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TRANSCRIPT
Predictive validity of the Beers and STOPP criteria to detect adverse drug events, hospitalizations, and emergency department visits in the United StatesJoshua D. Brown, PharmD1,3; Lisa C. Hutchison, PharmD, MPH2; Chenghui Li, PhD1; Jacob T. Painter, PharmD, MBA, PhD1; Bradley C. Martin, PharmD, PhD1
1Division of Pharmaceutical Evaluation and Policy, 2Department of Pharmacy Practice, College of Pharmacy, University of Arkansas for Medical Sciences; and 3Institute for Pharmaceutical Outcomes and Policy, University of Kentucky College of Pharmacy.
Corresponding author: Bradley C. Martin, PharmD, PhD, Professor and Head, Division of Pharmaceutical Evaluation and Policy, College of Pharmacy, University of Arkansas for Medical Sciences, 4301 West Markham St #522, Little Rock, AR 72205, [email protected], P: (501) 603-1992, F: (501) 686-5156
Alternate Contact: Joshua D. Brown, PharmD, Institute for Pharmaceutical Outcomes and Policy, University of Kentucky College of Pharmacy, 292 Pharmacy Building, 789 South Limestone, Lexington, KY 40536, P: (479) 650-8047, F: (501) 686-5156
Funding: The project described was supported by the Translational Research Institute (TRI), grant UL1TR000039 through the NIH National Center for Research Resources and National Center for Advancing Translational Sciences.
Meeting submission: This study was presented as a poster presentation at the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) 17th Annual European Congress, November 8-12, 2014, Amsterdam, the Netherlands.
Abbreviated title: Comparison of Beers and STOPP in the U.S.
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Abstract
OBJECTIVES: To compare the predictive validity of the 2003 Beers, 2012 Beers, and STOPP
inappropriate prescribing criteria.
DESIGN: Retrospective cohort.
SETTING: Managed care administrative claims data from 2006 to 2009
PARTICIPANTS: 174,275 commercially insured persons 65 and older in the United States.
MEASUREMENTS: Association between adverse drug event, emergency department (ED)
visits, and hospitalization outcomes and inappropriate medications using time-varying Cox
proportional hazard models. Measures of model discrimination (c-index) and hazard ratios (HR)
were calculated to compare unadjusted and adjusted models for associations.
RESULTS: The prevalence of inappropriate prescribing was 34.1%, 32.2%, and 27.6% for the
2012 Beers, 2003 Beers, and the STOPP criteria. Each criteria modestly discriminated ADEs in
unadjusted analyses: STOPP (HR=2.89 [2.68-3.12]; C-index=0.607), 2012 Beers (HR=2.51
[2.33-2.70]; C-index=0.603), 2003 Beers (HR=2.65 [2.46-2.85]; C-index=0.605). Similar results
were observed for ED visits and hospitalizations. Adjusted analyses increased the c-indices to
between 0.65 and 0.70. The kappa for agreement between criteria was 0.80 for the 2003 and
2012 Beers, 0.58 for the 2012 Beers and STOPP, and 0.59 for the 2003 Beers and STOPP. For
the three outcomes, 2012 Beers had the highest sensitivity (61.2%-71.2%) and the lowest
specificity (41.2%-70.7%) while STOPP criteria had the lowest sensitivity (53.8%-64.7%) but
the highest specificity (47.8%-78.1%).
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CONCLUSIONS: All three criteria were modestly prognostic for ADEs, EDs, and
hospitalizations with STOPP slightly outperforming Beers. With low sensitivity, low specificity,
as well as low agreement between the criteria, further updates to each criteria are needed to
develop a better predictive tool.
Key words: Beers Criteria, STOPP Criteria, inappropriate prescribing, adverse drug events
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INTRODUCTION
A potentially inappropriate medication (PIM) exists when the risk of adverse events due
to treatment outweighs the clinical benefit (1). PIMs are associated with adverse health and
economic outcomes (2-11), making detection and prevention a primary goal of clinicians, payers,
and policymakers. Since its development in 1991 (12), the Beers Criteria has become the most
widely used and recognized explicit criteria for the detection of PIMs in older adults (8, 13, 14).
The criteria were updated in 1997 (15), 2003 (16), and again in 2012 by an American Geriatrics
Society (AGS) expert panel and includes drugs to always avoid, drugs to use with caution, and
drug-disease interactions (17).
The Screening Tool of Older Persons’ Prescriptions (STOPP) Criteria is an alternative
criteria developed in 2008 by a European consensus group (1). STOPP is organized by
physiological system and includes drugs to avoid, drug-drug and drug-disease interactions, and
therapeutic duplication to define PIMs. It is purported to be more effective in a European
population where many of the medications considered inappropriate by the Beers Criteria are not
available (1, 18). As a result, STOPP has little overlap with the 2012 Beers Criteria – 55% of the
65 criteria are not found in 2012 Beers (19).
STOPP and the 2003 Beers have been compared in European populations where STOPP
identified more PIMs and increased the odds of having a serious adverse drug event (ADE) by
85% (20-26). A study conducted in Spain compared the 2003 Beers and STOPP along with the
updated 2012 Beers Criteria (27). The PIM prevalence was 24.3%, 35.4%, and 44% for 2003
Beers, STOPP, and 2012 Beers and the agreement between 2012 Beers and STOPP was 0.35.
That study did not compare the criteria on adverse outcomes.
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Because of the lack of evidence comparing Beers with STOPP in a United States (US)
population, a comparison of the ability of each criteria to predict relevant clinical outcomes is
warranted (19). Therefore, the current study sought to compare the predictive validity of 2003
Beers, 2012 Beers, and STOPP using three outcome measures: 1) ADEs, 2) all cause emergency
department (ED) visits, and 3) all cause hospitalizations. Further, the prevalence of PIMs
detected with each criteria was investigated as well as measures of agreement between the
criteria.
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METHODS
Data source
The study sample was selected from a 10% random sample of the proprietary Lifelink
Health Plans Claims Database comprised of administrative claims from 80 Managed Care
Organizations within the US. The data capture the health claims data of the elderly enrolled in
health plans offering employer sponsored coverage and Medicare Advantage plans but do not
capture data for persons enrolled in traditional Medicare.
Study subjects and design
We used a retrospective cohort study design. Inclusion into the cohort was based on a
person being at least 65 years old and having at least 9 months of continuous medical and
pharmacy coverage, including a 6 month pre-index period and a minimum 3 months of follow-up
between January 1, 2006 and December 31, 2009. The index date was defined as the first day of
the seventh month of continuous eligibility. Individuals were followed until the end of
continuous enrollment, the end of the study period, or until an outcome event occurred. Because
full medical and pharmacy claims data may not be captured, individuals with the payer identified
as “Medicaid” were excluded as this group may have additional insurance or incomplete records.
Potentially inappropriate medication exposure
Exposure definitions were created according to the 2003 (16) and 2012 Beers Criteria
(17) and the STOPP Criteria (1). Therapeutic duplication, present as an over-arching item in
STOPP, was excluded as this is not unique to the elderly population and was deliberately
excluded from the Beers Criteria (17). Additionally, dabigatran (2012 Beers) was not on the
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market during the time period of this study (2006-2009) and propoxyphene (2003 Beers) was not
included because it is no longer on the market. Otherwise, all items from each criteria were
included.
Drug-only criteria were mapped using the Medi-Span Generic Product Identifier (GPI,
Wolters Kluwer Health, Philadelphia, PA) classification system and the American Hospital
Formulary Service Pharmacologic-Therapeutic Classification codes (AHFSCC). These
hierarchical coding systems allowed for classification from the drug class, individual
medications, formulations (e.g. extended release), or dosing of individual products.
Disease-dependent PIM definitions were based on International Classifications of
Disease, 9th Revision, Clinical Modification (ICD-9-CM) codes in conjunction with the GPI and
AHFSCC medication codes. As an initial basis for defining disease concepts, the validated
Clinical Classification Software (CCS) codes were used to map ICD-9-CM definitions (28).
These codes were compared to other validated coding algorithms used by the Center for
Medicare and Medicaid Services (CMS) (29), Agency for Healthcare Research and Quality
(AHRQ) (30), and coding algorithms used widely in administrative claims data (31, 32).
Identified codes were included if they were present in at least two of these sources.
For disease states not defined using the above sources, literature searches were performed
on PubMed using “ICD-9” and “administrative claims” with a description of the disease.
Additionally, a manual search of an ICD-9-CM dataset and web pages for ICD-9-CM coding
were queried using disease specific terms
(http://www.cms.gov/medicare-coverage-database/staticpages/icd-9-code-lookup.aspx;
http://icd9.chrisendres.com/). A review of all code selections was conducted by two clinical
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pharmacists with experience in administrative claims research and a geriatric pharmacy
specialist. (The full details of PIM disease definitions are provided in Supplement 1.)
A time varying approach was used to assess PIM exposure as a monthly binary variable.
For drug-only criteria, a subject was only considered exposed to a PIM for the month a
medication was dispensed. For PIM definitions based on co-existing disease states, a patient was
considered to have that disease in the month of the first inpatient or non-ancillary outpatient
claim with a primary or secondary diagnosis for that disease and for all subsequent months of the
study.
