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Member Non-member

Regional hospital County hospital Local hospital

The Swedish Intensive Care Registry:

Source for research

http://www.icuregswe.org

Sten Walther, MDChairman, Swedish Intensive Care RegistryHeart Centre, Linköping University Hospital

http://www.icuregswe.org/

Member Non-member

Regional hospital County hospital Local hospital

Outline: Basics

– Data sources– Coverage and accuracy

Case studies– Data completeness and SAPS3– Active cooling after cardiac arrest– Life after ICU-care

The Swedish Intensive Care Registry:

Source for research

Data sources

Swedish Intensive Care Registry

Critical care outreach

ICU-care aftercare

Swedish population

registryMicrobiology lab

data

Many other ICUs

Your ICU

My ICU

Data sources



ICU-care aftercare

Swedish population


data

Many other ICUs

Your ICU

My ICU

Data coupling possible using Unique admission identifier Unique person identifier

Data sources



ICU-care aftercare

Swedish population


data

Many other ICUs

Your ICU

My ICU

Data coupling possible using Unique admission identifier Unique person identifier

National Quality Registry legislation Person identifier permitted if purpose is audit and benchmarking Written information to the patient must be provided Consent presumed Active withdrawal of consent possible

Consult

Admit Treat Discharge

Follow upCritical care outreach

ICU outcome

Withdrawal / Withholding

Adverse eventsSOFA

Nursing workload

Diagnosis

Key diagnosis

Renal RT

Ventilator therapy

Procedures

ICU-care aftercare

SAPS 3

ICU-Higgins

APACHE II

PIM 2

Reason for admission

Minimal dataset

CardioThor ICU

Pediatric ICU

ICU

Which data?

My ICU

No errorErrors


Swedish Population Registry

Data transfer: interaction over time

My ICU




Old admissionsCorrected errorsNew admissions

My ICU


Preferably weeklyAt least monthly



My ICU


Preferably weeklyAt least monthly


Weekly

Vital status update


Registry metrics (DocDAT stuk)

Criteria for assessing coverage and accuracy

Criteria …. (cont’d)

Black et al, Qual Saf Health Care 2003 12: 348-352

Case study I:Risk adjustment – SAPS3

Background Transition to SAPS3 model from APACHE model

2005 2006 2007 2008 2009 20100%

20%

40%

60%

80%

100%

APACHE IISAPS 3

Case study I:

Risk adjustment – SAPS3

Background Transition to SAPS3 model from APACHE model 2 vs. 24 hrs time window to capture physiologic data

Admission to ICU

Time (hrs)

Box IIIPhysiologic variables

www.saps3.org

SAPS 3 Admission ScoreBox I Age, yearsLength of stay before ICU admission, daysIntra-hospital location before ICU admissionCo-Morbidities:

Cancer therapy

Cancer

Haematological cancer

Chron. HF (NYHA IV)

Cirrhosis

AIDS

Use of major therapeutic options before ICU admission: Vasoactive drugs

Box IIICU admission: Planned or UnplannedReason(s) for ICU admission:

Cardiovascular:

Hepatic:

Digestive:

Neurologic:

Surgical status at ICU admissionAnatomical site of surgeryAcute infection at ICU admission:

Nosocomial

Respiratory

Box IIIEstimated GCS (lowest), points

Total bilirubine (highest) mg/dL (µmol/L)

Body temperature (highest), Degrees Celsius

Creatinine (highest), mg/dL (µmol/L)

Heart rate (highest), beats/minute

Leukocytes (highest), G/L

Hydrogen ion concentration (lowest), pH

Plateletes (lowest), G/L

Systolic blood pressure (lowest), mmHg

Oxygenation

SAPS 3 points 16Probability of death (%) 0

Case study I:

Risk adjustment – SAPS3

Background Transition to SAPS3 model from APACHE model 2 vs. 24 hrs time window to capture physiologic data

Will this leave us with more missing data and worse model performance?

