Dataset Information

Opening the black box of the group-based trajectory modeling process to analyze medication adherence patterns: An example using real-world statin adherence data.

ABSTRACT: PURPOSE:The rationale for choosing a final group-based trajectory modeling (GBTM) specification and evaluations of patient adherence patterns within groups are often omitted in the GBTM medication adherence literature. We aimed to (1) reveal the complexity of GBTM and (2) assess model discrimination of patient medication adherence patterns. METHODS:Medicare administrative claims were used to measure statin medication adherence as a continuous value in the 6 months before an acute myocardial infarction (AMI) hospitalization. Different GBTM specifications beyond default settings were constructed and compared with the Bayesian information criterion. Spaghetti plots were used to compare individual adherence patterns with group averages. RESULTS:Overall, 113,296 prevalent statin users met eligibility criteria. Four adherence groups were identified: persistently adherent, moderately adherent, progressively nonadherent, and persistently nonadherent. Spaghetti plots showed the persistently adherent and persistently nonadherent groups had relatively homogeneous adherence patterns that matched predicted trajectories well. Spaghetti plots also showed that, while adherence patterns in the progressively nonadherent group were not as homogeneous, most patients in this group appeared to be discontinuing statin therapy pre-AMI. CONCLUSIONS:Subjective decisions are necessary to identify a final trajectory model. Greater transparency and disclosure of these decisions in the medication adherence literature are needed. Individual patient adherence patterns from spaghetti plots provided additional diagnostic information about trajectory models beyond standard model-fit assessments to determine if group-average adherence estimates represent homogeneous patterns of medication adherence.

SUBMITTER: Hickson RP

PROVIDER: S-EPMC7058496 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Opening the black box of the group-based trajectory modeling process to analyze medication adherence patterns: An example using real-world statin adherence data.

Hickson Ryan P RP Annis Izabela E IE Killeya-Jones Ley A LA Fang Gang G

Pharmacoepidemiology and drug safety 20191204 3

<h4>Purpose</h4>The rationale for choosing a final group-based trajectory modeling (GBTM) specification and evaluations of patient adherence patterns within groups are often omitted in the GBTM medication adherence literature. We aimed to (1) reveal the complexity of GBTM and (2) assess model discrimination of patient medication adherence patterns.<h4>Methods</h4>Medicare administrative claims were used to measure statin medication adherence as a continuous value in the 6 months before an acute ...[more]

PMID: 31802581

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Project description:BACKGROUND:Robust evidence is lacking on optimal timing of statin administration and its impact on patient outcomes. OBJECTIVE:This study aims to evaluate among incident statin users the relationship between those prescribed evening vs. daily dosing instructions, medication adherence, and changes in low-density lipoprotein cholesterol (LDL-c). DESIGN:This is an observational cohort study at Sutter Health, a community-based healthcare system, 2010-2016. PARTICIPANTS:Patients were ??35 years of age as of the first statin prescription (baseline), with 12 to 36 months of electronic health record activity before and after baseline. Incident use was defined as no statin prescription in 12 months prior to baseline. MAIN MEASURES:Differences in medication adherence (proportion of days covered ??0.80) over 12 months from baseline and mean change in LDL-c between 12 and 24 months from baseline were measured using regression modeling, adjusting for baseline demographics and clinical, prescriber, and statin characteristics. KEY RESULTS:Among 31,252 patients with valid statin prescriptions between 2010 and 2016, 5099 eligible incident statin users (mean age, 63 years) were identified, of whom 53% were prescribed evening and 47% daily dosing instructions. No difference in likelihood of statin adherence over 12 months was observed for evening vs. daily dosing (adjusted odds ratio [OR] 0.90; 95% CI 0.75, 1.08). No differences were observed in mean change in LDL-c (adjusted mean difference 1.42 mg/dL; 95% CI -?1.02, 3.89) or likelihood of attaining LDL-c <?70 mg/dL (adjusted OR 0.83; 95% CI 0.67, 1.04) for evening vs. daily dosing over a mean of 19 months follow-up. CONCLUSIONS:Among incident statin users from a real-world clinical setting, those with daily and evening dosing instructions had similar adherence rates and mean changes in LDL-c. Given potential clinical equipoise for evening and daily dosing, clinicians should consider patient-tailored statin dosing instructions to reduce potentially unnecessary regimen complexity.

Project description:BackgroundOutcome measures for non-ambulant Duchenne muscular dystrophy (DMD) patients are limited, with only the Performance of the Upper Limb (PUL) approved as endpoint for clinical trials.ObjectiveWe assessed four outcome measures based on devices developed for the gaming industry, aiming to overcome disadvantages of observer-dependency and motivation.MethodsTwenty-two non-ambulant DMD patients (range 8.6-24.1 years) and 14 healthy controls (HC; range 9.5-25.4 years) were studied at baseline and 16 patients at 12 months using Leap Motion to quantify wrist/hand active range of motion (aROM) and a Kinect sensor for reached volume with Ability Captured Through Interactive Video Evaluation (ACTIVE), Functional Workspace (FWS) summed distance to seven upper extremity body points, and trunk compensation (KinectTC). PUL 2.0 was performed in patients only. A stepwise approach assessed quality control, construct validity, reliability, concurrent validity, longitudinal change and patient perception.ResultsLeap Motion aROM distinguished patients and HCs for supination, radial deviation and wrist flexion (range p = 0.006 to <0.001). Reliability was low and the manufacturer's hand model did not match the sensor's depth images. ACTIVE differed between patients and HCs (p < 0.001), correlated with PUL (rho = 0.76), and decreased over time (p = 0.030) with a standardized response mean (SRM) of -0.61. It was appraised as fun on a 10-point numeric rating scale (median 9/10). PUL decreased over time (p < 0.001) with an SRM of -1.28, and was appraised as fun (median 7/10). FWS summed distance distinguished patients and HCs (p < 0.001), but reliability in patients was insufficient. KinectTC differed between patients and HCs (p < 0.01), but correlated insufficiently with PUL (rho = -0.69).ConclusionsOnly ACTIVE qualified as potential outcome measure in non-ambulant DMD patients, although the SRM was below the commonly used threshold of 0.8. Lack of insight in technological constraints due to intellectual property and software updates made the technology behind these outcome measures a kind of black box that could jeopardize long-term use in clinical development.

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.

Dataset Information

Opening the black box of the group-based trajectory modeling process to analyze medication adherence patterns: An example using real-world statin adherence data.

Publications

Opening the black box of the group-based trajectory modeling process to analyze medication adherence patterns: An example using real-world statin adherence data.

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