Development and validation of a predictive model for end-stage renal disease risk prediction model in patients with autosomal dominant polycystic kidney disease

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https://storage.unitedwebnetwork.com/files/1099/5dfd720a479add5348b0b2c83459529c.pdf

Abstract Title *

Development and validation of a predictive model for end-stage renal disease risk prediction model in patients with autosomal dominant polycystic kidney disease

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Co-author 1

Su Hyun Song sudang_@naver.com Chonnam National Medical School Internal Medicine Gwangju Korea (Republic of) *

Co-author 2

Sang Heon Suh medssh19@daum.net Chonnam National Medical School Internal Medicine Gwangju Korea (Republic of) -

Co-author 3

Hong Sang Choi hongsang38@naver.com Chonnam National Medical School Internal Medicine Gwangju Korea (Republic of) -

Co-author 4

Chang Seong Kim laminion0820@daum.net Chonnam National Medical School Internal Medicine Gwangju Korea (Republic of) -

Co-author 5

Seong Kwon Ma drmsk@hanmail.net Chonnam National Medical School Internal Medicine Gwangju Korea (Republic of) -

Co-author 6

Soo Wan Kim kdksw@hanmail.net Chonnam National Medical School Internal Medicine Gwangju Korea (Republic of) -

Co-author 7

Eun Hui Bae baedak76@gmail.com Chonnam National Medical School Internal Medicine Gwangju Korea (Republic of) -

Co-author 8

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Co-author 10

Co-author 11

Co-author 12

Co-author 13

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Introduction

Autosomal dominant polycystic kidney disease (ADPKD) is the fourth leading cause of end-stage renal disease (ESRD) not only Korea but also worldwide. Therefore, predicting disease progression and the risk of progression of ESRD is of great clinical significance. This study aimed to develop a risk prediction model for progression to ESRD in Korea patients with ADPKD.

Methods

Between January 2015 and December 2024, we conducted a retrospective observational cohort study involving 487 Korean patients with ADPKD. The patients were randomly allocated to a training set and a validation set. A risk prediction model for ESRD was developed using a stepwise-selected model from the multivariable Cox regression analysis.

Results

During the follow-up period, 37 renal outcome events (ESRD) occurred (7.6%). Seven independent factors for prognosis prediction were age, htTKV, estimated glomerular filtration rate, hypertension, uric acid, proteinuria, and hemoglobin. The calibration curve of predicted probabilities against observed renal survival demonstrated excellent concordance. The model showed very good discrimination, with an area under the curve of 0.96 in year 1 and 0.90 in year 2.

Conclusion

The stepwise-selected model was effective for the prediction of renal survival in ADPKD patients. This model can support a useful clinical adjunct for evaluating the prognosis of patient with ADPKD and has the potential to support individualized decision-making in both research and clinical practice.

Kewords