Development and validation of an interpretable machine learning model for early prediction of deterioration in patients with severe fever with thrombocytopenia syndrome.

BACKGROUND

Severe Fever with Thrombocytopenia Syndrome (SFTS) is a severe tick-borne viral infection with high mortality, making the timely prediction of clinical deterioration critical. Current predictive models lack timeliness and generalizability. Therefore, this study aimed to develop and validate an interpretable machine learning model for the early prediction of deterioration in SFTS patients.

METHODS

We retrospectively analyzed 560 SFTS patients from two hospitals. Using clinical and laboratory data from a training set (n = 407), we developed eight ML models. An independent test set (n = 153) was used for external validation. Model performance was assessed via area under the ROC curve (AUC), calibration, decision curve analysis (DCA), and SHapley Additive exPlanations (SHAP).

RESULTS

The RF model outperformed others with an external validation AUC of 0.825. Key predictors included viral load, serum creatinine, D-dimer, procalcitonin, platelets, blood urea nitrogen, age, and lymphocytes. SHAP analysis revealed significant interactions, especially between blood urea nitrogen and viral load. The RF model provided reliable risk stratification, with net benefits surpassing no-treatment or all-treatment strategies when the threshold probability was between 0.09 and 0.76. An interactive web-based application was developed for real-time individualized risk prediction.

CONCLUSION

We successfully developed and validated a robust ML model using RF, integrating eight readily measurable clinical variables to predict early deterioration in SFTS patients. This model offers improved timeliness and interpretability, facilitating early clinical interventions. Future multi-center studies will further validate its robustness and reliability, enhancing its clinical utility.