Admission to ICU

Time (hrs)

Case study I: Risk adjustment – SAPS3Number physiologic variables missing

Number of admissions

Discrimination(Area under ROC curve)

All admissions 31 650 0.85

SIR data from 2009-2010

Case study I: Risk adjustment – SAPS3Number physiologic variables missing

Number of admissions

Discrimination(Area under ROC curve)

All admissions 31 650 0.850 missing 16 977 0.83 1 missing 4 855 0.87 2 missing 3 788 0.89 3 missing 1 882 0.88 4 missing 1 491 0.86 5 missing 702 0.87 6 missing 1 107 0.88 7 missing 453 0.91 8 missing 96 0.86 9 missing 179 0.87 10 missing 120 0.73


Case study I: Risk adjustment – SAPS3

0

20

40

60

80

100

Ris

k (%

)

0 20 40 60 80 100Predicted risk (%)

observed predicted

0

20

40

60

80

100

Ris

k (%

)

0 20 40 60 80 100Predicted risk (%)

observed predicted

0

20

40

60

80

100

Ris

k (%

)

0 20 40 60 80 100Predicted risk (%)

observed predicted

0

20

40

60

80

100R

isk

(%)

0 20 40 60 80 100Predicted risk (%)

observed predicted

No physiologic variable missing 1 physiologic variable missing

3 physiologic variables missing 5 physiologic variables missing

Calibration

ConclusionGood discriminationPoor calibrationLimited influence of missing physiologic data

Customization necessary

Case study I:Risk adjustment – SAPS3

Background 2002: First randomized controlled trials (RCT)

supporting use of hypothermia after cardiac arrest are published

2003: International liaison committee on resuscitation (ILCOR) recommends hypothermia after cardiac arrest

Rapid dissemination into clinical practice

Case study II:Active cooling after cardiac arrest

Background 2002: First randomized controlled trials (RCT)

supporting use of hypothermia after cardiac arrest are published

2003: International liaison committee on resuscitation (ILCOR) recommends hypothermia after cardiac arrest

Rapid dissemination into clinical practice


2001 2002 2003 2004 2005 2006 2007 2008 2009 20100%

25%

50%

Proportion per year of patients with active cooling

Finnish Intensive Care Qual-ity Consortium



Alingsås

Arvika

Borås

Danderyd

Eksjö

Eskilstuna

Falun

Gävle

Halmstad

HelsingborgHudiksvall

Jönköping

K Huddinge IVA

K Solna CIVA

Kalmar

Karlstad

Kristianstad

Kungälv

Lidköping

Linköping IVA

Linköping TIVA

Ljungby

Lund IVA

Malmö IVA

NU TrollhättanNorrköping

Norrtälje

Nyköping

Skövde

Sollefteå

St Göran

Sunderby

Sundsvall

SÖS IVA

Södertälje

Torsby

Umeå IVA

Varberg

Värnamo

VästeråsVäxjö

Ystad

Örebro IVA

Örnsköldsvik

Östersund

0

.2

.4

.6

.8

1

Pro

port

ion

with

act

ive

coo

ling

0 10 20 30 40 50 60

Cardiac arrest (cases per ICU 2010)

N=1 301

0

.2

.4

.6

.8

1

Pro

port

ion

with

act

ive

coo

ling

0 10 20 30 40 50 60

Cardiac arrest out of hospital (cases per ICU 2010)


Alingsås

Arvika

Borås

Danderyd

Eksjö

Eskilstuna

Falun

Gävle

Halmstad

HelsingborgHudiksvall

Jönköping

K Huddinge IVA

K Solna CIVA

Kalmar

Karlstad

Kristianstad

Kungälv

Lidköping

Linköping IVA

Linköping TIVA

Ljungby

Lund IVA

Malmö IVA

NU TrollhättanNorrköping

Norrtälje

Nyköping

Skövde

Sollefteå

St Göran

Sunderby

Sundsvall

SÖS IVA

Södertälje

Torsby

Umeå IVA

Varberg

Värnamo

VästeråsVäxjö

Ystad

Örebro IVA

Örnsköldsvik

Östersund

0

.2

.4

.6

.8

1

Pro

port

ion

with

act

ive

coo

ling

0 10 20 30 40 50 60

Cardiac arrest (cases per ICU 2010)

All cases 2010 (N=1 301)

Out-of-hospital 2010 (N=791)

