Machine learning using multimodal and autonomic nervous system parameters predicts clinically apparent stroke-associated pneumonia in a development and testing study

Background Stroke-associated pneumonia (SAP) is a preventable determinant for poor outcome after stroke. Machine learning (ML) using large-scale clinical data warehouses may be able to predict SAP and identify patients for targeted interventions. The aim of this study was to develop a prediction model for identifying clinically apparent SAP using automated ML.Methods The ML model used clinical and laboratory parameters along with heart rate (HR), heart rate variability (HRV), and blood pressure (BP) values obtained during the first 48 h after stroke unit admission. A logistic regression classifier was developed and internally validated with a nested-cross-validation (nCV) approach. For every shuffle, the model was first trained and validated with a fixed threshold for 0.9 sensitivity, then finally tested on the out-of-sample data and benchmarked against a widely validated clinical score (A2DS2).Results We identified 2390 eligible patients admitted to two-stroke units at Charite between October 2020 and June 2023, of whom 1755 had all parameters available. SAP was diagnosed in 96/1755 (5.5%). Circadian profiles in HR, HRV, and BP metrics during the first 48 h after admission exhibited distinct differences between patients with SAP diagnosis vs. those without. CRP, mRS at admission, leukocyte count, high-frequency power in HRV, stroke severity at admission, sex, and diastolic BP were identified as the most informative ML features. We obtained an AUC of 0.91 (CI 0.88-0.95) for the ML model on the out-of-sample data in comparison to an AUC of 0.84 (CI 0.76-0.91) for the previously established A2DS2 score (p < 0.001). The ML model provided a sensitivity of 0.87 (CI 0.75-0.97) with a corresponding specificity of 0.82 (CI 0.78-0.85) which outperformed the A2DS2 score for multiple cutoffs.Conclusions Automated, data warehouse-based prediction of clinically apparent SAP in the stroke unit setting is feasible, benefits from the inclusion of vital signs, and could be useful for identifying high-risk patients or prophylactic pneumonia management in clinical routine.

Automated summary

In this study, a machine learning model was developed to predict the risk of stroke patients developing SAP and identify high-risk patients for interventions in clinical practice. The model showed promising results in predicting clinically apparent SAP and outperformed traditional clinical scores.