A novel risk-stratification model that predicts long-term clinical outcomes in patients with recurrent pericarditis

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Abstract Background Recurrent Pericarditis (RP) is condition associated with significant debility and morbidity. Previous studies have identified variables such as elevated C-Reactive protein, corticosteroid usage, and presence of delayed hyper-enhancement (DHE) on cardiac magnetic resonance imaging are associated with recurrence. To date however, there is no established model for risk stratifying outcomes in these patients, therefore we attempted to develop a model that could predict long-term outcomes in patients with RP and stratify them into groups based on risk. Methods A total of 365 consecutive patients with RP from 2012-2019 were retrospectively examined. Our primary outcome was clinical remission (CR) defined as symptom resolution in addition to complete cessation of all anti-inflammatory therapy. We employed 5 survival models: CoxPH, RSF, SSVM, GBSA and XGBoost with Cox loss function to determine the likelihood of achieving CR within 5 years and to stratify patients into specific risk groups. We divided the dataset into training (70%) and test (30%) sets. Model training and optimization of the training set occurred via 5-fold cross validation. Model evaluation and selection was based on C-index. Finally, we employed an explainable artificial intelligence approach, SHapley Additive exPlanations to generate both individual and global explanation of the models decision. Results Amongst the cohort, the mean age was 46 ± 15 years, 205 (56%) were female, and the main etiology was idiopathic in 223 (61%). CR was achieved in 118 (32%) patients. The final model included steroid dependency, total number of recurrences, pericardial DHE, age, etiology, gender, ejection fraction and heart rate as its most important parameters. We observed similar CR rates across three risk score ranges and accordingly stratified patients into low (n=62), intermediate (n=154), and high-risk (n=149) groups. Scores of 3 or lower were categorized as low risk, with CR rates exceeding 90%. Scores of 4 to 7 were associated with CR rates of 50%. Scores of 8 or higher were deemed high-risk, with CR rates notably below 10%. Overall, the model predicted the outcome with C-index of 0.800 on the test set and exhibited a significant ability in stratification of patients into low-, intermediate-, and high-risk groups (log-rank test; P <0.0001). Conclusion We created a novel risk prediction model that can aid in predicting rates clinical remission in patients with recurrent pericarditis as well as aid in stratification into low, medium, and high risk groups. This risk prediction model may be helpful in diagnosis and management of these complicated patients.Figure 1