Outcome variables
ADEs were based on ICD-9-CM codes previously used for surveillance in hospital claims
data (33). A similar manual search strategy was performed with the terms “drug-induced”,
“adverse effect”, “caused by”, “poisoning”, “drug”, and “allergy” appearing in code descriptions.
ADEs were classified based on the subgroups in the original publication (33) with the addition of
those identified through the manual search and the removal of ADEs specific to the perinatal
period. (ADEs identified in this study, along with the ICD-9-CM codes and rates, are available
in Supplement 2.)
All-cause ED visits were defined by procedure and place of service codes and
hospitalizations were identified by unique confinement numbers. ADEs, all-cause ED visits, and
all-cause hospitalizations were considered separate outcomes; therefore, an individual could
experience one or more of the outcomes.
Subject characteristics
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Cohort demographics and plan characteristics were determined at the beginning of the
post-index period. Age was categorized as 65-74, 75-84, and 85 years and older. Region was
classified as South, West, East, and Midwest. Insurance coverage was categorized into five
categories based on payer type (Medicare or commercial) and plan type: HMO (health
maintenance organization, non-HMO (Preferred Provider Organization, Consumer Directed,
Indemnity, Point of Service), or unknown.
Comorbidities were based on the Charlson Comorbidity Index using the ICD-9-CM
coding algorithms by Quan et al. (32) and were assessed during the 6 month pre-index period.
Use of long term care was determined during the pre-index period and included the use of skilled
nursing facilities, nursing homes, or hospice care. Additionally, prescription utilization was
evaluated separately as the total number of prescription fills and refills annualized to number per
12 months as well as the total number of unique drug classes used during the post-index period.
Data analysis
Baseline variables for the total cohort were compared for those exposed to a PIM from at
least one of the three criteria using two-sided Student t-tests for continuous variables and the
Chi-square tests for categorical variables. Unadjusted and adjusted Cox proportional hazards
models were used to estimate the relationship between PIM exposure and outcomes. In order to
preserve the temporal relationship between PIM exposure and outcomes, individuals having an
outcome during the pre-index period were excluded in the model assessing the influence of PIMs
on that particular outcome but were included in models exploring one of the other two outcomes.
Three time-varying models and one time invariant approach were estimated to explore the
temporal relationship between PIM exposure and outcome. The primary model assessed PIM
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exposure in month t(i) and looked for an outcome in month t(i+1); providing stronger assurances
that the exposure preceded the experience of the outcome event. The alternative time-varying
model assessed exposures and outcomes within the same month. An additional third time-
varying approach used a once-exposed-always-exposed exposure classification where a subject
was considered exposed the first month a PIM was detected and all subsequent months.
Dummy variables were created for each covariate. Reference categories were as follows:
Age 65-74; Male gender, East region, and Medicare HMO insurance coverage. The Charlson
Comorbidity Index diseases were used as individual binary disease states. Prescription
utilization variables were considered to possibly exist along the causal pathway and were
excluded from the primary analyses. Sensitivity analyses were conducted which considered each
prescription utilization variable as a categorical and continuous variable. Additionally, separate
models were estimated which stratified the cohort by these measures. The proportionality
assumption was evaluated for each covariate in models by specifying interaction terms between
each covariate and log-time – where statistically significant coefficients would indicate a
violation of the assumption. Hazards ratios and 95% confidence intervals are reported.
To compare the predictive validity of the criteria, a c-index specifically developed for
time-varying models was used (34). The c-index is analogous to the c-statistic often used with
logistic regression and ranges between 0.5 and 1 where a value of 0.5 indicates model prediction
no better than chance and a value of 1 indicates a model which predicts events perfectly.
Concordance or discordance occur when the predictor score for the individual having an event is
greater or lesser than individuals not having an event at that time (34).
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Additionally, Cohen’s kappa was calculated to assess the person-level agreement
between all possible pairwise PIM criteria. The sensitivity and specificity of each of the PIM
criteria were calculated using each of the outcome measures and a composite outcome measure
as gold standards. All analyses were conducted using SAS version 9.3 (SAS Institute, Inc., Cary,
NC).
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RESULTS
A total of 538,532 individuals were 65 or older during the time period January 1, 2006
and December 31, 2009. Applying eligibility inclusion criteria, 257,206 had at least 9 months of
continuous medical insurance enrollment and 175,696 also had 9 months of continuous
pharmacy benefit enrollment. Combined, 175,581 had at least 9 months continuous enrollment
with both medical and pharmacy benefit during the study period. An additional 1,306 (<1%)
individuals were excluded having "Medicaid" identified as the payer type. The final cohort
consisted of 174,275 individuals representing 32.4% of the original elderly sample (Supplement
3). The mean follow up time of the cohort was slightly over 2 years (24.9 months, Median 27.0
months, IQR 12-39 months) and the cohort contributed a combined 361,621 person-years.
Baseline cohort demographics by PIM exposure are presented in Table 1.
Over the entire post-index period, 72,493 (41.6%) of the cohort were exposed to at least
one of the criteria and 19.7% were exposed to all three. Exposure to at least one PIM from 2012
Beers criteria was 34.1%, 2003 Beers 32.2%, and STOPP 27.6%. Overall exposure for those
experiencing outcome events was nearly double that of the total cohort and tended to be 1.5 to 2
times more prevalent for individual items. Person-level agreement between each of the PIM
criteria, measured by Cohen’s kappa, was “good” between 2012 and 2003 Beers (κ = 0.80, Table
2), and “moderate” between STOPP and the 2012 and 2003 Beers (κ = 0.58 and 0.59) (35).
The top 5 individual PIMs for each criteria included many of the same medication groups
but differed in prevalence because of different definitions of inappropriateness. A “use with
caution” criteria which included SSRIs, SNRIs, antipsychotics, and other medications associated
with syndrome of inappropriate anti-diuretic hormone (SIADH) was the most prevalent PIM for
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the 2012 Beers (16.2% of cohort). This was followed by benzodiazepines (11.3%), skeletal
muscle relaxants (6.6%), non-benzodiazepine hypnotics (5.8%), and NSAIDs (5.4%). The top
five 2003 Beers PIMs included anticholinergics and first generation antihistamines (19.4%),
SSRIs (“with caution”, 10.5%), benzodiazepines (11.2%), muscle relaxants and antispasmodics
(7.4%), and long-term NSAID use (5.1%). STOPP PIMs included NSAIDs (16.2%), opioids
(4.8%), beta-blockers (4.7%), corticosteroids (3.8%), and first generation antihistamines (3.8%).
(Complete PIM exposure prevalence is available in Supplements 4-6.)
A total of 1,911 individuals with a post-index ADE in the cohort (67 ADEs per 10,000
person-years, 1.12% of the total cohort) after excluding 3,558 people who had pre-index adverse
events. Additionally, 24,614 individuals were excluded who had a pre-index ED visit and an
additional 29,864 had a post-index event (140 ED visits per 1,000 person-years, 17.1% of the
total cohort). Post-index hospitalizations occurred for 16,444 persons (67 hospitalizations per
1,000 person-years, 9.4% of the total cohort) with 22,190 individuals excluded with
hospitalizations occurring in the pre-index period. The associations of demographic and health-
related characteristics with each outcome are shown in Supplement 7.
PIM exposure was strongly associated with all study outcomes in both adjusted and
unadjusted models (Table 3). In the primary unadjusted model, PIM exposure was associated
with a 2 to 3 fold increase risk across all outcomes for 2003 Beers, 2012 Beers, and STOPP. The
associations were similar across the three outcome measures. A stronger relationship between
PIM exposure with all three of the criteria and each of the three outcomes (HRs: 3.67 – 5.30) was
observed in the time varying models that assessed exposure and outcome in the same month.
The time dependent once exposed always exposed model found more modest associations
between all the PIM criteria (HRs: 1.30 – 1.76), however all remained significant. The hazard
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ratios for the STOPP criteria in the primary time varying model trended higher than those for
either of the Beers criteria.
For the primary unadjusted model, the c-indices were similar for each of the criteria for
each of the outcomes and indicated modest levels of discrimination with c-indices between 0.58
and 0.61 (Table 4). When the models included the pre-index covariates, the levels of
discrimination increased to 0.65 to 0.70 and were similar across the criteria for each of the
outcomes. The model that assessed PIM exposure and outcome in the same month had the
highest measures of discrimination than the other models. Inclusion of prescription utilization
measures as covariates increased the discrimination of the models less than 1% and stratification
had no significant effect. The sensitivity and specificity of the 2012 Beers, 2003 Beers, and
STOPP for the separate composite outcomes are shown in Table 5.
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DISCUSSION
In studies using the Beers Criteria, PIM rates of 40-50% are common and have ranged as
low as 12% (7, 25, 36, 37) while rates for STOPP have ranged from 13-70% (20, 21, 23, 38, 39).
Our study found 41.6% of the cohort to be exposed to at least one of the criteria. The 2003 and
2012 Beers Criteria identified PIMs in 32.2% and 34.1%, and 27.6% of the cohort were
classified as having a PIM using the STOPP Criteria. These rates are similar to a study in Spain
comparing the three criteria in an ambulatory population (27). Differences between criteria with
similar drug classes are due to inherent differences in the criteria definitions.
