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
DOI/URL:
Notes: Conference Paper -- Export Date: 3 May 2021 -- Source: Scopus
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Citation Impact
MSK Authors
  1. Meier Hsu
    136 Hsu
  2. James Huang
    150 Huang
  3. Bernard J Park
    191 Park
  4. Robert J Downey
    226 Downey
  5. David Randolph Jones
    240 Jones
  6. Daniela   Molena
    117 Molena
  7. Kay See   Tan
    139 Tan
  8. James Michael Isbell
    63 Isbell
  9. Smita Sihag
    28 Sihag
  10. Tamar B Nobel
    26 Nobel
  11. Gaetano Rocco
    39 Rocco
  12. Katherine D. Gray
    5 Gray
  13. Shi Yan
    4 Yan