Task sharing in an interprofessional medication management program - a survey of general practitioners and community pharmacists.

BACKGROUND: Pharmacist-led medication review and medication management programs (MMP) are well-known strategies to improve medication safety and effectiveness. If performed interprofessionally, outcomes might even improve. However, little is known about task sharing in interprofessional MMP, in which general practitioners (GPs) and community pharmacists (CPs) collaboratively perform medication reviews and continuously follow-up on patients with designated medical and pharmaceutical tasks, respectively. In 2016, ARMIN (Arzneimittelinitiative Sachsen-Thüringen) an interprofessional MMP was launched in two German federal states, Saxony and Thuringia. The aim of this study was to understand how GPs and CPs share tasks in MMP when reviewing the patients' medication. METHODS: This was a cross-sectional postal survey among GPs and CPs who participated in the MMP. Participants were asked who completed which MMP tasks, e.g., checking drug-drug interactions, dosing, and side effects. In total, 15 MMP tasks were surveyed using a 5-point Likert scale ranging from "I complete this task alone" to "GP/CP completes this task alone". The study was conducted between 11/2020 and 04/2021. Data was analyzed using descriptive statistics. RESULTS: In total, 114/165 (69.1%) GPs and 166/243 (68.3%) CPs returned a questionnaire. The majority of GPs and CPs reported (i) checking clinical parameters and medication overuse and underuse to be completed by GPs, (ii) checking storage conditions of drugs and initial compilation of the patient's medication including brown bag review being mostly performed by CPs, and (iii) checking side-effects, non-adherence, and continuous updating of the medication list were carried out jointly. The responses differed most for problems with self-medication and adding and removing over-the-counter medicines from the medication list. In addition, the responses revealed that some MMP tasks were not sufficiently performed by either GPs or CPs. CONCLUSIONS: Both GPs' and CPs' expertise are needed to perform MMP as comprehensively as possible. Future studies should explore how GPs and CPs can complement each other in MMP most efficiently.

Mortality and Hospitalizations Among Patients Enrolled in an Interprofessional Medication Management Program.

BACKGROUND: Measures for improving medication safety in outpatient care are often complex and involve medication reviews. Over the period 2016-2022 (with a preceeding one-year pilot phase), an interprofessional medication management program- the Medicines Initiative Saxony-Thuringia (Arzneimittelinitiative Sachsen-Thüringen, ARMIN)-was implemented in two German federal states. More than 5000 patients received a medication review by the end of 2019 by a team composed of physicians and pharmacists and were provided with joint, continuous care thereafter. METHODS: In the framework of a retrospectively registered cohort study, the mortality and hospitalizations of this population (5033 patients) were studied using routine data from a statutory health insurer (observation period 2015-2019) and compared with those of a control group (10 039 patients) determined from the routine data by propensity score matching. Mortality was compared by survival analysis (Cox regression), and hospitalization rates were compared in terms of event probabilities within two years of enrollment in the medication management program. Robustness was tested in multiple sensitivity analyses. RESULTS: Over the observation period, 9.3% of the ARMIN participants and 12.9% of persons in the control group died (hazard ratio of the adjusted Cox regression, 0.84; 95% confidence interval [0.76; 0.94], P = 0.001). In the first two years after inclusion, the ARMIN participants were hospitalized just as often as the persons in the control group (52.4% versus 53.4%; odds ratio from the adjusted model, 1.04 [0.96; 1.11], P = 0.347). The effects were consistent in sensitivity analyses. CONCLUSION: In this retrospective cohort study, participation in the ARMIN program was associated with a lower risk of death. Exploratory analyses provide clues to the potential origin of this association.

Can Machine Learning from Real-World Data Support Drug Treatment Decisions? A Prediction Modeling Case for Direct Oral Anticoagulants.

