Effect of a machine learning-based severe sepsis prediction algorithm on patient survival and hospital length of stay: a randomised clinical trial.
TLDR
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.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.read more
Citations
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The potential for artificial intelligence in healthcare.
TL;DR: The complexity and rise of data in healthcare means that artificial intelligence will increasingly be applied within the field, and several types of AI are already being employed by payers and providers of care, and life sciences companies.
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Surviving sepsis campaign: international guidelines for management of sepsis and septic shock 2021.
Laura Evans,Andrew Rhodes,Waleed Alhazzani,Massimo Antonelli,Craig M. Coopersmith,Craig French,Flávia Ribeiro Machado,Lauralyn McIntyre,Marlies Ostermann,Hallie C. Prescott,Christa Schorr,Steven Q. Simpson,W. Joost Wiersinga,Fayez Alshamsi,Derek C. Angus,Yaseen M. Arabi,Luciano Cesar Pontes Azevedo,Richard Beale,Gregory J. Beilman,Emilie P. Belley-Côté,Lisa Burry,Maurizio Cecconi,John Centofanti,Angel Coz Yataco,Jan De Waele,R. Phillip Dellinger,Kent Doi,Bin Du,Elisa Estenssoro,Ricard Ferrer,Charles D. Gomersall,Carol L. Hodgson,Morten Hylander Møller,Theodore J. Iwashyna,Shevin T. Jacob,Ruth M. Kleinpell,Michael Klompas,Michael Klompas,Younsuck Koh,Anand Kumar,Arthur Kwizera,Suzana Margareth Lobo,Henry Masur,Steven McGloughlin,Sangeeta Mehta,Yatin Mehta,Mervyn Mer,Mark E. Nunnally,Simon Oczkowski,Tiffany M. Osborn,Elizabeth Papathanassoglou,Anders Perner,Michael A. Puskarich,Jason A. Roberts,William D. Schweickert,Maureen A. Seckel,Jonathan E. Sevransky,Charles L. Sprung,Charles L. Sprung,Tobias Welte,Janice L. Zimmerman,Mitchell M. Levy +61 more
TL;DR: The Surviving Sepsis Campaign (SSC) guidelines provide evidence-based recommendations on the recognition and management of sepsis and its complications as discussed by the authors, which are either strong or weak, or in the form of best practice statements.
Journal ArticleDOI
Surviving Sepsis Campaign: International Guidelines for Management of Sepsis and Septic Shock 2021.
Laura Evans,Andrew Rhodes,Waleed Alhazzani,Massimo Antonelli,Craig M. Coopersmith,Craig French,Flávia Ribeiro Machado,Lauralyn McIntyre,Marlies Ostermann,Hallie C. Prescott,Christa Schorr,Steven Q. Simpson,W. Joost Wiersinga,Fayez Alshamsi,Derek C. Angus,Yaseen M. Arabi,Luciano Cesar Pontes Azevedo,Richard Beale,Gregory J. Beilman,Emilie P. Belley-Côté,Lisa Burry,Maurizio Cecconi,John Centofanti,Angel Coz Yataco,Jan De Waele,R. Phillip Dellinger,Kent Doi,Bin Du,Elisa Estenssoro,Ricard Ferrer,Charles D. Gomersall,Carol L. Hodgson,Morten Hylander Møller,Theodore J. Iwashyna,Shevin T. Jacob,Ruth M. Kleinpell,Michael Klompas,Younsuck Koh,Anand Kumar,Arthur Kwizera,Suzana Margareth Lobo,Henry Masur,Steven McGloughlin,Sangeeta Mehta,Yatin Mehta,Mervyn Mer,Mark E. Nunnally,Simon Oczkowski,Tiffany M. Osborn,Elizabeth Papathanassoglou,Anders Perner,Michael A. Puskarich,Jason A. Roberts,William D. Schweickert,Maureen A. Seckel,Jonathan E. Sevransky,Charles L. Sprung,Tobias Welte,Janice L. Zimmerman,Mitchell M. Levy +59 more
TL;DR: The Surviving Sepsis Campaign (SSC) guidelines provide evidence-based recommendations on the recognition and management of sepsis and its complications as mentioned in this paper, which are either strong or weak, or in the form of best practice statements.
Journal ArticleDOI
Artificial intelligence, bias and clinical safety.
