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Abstract Aims This study aimed to describe baseline characteristics and adherence among patients with transthyretin amyloid cardiomyopathy (ATTR‐CM) treated with tafamidis (VYNDAQEL®) in Japan using the Japanese Medical Data Vision (MDV) database. Methods and results This study was a non‐interventional, retrospective cohort study of adult (≥18 years old) patients in the Japanese MDV claims database diagnosed with ATTR‐CM and with at least two tafamidis prescriptions of dose strength 4 × 20 mg/day between 1 March 2019 and 31 August 2021. The date of the first prescription was defined as the index date, with follow‐up time defined as the time between the first and last prescription plus the days' supply from the last refill. Baseline characteristics were assessed during a 12 month pre‐index period. Adherence was measured using two metrics: (i) the modified medication possession ratio (mMPR), calculated by taking the sum of days supplied for all fills within the follow‐up period, divided by the number of days of follow‐up, and reported as a percentage, with patients classified as adherent with an mMPR of ≥80%, and (ii) the proportion of days covered (PDC), calculated by taking the total number of days' supply dispensed during the follow‐up period divided by the number of days of follow‐up, adjusting for any days' supply overlap. A total of 210 patients were identified; the mean (standard deviation) age of the cohort was 77 (5.9) years, and the majority (89%) were male. The most common baseline cardiovascular comorbidities were heart failure (85%), ischaemic heart disease (66%), hypertensive diseases (49%), and diabetes (35%); 75% of patients received heart failure medications in the 12 months prior to index, with the most common being beta‐blockers (49%), diuretics (48%), angiotensin receptor blockers (30%), angiotensin‐converting enzyme inhibitors (22%), and sodium–glucose cotransporter‐2 inhibitors (8.1%). Over an average 14 month follow‐up, mean mMPR was 96% with a median of 100% [inter‐quartile range (IQR): 97–101%]; 93% of patients were adherent (defined as an mMPR ≥ 80%). In the same follow‐up period, mean PDC was 93.6% with a median of 99% (IQR: 93–100%). Persistence was high with 78% of patients having a 0 day gap between prescription refills. Conclusions This study found high adherence rates to tafamidis in this real‐world Japanese patient population. Adherence rates in this study were similar to those reported by the tafamidis clinical trial and a previously published US commercial claims adherence analysis. Further studies should be conducted to assess the impact of real‐world adherence on real‐world outcomes.

Abstract Background Evaluating outcome reliability is critical in real-world evidence studies. Overall survival is a common outcome in these studies; however, its capture in real-world data (RWD) sources is often incomplete and supplemented with linked mortality information from external sources. Conflicting recommendations exist for censoring overall survival in real-world evidence studies. This simulation study aimed to understand the impact of different censoring methods on estimating median survival and log hazard ratios when external mortality information is partially captured. Methods We used Monte Carlo simulation to emulate a non-randomized comparative effectiveness study of two treatments with RWD from electronic health records and linked external mortality data. We simulated the time to death, the time to last database activity, and the time to data cutoff. Death events after the last database activity were attributed to linked external mortality data and randomly set to missing to reflect the sensitivity of contemporary real-world data sources. Two censoring schemes were evaluated: (1) censoring at the last activity date and (2) censoring at the end of data availability (data cutoff) without an observed death. We assessed the performance of each method in estimating median survival and log hazard ratios using bias, coverage, variance, and rejection rate under varying amounts of incomplete mortality information and varying treatment effects, length of follow-up, and sample size. Results When mortality information was fully captured, median survival estimates were unbiased when censoring at data cutoff and underestimated when censoring at the last activity. When linked mortality information was missing, censoring at the last activity date underestimated the median survival, while censoring at the data cutoff overestimated it. As missing linked mortality information increased, bias decreased when censoring at the last activity date and increased when censoring at data cutoff. Conclusions Researchers should consider the completeness of linked external mortality information when choosing how to censor the analysis of overall survival using RWD. Substantial bias in median survival estimates can occur if an inappropriate censoring scheme is selected. We advocate for RWD providers to perform validation studies of their mortality data and publish their findings to inform methodological decisions better.

Background: Conventional statistical methods used in clinical trials lack the ability to predict patient specific risk and very often do not consider the effects of time varying interventions. The aim of this study is to test a novel artificial neural network based model for clinical trial analysis to represent the continuous time evolution of risk. Methods: The novel methodology tested utilizes system dynamics and artificial neural networks. This methodology was applied to analyze data from 2,221 patients with acute myocardial infarction enrolled in a well characterized randomized study, the HORIZONS-AMI trial. Outcomes analyzed included: (1) target lesion revascularization (TLR) and (2) stent thrombosis. The proposed neural system dynamics (NSD) model was compared against traditional Cox Proportional Hazards (Cox PH) and Cox neural network (NN) models. Model performance was evaluated using C-statistic at 1, 2 -and 3- years follow-up and model-based simulation studies were performed to examine the effect of different variables on the predicted risk of TLR and stent thrombosis. Results: The NSD model achieved comparable performance to Cox models for TLR. For stent thrombosis, the NSD model outperformed Cox models (1-year C-statistic: Stent thrombosis – Cox PH: 0.60, Cox NN: 0.66, neural SD model: 0.69). The neural SD model identified clinically relevant variables such as stent count, stent type and multiple lesions treated as significant predictors for TLR; and stent count and peak platelet count for stent thrombosis. Simulations illustrated change in predicted TLR risk maximal for the neural SD model, compared to other models. For stent thrombosis, simulation scenario illustrated predicted event rate doubling when clopidogrel is discontinued at 6 months compared to extended use. Conclusions: We have demonstrated an alternative analytical methodology that combines system dynamics and artificial neural networks to analyze results from a randomized trial. This novel approach can incorporate patient-specific longitudinal data and provide personalized risk prediction.