We found that exposure to a PIM from any criteria was associated with an increased risk
of ADEs, ED visits, and hospitalizations. Individuals with exposure to PIM from STOPP had
slightly higher risks than either of the Beers. Despite the slightly higher risk associations for
STOPP compared to Beers, there were only marginal differences in discrimination between the
criteria. 2012 Beers performed better in terms of sensitivity across all outcomes but was less
specific while STOPP was less sensitive but more specific.
For the Beers criteria, the slightly lower performance appears to be a result of higher
exposures resulting in more false-positives weakening the association with outcomes. The
STOPP detected only 53% of individuals having any outcome while the 2012 Beers detected an
additional 7% of individuals having each outcome. When the combined “any criteria” exposure
was considered, sensitivity increased for ADEs, ED visits, and hospitalizations. Overall, the
combined exposure had a sensitivity of 71.4% and specificity of 67.4% for the composite
outcome. Therefore, future updates of the Beers Criteria should consider evidence-based
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refinement of the criteria to include more drug classes that are predictive of serious adverse
outcomes (40, 41).
AGS has adapted the Beers Criteria into a mobile application and a pocket guide for the
practicing clinician who they acknowledge as the target audience (17). However, Beers Criteria
have been widely used by researchers, pharmacy benefit managers, and policy-makers – greatly
broadening the impact of the Beers Criteria over the last twenty years. For example, the criteria
have been used by the CMS and the National Committee for Quality Assurance (NCQA) as
quality indicators in long-term care and ambulatory settings (42). There have even been cases of
“misuse” of the criteria to deny coverage of medications (43). Given this broad impact and
implications beyond education and prescribing, future updates and further research should
identify medications which pose the largest safety risk and are the most predictive of important
outcomes such as ADEs, ED visits, and hospitalizations.
One of the notable limitations of this study is the outcomes measures selected. We used a
narrow set of ICD-9-CM codes specific to drug-induced syndromes to define an adverse drug
event, some of which are based on supplementary E-codes. These codes were based on previous
work which measured the performance of these codes as an ADE surveillance system. They
found that the codes had an overall sensitivity and specificity of 55% and 97% for ADEs causing
hospital admission and positive predictive value greater than 70% (33). Though these codes may
have only detected half of all adverse drug events in that study, the codes can be expected to
detect true ADEs. Conversely, the all-cause hospitalizations and ED visits are not specific to
ADE events and may have higher sensitivity detecting serious ADEs but will be less specific.
For example, up to 31% of hospitalizations may be medication related (44) leaving two thirds
that are not. This should be considered when interpreting our findings, particularly when we
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report the sensitivity and specificity which should not be interpreted in the conventional fashion
as measures of diagnostic or screen accuracy for a specified verified outcome.
This study was strengthened by considering the temporal relationship of exposure and
outcomes with a time-varying approach. This allowed for the observation of the initial period of
PIM exposure when adverse events may be more apt to occur (45). This method also allows
individuals to move to and from exposed and unexposed status taking into account changes,
additions, and discontinuations of therapy. Our primary model in which exposure was assessed
in a month and outcomes assessed in the following month strongly preserves the temporal
relationship where exposure precedes outcomes. Though the month-to-month model provided
stronger associations between exposure and outcome, reverse causality may explain the stronger
association.
Non-prescription medications, such as aspirin or NSAIDs, and prescriptions not
processed through claims were not present in the data. For example, inappropriate use of proton
pump inhibitors based on STOPP has been highly prevalent and its underrepresentation in our
data may bias the associations between STOPP and the outcomes toward the null. The absence
of medications considered by all three criteria sets would have a similar but balanced effect.
Similarly, disease-dependent PIM definitions may suffer from missing data due to undercoding
(46, 47). Thus, our findings are likely conservative as more individuals are likely to be exposed
to PIMs than were observed in this study.
We excluded the therapeutic duplication criteria from the STOPP PIM definition because
this item has been specifically mentioned for exclusion from the Beers Criteria as it is not a
problem unique to the elderly (41). While therapeutic duplication has been reported to have a
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prevalence of nearly 5%, it has not been associated with ADEs in published studies (5, 38). Our
exclusion of this item may have decreased the exposure prevalence and the association of
STOPP with the outcomes.
The most prevalent PIM from the 2012 Beers criteria considered “use with caution”
medications because of the risk of SIADH. Based on the original wording of 2012 Beers, this
criterion did not require an individual to have had previous episodes, compared to STOPP which
did require a previous diagnosis of hyponatremia or SIADH. Thus, all individuals exposed to
these commonly used medications, including selective serotonin and norepinephrine reuptake
inhibitors, were considered exposed to the Beers Criteria. While this may over-estimate the
exposed, the time-varying exposure approach accounted for the risk associated with new
exposure when persons are at greater risk of experiencing adverse events.
The administrative claims capture the healthcare utilization of members enrolled in
commercial coverage and Medicare Advantage plans and would be expected to be generalizable
to that population. Individuals covered under traditional Medicare are not included. This
population may differ from the general Medicare population by demographic characteristics such
as income status, education, and health behaviors which could not be compared in the current
study.
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CONCLUSIONS
This was the first study to compare the predictive validity of the updated Beers Criteria to
the STOPP Criteria in a population of older adults as well as the first application of the full Beers
Criteria including drug-disease items in the US. Our study showed low agreement and no
significant differences between the two iterations of the Beers Criteria and the STOPP Criteria in
the level of discrimination for ADEs, ED visits, and hospitalizations, though each was
moderately prognostic of these outcomes. Future evidence-guided updates of these widely used
tools should identify medications and medication classes that may increase the predictive ability
of the criteria.
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ACKNOWLEDGEMENTS
Conflict of Interest Checklist:
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Financial/Personal
ConflictsJDB CL LCH JTP BCM
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Affiliation
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Grants/Funds x x x x x
Honoraria x x x x x
Speaker Forum x x x x x
Consultant x x x x x
Stocks x x x x x
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Royalties x x x x x
Expert Testimony x x x x x
Board Member x x x x x
Patents x x x x x
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BCM was paid by the International Society for Pharmacoeconomics and Outcomes Research
(ISPOR) to teach courses in retrospective database analysis. This study was unrelated to that
course content and ISPOR had no affiliation or review of the submitted work. BCM received a
grant (NIH Grant # 1UL1RR029884) which supported acquisition of the data used in this study.
CL is a consultant for eMaxHealth Systems on unrelated studies. LCH received a grant from
MedEdPortal/Josiah Macy Foundation on interprofessional education development and served
as a consultant for the Arkansas Foundation for Medical Care drug safety quality improvement
projects. LCH has stock in Cardinal Health and CareFusion and has received royalties from the
American Society of Healthsystem Pharmacists for a pharmacy textbook which are unrelated to
this work.
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JDB is now the University of Kentucky, Humana, Pfizer Doctoral Fellow at the Institute for
Pharmaceutical Outcomes and Policy in Lexington, KY. This work was completed before taking
this new position and the aforementioned companies had no involvement in the concept, design,
interpretation, or drafting of this manuscript.
We do not believe these are potential conflicts of interest, but report them in the interest of full
disclosure.
Author Contributions: Brown: study concept and design, data analysis and interpretation,
preparation and editing of manuscript. Hutchison: study concept and design, data interpretation,
editing of manuscript. Li: study design, data analysis and interpretation, editing of manuscript.
Painter: study concept and design, data interpretation, editing of manuscript. Martin: study
concept and design, data analysis and interpretation, preparation and editing of manuscript.
Sponsor’s Role: This project was supported by the UAMS Translational Research Institute (NIH Grant #
1UL1RR029884) which supported acquisition of the data. The sponsor had no other role in this study.
Meeting submission: This study has been accepted as a poster presentation at the International
Society for Pharmacoeconomics and Outcomes Research (ISPOR) 17th Annual European
Congress, November 8-12, 2014, Amsterdam, the Netherlands.
