Promoting head CT exams in the emergency department triage using a machine learning model.
Purpose: In this study, we aimed to develop a novel prediction model to identify patients in need of a non-contrast head CT exam during emergency department (ED) triage. Methods: We collected data of all adult ED visits in our institution for five consecutive years (1/2013–12/2017). Retrieved variab...
| Publicado en: | Neuroradiology Vol. 62; no. 2; pp. 153 - 161 |
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| Autores principales: | , , , , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2020
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| 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=141292367&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 141292367 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00283940 NYZ jtl: Neuroradiology issn: 00283940 maglogo: N pubinfo: dt: Feb2020 vid: 62 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 141292367 141292367 141292367 10.1007/s00234-019-02293-y 141292367 ppf: 153 ppct: 8 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Promoting head CT exams in the emergency department triage using a machine learning model. aug: au: Klang, Eyal Barash, Yiftach Soffer, Shelly Bechler, Sigalit Resheff, Yehezkel S. Granot, Talia Shahar, Moni Klug, Maximiliano Guralnik, Gennadiy Zimlichman, Eyal Konen, Eli affil: Department of Diagnostic Imaging, Sheba Medical Center, Emek HaEla St 1, Ramat Gan, Israel sug: subj: Tomography, X-Ray Computed Head Radiography Machine Learning Emergency Service Health Promotion Human Neuroradiography Adult Youden's J Statistic Sensitivity and Specificity Predictive Validity Adult: 19-44 years ab: Purpose: In this study, we aimed to develop a novel prediction model to identify patients in need of a non-contrast head CT exam during emergency department (ED) triage. Methods: We collected data of all adult ED visits in our institution for five consecutive years (1/2013–12/2017). Retrieved variables included the following: demographics, mode of arrival to the ED, comorbidities, home medications, structured and unstructured chief complaints, vital signs, pain scale score, emergency severity index, ED wing assignment, documentation of previous ED visits, hospitalizations and CTs, and current visit non-contrast head CT usage. A machine learning gradient boosting model was trained on data from the years 2013–2016 and tested on data from 2017. Area under the curve (AUC) was used as metrics. Single-variable AUCs were also determined. Youden's index evaluated optimal sensitivity and specificity of the models. Results: The final cohort included 595,561 ED visits. Non-contrast head CT usage rate was 11.8%. Each visit was coded into an input vector of 171 variables. Single-variable analysis showed that chief complaint had the best single predictive analysis (AUC = 0.87). The best model showed an AUC of 0.93 (95% CI 0.931–0.936) for predicting non-contrast head CT usage at triage level. The model had a sensitivity of 88.1% and specificity of 85.7% for non-contrast head CT utilization. Conclusion: The developed model can identify patients that need to undergo head CT exam already in the ED triage level and by that allow faster diagnosis and treatment. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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