measuring frailty using claims data for pharmacoepidemiologic studies of mortality in older adults:...

REVIEW

Measuring frailty using claims data for pharmacoepidemiologicstudies of mortality in older adults: evidence and recommendations

Dae Hyun Kim1,2* and Sebastian Schneeweiss1

1Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA2Division of Gerontology, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA

ABSTRACTPurpose Geriatric frailty is a common syndrome of older adults that is characterized by increased vulnerability to adverse health outcomesand influences treatment choice. Pharmacoepidemiologic studies that rely on administrative claims data in older adults are limited byconfounding due to unmeasured frailty. A claims-based frailty score may be useful to minimize confounding by frailty in such databases.We provide an overview of definitions and measurement of frailty, evaluated the claims-based models of frailty in literature, and recommendways to improve frailty adjustment in claims analysis.Methods We searched MEDLINE and EMBASE from inception to April 2014, without language restriction, to identify claims-basedmultivariable models that predicted frailty or its related outcome, disability. We critically appraised their approach, including population,predictor selection, outcome definition, and model performance.Results Of 152 reports, three models were identified. One model that predicted poor functional status using healthcare service claims in arepresentative sample of community-dwelling and institutionalized older adults showed an excellent discrimination (C statistic, 0.92). Theother two models that predicted disability using either diagnosis codes or prescription claims alone in institutionalized or frail adults hadlimited generalizability and modest model performance. None of the models have been applied to reduce confounding bias inpharmacoepidemiologic studies of drug therapy.Conclusions We found little research conducted on development and application of a claims-based frailty index for confoundingadjustment in pharmacoepidemiologic studies in older adults. More research is needed to advance this innovative, potentially usefulapproach by incorporating the expertise from aging research. Copyright © 2014 John Wiley & Sons, Ltd.

key words—frailty; prediction; administrative claims database; pharmacoepidemiology

Received 7 October 2013; Revised 2 May 2014; Accepted 2 June 2014

INTRODUCTION

Pharmacoepidemiologic studies of mortality in olderadults using administrative claims data are mainlycriticized for their limited ability to capture importantclinical information and prognostic factors thatare available to physicians who make treatmentdecisions.1 Physicians are more likely to withholdtreatments to patients who have limited life expectancyor high vulnerability to treatment-related adverseevents. As a result, incomplete measurement andadjustment for such patient characteristics can result

in confounding bias. For example, vaccinations were lesslikely to be given to patients who had long-term hospital-izations or skilled nursing stays.2 Users of lipid-loweringagents, non-steroidal anti-inflammatory drugs, andglaucoma drugs were healthier and had lower mortalitythan non-users among older adults.3–5 The protectiveassociation persisted after adjusting for age, sex, andcomorbidities.3,5 This suggests that some importantprognostic factors were not captured in claims data.One such prognostic factor that is increasingly recog-nized by physicians is frailty. In this paper, we providean overview of the definition and measurement of frailtyfrom aging literature, its relationship with disability andcomorbidity, and approaches to quantify and adjust forfrailty in pharmacoepidemiologic research. Followingthe overview, we made several recommendations toquantify frailty in administrative claims data.

*Correspondence to: D. H. Kim, Division of Pharmacoepidemiology andPharmacoeconomics, Department of Medicine, Brigham andWomen’s Hospital,1620 Tremont St., Suite 3030, Boston, MA 02120, USA.E-mail: [email protected]

Copyright © 2014 John Wiley & Sons, Ltd.

pharmacoepidemiology and drug safety 2014; 23: 891–901Published online 24 June 2014 in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/pds.3674

Frailty as an unmeasured confounder

A frail older person is typically described as a “slow,weak, and thin” person who appears older than one’schronologic age. In the geriatrics and gerontologyliterature, frailty is defined as a state of increasedvulnerability and reduced ability to recover homeosta-sis after a stressful event, thereby leading to adverseoutcomes, such as falls, disability, delirium, and mor-tality.6–11 It typically results from accumulated impair-ments in multiple physiologic systems.7 Frail patientsare at higher risk of treatment-related adverse eventsdue to their reduced physiologic reserve and limitedadaptability to a stressful event. As a result, physiciansare more likely to use a lower intensity or avoid treat-ments that may cause serious adverse events and dis-continue treatments that may not result in immediatebenefits in order to minimize polypharmacy. Severalrecent practice guidelines even beyond the geriatrics fieldrecommend considering frailty and assessment of risksand benefits in treatment choice in older adults.12–18 Adifferential use of various medical and surgical interven-tions by frailty status has been well documented inobservational studies that reflect real-world clinicalpractice (Table 1). How strongly frailty influencestreatment choice varies across the types of treatmentand associated risks. For instance, the prevalencedifference in frailty was larger for high-risk interventions(e.g., chemotherapy and warfarin) than for low-risk inter-ventions (e.g., statin). This difference may be morepronounced in the oldest old population, up to 50% ofwhom are frail.19 As such, modification of treatmentaccording to frailty status results in confounding bias inpharmacoepidemiologic studies in older adults.

In addition, some emerging evidence suggests thatfrailty may predict older patients who are more likelyto be harmed from chemotherapy or surgical proce-dures.20,21 On the other hand, the association betweensleep medications and hip fracture among nursinghome residents was greater in those with mild frailty(who were ambulatory and had more opportunities tofall) than those with severe frailty (who were almostnon-ambulatory).22 This reflects that the safety profileof a treatment may depend on frailty status. Therefore,frailty can be an effect modifier in studies of treatmenteffectiveness and safety in older adults.

