Establishment of nomogram model of acute kidney injury risk prediction based on clinical database / 中华肾脏病杂志

ABSTRACT

Objective:To construct the risk prediction nomogram model of acute kidney injury (AKI) with R language and traditional statistical methods based on the large sample clinical database, and verify the accuracy of the model.Methods:It was a a retrospective case control study. The patients who met the diagnostic criteria of AKI in Tongji Hospital of Tongji University from January 1 to December 31, 2021 were screened in the clinical database, and the patients with monitored serum creatinine within 48 hours but without AKI were included as the control group. The demographic data, disease history, surgical history, medication history and laboratory test data were collected to screen the risk factors of AKI in clinic.Firstly, based on multivariate logistic regression analysis and forward stepwise logistic regression analysis, the selected risk factors were included to construct the nomogram model. At the same time, cross validation, bootstrap validation and randomly split sample validation were used for internal verification, and clinical data of patients in the sane hospital after one year (January to December, 2022) were collected for external verification. The receiver-operating characteristic curve was used to determine the discrimination of the model, and calibration curve and decision curve analysis were carried out to evaluate the accuracy and clinical net benefit, respectively.Results:A total of 5 671 patients were enrolled in the study, with 1 884 AKI patients (33.2%) and 3 787 non-AKI patients (66.7%). Compared with non-AKI group, age, and proportions of surgical history, renal replacement therapy, hypertension, diabetes, cerebrovascular accident,chronic kidney disease, drug use histories and mortality in AKI group were all higher (all P<0.05). Multivariate logistic regression analysis showed that the independent influencing factors of AKI were surgical history, hypertension, cerebrovascular accident, diabetes, chronic kidney disease, diuretics, nitroglycerin, antidiuretic hormones, body temperature, serum creatinine, C-reactive protein, red blood cells, white blood cells, D-dimer, myoglobin, hemoglobin, blood urea nitrogen, brain natriuretic peptide, aspartate aminotransferase, alanine aminotransferase, triacylglycerol, lactate dehydrogenase, total bilirubin, activated partial thromboplastin time, blood uric acid and potassium ion (all P<0.05). Finally, the predictive factors in the nomogram were determined by forward stepwise logistic regression analysis, including chronic kidney disease, hypertension, myoglobin, serum creatinine and blood urea nitrogen, and the area under the curve of the prediction nomogram model was 0.926 [95% CI 0.918-0.933, P<0.001]. The calibration curve showed that the calibration effect of nomogram was good ( P>0.05). The decision curve showed that when the risk threshold of nomogram model was more than 0.04, the model construction was useful in clinic. In addition, the area under the curve of receiver-operating characteristic curve predicted by nomograph model in external validation set was 0.876 (95% CI 0.865-0.886), which indicated that nomograph model had a high discrimination degree. Conclusion:A nomogram model for predicting the occurrence of AKI is established successfully, which is helpful for clinicians to find high-risk AKI patients early, intervene in time and improve the prognosis.