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Novel application of approaches to predicting medication adherence using medical claims data.

ABSTRACT: OBJECTIVE:To compare predictive analytic approaches to characterize medication nonadherence and determine under which circumstances each method may be best applied. DATA SOURCES/STUDY SETTING:Medicare Parts A, B, and D claims from 2007 to 2013. STUDY DESIGN:We evaluated three statistical techniques to predict statin adherence (proportion of days covered [PDC ? 80 percent]) in the year following discharge: standard logistic regression with backward selection of covariates, least absolute shrinkage and selection operator (LASSO), and random forest. We used the C-index to assess model discrimination and decile plots comparing predicted values to observed event rates to evaluate model performance. DATA EXTRACTION:We identified 11 969 beneficiaries with an acute myocardial infarction (MI)-related admission from 2007 to 2012, who filled a statin prescription at, or shortly after, discharge. PRINCIPAL FINDINGS:In all models, prior statin use was the most important predictor of future adherence (OR = 3.65, 95% CI: 3.34-3.98; OR = 3.55). Although the LASSO regression model selected nearly 90 percent of all candidate predictors, all three analytic approaches had moderate discrimination (C-index ranging from 0.664 to 0.673). CONCLUSIONS:Although none of the models emerged as clearly superior, predictive analytics could proactively determine which patients are at risk of nonadherence, thus allowing for timely engagement in adherence-improving interventions.

SUBMITTER: Zullig LL

PROVIDER: S-EPMC6863234 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Novel application of approaches to predicting medication adherence using medical claims data.

Zullig Leah L LL Jazowski Shelley A SA Wang Tracy Y TY Hellkamp Anne A Wojdyla Daniel D Thomas Laine L Egbuonu-Davis Lisa L Beal Anne A Bosworth Hayden B HB

Health services research 20190820 6

<h4>Objective</h4>To compare predictive analytic approaches to characterize medication nonadherence and determine under which circumstances each method may be best applied.<h4>Data sources/study setting</h4>Medicare Parts A, B, and D claims from 2007 to 2013.<h4>Study design</h4>We evaluated three statistical techniques to predict statin adherence (proportion of days covered [PDC ≥ 80 percent]) in the year following discharge: standard logistic regression with backward selection of covariates, l ...[more]

PMID: 31429471

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Project description:Poor adherence to medication regimens and medical record inconsistencies result in incomplete knowledge of medication therapy in polypharmacy patients. By quantitatively identifying medications in the blood of patients and reconciling detected medications with the medical record, we have defined the severity of this knowledge gap and created a path toward optimizing medication therapy.We validated a liquid chromatography-tandem mass spectrometry assay to detect and/or quantify 38 medications across a broad range of chronic diseases to obtain a comprehensive survey of patient adherence, medical record accuracy, and exposure variability in two patient populations. In a retrospectively tested 821-patient cohort representing U.S. adults, we found that 46% of medications assessed were detected in patients as prescribed in the medical record. Of the remaining medications, 23% were detected, but not listed in the medical record while 30% were prescribed to patients, but not detected in blood. To determine how often each detected medication fell within literature-derived reference ranges when taken as prescribed, we prospectively enrolled a cohort of 151 treatment-regimen adherent patients. In this cohort, we found that 53% of medications that were taken as prescribed, as determined using patient self-reporting, were not within the blood reference range. Of the medications not in range, 83% were below and 17% above the lower and upper range limits, respectively. Only 32% of out-of-range medications could be attributed to short oral half-lives, leaving extensive exposure variability to result from patient behavior, undefined drug interactions, genetics, and other characteristics that can affect medication exposure.This is the first study to assess compliance, medical record accuracy, and exposure as determinants of real-world treatment and response. Variation in medication detection and exposure is greater than previously demonstrated, illustrating the scope of current therapy issues and opening avenues that warrant further investigation to optimize medication therapy.

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Novel application of approaches to predicting medication adherence using medical claims data.

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Novel application of approaches to predicting medication adherence using medical claims data.

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