Improving sepsis prediction in intensive care with SepsisAI: A clinical decision support system with a focus on minimizing false alarms

Prediction of sepsis using machine-learning approaches has recently gained traction. However, the lack of translation of these algorithms into clinical routine remains a major issue. Existing early sepsis detection methods are either based on the older definition of sepsis or do not accurately detect sepsis leading to the high frequency of false-positive alarms. This results in a well-known issue of clinicians’ “alarm fatigue”, leading to decreased responsiveness and identification, ultimately resulting in delayed clinical intervention. Hence, there is a fundamental, unmet need for a clinical decision system capable of accurate and timely sepsis diagnosis, running at the point of need. In this work, SepsisAI–a deep-learning algorithm based on long short-term memory (LSTM) networks was developed to predict the early onset of hospital-acquired sepsis in real-time for patients admitted to the ICU. The models are trained and validated with data from the PhysioNet Challenge, consisting of 40,336 patient data files from two healthcare systems: Beth Israel Deaconess Medical Center and Emory University Hospital. In the short term, the algorithm tracks frequently measured vital signs, sparsely available lab parameters, demographic features, and certain derived features for making predictions. A real-time alert system, which monitors the trajectory of the predictions, is developed on top of the deep-learning framework to minimize false alarms. On a balanced test dataset, the model achieves an AUROC, AUPRC, sensitivity, and specificity of 0.95, 0.96, 88.19%, and 96.75%, respectively at the patient level. In terms of lookahead time, the model issues a warning at a median of 6 hours (IQR 6 to 20 hours) and raises an alert at a median of 4 hours (IQR 2 to 5 hours) ahead of sepsis onset. Most importantly, the model achieves a false-alarm ratio of 3.18% for alerts, which is significantly less than other sepsis alarm systems. Additionally, on a disease prevalence-based test set, the algorithm reported similar outcomes with AUROC and AUPRC of 0.94 and 0.87, respectively, with sensitivity, and specificity of 97.05%, and 96.75%, respectively. The proposed algorithm might serve as a clinical decision support system to assist clinicians in the accurate and timely diagnosis of sepsis. With exceptionally high specificity and low false-alarm rate, this algorithm also helps mitigate the well-known issue of clinician alert fatigue arising from currently proposed sepsis alarm systems. Consequently, the algorithm partially addresses the challenges of successfully integrating machine-learning algorithms into routine clinical care.Author summary: Sepsis is a serious life-threatening complication that results from the body’s exaggerated response to an infection, leading to organ dysfunction. Distinguishing sepsis from other inflammatory conditions is challenging, leading to significant morbidity, mortality, and healthcare costs. We’ve developed a deep-learning based approach—SepsisAI, leveraging Long Short-Term Memory networks. It enables real-time monitoring and prediction of hospital-acquired sepsis for ICU patients, using routine parameters. The algorithm, trained and validated using data from two healthcare systems, demonstrates impressive results. Achieving a high AUROC, AUPRC, sensitivity, and specificity, it predicts sepsis hours before sepsis onset. It also uses a warnings and alert system resulting in a notably low false-alarm ratio, hence addressing the prevalent issue of alert fatigue, marking a positive step towards integrating machine-learning into routine clinical care. Our goal is that this effort will ultimately enhance patient survival and yield positive outcomes in terms of antimicrobial stewardship for complex and diverse conditions, such as sepsis.