Effect of a machine learning-based severe sepsis prediction algorithm on patient survival and hospital length of stay: a randomised clinical trial
By David Shimabukuro, C. Barton, Mitchell D. Feldman, S. Mataraso, Ritankar Das. BMJ Open Respiratory Research, 2017
329 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Epidemiology, Surgery
View Paper: https://www.semanticscholar.org/paper/04df1188faf6674173ea875ce187a32c8992e911
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Citations in the corpus
- Axes of a revolution: challenges and promises of big data in healthcare (2020, methodology): “Several machine-learning-based sepsis-prediction algorithms have been published 90 , and a randomized clinical trial demonstrated the beneficial real-life potential of this approach, decreasing patient length of stay in the hospital and in-hospital mortality 91 .”
- Role of Artificial Intelligence Applications in Real-Life Clinical Practice: Systematic Review (2020, background): “Other potential sources of bias included a small sample size [62,70,72], a short study period [62], and a lack of detailed information regarding RCTs and follow-ups [57,66].”
- Artificial intelligence, bias and clinical safety (2019, unknown): “42 53 As with all clinical safety discussions we need to maintain a realistic perspective.”