Validation of a Discharge Risk Calculator for Rural Patients Following Total Joint Arthroplasty.

Autor: Ozdag Y; Geisinger Musculoskeletal Institute, Department of Orthopaedic Surgery, Geisinger Commonwealth School of Medicine, Scranton, Pennsylvania., Makar GS; Geisinger Musculoskeletal Institute, Department of Orthopaedic Surgery, Geisinger Commonwealth School of Medicine, Scranton, Pennsylvania., Goltz DE; Department of Orthopaedic Surgery, Duke University Medical Center, Durham, North Carolina., Seyler TM; Department of Orthopaedic Surgery, Duke University Medical Center, Durham, North Carolina., Mercuri JJ; Geisinger Musculoskeletal Institute, Department of Orthopaedic Surgery, Geisinger Commonwealth School of Medicine, Scranton, Pennsylvania., Pallis MP; Geisinger Musculoskeletal Institute, Department of Orthopaedic Surgery, Geisinger Commonwealth School of Medicine, Scranton, Pennsylvania.
Jazyk: angličtina
Zdroj: The Journal of arthroplasty [J Arthroplasty] 2024 Jun 24. Date of Electronic Publication: 2024 Jun 24.
DOI: 10.1016/j.arth.2024.06.047
Abstrakt: Background: As the volume of total joint arthroplasty in the US continues to grow, new challenges surrounding appropriate discharge surface. Arthroplasty literature has demonstrated discharge disposition to postacute care facilities carries major risks regarding the need for revision surgery, patient comorbidities, and financial burden. To quantify, categorize, and mitigate risks, a decision tool that uses preoperative patient variables has previously been published and validated using an urban patient population. The aim of our investigation was to validate the same predictive model using patients in a rural setting undergoing total knee arthroplasty (TKA) and total hip arthroplasty.
Methods: All TKA and THA procedures that were performed between January 2012 and September 2022 at our institution were collected. A total of 9,477 cases (39.6% TKA, 60.4% THA) were included for the validation analysis. There were 9 preoperative variables that were extracted in an automated fashion from the electronic medical record. Included patients were then run through the predictive model, generating a risk score representing that patient's differential risk of discharge to a skilled nursing facility versus home. Overall accuracy, sensitivity and specificity were calculated after obtaining risk scores.
Results: Score cutoff equally maximizing sensitivity and specificity was 0.23, and the proportion of correct classifications by the predictive tool in this study population was found to be 0.723, with an area under the curve of 0.788 - both higher than previously published accuracy levels. With the threshold of 0.23, sensitivity and specificity were found to be 0.720 and 0.723, respectively.
Conclusions: The risk calculator showed very good accuracy, sensitivity, and specificity in predicting discharge location for rural patients undergoing TKA and THA, with accuracy even higher than in urban populations. The model provides an easy-to-use interface, with automation representing a viable tool in helping with shared decision-making regarding postoperative discharge plans.
(Copyright © 2024. Published by Elsevier Inc.)
Databáze: MEDLINE
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