened by two chart abstractors independently. 146

The total percentage of agreement between these abstractors was 95.1%, and ranged for the 147

variables of interest (Box 1) between 80.6% (electrocardiographic findings) and 100% 148

(gender), indicating good to excellent data reliability. 149

150

Box 1 Independent variables in Generalized Linear Mixed Model

Patient characteristics Patient characteristics Patient characteristics

Demographics

• Age

• Gender

Presenting factors

• Heart rate

• Systolic blood pressure

• Resuscitation at admission

• Cardiogenic shock

• In-hospital heart failure

• ST deviations on ECG

Discharge diagnosis

• UA

• NSTEMI

Cardiac history

• Coronary artery disease

• Peripheral vascular disease

• (Unstable) angina pectoris

• Acute myocardial infarction

• Previous CABG

• Previous PCI

• Revascularization/AMI < 6

months

Risk factors

• Diabetes mellitus

• Hypertension

• Renal failure

• Chronic heart failure

• Positive family history

• Smoking

• Former smoker

• Hypercholesterolemia*

• Obesity (BMI > 30)

• Coronary stenosis ≥50%

(in history)

Hospital characteristics

• Presence of revascularisation

options

• Teaching status

Abbreviations: ECG, electrocardiogram; UA, unstable angina; NSTEMI, non-ST-elevation myocardial

infarction; CABG, Coronary artery bypass graft surgery; PCI, Percutaneous coronary intervention; AMI, acute

myocardial infarction; BMI, body mass index. *Defined as statin use prior to admission, or described in

patients history (elevated cholesterol levels, hyperlipidaemia or hypercholesterolemia).

151

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Missing data 152

In total 1.5% of the values in the dataset were missing, ranging from 0.1% to 22% per 153

variable. Eleven variables had no missing values, including cardiac risk score use. Despite the 154

small amount of missing data and the spread of missing data in the dataset, a complete case 155

analysis would have led to a large loss of information and power. Therefore, missing values 156

were imputed using a multiple imputation procedure following the approach of van Buuren, 157

[32] resulting in five imputed datasets. In imputing missing values it was assumed that the 158

data were missing at random. The estimated values were corrected for the variables ‘hospital’ 159

and ‘cardiac risk score use’ as these variables were of primary interest in the analyses. By 160

means of the Kolmogorov-Smirnov test, and density and residual plots, it was determined, 161

whether the missing at random assumption was sustainable and the imputed values were 162

plausible. In addition, a sensitivity analysis was conducted by comparing the results from the 163

analyses of the imputed data with the results of a complete case analysis. Between these 164

models, only small differences were found. The missing value analyses and their imputations 165

were conducted in R (version 3.0.2 for Microsoft Windows) using the MICE package.[32, 33] 166

167

Data analysis 168

Sample characteristics were calculated using descriptive statistics, and included frequencies 169

and percentages for categorical variables, and means and standard deviations for continuous 170

variables. Associations of independent variables (box 1) with the use of cardiac risk scores 171

(yes/no) were studied with a generalized linear mixed model (GLMM), taking into account 172

the clustering of data within hospitals.[34] Odds ratios, that are based on median probabilities 173

over hospitals for cardiac risk score use, are presented. To facilitate interpretation, relevant 174

explanatory variables were transformed into categorical variables (i.e. age, heart rate and 175

systolic blood pressure). Furthermore, month of discharge was represented by a categorical 176

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variable with 12 levels in every model, to account for the fact that chart abstractors were 177

present to abstract data. In univariate analyses, associations between cardiac risk score use 178

and the independent variables were tested. All variables with a significance level of p≤0.15 179

were entered in a multivariable model. Variables significantly associated (p≤0.05) with 180

cardiac risk score use in the multivariable model were considered important in predicting risk 181

score adherence. In addition, based on previous literature two factor interactions with on-site 182

revascularization options, teaching status, age and gender were tested. All analyses were 183

conducted in R for windows (version 3.0.2) using the package lme4 on pooled data of five 184

imputed data sets.[33] The script of the pool function in MICE was rewritten for pooling 185

GLMM models. 186

187

RESULTS 188

189

Study population 190

A total of 1933 charts of patients with a confirmed diagnoses of UA or NSTEMI were 191

screened. Of these, 145 (7.5%) were excluded from the study as these concerned patients 192

transferred from one hospital to another, leaving 1788 patients for further analysis (Figure 1). 193

The majority (62.6%) of these patients had a discharge diagnosis of NSTEMI (Table 1). 194

Males accounted for 66.9% of the patients, and more than a third (35.9%) of the patients were 195

aged 75 years or older. Three quarters (75.3%) of the total population underwent coronary 196

catheterization. The average length of hospital stay was five days (SD 4.97). 197

198

199

200

201

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Table 1. Baseline characteristics (pooled data) (n= 1788) n (%)†

Age (<75 years) 1146 (64.1)

Gender (male) 1196 (66.9)

Discharge diagnoses (NSTEMI) 1119 (62.6)

Length of hospital stay (days) (mean ± SD) 5 ± 4.97

Systolic blood pressure (mmHg)

High (≥ 160) 552 (30.9)

Normal to slightly elevated (81-159) 1236 (69.1)

Low (≤ 80) 0 (0)

Heart rate (bpm)

Tachycardia (≥ 110) 103 (5.8)

Normal (51-109) 1634 (91.4)

Bradycardia (≤ 50) 51 (2.8)

Resuscitation at admission 33 (1.9)

Cardiogenic shock 7 (0.4)

In-hospital heart failure 103 (5.8)

ST deviations on electrocardiogram 810 (45.3)

History of coronary artery disease 252 (14.1)

History of peripheral vascular disease 131 (7.3)

Previous (U)A 432 (24.1)

Previous MI 499 (27.9)

Previous PCI 523 (29.3)

Previous CABG 289 (16.2)

MI or PCI/CABG 6 months prior to admission 125 (7)

Diabetes mellitus 451 (25.2)

Hypertension 936 (52.4)

Renal failure 88 (4.9)

Chronic heart failure 101 (5.7)

Hypercholesterolemia‡ 986 (55.1)

Obesity (BMI>30) 203 (11.3)

Smoking 427 (23.9)

Former smoker 350 (19.6)

Coronary stenosis (≥50%) 192 (10.8)

Positive family history 618 (34.6)

Coronary catheterization 1346 (75.3)

Management strategy

Pharmacological therapy 754 (42.2)

(scheduled) PCI 846 (47.3)

(scheduled) CABG 188 (10.5)

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†Data are presented in n(%), unless stated otherwise. ‡ Defined as statin use prior to

admission, or described in patients history (elevated cholesterol levels, hyperlipidaemia or

hypercholesterolemia).Abbreviations: n.a., not applicable; (U)A, (Unstable)Angina; MI,

myocardial infarction; PCI, percutaneous coronary intervention; CABG, coronary artery

bypass grafting; BMI, body mass index.

202

Cardiac risk score use 203

In 57% of the patient charts, a cardiac risk score was documented, though, substantial 204

variation between hospitals was observed i.e. 16.7% to 87% (Table 2). The variance 205

component for the random hospital effect in the GLMM ranged between 1.29 and 1.31 in the 206

five imputed datasets, confirming the great variety between hospitals in the use of cardiac risk 207

scores. When, for instance, the effects for two hospitals are equal to the 5 and 95 percentiles 208

of the normal distribution with variance 1.3 for hospital effects, the odds ratio of one hospital 209

relative to the other for cardiac risk score use is 42.6. 210

In univariate analyses, 15 patient related factors were significantly (p≤0.15) associated with 211

cardiac risk score use (Table 3). No significant associations with hospital related factors were 212

found (teaching status p=0.25, on-site revascularisation facilities p=0.67). In multivariable 213

analyses, patients with obesity (OR=1.49; 95%CI= 1.03-2.15; p=0.04) and former smokers 214

(OR=1.56; 95% CI=1.15-2.11; p≤0.01) were more likely to have a cardiac risk score 215

documented. Conversely patients with UA (OR=0.60; 95% CI=0.46-0.77; p≤0.01), in-hospital 216

heart failure (OR=0.46; 95% CI=0.27-0.76; p≤0.01), tachycardia (OR=0.45; 95% CI=0.26-217

0.75; p≤0.01) or who had been resuscitated at admission (OR=0.23; 95% CI=0.09-0.64; 218

p≤0.01) were less likely to have a cardiac risk score documented (Table 3). 219

220

221

222

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225

226

227

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235

236

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239

Table 2. Adherence to cardiac risk score use per hospital (pooled data)

Hospital

ID†

Teaching

status

Revascularization

options

Screened patient

charts n‡

Risk score use

n (%)

1 No No 84 14 (16.7)

2 Yes Yes 109 22 (20.2)

3 No No 110 26 (23.6)

4 No No 171 57 (33.3)

5 Yes Yes 132 46 (34.8)

6 Yes No 53 19 (35.8)

7 Yes Yes 145 79 (54.5)

8 Yes Yes 182 108 (59.3)

9 Yes Yes 96 68 (70.8)

10 Yes Yes 140 107 (76.4)

11 Yes Yes 108 87 (80.6)

12 No No 205 166 (81.0)

13 Yes No 253 220 (87.0)

Total -- -- 1788 1019 (57%)

† ranging from lowest to highest scoring hospital. ‡ Large variation in screened patient charts

per hospital is explained by differences in the amount of monthly admission for

UA/NSTEMI.

