Department of Physical Therapy Student Theses, Dissertations and Capstones

Document Type

Dissertation - NSU Access Only

Degree Name

Doctor of Philosophy (PhD) in Physical Therapy

Copyright Statement

All rights reserved. This publication is intended for use solely by faculty, students, and staff of Nova Southeastern University. No part of this publication may be reproduced, distributed, or transmitted in any form or by any means, now known or later developed, including but not limited to photocopying, recording, or other electronic or mechanical methods, without the prior written permission of the author or the publisher.

First Advisor

Cheryl Hill

Date of original Performance / Presentation

January 2023

Publication Date / Copyright Date

1-1-2023

Abstract

Background: With a changing regulatory climate for total joint arthroplasty (TJA) and movement toward outpatient (OP) surgery, there is a need to establish new clinical pathways to ensure safe, efficient, and cost-effective care. Patient selection is essential for successful TJA to ensure optimal outcome for patients and healthcare organizations. Previously established predictors of surgical classification (SC) and same day discharge (SDD) have been utilized by providers to aid in patient selection for OP TJA and more specifically, appropriateness for SDD. Novel patient and facility factors, namely a patient’s mobility and participation in pre-operative education classes, have been established as factors which are influential on TJA outcomes, but have not been included in previously established models. The addition of these novel predictors to previously established models may increase the predictive strength of the models for predicting SC and SDD. Methods: This dissertation project utilized a retrospective review of electronic medical record data for patients undergoing TJA at the Langone Orthopedic Hospital, to examine the relationships between pre-operative patient and facility factors and SC and SDD outcomes. To address the research questions, independent samples t-tests and univariate and multiple logistic regression were utilized to determine whether between-group differences exist, and if so, to establish odds ratios relating independent to dependent variables (outcomes). Concordance analysis was performed to compare the novel model created in this analysis with the current state of prediction (surgical booking). ROC analysis was also performed to determine score cutoffs for the FTSTS which can be used to predict SC and SDD. Results: Data was obtained for 9,401 participants and re-coded for the analyses. Individual binary logistic regression for patient and facility factors in relation to SDD and SC revealed relationships of varying strength. Multiple variable analyses resulted in the creation of Models 1 through 4 which had varying classification accuracy and area under the curve (AUC) values for prediction of SDD and SC. Akaike information criteria (AIC) score comparisons revealed that the models including surgical booking as a variable had the strongest classification accuracy and AUC values. Cut score analysis for the FTSTS test did not result in identification of a score which was highly specific and/or highly sensitive for the SDD or SC outcomes. Discussion/Conclusions: The novel models created through this analysis of data have both clinical and fiscal implications to healthcare organizations who perform TJA care. Improvement in the accuracy of patient classification for SDD and SC outcomes has the potential to improve clinical outcomes, maximize profit, and facilitate a satisfactory experience for patients. Study limitations include that of the retrospective design, sampling bias, and testing bias, which may have impacted the results.

Disciplines

Physical Therapy

Keywords

Five Times Sit to Stand Test; Preoperative education; Same day discharge; Total joint arthroplasty; Total joint replacement

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