BACKGROUND: Decision making for the "best" treatment is particularly challenging in situations in which individual patient response to drugs can largely differ from average treatment effects. By estimating individual treatment effects (ITEs), we aimed to demonstrate how strokes, major bleeding events, and a composite of both could be reduced by model-assisted recommendations for a particular direct oral anticoagulant (DOAC). METHODS: In German claims data for the calendar years 2014-2018, we selected 29 901 new users of the DOACs rivaroxaban and apixaban. Random forests considered binary events within 1 y to estimate ITEs under each DOAC according to the X-learner algorithm with 29 potential effect modifiers; treatment recommendations were based on these estimated ITEs. Model performance was evaluated by the c-for-benefit statistics, absolute risk reduction (ARR), and absolute risk difference (ARD) by trial emulation. RESULTS: A significant proportion of patients would be recommended a different treatment option than they actually received. The stroke model significantly discriminated patients for higher benefit and thus indicated improved decisions by reduced outcomes (c-for-benefit: 0.56; 95% confidence interval [0.52; 0.60]). In the group with apixaban recommendation, the model also improved the composite endpoint (ARR: 1.69 % [0.39; 2.97]). In trial emulations, model-assisted recommendations significantly reduced the composite event rate (ARD: -0.78 % [-1.40; -0.03]). CONCLUSIONS: If prescribers are undecided about the potential benefits of different treatment options, ITEs can support decision making, especially if evidence is inconclusive, risk-benefit profiles of therapeutic alternatives differ significantly, and the patients' complexity deviates from "typical" study populations. In the exemplary case for DOACs and potentially in other situations, the significant impact could also become practically relevant if recommendations were available in an automated way as part of decision making.HighlightsIt was possible to calculate individual treatment effects (ITEs) from routine claims data for rivaroxaban and apixaban, and the characteristics between the groups with recommendation for one or the other option differed significantly.ITEs resulted in recommendations that were significantly superior to usual (observed) treatment allocations in terms of absolute risk reduction, both separately for stroke and in the composite endpoint of stroke and major bleeding.When similar patients from routine data were selected (precision cohorts) for patients with a strong recommendation for one option or the other, those similar patients under the respective recommendation showed a significantly better prognosis compared with the alternative option.Many steps may still be needed on the way to clinical practice, but the principle of decision support developed from routine data may point the way toward future decision-making processes.

A prediction model for nonpersistence or nonadherence to direct oral anticoagulants in hospitalized patients with atrial fibrillation.

BACKGROUND: Medication adherence and persistence is fundamental for drug effectiveness, which is also true for the prevention of strokes in patients with atrial fibrillation (AF). Adherence to direct oral anticoagulants (DOACs) as first-line agents is often high in the early posthospital period. However, adherence often sharply declines (or eventually leads to nonpersistence) in the post-discharge ambulatory period, rendering stroke prevention ineffective. If patients at high risk of nonpersistence or nonadherence could be identified early, they could be offered early intervention measures to improve adherence and/or persistence. OBJECTIVE: To develop and internally validate a predictive model for medication nonadherence and nonpersistence to DOAC treatment in patients with AF after discharge using health insurance claims data. METHODS: We selected health insurance claims data between 2011 and 2016 from 8,055 patients with AF who were newly treated with rivaroxaban or apixaban after a hospital admission for stroke, transient ischemic attack, or AF. In the post-discharge ambulatory period, medication adherence was derived as the proportion of days covered, calculated from drug dispensation data. A maximum permissible 90-day gap between the end of a prescription and the next dispensation was used to estimate persistence. Candidate predictors were either derived from the index hospital admission or summarized from the previous year (eg, comorbidities or medication adherence to long-term treatments, such as ß-blockers, renin-angiotensin system inhibitors, statins, and thyroid hormones). A regularized logistic regression model was fitted using the least absolute shrinkage and selection operator in a split-sample approach (66.7% training data; 33.3% test data) to predict a composite of medication nonadherence/nonpersistence. Discrimination performance was assessed using the area under the receiver operating characteristic curve, the maximum sensitivity/specificity, and the scaled Brier score. A calibration curve fitted by linear regression was used to evaluate model calibration. RESULTS: The average age of the study participants was 79.7 years, 62% were female, and 3,515 patients (44%) were adherent and persistent (median follow-up of 185 days). Medication adherence to previous long-term treatments showed strong predictive properties. The developed model discriminated well (concordance statistic: 0.755), was well calibrated, and showed a scaled Brier score of 0.202 for identification of patients at risk. CONCLUSIONS: The model successfully predicted medication non-adherence/nonpersistence to DOAC treatment after discharge. Such a model could help ensure that targeted interventions are already in place at the time of hospital discharge, potentially preventing strokes and reducing costs. DISCLOSURES: Mr Wirbka is funded by the German Innovation Funds according to § 92a (2) Volume V of the Social Insurance Code (§ 92a Abs. 2, SGBV-Fünftes Buch Sozialgesetzbuch), grant number: 01VSF18019. Dr Haefeli received financial support from Daiichi-Sankyo, app development (https://www.easydoac.de/), and Bayer. He also received personal speaker fees from Bristol Myers-Squibb and Daiichi-Sankyo Online Seminar. Dr Meid is funded by the Physician-Scientist Programme of the Medical Faculty of Heidelberg University.