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Table 3. Univariate and multivariable associations between risk score documentation in patient charts and

hospital- and patient related factors (pooled data) (n=1788) †

Univariate associations Multivariable associations

OR (95% CI) P OR (95% CI) P

Hospital factors

Teaching status 2.15 (0.59 – 7.85) 0.25 n.a. n.a.

On-site revascularisation facilities 1.32 (0.38 – 4.60) 0.67 n.a. n.a.

Patient factors‡

Discharge diagnosis (reference NSTEMI) 0.65 (0.52 – 0.83) ≤0.01** 0.60 (0.46 – 0.77) ≤0.01**

Age (reference <75 years) 0.76 (0.61 – 0.96) 0.02* 0.86 (0.67 – 1.11) 0.24

Resuscitation at admission 0.25 (0.10 – 0.67) ≤0.01** 0.23 (0.09 – 0.64) ≤0.01**

In-hospital heart failure 0.38 (0.24 -0.62) ≤0.01** 0.46 (0.27 – 0.76) ≤0.01**

History of coronary artery disease 0.65 (0.47 – 0.89) ≤0.01** 0.87 (0.59 – 1.27) 0.46

History of peripheral artery disease 0.72 (0.47 – 1.09) 0.12 0.81 (0.53 – 1.26) 0.35

Previous (U)A 0.83 (0.64 – 1.07) 0.15 1.00 (0.75 – 1.34) 0.98

Previous MI 0.77 (0.61 – 0.98) 0.03* 0.89 (0.68 – 1.18) 0.43

Previous PCI 0.83 (0.65 – 1.05) 0.13 1.07 (0.80 – 1.43) 0.66

Renal failure 0.54 (0.33 – 0.90) 0.02* 0.72 (0.42 – 1.23) 0.23

Obesity (BMI>30) 1.49 (1.05 – 2.13) 0.03* 1.49 (1.03 – 2.15) 0.04*

Smoking 1.23 (0.95 – 1.60) 0.11 1.16 (0.86 – 1.55) 0.33

Former smoker 1.48 (1.12 – 1.97) ≤0.01** 1.56 (1.15 – 2.11) ≤0.01**

Coronary stenosis (≥50%) 0.65 (0.46 – 0.93) 0.02* 0.81 (0.54 – 1.22) 0.31

Heart rate (bpm) (reference Normal)

Tachycardia 0.46 (0.28 – 0.76) ≤0.01** 0.45 (0.26 – 0.75) ≤0.01**

Bradycardia 0.85 (0.44 – 1.63) 0.62 0.92 (0.46 – 1.86) 0.82

† pooled p-value based on normal approximation. ‡Only variables (patient characteristics) with p ≤ 0.15 in the

univariate analyses are presented in this table. Reference category is ‘no’, unless stated otherwise. *p-value ≤

0.05; **p-value ≤0.01. Abbreviations: OR, odds ratio; CI, 95% confidence interval; n.a., not applicable; PCI,

percutaneous coronary intervention; CABG: coronary artery bypass grafting; (U)A, (Unstable)Angina; MI,

myocardial infarction; BMI, body mass index.

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Interactions 241

Besides the interactions with on-site revascularization options, teaching status, age and 242

gender, it was decided to also test whether interactions with former smoker were present. This 243

was because an unexpected significant association between former smoker and risk score use 244

was found. Significant interactions were found between the variables former smoker and 245

discharge diagnosis (p=0.03), age and previous PCI (p=0.02), age and in-hospital heart failure 246

(p=0.04), age and history of peripheral artery disease (p=0.03), and age and heart rate 247

(p=0.04) (Table 4). 248

Looking at the interaction effects with age, it was found that patients aged 75 years or over 249

presenting with a previous PCI had a higher odds of cardiac risk score documentation 250

compared to patients without a previous PCI (OR=1.53; 95% CI=1.00 – 2.34; p=0.05). In 251

contrast, older patients were less likely to have a cardiac risk score documented in case they 252

presented with heart failure (OR=0.29; 95% CI=0.14 – 0.57; p<0.001), with a history of 253

peripheral artery disease (OR=0.47; 95% CI=0.24 – 0.91; p=0.02) or with tachycardia 254

(OR=0.20; 95% CI=0.08 – 0.52; p=<0.001). 255

256

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Table 4. Estimated odds ratio’s and 95% confidence intervals for significant (p≤0.05) interactions terms

added to multivariable model of GLMM (pooled data) †

OR CI 95% P-value‡

Former smoker*discharge diagnosis

Discharge diagnosis (UA vs. NSTEMI) within former smoker (no) 0.52 0.40 – 0.69 <0.001**

Discharge diagnosis (UA vs. NSTEMI) within former smoker (yes) 1.00 0.59 – 1.71 0.98

Former smoker (yes vs. no) within discharge diagnosis (UA) 2.28 1.43 – 3.61 <0.001**

Former smoker (yes vs. no) within discharge diagnosis (NSTEMI) 1.19 0.81 – 1.75 0.38

Age*previous PCI

Age (≥75 vs. <75 years) within previous PCI (no) 0.71 0.52 – 0.96 0.02*

Age (≥75 vs. <75 years) within previous PCI (yes) 1.30 0.84 – 1.99 0.23

Previous PCI (yes vs. no) within age (<75 years) 0.84 0.58 – 1.19 0.32

Previous PCI (yes vs. no) within age (≥75 years) 1.53 1.00 – 2.34 0.05*

Age*in-hospital heart failure

Age (≥75 vs. <75 years) within in-hospital heart failure (no) 0.92 0.71 – 1.20 0.54

Age (≥75 vs. <75 years) within in-hospital heart failure (yes) 0.32 0.12 – 0.85 0.02*

In-hospital heart failure (yes vs. no) within age (<75 years) 0.84 0.39 – 1.82 0.66

In-hospital heart failure (yes vs. no) within age (≥75 years) 0.29 0.14 – 0.57 <0.001**

Age*History of peripheral artery disease

Age (≥75 vs. <75 years) within history of peripheral artery disease (no) 0.93 0.72 – 1.21 0.60

Age (≥75 vs. <75 years) within history of peripheral artery disease (yes) 0.34 0.14 – 0.80 0.01**

History of peripheral artery disease (yes vs. no) within age (<75 years) 1.28 0.70 – 2.32 0.42

History of peripheral artery disease (yes vs. no) within age (≥75 years) 0.47 0.24 – 0.91 0.02*

Age*Heart rate

Age (≥75 vs. <75 years) within heart rate (normal) 0.91 0.70 – 1.18 0.47

Age (≥75 vs. <75 years) within heart rate (tachycardia) 0.28 0.09 – 0.85 0.03*

Age (≥75 vs. <75 years) within heart rate (bradycardia) 1.25 0.31 – 5.00 0.75

Heart rate (tachycardia vs. normal) within age (<75 years) 0.67 0.34 – 1.30 0.23

Heart rate (bradycardia vs. normal) within age (<75 years) 0.81 0.33 – 2.01 0.65

Heart rate (tachycardia vs. normal) within age (≥75 years) 0.20 0.08 – 0.52 <0.001**

Heart rate (bradycardia vs. normal) within age (≥75 years) 1.12 0.38 – 3.27 0.84

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†All four OR’s per interaction term are presented in the table to form an impression of the nature of the interaction.

For instance, two separate OR’s for former smoker no versus yes for UA and NSTEMI patients and two separate

OR’s for UA versus NSTEMI for former smoker no and yes. These four OR’s are all shown, because when

interaction between two factors is added to the model, the OR of one factor may depend upon the level of the other

factor. ‡P-value indicates if OR is significantly different from one. *p-value ≤ 0.05; **p-value ≤0.01.Abbreviations:

OR, odds ratio; CI 95%, 95% confidence interval; UA, unstable angina; NSTEMI, non-ST-elevation myocardial

infarction.

