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: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.

Project description:BackgroundSimple nudges such as reminders and feedback reports to either a patient or a partner may facilitate improved medication adherence.ObjectiveTo test the impact of a pill bottle used to monitor adherence, deliver a daily alarm, and generate weekly medication adherence feedback reports on statin adherence.DesignThree-month, three-arm randomized clinical trial (ClinicalTrials.gov identifier: NCT02480530).ParticipantsOne hundred and twenty-six veterans with known coronary artery disease and poor adherence (medication possession ratio <80 %).InterventionPatients were randomized to one of three groups: (1) a control group (n = 36) that received a pill-monitoring device with no alarms or feedback; (2) an individual feedback group (n = 36) that received a daily alarm and a weekly medication adherence feedback report; and (3) a partner feedback group (n = 54) that received an alarm and a weekly feedback report that was shared with a friend, family member, or a peer. The intervention continued for 3 months, and participants were followed for an additional 3 months after the intervention period.Main measuresAdherence as measured by pill bottle. Secondary outcomes included change in LDL (mg/dl), patient activation, and social support.Key resultsDuring the 3-month intervention period, medication adherence was higher in both feedback arms than in the control arm (individual feedback group 89 %, partner feedback group 86 %, control group 67 %; p < 0.001 and = 0.001). At 6 months, there was no difference in medication adherence between either of the feedback groups and the control (individual feedback 60 %, partner feedback 52 %, control group 54 %; p = 0.75 and 0.97).ConclusionsDaily alarms combined with individual or partner feedback reports improved statin medication adherence. While neither an individual feedback nor partner feedback strategy created a sustainable medication adherence habit, the intervention itself is relatively easy to implement and low cost.

Project description:State-of-the-art machine learning (ML) artificial intelligence methods are increasingly leveraged in clinical predictive modeling to provide clinical decision support systems to physicians. Modern ML approaches such as artificial neural networks (ANNs) and tree boosting often perform better than more traditional methods like logistic regression. On the other hand, these modern methods yield a limited understanding of the resulting predictions. However, in the medical domain, understanding of applied models is essential, in particular, when informing clinical decision support. Thus, in recent years, interpretability methods for modern ML methods have emerged to potentially allow explainable predictions paired with high performance. To our knowledge, we present in this work the first explainability comparison of two modern ML methods, tree boosting and multilayer perceptrons (MLPs), to traditional logistic regression methods using a stroke outcome prediction paradigm. Here, we used clinical features to predict a dichotomized 90 days post-stroke modified Rankin Scale (mRS) score. For interpretability, we evaluated clinical features' importance with regard to predictions using deep Taylor decomposition for MLP, Shapley values for tree boosting and model coefficients for logistic regression. With regard to performance as measured by Area under the Curve (AUC) values on the test dataset, all models performed comparably: Logistic regression AUCs were 0.83, 0.83, 0.81 for three different regularization schemes; tree boosting AUC was 0.81; MLP AUC was 0.83. Importantly, the interpretability analysis demonstrated consistent results across models by rating age and stroke severity consecutively amongst the most important predictive features. For less important features, some differences were observed between the methods. Our analysis suggests that modern machine learning methods can provide explainability which is compatible with domain knowledge interpretation and traditional method rankings. Future work should focus on replication of these findings in other datasets and further testing of different explainability methods.

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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