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.
- severe sepsis
- machine learning
- electronic health records
- patient monitoring
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Contributors DWS, CWB, MF, SJM and RD contributed to the conception and design of this study, and to acquisition, analysis or interpretation of data. SJM and RD were responsible for statistical analysis. SJM, DWS and RD drafted the manuscript. DWS, CWB, MDF, SJM and RD revised the manuscript and have approved it in this final form.
Funding This material is based on work supported by the National Science Foundation under Grant No. 1549867. The funder had no role in the conduct of the study; collection, management, analysis and interpretation of data; preparation, review and approval of the manuscript; and decision to submit the manuscript for publication. Research reported in this publication was supported by the National Institute of Nursing Research, of the National Institutes of Health, under award number R43NR015945. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
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.
Ethics approval This study was approved by the University of California, San Francisco Institutional Review Board with a waiver of informed consent for all patients.
Provenance and peer review Not commissioned; externally peer reviewed.
Data sharing statement Any inquiries regarding the dataset can be addressed to the corresponding author.
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