Abstract
Abstract Background Predicting the risk of clinically significant bleeding (CSB), bleeding requiring hospitalisation or fatal bleeding, during and following anticoagulation is complex. Current models lack the ability to adapt to changing patient conditions over time and fail to account for the direct impact of anticoagulation in real-world clinical settings. Purpose To develop a dynamic risk prediction model for CSB to help clinicians decide on the duration of anticoagulant therapy in conjunction with a model for recurrences of VTE (also submitted to the conference). Methods UK Clinical Practice Research Datalink data (2001-2020) was used to generate a retrospective cohort with first VTE who had received 3 months of anticoagulation. Patient episodes of CSB and recurrent VTE were evaluated. Covariates were collected at baseline and subsequently as time-varying data to capture changes post-VTE. Hazards with 95% confidence intervals (CI) were estimated and covariates included in a Fine & Gray model used as predictors of CSB. An additive (logarithmic) scoring scheme was developed from subdistribution hazard ratios, discrimination (expressed by the C-statistic) estimated from 10-fold cross-validation. Results 51,621 patients with a first VTE were included; 3307 CSB were identified in 206,292 person-years of observation. Incidence rates for CSB were 1.60 per 100 person-years. 18 independent predictors of CSB (recorded before or after the VTE diagnosis) were included in the model, Table 1. Patients recognised as lower-risk, medium and higher-risk (67.5 %, 15.9 % and 16.7 % of the population, respectively, at 90 days after first VTE, assuming no anticoagulation treatment) had an annualised incidence rate of 0.84, 2.94, and 9.45 per 100 person-years. The C-statistic for CSB was 0.78 (95%CI, 0.77-0.79). The points scored can be added to predict risk (Table 2). Conclusions Our dynamic score effectively identifies patients at risk of CSB three months post their initial VTE diagnosis. It allows for continuous monitoring of risk, and modelling of treatment impact, providing clinicians with a pragmatic tool to help determine treatment duration, and adapt their strategies in response to evolving patient conditions.Table 1:Predictors for CSBTable 2:CSB risk score
Published Version
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