Background: Early identification of patients at risk for surgical complications enables surgeons to make better treatment decisions and optimize resource utilization. We propose to develop a nomogram for predicting the risk of moderate-to-severe liver surgery-specific complications after hepatectomy in hepatocellular carcinoma (HCC) patients.
Methods: We retrospectively enrolled HCC patients who underwent radical hepatectomy at four medical centers from January 2014 to January 2019 in southwestern China, randomly (7:3) divided into training and validation cohorts. We used least absolute shrinkage and selection operator (LASSO) logistic regression to build a nomogram model.
Results: The nomogram model contained 6 variables: diabetes mellitus (yes vs. no, OR: 2.32, 95% CI: 1.16-4.64, P = 0.02), major hepatectomy (yes vs. no, OR: 2.65, 95% CI: 1.64-4.27, P < 0.001), platelets (PLT, ≥100 × 10/μl vs. <100 × 10/μl, OR: 0.53, 95% CI: 0.33-0.87, P = 0.01), prothrombin time (PT, >13 s vs. ≤13 s, OR: 1.78, 95% CI: 1.04-3.05, P = 0.04), albumin-indocyanine green evaluation grade (ALICE grade, grade B vs. grade A, OR: 2.06, 95% CI: 1.17-3.61, P = 0.01), and prognostic nutrient index (PNI, >48 vs. ≤48, OR: 0.55, 95% CI: 0.33-0.92, P = 0.02). The concordance index (C-index) and area under the receiver operating characteristic curve (AUC) were 0.751 (95% CI, 0.703-0.799) and 0.743 (95% CI, 0.653-0.833) for the training and validation cohorts, respectively. Decision curve analysis (DCA) showed that the nomogram had good clinical value.
Conclusion: We provide good preoperative predictors for the risk of moderate-to-high FABIB score complications in patients with HBV-related HCC posthepatectomy.
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