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26
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487
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495496
497
Table 1: Baseline Characteristics for Cohort and Those Exposed to 2012 Beers, 2003
Beers, and STOPP criteria
Characteristics
Total Cohort
N=174,275
No. (%)
2012 Beers
N=59,426
No. (%)
2003 Beers
N=56,144
No. (%)
STOPP
N=48,121
No. (%)
Age (years)*
65-74
75-84
85 and older
128,306 (73.6)
34,637 (19.9)
11,332 (6.5)
37,150 (62.5)
17,098 (28.8)
5,178 (8.7)
36,603 (65.2)
14,991 (26.7)
4,550 (8.1)
30,951 (64.3)
13,098 (27.2)
4,072 (8.5)
Female* 94,588 (54.3) 34,779 (58.5) 33,997 (60.6) 27,809 (57.8)
Insurance Type*
Medicare HMO
Medicare non-HMO
Commercial HMO
Commercial non-HMO
Unspecified
22,570 (13.0)
24,992 (14.3)
20,432 (11.7)
96,412 (55.3)
9,869 (5.7)
11,071 (18.6)
8,995 (15.1)
5,449 (9.2)
31,116 (52.4)
2,795 (4.7)
9,907 (17.7)
8,357 (14.9)
5,311 (9.5)
29,868 (53.2)
2,701 (4.8)
8,630 (17.9)
7,248 (15.1)
4,755 (9.9)
25,214 (52.4)
2,274 (4.7)
Region
27
East
Midwest
South
West
35,987 (20.7)
57,514 (33.0)
43,528 (25.0)
37,246 (21.4)
11,739 (19.8)
20,388 (34.3)
14,294 (24.1)
13,005 (21.9)
11,333 (20.2)
19,219 (34.2)
13,393 (23.9)
12,199 (21.7)
10,233 (21.3)
16,688 (34.7)
11,508 (23.9)
9,692 (20.1)
Charlson Co-morbidity
Index (Pre-index)
Mean (SD)*
0-1
2-3
3+
1.3 (1.7)
117,690 (67.5)
39,736 (22.8)
16,849 (9.7)
1.6 (1.9)
35,013 (58.9)
16,513 (27.8)
7,900 (13.3)
1.6 (1.8)
33,803 (60.2)
15,265 (27.2)
7,076 (12.6)
1.8 (2.0)
27,111 (56.3)
13,744 (28.6)
7,266 (15.1)
Prescription utilization
Total prescription fills
per 12 months
Mean (SD)*
Unique drug classes
Mean (SD)*
12.1 (36.5)
4.4 (5.3)
20.5 (17.7)
8.8 (5.5)
20.0 (16.3)
8.7 (5.5)
19.4 (16.3)
8.8 (5.7)
Long term care 3,682 (2.1) 2,287 (3.9) 2,088 (3.7) 2,126 (4.42)
28
Follow-up time
(months)
Mean (SD)*
Median
IQR
24.9 (13.2)
27.0
12-39
29.1 (11.8)
36.0
18-39
29.4 (11.6)
36.0
19-39
30.1 (11.2)
36.0
12-39
*p<0.01 for comparison between “Any Exposure” and Total Cohort.
Significant differences were not observed between criteria
Abbreviations: HMO (health maintenance organization); SD (standard deviation); IQR
(inter-quartile range)
29
498
499
Table 2 - Inappropriate prescribing criteria person-level concordance and agreement
Exposure to inappropriate prescribing No. (% of cohort)
N=174,275
Any exposure to criteria
Exposed to more than one criteria
Exposed to all criteria
72,493 (41.6)
58,915 (32.7)
34,283 (19.7)
Concordance Between Criteria No. (% Agree)
2012 Beers*2003 Beers
2012 Beers*STOPP
2003 Beers*2012 Beers
2003 Beers*STOPP
STOPP*2012 Beers
STOPP*2003 Beers
2012 Beers*All Criteria
2003 Beers*All Criteria
STOPP*All Criteria
50,182 (84.4)
38,006 (64.0)
50,182 (89.4)
37,293 (66.4)
38,006 (79.0)
37,293 (77.5)
59,426 (82.0)
56,144 (77.4)
48,121 (66.4)
Agreement Between Criteria Cohen’s Kappa
2012 Beers*2003 Beers
2012 Beers*STOPP
2003 Beers*STOPP
2012 Beers*All Criteria
2003 Beers*All Criteria
STOPP*All Criteria
0.80
0.58
0.59
0.84
0.80
0.70
30
Table 3: Adjusted and Unadjusted Hazards ratios for the 2012 Beers, 2003 Beers, and STOPP criteria for time varying
and non-time varying models.
Unadjusted models (exposure only) Adjusted modelsa
Criteria Time-varying monthly lag (Primary Model)b
ADEs Emergency Hospitalization ADEs Emergency Hospitalization
2012 Beers 2.51 (2.33-2.70) 2.21 (2.16-2.25) 2.25 (2.20-2.30) 2.17 (2.01-2.34) 2.00 (1.96-
2.04)
2.03 (1.98-2.07)
2003 Beers 2.65 (2.46-2.85) 2.29 (2.25-2.34) 2.31 (2.26-2.37) 2.33 (2.16-2.52) 2.14 (2.10-
2.19)
2.16 (2.11-2.21)
STOPP 2.89 (2.68-3.12) 2.66 (2.60-2.72) 2.80 (2.74-2.87) 2.43 (2.24-2.63) 2.38 (2.32-
2.43)
2.46 (2.40-2.52)
Time-varying month to monthc
ADEs Emergency Hospitalization ADEs Emergency Hospitalization
2012 Beers 4.33 (4.11-4.56) 4.38 (4.31-4.44) 4.27 (4.20-4.34) 3.67 (3.48-3.87) 3.93 (3.87- 3.75 (3.68-3.81)
1
2
3.99)
2003 Beers 5.01 (4.75-5.28) 4.89 (4.81-4.97) 4.76 (4.68-4.84) 4.30 (4.08-4.54) 4.51 (4.44-
4.58)
4.32 (4.25-4.40)
STOPP 5.21 (4.91-5.52) 5.18 (5.09-5.28) 5.30 (5.20-5.41) 4.18 (3.92-4.44) 4.52 (4.43-
4.60)
4.47 (4.38-4.56)
Time-dependent once exposed, always exposedd
ADEs Emergency Hospitalization ADEs Emergency Hospitalization
2012 Beers 1.71 (1.57-1.87) 1.45 (1.42-1.48) 1.46 (1.42-1.49) 1.43 (1.31-1.56) 1.32 (1.29-
1.35)
1.30 (1.26-1.33)
2003 Beers 1.66 (1.53-1.81) 1.39 (1.36-1.42) 1.38 (1.35-1.42) 1.45 (1.33-1.58) 1.32 (1.29-
1.35)
1.30 (1.26-1.33)
STOPP 1.76 (1.62-1.91) 1.50 (1.46-1.53) 1.54 (1.51-1.58) 1.47 (1.35-1.60) 1.37 (1.34-
1.40)
1.38 (1.34-1.42)
3
4
Ever exposuree
ADEs Emergency Hospitalization ADEs Emergency Hospitalization
2012 Beers 3.06 (2.77-3.37) 2.34 (2.28-2.39) 2.58 (2.51-2.65) 2.60 (2.35-2.88) 2.08 (2.03-
2.13)
2.27 (2.21-2.34)
2003 Beers 2.83 (2.57-3.12) 2.18 (2.13-2.23) 2.33 (2.27-2.39) 2.49 (2.25-2.74) 2.01 (1.97-
2.06)
2.15 (2.09-2.21)
STOPP 3.11 (2.83-3.42) 2.44 (2.38-2.49) 2.71 (2.64-2.78) 2.64 (2.39-2.91) 2.18 (2.13-
2.23)
2.38 (2.32-2.45)
a Covariates included: Age, gender, insurance status, region, long term care, and Charlson comoribidities
b Outcome events associated with time-varying exposure in the preceding month (i.e. March outcome associated with
February exposure
c Outcome events associated with time-varying exposure in the same month
d Once exposed to a criteria, always exposed whether or not exposure status changes
5
6
e Exposed at any point during the post-index follow up period
Sample size for final model excluding pre-index events: ADEs (170,717); ED visits (147,661); Hospitalizations (152,085)
7
500
501
8
Table 4: C-indices and 95% confidence intervals for 2003 Beers, 2012 Beers, and STOPP criteria for the time varying and
non-time varying models
Unadjusted Model (exposure only) Adjusted Modela*
Time-varying monthly lag (Primary Model)b
Criteria ADEs Emergency Hospitalization ADE Emergency Hospitalization
2012 Beers 0.603 (0.597-
0.609)
0.585 (0.583-
0.587)
0.590 (0.588-
0.592)
0.688 (0.677-
0.700)
0.652 (0.649-
0.655)
0.673 (0.670-
0.677)
2003 Beers 0.605 (0.599-
0.611)
0.585 (0.583-
0.587)
0.588 (0.586-
0.590)
0.695 (0.684-
0.706)
0.653 (0.650-
0.656)
0.673 (0.670-
0.676)
STOPP 0.607 (0.601-
0.614)
0.590 (0.588-
0.592)
0.599 (0.597-
0.601)
0.695 (0.685-
0.706)
0.661 (0.658-
0.664)
0.683 (0.680-
0.686)
Time-varying month to monthc
ADEs Emergency Hospitalization ADE Emergency Hospitalization
2012 Beers 0.642 (0.639- 0.635 (0.634- 0.636 (0.634- 0.733 (0.723- 0.709 (0.706- 0.720 (0.717-
9
10
0.645) 0.636) 0.638) 0.744) 0.712) 0.723)
2003 Beers 0.646 (0.643-
0.650)
0.635 (0.634-
0.636)
0.637 (0.636-
0.638)
0.741 (0.730-
0.751)
0.708 (0.705-
0.711)
0.721 (0.718-
0.724)
STOPP 0.642 (0.638-
0.647)
0.626 (0.625-
0.628)
0.634 (0.633-
0.634)
0.741 (0.730-
0.752)
0.707 (0.704-
0.710)
