Heart rate variability based machine learning models for risk prediction of suspected sepsis patients in the emergency department

Abstract

Early identification of high-risk septic patients in the emergency department (ED) may guide appropriate management and disposition, thereby improving outcomes. We compared the performance of machine learning models against conventional risk stratification tools, namely the Quick Sequential Organ Failure Assessment (qSOFA), National Early Warning Score (NEWS), Modified Early Warning Score (MEWS), and our previously described Singapore ED Sepsis (SEDS) model, in the prediction of 30-day in-hospital mortality (IHM) among suspected sepsis patients in the ED. Adult patients who presented to Singapore General Hospital (SGH) ED between September 2014 and April 2016, and who met ≥2 of the 4 Systemic Inflammatory Response Syndrome (SIRS) criteria were included. Patient demographics, vital signs and heart rate variability (HRV) measures obtained at triage were used as predictors. Baseline models were created using qSOFA, NEWS, MEWS, and SEDS scores. Candidate models were trained using k-nearest neighbors, random forest, adaptive boosting, gradient boosting and support vector machine. Models were evaluated on F1 score and area under the precision-recall curve (AUPRC). A total of 214 patients were included, of whom 40 (18.7%) met the outcome. Gradient boosting was the best model with a F1 score of 0.50 and AUPRC of 0.35, and performed better than all the baseline comparators (SEDS, F1 0.40, AUPRC 0.22; qSOFA, F1 0.32, AUPRC 0.21; NEWS, F1 0.38, AUPRC 0.28; MEWS, F1 0.30, AUPRC 0.25). A machine learning model can be used to improve prediction of 30-day IHM among suspected sepsis patients in the ED compared to traditional risk stratification tools.

Sepsis 2019: What Surgeons Need to Know

“…Specifically, because of the non-linear nature of its algorithm, its accuracy in predicting postoperative sepsis has been shown to be several folds superior in this patient population [95]. In addition to POTTER, machine learning techniques have been used to analyze vital signs and heart rate variability in real time to predict patients in early sepsis [96][97][98][99][100][101]. For example, the machine learning algorithm Insight was derived from six vital signs and outperformed existing scoring systems for sepsis and septic shock [99].…”

Prediction of Vestibular Dysfunction by Applying Machine Learning Algorithms to Postural Instability

“…The gradient boosting classifier was the best algorithm in our datasets. This algorithm has recently been studied in varied medical fields (20,21,25,26), and has shown highly predictive performance. Other algorithms including SVC (24,(27)(28)(29), decision trees (30), genetics-based algorithms (31), and the multilayer perceptron classifier (32) did not show the best predictive performance in our datasets.…”

The Association of Autonomic Nervous System Function With Ischemic Stroke, and Treatment Strategies

“…The HRV-based classifier could identify hypertensive patients at high risk of developing vascular events with high sensitivity and specificity (71.4 and 87.8%, respectively) (51). A combination model incorporating HRV and other disease severity score variables showed optimal predictive ability of 30-day in-hospital mortality for septic patients at the emergency department against conventional risk stratification tools (AUC = 0.91, 95% confidence interval: 0.88-0.95) (52,53). The exploitation of HRV in risk stratification tools of stroke becomes one of the vital and prophylactic measures for high-risk individuals to ring alarm bells.…”