Development and validation of a nomogram for renal survival prediction in patients with autosomal dominant polycystic kidney disease

• Main Authors: Xiaomei Wang (Author), Rui Zheng (Author), Zhende Liu (Author), Ling Qi (Author), Liang Gu (Author), Xiaoping Wang (Author), Shan Zhu (Author), Mingyue Zhang (Author), Danya Jia (Author), Zhen Su (Author)
• Format: Book
• Published: Karger Publishers, 2023-06-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 2296-9381; 2296-9357; 10.1159/000531329
• Keywords: Internal medicine; RC31-1245
• Source: Kidney Diseases, Pp 1-1 (2023)

Introduction：Due to the wide variation in the prognosis of autosomal dominant polycystic kidney disease (ADPKD), prediction of risk of renal survival in ADPKD patients is a tough challenge. We aimed to establish a nomogram for the prediction of renal survival in ADPKD patients. Methods: We conducted a retrospective observational cohort study in 263 patients with ADPKD. The patients were randomly assigned to a training set (N= 198) and a validation set (N=65) and demographic and statistical data at baseline were collected. The total kidney volume (TKV) was measured using stereology. A clinical prediction nomogram was developed based on multivariate Cox regression results. The performance and clinical utility of the nomogram were assessed by calibration curves, the concordance index (C-index), and decision curve analysis (DCA). The nomogram was compared with the height-adjusted total kidney volume (htTKV) model by receiver operating characteristic curve analysis, and DCA. Results：The five independent factors used to construct the nomogram for prognosis prediction were age, htTKV, estimated glomerular filtration rate (eGFR), hypertension, and hemoglobin. The calibration curve of predicted probabilities against observed renal survival indicated excellent concordance. The model showed very good discrimination with a C-index of 0.91 (0.83-0.99), and an area under the curve (AUC) of 0.94, which were significantly higher than those of the htTKV model. Similarly, DCA demonstrated that the nomogram had a better net benefit than the htTKV model. Conclusion: The risk prediction nomogram, incorporating easily assessable clinical parameters, was effective for the prediction of renal survival in ADPKD patients. It can be a useful clinical adjunct for clinicians to evaluate the prognosis of ADPKD patients and provide individualized decision-making.