Measurement of frailty

How to operationalize the concept of frailty forresearch and clinical practice has been a hot topic ofgeriatrics and gerontology research in the pastdecade.9 In research, the frailty phenotype7 and thefrailty index11 are most widely used among severalvalidated frailty models.23 The frailty phenotype,proposed by Fried et al., is constructed on the biologicalcycle of frailty that consists of shrinking, weakness,exhaustion, slowness, and low physical activity.7 Thefrailty index, proposed by Mitnitski et al., countsthe number (or proportion) of health deficits from apre-specified number (usually≥30) of symptoms, signs,diseases, test abnormalities, and disability in physical,psychological, and social domains.11 The frailty pheno-type requires very specific information from self-reportand geriatric assessment, whereas the frailty indexplaces few restrictions on the type and number ofindividual components to define frailty (Table 2).Because of its flexibility in choosing individual items,

Table 1. Examples of differential use of medical and surgical treatments by frailty status

Treatment or target condition Treatment options Frail patients, N (%) Non-frail patients, N (%)

Aortic stenosis (Bainey et al.)51 Total sample (mean age: 82 years) 68 (100) 85 (100)Surgical aortic valve replacement 6 (9) 27 (32)Transcatheter aortic valve replacement 32 (47) 26 (31)Medical treatment 30 (44) 32 (37)

Bisphosphonate (Sambrook et al.)52 Total sample (mean age: 83 years) 326 (100) 1675 (100)Bisphosphonate 2 (1) 76 (5)No bisphosphonate 324 (99) 1599 (95)

Chemotherapy for breast cancer (Aaldriks et al.)53 Total sample (mean age: 76 years) 28 (100) 27 (100)≥4 cycles of chemotherapy 18 (64) 21 (78)<4 cycles of chemotherapy 10 (36) 6 (22)

Coronary artery disease (Purser et al.)54 Total sample (mean age: 77 years) 194 (100) 112 (100)Coronary artery bypass surgery 23 (13) 21 (19)Percutaneous coronary intervention 70 (38) 59 (53)Medical treatment 91 (49) 32 (28)

Statin (Gnjidic et al.)55 Total sample (age: 77 years) 147 (100) 1518 (100)Statin 53 (36) 659 (43)No statin 94 (64) 859 (57)

Warfarin for atrial fibrillation (Perera et al.)56 Total sample (mean age: 83 years) 130 (100) 77 (100)Warfarin 30 (23) 53 (69)No warfarin 100 (77) 24 (31)

d. h. kim and s. schneeweiss892

Copyright © 2014 John Wiley & Sons, Ltd. Pharmacoepidemiology and Drug Safety, 2014; 23: 891–901DOI: 10.1002/pds

the frailty index has been successfully applied to variouspopulation-based surveys, cohorts, and patient databasesthat did not contain the same components of healthdeficits as the original database where it was initiallydeveloped.24 This is an important advantage of the frailtyindex that may be useful in developing a claims-basedfrailty score. In clinical practice, most physicians recog-nize frailty based on information from a combination ofsources, which include observation of the overall appear-ance (“eyeball” test), review of comorbidities, medica-tions, and activity of daily living (ADL) disability, andexamination of mobility and muscle strength.7,25

In administrative claims data, most of the compo-nents that are used to determine frailty are not avail-able or incompletely measured. Thus, two individualswith the same age and seemingly similar comorbidityprofile in claims data may have a considerably differ-ent level of frailty and mortality risk. Individualswho are near the end of life with advanced frailtymay have fewer encounters with the healthcaresystem, which further limits our ability to capture theirhealth status completely and similarly to thosewith more encounters. Moreover, there is no specificInternational Classification of Diseases (ICD) codefor frailty. Although physicians commonly considerfrailty before recommending treatments, they do notreliably document manifestations of frailty in medical

records or submit relevant ICD codes (e.g., abnormal-ity of gait, cachexia, debility, exhaustion, fall, failureto thrive, fatigue, loss of weight, pressure ulcer,senility, or muscle weakness).

The relationship of frailty, disability, and comorbidityand relevance to confounding adjustment

The interrelationship among frailty, disability, andcomorbidity is complex, and untangling these overlap-ping concepts may be difficult.9,26 Disability is definedas difficulty in performing basic or instrumental ADLsand viewed as a consequence of frailty and comorbid-ities.26 In general, individuals with a high burden ofcomorbidities are more likely to become frail anddisabled. Nonetheless, it is important to recognize thatfrailty can identify patients at high risk for poor out-comes who otherwise may not be identified as suchon the basis of comorbidity and disability. In theCardiovascular Health Study, 27% of frail older adultsin the community did not have major comorbidities ordisability (Figure 1).7 Frailty was associated with 1.3-to 2.2-fold elevated risk of worsening disability,hospitalization, and mortality, independently of comor-bidities and disability.7 This suggests that adjusting forcomorbidities and disability may result in residualconfounding by frailty. The magnitude of this bias will

Table 2. Measurement of frailty in geriatrics and gerontology research

Measurement of frailty phenotype7 Measurement of frailty index11

Core features of frailty50 • Increased vulnerability and reduced ability to recover homeostasis after a stressful event• Changes over time• Include impairments in multiple physiologic systems• More common in advanced age, women, and related to disability• Predict adverse outcomes (e.g., falls, disability, delirium, and mortality)

Operationalization Frailty is quantified as the presence of thefollowing characteristics:

Frailty is quantified as the number (or proportion)of health deficits from a pre-specified number of items(at least 30) of any symptoms, signs, diseases, testabnormalities, and disability that satisfy the followingcriteria:

(1) Shrinking: unexplained weight loss>10 pounds in the last year

(1) Health deficits should be acquired, increase with age,and predict adverse outcomes.

(2) Weakness: gender-specific and bodymass index-specific grip strength in thelowest 20%

(2) Health deficits should not saturate prematurely in thepopulation (should not be present in almost all participantsin the population).

(3) Exhaustion: self-report of exhaustion Although it is used as a continuous variable, the cutoff of0.20 or 0.25 has been used.(4) Slowness: gender-specific and height-

specific time to walk 15 ft in the lowest 20%(5) Low physical activity: gender-specificphysical activity level (kcal) per week inthe lowest 20%

Frailty is defined if three of the preceding items aremet; pre-frail if one or two of the items are met.

Advantages • Require only five items • Few restrictions on individual items required• Better clinical translation: targeted interventions andmonitoring for each item are possible.

