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Controlling for Frailty in Pharmacoepidemiologic Studies of Older Adults: Validation of an Existing Medicare Claims-based Algorithm

Cuthbertson, Carmen, C.a; Kucharska-Newton, Annaa; Faurot, Keturah, R.a; Stürmer, Tila; Jonsson Funk, Michelea; Palta, Priyaa; Windham, B., Gwenb; Thai, Sydneya; Lund, Jennifer, L.a

doi: 10.1097/EDE.0000000000000833
Pharmacoepidemiology

Background: Frailty is a geriatric syndrome characterized by weakness and weight loss and is associated with adverse health outcomes. It is often an unmeasured confounder in pharmacoepidemiologic and comparative effectiveness studies using administrative claims data.

Methods: Among the Atherosclerosis Risk in Communities (ARIC) Study Visit 5 participants (2011–2013; n = 3,146), we conducted a validation study to compare a Medicare claims-based algorithm of dependency in activities of daily living (or dependency) developed as a proxy for frailty with a reference standard measure of phenotypic frailty. We applied the algorithm to the ARIC participants’ claims data to generate a predicted probability of dependency. Using the claims-based algorithm, we estimated the C-statistic for predicting phenotypic frailty. We further categorized participants by their predicted probability of dependency (<5%, 5% to <20%, and ≥20%) and estimated associations with difficulties in physical abilities, falls, and mortality.

Results: The claims-based algorithm showed good discrimination of phenotypic frailty (C-statistic = 0.71; 95% confidence interval [CI] = 0.67, 0.74). Participants classified with a high predicted probability of dependency (≥20%) had higher prevalence of falls and difficulty in physical ability, and a greater risk of 1-year all-cause mortality (hazard ratio = 5.7 [95% CI = 2.5, 13]) than participants classified with a low predicted probability (<5%). Sensitivity and specificity varied across predicted probability of dependency thresholds.

Conclusions: The Medicare claims-based algorithm showed good discrimination of phenotypic frailty and high predictive ability with adverse health outcomes. This algorithm can be used in future Medicare claims analyses to reduce confounding by frailty and improve study validity.

From the aDepartment of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC

bUniversity of Mississippi, Jackson, MS.

Submitted August 18, 2017; accepted March 28, 2018.

The Atherosclerosis Risk in Communities Study is carried out as a collaborative study supported by National Heart, Lung, and Blood Institute contracts (HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN268201100011C, and HHSN268201100012C).

Statement of data and code availability: The ARIC Study cohort data are available to researchers who request it through the ARIC Study Coordinating Center. SAS code for all published analyses are available upon request.

This work was supported by the National Institutes of Health: K12CA120780 (J.L.L.) R01/56 AG023178 (T.S., M.J.F.).

T.S. receives investigator-initiated research funding as the Principal Investigator (R01/56 AG023178) and as Co-Investigator (R01 CA174453; R01 HL118255, R21-HD080214) from the National Institutes of Health. He also receives salary support as Director of the Comparative Effectiveness Research Strategic Initiative, NC TraCS Institute, UNC Clinical and Translational Science Award (UL1TR001111) and as Director of the Center for Pharmacoepidemiology (current members: GlaxoSmithKline, UCB BioSciences, Merck) and received research support from pharmaceutical companies (Amgen, AstraZeneca) to the Department of Epidemiology at UNC. He does not accept personal compensation of any kind from any pharmaceutical company. He owns stock in Novartis, Roche, BASF, AstraZeneca, and Novo Nordisk. M.J.F. receives investigator-initiated research funding as the Principal Investigator (R01 HL118255) and as Co-Investigator (R01/56 AG023178) from the National Institutes of Health. She also receives salary support as Core Faculty of the Comparative Effectiveness Research Strategic Initiative, NC TraCS Institute, UNC Clinical and Translational Science Award (UL1TR001111), and from the Center for Pharmacoepidemiology (current members: GlaxoSmithKline (GSK), UCB BioSciences, and Merck) and received research support from AstraZeneca to the Department of Epidemiology at UNC. She is a member of the Scientific Steering Committee (SSC) for a postapproval safety study funded by GSK. All compensation for services provided on the SSC is invoiced by and paid to UNC Chapel Hill. She does not accept personal compensation of any kind from any pharmaceutical company. J.L.L. was supported by a PhRMA Foundation Research Starter Award to the Department of Epidemiology at the University of North Carolina at Chapel Hill (UNC). The other authors have no conflicts to report.

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Correspondence: Jennifer L. Lund, Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, 2102-D McGavran-Greenberg Hall, CB#7435, Chapel Hill, NC 27599. E-mail: Jennifer.Lund@unc.edu.

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