Development and External Validation of a Nomogram for Predicting Renal Function Based on Preoperative Data from In-Hospital Patients with Simple Renal Cysts.

Objective To develop and validate a nomogram for predicting renal dysfunction in patients with simple renal cysts (SRCs). Methods We performed a multivariable logistic regression analysis of an in-hospital retrospective cohort of patients with SRCs in the Urology Department of the First Affiliated Hospital of Anhui Medical University. For prognostic model development, 386 patients with SRCs were enrolled from January 2016 to December 2018. External validation was performed in 46 patients with SRCs from January 2019 to April 2019. The primary outcome was renal dysfunction. Results Patients were divided into normal or abnormal estimated glomerular filtration rate groups (293 vs. 93) based on the cut-off value of 90 mL/minute/1.73 m2. Logistical regression analysis determined that age, haemoglobin, globulin, and creatinine might be associated with renal dysfunction, and a novel nomogram was established. Calibration curves showed that the true prediction rate was 77.42%, and decision curve analysis revealed that the nomogram was more effective with threshold probabilities ranging from 0.1 to 0.8. The area under the curves were 0.829, 0.752, and 0.888 in the overall training, internal, and external validation cohorts, respectively. Conclusions We established a nomogram to predict the probability of developing renal dysfunction in patients with SRCs.