• Allows finer discrimination of outcome risk than thefrailty phenotype

Disadvantages • Specific individual items required • Require at least 30 items• Allows only broad discrimination of outcome risk • Less clear clinical translation

frailty index in claims database 893


depend on the prevalence difference in frailty betweentreated and untreated patients as well as the associationbetween frailty and the outcome of interest that is notcaptured by comorbidities or disability.

Existing approaches that may reduce unmeasuredconfounding by frailty

To date, much progress has been made in risk adjust-ment methods using summary scores of measuredconfounding, such as propensity scores or disease riskscores.27–30 The impact of unmeasured confoundingby surrogates of frailty (e.g., disability and cognitiveand physical function impairment) has been assessedby a sensitivity analysis using an independent datasetand the confounder–outcome associations from theliterature.31–34 If a subset of claims data is linked to avalidation dataset that contains frailty measures,propensity score calibration or reweighting can be ap-plied to reduce confounding by frailty.35,36 Thesemethods require an external data source with frailtymeasures. In the absence of such external data, a self-controlled design or an active comparator design can beuseful. High-dimensional propensity score analysis37,38

that adjusts for proxies (which may be associated withfrailty) and instrumental variable analysis37 may alsoreduce unmeasured confounding by frailty under some

untestable assumptions. In a study of conventional versusatypical antipsychotic use on mortality in nursing homeresidents, high-dimensional propensity score and instru-mental variable analyses resulted in effect estimatessimilar to those when additional clinical data, includingmanifestations of frailty, were adjusted for.37

Claims-based frailty score as an alternative approach

As an alternative approach, a frailty score can be devel-oped and validated from claims data. Although geriatri-cians and gerontologists have developed several modelsof frailty for clinical and research use,23,39 little efforthas been made to translate these models into pharma-coepidemiologic studies. A successful adaptation of thesemodels of frailty for claims data has a great potentialto improve the validity of pharmacoepidemiologicstudies in older adults. In the following sections of thisreview, we critically evaluated the existing claims-basedmodels of frailty in the literature and proposed how todevelop and validate a claims-based frailty score forpharmacoepidemiologic studies.

METHODS

We identified claims-based models of frailty in theliterature by searching MEDLINE and EMBASE frominception to 30/4/2014, using the following key termsand their variations: frailty, disability, activity of dailyliving, vulnerability, bias, confounding, case mix, riskadjustment, and administrative claims database. Wealso searched the reference lists of included papers.Because functional impairment and disability aremanifestations of frailty, any papers that reported a multi-variable model to predict frailty or disability using claimsdata were included. In each paper, we examined datasource, population characteristics, outcome definition,predictors, and model performance (calibration anddiscrimination) in the derivation and validation samples.

RESULTS

Of 152 citations identified in the literature search, wefound three claims-based multivariable models40–43

that predicted frailty or disability in older adults(Table 3). These models varied widely with respectto population source, outcome definition, type of pre-dictors, and model performance. Rosen et al. analyzedthe national Veterans Affairs database of long-term-care residents to develop a model that used 13diagnostic categories to predict worsening disabilityin three basic ADLs over 6months.40,41 The modelwas limited by moderate discrimination (C statistic

(n=196)

21.5%(n=79)

5.7%(n=21)

46.2%(n=170)

26.6%(n=98)

(n=2131)(n=67)

Comorbidity*Disability: 1 ADL**

Frailty+

Figure 1. The Relationship among Frailty, Comorbidity, and Disability inthe Cardiovascular Health Study (reprinted with permission7).Percentageslisted indicate the proportion among those who were frail (n= 368), whohad comorbidity and/or disability, or neither. Total represented: 2762 par-ticipants who had comorbidity and/or disability and/or frailty. n of eachsubgroup indicated in parentheses. +Frail: overall n= 368 frail participants.*Comorbidity: overall n= 2576 with two or more out of the following ninediseases: myocardial infarction, angina, congestive heart failure, claudica-tion, arthritis, cancer, diabetes, hypertension, and chronic obstructive pul-monary disease. Of these, 249 were also frail. **Disabled: overall n= 363with an activity of daily living (ADL) disability; of these, 100 were frail



Table

3.Predictionmodelsof

frailty

anddisabilitybasedon

claimsdata

Model

developm

ent

Model

performance

Author(year)

Population

Outcomedefinitio

nPredictors

Derivationcohort

Validationcohort

Rosen

(2000,

2001)40,41

Derivationsample:

Alllong-term-care

residentsin

theVeteransAffairs

system

,USA,in1996

Worsening

disability,

definedas

≥2-pointincrease

inADLdependence

index(range:3–15),derivedfrom

eatin

g,toiletin

g,and

transferring

over

6months

Diagnosiscodesforthefollo

wing

conditions:

Characteristics:

Characteristics:

Pressureulcer

N=15,639

N=13,723

Terminal

illness

Meanage:

72years

Meanage:

NR

Hem

iplegia/quadriplegia

Men:96%

Men:NR

Multip

lesclerosis

Outcome:

15%

Outcome:

NR

Pulmonarydisease

Discrim

ination:

Discrim

ination:

Validationsample:

Alllong-term-care

residentsin

theVeteransAffairs

system

,USA,in1997

Congestiveheartfailu

reCstatistic:0.70

Cstatistic:0.68

Arthritis

Cancer

Calibratio

n:Calibratio

n:Alzheim

er’s

disease

H-L

GOF:p=0.10

H-L

GOF:p=0.02

Dem

entia

otherthan

Alzheim

er’s

disease

Predicted/observed

risk

Predicted/observed

risk

Parkinson

disease

Decile

1:2%

/3%

Decile

1:2%

/3%

Seizure

disorder

Decile

5:14%/13%

Decile

5:14%/14%

Substance-related

disorder

Decile

10:32%/33%

Decile

10:29%/29%

Dubois(2010)

42

Derivationsample:

Arandom

sample

offrailcommunity

-based

older

adults,C

anada

FunctionalAutonom

yMeasurement

System

score(0–87points),derived

from

ADL,IA

DL,m

obility,

communication,

andmentalfunctio

n

Prescriptionclaimsforthefollo

wing

conditions:

Characteristics:

Characteristics:

Anxiety

andsleepdisorders

N=1000

N=402

Vasculardisease

Meanage:

83years

Meanage:

83years

Diabetes

Men:38%

Men:38%

Cardiac

diseases

Outcome:

19points

Outcome:

20points

Validationsample:

Sam

eas

derivatio

nsample(a

random

split

ofthetotal

populatio

n)Mentaldisorders

Goodnessof

fit:

Goodnessof

fit:

Anemia

Respiratory

illness

R2=0.16

R2=0.12

Severepain

Neurologiccondition

Behaviorproblems

Hyperlip

idem

ia

Davidoff(2013)

43

Derivationsample:

Arepresentativ

esampleof

community

-based

and

institu

tionalized

Medicarebeneficiaries

Functionalstatus

(poorversus

good),

derivedfrom

ADL,IADL,strength,

stam

ina,andexercise

Health

care

serviceclaimsin

the

follo

wingcategories:

Characteristics:

Characteristics:

Evaluationandmanagem

ent

(E&M)/othervisits

N=7394

N=7394

Nursing

homevisit

Meanage:

78years

Meanage:

78years

DermatologyE&M

visit

Men:39%

Men:38%

Neurology

E&M

visit

Outcome:

9%Outcome:

9%Validationsample:

Sam

eas

derivatio

nsample(a

random

split

ofthetotal

populatio

n)

Rheum

atologyE&M

visit

Discrim

ination:

Discrim

ination:

Chiropractic

Cstatistic:0.92

Cstatistic:NR

Hom

evisit

Using

thecutoff0.11

Using

thecutoff0.11

Hospice

visit

Sensitiv

ity:0.81

Sensitiv

ity:0.79

Minor

procedures

Specificity:0.92

Specificity:0.92

Skin

PPV:0.50

PPV:0.48

Ambulatory

procedures

NPV:0.98

NPV:0.98

Musculoskeletal

Preventiveservices

Calibratio

n:Calibratio

n:Screening

H-L

GOF:p=0.32

H-L

GOF:NR

(Contin

ues)



Table

3.(Contin

ued)

Model

developm

ent

Model

performance

Author(year)

Population

Outcomedefinitio

nPredictors

Derivationcohort

Validationcohort

Immunization/vaccination

Major

procedures

Cardiovascular

Colectomy

Orthopedic—

other

Durable

medical

equipm

ent

Bathandtoiletaids

Wheelchairs

Hospitalbed

Enteral

andparenteral

Medical/surgicalsupplies

Other

Imaging

Imaging/procedures—heart

includingcardiaccatheter

Standardim

aging—

nuclearmedicine

Other Ambulance

Electrocardiography

monito

ring

andcardiovascular

stress

tests

Endoscopy

Upper

gastrointestinal

Sigmoidoscopy

Colonoscopy

Medicaidenrollm

ent

Regionof

country

Count

ofE&M

office

visits

Sex

ADL,activity

ofdaily

living;

E&M,evaluationandmanagem

ent;H-L

GOF,H

osmer–L

emeshowgoodness-of-fittest;IA

DL,instrum

entalactivity

ofdaily

living;

NR,n

otreported;N

PV,n

egativepre-

dictivevalue;

PPV,p

ositive

predictiv

evalue.



0.68–0.70) and uncertain generalizability to womenand community-dwelling non-veterans. In a randomcommunity-based sample of frail older Canadiansidentified by a postal screening questionnaire, Duboiset al. developed an index that predicts a summary dis-ability score that consists of basic and instrumentalADLs, mobility, communication, and mental function,on the basis of prescription claims for 11 conditions.42

However, the overall model fit was poor (R2 = 0.12–0.16), and its generalizability to non-frail older adultsremains uncertain. The index by Davidoff et al.overcomes most limitations of the earlier indices byusing a nationally representative sample of bothcommunity-dwelling and institutionalized Medicarebeneficiaries.43 Instead of using diagnosis codes orprescription claims, this index used healthcare serviceclaims (e.g., types of visits, procedures, and durablemedical equipment) that are both positively andinversely associated with poor functional status. Poorfunctional status was an approximation of the EasternCooperative Oncology Group performance status frombasic and instrumental ADLs, strength, stamina, andexercise. This outcome definition seems to overlapwith some components of frailty phenotype (weak-ness, slowness, and low physical activity). Althoughthe discrimination (C statistic 0.92) and calibrationof this index were excellent, they might have beenoverestimated owing to the inclusion of individualswho are already institutionalized or on hospice carefor terminal illness. None of the three identifiedmodels have been applied to reduce confounding biasin pharmacoepidemiologic studies of a drug treatment.

DISCUSSION

To date, we found very little research conducted ondevelopment and application of a claims-based frailtyscore in pharmacoepidemiologic studies. Two modelsthat were developed from institutionalized or alreadyfrail individuals had limited generalizability to thegeneral population of older adults in the commu-nity.40–42 Only the model by Davidoff et al. thatwas developed from a representative population ofolder adults seemed useful for pharmacoepidemiologicstudies.43 Nonetheless, these models focused onpredicting disability rather than the accepted defini-tions of frailty from aging research. Measuring frailtyis attractive, because it can identify high-risk olderpatients even when they are not considered high riskbased on comorbidities and disability as well as thosewho are more likely to benefit (or harm) from a treat-ment.20,21 In the following paragraphs, we propose

two potential approaches to develop a claims-basedfrailty score that incorporates insights from geriatricsand gerontology research.

