Physiological machine learning models for prediction of sepsis in hospitalized adults: An integrative review

Diagnosing sepsis remains challenging. Data compiled from continuous monitoring and electronic health records allow for new opportunities to compute predictions based on machine learning techniques. There has been a lack of consensus identifying best practices for model development and validation towards early identification of sepsis.

To evaluate the modeling approach and statistical methodology of machine learning prediction models for sepsis in the adult hospital population.

PubMed, CINAHL, and Cochrane databases were searched with the Preferred Reporting Items for Systematic Reviews guided protocol development. We evaluated studies that developed or validated physiologic sepsis prediction models or implemented a model in the hospital environment.

Fourteen studies met the inclusion criteria, and the AUROC of the prediction models ranged from 0.61 to 0.96. We found a variety of sepsis definitions, methods used for event adjudication, model parameters used, and modeling methods. Two studies tested models in clinical settings; the results suggested that patient outcomes were improved with implementation of machine learning models.

Nurses have a unique perspective to offer in the development and implementation of machine learning models detecting patients at risk for sepsis. More work is needed in developing model harmonization standards and testing in clinical settings.