Predicting Hospital Readmissions from Health Insurance Claims Data: A Modeling Study Targeting Potentially Inappropriate Prescribing.

BACKGROUND: Numerous prediction models for readmissions are developed from hospital data whose predictor variables are based on specific data fields that are often not transferable to other settings. In contrast, routine data from statutory health insurances (in Germany) are highly standardized, ubiquitously available, and would thus allow for automatic identification of readmission risks. OBJECTIVES: To develop and internally validate prediction models for readmissions based on potentially inappropriate prescribing (PIP) in six diseases from routine data. METHODS: In a large database of German statutory health insurance claims, we detected disease-specific readmissions after index admissions for acute myocardial infarction (AMI), heart failure (HF), a composite of stroke, transient ischemic attack or atrial fibrillation (S/AF), chronic obstructive pulmonary disease (COPD), type-2 diabetes mellitus (DM), and osteoporosis (OS). PIP at the index admission was determined by the STOPP/START criteria (Screening Tool of Older Persons' Prescriptions/Screening Tool to Alert doctors to the Right Treatment) which were candidate variables in regularized prediction models for specific readmission within 90 days. The risks from disease-specific models were combined ("stacked") to predict all-cause readmission within 90 days. Validation performance was measured by the c-statistics. RESULTS: While the prevalence of START criteria was higher than for STOPP criteria, more single STOPP criteria were selected into models for specific readmissions. Performance in validation samples was the highest for DM (c-statistics: 0.68 [95% confidence interval (CI): 0.66-0.70]), followed by COPD (c-statistics: 0.65 [95% CI: 0.64-0.67]), S/AF (c-statistics: 0.65 [95% CI: 0.63-0.66]), HF (c-statistics: 0.61 [95% CI: 0.60-0.62]), AMI (c-statistics: 0.58 [95% CI: 0.56-0.60]), and OS (c-statistics: 0.51 [95% CI: 0.47-0.56]). Integrating risks from disease-specific models to a combined model for all-cause readmission yielded a c-statistics of 0.63 [95% CI: 0.63-0.64]. CONCLUSION: PIP successfully predicted readmissions for most diseases, opening the possibility for interventions to improve these modifiable risk factors. Machine-learning methods appear promising for future modeling of PIP predictors in complex older patients with many underlying diseases.

Estimated Thresholds of Minimum Necessary Adherence for Effective Treatment with Direct Oral Anticoagulants - A Retrospective Cohort Study in Health Insurance Claims Data.

BACKGROUND: An essential contribution regarding the prevention of thromboembolic events in patients with (non-valvular) atrial fibrillation (AF) is good adherence to direct oral anticoagulants (DOACs). However, it is an open question what "good" adherence means for DOACs or below which threshold non-adherence is clinically relevant for AF patients. Ultimately, such a classification could prevent strokes and associated costs through adjusted treatment regimens or supportive measures. METHODS: We selected 10,092 AF patients from health insurance claims data between 2014 and 2018 who were issued a majority (at least half of the number) of maximum approved strength prescriptions of one of the following DOACs, namely rivaroxaban, apixaban, or dabigatran. Due to the limited sample size, the prescriptions of dabigatran had to be finally excluded for the cut-off analysis. DOAC adherence was calculated as the proportion of days covered (PDC) by dividing the days of theoretical use (days covered) of the drug by the duration in days of the observation interval. PDC cut-off values were derived from stroke risk as a function of continuous PDC values in time-to-event analyses and corresponding dose-response models. The influence of adherence-promoting interventions (targeted and untargeted) on the occurrence of strokes and related costs was then projected, considering intervention costs per patient. RESULTS: The population had a mean age of 74.5 years and 50% were female. The median PDC was 0.79 ± 0.28 with a median follow-up time of 1218 days, in which 2% of all DOAC patients had a stroke. The adherence cut-offs for good adherence were identified at 0.78 for rivaroxaban and 0.8 for apixaban. Targeted interventions appeared to be far more cost-effective than untargeted interventions. CONCLUSION: Clear adherence cut-offs enable healthcare professionals to identify patients with clinically relevant non-adherence. Interventions based on these cut-offs appear to be a promising means to optimize DOAC treatment.

