ASA Score as a Predictor of 30-Day Perioperative Readmission in Patients With Orthopaedic Trauma Injuries: An NSQIP Analysis

作者：Sathiyakumar Vasanth; Molina Cesar S; Thakore Rachel V; Obremskey William T; Sethi Manish K^*

来源：Journal of Orthopaedic Trauma, 2015, 29(3): E127-E132.

• ASA
• readmissions
• NSQIP
• orthopaedic trauma injury
• trauma

Objective: Our purpose was to identify the impact of the physical status of the American Society of Anesthesiologists (ASA) on the 30-day readmission of patients receiving operative management of orthopaedic fractures using the National Surgical Quality Improvement Program (NSQIP) database.
Methods: We analyzed all patients with orthopaedic trauma injuries in the American College of Surgeons NSQIP database from 2005 to 2011. A total of 8761 patients representing 91 orthopaedic trauma procedures were identified and included in analysis after selection. Logistic regressions were conducted to identify the predictive ability of ASA on the likelihood of readmission for patients in each anatomic category (upper extremity, pelvis/acetabulum, lower extremity) and the combined study population.
Results: The ASA physical status proved the strongest predictor of 30-day readmission for the selected orthopaedic trauma procedures. After controlling for age, gender, race, and medical comorbidities that were shown to be significant independent risk factors for readmission, ASA score continued to have a significant association on 30-day readmissions in the combined population (odds ratio = 1.45, 95% confidence interval = 1.13-1.88, P = 0.001). For the combined analysis, compared with patients with an ASA score of 1, patients with an ASA score of 2 were 1.04 times as likely to have a readmission (P = 0.001), patients with an ASA score of 3 were 3.77 times as likely to have a readmission (P = 0.001), and patients with an ASA score of 4 were 13.7 times as likely to have a readmission (P = 0.001).
Conclusions: ASA classification is an indicator for variance in readmission for patients receiving operative treatment of orthopaedic fractures. Given that ASA classification is a universally collected data point, this method can be used in almost any hospital system and for any operative service. This model may be used to more accurately predict a patient's postoperative course and the expected risk for readmission, such that hospitals can target these "at-risk" individuals and reduce 30-day readmissions.

• 出版日期2015-3