0.726 (0.723-
0.729)
Time-varying once exposed, always exposed
ADEs Emergency Hospitalization ADE Emergency Hospitalization
2012 Beers 0.566 (0.557-
0.574)
0.548 (0.546-
0.551)
0.551 (0.549-
0.554)
0.666 (0.654-
0.679)
0.628 (0.624-
0.631)
0.653 (0.648-
0.655)
2003 Beers 0.563 (0.554-
0.571)
0.542 (0.540-
0.545)
0.544 (0.541-
0.546)
0.667 (0.655-
0.680)
0.626 (0.622-
0.629)
0.651 (0.647-
0.654)
STOPP 0.567 (0.559-
0.574)
0.548 (0.546-
0.551)
0.554 (0.552-
0.557)
0.670 (0.658-
0.682)
0.630 (0.627-
0.634)
0.657 (0.652-
0.660)
11
12
Ever exposuree
ADEs Emergency Hospitalization ADE Emergency Hospitalization
2012 Beers 0.566 (0.557-
0.574)
0.548 (0.546-
0.551)
0.551 (0.549-
0.554)
0.666 (0.654-
0.679)
0.628 (0.624-
0.631)
0.652 (0.648-
0.655)
2003 Beers 0.563 (0.554-
0.571)
0.542 (0.540-
0.545)
0.544 (0.541-
0.546)
0.667 (0.655-
0.680)
0.626 (0.622-
0.629)
0.650 (0.647-
0.654)
STOPP 0.636 (0.624-
0.647)
0.599 (0.596-
0.603)
0.612 (0.608-
0.615)
0.713 (0.701-
0.725)
0.659 (0.656-
0.663)
0.687 (0.683-
0.691)
*Covariate only model c-indices: ADE 0.664 (0.651-0.676); Emergency 0.606 (0.603-0.610); Hospitalization 0.647 (0.644-
0.651)
a Covariates included: Age, gender, insurance status, region, long term care, and Charlson comoribidities
b Outcome events associated with time-varying exposure in the preceding month (i.e. March outcome associated with
February exposure
13
14
c Outcome events associated with time-varying exposure in the same month
d Once exposed to a criteria, always exposed whether or not exposure status changes
e Exposed at any point during the post-index follow up period
Sample size for final model excluding pre-index events: ADEs (170,717); ED visits (147,661); Hospitalizations (152,085)
15
16
Table 5: Sensitivity and specificity of PIM criteria predicting study outcomes
Sensitivity (%) Specificity (%)
2012 Beers
ADEs
Emergency Visits
Hospitalizations
Composite Outcome
71.2
61.2
64.3
60.6
41.2
70.7
69.0
73.9
2003 Beers
ADEs
Emergency Visits
Hospitalizations
Composite Outcome
67.7
57.8
60.3
57.3
42.8
72.2
70.4
75.4
STOPP
ADEs
Emergency Visits
Hospitalizations
Composite Outcome
64.7
53.8
57.6
53.4
47.8
78.1
76.3
80.2
All Criteria exposure
ADEs
Emergency Visits
Hospitalizations
Composite Outcome
79.8
71.8
74.8
71.4
30.1
63.2
61.4
67.4
17
502
18
Study supplement to facilitate review only, not intended for publication or for inclusion in the word or table count.
Supplement 1 - Beers and STOPP Criteria PIM disease description, ICD-9-CM coding definitions and source of codes
Disease/Condition and Source
(Multi-level CCS unless specified)
ICD-9-CM Codes
*Additions to CCS from identified sources
Hypertension (7.1) 401.x- 404.x, 405.01,405.09, 405.11,405.19, 405.91,405.99, 437.2
Arthritis, osteo- and rheumatoid (13.2.1; 13.2.2)
714.x; 715.x, 720.0, 721.x*, V13.4
Arrhythmias (7.2.8, 7.2.9) 426.x, V45.x, V53.3, 427.x, 785.0, 785.1
Glaucoma (6.7.3)* 365.x, 377.14*
Chronic Obstructive Pulmonary Disease (COPD) (8.2)
490.x-492.x, 494.x, 496.x
Benign Prostatic Hyperplasia, BPH (10.2.1) 600.x
Atrial Fibrillation (7.2.9.3) 427.31
Depression (5.8.2)* 293.83, 296.x, 300.4,301.12*, 309.0*, 309.1, 311
Nutritional Deficiency (3.5) 260-269,799.4, V12.1
Chronic Kidney Disease (10.1.1, 10.1.3)* 403.x*,404.02*,404.03*,404.12*,404.13*, 404.92*,404.93*,580.x-583.x, 585.x- 588.x, 792.5, V42.0, V45.1, V45.11,V45.12, V56.0, V56.1, V56.2, V56.31, V56.32, V56.8
Obesity (3.11.2) 278.0x,793.91, V85.21-V85.54
Edema (48) 782.3, 276.6
19
503504
505
506
20
Heart Failure (7.2.11) 398.91,402.01, 402.11,402.91, 404.01,404.11, 404.91,404.03, 404.13,404.93, 428.x
Fractures (16.2, 13.5) 800-829, 733.x, 905.0x-905.5x,V13.51, V13.52,V54.1x,V54.2x, V66.4, V67.4
Urinary incontinence (49, 50) 596.5x,599.8x, 625.6, 788.3x, 344.61,596.5x, 599.8, 599.84, 625.6, 788.32, 788.39
Constipation (9.12.1) 564.0x
Gout (51) 274.x
Dementia, cognitive and memory disorders (5.4)
290.x, 293.0, 293.1, 294.x, 331.x, 797
Insomnia (52) 307.41,307.42, 780.51, 780.52
Breast Cancer (2.5) 174.x, 175.x, 233.0, V10.3
Syncope (17.1.1) 780.2
Falls (2603 single-level CCS) E880-E888, E968.1,E987.0, E987.1,E987.2, E987.9,V15.88
Bleeding Disorder, coagulation and hemorrhagic disorders (4.2)
286.x, 287.x, 289.81,289.82, 289.84, 782.7
Urinary Retention (10.1.8.2) 788.2x
Hypotension (7.4.4.1) 458.x
Diarrhea, Intestinal infection, Enteritis (9.1, 9.6.2)
001.x-009.x, 021.1, 022.2, 555.x-556.x, 564.5*
Extrapyramidal Symptoms (EPS) (53, 54) 333.x
Hyponatremia (55, 56) 276.1
Hypogonadism (57) 257.1, 257.2, 257.8, 257.9+
Deep Vein Thrombosis (58, 59) 451.11,451.19, 451.2,451.81,451.9,453.2,453.40,453.51,453.42,453.8,453.9,671.30,671.31,671.33,671.40,671.42,671.44, 671.9,997.2, 999.2
Pulmonary Embolism (58) 415.1, 639.6, 996.7
21
22
Hypoglycemia (60, 61) 250.3, 250.8, 251.0, 251.1, 251.2, 270.3, 775.0, 775.6, 962.3
Stress or mixed urinary incontinence
(49, 50)
625.6, 599.82, 788.33
Epilepsy (6.4) 345.x, 780.3x
Gastric or peptic ulcers (9.4.2, 9.10.1) 531.x-534.x, V12.71
Parkinson's Disease (6.2.1) 332.x
Delirium (Adapted from CCS 5.4) 293.0, 293.1, 290.11,290.30, 290.41
Gastroparesis (62) 536.3
Urinary Catheter V53.6, V58.82
SIADH (55, 56) 253.6
Anorexia (5.15.2 ) 307.1, 307.5x
References to the supplement
1. Medina-Ramon M, Goldberg R, Melly S, Mittleman MA, Schwartz J. Residential exposure to traffic-related air pollution and survival after heart failure. Environ Health Perspect. 2008 Apr;116(4):481-5.
2. Oliphant SS, Wang L, Bunker CH, Lowder JL. Trends in stress urinary incontinence inpatient procedures in the united states, 1979-2004. Am J Obstet Gynecol. 2009 May;200(5):521.e1,521.e6.
3. Anger JT, Saigal CS, Madison R, Joyce G, Litwin MS, Urologic Diseases of America Project. Increasing costs of urinary incontinence among female medicare beneficiaries. J Urol. 2006 Jul;176(1):247,51; discussion 251.
4. Wallace KL, Riedel AA, Joseph-Ridge N, Wortmann R. Increasing prevalence of gout and hyperuricemia over 10 years among older adults in a managed care population. J Rheumatol. 2004 Aug;31(8):1582-7.
5. Asche CV, Joish VN, Camacho F, Drake CL. The direct costs of untreated comorbid insomnia in a managed care population with major depressive disorder. Curr Med Res Opin. 2010 Aug;26(8):1843-53.
23
507
508
509510511
512513514
515516517
518519520
521522523
24
6. Finkelstein MM, Jerrett M. A study of the relationships between parkinson's disease and markers of traffic-derived and environmental manganese air pollution in two canadian cities. Environ Res. 2007 Jul;104(3):420-32.
7. Oliveria SA, Liperoti R, L'italien G, Pugner K, Safferman A, Carson W, et al. Adverse events among nursing home residents with alzheimer's disease and psychosis. Pharmacoepidemiol Drug Saf. 2006 Nov;15(11):763-74.
