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. 2017 Nov 9;4(1):e000234.
doi: 10.1136/bmjresp-2017-000234. eCollection 2017.

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

David W Shimabukuro  1 , Christopher W Barton  2 , Mitchell D Feldman  3 , Samson J Mataraso  4   5 , Ritankar Das  6
Affiliations

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

David W Shimabukuro et al. BMJ Open Respir Res. .

Abstract

Introduction: Several 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.

Methods: We 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.

Results: Outcomes 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.

Conclusion: The 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 registration: NCT03015454.

Keywords: alerts; electronic health records; machine learning; patient monitoring; prediction; sepsis; severe sepsis.

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Conflict of interest statement

Competing interests: SM and RD are employees of Dascena. CB reports receiving consulting fees from Dascena. CB, DS and MF report receiving research grant funding from Dascena.

Figures

Figure 1
Figure 1
Patients assessed, enrolled, randomised and analysed in each arm of the randomised controlled trial.
Figure 2
Figure 2
Decrease in average hospital and ICU length of stay with the use of the machine learning algorithm. The error bars represent one standard error above and below the mean length of stay. ICU, intensive care unit.
Figure 3
Figure 3
Reduction of in-hospital mortality rate when using the machine learning algorithm. The error bars represent one standard error above and below the average in-hospital mortality rate.
See this image and copyright information in PMC

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