[Establishment and validation of a nomogram model for predicting acute kidney injury in patients undergoing extracorporeal cardiopulmonary resuscitation: a multicenter retrospective cohort study spanning nine consecutive years].

内容摘要

To establish a predictive model for acute kidney injury (AKI) in patients undergoing extracorporeal cardiopulmonary resuscitation (ECPR), and to evaluate and validate its predictive value. A multicenter retrospective cohort study was conducted. 1) The clinical data of the patients undergoing ECPR during cardiopulmonary resuscitation (CPR) admitted to the First Hospital of Jiaxing between January 2016 and August 2024 were collected as the modeling cohort. The clinical data included patient characteristic information, relevant treatment information during ECPR, post-extracorporeal membrane oxygenation (ECMO) operation parameters, and ECMO-related variable information. The patients were divided into an AKI group and a non-AKI group according to the occurrence of AKI during ECMO support. The differences in clinical characteristics between the two groups were compared. Multivariate Logistic regression analysis was used to screen independent risk factors for AKI during ECMO therapy, and a nomogram model was established. The predictive value of the model was evaluated by receiver operator characteristic curve (ROC curve). Internal validation of the model was performed using the Bootstrap method with 1 000 resamplings. The predictive performance of the nomogram model was verified using calibration curves and the Hosmer-Lemeshow test, and the clinical utility of the model was assessed by decision curve analysis (DCA). 2) The clinical data of the patients who received ECPR during CPR admitted to the First People's Hospital of Tongxiang and the First People's Hospital of Pinghu from May 2024 to June 2025 were selected for external validation. The predictive efficacy of the model was evaluated by ROC curve analysis. 1) A total of 108 ECPR patients were finally enrolled in the modeling cohort, among whom 78 developed AKI during ECMO treatment and 30 did not, with an AKI incidence of 72.2%. Compared with the non-AKI group, the patients in the AKI group had higher Sequential Organ Failure Assessment (SOFA) score, serum creatinine (SCr), blood lactic acid, lower procalcitonin (PCT), and longer hypoperfusion time (all P<0.05). There were no significant differences in other clinical data between the two groups. Multivariate Logistic regression analysis showed that increased SOFA score [odds ratio (OR)=1.288, 95% confidence interval (95%CI) was 1.055-1.571, P=0.013], SCr (OR=1.010, 95%CI was 1.002-1.018, P=0.015) and blood lactic acid (OR=1.151, 95%CI was 1.036-1.279, P=0.009), and prolonged hypoperfusion time (OR=1.059, 95%CI was 1.007-1.114, P=0.026) were independent risk factors for AKI during ECMO in ECPR patients. A nomogram prediction model was constructed based on the above independent risk factors. ROC curve analysis showed that the area under the ROC curve (AUC) of the nomogram model for predicting AKI in ECPR patients was 0.858 (95%CI was 0.782-0.934, P<0.001), with a sensitivity of 71.8% and a specificity of 83.3%. After 1 000 Bootstrap resamplings, the C-index was 0.822. The Hosmer-Lemeshow test and calibration curve showed good fitness between the predicted and ideal probabilities (&#x3c7;2=6.402, P=0.602), indicating favorable model performance. DCA results suggested that using the nomogram model achieved higher net benefit for most patients. 2) A total of 31 patients who received ECPR were enrolled for external validation. There were no significant differences in baseline data such as gender, age, or underlying diseases as well as four core independent risk factors for constructing a nomogram predictive model between the external validation cohort and the primary cohort, meeting the requirements of external validation design. ROC curve analysis showed that the AUC of the nomogram model for predicting AKI in ECPR patients was 0.833 (95%CI was 0.654-1.000, P<0.001), with a sensitivity of 70.0% and a specificity of 85.7%. A nomogram model for predicting AKI in ECPR patients is established based on hypoperfusion time combined with SOFA score, SCr, and blood lactic acid. The model has been confirmed to possess good predictive value through both internal and external validation.