Robert Challen,Joshua C. Denny,Martin Pitt,Luke Gompels,Tom Edwards,Krasimira Tsaneva-Atanasova +5 more
TL;DR: This analysis is written with the dual aim of helping clinical safety professionals to critically appraise current medical AI research from a quality and safety perspective, and supporting research and development in AI by highlighting some of the clinical safety questions that must be considered if medical application of these exciting technologies is to be successful.
Journal ArticleDOI
Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy.
Lucas M. Fleuren,Thomas Klausch,Charlotte L. Zwager,Linda J. Schoonmade,Tingjie Guo,Luca F. Roggeveen,Eleonora L. Swart,Armand R. J. Girbes,Patrick Thoral,Ari Ercole,Mark Hoogendoorn,Paul W. G. Elbers +11 more
TL;DR: It is shown that on retrospective data, individual machine learning models can accurately predict sepsis onset ahead of time and between-study heterogeneity limits the assessment of pooled results.
References
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Journal ArticleDOI
The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3)
Mervyn Singer,Clifford S. Deutschman,Christopher W. Seymour,Manu Shankar-Hari,Djillali Annane,Michael Bauer,Rinaldo Bellomo,Gordon R. Bernard,Jean-Daniel Chiche,Craig M. Coopersmith,Richard S. Hotchkiss,Mitchell M. Levy,John C. Marshall,Greg S. Martin,Steven M. Opal,Gordon D. Rubenfeld,Gordon D. Rubenfeld,Tom van der Poll,Jean Louis Vincent,Derek C. Angus +19 more
TL;DR: The task force concluded the term severe sepsis was redundant and updated definitions and clinical criteria should replace previous definitions, offer greater consistency for epidemiologic studies and clinical trials, and facilitate earlier recognition and more timely management of patients with sepsi or at risk of developing sepsic shock.
Journal ArticleDOI
Early Goal-Directed Therapy in the Treatment of Severe Sepsis and Septic Shock
Emanuel P. Rivers,Bryant Nguyen,Suzanne Havstad,Julie Ressler,Alexandria Muzzin,Bernhard P. Knoblich,Edward L. Peterson,Michael C. Tomlanovich +7 more
TL;DR: This study randomly assigned patients who arrived at an urban emergency department with severe sepsis or septic shock to receive either six hours of early goal-directed therapy or standard therapy (as a control) before admission to the intensive care unit.
Journal ArticleDOI
The SOFA (Sepsis-related Organ Failure Assessment) score to describe organ dysfunction/failure. On behalf of the Working Group on Sepsis-Related Problems of the European Society of Intensive Care Medicine.
Jean Louis Vincent,R. Moreno,Jukka Takala,Sheila Willatts,A. de Mendonça,Hajo A. Bruining,C. K. Reinhart,P. M. Suter,L. G. Thijs +8 more
TL;DR: The ESICM developed a so-called sepsis-related organ failure assessment (SOFA) score to describe quantitatively and as objectively as possible the degree of organ dysfunction/failure over time in groups of patients or even in individual patients.
Journal ArticleDOI
Epidemiology of severe sepsis in the United States: analysis of incidence, outcome, and associated costs of care.
Derek C. Angus,Walter T. Linde-Zwirble,Jeffrey Lidicker,Gilles Clermont,Joseph A. Carcillo,Michael R. Pinsky +5 more
TL;DR: Severe sepsis is a common, expensive, and frequently fatal condition, with as many deaths annually as those from acute myocardial infarction, and is especially common in the elderly and is likely to increase substantially as the U.S. population ages.
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Surviving Sepsis Campaign: International Guidelines for Management of Severe Sepsis and Septic Shock, 2012
R. Phillip Dellinger,Mitchell M. Levy,Andrew Rhodes,Djillali Annane,Herwig Gerlach,Steven M. Opal,Jonathan E. Sevransky,Charles L. Sprung,Ivor S. Douglas,Roman Jaeschke,Tiffany M. Osborn,Mark E. Nunnally,Sean R. Townsend,Konrad Reinhart,Ruth M. Kleinpell,Derek C. Angus,Clifford S. Deutschman,Flávia Ribeiro Machado,Gordon D. Rubenfeld,Steven A R Webb,Richard Beale,Jean Louis Vincent,Rui Moreno +22 more
TL;DR: A consensus committee of 68 international experts representing 30 international organizations was convened in 2008 to provide an update to the "Surviving Sepsis Campaign Guidelines for Management of Severe Sepsis and Septic Shock".
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