Mayo imaging classification is a good predictor of rapid progress among Korean patients with autosomal dominant polycystic kidney disease: results from the KNOW-CKD study

Park, Hayne Cho; Hong, Yeji; Yeon, Jeong-Heum; Ryu, Hyunjin; Kim, Yong Chul; Lee, Joongyub; Kim, Yeong Hoon; Chae, Dong Wan; Chung, Wookyung; Ahn, Curie; Oh, Kook Hwan; Oh, Yun Kyu

doi:10.23876/j.krcp.21.261

Cited by 7 publications

(5 citation statements)

References 28 publications

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“…Co. Ltd.) to identify the rapid progressors among ADPKD patients. Mayo imaging classification is currently the best prediction model for selecting rapid progressors among ADPKD patients and is also a good predictor for Korean ADPKD population [ 10 ]. The SaaS included uploading of Digital Imaging and Communications in Medicine (DICOM) images for TKV measurement, and other variables including age, sex, race, height, serum creatinine, and genotype.…”

Section: Methodsmentioning

confidence: 99%

Feasibility of artificial intelligence-based decision supporting system in tolvaptan prescription for autosomal dominant polycystic kidney disease

et al. 2023

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Purpose Total kidney volume (TKV) measurement is crucial for selecting treatment candidates in autosomal dominant polycystic kidney disease (ADPKD). We developed and investigated the performance of fully-automated 3D-volumetry model and applied it to software as a service (SaaS) for clinical support on tolvaptan prescription in ADPKD patients. Materials and Methods Computed tomography scans of ADPKD patients taken between January 2000 and June 2022 were acquired from seven institutions. The quality of the images was manually reviewed in advance. The acquired dataset was split into training, validation, and test datasets at a ratio of 8.5:1:0.5. Convolutional, neural network-based automatic segmentation model was trained to obtain 3D segment mask for TKV measurement. The algorithm consisted of three steps: data preprocessing, ADPKD area extraction, and post-processing. After performance validation with the Dice score, 3D-volumetry model was applied to SaaS which is based on Mayo imaging classification for ADPKD. Results A total of 753 cases with 95,117 slices were included. The differences between the ground-truth ADPKD kidney mask and the predicted ADPKD kidney mask were negligible, with intersection over union >0.95. The post-process filter successfully removed false alarms. The test-set performance was homogeneously equal and the Dice score of the model was 0.971; after post-processing, it improved to 0.979. The SaaS calculated TKV from uploaded Digital Imaging and Communications in Medicine images and classified patients according to height-adjusted TKV for age. Conclusions Our artificial intelligence-3D volumetry model exhibited effective, feasible, and non-inferior performance compared with that of human experts and successfully predicted the rapid ADPKD progressor.

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Section: Methodsmentioning

confidence: 99%

Feasibility of artificial intelligence-based decision supporting system in tolvaptan prescription for autosomal dominant polycystic kidney disease

et al. 2023

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“…In addition, the assessment of age and height-adjusted TKV could support the classification of kidney growth curves. It is usually divided into five categories from 1A to 1E based on the mayo classification, and patients with 1C-1E are generally regarded as a rapid progressors [15][16][17]. In this regard, the Tolvaptan Efficacy and Safety in Management of ADPKD and Its Outcomes (TEMPO) 3:4 study, a landmark trial of tolvaptan, aimed to evaluate the efficacy of tolvaptan with the annual rate of change in TKV and estimated glomerular filtration rate (eGFR) slope.…”

Section: Impact Of Tolvaptan On Total Kidney Volume (Tkv)mentioning

confidence: 99%

Recent updates in therapeutic approach using tolvaptan for autosomal dominant polycystic kidney disease