258

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DISCUSSION 259

260

This study aimed to provide insight in the extent of cardiac risk score use in Dutch hospitals 261

as recommended by international cardiac guidelines. In addition, associations with patient and 262

hospital related factors were studied. Substantial variation between hospitals’ cardiac risk 263

score use was observed, with in approximately 40% of patient charts a cardiac risk score was 264

not documented. Several patient related factors including a diagnosis of UA, the presence of 265

in-hospital heart failure, tachycardia, and resuscitation at admission were associated with a 266

lower likelihood of cardiac risk score use. Also the likelihood of cardiac risk score use was 267

often lower in older patients (≥ 75 years) with additional conditions, such as in-hospital heart 268

failure, a history of peripheral artery disease or tachycardia. 269

Previous studies also reported advanced age, heart failure and tachycardia as important 270

predictors of lower guideline adherence in patients with acute coronary syndromes.[5, 35-40] 271

Moreover, several of these studies also reported a decreased likelihood of survival. [36 ,37, 272

39] Implying that patients at high risk for adverse cardiac outcomes are less likely to receive 273

guideline recommended care. However, according to the European guidelines these high risk 274

subgroups of patients benefit most from early invasive treatments[9]. It may, however, be 275

discussed to what degree an invasive treatment may be desired in these high risk subgroups of 276

patients. Also, it could be questionable to what degree risk stratification using a cardiac risk 277

score adds value in deciding on the treatment for these patients, e.g. in the case of 278

resuscitation the decision for a certain procedure may be evident. The European Society of 279

Cardiology guidelines however, do not take these circumstances into account and recommend 280

estimating risk levels with a cardiac risk scoring instrument for every patient suspected of 281

UA/NSTEMI.[9] 282

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An interesting finding, that contrasted the findings of previous studies, was that a hospitals’ 283

teaching status or the presence of on-site revascularisation facilities were not significantly 284

associated with cardiac risk score use.[6, 35] These differences may be explained by the 285

relatively small number of hospitals participating in the present study compared to previous 286

studies. A large variation between hospitals in adherence scores regarding cardiac risk score 287

use were found. The large component of variance explained by the random hospital effect 288

suggests that cardiac risk score use in patients presenting with the same characteristics may 289

heavily depend on which hospital the patient is presented in and that other factors, beside a 290

hospital’s teaching status or on-site revascularisation facilities, are of influence. 291

Common barriers in the implementation of cardiac risk scores, including the absence of 292

necessary resources for implementation and cultural differences, may explain this substantial 293

variation.[41] It has also been suggested that physicians find the evidence underlying cardiac 294

risk scores unconvincing.[23] To increase the use of cardiac risk scores in clinical practice 295

several implementation strategies, which pay explicit attention to patients with suspected UA 296

may be employed. A recent improvement initiative in the USA for instance, in which 297

continuous education was the primary intervention, led to a significant increase in cardiac risk 298

score documentation in UA and NSTEMI patients.[42] The use of continuous education has 299

proven to be effective in achieving change in practice, however it is recommended to also 300

take into account facilitating factors and barriers on a patient, provider and organizational 301

level.[43] Therefore, further research is needed to carefully understand factors that explain the 302

variation between hospitals’ cardiac risk score use. 303

304

Study limitations 305

Several limitations potentially affect the interpretation of the results of this study. First, the 306

use of cardiac risk scores was measured by screening charts on the documentation of a cardiac 307

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risk score. As a result it is unknown to what degree a cardiac risk score influenced physicians’ 308

decision making regarding appropriate management strategies. However, it is plausible that 309

when a cardiac risk score was documented, it was also used in practice. 310

Second, four predictors reported in previous studies of risk score use i.e. aspirin use prior to 311

admission, creatinine level, troponin level and biomarkers, were not considered in the present 312

study. These data could not be abstracted reliably. As a result, the precision of the model 313

reported in this study might be smaller compared to other studies. 314

Third, in two hospitals the method of selection of patient charts differed, as it was not possible 315

to select patients based on the hospital’s billing system. This could have influenced the 316

selection of patients. However, their effects may be limited as it appeared that the random 317

effects of these two hospitals were well in range with those of the other hospitals. 318

Finally, it was not possible to extract all data from the charts at one time point per hospital. 319

Therefore, monthly data collection visits were deemed necessary. For this reason, the reported 320

associations were corrected for month of discharge, though, the frequent presence of the 321

researchers on-site may have led to more awareness of the health care providers using cardiac 322

risk scores, and as a result higher adherence scores than in hospital not participating in the 323

evaluation of the quality improvement program. 324

325

CONCLUSIONS 326

327

The results of the present study indicate that cardiac risk scores have not been fully 328

implemented in Dutch clinical practice, as a substantial number of patients had no risk score 329

documented in their chart. The large variation between hospitals could not be explained by the 330

presence of on-site revascularization facilities or a hospitals’ teaching status, as well as by 331

several patient related factors that were associated with higher or lower usage of cardiac risk 332

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scores in clinical practice. It is recommended that further research should focus first on 333

explanatory factors for differences between hospitals, which could provide a basis for future 334

improvement initiatives in which strategies are targeted towards patient groups in which risk 335

scores were less often documented, as these patients may currently be undertreated. 336

337

ACKNOWLEDGEMENTS 338

We would like to thank Marie-Julie Heeren, Felix van Urk, Suzanne Vonk and Rixt Zuidema 339

for their support in data collection. 340

341

COMPETING INTERESTS 342

The authors declare that they have no competing interests. 343

344

AUTHORS’ CONTRIBUTION 345

All authors made substantial contributions to conception and design of the study. JE carried 346

out data collection, performed statistical analysis, and drafted the manuscript, and prepared 347

the manuscript for publication. IvdW provided substantial support in imputation of data and 348

statistical analysis. IvdW, MdB and CW made substantial contributions to interpretation of 349

the data and revised the manuscript critically for important intellectual content. All authors 350

read and approved the final manuscript. 351

352

FUNDING 353

The study was funded by the Dutch Ministry of Public health, Welfare and Sports. The study 354

sponsor had no role in the study design, collection, analysis and interpretation of the data, or 355

in writing of the manuscript and decision to submit the manuscript for publication. 356

357

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358

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REFERENCES 359

360

1. Fox KAA, Steg PG, Eagle KA, et al. Decline in rates of death and heart failure in acute 361

coronary syndromes, 1999-2006. JAMA 2007,297:1892-1900. 362

2. Yeh RW, Sidney S, Chandra M, et al. Population trends in the incidence and outcomes 363

of acute myocardial infarction. N Engl J Med 2010,362:2155-2165. 364

3. Lopez AD, Murray CJ. The global burden of disease. A comprehensive assessment of 365

mortality and disability from diseases, injuries and risk factors in 1990 and projected to 366

2020. Cambridge MA: Harvard University Press 1996. 367

4. Murray CJ, Lopez AD. Alternative projections of mortality and disability by cause 368

1990-2020: Global Burden of Disease Study. Lancet 1997,349:1498-1504. 369

5. Roe MT, Peterson ED, Newby LK, et al. The influence of risk status on guideline 370

adherence for patients with non-ST-segment elevation acute coronary syndromes. Am 371

Heart J 2006,151:1205-1213. 372

6. Peterson ED, Roe MT, Mulgund J, et al. Association between hospital process 373

performance and outcomes among patients with acute coronary syndromes. JAMA 374

2006,295:1912-1920. 375

7. Gulati M, Patel S, Jaffe AS, et al. Impact of contemporary guideline compliance on risk 376

stratification models for acute coronary syndromes in The Registry of Acute Coronary 377

Syndromes. Am J Cardiol 2004,94:873-878. 378

8. Patel MR, Chen AY, Roe MT, et al. A comparison of acute coronary syndrome care at 379

academic and nonacademic hospitals. Am J Med 2007,120:40-46. 380

Page 22 of 32


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j.com/

BM



ber 2015. Dow

nloaded from



23

9. Hamm CW, Bassand JP, Agewall S, et al. ESC Guidelines for the management of acute 381

coronary syndromes in patients presenting without persistent ST-segment elevation: The 382

Task Force for the management of acute coronary syndromes (ACS) in patients 383

presenting without persistent ST-segment elevation of the European Society of 384

Cardiology (ESC). Eur Heart J 2011,32:2999-3054. 385

10. Anderson JL, Adams CD, Antman EM, et al. ACC/AHA 2007 guidelines for the 386

management of patients with unstable angina/non ST-elevation myocardial infarction: a 387

report of the American College of Cardiology/American Heart Association Task Force 388

on Practice Guidelines (Writing Committee to Revise the 2002 Guidelines for the 389

Management of Patients With Unstable Angina/Non ST-Elevation Myocardial 390

Infarction): developed in collaboration with the American College of Emergency 391

Physicians, the Society for Cardiovascular Angiography and Interventions, and the 392

Society of Thoracic Surgeons: endorsed by the American Association of Cardiovascular 393

and Pulmonary Rehabilitation and the Society for Academic Emergency Medicine. 394

Circulation 2007,116:e148-e304. 395

11. Amsterdam EA, Wenger NK, Brindis RG, et al. 2014 AHA/ACC Guideline for the 396

Management of Patients With Non-ST-Elevation Acute Coronary Syndromes: A Report 397

of the American College of Cardiology/American Heart Association Task Force on 398

Practice Guidelines. Circulation 2014,130:2354-2394. 399

12. Granger CB, Goldberg RJ, Dabbous O, et al. Predictors of hospital mortality in the 400

global registry of acute coronary events. Arch Intern Med 2003,163:2345-2353. 401

13. Fox KA, Dabbous OH, Goldberg RJ, et al. Prediction of risk of death and myocardial 402

infarction in the six months after presentation with acute coronary syndrome: 403

prospective multinational observational study (GRACE). BMJ 2006,333:1091. 404

Page 23 of 32


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BM



ber 2015. Dow

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24

14. Antman EM, Cohen M, Bernink PJ, et al. The TIMI risk score for unstable angina/non-405

ST elevation MI: A method for prognostication and therapeutic decision making. JAMA 406

2000,284:835-842. 407

15. Lagerqvist B, Diderholm E, Lindahl B, et al. FRISC score for selection of patients for an 408

early invasive treatment strategy in unstable coronary artery disease. Heart 409

2005,91:1047-1052. 410

16. Boersma E, Pieper KS, Steyerberg EW, et al. Predictors of outcome in patients with 411

acute coronary syndromes without persistent ST-segment elevation. Results from an 412

international trial of 9461 patients. The PURSUIT Investigators. Circulation 413

2000,101:2557-2567. 414

17. Six AJ, Backus BE, Kelder JC. Chest pain in the emergency room: value of the HEART 415

score. Neth Heart J 2008,16:191-196. 416

18. de Araujo GP, Ferreira J, Aguiar C, et al. TIMI, PURSUIT, and GRACE risk scores: 417

sustained prognostic value and interaction with revascularization in NSTE-ACS. Eur 418

