Accuracy of the ACS NSQIP surgical risk calculator in predicting postoperative outcomes in colorectal surgery in Saudi Arabia
Bibliographic record
Abstract
Background: Accurate perioperative risk assessment is essential for surgical decision-making and predicting patient outcomes. The American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) Surgical Risk Calculator (SRC) is widely used for estimating postoperative complications. However, its accuracy across different populations remains uncertain. Objectives: This study aimed to externally validate the ACS NSQIP SRC in Saudi Arabia, focusing on colorectal cancer (CRC) patients at King Abdulaziz Medical City, Riyadh. It assessed the accuracy of the SRC in predicting 30-day postoperative complications, particularly cardiac events, mortality, and surgical site infections. Additionally, the study evaluated the SRC’s role in improving informed consent and surgical decision-making. Methods: A retrospective review was conducted on CRC patients who underwent surgery between 2016 and 2018. Demographic and clinical data, including 20 ACS NSQIP SRC parameters, were collected. Predicted risks were calculated using the SRC and compared to actual outcomes, including surgical site infections, myocardial infarction, pneumonia, and mortality. Predictive accuracy was assessed using the Brier score and the area under the receiver operating characteristic curve (AUC). Results: Among 109 patients, 40.37% experienced complications, with serious complications in 18.35%. The SRC demonstrated high accuracy for cardiac complications (AUC = 0.8, Brier score = 0.01) and moderate accuracy for pneumonia (AUC = 0.71, Brier score = 0.02). Conclusions: The ACS NSQIP SRC accurately predicted key postoperative complications, supporting its use in Saudi surgical practice. Further research is needed to explore its applicability across other specialties.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".