Approach 1: deriving the frailty index directly fromclaims database

Given the challenges in measuring individual compo-nents to define the frailty phenotype from claims data,we believe that creating a frailty index with diagnosisand procedure codes from claims data (as “healthdeficit”) may be feasible and offer some advantagesover the frailty phenotype. The flexibility in choosingindividual items for the frailty index can allow selec-tion of a pre-specified list of claims in the database athand, as long as a sufficient number of claims areselected to cover multiple physiologic systems andtheir prevalence increases gradually with age (withoutsaturating too early). One way of selecting a list ofclaims is to examine the prevalence of claims in eachdisease and symptom classification (e.g. ICD classifi-cation or Major Diagnostic Categories of the Diagnosis-Related Group). In addition, the health deficits requiredto construct the frailty index may differ betweencommunity-dwelling and institutionalized populations.For instance, the prevalence of hearing loss graduallyincreases with age in community-dwelling older adults;but the prevalence reaches almost 100% in institutiona-lized older adults. When counted as a health deficit,hearing loss does not help discriminate the health riskin institutionalized populations. Therefore, it can beincluded in the frailty index only for the community-dwelling population. Another advantage is that thefrailty index provides better discrimination inpredicting the risk of adverse outcomes than thefrailty phenotype.44,45 Whereas the frailty phenotypemay be clinically appropriate to identify vulnerablepatients for interventions and monitor the changein individual components over time, the frailtyindex is more suitable for the purpose ofconfounding adjustment in pharmacoepidemiologicstudies of mortality.

Approach 2: gold-standard-driven development of aclaims-based frailty score

As an alternative to deriving the frailty index directlyfrom the claims database, we can build a multivariablemodel that uses claims to predict a “gold standard”frailty (either the frailty phenotype or the frailty index)that is defined from in-person assessment. Here weassume that such an in-person assessment of cognitiveand physical function, mood, social support, and med-ical history allows an accurate frailty measurement. In



the following, we describe steps to develop and evalu-ate a claims-based frailty score (Table 4).The first step is to find a database that has both

claims data and in-person assessment that is neededto define a “gold standard” frailty. A few population-based epidemiologic studies of aging that havebeen linked to claims data can be valuable datasources (e.g., Adult Changes in Thought Study, Car-diovascular Health Study, Health and RetirementStudy, Longitudinal Study of Aging, and NationalLong-term Care Survey; the studies’ main websiteswere provided in Table 4). Electronic health records(EHR) are a potentially useful source to capture frailtyin a real-world population that can be linked to claims.

EHR contains clinical data (laboratory, pharmacy, ra-diology, and physician orders) and narrative clinicalnotes (medical history, symptoms, and clinical reason-ing behind treatment decision making) that are diffi-cult to obtain from claims data. However, there arechallenges in measuring a “gold standard” frailty fromEHR. As mentioned earlier, most physicians do notuse one of the accepted models of frailty but rely ontheir subjective assessment (“eyeball” test). Althoughthere are a few clinical prediction models based onEHR data,46–48 how to create an EHR-based frailtyscore remains to be determined. Selective availabilityof certain clinical data, complex patterns of healthcaresystem use and difficulties in mining information from

Table 4. Recommendations for development and evaluation of a claims-based frailty score

Recommendations

Database Consider population-based epidemiologic studies of aging that have claims and detailed clinical or survey data. Thefollowing are some examples:• Adult Changes in Thought Study (http://www.grouphealthresearch.org/capabilities/clinic/clin_std.html#act)• Canadian Study of Health and Aging (http://www.csha.ca)• Cardiovascular Health Study (https://chs-nhlbi.org)• Health and Retirement Study (http://hrsonline.isr.umich.edu)• Longitudinal Studies of Aging (http://www.cdc.gov/nchs/lsoa.htm)• National Long-term Care Survey (http://www.nltcs.aas.duke.edu)

Consider linking claims data to electronic health records.Population Develop claims-based frailty scores separately for community-dwelling population and institutionalized population.Definition of “gold standard”frailty

Use the frailty index as a “gold standard” rather than the frailty phenotype for the purpose of confounding adjustment.Determine a pre-specified list (≥30) of health deficits (symptoms, signs, diseases, test abnormalities, and disability) inmultiple physiologic systems available in the database.Construct the “gold standard” frailty index as proportion of deficits.

Development of claims-basedfrailty score

Determine a time frame in which potential predictors are measured (e.g., 1 year).Consider the following a priori list of ICD-9-CM diagnosis codes and HCPCS codes that represent clinical manifestations offrail patients.• Abnormality of gait: 781.2• Abnormal loss of weight and underweight: 783.2• Adult failure to thrive: 783.7• Cachexia: 799.4• Debility: 799.3• Difficulty in walking: 719.7• Fall: V15.88• Malaise and fatigue: 780.7• Muscular wasting and disuse atrophy: 728.2• Muscle weakness: 728.87• Pressure ulcer: 707.0, 707.2• Senility without mention of psychosis: 797• Durable medical equipment (cane, walker, bath equipment, and commode): E0100, E0105, E0130, E0135, E0140,E0141, E0143, E0144, E0147–E0149, E0160–E0171• Nursing or personal care services: T1000–T1005, T1019–T1022, T1030, T1031

Consider additional diagnosis codes, prescription claims, and healthcare service claims to identify predictors that are eitherpositively or negatively associated with the “gold standard” frailty.

Evaluation of claims-basedfrailty score

Validate the performance of a claims-based frailty score against the “gold standard” definition of frailty in an independentholdout sample, cross-validation, or bootstrap resampling.Test the associations with the following clinical outcomes that are known to be associated with frailty:• Fall-related injury (including fracture)• Hospitalizations• Institutionalizations• Home health service use• Mortality

Assess the effectiveness of using claims-based frailty score (e.g., restriction, stratification, and matching) on bias reductionusing a known example of a treatment that has substantial confounding by frailty.