Heart J 2005,26:865-872. 419

19. Backus BE, Six AJ, Kelder JC, et al. Chest pain in the emergency room: a multicenter 420

validation of the HEART Score. Crit Pathw Cardiol 2010,9:164-169. 421

20. Yan AT, Yan RT, Tan M, et al. Risk scores for risk stratification in acute coronary 422

syndromes: useful but simpler is not necessarily better. Eur Heart J 2007,28:1072-1078. 423

21. Chew DP, Juergens C, French J, et al. An examination of clinical intuition in risk 424

assessment among acute coronary syndromes patients: observations from a prospective 425

multi-center international observational registry. Int J Cardiol 2014,171:209-216. 426

Page 24 of 32


BMJ Open

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960


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j.com/

BM



ber 2015. Dow

nloaded from



25

22. Manfrini O, Bugiardini R. Barriers to clinical risk scores adoption. Eur Heart J 427

2007,28:1045-1046. 428

23. Van de Werf F, Ardissino D, Bueno H, et al. Acute coronary syndromes: considerations 429

for improved acceptance and implementation of management guidelines. Expert Rev 430

Cardiovasc Ther 2012,10:489-503. 431

24. Yan AT, Yan RT, Tan M, et al. In-hospital revascularization and one-year outcome of 432

acute coronary syndrome patients stratified by the GRACE risk score. Am J Cardiol 433

2005,96:913-916. 434

25. Yan AT, Yan RT, Tan M, et al. Management patterns in relation to risk stratification 435

among patients with non-ST elevation acute coronary syndromes. Arch Intern Med 436

2007,167:1009-1016. 437

26. Fox KA, Anderson FA, Jr., Dabbous OH et al. Intervention in acute coronary 438

syndromes: do patients undergo intervention on the basis of their risk characteristics? 439

The Global Registry of Acute Coronary Events (GRACE). DHeart 2007,93:177-182. 440

27. Heras M, Bueno H, Bardaji A et al. Magnitude and consequences of undertreatment of 441

high-risk patients with non-ST segment elevation acute coronary syndromes: insights 442

from the DESCARTES Registry. Heart 2006,92:1571-1576. 443

28. Lee CH, Tan M, Yan AT, et al. Use of cardiac catheterization for non-ST-segment 444

elevation acute coronary syndromes according to initial risk: reasons why physicians 445

choose not to refer their patients. Arch Intern Med 2008,168: 291-296. 446

Page 25 of 32


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BM



ber 2015. Dow

nloaded from



26

29. Motivala AA, Cannon CP, Srinivas VS, et al. Changes in myocardial infarction 447

guideline adherence as a function of patient risk: an end to paradoxical care? J Am Coll 448

Cardiol 2011,58: 1760-1765. 449

30. Tra J, Engel J, van der Wulp I et al. Monitoring guideline adherence in the management 450

of acute coronary syndrome in hospitals: design of a multicentre study. Neth Heart J 451

2014,22: 346-53. 452

31. VMSzorg. Optimal care in Acute Coronary Syndromes [Original title in Dutch: Optimale 453

zorg in Acute Coronaire Syndromen]. Dutch National Patient Safety Programme. 2010 454

Http://www.vmszorg.nl/Themas/ACS 455

32. van Buuren S, Groothuis-Oudshoorn K (Eds). mice: Multivariate Imputation by Chained 456

Equations in R. In Journal of Statistical Software 2011,45:1-67. 457

33. R Core Team. R: A language and environment for statistical computing. 2013. Vienna, 458

Austria, R Foundation for Statistical Computing. http://www.R-project.org. 459

34. McCulloch CE, Searle SR, Neuhaus JM: Generalized, Linear, and Mixed Models., 2 460

edn. New Jersey: John Wiley & Sons, 2008. 461

35. Kumbhani DJ, Fonarow GC, Cannon CP et al. Predictors of adherence to performance 462

measures in patients with acute myocardial infarction. Am J Med 2013,126:74-79. 463

36. Scott IA, Harper CM. Guideline-discordant care in acute myocardial infarction: 464

predictors and outcomes. Med J Aust 2002,177:26-31. 465

37. Allen LA, O'Donnell CJ, Camargo CAJ et al. Comparison of long-term mortality across 466

the spectrum of acute coronary syndromes. Am Heart J 2006,151:1065-1071. 467

Page 26 of 32


BMJ Open

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j.com/

BM



ber 2015. Dow

nloaded from



27

38. Tran CTT, Laupacis A, Mamdani MM, et al. Effect of age on the use of evidence-based 468

therapies for acute myocardial infarction. Am Heart J 2004,148:834-841. 469

39. Ohman EM, Roe MT, Smith SCJ et al. Care of non-ST-segment elevation patients: 470

insights from the CRUSADE national quality improvement initiative. Am Heart J 471

2004,148:S34-S39. 472

40. Haim M, Battler A, Behar S et al. Acute coronary syndromes complicated by 473

symptomatic and asymptomatic heart failure: does current treatment comply with 474

guidelines? Am Heart J 2004,147:859-864. 475

41. Glickman SW, Boulding W, Staelin R et al. A framework for quality improvement: an 476

analysis of factors responsible for improvement at hospitals participating in the Can 477

Rapid Risk Stratification of Unstable Angina Patients Suppress Adverse Outcomes with 478

Early Implementation of the ACC/AHA Guidelines (CRUSADE) quality improvement 479

initiative. Am Heart J 2007,154:1206-1220. 480

42. Cannon CP, Hoekstra JW, Larson DM et al. A report of quality improvement in the care 481

of patients with acute coronary syndromes. Crit Pathw Cardiol 2011,10: 29-34. 482

43. Grol R, Grimshaw J. From best evidence to best practice: effective implementation of 483

change in patients' care. Lancet 2003,362: 1225-1230. 484

485

486

487

488

489

490

491

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Figure 1. Inclusion and exclusion procedure of chart selection and screening 492

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Figure 1. Inclusion and exclusion procedure of chart selection and screening

Screened charts

n=1933

Sample study

n=1788

Exclusion chart, because:

- Age < 18

- Elective admission/procedure

- UA/NSTEMI due to non-ischemic cause

- Insufficient information in chart

Exclusion chart, because:

- patient is transferred in from another

hospital (n=145).

Identified

records from

hospital billing

system based on

discharge

diagnosis

Confirmation

discharge

diagnosis of UA

or NSTEMI Consultation of attending physician:

UA/NSTEMI?

Charts fulfill all

eligible criteria No

Yes

Exclusion of chart

Doubt

Yes

No No

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STROBE 2007 (v4) Statement—Checklist of items that should be included in reports of cross-sectional studies

Although we are aware of the SPIRIT checklist, the STROBE checklist seems most suited for this manuscript, as it describes the study design of a cross-sectional

study. The SPIRIT checklist is specifically developed for reporting randomized trials and therefore its applicability to the contents of the present manuscript is

limited. In addition, some parts of the STROBE checklist will not be applicable to our manuscript, these will be left empty.

Section/Topic Item

# Recommendation Reported on page #

Title and abstract 1 (a) Indicate the study’s design with a commonly used term in the title or the abstract 1

(b) Provide in the abstract an informative and balanced summary of what was done and what was found 3

Introduction

Background/rationale 2 Explain the scientific background and rationale for the investigation being reported 4-5

Objectives 3 State specific objectives, including any prespecified hypotheses 5

Methods

Study design 4 Present key elements of study design early in the paper 5

Setting 5 Describe the setting, locations, and relevant dates, including periods of recruitment, exposure, follow-up, and data

collection

5-7

Participants

6

(a) Give the eligibility criteria, and the sources and methods of selection of participants 6

Variables 7 Clearly define all outcomes, exposures, predictors, potential confounders, and effect modifiers. Give diagnostic criteria, if

applicable

6

Data sources/

measurement

8* For each variable of interest, give sources of data and details of methods of assessment (measurement). Describe

comparability of assessment methods if there is more than one group

6

Bias 9 Describe any efforts to address potential sources of bias 7

Study size 10 Explain how the study size was arrived at 6

Quantitative variables 11 Explain how quantitative variables were handled in the analyses. If applicable, describe which groupings were chosen and

why

8-9

Statistical methods 12 (a) Describe all statistical methods, including those used to control for confounding 8-9

(b) Describe any methods used to examine subgroups and interactions 8-9

(c) Explain how missing data were addressed 8

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(d) If applicable, describe analytical methods taking account of sampling strategy n.a.

(e) Describe any sensitivity analyses 8

Results

Participants 13* (a) Report numbers of individuals at each stage of study—eg numbers potentially eligible, examined for eligibility,

confirmed eligible, included in the study, completing follow-up, and analysed

9

(b) Give reasons for non-participation at each stage n.a.