8. Williams C, Simon TD, Riva-Cambrin J, Bratton SL. Hyponatremia with intracranial malignant tumor resection in children. J Neurosurg Pediatr. 2012 May;9(5):524-9.
9. Movig KL, Leufkens HG, Lenderink AW, Egberts AC. Validity of hospital discharge international classification of diseases (ICD) codes for identifying patients with hyponatremia. J Clin Epidemiol. 2003 Jun;56(6):530-5.
10. Khan N, Abbas AM, Almukhtar RM, Khan A. Prevalence and predictors of low bone mineral density in males with ulcerative colitis. J Clin Endocrinol Metab. 2013 Jun;98(6):2368-75.
11. Anderson FA,Jr, Wheeler HB, Goldberg RJ, Hosmer DW, Patwardhan NA, Jovanovic B, et al. A population-based perspective of the hospital incidence and case-fatality rates of deep vein thrombosis and pulmonary embolism. the worcester DVT study. Arch Intern Med. 1991 May;151(5):933-8.
12. Zhan C, Battles J, Chiang YP, Hunt D. The validity of ICD-9-CM codes in identifying postoperative deep vein thrombosis and pulmonary embolism. Jt Comm J Qual Patient Saf. 2007 Jun;33(6):326-31.
13. Shorr RI, Ray WA, Daugherty JR, Griffin MR. Incidence and risk factors for serious hypoglycemia in older persons using insulin or sulfonylureas. Arch Intern Med. 1997 Aug 11-25;157(15):1681-6.
14. Ginde AA, Blanc PG, Lieberman RM, Camargo CA,Jr. Validation of ICD-9-CM coding algorithm for improved identification of hypoglycemia visits. BMC Endocr Disord. 2008 Apr 1;8:4,6823-8-4.
15. Wang YR, Fisher RS, Parkman HP. Gastroparesis-related hospitalizations in the united states: Trends, characteristics, and outcomes, 1995-2004. Am J Gastroenterol. 2008 Feb;103(2):313-22.
25
524525526
527528529
530531
532533534
535536
537538539540
541542543
544545546
547548549
550551552
553
26
Supplement 2 - Adverse drug event outcome categories, ICD-9-CM coding, and observed post-index rates
Adverse Drug Event Classification
ICD-9-CM Codes N % of all
ADEs
% of cohort
Rate per 10,000 person-years
All Adverse drug events 1191 --- 1.12 67.2
Addendum from manual search (drug-induced anemia, drug-induced glaucoma, etc.)
284.11, 284.12, 285.3, 288.03, 339.3, 359.24, 365.32, 365.32, 357.6,
333.72, 333.85, 528.02, 995.2, 995.23, 995.27
504 42.3 0.30 17.7
Drug psychosis 292.x 255 21.4 0.15 9.0
Other agents (GI agents, vaccines)
909.0, 909.5, 970.x, 971.x, 973.x-979.x,
E858.x, E929.2, E943.x-E949.x
247 20.7 0.14 8.7
Agents affection blood constituents; e.g. Iron, anti-anemic, anticoagulants, etc.
964.x, E858.2, E934.x
203 17.0 0.12 7.1
Agents affecting the CV system
972.x, E942.x147 12.3 0.09 5.2
Dermatitis 692.3, 693.0, 693.8, 693.9 121 10.2 0.07 4.3
Anti-allergy, anti-emetic, pH agents, enzymes, vitamins, other systemic agents
963.x, E858.1, E933.x
105 8.8 0.06 3.7
Hormones, natural and synthetic
962.x, E858, E932.x94 7.9 0.06 3.3
Analgesics, antipyretics, antirheumatics
965.x, E850.x, E936.x73 6.1 0.04 2.6
27
28
Antibiotics and anti-infectives
960.x, 961.x, E856.x, E857.x, E930.x, E931.x
66 5.5 0.04 2.3
Anticonvulsants, antiparkinsonism
966.x, E855.0, E936.x33 2.8 0.02 1.2
Psychotropics 969.x, E853.x, E854.x, E939.x
26 2.2 0.02 0.9
Sedatives, hypnotics 967.x, E851, E852.x, E937.x
19 1.6 0.01 0.7
CNS depressants, stimulants, anesthetics
968.x, E855.x, E938.x, E940.0, E940.x, E941.x
18 1.5 0.01 0.6
Abbreviation: GPI (generic product identifier); AHFSCC (American Hospital Formulary Service Pharmacologic-Therapeutic Classification)
29
554
555
30
Supplement 3 - Application of inclusion criteria to study sample
Excluded those with payer type identified as "Medicaid”(1,306, <1% excluded)
Final Cohort174,275
Combined Medical and Pharmacy Benefits for at least 9 months
during study period175,581
(115, <1% excluded)
Pharmacy Benefits for at least 9 months during study period
175,696(362,836, 67.38% excluded)
Medical Benefits for at least 9 months during study period
257,206(281,326, 52.24% excluded)
Age greater than 65 years during the years 2006 to 2009
538,532
31
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
32
Supplement 4 - 2003 Beers PIM Exposure
Criterion DefinitionADEs
(N=1,191)
ER Visits
(N=29,864)
Hospitalizations
(N=16,444)
Total cohort
(N=174,275)
N % N % N % N %
Any Old Beers Exposure
727 61.02 17402 58.27 9996 60.79 56151 32.22
Drugs always considered inappropriate
Indomethacin 27 2.30 663 2.22 398 2.42 2422 1.39
Pentazocine 1 0.05 33 0.11 21 0.13 105 0.06
Trimethobenzamide 3 0.27 45 0.15 25 0.15 139 0.08
Muscle relaxants and antispasmodics
155 13.00 3575 11.97 2036 12.38 12896 7.40
Flurazepam 2 0.18 39 0.13 26 0.16 139 0.08
Amitriptylline 45 3.74 899 3.01 518 3.15 3381 1.94
Doxepin 16 1.32 176 0.59 102 0.62 627 0.36
Meprobamate 2 0.20 39 0.13 25 0.15 174 0.10
Benzodiazepines 236 19.8 5205 17.4 3068 18.7 19554 11.2
Disopyramide 0 0.00 15 0.05 8 0.05 52 0.03
Digoxin >0.125mg/d 32 2.71 741 2.48 487 2.96 2196 1.26
Dipyramidole 2 0.17 78 0.26 44 0.27 227 0.13
Methyldopa 1 0.08 24 0.08 15 0.09 105 0.06
Reserpine >0.25mg 0 0.03 12 0.04 5 0.03 35 0.02
Chlorpropamide 0 0.02 9 0.03 7 0.04 17 0.01
GI antispasmodics 36 3.05 866 2.90 470 2.86 3172 1.82
Anticholinergics and 407 34.19 9276 31.06 5334 32.44 33809 19.40
33
34
first generation antihistamines
Ergot mesylates 0 0.02 3 0.01 2 0.01 17 0.01
Iron sulfate 3 0.27 66 0.22 53 0.32 122 0.07
Barbiturates (except phenobarbital) unless used for seizures
0 0.02 3 0.01 5 0.03 17 0.01
Meperidine 6 0.47 179 0.60 100 0.61 610 0.35
Ticlopidine 2 0.20 36 0.12 25 0.15 87 0.05
Ketorolac 6 0.54 185 0.62 97 0.59 540 0.31
Amphetamines and anorexants
41 3.45 1024 3.43 605 3.68 3050 1.75
Long term NSAIDs 81 6.77 2093 7.01 1074 6.53 8905 5.11
Fluoxetine 35 2.98 765 2.56 460 2.80 3154 1.81
Stimulant laxatives unless used with opioids
2 0.20 66 0.22 44 0.27 157 0.09
Amiodarone 30 2.56 726 2.43 584 3.55 1603 0.92
Orphenadrine 5 0.40 110 0.37 58 0.35 418 0.24
Guanethidine 0 0.00 0 0.00 0 0.00 0 0.00
Guanadrel 0 0.00 0 0.00 0 0.00 0 0.00
Cyclandelate 0 0.00 0 0.00 0 0.00 0 0.00
Isoxsuprine 0 0.00 3 0.01 0 0.00 0 0.00
Nitrofurantoin 94 7.88 1872 6.27 1179 7.17 5647 3.24
Doxazosin 28 2.33 800 2.68 488 2.97 3242 1.86
Methyltestosterone 0 0.02 0 0.00 2 0.01 17 0.01
Thioridazine 0 0.00 9 0.03 5 0.03 35 0.02
35
36
Mesoridazine 0 0.00 0 0.00 0 0.00 0 0.00
Nifedipine IR 0 0.03 24 0.08 12 0.07 52 0.03
