Kidney Res Clin Pract. 2022;41(4):432-441.

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.

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제1저자 박혜인조 · 교신저자 오윤규

PMID 35286789 PMC9346394 DOI 10.23876/j.krcp.21.261 IF 3.5 신장내과

소속 (11)

Department of Internal Medicine, Hallym University Kangnam Sacred Heart Hospital, Seoul, Republic of Korea.
Kidney Research Institute, Hallym University College of Medicine, Seoul, Republic of Korea.
Rehabilitation Medical Research Center, Korea Workers' Compensation and Welfare Service Incheon Hospital, Incheon, Republic of Korea.
Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea.
Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea.
Department of Prevention and Management, Inha University School of Medicine, Incheon, Republic of Korea.
Department of Internal Medicine, Inje University Busan Paik Hospital, Busan, Republic of Korea.
Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.
Department of Internal Medicine, Gachon University Gil Medical Center, Incheon, Republic of Korea.
Department of Internal Medicine, National Medical Center, Seoul, Republic of Korea.
Department of Internal Medicine, SMG-SNU Boramae Medical Center, Seoul, Republic of Korea.

Abstract

BACKGROUND Mayo imaging classification (MIC) is a useful biomarker to predict disease progression in autosomal dominant polycystic kidney disease (ADPKD). This study was performed to validate MIC in the prediction of renal outcome in a prospective Korean ADPKD cohort and evaluate clinical parameters associated with rapid disease progression.

METHODS A total of 178 ADPKD patients were enrolled and prospectively observed for an average duration of 6.2 ± 1.9 years. Rapid progressor was defined as MIC 1C through 1E while slow progressor was defined as 1A through 1B. Renal composite outcome (doubling of serum creatinine, 50% decline of estimated glomerular filtration rate [eGFR], or initiation of renal replacement therapy) as well as the annual percent change of height-adjusted total kidney volume (mHTKV-α), and eGFR decline (mGFR-α) were compared between groups.

RESULTS A total of 110 patients (61.8%) were classified as rapid progressors. These patients were younger and showed a higher proportion of male patients. Rapid progressor was an independent predictor for renal outcome (hazard ratio, 4.09; 95% confidence interval, 1.23-13.54; p = 0.02). The mGFR-α was greater in rapid progressors (-3.58 mL/min per year in 1C, -3.7 in 1D, and -4.52 in 1E) compared with that in slow progressors (-1.54 in 1A and -2.06 in 1B). The mHTKV-α was faster in rapid progressors (5.3% per year in 1C, 9.4% in 1D, and 11.7% in 1E) compared with that in slow progressors (1.2% in 1A and 3.8% in 1B).

CONCLUSION MIC is a good predictive tool to define rapid progressors in Korean ADPKD patients.

Keywords

Autosomal dominant polycystic kidneyComputer-assisted image interpretationGlomerular filtration ratePrognosisRenal insufficiency

Figures

**Figure 1.** Study population. Among 2,238 participants enrolled in the KNOW-CKD (KoreaN Cohort Study for Outcomes in Patients With Chronic Kidney Disease) observational cohort study, a total of 364 patients with ADPKD were identified. Among them, 140 patients without initial htTKV data and 37 patients without follow-up computed tomography exam were excluded from the analysis; in addition, nine patients who received tolvaptan treatment during the observational period were excluded from the analysis. Finally, a total of 178 patients were included in the current study. Among them, 110 patients (61.8%) were classified as rapid progressors and 68 patients (38.2%) were classified as slow progressors according to the original equation of Mayo imaging classification (A = 0 and K = 150). ADPKD, autosomal dominant polycystic kidney disease; CKD, chronic kidney disease; FU, follow-up; htTKV, height-adjusted total kidney volume.

**Figure 2.** Correlation between TKVe and TKVs. (A) TKVe and TKVs strongly correlated with each other. (B) Systematic underestimation or overestimation of TKVe was noticed with a mean difference of 5.3%. SD, standard deviation; TKVe, total kidney volume using ellipsoid methods; TKVs, total kidney volume using stereologic methods.

**Figure 3.** Difference between eHTKV-α at initial and final points of TKV measurement. The eHTKV-α was predicted from htTKV at a certain age. The eHTKV-α at the initial point of TKV measurement and at the final point of TKV measurement was compared in each patient. Using the original equation in MIC (A = 0 and K = 150), the difference between initial and final values was larger than the modified equation from Higashihara’s group (A = 0 and K = 130). (A) Using the original equation (A = 0 and K = 150), 10 of the 178 patients (5.6%) showed more than 1% change in final eHTKV-α from the initial value. (B) Using the modified equation (A = 0 and K = 130), only six out of 178 patients (3.4%) showed more than 1% difference from the initial value. (C, D) We analyzed the associations between eHTKV-α and mHTKV-α, and both equations demonstrated good association between eHTKV-α and mHTKV-α. eHTKV-α, estimated htTKV slope; htTKV, height-adjusted TKV; mHTKV-α, annual percent change of htTKVs; MIC, Mayo imaging classification; SD, standard deviation; TKV, total kidney volume.

**Figure 4.** Renal outcome by the original MIC (A = 0 and K = 150). A total of 46 renal events occurred within 6.2 years. No renal event occurred in patients with MIC 1A. Four events (7.8%) occurred in 1B, 21 events (33.9%) in 1C, 15 events (42.9%) in 1D, and six events (46.2%) in 1E. Rapid progressors defined by original MIC predicted more frequent renal events compared to slow progressors (p < 0.001). MIC, Mayo imaging classification.

그림 출처: PubMed Central 공개 논문 (PMC9346394)

Conflict of interest statement

Conflicts of interest . All authors have no conflicts of interest to declare.

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