(c) Consider use of a flow diagram Figure 1

Descriptive data 14* (a) Give characteristics of study participants (eg demographic, clinical, social) and information on exposures and potential

confounders

9

(b) Indicate number of participants with missing data for each variable of interest 7-8

Outcome data 15* Report numbers of outcome events or summary measures 9

Main results 16 (a) Give unadjusted estimates and, if applicable, confounder-adjusted estimates and their precision (eg, 95% confidence

interval). Make clear which confounders were adjusted for and why they were included

9-11

(b) Report category boundaries when continuous variables were categorized

(c) If relevant, consider translating estimates of relative risk into absolute risk for a meaningful time period

Other analyses 17 Report other analyses done—eg analyses of subgroups and interactions, and sensitivity analyses 14

Discussion

Key results 18 Summarise key results with reference to study objectives 17-18

Limitations 19 Discuss limitations of the study, taking into account sources of potential bias or imprecision. Discuss both direction and

magnitude of any potential bias

18-19

Interpretation 20 Give a cautious overall interpretation of results considering objectives, limitations, multiplicity of analyses, results from

similar studies, and other relevant evidence

17-18

Generalisability 21 Discuss the generalisability (external validity) of the study results 17-18

Other information

Funding 22 Give the source of funding and the role of the funders for the present study and, if applicable, for the original study on

which the present article is based

19

*Give information separately for cases and controls in case-control studies and, if applicable, for exposed and unexposed groups in cohort and cross-sectional studies.

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Note: An Explanation and Elaboration article discusses each checklist item and gives methodological background and published examples of transparent reporting. The STROBE

checklist is best used in conjunction with this article (freely available on the Web sites of PLoS Medicine at http://www.plosmedicine.org/, Annals of Internal Medicine at

http://www.annals.org/, and Epidemiology at http://www.epidem.com/). Information on the STROBE Initiative is available at www.strobe-statement.org.

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Journal: BMJ Open

Manuscript ID: bmjopen-2015-008523.R1


Date Submitted by the Author: 07-Jul-2015










jopen.bmj.com

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ovember 2015. D

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3

Josien Engel, MSc*1



, Cordula Wagner, 4

PhD 1,2

5

6

1



2


10





15


chart review 17

18



21

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• This multicenter study is the first to provide thorough insight in the extent of cardiac 23

risk score use in clinical practice by reviewing patient charts in thirteen hospitals 24

throughout the Netherlands; 25


factors associated with lower or higher cardiac risk score use; 27

• The results can serve as a basis for future improvement initiatives aimed to enhance 28

cardiac risk score use in practice, by tailoring strategies to patient groups in which risk 29

scores are less often documented; 30



constraints; 33


collection made it impossible to determine the actual influence of cardiac risk score 35



38

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ABSTRACT 39

40

Objectives: Quantitative risk assessment in unstable angina (UA) and non-ST-elevation 41



exists, as these instruments seem underused in practice. The present study aimed to determine 44

the extent of cardiac risk score use and to study factors associated with lower or higher 45


Setting: Thirteen hospitals throughout the Netherlands 47

Participants: A retrospective chart review of 1788 charts of UA and NSTEMI patients, 48

discharged in 2012 49

Primary and secondary outcomes: The extent of cardiac risk score use reflected in a 50

documented risk score outcome in the patient’s chart. And factors associated with cardiac 51

risk score use determined by generalized linear mixed models. 52

Results: In 57% (n=1019) of the charts, physicians documented the use of a cardiac risk 53

score. Substantial variation between hospitals was observed (16.7% to 87%), although this 54

variation could not be explained by the presence of on-site revascularization facilities or a 55

hospitals’ teaching status. Obese patients (OR=1.49; CI 95%= 1.03–2.15) and former smokers 56

(OR=1.56; CI 95%= 1.15–2.11) were more likely to have a cardiac risk score documented. 57

Risk scores were less likely being used among patients diagnosed with unstable angina 58

(OR=0.60; CI 95%= 0.46–0.77), in-hospital resuscitation (OR=0.23; CI 95%= 0.09–0.64), in-59

hospital heart failure (OR=0.46; CI 95%= 0.27–0.76) or tachycardia (OR=0.45; CI 95%= 60

0.26–0.75). 61



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BACKGROUND 67

68




conditions still account for a large part of the annual deaths worldwide and are expected to be 72

the leading cause of death and to account for the largest disease burden worldwide by 2020-73

2030.[3-5] Part of these deaths may be prevented, as it has previously been reported that a 74

substantial number of patients were not treated according to the current standards of care.[6-8] 75

Patients with diabetes mellitus, renal insufficiency, signs of heart failure and patients aged 75 76

years or older, were often neglected guideline recommended care.[6] On the other hand, 77

patients presenting to academic hospitals and to hospitals with revascularisation facilities on-78

site (e.g. percutaneous coronary intervention (PCI) or coronary artery bypass grafting 79

(CABG)), were more often treated in accordance to the guidelines.[7,9] Patients diagnosed 80

with UA or NSTEMI can be treated with medication or invasive procedures such as PCI or 81

CABG. According to international cardiac guidelines the decision to treat such patients with 82

one or the other may be made on the basis of a quantitative assessment of the patient’s risk of 83

re-infarction or death.[10-12] To assist clinicians in identifying patients at high risk of adverse 84

cardiac events that would benefit most from invasive therapies, several instruments have been 85

developed.[10-12] The GRACE (Global registry of Acute Coronary Events),[13, 14] TIMI 86

(Thrombolysis in Myocardial Infarction),[15] FRISC (fast revascularization in instability in 87

coronary disease),[16] PURSUIT (Platelet glycoprotein IIb/IIIa in Unstable angina: Receptor 88

Suppression Using Integrilin),[17] and HEART risk scores [18] are examples of validated 89

cardiac risk scoring instruments. In estimating risk, these instruments incorporate and 90

combine several diagnostic elements including a patients’ history, biomarkers and 91

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electrocardiogram findings, and can be used in the emergency department or coronary care 92

unit. The predictive validity of these instruments was reported to be good.[16, 19, 20] 93

Previous research found that cardiac risk scores were effective in identifying patients at high 94

risk for cardiac events.[21, 22] However, a gap between recommended care in the guidelines 95

and actual practice seems to exist, as it has been suggested before that cardiac risk scores are 96

not routinely used in clinical practice. [21, 23, 24] This possibly contributed to the 97

perpetuating “treatment risk paradox”, in which patients with low risk of adverse cardiac 98

events, opposite to cardiac guideline recommendations, were more likely to receive invasive 99

cardiac treatment compared to high risk patients.[6, 25-30] Prior to creating future 100

improvement initiatives to increase cardiac risk score use, knowledge about the extent of this 101

gap and associated factors is necessary. The present study, therefore, aimed to determine the 102

extent of cardiac risk score use in Dutch clinical practice and to study factors associated with 103

lower or higher cardiac risk score use. 104

105

METHODS 106

107





112

Setting 113

In 2008 all hospitals in the Netherlands committed themselves to the implementation of a 114

quality improvement program aimed to enhance patient safety in Dutch hospitals. The 115

program comprised several themes, including the theme ‘Optimal care for Acute Coronary 116

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Syndromes’ which, among other things, aimed to increase the application of cardiac risk 117

scores in clinical practice.[32] A random selection of 40 hospitals participated voluntarily in 118

the evaluation of the nationwide quality improvement program. By a multistage random 119

sampling procedure, initially 12 hospitals were selected from the pool of 40 hospitals to 120

participate in the current study (i.e. evaluation of cardiac risk score use). Three PCI-capable 121

hospitals declined participation, for which three additional PCI-capable hospitals were 122

selected. Additionally, one hospital was selected to obtain optimal diversity in on-site 123

revascularization facilities and teaching status. The final sample consisted of thirteen 124

hospitals, of which two university hospitals, seven tertiary teaching hospitals, and four 125

general hospitals. Bed capacity in the hospitals varied between 200 and 1200 beds. 126

127

Data collection 128


management of UA and NSTEMI patients, reflected in a documented risk score outcome in 130

the patient’s chart. Data were collected monthly by means of retrospective chart review. 131










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Charts of patients were selected per month in chronological order of discharge until the 141





standardized data extraction forms, including the following: demographic characteristics, 146

cardiac history, presence of cardiac risk factors, presenting symptoms, biochemical and 147

electrocardiographic findings and treatment practices. In addition, information regarding 148

cardiac risk score use was registered, including the use of a validated risk score (yes/no), date 149

of application, type of risk score used and risk score outcome and classification. Besides 150

patient related information, the following hospital factors were registered: teaching status 151

(yes/no) and the presence of on-site revascularization facilities. 152









Demographics

• Age

• Gender

Presenting factors

• Heart rate





Cardiac history





• Previous CABG

• Previous PCI


months

Risk factors


• Hypertension

• Renal failure



• Smoking

• Former smoker

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Discharge diagnosis

• UA

• NSTEMI



options

• Teaching status




(in history)





159

Missing data 160






[33] resulting in five imputed datasets. In imputing missing values it was assumed that the 166









175

Data analysis 176



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present to abstract data. In univariate analyses, associations between cardiac risk score use 186

and the independent variables were tested. All variables with a significance level of p≤0.15 187

were entered in a multivariable model. Variables significantly associated (p≤0.05) with 188

cardiac risk score use in the multivariable model were considered important in predicting risk 189

score adherence. In addition, based on previous literature two factor interactions with on-site 190

revascularization options, teaching status, age and gender were tested. All analyses were 191

conducted in R for windows (version 3.0.2) using the package lme4 on pooled data of five 192

imputed data sets.[34] The script of the pool function in MICE was rewritten for pooling 193

