Development and validation of a multivariable nomogram predictive of kidney function after cardiopulmonary resuscitation

Background Kidney injury is an important manifestation of post resuscitation syndrome (PRS) and a significant factor leading to high mortality rates after cardiopulmonary resuscitation (CPR).This study aimed to develop and validate a multivariable nomogram to predict estimated glomerular filtration rate (eGFR) after CPR to aassess the degree of kidney injury and provide protective strategies. Methods The clinical data of patients after CPR admitted to Tianjin Medical University General Hospital from January 2017 to June 2024 and Tianjin Medical University General Hospital Airport Hospital from January 2017 to December 2019 were retrospectively analyzed.The patients those who met the inclusion criteria were randomly divided into training and validation cohorts at a ratio of 7∶3.We obtained clinical data from January 2021 to June 2023 at First Affiliated Hospital of Hebei North University as external validation.Univariate and multivariate linear regression method were used to identify independent risk factors for 7d-eGFR after CPR,develope and validate (internal and external) a multivariate nomogram model.Calibration curve, Bland-Altman plot and Paired-T validation were used to validate the predictive performance of the model. Results We included 439 patients after CPR, of whom 307 were in training cohort and 132 were in validation cohort.And 105 patients were included as an external validation cohort.Multivariable linear analysis showed that age (Beta coefficient[β] 95% confidence interval [CI]:-0.344[-0.528, -0.160])、hypertension (-3.610[-5.968,-1.252])、diabetes mellitus (-2.992[-5.295,-0.689])、no flow time (-0.577[-0.996, -0.158])、baseline eGFR (0.349[0.269 ~ 0.429])、ACR (-0.042[-0.073, -0.011])、lactic acid (-0.650[-1.214,-0.086]) were the independent risk factors for eGFR after CPR.A composite nomogram predicted eGFR with good accuracy in training(97.07%), internal validation (95.45%) and external validation (91.08%) cohorts.The nomogram model has good predictive ability for AKI and CKD in training (AUC = 0.933 and 0.882), internal validation (AUC = 0.915 and 0.859) and external validation (AUC = 0.823 and 0.784) cohorts. Conclusion The developed nomogram could be used to predict 7d-eGFR after CPR, which helped to accurately quantify kidney function levels and early predict the probability of AKI and CKD progression, achieving early detection and intervention, thereby improving the prognosis of patients after CPR.