A validated preoperative risk prediction tool for discharge to skilled nursing or rehabilitation facility following anatomic or reverse shoulder arthroplasty

Predicting Hospital Readmissions After Total Shoulder Arthroplasty Within a Bundled Payment Cohort

INTRODUCTION

Given the rising demand for shoulder arthroplasty, understanding risk factors associated with unplanned hospital readmission is imperative. No study to date has examined the influence of patient and hospital-specific factors as a predictive model for 90-day readmissions within a bundled payment cohort after primary shoulder arthroplasty. The purpose of this study was to determine predictive factors for 90-day readmissions after primary shoulder arthroplasty within a bundled payment cohort.

METHODS

After obtaining IRB approval, a retrospective review of a consecutive series of Medicare patients undergoing primary shoulder arthroplasty from 2014 to 2020 at a single academic institution was conducted. Patient demographic data, surgical variables, medical comorbidity profiles, medical risk scores, and social risk scores were collected. Postoperative variables included length of hospital stay, discharge location, and 90-day readmissions. Multivariate analysis was conducted to determine the independent risk factors of 90-day readmission.

RESULTS

Overall, 3.6% of primary shoulder arthroplasty patients (127/3,523) were readmitted within 90 days. Readmitted patients had a longer hospital course (1.75 versus 1.45 P = 0.006), higher comorbidity profile (4.64 versus 4.24 P = 0.001), higher social risk score (7.96 versus 6.9 P = 0.008), and higher medical risk score (10.1 versus 6.96 P < 0.001) and were more likely to require a home health aide or be discharged to an inpatient rehab facility or skilled nursing facility ( P = 0.002). Following multivariate analysis, an elevated medical risk score was associated with an increased risk of readmission (odds ratio = 1.05, P < 0.001).

DISCUSSION

This study demonstrates medical risk scores to be an independent risk factor of increased risk of 90-day hospital readmissions after primary shoulder arthroplasty within a bundled payment patient population. Additional incorporation of medical risk scores may be a beneficial adjunct in preoperative prediction for readmission and the potentially higher episode-of-care costs.

LEVEL OF EVIDENCE

Level III, retrospective cohort.

Perioperative risk stratification tools for shoulder arthroplasty: a systematic review

BACKGROUND

Risk stratification tools are being increasingly utilized to guide patient selection for outpatient shoulder arthroplasty. The purpose of this study was to identify the existing calculators used to predict discharge disposition, postoperative complications, hospital readmissions, and patient candidacy for outpatient shoulder arthroplasty and to compare the specific components used to generate their prediction models.

METHODS

This review was conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analysis protocol. PubMed, Cochrane Library, Scopus, and OVID Medline were searched for studies that developed calculators used to determine patient candidacy for outpatient surgery or predict discharge disposition, the risk of postoperative complications, and hospital readmissions after anatomic or reverse total shoulder arthroplasty (TSA). Reviews, case reports, letters to the editor, and studies including hemiarthroplasty cases were excluded. Data extracted included authors, year of publication, study design, patient population, sample size, input variables, comorbidities, method of validation, and intended purpose. The pros and cons of each calculator as reported by the respective authors were evaluated.

RESULTS

Eleven publications met inclusion criteria. Three tools assessed patient candidacy for outpatient TSA, 3 tools evaluated the risk of 30- or 90-day hospital readmission and postoperative complications, and 5 tools predicted discharge destination. Four calculators validated previously constructed comorbidity indices used as risk predictors after shoulder arthroplasty, including the Charlson Comorbidity Index, Elixhauser Comorbidity Index, modified Frailty Index, and the Outpatient Arthroplasty Risk Assessment, while 7 developed newcalculators. Nine studies utilized multiple logistic regression to develop their calculators, while 1 study developed their algorithm based on previous literature and 1 used univariate analysis. Five tools were built using data from a single institution, 2 using data pooled from 2 institutions, and 4 from large national databases. All studies used preoperative data points in their algorithms with one tool additionally using intraoperative data points. The number of inputs ranged from 5 to 57 items. Four calculators assessed psychological comorbidities, 3 included inputs for substance use, and 1 calculator accounted for race.

CONCLUSION

The variation in perioperative risk calculators after TSA highlights the need for standardization and external validation of the existing tools. As the use of outpatient shoulder arthroplasty increases, these calculators may become outdated or require revision. Incorporation of socioeconomic and psychological measures into these calculators should be investigated.

A validated preoperative risk prediction tool for extended inpatient length of stay following anatomic or reverse total shoulder arthroplasty

BACKGROUND

Recent work has shown inpatient length of stay (LOS) following shoulder arthroplasty to hold the second strongest association with overall cost (after implant cost itself). In particular, a preoperative understanding for the patients at risk of extended inpatient stays (≥3 days) can allow for counseling, optimization, and anticipating postoperative adverse events.

METHODS

A multicenter retrospective review was performed of 5410 anatomic (52%) and reverse (48%) total shoulder arthroplasties done at 2 large, tertiary referral health systems. The primary outcome was extended inpatient LOS of at least 3 days, and over 40 preoperative sociodemographic and comorbidity factors were tested for their predictive ability in a multivariable logistic regression model based on the patient cohort from institution 1 (derivation, N = 1773). External validation was performed using the patient cohort from institution 2 (validation, N = 3637), including area under the receiver operator characteristic curve (AUC), sensitivity, specificity, and positive and negative predictive values.

RESULTS

A total of 814 patients, including 318 patients (18%) in the derivation cohort and 496 patients (14%) in the validation cohort, experienced an extended inpatient LOS of at least 3 days. Four hundred forty-five (55%) were discharged to a skilled nursing or rehabilitation facility. Following parameter selection, a multivariable logistic regression model based on the derivation cohort (institution 1) demonstrated excellent preliminary accuracy (AUC: 0.826), with minimal decrease in accuracy under external validation when tested against the patients from institution 2 (AUC: 0.816). The predictive model was composed of only preoperative factors, in descending predictive importance as follows: age, marital status, fracture case, ASA (American Society of Anesthesiologists) score, paralysis, electrolyte disorder, body mass index, gender, neurologic disease, coagulation deficiency, diabetes, chronic pulmonary disease, peripheral vascular disease, alcohol dependence, psychoses, smoking status, and revision case.

CONCLUSION

A freely-available, preoperative online clinical decision tool for extended inpatient LOS (≥ 3 days) after shoulder arthroplasty reaches excellent predictive accuracy under external validation. As a result, this tool merits consideration for clinical implementation, as many risk factors are potentially modifiable as part of a preoperative optimization strategy.