Explainable models for predicting long-term outcomes in patients with spontaneous intracerebral haemorrhage: a retrospective cohort study.

Abstract

Background and aim: Recently, long-term outcomes in patients with spontaneous intracerebral haemorrhage (sICH) have gained increasing attention besides acute-phase characteristics. Predictive models for long-term outcomes are valuable for risk stratification and treatment strategies. This study aimed to develop and validate an explainable model for predicting long-term recurrence and all-cause death in patients with ICH, using clinical and imaging markers of cerebral small vascular diseases from MRI.

Method: We retrospectively analysed data from a prospectively collected large-scale cohort of patients with acute ICH admitted to the Neurology Department of The Second Affiliated Hospital of Zhejiang University between November 2016 and April 2023. After comprehensive variable selection using least absolute shrinkage and selection operator and stepwise Cox regression, we constructed Cox proportional hazards models to predict recurrence and all-cause death. Model performance was evaluated using the concordance index, integrated Brier score and time-dependent area under the curve. Global and local interpretability were assessed using variable importance calculated as SurvSHAP(t) and SurvLIME methods for the entire training set and individual patients, respectively.

Results: A total of 842 eligible patients were included. Over a median follow-up of 36 months (IQR: 12-51), 86 patients (9.1%) died, and 62 patients (6.6%) experienced recurrence of ICH. The concordance indexes for the all-cause death and recurrence models were 0.841 (95% CI 0.767 to 0.913) and 0.759 (95% CI 0.651 to 0.867), respectively, with integrated Brier scores of 0.079 and 0.063. The interpretability maps highlighted age, aetiology of ICH and low haemoglobin as key predictors of long-term death, while cortical superficial siderosis and previous haemorrhage were crucial for predicting recurrence.

Conclusions: This model demonstrates high predictive accuracy and emphasises the crucial factors in predicting long-term outcomes of patients with sICH.