Discharge to rehabilitation or skilled nursing facilities (post-acute care) is a major driver of costs associated with total joint arthroplasty. The authors suggest that an accurate preoperative risk calculator for shoulder arthroplasty would help counsel patients in clinic during shared decision-making conversations and also identify high-risk individuals who may benefit from preoperative optimization and discharge planning.
They studied shoulder arthroplasty cohorts from two geographically diverse, high-volume centers, including 1,773 cases from Institution #1 (56% anatomic) and 3,637 from Institution #2 (50% anatomic).
Of these, 485 (9%) shoulder arthroplasties overall were discharged to post-acute care (anatomic: 6%, reverse: 14%, p < 0.0001), and these patients had significantly higher rates of unplanned 90-day readmission (5% vs 3%, p = 0.0492).
Cases performed for preoperative fracture were more likely to require post-acute care (13% vs 3%, p < 0.0001), while revision cases were not (10% vs 10%, p = 0.8015).
A multivariable logistic regression model derived from the Institution #1 cohort demonstrated excellent preliminary accuracy (AUC: 0.87), requiring 11 preoperative variables (in order of importance): age, marital status, fracture, neurologic disease, paralysis, ASA, gender, electrolyte disorder, chronic pulmonary disease, diabetes, and coagulation deficiency. This model performed exceptionally well during external validation using the Institution #2 cohort (AUC: 0.84).
This model was incorporated into a freely-available, online prediction tool (see this link).
The authors suggest that these model parameters should form the basis for reimbursement legislation adjusting for patient comorbidities, ensuring no disparities in access arise for at-risk populations.
Comment: A particular strength of this study is that they "trained' the model on a data set from one institution and validated it by applying it to another.
This area of research is important in that there are penalties imposed on health systems for readmissions within 90 days and extra costs and other concerns (e.g. Covid exposure) about discharge to institutions other than home as well as for readmission. Predicting non-home discharge and readmission as well as managing the risk factors may help lower these risks and costs. While many of the risk factors are not modifiable (age, marital status, fracture diagnosis, diabetes, coagulation deficiency, ASA, paralysis, gender, and pulmonary disease), the severity of electrolyte disorders, pulmonary disease, diabetes, and coagulation status may be managed to an extent. It is unclear whether attempts at managing these conditions would actually reduce the risk of non-home discharge or readmission.
The authors suggest that legislation may be considered that would lessen penaties for readmission of high risk patients so that care of these individuals would not be disincented. This is a well-intended goal that would have to navigate the challenges of a risk stratification method and a way of using that risk stratification to drive a novel reimbursement and penalty-forgiveness formula.
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