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Performance Comparison Between SURPAS and ACS NSQIP Surgical Risk Calculator in Pulmonary Resection.

Neel P Chudgar¹, Shi Yan², Meier Hsu¹

¹Thoracic Surgery Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York.

The Annals of Thoracic Surgery

|October 19, 2020

Summary

This summary is machine-generated.

The Surgical Risk Preoperative Assessment System (SURPAS) and the National Surgical Quality Improvement Program (NSQIP) Surgical Risk Calculator (SRC) showed similar performance in predicting outcomes after pulmonary resection. NSQIP SRC demonstrated superior accuracy in predicting renal failure risk.

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Area of Science:

Thoracic Surgery
Surgical Outcomes Research
Predictive Analytics in Medicine

Background:

Accurate preoperative risk assessment is crucial for surgical decision-making.
The Surgical Risk Preoperative Assessment System (SURPAS) and the National Surgical Quality Improvement Program (NSQIP) Surgical Risk Calculator (SRC) are tools for predicting postoperative complications.
This study aimed to compare the predictive performance of SURPAS and NSQIP SRC for pulmonary resection outcomes.

Purpose of the Study:

To compare the observed and predicted postoperative complication rates between the SURPAS and NSQIP SRC models.
To evaluate the discrimination and calibration of both risk assessment tools in a cohort undergoing pulmonary resection.

Main Methods:

A retrospective analysis of 2514 patients undergoing pulmonary resection between January 2016 and December 2018.
Inputting patient demographics, preoperative risk factors, and procedural details into both NSQIP SRC and SURPAS prediction models.
Assessing model performance using discrimination (C-index) and calibration metrics.

Main Results:

Both SURPAS and NSQIP SRC demonstrated comparable discrimination for 30-day mortality, urinary tract infection, readmission, and discharge disposition.
NSQIP SRC showed significantly better discrimination for renal failure risk compared to SURPAS (C-index, 0.798 vs 0.694; P = .003).
Calibration curves indicated similar performance for both models, with a general tendency towards overestimation of risk, except for renal failure.

Conclusions:

SURPAS and NSQIP SRC exhibit similar predictive performance for pulmonary resection outcomes in a large, single-center validation study.
SURPAS utilizes a more concise set of input variables.
Incorporating thoracic-specific variables into risk assessment models may enhance predictive accuracy.