GLMM models. 194

195

RESULTS 196

197







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206


Age (<75 years) 1146 (64.1)





High (≥ 160) 552 (30.9)


Low (≤ 80) 0 (0)

Heart rate (bpm)

Tachycardia (≥ 110) 103 (5.8)

Normal (51-109) 1634 (91.4)


















Obesity (BMI>30) 203 (11.3)

Smoking 427 (23.9)




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Management strategy









207



variation between hospitals was observed i.e. 16.7% to 87% (Table 2). Six out of the thirteen 210

hospitals used more than one risk scoring instrument to calculate a risk score, being the 211

following: GRACE (12/13 hospitals), TIMI (3/13 hospitals), FRISC (1/13 hospitals) and the 212

HEART risk score (6/13 hospitals) (Table 2). The variance component for the random 213

hospital effect in the GLMM ranged between 1.29 and 1.31 in the five imputed datasets, 214

confirming the great variety between hospitals in the use of cardiac risk scores. When, for 215

instance, the effects for two hospitals are equal to the 5 and 95 percentiles of the normal 216

distribution with variance 1.3 for hospital effects, the odds ratio of one hospital relative to the 217

other for cardiac risk score use is 42.6. 218

In univariate analyses, 15 patient related factors were significantly (p≤0.15) associated with 219

cardiac risk score use (Table 3). No significant associations with hospital related factors were 220






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228


Hospital

ID†

Teaching

status

PCI/

CABG

options

Screened

charts n‡

Risk score

use n (%)˥

Type of risk score used∞

GRACE TIMI FRISC HEART

1 No No 84 14 (16.7) X - - -

2 Yes Yes 109 22 (20.2) X X - -

3 No No 110 26 (23.6) X - - -

4 No No 171 57 (33.3) - - - X

5 Yes Yes 132 46 (34.8) X - - X

6 Yes No 53 19 (35.8) X - - -

7 Yes Yes 145 79 (54.5) X - X X

8 Yes Yes 182 108 (59.3) X - - X

9 Yes Yes 96 68 (70.8) X - - -

10 Yes Yes 140 107 (76.4) X - - X

11 Yes Yes 108 87 (80.6) X X - X

12 No No 205 166 (81.0) X X - -

13 Yes No 253 220 (87.0) X - - -

Total -- -- 1788 1019 (57%) -- -- -- --

† ranging from lowest to highest scoring hospital. ‡ Large variation in screened patient charts per hospital is explained

by differences in the amount of monthly admission for UA/NSTEMI. ˥ Risk score use is represented by (one or more)

documented risk score outcome(s) in the patient’s chart. ∞Several hospitals calculated more than one risk score per

patient, using different risk scoring instruments. Abbreviations: CABG, coronary artery bypass grafting; FRISC, fast

revascularization in instability in coronary disease; GRACE, global registry of acute coronary events; PCI,

percutaneous coronary intervention; TIMI, Thrombolysis in Myocardial Infarction

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OR (95% CI) P OR (95% CI) P

Hospital factors



Patient factors‡







Previous (U)A 0.83 (0.64 – 1.07) 0.15 1.00 (0.75 – 1.34) 0.98

Previous MI 0.77 (0.61 – 0.98) 0.03* 0.89 (0.68 – 1.18) 0.43

Previous PCI 0.83 (0.65 – 1.05) 0.13 1.07 (0.80 – 1.43) 0.66

Renal failure 0.54 (0.33 – 0.90) 0.02* 0.72 (0.42 – 1.23) 0.23

Obesity (BMI>30) 1.49 (1.05 – 2.13) 0.03* 1.49 (1.03 – 2.15) 0.04*

Smoking 1.23 (0.95 – 1.60) 0.11 1.16 (0.86 – 1.55) 0.33

Former smoker 1.48 (1.12 – 1.97) ≤0.01** 1.56 (1.15 – 2.11) ≤0.01**



Tachycardia 0.46 (0.28 – 0.76) ≤0.01** 0.45 (0.26 – 0.75) ≤0.01**

Bradycardia 0.85 (0.44 – 1.63) 0.62 0.92 (0.46 – 1.86) 0.82






229

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Interactions 230


gender, it was decided to also test whether interactions with former smoker were present. This 232

was because an unexpected significant association between former smoker and risk score use 233




(p=0.04) (Table 4). 237







(OR=0.20; 95% CI=0.08 – 0.52; p=<0.001). 244

245

246

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Age*previous PCI















Age*Heart rate








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infarction.

247

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DISCUSSION 248

249


as recommended by international cardiac guidelines. In addition, associations with patient and 251

hospital related factors were studied. Substantial variation between hospitals’ cardiac risk 252


not documented. Several patient related factors including a diagnosis of UA, the presence of 254


lower likelihood of cardiac risk score use. Although evidence is not conclusive, the 256

probability of cardiac risk score use was often lower in older patients (≥ 75 years) with 257

additional conditions, such as in-hospital heart failure, a history of peripheral artery disease or 258

tachycardia. 259



Moreover, several of these studies also reported a decreased likelihood of survival. [37,38, 262









estimating risk levels with a cardiac risk scoring instrument for every patient suspected of 271

UA/NSTEMI.[10] 272

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Obese patients and former smokers were more likely to have a cardiac risk score documented. 273

The association of former smoking and the use of a cardiac risk score, however, was 274

unexpected and difficult to explain. There are no indications for partial confounding with 275

other factors in the model as odds ratio’s for former smoker in univariate and multivariable 276

models are sizeable and similar. Possibly, former smoking is an alias for some other 277

underlying and unknown variable. For instance, former smoking is seen as an indication of a 278

former more high-risk lifestyle and that way affects judgement. Further research may provide 279

more insight on this. 280

Another interesting finding, that contrasted the findings of previous studies, was that a 281

hospitals’ teaching status or the presence of on-site revascularisation facilities were not 282

significantly associated with cardiac risk score use.[7, 36] These differences may be explained 283

by the relatively small number of hospitals participating in the present study compared to 284

previous studies. A large variation between hospitals in adherence scores regarding cardiac 285

risk score use were found. The large component of variance, explained by the random hospital 286

effect, suggests that cardiac risk score use in patients presenting with the same characteristics 287

may heavily depend on which hospital the patient is presented in and that other factors, beside 288

a hospital’s teaching status or on-site revascularisation facilities, are of influence. 289



variation.[42] It has been suggested that physicians find the evidence underlying cardiac risk 292

scores unconvincing.[24] To increase the use of cardiac risk scores in clinical practice several 293

implementation strategies, which pay explicit attention to patients with suspected UA may be 294

employed. A recent improvement initiative in the USA for instance, in which continuous 295

education was the primary intervention, led to a significant increase in cardiac risk score 296

documentation in UA and NSTEMI patients.[43] The use of continuous education has proven 297

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to be effective in achieving change in practice, however it is recommended to also take into 298

account facilitating factors and barriers on a patient, provider and organizational level.[44] 299

Therefore, further research is needed to carefully understand factors that explain the variation 300

between hospitals’ cardiac risk score use. 301

302


Several limitations potentially affect the interpretation of the results of this study. 304

First, the use of cardiac risk scores was measured by screening charts on the documentation of 305

a cardiac risk score. As a result it is unknown to what degree a cardiac risk score influenced 306

physicians’ decision making regarding appropriate management strategies. However, it is 307

plausible that when a cardiac risk score was documented, it was also used in practice. 308




reported in this study might be smaller compared to other studies. In addition, it was not 312

possible to reliable extract at what time point a risk score was recorded. The time registered in 313

the patient’s file was often imprecise (i.e. time was entered retrospectively and did not 314

represent the actual time point at which the risk score was used) or lacking. Making it 315

impossible to provide any additional contextual information regarding the use of cardiac risk 316

scores in clinical practice. 317

Third, in two hospitals the method of selection of patient charts differed, as it was not possible 318

to select patients based on the hospital’s billing system. This could have influenced the 319

selection of patients. However, their effects may be limited as it appeared that the random 320

effects of these two hospitals were well in range with those of the other hospitals. 321

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Fourth, it was not possible to extract all data from the charts at one time point per hospital. 322


associations were corrected for month of discharge, though, the frequent presence of the 324


risk scores, and as a result higher adherence scores than in hospitals not participating in the 326

evaluation of the quality improvement program. This overestimation of adherence rates can 327

also be a result of the fact that the evaluation of the improvement program took place in a 328

cohort of highly motivated hospitals, as they all voluntarily agreed to participate. 329

Finally, three of the randomly selected hospitals declined participation in this study, which 330

may have introduced selection-bias. Hospitals that declined participation were possibly 331

lagging behind in implementation. The actual use of cardiac risk scores in practice might 332

therefore be even lower than estimated in this paper. 333

334

CONCLUSIONS 335

336



documented in their chart. The large variation between hospitals could not be explained by 339

the presence of on-site revascularization facilities or a hospitals’ teaching status, as well as by 340






346

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350



353








361

FUNDING 362




366

DATA SHARING STATEMENT 367

No additional data are available. 368

369

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REFERENCES 370

371










5. Mathers CD, Loncar D. Projections of global mortality and burden of disease from 2002 381

to 2030. PLoS Med 2006,3:e442. 382



Heart J 2006,151:1205-1213. 385



2006,295:1912-1920. 388




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Circulation 2007,116:e148-e304. 408







