# Integrating phase-rectified signal averaging with machine learning to predict stroke-associated infections: a retrospective cohort study

## TL;DR

This study uses machine learning with heart rate data to predict infections in stroke patients, aiming to improve early diagnosis and treatment.

## Contribution

A novel machine learning model integrating PRSA indicators for early prediction of stroke-associated infections is developed and validated.

## Key findings

- The CAT machine learning model achieved 91% accuracy and 88% sensitivity in predicting stroke-associated infections.
- Cardiac deceleration capacity and NIHSS at admission were identified as key predictors by SHAP analysis.
- PRSA markers show potential for targeted antibiotic use and prophylactic management of stroke-associated infections.

## Abstract

Stroke-associated infection (SAI) adversely affects the prognosis of acute ischemic stroke (AIS) patients, contributing to poorer functional outcomes and survival. The absence of validated tools for early SAI diagnosis and risk stratification in AIS remains a critical clinical gap. This study aims to develop and validate a machine learning-based prediction model that leverages phase-rectified signal averaging (PRSA) indicators closely linked to SAI pathogenesis for timely risk assessment in emergency settings.

This derivative cohort comprised 392 patients diagnosed with AIS between 2021 and 2023. The variables considered in this study included age, sex, heart rate variability (HRV) parameters, and PRSA parameters. Variable selection was performed using the Boruta algorithm and correlation analysis. Ten machine learning methods were employed to construct the SAI diagnostic model, and its performance was evaluated using the area under the curve (AUC), decision curve analysis (DCA), sensitivity, specificity, and an internal validation cohort. The predictive model outcomes were interpreted using Shapley Additive Explanations (SHAP).

Through variable screening, 16 indicators were identified as independent predictive factors for SAI in AIS patients. Utilizing these indicators, 10 machine learning models were developed. Among the machine learning algorithms, the Categorical Boosting (CAT) model demonstrated superior performance, achieving an accuracy of 91%, sensitivity of 88%, specificity of 92%, F1-score of 74%, and an AUC of 0.939 (95% CI: 0.894–0.984). Furthermore, SHAP identified cardiac deceleration capacity (DC) and the National Institute of Health Stroke Scale (NIHSS) at admission as the primary determinants influencing the predictions of the machine learning models.

Machine learning algorithms, when integrated with demographic and clinical factors, demonstrated accurate prediction of SAI in patients with AIS. The CAT model exhibited robust performance, highlighting its potential to enhance early detection and treatment in clinical practice. Additionally, PRSA markers may serve as potential targets for preventive interventions, enabling more judicious, timely, and targeted use of antibiotics. This approach opens new avenues for research into the prophylactic management of SAI.