[Construction of a risk prediction model for the timing of extracorporeal membrane oxygenation initiation]

To identify the risk factors related to the timing of patients receiving extracorporeal membrane oxygenation (ECMO) initiation and construct a risk prediction model for ECMO initiation timing.

Patients who received ECMO admitted to the Second Xiangya Hospital of Central South University from January 2020 to January 2024 were retrospectively collected. The case data mainly included physiological and biochemical indicators 1 hour before ECMO initiation. According to the outcome of the patients, they were divided into survival group and death group. Univariate and multivariate Logistic regression analysis were used to analyze the predictors of mortality risk in patients with ECMO, and a nomogram prediction model was constructed. The discrimination, calibration accuracy, and goodness of the model were evaluated by the receiver operator characteristic curve (ROC curve), calibration curve, and the Hosmer-Lemeshow test, respectively. Decision curve analysis (DCA) evaluated the clinical net benefit rate of the model.

A total of 81 ECMO patients were included, including 59 males and 22 females; age range from 16 to 61 years old, with a median age of 56.0 (39.5, 61.5) years old; 20 patients received veno-arterial (V-A) ECMO, and 61 patients received veno-venous (V-V) ECMO; 23 patients ultimately survived and 58 patients died. Univariate analysis showed that age, blood urea nitrogen, serum creatinine, D-dimer, arterial blood carbon dioxide partial pressure, and prothrombin time of the death group were all higher than those of the survival group, while albumin was slightly lower than that of the survival group. There was a statistically significant difference in the direct cause of ECMO initiation between the two groups. Multivariate Logistic regression analysis showed that age [odds ratio (OR) = 1.069, 95% confidence interval (95%CI) was 1.015-1.125, P = 0.012], direct cause of ECMO initiation [with heart failure as the reference, return of spontaneous circulation (ROSC) after cardiopulmonary support (OR = 30.672, 95%CI was 1.265-743.638, P = 0.035), novel coronavirus infection (OR = 8.666, 95%CI was 0.818-91.761, P = 0.073), other severe pneumonia (OR = 4.997, 95%CI was 0.558-44.765, P = 0.150)], pre-ECMO serum creatinine (OR = 1.008, 95%CI was 1.000-1.016, P = 0.044), prothrombin time (OR = 1.078, 95%CI was 0.948-1.226, P = 0.252), and D-dimer (OR = 1.135, 95%CI was 1.047-1.231, P = 0.002) were entered into the final regression equation. A nomogram prediction model was developed based on these five factors. The area under the ROC curve (AUC) of the model was 0.889 (95%CI was 0.819-0.959), higher than the AUC of the sequential organ failure assessment (SOFA; AUC = 0.604, 95%CI was 0.467-0.742). The calibration curve showed good consistency between the model predictions and the observed results. The Hosmer-Lemeshow goodness-of-fit test showed that χ ² = 4.668, P = 0.792. DCA analysis showed that when the risk threshold was 0-0.8, the net benefit rate was greater than 0, which was significantly better than that of SOFA score.

The risk prediction model for the timing of ECMO initiation, constructed using five factors (age, direct cause of ECMO initiation, thrombin time, serum creatinine, and D-dimer), demonstrated good discrimination and calibration. It can serve as a pre-initiation assessment tool to identify and predict post-initiation mortality risk in ECMO patients.