Development of a Perioperative Risk Mortality Calculator for Humanitarian Surgical Care.

Background: Risk models to predict perioperative mortality rates (POMR) are critical to surgical quality improvement yet are not widely adapted for use in humanitarian and low-resource settings (LRS). We developed a POMR and corresponding nomogram and calculator for use in humanitarian surgical care.

Methods: Electronic health record data from a high-income academic medical center from 2015 to 2019 were retrospectively extracted, selecting variables and operations specific to LRS. This development dataset was used to create a logistic regression POMR model, which was then prospectively validated using data from 2022 to 2023 from the same institution.

Results: EHR from a total of 49,277 patients were used. The model fitted eight variables feasibly obtainable in LRS: age > 65 years (OR = 4.05 and 95% CI: 3.71-4.43), male sex (OR = 1.32 and 95% CI: 1.25-1.40), GCS < 13 (OR = 5.20 and 95% CI: 4.73-5.73), glucose > 200 mg/dL (OR = 2.19 and 95% CI: 2.01-2.38), Hgb ≤ 11 g/dL (OR = 2.65 and 95% CI: 2.43-2.89), HR > 120 bpm (OR = 2.49 and 95% CI: 2.35-2.64), T > 38 degrees Celsius (OR = 1.32 and 95% CI: 1.19-1.45), and SBP > 180 mmHg (OR = 1.18 and 95% CI: 1.02-1.37). The model demonstrated a high area under the curve (0.847, 0.867, and 0.925), sensitivity (0.739, 0.886, and 0.844), specificity (0.807, 0.780, and 0.864), and negative predictive value (0.750, 0.997, and 0.999) on training, holdout, and prospective validation sets.

Conclusion: We validated a POMR model for use in LRS using eight variables that are readily available in the target environment. This model's predictors and accompanying clinical tools of an Excel calculator and nomogram make it simultaneously comprehensive and accessible in LRS.