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

David N Bernstein1, Aakash Keswani2, David Ring3

  • 1University of Rochester School of Medicine & Dentistry, Rochester, NY, USA.

Clinical Orthopaedics and Related Research
|December 2, 2016
PubMed
Summary

A statistically driven model for total shoulder arthroplasty (TSA) better predicts unplanned readmissions and adverse events than a clinically driven model. Large database research is valuable, but more factors are needed to explain outcome variations.

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Area of Science:

  • Orthopaedic Surgery
  • Health Services Research
  • Biostatistics

Background:

  • Growing interest in value-based healthcare necessitates understanding factors influencing surgical outcomes.
  • Statistical analysis of large datasets can identify predictors of adverse events and unplanned readmissions.
  • Comparing clinician-based risk assessments with data-driven models is crucial for evidence-based practice in total shoulder arthroplasty (TSA).

Purpose of the Study:

  • To compare the explanatory power of a statistically driven risk model versus a clinically driven model for 30-day unplanned readmissions after TSA.
  • To evaluate which model better explains variations in 30-day adverse events following TSA.

Main Methods:

  • Logistic regression models were constructed using data from 4030 patients undergoing TSA for osteoarthritis.
  • Two models were developed: one based on five expert-opinion variables and another using all variables with p < 0.10.
  • Patients deemed unfit for discretionary surgery were excluded, and models were re-evaluated on the remaining 3160 patients.

Main Results:

  • The statistically driven model explained more variation in unplanned readmissions (4.6%) compared to the clinically driven model (1.4%), identifying operating time and hypertension as significant factors.
  • For adverse events, the statistically driven model explained 3.3% of variation, highlighting age, male sex, operating time, and high blood urea nitrogen (BUN) as predictors.
  • Neither model provided substantial explanatory power for readmissions or adverse events.

Conclusions:

  • Statistically derived risk models demonstrate superior performance over clinically driven models in predicting TSA outcomes.
  • Large database research is valuable for identifying risk factors, but further investigation is needed to explain the limited variation in outcomes.
  • Clinician intuition may not always be the most accurate for risk adjustment; data-driven insights are essential for improving TSA patient care.