Predicting Progression to Septic Shock in the Emergency Department Using an Externally Generalizable Machine-Learning Algorithm - 22/03/21
, Morgan Carlile, MD a, Andre Holder, MD, MSc d, Supreeth Shashikumar, PhD c, Stephen R. Hayden, MD a, Shamim Nemati, PhD cAbstract |
Study objective |
Machine-learning algorithms allow improved prediction of sepsis syndromes in the emergency department (ED), using data from electronic medical records. Transfer learning, a new subfield of machine learning, allows generalizability of an algorithm across clinical sites. We aim to validate the Artificial Intelligence Sepsis Expert for the prediction of delayed septic shock in a cohort of patients treated in the ED and demonstrate the feasibility of transfer learning to improve external validity at a second site.
Methods |
This was an observational cohort study using data from greater than 180,000 patients from 2 academic medical centers between 2014 and 2019, using multiple definitions of sepsis. The Artificial Intelligence Sepsis Expert algorithm was trained with 40 input variables at the development site to predict delayed septic shock (occurring greater than 4 hours after ED triage) at various prediction windows. We then validated the algorithm at a second site, using transfer learning to demonstrate generalizability of the algorithm.
Results |
We identified 9,354 patients with severe sepsis, of whom 723 developed septic shock at least 4 hours after triage. The Artificial Intelligence Sepsis Expert algorithm demonstrated excellent area under the receiver operating characteristic curve (>0.8) at 8 and 12 hours for the prediction of delayed septic shock. Transfer learning significantly improved the test characteristics of the Artificial Intelligence Sepsis Expert algorithm and yielded comparable performance at the validation site.
Conclusion |
The Artificial Intelligence Sepsis Expert algorithm accurately predicted the development of delayed septic shock. The use of transfer learning allowed significantly improved external validity and generalizability at a second site. Future prospective studies are indicated to evaluate the clinical utility of this model.
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| Please see page 396 for the Editor’s Capsule Summary of this article. |
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| Supervising editor: Donald M. Yealy, MD. Specific detailed information about possible conflict of interest for individual editors is available at editors. |
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| Author contributions: GW, SS, and SN conceived the study, designed the trial, and obtained research funding. SN and SS developed the algorithm used for retrospective data collection and provided data governance, as well as performed statistical analysis. GW, MC, AH, and SRH drafted the article, and all authors contributed substantially to its revision. GW takes responsibility for the paper as a whole. |
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| All authors attest to meeting the four ICMJE.org authorship criteria: (1) Substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data for the work; AND (2) Drafting the work or revising it critically for important intellectual content; AND (3) Final approval of the version to be published; AND (4) Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. |
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| Funding and support: By Annals policy, all authors are required to disclose any and all commercial, financial, and other relationships in any way related to the subject of this article as per ICMJE conflict of interest guidelines (see www.icmje.org). Dr. Wardi is supported by the National Foundation of Emergency Medicine and funding from the Gordon and Betty Moore Foundation (GBMF9052). He has received speaker’s fees from Thermo-Fisher and consulting fees from General Electric. Dr. Holder is supported by the National Center for Advancing Translational Sciences of the National Institutes of Health (UL1TR002378 and KL2TR002381). Dr. Nemati is funded by the National Institutes of Health (K01ES025445), National Science Foundation (1822378), Biomedical Advanced Research and Development Authority (HHSO100201900015C), and the Gordon and Betty Moore Foundation (GBMF9052). |
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Vol 77 - N° 4
P. 395-406 - avril 2021 Retour au numéro
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