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Early prediction of sepsis in intensive care patients using the machine learning algorithm NAVOY® Sepsis, a prospective randomized clinical validation study
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- Persson, Inger (author)
- Uppsala universitet,Statistiska institutionen
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- Macura, Andreas (author)
- AlgoDx AB, Stockholm, Sweden
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- Becedas, David (author)
- AlgoDx AB, Stockholm, Sweden
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- Elsevier, 2024
- 2024
- English.
- In: Journal of critical care. - : Elsevier. - 0883-9441 .- 1557-8615. ; 80
- Journal article (peer-reviewed)
Abstract
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- Purpose:To prospectively validate, in an ICU setting, the prognostic accuracy of the sepsis prediction algorithm NAVOY® Sepsis which uses 4 h of input for routinely collected vital parameters, blood gas values, and lab values.Materials and methods:Patients 18 years or older admitted to the ICU at Skåne University Hospital Malmö from December 2020 to September 2021 were recruited in the study. A total of 304 patients were randomized into one of two groups: Algorithm group with active sepsis alerts, or Standard of care. NAVOY® Sepsis made silent predictions in the Standard of care group, in order to evaluate its performance without disturbing the outcome. The study was blinded, i.e., study personnel did not know to which group patients were randomized. The healthcare provider followed standard practices in assessing possible development of sepsis and intervening accordingly. The patients were followed-up in the study until ICU discharge. Results:NAVOY® Sepsis could predict the development of sepsis, according to the Sepsis-3 criteria, three hours before sepsis onset with high performance: accuracy 0.79; sensitivity 0.80; and specificity 0.78.Conclusions:The accuracy, sensitivity, and specificity were all high, validating the prognostic accuracy of NAVOY® Sepsis in an ICU setting, including Covid-19 patients.
Subject headings
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Anestesi och intensivvård (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Anesthesiology and Intensive Care (hsv//eng)
Keyword
- Prediction
- Early detection
- Machine learning
- Software as a medical device
- Intensive care unit
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- ref (subject category)
- art (subject category)
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- Persson, Inger
- Macura, Andreas
- Becedas, David
- Sjövall, Fredrik
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