Preventing sepsis; how can artificial intelligence inform the clinical decision-making process? A systematic review.

Autor: Hassan N; School of Pharmacy, Newcastle University, King George VI Building, Newcastle upon Tyne, NE1 7RU, UK. Electronic address: n.a.m.hassan2@newcastle.ac.uk., Slight R; Newcastle Upon Tyne Hospitals NHS Foundation Trust, Freeman Hospital, High Heaton, Newcastle upon Tyne, NE7 7DN, UK. Electronic address: bob.slight@nhs.net., Weiand D; Newcastle Upon Tyne Hospitals NHS Foundation Trust, Freeman Hospital, High Heaton, Newcastle upon Tyne, NE7 7DN, UK. Electronic address: dweiand@nhs.net., Vellinga A; School of Medicine, National University of Ireland Galway, University Road, Galway, H91 TK33, Ireland. Electronic address: akke.vellinga@nuigalway.ie., Morgan G; School of Computing, Newcastle University, Urban Sciences Building, Newcastle upon Tyne, NE4 5TG, UK. Electronic address: graham.morgan@ncl.ac.uk., Aboushareb F; Northumbria Healthcare NHS Foundation Trust, Rake Lane, North Shields, Tyne and Wear, NE29 8NH, UK. Electronic address: fathy.aboushareb@nhs.net., Slight SP; School of Pharmacy, Newcastle University, King George VI Building, Newcastle upon Tyne, NE1 7RU, UK. Electronic address: Sarah.Slight@newcastle.ac.uk.
Jazyk: angličtina
Zdroj: International journal of medical informatics [Int J Med Inform] 2021 Jun; Vol. 150, pp. 104457. Date of Electronic Publication: 2021 Apr 10.
DOI: 10.1016/j.ijmedinf.2021.104457
Abstrakt: Background and Objectives: Sepsis is a life-threatening condition that is associated with increased mortality. Artificial intelligence tools can inform clinical decision making by flagging patients at risk of developing infection and subsequent sepsis. This systematic review aims to identify the optimal set of predictors used to train machine learning algorithms to predict the likelihood of an infection and subsequent sepsis.
Methods: This systematic review was registered in PROSPERO database (CRD42020158685). We conducted a systematic literature review across 3 large databases: Medline, Cumulative Index of Nursing and Allied Health Literature, and Embase. Quantitative primary research studies that focused on sepsis prediction associated with bacterial infection in adults in all care settings were eligible for inclusion.
Results: Seventeen articles met our inclusion criteria. We identified 194 predictors that were used to train machine learning algorithms, with 13 predictors used on average across all included studies. The most prevalent predictors included age, gender, smoking, alcohol intake, heart rate, blood pressure, lactate level, cardiovascular disease, endocrine disease, cancer, chronic kidney disease (eGFR<60 mL/min), white blood cell count, liver dysfunction, surgical approach (open or minimally invasive), and pre-operative haematocrit < 30 %. All included studies used artificial intelligence techniques, with average sensitivity 75.7 ± 17.88, and average specificity 63.08 ± 22.01.
Conclusion: The type of predictors influenced the predictive power and predictive timeframe of the developed machine learning algorithm. Predicting the likelihood of sepsis through artificial intelligence can help concentrate finite resources to those patients who are most at risk. Future studies should focus on developing more sensitive and specific algorithms.
(Copyright © 2021 Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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