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

Minimal Adherence Threshold to Non-Vitamin K Antagonist Oral Anticoagulants in Patients with Atrial Fibrillation to Reduce the Risk of Thromboembolism and Death: A Nationwide Cohort Study

PURPOSE

Poor adherence to non-vitamin K antagonist oral anticoagulants (NOACs) may raise thromboembolic risks in patients with atrial fibrillation (AF). However, the minimal adherence to maintain the protective effect of NOACs is currently unknown. Therefore, we investigated thresholds of NOAC adherence in association with thromboembolic and mortality risks.

METHODS

Patients with AF initiating NOACs between 2013 and 2019 were identified in Belgian nationwide data. Adherence was measured using the proportion of days covered (PDC) after one year of treatment. Inverse probability of treatment weighted Cox regression was used to investigate outcomes.

RESULTS

92,111 persons were included (250,750 person-years). Compared to NOAC users with a one-year PDC of 100%, significantly higher risks of stroke or systemic embolism were observed among NOAC users with PDCs of 85-89% (adjusted hazard ratio (aHR) 1.35, 95% confidence interval (CI) (1.19-1.54)), 80-84% (aHR 1.31, 95%CI (1.08-1.58)) and < 80% (aHR 1.64, 95%CI (1.34-2.01)), while no significant differences were observed among NOAC users with one-year PDCs of 95-99% (aHR 1.02, 95%CI (0.94-1.12)) or 90-94% (aHR 1.06, 95%CI (0.95-1.18)). Significantly higher risks of all-cause mortality were observed with decreasing levels of NOAC adherence, which were already higher among NOAC users with a one-year PDC of 90-94% versus 100% (aHR 1.09, 95%CI (1.01-1.17)). Findings were similar with once-daily and twice-daily dosed NOACs.

CONCLUSION

Poor adherence to NOACs is associated with increased risks of thromboembolism and all-cause mortality. The minimal adherence threshold should be ≥ 90%, preferably even ≥ 95%.

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.

Adherence and persistence to oral anticoagulants in patients with atrial fibrillation: A Belgian nationwide cohort study

Background

Since non-vitamin K antagonist oral anticoagulants (NOACs) do not require coagulation monitoring, concerns of lower adherence and persistence to NOACs than vitamin K antagonists (VKAs) have been raised. Moreover, little is known on the frequency of permanent cessation and switching between anticoagulants in patients with atrial fibrillation (AF). Therefore, persistence, reinitiation, switching and adherence to oral anticoagulants (OACs) were investigated.

Materials and methods

AF patients with a first OAC prescription claim between 2013 and 2019 were identified in Belgian nationwide data. Persistence, reinitiation and switching were estimated using Kaplan-Meier analyses. Adherence was investigated using the proportion of days covered (PDC). Predictors for non-adherence and non-persistence were identified by multivariable logistic regression.

Results

Among 277,782 AF patients, 69.6% NOAC and 37.2% VKA users were persistent after 1 year, whereas 44.3% and 18.9% after 5 years, respectively. After one year, 67.1% rivaroxaban, 68.1% dabigatran, 69.8% apixaban, and 76.9% edoxaban users were persistent. Among subjects having discontinued NOAC or VKA treatment, 75.4% and 46.1% reinitiated any OAC within 5 years, respectively. VKAs were more frequently switched to NOACs than vice versa (17.6% versus 2.5% after 1 year). After 1 year, a high PDC (≥ 90%) was observed in 87.8% apixaban, 88.6% dabigatran, 91.3% rivaroxaban, and 94.7% edoxaban users (90.2% NOAC users). Adherence and persistence were higher in older, female subjects, while lower in subjects with dementia or hyperpolypharmacy.

Conclusion

Adherence and persistence to NOACs were high. However, 10% of subjects were non-adherent after 1 year and one-fourth did not reinitiate anticoagulation within 5 years after NOAC discontinuation.

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.