Case study II:Active cooling after out-of-hospital cardiac arrest

No active cooling

Active cooling

P < 0.001, Cox

0.00

0.20

0.40

0.60

0.80

1.00

Pro

port

ion

aliv

e

941 280 188 113 71 31No active cooling1162 232 170 118 71 36Active cooling

Number at risk

0 1 2 3 4 5Survival (years)


Variable Risk adjustment using APACHE II

2005-2010, N=1102

Active cooling 0.59 (0.51 – 0.68)

Age(increase per 10 yrs)

1.17 (1.11 – 1.23)

Female sex 1.11 (0.96 – 1.28)

Trained ICU (>20 admissions)

0.82 (0.49 – 1.38)

APACHE(increase per point)

1.05 (1.04 – 1.06)

Out of hospital 2005-2010, hazard ratios (95% CI)


Variable Risk adjustment using APACHE II

2005-2010, N=1102

Risk adjustment using SAPS3

2008-2010, N=980

Active cooling 0.59 (0.51 – 0.68) 0.71 (0.61 – 0.83)

Age(increase per 10 yrs)

1.17 (1.11 – 1.23) 1.01 (0.95 – 1.07)

Female sex 1.11 (0.96 – 1.28) 1.18 (1.01 – 1.38)

Trained ICU (>20 admissions)

0.82 (0.49 – 1.38) 0.91 (0.70 – 1.20)

APACHE II / SAPS3(increase per point)

1.05 (1.04 – 1.06) 1.03 (1.02 – 1.04)


Out of hospital 2005-2010, hazard ratios (95% CI)

SIR SSAI2011

HACANEJM 2002

Bernard et alNEJM 2002

Oksanen et alAAScand 2007

Arrich et alCCM 2007

Nielsen et alAAScand 2009

Registry RCT RCT Registry Registry Registry

SurvivalShort term

30 daysNormo: 28%Hypo: 42%

HospitalNormo: 33%Hypo: 49%

Hospital

Hypo: 67%


Hospital Hypo: 56%

Survival Long term

6 monthsNormo: 23%Hypo: 36%


6 months Hypo: 55%

6-12 months Hypo: 50%


Normo = No active coolingHypo = Active cooling

SIR SSAI2011

HACANEJM 2002

Bernard et alNEJM 2002

Oksanen et alAAScand 2007

Arrich et alCCM 2007

Nielsen et alAAScand 2009

Registry RCT RCT Registry Registry Registry

SurvivalShort term

30 daysNormo: 28%Hypo: 42%


Hospital

Hypo: 67%


Hospital Hypo: 56%

Survival Long term



6 months Hypo: 55%

6-12 months Hypo: 50%


Conclusion Active cooling improves survival in clinical practice Effectiveness less than in RCT and prior registry studies

Assessing health related quality of life may give important insights

You only manage what you measure

Case study III:Health related quality of life after ICU

Assessing health related quality of life may give important insights

You only manage what you measure

Differences related to illness severity? length of ICU-stay? treatment protocols?……

Differences between diagnoses? gender?

Is there anything we can do about it? Designing and exploring interventions



PF

RP

BP

GH

VT

SF

RE

MH

20

40

60

80

100

PF Physical FunctioningRP Role - PhysicalBP Bodily PainGH General HealthVT VitalitySF Social FunctioningRE Role - EmotionalMH Mental Health

Reference

2 months, N=9826 months, N=70112 months, N=302

At 2 months (N=982):Age 61 (17 – 99) yrsICU LOS 9 (2 – 48) days

SF-36: All assessments (27 ICUs)



PF

RP

BP

GH

VT

SF

RE

MH

20

40

60

80

100

PF Physical FunctioningRP Role - PhysicalBP Bodily PainGH General HealthVT VitalitySF Social FunctioningRE Role - EmotionalMH Mental Health

Reference

2 months, N=2226 months, N=22212 months, N=222

SF-36: Complete follow-up

What is the appropriate reference?

For how long should we measure?

Can we accelerate recovery?

Designing and exploring interventions

The Swedish Intensive Care Registry Not a database Large group of people devoted to audit and benchmarking to be able to deliver the very best care

SIR 10th AnniversarySaltsjöbaden 2011

member non-member regional hospital county hospital local hospital the swedish intensive care...

Documents

icu slide

icu data

missing data

time slide

cardiac arrest slide

calibration slide

research slide

accuracy slide