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On Scene Injury Sev...
On Scene Injury Severity Prediction (OSISP) model for trauma developed using the Swedish Trauma Registry
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- Bakidou, Anna, 1996 (author)
- Högskolan i Borås,Akademin för vård, arbetsliv och välfärd,Department of Electrical Engineering, Chalmers University of Technology, 412 96, Gothenburg, Sweden,PreHospen
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- Caragounis, Eva Corina (author)
- Gothenburg University,Göteborgs universitet,Institutionen för kliniska vetenskaper, Avdelningen för kirurgi,Institute of Clinical Sciences, Department of Surgery,Sahlgrenska universitetssjukhuset,Sahlgrenska University Hospital,Department of Surgery, Institute of Clinical Sciences, Sahlgrenska University Hospital, Sahlgrenska Academy, University of Gothenburg, Per Dubbsgatan 15, 413 45, Gothenburg, Sweden
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- Andersson Hagiwara, Magnus (author)
- Högskolan i Borås,Akademin för vård, arbetsliv och välfärd,PreHospen
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- Jonsson, Anders, 1955- (author)
- Högskolan i Borås,Akademin för vård, arbetsliv och välfärd
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- Sjöqvist, Bengt-Arne, 1952 (author)
- Chalmers tekniska högskola,Chalmers University of Technology,Department of Electrical Engineering, Chalmers University of Technology, 412 96, Gothenburg, Sweden
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- Candefjord, Stefan, 1981 (author)
- Chalmers tekniska högskola,Chalmers University of Technology,Department of Electrical Engineering, Chalmers University of Technology, 412 96, Gothenburg, Sweden
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(creator_code:org_t)
- 2023
- 2023
- English.
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In: BMC Medical Informatics and Decision Making. - 1472-6947. ; 23:1
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Abstract
Subject headings
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- Background: Providing optimal care for trauma, the leading cause of death for young adults, remains a challenge e.g., due to field triage limitations in assessing a patient’s condition and deciding on transport destination. Data-driven On Scene Injury Severity Prediction (OSISP) models for motor vehicle crashes have shown potential for providing real-time decision support. The objective of this study is therefore to evaluate if an Artificial Intelligence (AI) based clinical decision support system can identify severely injured trauma patients in the prehospital setting. Methods: The Swedish Trauma Registry was used to train and validate five models – Logistic Regression, Random Forest, XGBoost, Support Vector Machine and Artificial Neural Network – in a stratified 10-fold cross validation setting and hold-out analysis. The models performed binary classification of the New Injury Severity Score and were evaluated using accuracy metrics, area under the receiver operating characteristic curve (AUC) and Precision-Recall curve (AUCPR), and under- and overtriage rates. Results: There were 75,602 registrations between 2013–2020 and 47,357 (62.6%) remained after eligibility criteria were applied. Models were based on 21 predictors, including injury location. From the clinical outcome, about 40% of patientswere undertriaged and 46% were overtriaged. Models demonstrated potential for improved triaging and yielded AUC between 0.80–0.89 and AUCPR between 0.43–0.62. Conclusions: AI based OSISP models have potential to provide support during assessment of injury severity. The findings may be used for developing tools to complement field triage protocols, with potential to improve prehospital trauma care and thereby reduce morbidity and mortality for a large patient population.
Subject headings
- MEDICIN OCH HÄLSOVETENSKAP -- Medicinsk bioteknologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Medical Biotechnology (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Medicinteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Medical Engineering (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Hälsovetenskap (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Health Sciences (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Annan medicin och hälsovetenskap (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Other Medical and Health Sciences (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Hälsovetenskap -- Omvårdnad (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Health Sciences -- Nursing (hsv//eng)
Keyword
- Artificial Intelligence (AI)
- Clinical Decision Support System (CDSS)
- Field triage
- On Scene Injury Severity Prediction (OSISP)
- Prehospital care
- Trauma
- Clinical Decision Support System (CDSS)
- Människan i vården
Publication and Content Type
- ref (subject category)
- art (subject category)
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