Influencing factors of early mortality after heart transplantation and constructing a prediction model / 中华器官移植杂志

ABSTRACT

Objective:To explore the risk factors for early mortality in heart transplant(HT)recipients and construct a nomogram prediction model.Methods:From 2018 to 2022, preoperative clinical data were retrospectively reviewed for 163 consecutive HT recipients.Risk factor variables were shortlisted by univariate correlation analysis based upon early(90-day)postoperative patient survival.Lasso regression was then employed for screening all variables and common variables were combined.A nomogram was constructed for predicting the probability of early mortality after considering actual circumstance.Receiver operating characteristic(ROC)curve, area under the ROC curve(AUC), Harrell's C-index and calibration curves were employed for evaluating and internally validate the performance of the model.Decision curve analysis was performed for assessing clinical utility of the model.Results:In survival and mortality groups, mechanical ventilation, nervous system lesions, use of extracorporeal membrane oxygenation, red blood cell count ≤3.52×10 12/L, mean pulmonary arterial pressure>27 mmHg, pulmonary vascular resistance>4.01 Wood Unit, albumin≤33 g/L, aspartate aminotransferase >50 U/L, hemoglobin ≤108 g/L, platelet count ≤109×10 9/L and total bilirubin>57 μmol/L demonstrated statistically significant differences( P<0.05). At the same time, according to actual situations and different variables, hemoglobin ≤108 g/L, albumin ≤33 g/L, platelet count ≤109×10 9/L, total bilirubin>57μmol/L, aspartate aminotransferase>50 U/L, nervous system lesions and average pulmonary arterial pressure >27 mmHg were seven variables.And a nomogram with relatively high reliability was constructed for predicting the probability of early mortality post-HT(nomogram model evaluation, AUC 0.917, C index 0.910 and good calibration curve). Decision curve analysis indicated that the nomogram could benefit HT recipients. Conclusions:Risk factors have been identified for early mortality in HT recipients.And the nomogram prediction model offers a simple and reliable tool for predicting early mortality post-HT.It has important implications for individualized treatment of HT candidates.