Performance comparison between SURPAS and ACS NSQIP Surgical Risk Calculator in pulmonary resection Journal Article


Authors: Chudgar, N. P.; Yan, S.; Hsu, M.; Tan, K. S.; Gray, K. D.; Molena, D.; Nobel, T.; Adusumilli, P. S.; Bains, M.; Downey, R. J.; Huang, J.; Park, B. J.; Rocco, G.; Rusch, V. W.; Sihag, S.; Jones, D. R.; Isbell, J. M.
Article Title: Performance comparison between SURPAS and ACS NSQIP Surgical Risk Calculator in pulmonary resection
Abstract: Background: Accurate preoperative risk assessment is critical for informed decision making. The Surgical Risk Preoperative Assessment System (SURPAS) and the National Surgical Quality Improvement Program (NSQIP) Surgical Risk Calculator (SRC) predict risks of common postoperative complications. This study compares observed and predicted outcomes after pulmonary resection between SURPAS and NSQIP SRC. Methods: Between January 2016 and December 2018, 2514 patients underwent pulmonary resection and were included. We entered the requisite patient demographics, preoperative risk factors, and procedural details into the online NSQIP SRC and SURPAS formulas. Performance of the prediction models was assessed by discrimination and calibration. Results: No statistically significant differences were found between the 2 models in discrimination performance for 30-day mortality, urinary tract infection, readmission, and discharge to a nursing or rehabilitation facility. The ability to discriminate between a patient who will develop a complication and a patient who will not was statistically indistinguishable between NSQIP and SURPAS, except for renal failure. With a C index closer to 1.0, the NSQIP performed significantly better than the SURPAS SRC in discriminating risk of renal failure (C index, 0.798 vs 0.694; P = .003). The calibration curves of predicted and observed risk for each model demonstrate similar performance with a tendency toward overestimation of risk, apart from renal failure. Conclusions: Overall, SURPAS and NSQIP SRC performed similarly in predicting outcomes for pulmonary resections in this large, single-center validation study with moderate to good discrimination of outcomes. Notably, SURPAS uses a smaller set of input variables to generate the preoperative risk assessment. The addition of thoracic-specific input variables may improve performance. © 2021 The Society of Thoracic Surgeons
Journal Title: Annals of Thoracic Surgery
Volume: 111
Issue: 5
ISSN: 0003-4975
Publisher: Elsevier Science, Inc.  
Date Published: 2021-05-01
Start Page: 1643
End Page: 1651
Language: English
DOI: 10.1016/j.athoracsur.2020.08.021
PUBMED: 33075322
PROVIDER: scopus
PMCID: PMC8109003
DOI/URL:
Notes: Conference Paper -- Export Date: 3 May 2021 -- Source: Scopus
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MSK Authors
  1. Meier Hsu
    169 Hsu
  2. James Huang
    214 Huang
  3. Bernard J Park
    263 Park
  4. Robert J Downey
    254 Downey
  5. David Randolph Jones
    417 Jones
  6. Daniela   Molena
    270 Molena
  7. Neel Pankaj Chudgar
    15 Chudgar
  8. Kay See   Tan
    241 Tan
  9. James Michael Isbell
    127 Isbell
  10. Smita Sihag
    96 Sihag
  11. Tamar B Nobel
    41 Nobel
  12. Gaetano Rocco
    130 Rocco
  13. Katherine D. Gray
    24 Gray
  14. Shi Yan
    8 Yan