Prognostic nomogram integrated with inflammatory marker ratios for assessing in-hospital mortality risk in patients with acute type A aortic dissection - Xiao-Chai Lv; Lin-feng Xie; Min-xia Xie; Lei Wang; Yan-ting Hou; Liang-wan Chen (PLoS ONE, 2025)
Background Preoperative inflammatory biomarker ratios to predict adverse outcomes in patients with acute type A aortic dissection (AAD) were assessed in this study, and a prognostic nomogram to guide anti-inflammatory therapy was developed.. Methods We retrospectively analyzed 673 adult AAD patients who underwent surgery. Preoperative hematological parameters, including neutrophil, lymphocyte, and platelet counts; hemoglobin (Hb) and albumin levels; and composite indices including the neutrophil‒lymphocyte ratio (NLR), platelet‒lymphocyte ratio, neutrophil‒platelet ratio, and platelet‒albumin ratio, were evaluated. The univariate and multivariate logistic regression identified in-hospital mortality predictors, and a nomogram was constructed. The internal validation included bootstrapping with discrimination assessed by the C-index and calibration by the Hosmer–Lemeshow test. Results The univariate analysis revealed Hb, D-dimer, blood urea nitrogen, and albumin levels; the NLR; the aortic root concomitant procedure; ventilation support time and multiple organ dysfunction syndrome (MODS) as perioperative mortality predictors; after multivariate adjustment, decreased Hb level, elevated NLR, and the presence of MODS independently predicted in-hospital mortality. The nomogram that integrated these predictors achieved a corrected C-index of 0.846 and an area under the curve of 0.843, which demonstrated strong calibration and a Hosmer–Lemeshow P = 0.91. At the optimal probability cutoff of 0.124, the sensitivity was 77.2%, the specificity was 78.2%, and the accuracy was 78.1%. Conclusion The NLR and preoperative Hb level, combined with postoperative MODS, independently predict in-hospital death in patients with AAD. Additionally, a nomogram combining these factors accurately predicts short-term mortality and aids in the personalized risk assessment and may assist in improving the prognosis.