Construction and verification of prediction model of Iopamidol-induced renal damage in enhanced CT scan
Objective To construct and verificate a prediction model to explore the related risk factors for Iopamidol-induced blood coagulation and renal damage,aiming to provide a basis for rational use of iopatol.Methods A total of 136 patients who received enhanced CT scan for Iopamidol-induced renal damage in our hospital from January 2022 to June 2023 were randomized to a training set(n=96)or a validation set(n=40).Univariate and multivariate Logistic regression analyses were successively used to construct a prediction nomogram model.The receiver operating characteristic(ROC)curve,calibration curve and decision curve were conducted to evaluate the efficacy of the model.Results Univariate Logistic regression analysis showed that smoking history,faster pulse,drinking history and lower activated partial thromboplastin time had statistically significant differences(P＜0.05).Multivariate logistic regression analysis verified that faster pulse,smoking history and lower activated partial thromboplastin time were independent risk factors for Iopatol-induced blood coagulation and kidney damage(P＜0.05).The AUC of the nomogram model of the training set and validation set was 0.726 and 0.779,respectively,with no statistical significance(P＞0.05).There was no significant difference in the results of goodness of fit test between sets(P＞0.05).When the threshold probability of decision curve of training set model was 0.16-0.72,the net benefit rate of patients was＞0.Conclusion When patients have no smoking history,faster pulse and lower partial thromboplastin activation time,the risk of Iopamidol-induced renal damage is significantly increased,and relevant measures should be taken.
万方数据
维普