HCPCS, Healthcare Common Procedure Coding System; ICD-9-CM, the International Classification of Diseases, Ninth Revision, Clinical Modification.



http://www.nltcs.aas.duke.edu

narrative clinical notes pose several technical and ana-lytical challenges, such as missing data problems49

and differential ascertainment or misclassification ofconfounders. Routine use of clinical templates andadvances in natural language processing programsmay mitigate these challenges in the future.The next step is to define a “gold standard” frailty

using detailed clinical information in the surveys,epidemiologic cohorts, or EHR. Although both thefrailty phenotype and frailty index can be used, werecommend modeling the frailty index that has betterdiscrimination than the frailty phenotype in predictingthe outcome risk. Once a “gold standard” frailty isdefined, a wide range of claims that are measuredwithin a time frame (e.g., 1 year)—diagnoses,prescriptions, and healthcare service claims—need tobe considered. Based on our understanding about theclinical phenotype or underlying biology of frailty,we should consider certain predictors a priori, suchas diagnosis codes, durable medical equipment claims,and nursing or personal care service claims thatcharacterize frail patients (Table 4). In addition tothese claims, we need to include claims that are nega-tively associated with frailty. As shown by Davidoffet al.,43 some healthcare services (e.g., ambulance,upper gastrointestinal tract endoscopy, and minor skinprocedures) are more utilized by frail patients, whilepreventive services (e.g., screening and vaccination)are less utilized.The performance (calibration and discrimination) of

the derived claims-based frailty score needs to beevaluated in an independent holdout sample, or usingcross-validation or bootstrap resampling to avoid over-estimation of model performance. The criteria for asuccessful frailty measure have been proposed interms of content validity, construct validity, and crite-rion validity.50 According to the criteria, a successfulfrailty measure should be dynamic (changes overtime), include multiple determinants, and replicatesome of the known relationships with age (morecommon in advancing age), gender (more commonin women), and adverse clinical outcomes (fall, hospi-talization, institutionalization, home health serviceuse, and mortality). Finally, a validated claims-basedfrailty score should be applied to reduce confoundingbias in a known example of a treatment that hassubstantial confounding by frailty.

Areas of uncertainty

First, it may be useful to examine the inherent charac-teristics of a claims-based frailty score. It is importantto study the optimal period length to capture claims

data for development of a claims-based frailty scoreand the sensitivity of the score to reflect changes inclinical frailty over time. Second, it is unknown whichof the two approaches we outlined leads to betterconfounding adjustment. This can be indirectlyassessed by comparing C statistics for mortalitybetween the derived frailty scores and ultimately byapplying the scores to an example where the true effectis known from randomized controlled trials. Third,when the frailty score, comorbidity score, and dis-ability score are created using the same claimsdatabase, it is unclear whether the claims-basedfrailty score can provide additional prognostic informa-tion beyond existing confounding summary scores(e.g., comorbidity score, disease risk score, or propen-sity score). Lastly, the transportability of a frailty scoredeveloped from one population to similar populationsin different databases that might have differenthealthcare utilization and practice patterns should beevaluated. This may be less problematic with our firstproposed approach, that is, deriving the frailty indexdirectly from claims data using a pre-selected list ofhealth deficits available in that particular claims data-base. Our second approach, however, needs validationin independent databases.

CONCLUSIONS

As observational data remain a major source ofcomparative effectiveness research in older adults,there is a critical need to advance methods to adjustfor confounding bias by frailty by incorporating theaccumulated expertise from geriatrics and gerontologyresearch. Among different operationalized definitionsof frailty used in clinical practice, we believe that thefrailty index, the count of health deficits in multiplephysiologic systems, is more promising for thepurpose of confounding adjustment. A claims-basedfrailty score should consider including a certain a priorilist of diagnoses and healthcare service claims thatcharacterize frail patients as well as consider additionaldiagnoses, prescriptions, and healthcare service claimsthat are either positively or negatively associated withfrailty. More research is needed to evaluate theperformance and transportability of claims-based frailtyscores in different databases and additional reduction inconfounding bias beyond existing methods.

CONFLICT OF INTEREST

Dr. Kim has no disclosure. Dr. Schneeweiss is aconsultant to WHISCON, LLC, and to Aetion, Inc., a



software manufacturer in which he owns shares. He isa principal investigator of investigator-initiated grantsto the Brigham and Women’s Hospital from Novartisand Boehringer-Ingelheim unrelated to the topic ofthis study.

KEY POINTS• Pharmacoepidemiologic studies in older adultsare limited by unmeasured confounding biasdue to frailty that increases the risk of adversehealth outcomes and influences treatment choice.

• Little research has been conducted on developmentand application of a claims-based frailty index forconfounding adjustment in pharmacoepidemiologicstudies in older adults.

• Based on the widely accepted concepts of frailtyin geriatrics and gerontology research, a frailtyindex can be developed by counting the numberof “health deficits” using a priori list of diagnosesand healthcare service claims that representhealth status.

• More research is needed to evaluate theperformance and appropriate applications ofthe frailty score.

ACKNOWLEDGEMENTS

Dr. Kim has full access to all of the data in the studyand takes responsibility for the integrity of the dataand the accuracy of the data analysis. Dr. Kim wassupported by the Charles A. King Trust PostdoctoralFellowship award from the Medical Foundation, a di-vision of Health Resources in Action, and KL2Medical Research Investigator Training award fromthe Harvard Catalyst, the Harvard Clinical andTranslational Science Center, and the National Centerfor Research Resources and the National Center forAdvancing Translational Sciences, National Institutes ofHealth (1KL2 TR001100-01). Dr. Schneeweiss is a prin-cipal investigator of the Harvard–Brigham Drug Safetyand Risk Management Research Center funded by theFood and Drug Administration. His work is partiallyfunded by grants and contracts from the Patient CenteredOutcomes Research Institute, Food and Drug Adminis-tration, and National Heart, Lung, and Blood Institute.The sponsor had no role in the design, methods, data

collection, analysis and preparation of this paper.

ETHICS STATEMENT

The authors state that no ethical approval was needed.

AUTHOR CONTRIBUTIONS

Dr. Kim contributed to study concept and design, dataacquisition, analysis and interpretation of data, andpreparation of the manuscript. Dr. Schneeweisscontributed to study concept and design, analysis andinterpretation of data, and preparation of the manuscript.

