Machine Learning Predicts Unplanned Care Escalations for Post-Anesthesia Care Unit Patients during the Perioperative Period: A Single-Center Retrospective Study.
Background: Despite low mortality for elective procedures in the United States and developed countries, some patients have unexpected care escalations (UCE) following post-anesthesia care unit (PACU) discharge. Studies indicate patient risk factors for UCE, but determining which factors are most imp...
| Publicado en: | Journal of Medical Systems Vol. 48; no. 1; pp. 1 - 12 |
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| Autores principales: | , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Springer Nature
7/23/2024
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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=178623199&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 178623199 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: 7/23/2024 vid: 48 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 178623199 178623199 178623199 10.1007/s10916-024-02085-9 178623199 ppf: 1 ppct: 11 formats: fmt: @attributes: type: P tig: atl: Machine Learning Predicts Unplanned Care Escalations for Post-Anesthesia Care Unit Patients during the Perioperative Period: A Single-Center Retrospective Study. aug: au: Barker, Andrew B. Melvin, Ryan L. Godwin, Ryan C. Benz, David Wagener, Brant M. affil: https://ror.org/008s83205 Division of Critical Care Medicine, Department of Anesthesiology and Perioperative Medicine, University of Alabama at Birmingham, 901 19th Street South, PBMR 302, 35294, Birmingham, AL, United States of America sug: subj: Post Anesthesia Care Units Surgical Patients Clinical Deterioration Risk Factors Postoperative Complications Risk Factors Risk Assessment Preoperative Period Machine Learning Human Funding Source Male Female Adult Middle Age Retrospective Design Record Review Vital Signs Emergencies Anesthesia Time Factors Oxygen Saturation Systolic Pressure Heart Rate Respiratory Rate Regression Data Analysis Software T-Tests Chi Square Test Fisher's Exact Test ROC Curve Adult: 19-44 years Middle Aged: 45-64 years Male Female ab: Background: Despite low mortality for elective procedures in the United States and developed countries, some patients have unexpected care escalations (UCE) following post-anesthesia care unit (PACU) discharge. Studies indicate patient risk factors for UCE, but determining which factors are most important is unclear. Machine learning (ML) can predict clinical events. We hypothesized that ML could predict patient UCE after PACU discharge in surgical patients and identify specific risk factors. Methods: We conducted a single center, retrospective analysis of all patients undergoing non-cardiac surgery (elective and emergent). We collected data from pre-operative visits, intra-operative records, PACU admissions, and the rate of UCE. We trained a ML model with this data and tested the model on an independent data set to determine its efficacy. Finally, we evaluated the individual patient and clinical factors most likely to predict UCE risk. Results: Our study revealed that ML could predict UCE risk which was approximately 5% in both the training and testing groups. We were able to identify patient risk factors such as patient vital signs, emergent procedure, ASA Status, and non-surgical anesthesia time as significant variable. We plotted Shapley values for significant variables for each patient to help determine which of these variables had the greatest effect on UCE risk. Of note, the UCE risk factors identified frequently by ML were in alignment with anesthesiologist clinical practice and the current literature. Conclusions: We used ML to analyze data from a single-center, retrospective cohort of non-cardiac surgical patients, some of whom had an UCE. ML assigned risk prediction for patients to have UCE and determined perioperative factors associated with increased risk. We advocate to use ML to augment anesthesiologist clinical decision-making, help decide proper disposition from the PACU, and ensure the safest possible care of our patients. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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