To develop and validate a nomogram model to predict the occurrence of acute kidney disease (AKD) after nephrectomy.
A retrospective cohort including 378 patients with renal cell carcinoma (RCC) who had undergone radical or partial nephrectomy between March 2013 and December 2017 at the First Affiliated Hospital of Zhengzhou University was analyzed. Of these, patients who had undergone surgery in an earlier period of time formed the training cohort (n=265) for nomogram development, and those who had undergone surgery thereafter formed the validation cohort (n=113) to confirm the model's performance. The incidence rate of AKD was measured. Univariate and multivariate logistics regression analysis was used to estimate the independent risk factors associated with AKD. The independent risk factors were incorporated into the nomogram. The accuracy and utility of the nomogram were evaluated by calibration curve and decision curve analysis, respectively.
Overall, AKD occurred in 27.5% and 28.3% of patients in the training and validation cohorts, separately. The final nomogram included surgery approach, Charlson comorbidity index (CCI), and the decrement of eGFR. This model achieved good concordance indexes of 0.78 (95% CI=0.71-0.84) and 0.76 (95% CI=0.67-0.86) in the training and validation cohorts, respectively. The calibration curves and decision curve analysis (DCA) demonstrated the accuracy and the clinical usefulness of the proposed nomogram, separately.
The nomogram accurately predicts AKD after nephrectomy in patients with RCC. The risk for patients' progress into AKD can be determined, which is useful in guiding clinical decisions.
Cancer management and research. 2020 Nov 17*** epublish ***
Xiao-Ying Hu, Dong-Wei Liu, Ying-Jin Qiao, Xuan Zheng, Jia-Yu Duan, Shao-Kang Pan, Zhang-Sou Liu
Department of Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, People's Republic of China., Department of Radiation Oncology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences (CAMS) and Peking Union Medical College (PUMC), Beijing, 100021, People's Republic of China.