REFERENCES

1. Ray WA. Observational studies of drugs and mortality. N Engl J Med 2005;353(22): 2319–2321.

2. McGrath LJ, Cole SR, Kshirsagar AV, Weber DJ, Sturmer T, Brookhart MA.Hospitalization and skilled nursing care are predictors of influenza vaccinationamong patients on hemodialysis: evidence of confounding by frailty. Med Care2013; 51(12): 1106–1113.

3. Glynn RJ, Knight EL, Levin R, Avorn J. Paradoxical relations of drug treatmentwith mortality in older persons. Epidemiology 2001; 12(6): 682–689.

4. Sturmer T, Schneeweiss S, Brookhart MA, Rothman KJ, Avorn J, Glynn RJ.Analytic strategies to adjust confounding using exposure propensity scores anddisease risk scores: nonsteroidal antiinflammatory drugs and short-term mortalityin the elderly. Am J Epidemiol 2005; 161(9): 891–898.

5. Dormuth CR, Patrick AR, Shrank WH, et al. Statin adherence and risk ofaccidents: a cautionary tale. Circulation 2009; 119(15): 2051–2057.

6. Clegg A, Young J, Iliffe S, Rikkert MO, Rockwood K. Frailty in elderly people.Lancet 2013; 381(9868): 752–762.

7. Fried LP, Tangen CM, Walston J, et al. Frailty in older adults: evidence for aphenotype. J Gerontol A Biol Sci Med Sci 2001; 56(3): M146–M156.

8. Mitnitski AB, Song X, Rockwood K. The estimation of relative fitness and frailtyin community-dwelling older adults using self-report data. J Gerontol A Biol SciMed Sci 2004; 59(6): M627–M632.

9. Bergman H, Ferrucci L, Guralnik J, et al. Frailty: an emerging research andclinical paradigm—issues and controversies. J Gerontol A Biol Sci Med Sci2007; 62(7): 731–737.

10. Lacas A, Rockwood K. Frailty in primary care: a review of its conceptualizationand implications for practice. BMC Med 2012; 10: 4.

11. Mitnitski AB, Mogilner AJ, Rockwood K. Accumulation of deficits as a proxymeasure of aging. Scientific World Journal 2001; 1: 323–336.

12. American Geriatrics Society Expert Panel on Care of Older Adults with DiabetesMellitus, Moreno G, Mangione CM, Kimbro L, Vaisberg E. Guidelinesabstracted from the American Geriatrics Society Guidelines for Improving theCare of Older Adults with Diabetes Mellitus: 2013 update. J Am Geriatr Soc2013; 61(11): 2020–2026.

13. James PA, Oparil S, Carter BL, et al. 2014 evidence-based guideline for themanagement of high blood pressure in adults: report from the panel membersappointed to the Eighth Joint National Committee (JNC 8). JAMA 2014;311(5): 507–520.

14. European Heart Rhythm Association, European Association for Cardio-thoracicSurgery, Camm AJ, et al. Guidelines for the management of atrial fibrillation:the Task Force for the Management of Atrial Fibrillation of the European Societyof Cardiology (ESC). Eur Heart J 2010; 31(19): 2369–2429.

15. Wildiers H, Kunkler I, Biganzoli L, et al.Management of breast cancer in elderlyindividuals: recommendations of the International Society of Geriatric Oncology.Lancet Oncol 2007; 8(12): 1101–1115.

16. Hurria A, Wildes T, Blair SL, et al. Senior adult oncology, version 2.2014: clinicalpractice guidelines in oncology. J Natl Compr Canc Netw 2014; 12(1): 82–126.

17. American Geriatrics Society Beers Criteria Update Expert Panel. AmericanGeriatrics Society updated Beers criteria for potentially inappropriate medicationuse in older adults. J Am Geriatr Soc 2012; 60(4): 616–631.

18. Durso SC. Using clinical guidelines designed for older adults with diabetesmellitus and complex health status. JAMA 2006; 295(16): 1935–1940.

19. Song X, Mitnitski A, Rockwood K. Prevalence and 10-year outcomes of frailty in olderadults in relation to deficit accumulation. J Am Geriatr Soc 2010; 58(4): 681–687.

20. Stortecky S, Schoenenberger AW, Moser A, et al. Evaluation of multidimen-sional geriatric assessment as a predictor of mortality and cardiovascular eventsafter transcatheter aortic valve implantation. JACC Cardiovasc Interv 2012;5(5): 489–496.

21. Puts MT, Santos B, Hardt J, et al. An update on a systematic review of the useof geriatric assessment for older adults in oncology. Ann Oncol 2014; 25(2):307–315.

22. Berry SD, Lee Y, Cai S, Dore DD. Nonbenzodiazepine sleep medication useand hip fractures in nursing home residents. JAMA Intern Med 2013; 173(9):754–761.

23. Morley JE, Vellas B, van Kan GA, et al. Frailty consensus: a call to action. J AmMed Dir Assoc 2013; 14(6): 392–397.



24. Mitnitski A, Song X, Skoog I, et al. Relative fitness and frailty of elderly menand women in developed countries and their relationship with mortality. J AmGeriatr Soc 2005; 53(12): 2184–2189.

25. Rockwood K, Song X, MacKnight C, et al. A global clinical measure of fitnessand frailty in elderly people. CMAJ 2005; 173(5): 489–495.

26. Fried LP, Ferrucci L, Darer J, Williamson JD, Anderson G. Untangling theconcepts of disability, frailty, and comorbidity: implications for improvedtargeting and care. J Gerontol A Biol Sci Med Sci 2004; 59(3): 255–263.

27. Seeger JD, Williams PL, Walker AM. An application of propensityscore matching using claims data. Pharmacoepidemiol Drug Saf 2005; 14(7):465–476.

28. Quan H, Li B, Couris CM, et al. Updating and validating the CharlsonComorbidity Index and score for risk adjustment in hospital discharge abstractsusing data from 6 countries. Am J Epidemiol 2011; 173(6): 676–682.

29. Sharabiani MT, Aylin P, Bottle A. Systematic review of comorbidity indices foradministrative data. Med Care 2012; 50(12): 1109–1118.

