Establishment and Validation of Risk Nomogram for Individualized Prediction of Acute Kidney Injury in Patients with Sepsis
Objective To establish and verify a nomogram model for early prediction of acute kidney injury(AKI)in patients with sepsis.Methods A total of 162 patients with sepsis admitted to the Second Hospital of Lanzhou University from January 2020 to Decem-ber 2021 were selected as the modeling group,A total of 93 patients with sepsis admitted from January 2022 to December 2022 were se-lected as the verification group.The patients in the modeling group were divided into the sepsis with AKI group(n=52)and sepsis with-out AKI group(n=110).Logistic regression analysis was used to screen the risk factors affecting the occurrence of AKI in patients with sepsis in the modeling group;R software was used to construct a nomogram model for predicting the occurrence of AKI in patients with sepsis.Results Mechanical ventilation,hospitalization time of intensive care unit(ICU),procalcitonin(PCT)level,interleukin-6(IL-6)level,and tumor necrosis factor-α(TNF-α)level,and blood lactate level showed statistically significant differences in sepsis with AKI and sepsis without AKI(P＜0.05).Multivariate Logistic regression analysis showed that mechanical ventilation,hospitalization time of ICU≥3days,and high PCT level,and high blood lactate level were independent risk factors for the occurrence of AKI in patients with sepsis(P＜0.05).Based on the risk factors,a nomogram model was established with R software,and the area under the receiver operating characteristic(ROC)curve was 0.830 in the modeling group,and 0.845 in the validation group.According to the results of the nomogram model,the calibration curves were in general agreement between the predicted values and actual values.The results of the Hos-mer-Lemeshow goodness-of-fit test showed that for the modelling group x2=7.340,P=0.501,and for the validation group x2=7.758,P=0.458.Conclusion The construction of a risk nomogram model for predicting the occurrence of AKI in patients with sepsis in the ICU is of great clinical value,and can be used in the clinic to guide individualised treatment.
SepsisAcute kidney injuryRisk factorsPrediction model
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