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Comparing short-term AIS post-operative complications between ACS-NSQIP and a surgeon study group
Jennifer M Bauer1, Suken A Shah2, Paul D Sponseller3
1Dept. of Orthopaedic Surgery, Seattle Children's Hospital, University of Washington, 4800 Sand Point Way NE, Seattel, WA, 98105, USA. jennifer.bauer@seattlechildrens.org.
Study Design:
Prospective cohort review.
Objective:
To compare two AIS databases to determine if a performance improvement-based surgeon group has different outcomes compared to a national database. The American College of Surgeon's National Quality Improvement Program (ACS-NSQIP) and a surgeon study group (SG) collect prospective data on AIS surgery outcomes. NSQIP offers open enrollment to all institutions, and SG membership is limited to 15 high-volume institutions, with a major initiative to improve surgeon performance. While both provide important outcome benchmarks, they may reflect outcomes that are not relatable nationwide.
Methods:
The ASC-NSQIP Pediatric Spine Fusion and SG database were queried for AIS 30- and 90-day complication data for 2014 and 2015. Prospective enrollment and a dedicated site coordinator with rigorous data quality assurance protocols existed for both registries. Outcomes were compared between groups with respect to superficial and deep surgical site infections (SSI), neurologic injury, readmission, and reoperation.
Results:
There were a total of 2927 AIS patients included in the ASC-NSQIP data and 721 in the SG database. Total complication rate was 9.4% NSQIP and 3.6% SG. At 90 days, there were fewer surgical site infections reported by SG than ASC-NSQIP (0.6% vs. 1.6%, p = 0.03). Similarly, there were less spinal cord injuries (0.8% vs 1.5%, p = 0.006), 30-day readmissions (0.8% vs. 2.6%, p = 0.002), and 30-day reoperations (0.6% vs. 1.7%, p = 0.02) in the SG cohort.
Conclusions:
Comparison of these two data sets suggests a range of complications and readmission rates, with the SG demonstrating lower values. These results are likely multi-factorial with the performance improvement initiative of the SG playing a role. Understanding the rate and ultimate risk factors for readmission and complications from big data sources has the potential to further drive quality improvement.
Level Of Evidence:
III.
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