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2000,284:835-842. 420



2005,91:1047-1052. 423




2000,101:2557-2567. 427





Heart J 2005,26:865-872. 432





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2007,28:1045-1046. 441



Cardiovasc Ther 2012,10:489-503. 444



2005,96:913-916. 447



2007,167:1009-1016. 450







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Cardiol 2011,58: 1760-1765. 462



2014,22: 346-53. 465














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2004,148:S34-S39. 485













498

499

500

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147x216mm (300 x 300 DPI)

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Section/Topic Item




Introduction



Methods



collection

5-7

Participants

6



applicable

6

Data sources/

measurement



6




why

8-9




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Results



9




confounders

9





9-11




Discussion




18-19



17-18


Other information



19


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Journal: BMJ Open

Manuscript ID: bmjopen-2015-008523.R2


Date Submitted by the Author: 16-Jul-2015










jopen.bmj.com

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3

Josien Engel, MSc*1



, Cordula Wagner, 4

PhD 1,2

5

6

1



2


10





15


chart review 17

18



21

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• This multicenter study is the first to provide thorough insight into the extent of cardiac 23

risk score use in clinical practice by reviewing patient charts in thirteen hospitals 24

throughout the Netherlands; 25


factors associated with lower or higher cardiac risk score use; 27

• The results can serve as a basis for future improvement initiatives aimed to enhance 28

cardiac risk score use in practice, by tailoring strategies to patient groups in which risk 29

scores are less often documented; 30



constraints; 33


collection, made it impossible to determine the actual influence of cardiac risk score 35



38

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ABSTRACT 39

40

Objectives: Quantitative risk assessment in unstable angina (UA) and non-ST-elevation 41



exists, as these instruments seem underused in practice. The present study aimed to determine 44

the extent of cardiac risk score use and to study factors associated with lower or higher 45


Setting: Thirteen hospitals throughout the Netherlands 47

Participants: A retrospective chart review of 1788 charts of UA and NSTEMI patients, 48

discharged in 2012 49

Primary and secondary outcomes: The extent of cardiac risk score use reflected in a 50

documented risk score outcome in the patient’s chart. Factors associated with cardiac risk 51

score use determined by generalized linear mixed models. 52

Results: In 57% (n=1019) of the charts, physicians documented the use of a cardiac risk 53

score. Substantial variation between hospitals was observed (16.7% to 87%), although this 54

variation could not be explained by the presence of on-site revascularization facilities or a 55

hospitals’ teaching status. Obese patients (OR=1.49; CI 95%= 1.03–2.15) and former smokers 56

(OR=1.56; CI 95%= 1.15–2.11) were more likely to have a cardiac risk score documented. 57

Risk scores were less likely to be used among patients diagnosed with unstable angina 58

(OR=0.60; CI 95%= 0.46–0.77), in-hospital resuscitation (OR=0.23; CI 95%= 0.09–0.64), in-59

hospital heart failure (OR=0.46; CI 95%= 0.27–0.76) or tachycardia (OR=0.45; CI 95%= 60

0.26–0.75). 61



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BACKGROUND 67

68




conditions still account for a large part of the annual deaths worldwide and are expected to be 72

the leading cause of death and to account for the largest disease burden worldwide by 2020-73

2030.[3-5] Part of these deaths may be prevented, as it has previously been reported that a 74

substantial number of patients were not treated according to the current standards of care.[6-8] 75

Patients with diabetes mellitus, renal insufficiency, signs of heart failure and patients aged 75 76

years or older, were often neglected guideline recommended care.[6] On the other hand, 77

patients presenting to academic hospitals and to hospitals with revascularisation facilities on-78

site (e.g. percutaneous coronary intervention (PCI) or coronary artery bypass grafting 79

(CABG)), were more often treated in accordance to the guidelines.[7,9] Patients diagnosed 80

with UA or NSTEMI can be treated with medication or invasive procedures such as PCI or 81

CABG. According to international cardiac guidelines the decision to treat such patients with 82

one or the other may be made on the basis of a quantitative assessment of the patient’s risk of 83

re-infarction or death.[10-12] To assist clinicians in identifying patients at high risk of adverse 84

cardiac events that would benefit most from invasive therapies, several instruments have been 85

developed.[10-12] The GRACE (Global registry of Acute Coronary Events),[13, 14] TIMI 86

(Thrombolysis in Myocardial Infarction),[15] FRISC (fast revascularization in instability in 87

coronary disease),[16] PURSUIT (Platelet glycoprotein IIb/IIIa in Unstable angina: Receptor 88

Suppression Using Integrilin),[17] and HEART risk scores [18] are examples of validated 89

cardiac risk scoring instruments. In estimating risk, these instruments incorporate and 90

combine several diagnostic elements including a patients’ history, biomarkers and 91

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electrocardiogram findings, and can be used in the emergency department or coronary care 92

unit. The predictive validity of these instruments was reported to be good.[16, 19, 20] 93

Previous research found that cardiac risk scores were effective in identifying patients at high 94

risk for cardiac events.[21, 22] However, a gap between recommended care in the guidelines 95

and actual practice seems to exist, as it has been suggested before that cardiac risk scores are 96

not routinely used in clinical practice. [21, 23, 24] This possibly contributes to perpetuating 97

the “treatment risk paradox”, in which patients with low risk of adverse cardiac events, 98

opposite to cardiac guideline recommendations, were more likely to receive invasive cardiac 99

treatment compared to high risk patients.[6, 25-30] Prior to creating future improvement 100

initiatives aimed to increase cardiac risk score use, knowledge about the extent of this gap and 101

associated factors is necessary. The present study, therefore, aimed to determine the extent of 102

cardiac risk score use in Dutch clinical practice and to study factors associated with lower or 103

higher cardiac risk score use. 104

105

METHODS 106

107





112

Setting 113

In 2008 all hospitals in the Netherlands committed themselves to the implementation of a 114

quality improvement program aimed to enhance patient safety in Dutch hospitals. The 115

program comprised several themes, including the theme ‘Optimal care for Acute Coronary 116

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Syndromes’ which, among other things, aimed to increase the application of cardiac risk 117

scores in clinical practice.[32] A random selection of 40 hospitals participated voluntarily in 118

the evaluation of the nationwide quality improvement program. By a multistage random 119

sampling procedure, initially 12 hospitals were selected from the pool of 40 hospitals to 120

participate in the current study (i.e. evaluation of cardiac risk score use). Three PCI-capable 121

hospitals declined participation, for which three additional PCI-capable hospitals were 122

selected. Additionally, one hospital was selected to obtain optimal diversity in on-site 123

revascularization facilities and teaching status. The final sample consisted of thirteen 124

hospitals, of which two university hospitals, seven tertiary teaching hospitals, and four 125

general hospitals. Bed capacity in the hospitals varied between 200 and 1200 beds. 126

127

Data collection 128


management of UA and NSTEMI patients, reflected in a documented risk score outcome in 130

the patient’s chart. Data were collected monthly by means of retrospective chart review. 131










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Charts of patients were selected per month in chronological order of discharge, until the 141





standardized data extraction forms: demographic characteristics, cardiac history, presence of 146

cardiac risk factors, presenting symptoms, biochemical and electrocardiographic findings and 147

treatment practices. In addition, information regarding cardiac risk score use was registered, 148

including the use of a validated risk score (yes/no), date of application, type of risk score used 149

and risk score outcome and classification. Besides patient related information, the following 150

hospital factors were registered: teaching status (yes/no) and the presence of on-site 151

revascularization facilities. 152









Demographics

• Age

• Gender

Presenting factors

• Heart rate





Cardiac history





• Previous CABG

• Previous PCI


months

Risk factors


• Hypertension

• Renal failure



• Smoking

• Former smoker

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Discharge diagnosis

• UA

• NSTEMI



options

• Teaching status




(in history)





159

Missing data 160






resulting in five imputed datasets. [33] In imputing missing values it was assumed that the 166









175

Data analysis 176



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present on hospital departments to abstract data. In univariate analyses, associations between 186

cardiac risk score use and the independent variables were tested. All variables with a 187

significance level of p≤0.15 were entered in a multivariable model. Variables significantly 188

associated (p≤0.05) with cardiac risk score use in the multivariable model were considered 189

important in predicting risk score adherence. In addition, based on previous literature two 190

factor interactions with on-site revascularization options, teaching status, age and gender were 191

tested. All analyses were conducted in R for windows (version 3.0.2) using the package lme4 192

on pooled data of five imputed data sets.[34] The script of the pool function in MICE was 193

rewritten for pooling GLMM models. 194

195

RESULTS 196

197







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206


Age (<75 years) 1146 (64.1)





High (≥ 160) 552 (30.9)


Low (≤ 80) 0 (0)

Heart rate (bpm)

Tachycardia (≥ 110) 103 (5.8)

Normal (51-109) 1634 (91.4)


















Obesity (BMI>30) 203 (11.3)

Smoking 427 (23.9)




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Management strategy









207



variation between hospitals was observed i.e. 16.7% to 87% (Table 2). Six out of the thirteen 210

hospitals used more than one risk scoring instrument to calculate a risk score, being the 211

following: GRACE (12/13 hospitals), TIMI (3/13 hospitals), FRISC (1/13 hospitals) and the 212

