Effect of a sepsis prediction algorithm on patient mortality, length of stay and readmission: a prospective multicentre clinical outcomes evaluation of real-world patient data from US hospitals

Abstract

BackgroundSevere sepsis and septic shock are among the leading causes of death in the USA. While early prediction of severe sepsis can reduce adverse patient outcomes, sepsis remains one of the most expensive conditions to diagnose and treat.ObjectiveThe purpose of this study was to evaluate the effect of a machine learning algorithm for severe sepsis prediction on in-hospital mortality, hospital length of stay and 30-day readmission.DesignProspective clinical outcomes evaluation.SettingEvaluation was performed on a multiyear, multicentre clinical data set of real-world data containing 75 147 patient encounters from nine hospitals across the continental USA, ranging from community hospitals to large academic medical centres.ParticipantsAnalyses were performed for 17 758 adult patients who met two or more systemic inflammatory response syndrome criteria at any point during their stay (‘sepsis-related’ patients).InterventionsMachine learning algorithm for severe sepsis prediction.Outcome measuresIn-hospital mortality, length of stay and 30-day readmission rates.ResultsHospitals saw an average 39.5% reduction of in-hospital mortality, a 32.3% reduction in hospital length of stay and a 22.7% reduction in 30-day readmission rate for sepsis-related patient stays when using the machine learning algorithm in clinical outcomes analysis.ConclusionsReductions of in-hospital mortality, hospital length of stay and 30-day readmissions were observed in real-world clinical use of the machine learning-based algorithm. The predictive algorithm may be successfully used to improve sepsis-related outcomes in live clinical settings.Trial registration numberNCT03960203

Early prediction of central line associated bloodstream infection using machine learning

“…The models described in the current study may provide early warning of which patients may be more vulnerable if they were to have a central line placed during the course of care. Our research team has previously demonstrated the utility of this approach with an algorithm designed to predict sepsis, resulting in significantly reduced rates of sepsis, costs related to sepsis, and mortality (21,22). Through multiple steps of eliminating the least important features that did not significantly impact the model performance, we identified the most prominent subset of features for CLABSI prediction with the XGBoost model to be age, race, temperature, hemoglobin, white blood cell count, neutrophil, and any comorbidity history of sepsis, chronic kidney disease, stoma, renal failure, valvular disease and previous CLABSI.…”

Early Prediction of Sepsis in the ICU using Machine Learning: A Systematic Review

“…While initial studies employing machine learning for the prediction of sepsis have demonstrated promising results [Calvert et al, 2016, Desautels et al, 2016, Kam and Kim, 2017], the literature since has been diverging on which are the most pressing open challenges that need to be addressed to further the goal of early sepsis detection. On the one hand, corporations have been propelling the deployment of the first interventional studies [Burdick et al, 2020, Shimabukuro et al, 2017], while on the other hand, recent findings have cast doubt on the validity and meaningfulness of the experimental pipeline that is currently being implemented in most retrospective analyses [Schamoni et al, 2019]. This can be partially attributed to circular prediction settings (for more details, please refer to Section 4.4).…”

Performance of national COVID-19 ‘symptom checkers’: a comparative case simulation study

“…Again, beyond the mortality impact, there is no evidence that such an approach actually reduces healthcare burden. Indeed, beyond the established evidence in pneumonia generally, [19][20][21][22] there is direct evidence that early correction of hypoxia in COVID-19 prevents progression to mechanical ventilation, 5 consistent with basic medical principles. Programming symptom checkers to aggressively triage patients to stay home may well lead to patients presenting to healthcare later, requiring more intensive healthcare to recover, and as such, symptom checkers 'set' to keep patients at home may actually increase the burden on intensive care facilities and perpetuate a healthcare crisis.…”

“…18 The stakes are high, in that a failure to triage serious medical conditions (such as severe COVID-19, bacterial pneumonia or sepsis) in for further assessment will inevitably lead to delayed treatment and higher mortality. [19][20][21][22] Here, we test the performance of four nationwide symptom checkers from four nations to ascertain how safe and efficient each symptom checker is in differentiating mild from severe COVID-19 cases, and how well they detect time-sensitive COVID-19 mimickers such as bacterial pneumonia and sepsis.…”