Predicting Postoperative Course After Total Shoulder Arthroplasty Using a Medical-Social Evaluation Model

Daniel E. Davis, Benjamin Zmistowski, Manan S. Patel, Alex Girden, Eric Padegimas, Matthew L. Ramsey

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

INTRODUCTION: The ability to predict successful outcomes is important for patient satisfaction and optimal results after shoulder arthroplasty. We hypothesize that a medical-social scoring tool will predict resource requirements in doing total shoulder arthroplasty (TSA). METHODS: A retrospective analysis of 453 patients undergoing TSA was undertaken. Preoperatively, medical and social surveys were completed by each patient. Demographics, comorbidity scores, hospital course, postdischarge disposition, and readmissions were collected. RESULTS: The average length of stay was 1.6 days (0 to 7). There was an association with utilization of home care or inpatient rehabilitation and both the medical (7.3 versus 3.9; P = 0.0002) and social (7.1 versus 3.4; P < 0.0001) components of the survey. There was a weak correlation between hospital length of stay and the social component of the survey (R = 0.29; P < 0.001), but not the medical component (R = 0.04; P = 0.38). No variable was predictive of readmission. Social score of eight was found to be predictive of postoperative requirement of home care or rehabilitation. CONCLUSION: This study found that Medical and Social Survey Scores can stratify patients who are at risk of requiring more advanced postdischarge care and/or a longer hospital stay. With this, we can match patients to the most appropriate level of postoperative care.

Original languageEnglish
Pages (from-to)808-813
Number of pages6
JournalThe Journal of the American Academy of Orthopaedic Surgeons
Volume28
Issue number19
DOIs
StatePublished - Oct 1 2020

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