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.

Predicting hospital admissions from individual patient data (IPD): an applied example to explore key elements driving external validity.

OBJECTIVE: To explore factors that potentially impact external validation performance while developing and validating a prognostic model for hospital admissions (HAs) in complex older general practice patients. STUDY DESIGN AND SETTING: Using individual participant data from four cluster-randomised trials conducted in the Netherlands and Germany, we used logistic regression to develop a prognostic model to predict all-cause HAs within a 6-month follow-up period. A stratified intercept was used to account for heterogeneity in baseline risk between the studies. The model was validated both internally and by using internal-external cross-validation (IECV). RESULTS: Prior HAs, physical components of the health-related quality of life comorbidity index, and medication-related variables were used in the final model. While achieving moderate discriminatory performance, internal bootstrap validation revealed a pronounced risk of overfitting. The results of the IECV, in which calibration was highly variable even after accounting for between-study heterogeneity, agreed with this finding. Heterogeneity was equally reflected in differing baseline risk, predictor effects and absolute risk predictions. CONCLUSIONS: Predictor effect heterogeneity and differing baseline risk can explain the limited external performance of HA prediction models. With such drivers known, model adjustments in external validation settings (eg, intercept recalibration, complete updating) can be applied more purposefully. TRIAL REGISTRATION NUMBER: PROSPERO id: CRD42018088129.

Comparison of Nine Instruments to Calculate Anticholinergic Load in a Large Cohort of Older Outpatients: Association with Cognitive and Functional Decline, Falls, and Use of Laxatives.

OBJECTIVE: A patient's risk for anticholinergic adverse effects is frequently estimated by instruments evaluating the drugs included in his medication profile. It remains unknown, however, which characteristics should be included in such an assessment instrument aiming to reliably predict adverse anticholinergic outcomes. DESIGN: Cross-sectional study. SETTING: ESTHER cohort (Germany). PARTICIPANTS: Home-dwelling participants (N = 2,761) aged between 60 and 87 years. MEASUREMENTS: The association between anticholinergic load calculated with nine different instruments and four anticholinergic adverse outcomes was investigated in univariate and multivariate analyses. Therefore, linear models complemented with Kendall's tau rank correlation coefficients (ԏ) were applied for continuous outcomes and generalized linear models were used to derive odds ratios (ORs) with 95% confidence intervals (CIs) for binary endpoints. RESULTS: Based on the respective identification criteria for anticholinergic drugs, the nine instruments identified between 245 (9%) and 866 (31%) anticholinergic drug users (mean age ± SD: 73 ± 6 years; Mini-Mental State Examination [MMSE] score: 28.3 ± 2.07; Barthel Index: 97.1 ± 7.5; 291 reporting falls; 29 taking laxatives [surrogate for constipation]). In the multivariate analysis, only two instruments indicated a significant association between anticholinergic load and all four outcomes. The instrument considering the prescribed dose showed the strongest association with MMSE scores (ԏ = -0.10), falls (OR: 2.30; 95% CI: 1.50-3.52), and the use of laxatives (OR: 3.11; 95% CI: 1.04-9.36). CONCLUSIONS: Instruments most reliably predicted anticholinergicadverse events if they were either based on the drugs' serum anticholinergic activity and the suggestions of clinician experts or considered the actual prescribed dose.