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A comparison of NSQIP and CESQIP in data quality and ability to predict thyroidectomy outcomes

Published:November 16, 2022DOI:https://doi.org/10.1016/j.surg.2022.05.046

      Abstract

      Background

      The Collaborative Endocrine Surgery Quality Improvement Program tracks thyroidectomy outcomes with self-reported data, whereas the National Surgical Quality Improvement Program uses professional abstractors. We compare completeness and predictive ability of these databases at a single-center and national level.

      Method

      Data consistency in the Collaborative Endocrine Surgery Quality Improvement Program and the National Surgical Quality Improvement Program at a single institution (2013–2020) was evaluated using McNemar’s test. At the national level, data from the Collaborative Endocrine Surgery Quality Improvement Program and the National Surgical Quality Improvement Program (2016–2019) were used to compare predictive capability for 4 outcomes within each data source: thyroidectomy-specific complication, systemic complication, readmission, and reoperation, as measured by area under curve.

      Results

      In the single-center analysis, 66 cases were recorded in both the Collaborative Endocrine Surgery Quality Improvement Program and the National Surgical Quality Improvement Program. The reoperation variable had the most discrepancies (2 vs 0 in the National Surgical Quality Improvement Program versus the Collaborative Endocrine Surgery Quality Improvement Program, respectively; χ2 = 2.00, P = .16). At the national level, there were 24,942 cases in the National Surgical Quality Improvement Program and 17,666 cases in the Collaborative Endocrine Surgery Quality Improvement Program. In the National Surgical Quality Improvement Program, 30-day thyroidectomy-specific complication, systemic complication, readmission, and reoperation were 13.25%, 2.13%, 1.74%, and 1.39%, respectively, and in the Collaborative Endocrine Surgery Quality Improvement Program 7.27%, 1.95%, 1.64%, and 0.81%. The area under curve of the National Surgical Quality Improvement Program was higher for predicting readmission (0.721 [95% confidence interval 0.703–0.737] vs 0.613 [0.581–0.649]); the area under curve of the Collaborative Endocrine Surgery Quality Improvement Program was higher for thyroidectomy-specific complication (0.724 [0.708–0.737] vs 0.677 [0.667–0.687]) and reoperation (0.735 [0.692–0.775] vs 0.643 [0.611-0.673]). Overall, 3.44% vs 27.22% of values were missing for the National Surgical Quality Improvement Program and the Collaborative Endocrine Surgery Quality Improvement Program, respectively.

      Conclusion

      The Collaborative Endocrine Surgery Quality Improvement Program was more accurate in predicting thyroidectomy-specific complication and reoperation, underscoring its role in collecting granular, disease-specific variables. However, a higher proportion of data are missing. The National Surgical Quality Improvement Program infrastructure leads to more rigorous data capture, but the Collaborative Endocrine Surgery Quality Improvement Program is better at predicting thyroid-specific outcomes.
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      References

        • Ingraham A.M.
        • Richards K.E.
        • Hall B.L.
        • Ko C.Y.
        Quality improvement in surgery: the American College of Surgeons national surgical quality improvement program approach.
        Adv Surg. 2010; 44: 251-267
        • Carty S.E.
        2014 American Association of Endocrine Surgeons presidential address: evolution.
        Surgery. 2014; 156: 1289-1296
        • Sippel R.S.
        • Chen H.
        Limitations of the ACS NSQIP in thyroid surgery.
        Ann Surg Oncol. 2011; 18: 3529-3530
        • Cohen M.E.
        • Ko C.Y.
        • Bilimoria K.Y.
        • et al.
        Optimizing ACS NSQIP modeling for evaluation of surgical quality and risk: patient risk adjustment, procedure mix adjustment, shrinkage adjustment, and surgical focus.
        J Am Coll Surg. 2013; 217: 336-346.e1
        • Talutis S.D.
        • Drake F.T.
        • Sachs T.
        • Rao S.R.
        • McAneny D.
        Evacuation of postoperative hematomas after thyroid and parathyroid surgery: an analysis of the CESQIP database.
        Surgery. 2019; 165: 250-256
        • Inabnet W.B.
        • Palazzo F.
        • Sosa J.A.
        • et al.
        Correlating the Bethesda system for reporting thyroid cytopathology with histology and extent of surgery: a review of 21,746 patients from four endocrine surgery registries across two continents.
        World J Surg. 2020; 44: 426-435
        • Kazaure H.S.
        • Thomas S.
        • Scheri R.P.
        • Stang M.T.
        • Roman S.A.
        • Sosa J.A.
        The devil is in the details: assessing treatment and outcomes of 6,795 patients undergoing remedial parathyroidectomy in the Collaborative Endocrine Surgery Quality Improvement Program.
        Surgery. 2019; 165: 242-249
        • Taye A.
        • Inabnet III, W.B.
        • Pan S.
        • et al.
        Post-thyroidectomy emergency room visits and readmissions: assessment from the Collaborative Endocrine Surgery Quality Improvement Program (CESQIP).
        Am J Surg. 2020; 220: 813-820
      1. Discussion.
        J Am Coll Surg. 2019; 228: 659-661
        • Weiss A.
        • Anderson J.E.
        • Chang D.C.
        Comparing the National Surgical Quality Improvement Program with the Nationwide Inpatient Sample Database.
        JAMA Surg. 2015; 150: 815-816
        • Davenport D.L.
        • Holsapple C.W.
        • Conigliaro J.
        Assessing surgical quality using administrative and clinical data sets: a direct comparison of the University HealthSystem Consortium Clinical Database and the National Surgical Quality Improvement Program data set.
        Am J Med Qual. 2009; 24: 395-402
        • Hall B.L.
        • Hirbe M.
        • Waterman B.
        • Boslaugh S.
        • Dunagan W.C.
        Comparison of mortality risk adjustment using a clinical data algorithm (American College of Surgeons National Surgical Quality Improvement Program) and an administrative data algorithm (Solucient) at the case level within a single institution.
        J Am Coll Surg. 2007; 205: 767-777
        • McNemar Q.
        Note on the sampling error of the difference between correlated proportions or percentages.
        Psychometrika. 1947; 12: 153-157
      2. MATLAB. MathWorks, Inc, 2020
        • Draper N.R.
        • Smith H.
        Applied Regression Analysis. 326. John Wiley & Sons, 1998
        • Courvoisier D.S.
        • Combescure C.
        • Agoritsas T.
        • Gayet-Ageron A.
        • Perneger T.V.
        Performance of logistic regression modeling: beyond the number of events per variable, the role of data structure.
        J Clin Epidemiol. 2011; 64: 993-1000
        • LaPar D.J.
        • Stukenborg G.J.
        • Lau C.L.
        • Jones D.R.
        • Kozower B.D.
        Differences in reported esophageal cancer resection outcomes between national clinical and administrative databases.
        J Thorac Cardiovasc Surg. 2012; 144: 1152-1159
        • Bedard N.A.
        • Pugely A.J.
        • McHugh M.
        • et al.
        Analysis of outcomes after TKA: do all databases produce similar findings?.
        Clin Orthop Relat Res. 2018; 476: 52-63