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ACS NSQIP Risk Calculator Performance Across Multiple Domains of Operative Risk and Risk-associated Features.

Mark E Cohen¹, Yaoming Liu¹, Bruce L Hall^1,2

¹Division of Research and Optimal Patient Care, American College of Surgeons, Chicago, IL.

Annals of Surgery

|May 13, 2025

Summary

This summary is machine-generated.

The American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) Risk Calculator (RC) accurately predicts surgical outcomes for high-risk patients. This validated tool offers excellent calibration and good discrimination across diverse patient populations.

Keywords:

NSQIP accuracy high risk surgery risk calculator

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Area of Science:

Surgical outcomes research
Health informatics
Machine learning in medicine

Background:

The ACS NSQIP Risk Calculator (RC) is crucial for surgical decision-making, especially in high-risk scenarios.
Previous evaluations of the RC did not focus on high-risk subsets or account for its updated machine learning (ML) algorithm.
This study addresses these limitations by assessing the RC's performance in complex patient groups.

Purpose of the Study:

To evaluate the accuracy of the ACS NSQIP Risk Calculator (RC) in predicting outcomes for high-risk surgical patient subsets.
To compare the performance of the current ML algorithm (XGB) with a potential future algorithm (CATB).

Main Methods:

Analysis of 1,085,707 patients from the ACS-NSQIP database (2021-2024).
Patients were categorized into 21 subsets based on risk factors, age, ASA class, functional status, and surgical urgency/type.
Assessed calibration (Absolute Percentage Error; APE) and discrimination (Area Under the Curve; AUC) for mortality and morbidity outcomes.

Main Results:

Observed mortality rates varied from 0.04% to 60.01%, and morbidity rates ranged from 3.33% to 59.17% across subsets.
Both XGB and CATB algorithms demonstrated excellent calibration (APE < 10%).
Discrimination was consistently good (AUC > 0.7), with only minor exceptions.

Conclusions:

The ACS NSQIP Risk Calculator (RC) provides reliable predictions for all patient risk levels.
The tool exhibits excellent calibration and good discrimination, proving valuable for surgical decision-making.
The findings support the continued use and potential enhancement of the RC for diverse surgical populations.