Effect of a machine learning-based severe sepsis prediction algorithm on patient survival and hospital length of stay: a randomised clinical trial

Abstract

IntroductionSeveral methods have been developed to electronically monitor patients for severe sepsis, but few provide predictive capabilities to enable early intervention; furthermore, no severe sepsis prediction systems have been previously validated in a randomised study. We tested the use of a machine learning-based severe sepsis prediction system for reductions in average length of stay and in-hospital mortality rate.MethodsWe conducted a randomised controlled clinical trial at two medical-surgical intensive care units at the University of California, San Francisco Medical Center, evaluating the primary outcome of average length of stay, and secondary outcome of in-hospital mortality rate from December 2016 to February 2017. Adult patients (18+) admitted to participating units were eligible for this factorial, open-label study. Enrolled patients were assigned to a trial arm by a random allocation sequence. In the control group, only the current severe sepsis detector was used; in the experimental group, the machine learning algorithm (MLA) was also used. On receiving an alert, the care team evaluated the patient and initiated the severe sepsis bundle, if appropriate. Although participants were randomly assigned to a trial arm, group assignments were automatically revealed for any patients who received MLA alerts.ResultsOutcomes from 75 patients in the control and 67 patients in the experimental group were analysed. Average length of stay decreased from 13.0 days in the control to 10.3 days in the experimental group (p=0.042). In-hospital mortality decreased by 12.4 percentage points when using the MLA (p=0.018), a relative reduction of 58.0%. No adverse events were reported during this trial.ConclusionThe MLA was associated with improved patient outcomes. This is the first randomised controlled trial of a sepsis surveillance system to demonstrate statistically significant differences in length of stay and in-hospital mortality.Trial registrationNCT03015454.

Effect of a Sepsis Prediction Algorithm on Patient Mortality, Length of Stay, and Readmission

“…At an average per diem cost of care of $2,271, the reduction of length of stay translates to approximately $14.5 million of annual cost savings across all nine hospitals included in this analysis. These findings on post-marketing real-world data confirm pre-marketing randomized clinical trial results [11]. The MLA system used in both analyses demonstrated higher sensitivity and specificity than comparable rule-based systems (MEWS, SOFA, and SIRS), and accuracy of the MLA suggests that this system may improve severe sepsis detection and patient outcomes over the use of comparators.…”

“…The latest state of the MLA was characterized in the retrospective analysis. Previous states of the algorithm have been studied retrospectively and prospectively [10][11][12][13][14][15][16]; however, this study was performed on significantly larger and more diverse datasets.…”

Evaluating a sepsis prediction machine learning algorithm in the emergency department and intensive care unit: a before and after comparative study

“…The algodiagnostic assessed in this study has previously been examined in several retrospective studies, where it has been validated for detection of sepsis [14], severe sepsis [13], and septic shock [15]. The algodiagnostic has also been previously evaluated in prospective studies, including a randomized controlled trial where use of the MLA resulted in statistically significant decreases in in-hospital mortality and average length of stay [17]. The present study presents further evidence that machine-learning methods for sepsis detection and prediction can provide routes towards improving sepsis-related patient outcomes.…”

“…Risk prediction scores were computed hourly throughout the duration of each patient's stay. The MLA used in this study is described in detail in prior prospective [16,17] and retrospective work [13].…”

“…The Dascena MLA was validated for sepsis prediction and detection in several studies [13][14][15], demonstrating an area under the receiver operator characteristic (ROC) curve (AUROC) over 0.90 using only six vital signs, in a multicenter cohort study of over 650,000 encounters [16]. In a recent randomized clinical trial, mortality decreased by 12.4 percentage points with use of the MLA, a relative reduction of 58% [17]. Comparison of the MLA to rules-based scores such as Systemic Inflammatory Response Syndrome (SIRS) [18] criteria have shown superior sensitivity and specificity up to four hours in advance of sepsis onset [13].…”