Clonidine 40 3.35 920 3.08 567 3.45 2858 1.64
Mineral oil 0 0.00 3 0.01 2 0.01 0 0.00
Cimetidine 9 0.75 137 0.46 74 0.45 488 0.28
Ethacrynic acid 2 0.18 18 0.06 12 0.07 35 0.02
Dessicated thyroid 6 0.54 161 0.54 74 0.45 749 0.43
Estrogens (oral) 50 4.24 1063 3.56 582 3.54 6640 3.81
Inappropriate prescribing in the presence of disease
Heart Failure: disopyramide
0 0.00 0 0.00 0 0.00 0 0.00
Hypertension: phenylpropanolamine, pseudoephedrine, anorexants, and amphetamines
13 1.07 284 0.95 173 1.05 1098 0.63
Ulcers: NSAIDs and aspirin >325 mg
2 0.18 54 0.18 43 0.26 139 0.08
Seizures: some typical antipsychotics
0 0.02 6 0.02 2 0.01 17 0.01
Clotting disorders: aspirin, NSAIDs, dipyridamole, ticlopidine, clopidogrel
56 4.74 1078 3.61 806 4.90 2370 1.36
Lower urinary tract symptoms of BPH: anticholinergics, antihistamines, GI antispasmodics, muscle relaxants, oxybutynin, flavoxate,
22 1.86 481 1.61 326 1.98 1063 0.61
37
38
antidepressants, decongestants, and tolterodine
Stress Incontinence: alpha-1 blockers, anticholinergics, TCAs, long-acting benzodiazepines
10 0.85 197 0.66 143 0.87 645 0.37
Arrhythmias: imipramine, doxepin, amitriptylline
9 0.77 173 0.58 120 0.73 366 0.21
Insomnia: decongestants, theophylline, methylphenidate, MAOIs, amphetamines
0 0.02 3 0.01 2 0.01 0 0.00
Parkinson’s: metoclopramide, typical antipsychotics, tacrine
5 0.42 69 0.23 53 0.32 157 0.09
Cognitive impairment: barbiturartes, anticholinergics, antispasmodics, muscle relaxants, CNS stimulants
25 2.11 523 1.75 372 2.26 1028 0.59
Depression: benzodiazepines, methyldopa, reserpine, guanethidine
0 0.02 3 0.01 3 0.02 17 0.01
Anorexia/malnutrition: CNS stimulants, fluoxetine
3 0.27 42 0.14 25 0.15 105 0.06
39
40
Syncope/falls: benzodiazepines, TCAs
15 1.27 287 0.96 179 1.09 505 0.29
SIADH: SSRIs 229 19.24 5023 16.82 3014 18.33 18369 10.54
Obesity: olanzapine 0 0.00 3 0.01 2 0.01 0 0.00
COPD: benzodiazepines, propanolol
6 0.47 119 0.40 81 0.49 296 0.17
Constipation: CCBs, anticholinergics, TCAs
23 1.93 436 1.46 301 1.83 976 0.56
41
576577
42
Supplement 5 - 2012 Beers PIM Exposure
Criterion Definition
ADEs
(N=1,191)
ER Visits
(N=29,864)
Hospitalizations
(N=16,444)
Total cohort
(N=174,275)
N % N % N % N %
Any New Beers Exposure
753 63.15 18375 61.53 10634 64.67 59,426
34.10
Drugs always considered inappropriate
First generation antihistamines (single or combination products
90 7.56 2123 7.11 1199 7.29 6884 3.95
Antiparkinsons agents: benztropine, trihexyphenidyl
3 0.23 48 0.16 36 0.22 192 0.11
Antispasmodics: belladonna, clidinium-chlordiazepoxide, dicyclomine, hyoscyamine, propantheline, scopolamine
43 3.64 1033 3.46 561 3.41 3973 2.28
Dipyridamole - oral short acting
2 0.17 78 0.26 44 0.27 227 0.13
Ticlopidine 2 0.20 36 0.12 25 0.15 87 0.05
Nitrofurantoin: long term or Stage 3+ CKD
11 0.89 188 0.63 140 0.85 349 0.20
Alpha-1 blockers: doxazosin, prazosin, terazosin
52 4.37 1436 4.81 844 5.13 5943 3.41
Central alpha agonsists: clonidine, guanabenz,
42 3.49 980 3.28 602 3.66 3120 1.79
43
44
guanfacine, methyldopa, reserpine (>0.1 mg/d)
Antiarrhythmic: Class IA, IC, III
50 4.24 1245 4.17 926 5.63 3189 1.83
Dronedarone 0 0.00 0 0.00 0 0.00 0 0.00
Digoxin >0.125 mg/d 32 2.71 741 2.48 487 2.96 2196 1.26
Nifedipine IR 1 0.03 24 0.08 12 0.07 52 0.03
Spironolactone >25 mg/d with CrCl<30 mL/min
3 0.28 78 0.26 62 0.38 174 0.10
Tertiary TCAs 79 6.67 1445 4.84 832 5.06 5333 3.06
Antipsychotics: 1st and 2nd generation
35 2.97 657 2.20 455 2.77 1220 0.70
Thioridazine, mesoridazine
0 0.00 9 0.03 5 0.03 35 0.02
Barbiturates 2 0.15 30 0.10 18 0.11 157 0.09
Benzodiazepines 233 19.6 5143 17.2 3032 18.4 19600 11.3
Chloral hydrate 4 0.34 39 0.13 15 0.09 105 0.06
Meprobamate 2 0.20 39 0.13 25 0.15 174 0.10
Non-benzodiazepine hyponotics
139 11.71 2822 9.45 1801 10.95 10125 5.81
Ergot mesylates, isoxsuprine
1 0.02 6 0.02 3 0.02 17 0.01
Androgens 8 0.64 137 0.46 76 0.46 593 0.34
Dessicated thyroid 6 0.54 161 0.54 74 0.45 749 0.43
Estrogens, oral or patch 58 4.86 1189 3.98 648 3.94 7616 4.37
45
46
Growth hormone 0 0.00 3 0.01 0 0.00 0 0.00
Insulin, sliding scale 32 2.70 738 2.47 479 2.91 2213 1.27
Megestrol 6 0.47 63 0.21 44 0.27 122 0.07
Chlorpropamide, glyburide
28 2.31 956 3.20 534 3.25 3520 2.02
Metoclopramide, unless for gastroparesis
45 3.79 1030 3.45 673 4.09 2649 1.52
Mineral oil, oral 0 0.00 3 0.01 2 0.01 0 0.00
Trimethobenzamide 3 0.27 45 0.15 25 0.15 139 0.08
Meperidine 6 0.47 179 0.60 100 0.61 610 0.35
Non-COX selective NSAIDs, oral, >75 y/o OR taking oral/IV corticosteroids, anticoagulants, antiplatelets
117 9.80 2918 9.77 1751 10.65 8400 4.82
Indomethacin, Ketorolac
17 1.46 370 1.24 235 1.43 924 0.53
Pentazocine 1 0.05 33 0.11 21 0.13 105 0.06
Skeletal muscle relaxants: carisoprodol, chlorzoxazone, cyclobenzaprine, metaxalone, methocarbamol, orphenadrine
136 11.35 3118 10.44 1735 10.55 11485 6.59
Inappropriate prescribing in the presence of disease
Heart Failure: NSAIDs, CCBs, TZD, cilostazole, dronedarone
59 4.99 1242 4.16 932 5.67 2492 1.43
Syncope: AChEIs, 22 1.88 561 1.88 367 2.23 1011 0.58
47
48
alpha-1 blockers, TCAs, some antipsychotics
Chronic seizures or epilepsy: bupropion, antipsychotics, tramadol
4 0.34 63 0.21 41 0.25 122 0.07
Delirium: TCAs, *Anticholinergics, benzodiazepines, chlorpromazine, corticosteroids, H2RA, meperidine, sedative hypnotics, thioridazine
16 1.34 239 0.80 196 1.19 366 0.21
Dementia and cognitive impairment: *Anticholinergics, benzodiazepines, H2RA, zolpidem, antipsychotics
71 5.95 1496 5.01 1039 6.32 2876 1.65
History of falls or fractures: anticonvulsants, antipsychotics, benzodiazepines, hypnotics, TCAs, SSRIs
114 9.59 2416 8.09 1478 8.99 5054 2.90
Insomnia: decongestants, stimulants, theobromines
1 0.08 6 0.02 5 0.03 35 0.02
Parkinson’s Disease: antipsychotics, antiemetics
10 0.84 146 0.49 107 0.65 296 0.17
Chronic constipation: antimuscarinics, CCBs, 1st generation antihistamines,
76 6.42 1224 4.10 811 4.93 2876 1.65
49
50
anticholinergics, antispasmodics4
History of gastric/duodenal ulcers: NSAIDs
7 0.59 179 0.60 123 0.75 436 0.25
CKD Stage 4+: NSAIDs, triamterene
2 0.17 69 0.23 43 0.26 122 0.07
Urinary incontinence: oral and transdermal estrogen
9 0.72 143 0.48 104 0.63 593 0.34
Lower urinary tract symptoms, BPH: inhaled anticholinergic, *anticholinergics (except antimuscarinics)