30. Arbogast PG, Ray WA. Use of disease risk scores in pharmacoepidemiologicstudies. Stat Methods Med Res 2009; 18(1): 67–80.

31. Schneeweiss S, Wang PS. Claims data studies of sedative-hypnotics and hipfractures in older people: exploring residual confounding using survey informa-tion. J Am Geriatr Soc 2005; 53(6): 948–954.

32. Schneeweiss S, Setoguchi S, Brookhart MA, Kaci L, Wang PS. Assessingresidual confounding of the association between antipsychotic medications andrisk of death using survey data. CNS Drugs 2009; 23(2): 171–180.

33. Schneeweiss S, Wang PS. Association between SSRI use and hip fractures andthe effect of residual confounding bias in claims database studies. J ClinPsychopharmacol 2004; 24(6): 632–638.

34. Pressley JC, Patrick CH. Frailty bias in comorbidity risk adjustments ofcommunity-dwelling elderly populations. J Clin Epidemiol 1999; 52(8): 753–760.

35. Nelson JC, Marsh T, Lumley T, et al. Validation sampling can reduce bias inhealth care database studies: an illustration using influenza vaccination effective-ness. J Clin Epidemiol 2013; 66(8 Suppl): S110–S121.

36. Sturmer T, Schneeweiss S, Avorn J, Glynn RJ. Adjusting effect estimates forunmeasured confounding with validation data using propensity score calibration.Am J Epidemiol 2005; 162(3): 279–289.

37. Huybrechts KF, Brookhart MA, Rothman KJ, et al. Comparison of differentapproaches to confounding adjustment in a study on the association ofantipsychotic medication with mortality in older nursing home patients. Am JEpidemiol 2011; 174(9): 1089–1099.

38. Schneeweiss S, Rassen JA, Glynn RJ, Avorn J, Mogun H, Brookhart MA.High-dimensional propensity score adjustment in studies of treatment effectsusing health care claims data. Epidemiology 2009; 20(4): 512–522.

39. Theou O, Brothers TD, Mitnitski A, Rockwood K. Operationalization of frailtyusing eight commonly used scales and comparison of their ability to predictall-cause mortality. J Am Geriatr Soc 2013; 61(9): 1537–1551.

40. Rosen A, Wu J, Chang BH, Berlowitz D, Ash A, Moskowitz M. Does diagnosticinformation contribute to predicting functional decline in long-term care? MedCare 2000; 38(6): 647–659.

41. Rosen A, Wu J, Chang BH, et al. Risk adjustment for measuring health out-comes: an application in VA long-term care. Am J Med Qual 2001; 16(4):118–127.

42. Dubois MF, Dubuc N, Kroger E, Girard R, Hebert R. Assessing comorbidityin older adults using prescription claims data. J Pharm Health Serv Res2010; 1(4): 157–165.

43. Davidoff AJ, Zuckerman IH, Pandya N, et al. A novel approach to improvehealth status measurement in observational claims-based studies of cancer treat-ment and outcomes. J Geriatr Oncol 2013; 4(2): 157–165.

44. Rockwood K, Andrew M, Mitnitski A. A comparison of two approachesto measuring frailty in elderly people. J Gerontol A Biol Sci Med Sci2007; 62(7): 738–743.

45. Kulminski AM, Ukraintseva SV, Kulminskaya IV, Arbeev KG, Land K, YashinAI. Cumulative deficits better characterize susceptibility to death in elderly peo-ple than phenotypic frailty: lessons from the Cardiovascular Health Study. J AmGeriatr Soc 2008; 56(5): 898–903.

46. Eapen ZJ, Liang L, Fonarow GC, et al. Validated, electronic health recorddeployable prediction models for assessing patient risk of 30-day rehospital-ization and mortality in older heart failure patients. JACC Heart Fail 2013;1(3): 245–251.

47. Rapsomaniki E, Shah A, Perel P, et al. Prognostic models for stable coronary ar-tery disease based on electronic health record cohort of 102 023 patients. EurHeart J 2014; 35(13): 844–852.

48. Harrold J, Byhoff E, Harris P, et al. All hospice patients are not equal: develop-ment of a visit-based acuity index. J Palliat Med 2014; 17(2): 135–140.

49. Schneeweiss S, Rassen JA, Glynn RJ, et al. Supplementing claims data withoutpatient laboratory test results to improve confounding adjustment ineffectiveness studies of lipid-lowering treatments. BMC Med Res Methodol2012; 12: 180.

50. Rockwood K. What would make a definition of frailty successful? Age Ageing2005; 34(5): 432–434.

51. Bainey KR, Natarajan MK, Mercuri M, et al. Treatment assignment of high-risksymptomatic severe aortic stenosis patients referred for transcatheter aortic valveimplantation. Am J Cardiol 2013; 112(1): 100–103.

52. Sambrook PN, Cameron ID, Chen JS, et al. Oral bisphosphonates are associatedwith reduced mortality in frail older people: a prospective five-year study.Osteoporos Int 2011; 22(9): 2551–2556.

53. Aaldriks AA, Giltay EJ, le Cessie S, et al. Prognostic value of geriatric assess-ment in older patients with advanced breast cancer receiving chemotherapy.Breast 2013; 22(5): 753–760.

54. Purser JL, Kuchibhatla MN, Fillenbaum GG, Harding T, Peterson ED,Alexander KP. Identifying frailty in hospitalized older adults with significantcoronary artery disease. J Am Geriatr Soc 2006; 54(11): 1674–1681.

55. Gnjidic D, Le Couteur DG, Blyth FM, et al. Statin use and clinical outcomes inolder men: a prospective population-based study. BMJ Open 2013; 3(3): e002333.

56. Perera V, Bajorek BV, Matthews S, Hilmer SN. The impact of frailty on theutilisation of antithrombotic therapy in older patients with atrial fibrillation.Age Ageing 2009; 38(2): 156–162.



measuring frailty using claims data for pharmacoepidemiologic studies of mortality in older adults:...

Documents