Machine learning for prognostic assessment in elderly patients with gastroduodenal perforation

BACKGROUND: Elderly gastroduodenal perforation (E-GDP) is prone to severe adverse effects (SAEs) and high mortality rates. This study aims to develop an intelligent prognostic assessment system for the comprehensive evaluation of E-GDP. METHODS: This study included a development cohort of 108 patients with E-GDP and an independent validation cohort of 30 patients. Patients’ clinical features, laboratory findings, and postoperative complications were retrospectively collected. Patients were categorized into superior prognosis group(SG) and inferior prognosis group(IG) using two-step clustering (TSC). Machine learning models predicted E-GDP prognosis, interpreted via SHapley Additive exPlanations (SHAP) analysis, and were validated at an external center. RESULTS: After TSC, patients were divided into two groups: the SG (76 patients) and the IG (32 patients). TSC better distinguished hospital days than SAE grade (p < 0.001 vs. p = 0.01). Compared with the SG, the IG had greater postoperative white blood cell counts (10.61 ± 4.82 vs. 8.01 ± 2.41, p = 0.006), intra-abdominal infections (94% vs. 0%, p < 0.001), anastomotic leakage (44% vs. 0%, p < 0.001), fluid time (8.01 ± 3.41 vs. 6.05 ± 2.12, p = 0.004), length of intensive care unit stay (114.20 ± 188.35 vs. 18.07 ± 48.55, p = 0.008), and costs (92180.79 ± 70920.55 vs. 38416.18 ± 20479.48, p < 0.001). The support vector machine (SVM) had the best performance, with an area under the curve (AUC) of 0.90 (0.77, 0.99) and 0.77 (0.61, 0.92) in the external validation set(30 patients). CONCLUSION: We developed a robust SVM-based machine learning model using the TSC assessment system to preoperatively predict the prognosis of E-GDP patients.