37 3.12 899 3.01 607 3.69 2056 1.18
Stress/mixed urinary incontinence: alpha-1 blockers
1 0.08 12 0.04 13 0.08 52 0.03
Use with caution
Aspirin for primary prevention: age >=80
31 2.58 765 2.56 480 2.92 1725 0.99
Dabigatran (not on market during study period)
0 0.00 0 0.00 0 0.00 0 0.00
Prasugrel: >=75 y/o 0 0.00 0 0.00 0 0.00 0 0.00
Antipsychotics, cisplatin, carboplatin, mirtazapine, SNRIs, SSRIs, TCA, vincristine
384 32.26 7890 26.42 4775 29.04 28302 16.24
Vasodilators: syncope 21 1.73 430 1.44 294 1.79 802 0.46
51
578
52
Supplement 6 - STOPP Criteria PIM Exposure
Criterion Definition
ADEs
(N=1,191)
ER Visits
(N=29,864)
Hospitalizations
(N=16,444)
Total cohort
(N=174,275)
N % N % N % N %
Any STOPP Exposure 674 56.59 16168 54.14 9531 57.96 60055 34.46
Colchicine, long term use
17 1.39 457 1.53 298 1.81 1603 0.92
Corticosteroids: COPD maintenance, over 3 months for arthritis
136 11.40 2323 7.78 1557 9.47 6605 3.79
NSAIDs: h/o ulcer or bleed without receiving PPI, H2RA, or misoprostol, w/ hypertension, heart failure, >3m with arthritis, with GFR<50 mL/min, for gout, with warfarin
270 22.66 6564 21.98 3554 21.61 28163 16.16
Opioids: TCAs, with enteritis, falls, with constipation w/o laxative, with dementia
172 14.43 3279 10.98 2233 13.58 8435 4.84
Aspirin: warfarin or ulcer disease and not receiving protective therapy; without indication; bleeding disorder
1 0.10 18 0.06 12 0.07 35 0.02
Beta blocker: COPD, verapamil, in diabetes with hypoglycemia
139 11.66 3043 10.19 2110 12.83 8208 4.71
53
54
Calcium channel blocker: constipation, TCA, diltiazem or verapamil with Class III+ HF, verapamil with beta blocker
76 6.40 1574 5.27 1075 6.54 4026 2.31
Cimetidine: warfarin 1 0.05 9 0.03 5 0.03 17 0.01
Clopidogrel: bleeding disorder
17 1.41 224 0.75 173 1.05 488 0.28
Digoxin: >0.125 mg/d and GFR <50 mL/min
3 0.22 57 0.19 35 0.21 122 0.07
Dipyridamole: monotherapy prevention, bleeding disorder
0 0.00 0 0.00 0 0.00 0 0.00
Loop diuretic: edema w/o HF, monotherapy w/ hypertension
40 3.37 941 3.15 650 3.95 2161 1.24
Thiazides: gout 4 0.35 84 0.28 49 0.30 209 0.12
Vasodilators: orthostatic hypotension
5 0.40 75 0.25 62 0.38 192 0.11
Warfarin: bleeding disorder, NSAIDs, with aspirin w/o protective agent
19 1.61 269 0.90 214 1.30 575 0.33
Anticholinergics: EPS associated with antipsychotics, with dementia, chronic constipation, BPH, glaucoma
13 1.12 257 0.86 174 1.06 523 0.30
Antihistamines, 1st generation: falls
90 7.56 2123 7.11 1199 7.29 6553 3.76
55
56
Benzodiazepines: long-acting more than one month, falls
64 5.32 1500 5.02 806 4.90 4392 2.52
Antipsychotics: parkinsonism, epilepsy, falls
8 0.64 110 0.37 82 0.50 227 0.13
Promethazine: epilepsy, parkinsonism, falls
29 2.41 663 2.22 426 2.59 1568 0.90
SSRIs: SIADH 4 0.37 60 0.20 48 0.29 139 0.08
Tricyclic antidepressants: dementia, glaucoma, arrhythmias, constipation, opioids, CCBs, BPH, urinary retention
8 0.69 155 0.52 112 0.68 349 0.20
Chlorpropamide: diabetes
0 0.00 0 0.00 0 0.00 0 0.00
Estrogens: h/o breast cancer or VTE
1 0.05 6 0.02 3 0.02 17 0.01
Glyburide: diabetes 3 0.27 116 0.39 61 0.37 261 0.15
Antispasmodics with anticholinergic effects: constipation
4 0.37 63 0.21 46 0.28 139 0.08
Diphenoxylate: enteritis 1 0.05 12 0.04 8 0.05 17 0.01
Loperamide: enteritis 1 0.05 6 0.02 3 0.02 17 0.01
Metoclopramide: parkonsonism
1 0.02 6 0.02 5 0.03 17 0.01
Prochlorperazine: parkinsonism
1 0.02 0 0.00 0 0.00 0 0.00
57
58
Proton pump inhibitor: full dose >8w
82 6.90 2010 6.73 1334 8.11 4810 2.76
Ipratropium nebulized: narrow angle glaucoma
1 0.03 12 0.04 10 0.06 17 0.01
Theophylline: COPD 0 0.00 0 0.00 0 0.00 0 0.00
Alpha-blockers: urinary incontinence in men, catheter, hypotension
7 0.59 134 0.45 100 0.61 279 0.16
Urinary antispasmodics, anticholinergics
2 0.13 27 0.09 21 0.13 52 0.03
59
579
580
60
Supplement 7 - Unadjusted hazards ratios and 95% confidence intervals of baseline covariates for ADEs, Emergency visits, and Hospitalizations.
ADEs ED Visits Hospitalization
Age
65-74 Ref. Ref. Ref.
75-84 1.39 (1.25-1.54) 1.55 (1.51-1.59) 1.64 (1.59-1.69)
85+ 1.26 (1.08-1.49) 2.07 (1.99-2.15) 2.01 (1.92-2.10)
Female 1.23 (1.12-1.35) 1.00 (0.97-1.02) 0.89 (0.87-0.92)
Region
East Ref. Ref. Ref.
Midwest 1.10 (0.97-1.26) 1.08 (1.05-1.12) 1.15 (1.11-1.20)
South 1.14 (0.98-1.32) 0.98 (0.94-1.02) 1.00 (0.96-1.04)
West 1.12 (0.97-1.30) 0.79 (0.76-0.82) 0.85 (0.82-0.89)
Insurance
Medicare HMO Ref. Ref. Ref.
Medicare non-HMO 1.03 (0.88-1.21) 0.93 (0.89-0.97) 1.11 (1.05-1.16)
Comm. HMO 0.97 (0.80-1.17) 0.91 (0.86-0.95) 0.90 (0.85-0.95)
Comm. non-HMO 0.86 (0.76-0.98) 0.96 (0.93-0.99) 0.92 (0.89-0.96)
Unknown 0.48 (0.35-0.65) 0.50 (0.47-0.54) 0.50 (0.46-0.54)
Long term care 1.71 (1.41-2.08) 2.10 (1.97-2.24) 4.02 (3.77-4.29)
Myocardial Infarction 1.49 (1.14-1.97) 1.27 (1.13-1.43) 1.35 (1.19-1.53)
Congestive Heart Failure 1.57 (1.33-1.85) 1.41 (1.33-1.48) 1.51 (1.42-1.60)
Peripheral Vascular Disease 1.06 (0.87-1.29) 1.15 (1.10-1.22) 1.24 (1.17-1.32)
Cerebrovascular Disease 1.15 (0.94-1.40) 1.32 (1.24-1.40) 1.31 (1.23-1.40)
Dementia 1.02 (0.70-1.50) 1.17 (1.04-1.31) 0.76 (0.67-0.87)
61
62
Chronic Pulmonary Disease 1.25 (1.09-1.43) 1.41 (1.36-1.47) 1.47 (1.41-1.53)
Connective Tissue Disease 2.26 (1.82-2.81) 1.38 (1.27-1.49) 1.39 (1.27-1.51)
Gastric Ulcers 0.76 (0.42-1.37) 1.28 (1.08-1.51) 1.23 (1.01-1.49)
Mild Liver Disease 1.44 (0.94-2.22) 1.29 (1.12-1.49) 1.23 (1.04-1.44)
Diabetes w/o complications 1.32 (1.18-1.49) 1.25 (1.21-1.30) 1.30 (1.26-1.35)
Diabetes w/ complications 1.01 (0.81-1.27) 1.17 (1.09-1.24) 1.21 (1.13-1.30)
Paraplegia, hemiplegia 1.58 (0.92-2.71) 1.67 (1.32-2.10) 1.56 (1.20-2.03)
Renal Disease 1.39 (1.13-1.72) 1.35 (1.26-1.45) 1.49 (1.39-1.61)
Cancer 1.74 (1.52-2.01) 1.25 (1.20-1.30) 1.32 (1.26-1.39)
Moderate/Severe Liver Disease 0 1.76 (1.10-2.82) 3.00 (1.97-4.55)
Metastatic carcinoma 3.80 (2.87-5.03) 1.17 (0.99-1.37) 1.39 (1.16-1.66)
AIDS/HIV 0 1.40 (0.70-2.80) 0.91 (0.34-2.41)
Depression 1.57 (1.32-1.87) 1.29 (1.22-1.37) 1.19 (1.11-1.26)
Hypertension 1.27 (1.15-1.39) 1.16 (1.13-1.18) 1.20 (1.17-1.24)
Skin ulcers/Cellulitis 1.25 (1.03-1.50) 1.26 (1.19-1.34) 1.20 (1.13-1.28)
Abbreviations: HMO (health maintenance organization); ADE (adverse drug event)
63
581
582
583
64