Machine learning in the prediction of massive transfusion in trauma: a retrospective analysis as a proof-of-concept.
Purpose: Early administration and protocolization of massive hemorrhage protocols (MHP) has been associated with decreases in mortality, multiorgan system failure, and number of blood products used. Various prediction tools have been developed for the initiation of MHP, but no single tool has demons...
| Publicado en: | European Journal of Trauma & Emergency Surgery Vol. 50; no. 3; pp. 1073 - 1082 |
|---|---|
| Autores principales: | , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2024
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=178443670&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 178443670 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 18639933 3C05 jtl: European Journal of Trauma & Emergency Surgery issn: 18639933 maglogo: N pubinfo: dt: Jun2024 vid: 50 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 178443670 174992937 178443670 178443670 10.1007/s00068-023-02423-5 178443670 ppf: 1073 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Machine learning in the prediction of massive transfusion in trauma: a retrospective analysis as a proof-of-concept. aug: au: Nikouline, Anton Feng, Jinyue Rudzicz, Frank Nathens, Avery Nolan, Brodie affil: https://ror.org/037tz0e16 Department of Emergency Medicine, London Health Sciences Centre, 800 Commissioners Road E, N6A 5W9, London, ON, Canada sug: subj: Trauma Therapy Blood Transfusion Machine Learning Prediction Models Human Descriptive Statistics Data Analysis Software Funding Source Male Female Adult Middle Age Emergency Medical Services Algorithms Adult: 19-44 years Middle Aged: 45-64 years Male Female ab: Purpose: Early administration and protocolization of massive hemorrhage protocols (MHP) has been associated with decreases in mortality, multiorgan system failure, and number of blood products used. Various prediction tools have been developed for the initiation of MHP, but no single tool has demonstrated strong prediction with early clinical data. We sought to develop a massive transfusion prediction model using machine learning and early clinical data. Methods: Using the National Trauma Data Bank from 2013 to 2018, we included severely injured trauma patients and extracted clinical features available from the pre-hospital and emergency department. We subsequently balanced our dataset and used the Boruta algorithm to determine feature selection. Massive transfusion was defined as five units at 4 h and ten units at 24 h. Six machine learning models were trained on the balanced dataset and tested on the original. Results: A total of 326,758 patients met our inclusion with 18,871 (5.8%) requiring massive transfusion. Emergency department models demonstrated strong performance characteristics with mean areas under the receiver-operating characteristic curve of 0.83. Extreme gradient boost modeling slightly outperformed and demonstrated adequate predictive performance with pre-hospital data only, as well as 4-h transfusion thresholds. Conclusions: We demonstrate the use of machine learning in developing an accurate prediction model for massive transfusion in trauma patients using early clinical data. This research demonstrates the potential utility of artificial intelligence as a clinical decision support tool. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|