Predicting Prolonged Length of Hospital Stay and Identifying Risk Factors Following Total Ankle Arthroplasty: A Supervised Machine Learning Methodology.
Ankle osteoarthritis (OA) is a debilitating condition that arises as a result of trauma or injury to the ankle and often progresses to chronic pain and loss of function that may require surgical intervention. Total ankle arthroplasty (TAA) has emerged as a means of operative treatment for end-stage...
| Published in: | Journal of Foot & Ankle Surgery Vol. 63; no. 5; pp. 557 - 562 |
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| Main Authors: | , , , , , |
| Format: | research Journal Article |
| Published: |
W B Saunders
Sep2024
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=179260297&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 179260297 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10672516 2S4 jtl: Journal of Foot & Ankle Surgery issn: 10672516 maglogo: N pubinfo: dt: Sep2024 vid: 63 iid: 5 pid: 1351 pub: W B Saunders place: Philadelphia, Pennsylvania artinfo: ui: 179260297 179260297 179260297 10.1053/j.jfas.2024.05.005 179260297 ppf: 557 ppct: 5 formats: tig: atl: Predicting Prolonged Length of Hospital Stay and Identifying Risk Factors Following Total Ankle Arthroplasty: A Supervised Machine Learning Methodology. aug: au: Chirongoma, Tadiwanashe Cabrera, Andrew Bouterse, Alexander Chung, David Patton, Daniel Essilfie, Anthony affil: School of Medicine, Loma Linda University, Loma Linda, CA sug: subj: Arthroplasty, Replacement, Ankle Adverse Effects Postoperative Complications Risk Factors Length of Stay Risk Assessment Machine Learning Algorithms Human Osteoarthritis, Ankle Surgery Surgery, Elective Male Female Sex Factors Postoperative Period Sodium Blood Preoperative Period Hematocrit Diabetes Mellitus Creatinine Blood Body Mass Index Leukocyte Count Hispanic Americans Race Factors Descriptive Statistics Sensitivity and Specificity Decision Making, Clinical Male Female ab: Ankle osteoarthritis (OA) is a debilitating condition that arises as a result of trauma or injury to the ankle and often progresses to chronic pain and loss of function that may require surgical intervention. Total ankle arthroplasty (TAA) has emerged as a means of operative treatment for end-stage ankle OA. Increased hospital length of stay (LOS) is a common adverse postoperative outcome that increases both the complications and cost of care associated with arthroplasty procedures. The purpose of this study was to employ four machine learning (ML) algorithms to predict LOS in patients undergoing TAA using the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database. The ACS-NSQIP database was queried to identify adult patients undergoing elective TAA from 2008 to 2018. Four supervised ML classification algorithms were utilized and tasked with predicting increased hospital length of stay (LOS). Among these variables, female sex, ASA Class III, preoperative sodium, preoperative hematocrit, diabetes, preoperative creatinine, other arthritis, BMI, preoperative WBC, and Hispanic ethnicity carried the highest importance across predictions generated by 4 independent ML algorithms. Predictions generated by these algorithms were made with an average AUC of 0.7257, as well as an average accuracy of 73.98% and an average sensitivity and specificity of 48.47% and 79.38%, respectively. These findings may be useful for guiding decision-making within the perioperative period and may serve to identify patients at increased risk for a prolonged LOS. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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