CORRInsights®: Perioperative Risk Adjustment for Total Shoulder Arthroplasty: Are Simple Clinically Driven Models Sufficient?

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With increasing pressure on healthcare budgets around the globe, it is vital for healthcare providers to demonstrate that their procedures deliver value [8]. If we want to improve the value of healthcare, we will need to institute substantial cost-saving measures [3]. Adverse events in hospitals are estimated to affect one in 10 patients [2], and the economic impact of events like infections, adverse-drug events, and surgical complications is substantial. If we could reduce their frequency, cost savings would likely follow.
There are potential risk factors for complications following total shoulder arthroplasty [1], a procedure that has more than tripled in incidence in the last 10 years [6]. Reducing adverse events and readmissions after surgery are two key areas that warrant close attention when improving surgical services [4, 5].
The current study by Bernstein and colleagues compared two models that predict unplanned readmission rates and adverse events after total shoulder arthroplasty. Traditionally, clinicians identify potential risk factors by highlighting the phenotypic traits of their patients and subjecting the data to regression analyses. The disadvantage to this is that other factors deemed unimportant might be overlooked. By preoperatively identifying patients with characteristics that might lead to a higher risk of adverse events or readmissions, these models can potentially modify the risk factors ahead of treatment.
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