HEART risk score (6/13 hospitals) (Table 2). The variance component for the random 213

hospital effect in the GLMM ranged between 1.29 and 1.31 in the five imputed datasets, 214

confirming the great variety between hospitals in the use of cardiac risk scores. When, for 215

instance, the effects for two hospitals are equal to the 5 and 95 percentiles of the normal 216

distribution with variance 1.3 for hospital effects, the odds ratio of one hospital relative to the 217

other for cardiac risk score use is 42.6. 218

In univariate analyses, 15 patient-related factors were significantly (p≤0.15) associated with 219

cardiac risk score use (Table 3). No significant associations with hospital-related factors were 220






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228

229

230

231

232

233

234

235

236

237

238

239

240

241

242

243

244

245

246

247

248


Hospital

ID†

Teaching

status

PCI/

CABG

options

Screened

charts n‡

Risk score

use n (%)˥

Type of risk score used∞

GRACE TIMI FRISC HEART

1 No No 84 14 (16.7) X - - -

2 Yes Yes 109 22 (20.2) X X - -

3 No No 110 26 (23.6) X - - -

4 No No 171 57 (33.3) - - - X

5 Yes Yes 132 46 (34.8) X - - X

6 Yes No 53 19 (35.8) X - - -

7 Yes Yes 145 79 (54.5) X - X X

8 Yes Yes 182 108 (59.3) X - - X

9 Yes Yes 96 68 (70.8) X - - -

10 Yes Yes 140 107 (76.4) X - - X

11 Yes Yes 108 87 (80.6) X X - X

12 No No 205 166 (81.0) X X - -

13 Yes No 253 220 (87.0) X - - -

Total -- -- 1788 1019 (57%) -- -- -- --

† ranging from lowest to highest scoring hospital. ‡ Large variation in screened patient charts per hospital is explained

by differences in the amount of monthly admission for UA/NSTEMI. ˥ Risk score use is represented by (one or more)

documented risk score outcome(s) in the patient’s chart. ∞Several hospitals calculated more than one risk score per

patient, using different risk scoring instruments. Abbreviations: CABG, coronary artery bypass grafting; FRISC, fast

revascularization in instability in coronary disease; GRACE, global registry of acute coronary events; PCI,

percutaneous coronary intervention; TIMI, Thrombolysis in Myocardial Infarction

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OR (95% CI) P OR (95% CI) P

Hospital factors



Patient factors‡







Previous (U)A 0.83 (0.64 – 1.07) 0.15 1.00 (0.75 – 1.34) 0.98

Previous MI 0.77 (0.61 – 0.98) 0.03* 0.89 (0.68 – 1.18) 0.43

Previous PCI 0.83 (0.65 – 1.05) 0.13 1.07 (0.80 – 1.43) 0.66

Renal failure 0.54 (0.33 – 0.90) 0.02* 0.72 (0.42 – 1.23) 0.23

Obesity (BMI>30) 1.49 (1.05 – 2.13) 0.03* 1.49 (1.03 – 2.15) 0.04*

Smoking 1.23 (0.95 – 1.60) 0.11 1.16 (0.86 – 1.55) 0.33

Former smoker 1.48 (1.12 – 1.97) ≤0.01** 1.56 (1.15 – 2.11) ≤0.01**



Tachycardia 0.46 (0.28 – 0.76) ≤0.01** 0.45 (0.26 – 0.75) ≤0.01**

Bradycardia 0.85 (0.44 – 1.63) 0.62 0.92 (0.46 – 1.86) 0.82






249

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Interactions 250


gender, it was decided to also test whether interactions with former smoker were present. 252

This, because an unexpected significant association between former smoker and risk score use 253




(p=0.04) (Table 4). 257







(OR=0.20; 95% CI=0.08 – 0.52; p=<0.001). 264

265

266

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Age*previous PCI















Age*Heart rate








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infarction.

267

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DISCUSSION 268

269


as recommended by international cardiac guidelines. In addition, associations with patient- 271

and hospital-related factors were studied. Substantial variation between hospitals’ cardiac risk 272


not documented. Several patient-related factors including a diagnosis of UA, the presence of 274


lower likelihood of cardiac risk score use. Although evidence is not conclusive, the 276

probability of cardiac risk score use was often lower in older patients (≥ 75 years) with 277

additional conditions, such as in-hospital heart failure, a history of peripheral artery disease or 278

tachycardia. 279



Moreover, several of these studies also reported a decreased likelihood of survival. [37,38, 282









to estimate risk levels with a cardiac risk scoring instrument for every patient suspected of 291

UA/NSTEMI.[10] 292

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Obese patients and former smokers were more likely to have a cardiac risk score documented. 293

The association of former smoking and the use of a cardiac risk score, however, was 294

unexpected and difficult to explain. There are no indications for partial confounding with 295

other factors in the model as odds ratio’s for former smoker in univariate and multivariable 296

models are sizeable and similar. Possibly, former smoking is an alias for some other 297

underlying and unknown variable. For instance, former smoking is seen as an indication of a 298

former more high-risk lifestyle and that way affects judgement. Further research may provide 299

more insight on this. 300

Another interesting finding, that contrasted the findings of previous studies, was that a 301

hospitals’ teaching status or the presence of on-site revascularisation facilities were not 302

significantly associated with cardiac risk score use.[7, 36] These differences may be explained 303

by the relatively small number of hospitals participating in the present study compared to 304

previous studies. A large variation between hospitals in adherence scores regarding cardiac 305

risk score use was found. The large component of variance, explained by the random hospital 306

effect, suggests that cardiac risk score use in patients presenting with the same characteristics 307

may heavily depend on which hospital the patient is presented in, and that other factors, 308

beside a hospital’s teaching status or on-site revascularisation facilities, are of influence. 309



variation.[42] Also, it has been suggested that physicians find the evidence underlying cardiac 312

risk scores unconvincing.[24] To increase the use of cardiac risk scores in clinical practice 313

several implementation strategies, which pay explicit attention to patients with suspected UA, 314

may be employed. A recent improvement initiative in the USA for instance, in which 315

continuous education was the primary intervention, led to a significant increase in cardiac risk 316

score documentation in UA and NSTEMI patients.[43] The use of continuous education has 317

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proven to be effective in achieving change in practice, however it is recommended to also 318

take into account facilitating factors and barriers on a patient, provider and organizational 319

level.[44] Therefore, further research is needed to carefully understand factors that explain the 320

variation between hospitals’ cardiac risk score use. 321

322


Several limitations potentially affect the interpretation of the results of this study. 324

First, the use of cardiac risk scores was measured by screening charts on the documentation of 325

a cardiac risk score. As a result it is unknown to what degree a cardiac risk score influenced 326

physicians’ decision making regarding appropriate management strategies. However, it is 327

plausible that when a cardiac risk score was documented, it was also used in practice. 328




reported in this study might be smaller compared to other studies. In addition, it was not 332

possible to reliable extract at what time point a risk score was recorded. The time registered in 333

the patient’s file was often imprecise (i.e. time was entered retrospectively and did not 334

represent the actual time point at which the risk score was used) or lacking. Making it 335

impossible to provide any additional contextual information regarding the use of cardiac risk 336

scores in clinical practice. 337

Third, in two hospitals the method of selection of patient charts differed, as in these hospitals 338

it was not possible to select patients based on the hospital’s billing system. This could have 339

influenced the selection of patients. However, their effects may be limited as it appeared that 340

the random effects of these two hospitals were well in range with those of the other hospitals. 341

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Fourth, it was not possible to extract all data from the charts at one time point per hospital. 342


associations were corrected for month of discharge. However, the frequent presence of the 344


risk scores, and as a result have higher adherence scores than hospitals not participating in the 346

evaluation of the quality improvement program. This overestimation of adherence rates can 347

also be a result of the fact that the evaluation of the improvement program took place in a 348

cohort of highly motivated hospitals, as they all voluntarily agreed to participate. 349

Finally, three of the randomly selected hospitals declined participation in this study, which 350

may have introduced selection-bias. Hospitals that declined participation were possibly 351

lagging behind in implementation. The actual use of cardiac risk scores in practice might 352

therefore be even lower than estimated in this paper. 353

354

CONCLUSIONS 355

356



documented in their chart. The large variation between hospitals could not be explained by 359

the presence of on-site revascularization facilities or a hospitals’ teaching status, as well as by 360






366

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370



373








381

FUNDING 382




386

DATA SHARING STATEMENT 387

No additional data are available. 388

389

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REFERENCES 390

391










5. Mathers CD, Loncar D. Projections of global mortality and burden of disease from 2002 401

to 2030. PLoS Med 2006,3:e442. 402



Heart J 2006,151:1205-1213. 405



2006,295:1912-1920. 408




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Circulation 2007,116:e148-e304. 428







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2000,284:835-842. 440



2005,91:1047-1052. 443




2000,101:2557-2567. 447





Heart J 2005,26:865-872. 452





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2007,28:1045-1046. 461



Cardiovasc Ther 2012,10:489-503. 464



2005,96:913-916. 467



2007,167:1009-1016. 470







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Cardiol 2011,58: 1760-1765. 482



2014,22: 346-53. 485














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2004,148:S34-S39. 505













518

519

520

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