Kim¹,

Han²

2023

Korean J Intern Med

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As a genetic disease, there has been a long-standing effort to identify therapeutic options for autosomal dominant polycystic kidney disease (ADPKD). Following the development of tolvaptan, a vasopressin 2 receptor antagonist, the treatment strategy for ADPKD patients with rapid disease progression has been changed with a disease-targeted approach. Tolvaptan showed significant efficacy in preserving kidney function and reducing the total kidney volume (TKV) growth rate. These effects were especially pronounced in patients with more severe clinical phenotypes, such as higher TKV and rapidly declining kidney function. Despite the therapeutic effects of tolvaptan, aquaretic symptoms are unavoidable side effects related to the mechanism of the drug and are also directly related to the quality of life. A shared decision-making process could be a valuable strategy for reducing the incidence of side effects and improving medication adherence. Herein, we aimed to review overall clinical trials for applying tolvaptan and suggest important factors during the shared decision-making process.

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“…7 MIC categories are associated with kidney function decline and kidney failure in several ADPKD cohorts, including Tolvaptan Efficacy and Safety in Management of Autosomal Dominant Polycystic Kidney Disease and its Outcomes, 8 HALT Progression of Polycystic Kidney Disease-PKD, 9,10 and KoreaN Cohort Study for Outcome in Patients With CKD. 11 In the original publication, the MIC was externally validated in a relatively small cohort of 173 participants of the Consortium for Radiologic Imaging Studies in Polycystic Kidney Disease study from the same region as the development cohort. 7 The widespread use of this classification makes it essential to validate its predictive capability.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Validation of the Mayo Imaging Classification System for Predicting Kidney Outcomes in ADPKD

Bais,

Geertsema,

Knol

et al. 2024

CJASN

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Background: The Mayo Imaging Classification (MIC) was developed to predict the rate of disease progression in patients with autosomal dominant polycystic kidney disease (ADPKD). This study aimed to validate its ability to predict kidney outcomes in a large multicenter ADPKD cohort. Methods: Included were patients with ≥1 height-adjusted total kidney volume (HtTKV) measurement and ≥3 estimated glomerular filtration rate (eGFR) values during ≥1 year follow-up. Mayo HtTKV class stability, kidney growth rates and eGFR decline rates were calculated. The observed eGFR decline was compared to predictions from the Mayo Clinic future eGFR equation. The future eGFR prediction equation was also tested for non-linear eGFR decline. Kaplan-Meier survival analysis and Cox regression models were used to assess time to kidney failure using Mayo HtTKV class as a predictor variable. Results: We analyzed 618 patients with a mean age of 47 ± 11 years and mean eGFR of 64 ± 25 ml/min/1.73m2 at baseline. Most patients (82%) remained in their baseline Mayo HtTKV class. During a mean follow-up of 5.1 ± 2.2 years, the mean TKV growth rates and eGFR decline were 5.33 ± 3.90 %/year and -3.31 ± 2.53 ml/min/1.73m2/year, respectively. Kidney growth and eGFR decline showed considerable overlap between the classes. The observed annual eGFR decline was not significantly different from the predicted values for classes 1A, 1B, 1C, and 1D but significantly slower for class 1E. This was also observed in patients aged <40 and >60 years and those with PKD2 mutations. A polynomial model allowing non-linear eGFR decline provided more accurate slope predictions. Ninety-seven patients (16%) developed kidney failure during follow-up. The classification predicted the development of kidney failure, although the sensitivity and positive predictive values were limited. Conclusion: The MIC demonstrated acceptable stability and generally predicted kidney failure and eGFR decline rate. However, there was marked interindividual variability in the rate of disease progression within each class.

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Mayo imaging classification is a good predictor of rapid progress among Korean patients with autosomal dominant polycystic kidney disease: results from the KNOW-CKD study

Cited by 7 publications

References 28 publications

Feasibility of artificial intelligence-based decision supporting system in tolvaptan prescription for autosomal dominant polycystic kidney disease

Feasibility of artificial intelligence-based decision supporting system in tolvaptan prescription for autosomal dominant polycystic kidney disease

Recent updates in therapeutic approach using tolvaptan for autosomal dominant polycystic kidney disease

Validation of the Mayo Imaging Classification System for Predicting Kidney Outcomes in ADPKD

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