Prediction of outcome in malignant hypertension patients using machine learning: a report from the West Birmingham malignant hypertension registry

Abstract

Abstract Objectives Malignant hypertension (MHT) is a rare, yet severe condition characterized by high morbidity and mortality rates. This study aimed to assess the potential of machine learning (ML) algorithms in forecasting prognostic outcomes in MHT patients. Methods The dataset from the West Birmingham MHT Registry was used. We evaluated the efficacy of 9 ML algorithms, CatBoost, Decision Tree (DT), Light-Gradient Boosting Machine (LightGBM), K-Nearest Neighbors (KNN), Logistic Regression (LR), Multi-Layer Perceptron (MLP), Random Forest (RF), Support Vector Machine (SVM) and XGBoost, in predicting a composite outcome of all-cause mortality or dialysis. Evaluation metrics included the area under the receiver operating characteristic curve (AUC) and the F1 score. The SHapley Additive exPlanations values were employed to quantify the importance of each feature in the models. Results The study cohort comprised 385 individuals diagnosed with MHT (mean age of 48±13 years, 66% male). During a median follow-up period of 11 years (interquartile range: 3-18 years), 282 patients (73%) experienced the composite outcome. From 24 demographic and clinical variables, 16 were selected into the ML models. Among 9 models, the SVM and LR models exhibited robust predictive performance, achieving AUCs of 0.86 (95% CI: 0.76-0.93) and 0.81 (95% CI: 0.72-0.93), respectively. Furthermore, these models demonstrated high F1 scores (SVM: 0.89, LR: 0.85) and sensitivity (SVM: 0.89, LR: 0.83) (Table). Age, smoking history, systolic blood pressure at follow-up and baseline creatinine levels were commonly identified as primary prognostic features in both SVM and LR models (Figure). Conclusions The application of ML algorithms facilitates effective prediction of prognostic outcomes in patients with MHT, illustrating their potential utility in clinical decision-making through more targeted risk stratification and individualized patient care in these high-risk patients.