Disease-dependent variations in the timing and causes of readmissions in Germany: A claims data analysis for six different conditions.

BACKGROUND: Hospital readmissions place a major burden on patients and health care systems worldwide, but little is known about patterns and timing of readmissions in Germany. METHODS: We used German health insurance claims (AOK, 2011-2016) of patients ≥ 65 years hospitalized for acute myocardial infarction (AMI), heart failure (HF), a composite of stroke, transient ischemic attack, or atrial fibrillation (S/AF), chronic obstructive pulmonary disease (COPD), type 2 diabetes mellitus, or osteoporosis to identify hospital readmissions within 30 or 90 days. Readmissions were classified into all-cause, specific, and non-specific and their characteristics were analyzed. RESULTS: Within 30 and 90 days, about 14-22% and 27-41% index admissions were readmitted for any reason, respectively. HF and S/AF contributed most index cases, and HF and COPD accounted for most all-cause readmissions. Distributions and ratios of specific to non-specific readmissions were disease-specific with highest specific readmissions rates among COPD and AMI. CONCLUSION: German claims are well-suited to investigate readmission causes if longer periods than 30 days are evaluated. Conditions closely related with the primary disease are the most frequent readmission causes, but multiple comorbidities among readmitted cases suggest that a multidisciplinary care approach should be implemented vigorously addressing comorbidities already during the index hospitalization.

A framework to build similarity-based cohorts for personalized treatment advice - a standardized, but flexible workflow with the R package SimBaCo.

Along with increasing amounts of big data sources and increasing computer performance, real-world evidence from such sources likewise gains in importance. While this mostly applies to population averaged results from analyses based on the all available data, it is also possible to conduct so-called personalized analyses based on a data subset whose observations resemble a particular patient for whom a decision is to be made. Claims data from statutory health insurance companies could provide necessary information for such personalized analyses. To derive treatment recommendations from them for a particular patient in everyday care, an automated, reproducible and efficiently programmed workflow would be required. We introduce the R-package SimBaCo (Similarity-Based Cohort generation) offering a simple, but modular, and intuitive framework for this task. With the six built-in R-functions, this framework allows the user to create similarity cohorts tailored to the characteristics of particular patients. An exemplary workflow illustrates the distinct steps beginning with an initial cohort selection according to inclusion and exclusion criteria. A plotting function facilitates investigating a particular patient's characteristics relative to their distribution in a reference cohort, for example the initial cohort or the precision cohort after the data has been trimmed in accordance with chosen variables for similarity finding. Such precision cohorts allow any form of personalized analysis, for example personalized analyses of comparative effectiveness or customized prediction models developed from precision cohorts. In our exemplary workflow, we provide such a treatment comparison whereupon a treatment decision for a particular patient could be made. This is only one field of application where personalized results can directly support the process of clinical reasoning by leveraging information from individual patient data. With this modular package at hand, personalized studies can efficiently weight benefits and risks of treatment options of particular patients.

Using the Causal Inference Framework to Support Individualized Drug Treatment Decisions Based on Observational Healthcare Data.

When healthcare professionals have the choice between several drug treatments for their patients, they often experience considerable decision uncertainty because many decisions simply have no single "best" choice. The challenges are manifold and include that guideline recommendations focus on randomized controlled trials whose populations do not necessarily correspond to specific patients in everyday treatment. Further reasons may be insufficient evidence on outcomes, lack of direct comparison of distinct options, and the need to individually balance benefits and risks. All these situations will occur in routine care, its outcomes will be mirrored in routine data, and could thus be used to guide decisions. We propose a concept to facilitate decision-making by exploiting this wealth of information. Our working example for illustration assumes that the response to a particular (drug) treatment can substantially differ between individual patients depending on their characteristics (heterogeneous treatment effects, HTE), and that decisions will be more precise if they are based on real-world evidence of HTE considering this information. However, such methods must account for confounding by indication and effect measure modification, eg, by adequately using machine learning methods or parametric regressions to estimate individual responses to pharmacological treatments. The better a model assesses the underlying HTE, the more accurate are predicted probabilities of treatment response. After probabilities for treatment-related benefit and harm have been calculated, decision rules can be applied and patient preferences can be considered to provide individual recommendations. Emulated trials in observational data are a straightforward technique to predict the effects of such decision rules when applied in routine care. Prediction-based decision rules from routine data have the potential to efficiently supplement clinical guidelines and support healthcare professionals in creating personalized treatment plans using decision support tools.