Validation of the Mayo Imaging Classification System for Predicting Kidney Outcomes in ADPKD
Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Society of Nephrology..
BACKGROUND: The Mayo Imaging Classification was developed to predict the rate of disease progression in patients with autosomal dominant polycystic kidney disease. This study aimed to validate its ability to predict kidney outcomes in a large multicenter autosomal dominant polycystic kidney disease cohort.
METHODS: Included were patients with ≥1 height-adjusted total kidney volume (HtTKV) measurement and ≥3 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 with predictions from the Mayo Clinic future eGFR equation. The future eGFR prediction equation was also tested for nonlinear 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 per 1.73 m 2 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 total kidney volume growth rates and eGFR decline were 5.33%±3.90%/yr and -3.31±2.53 ml/min per 1.73 m 2 per 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 younger than 40 years and older than 60 years and those with PKD2 mutations. A polynomial model allowing nonlinear 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.
CONCLUSIONS: The Mayo Imaging Classification 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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - year:2024 |
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| Enthalten in: |
Clinical journal of the American Society of Nephrology : CJASN - (2024) vom: 26. Feb. |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Bais, Thomas [VerfasserIn] |
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Date Revised 04.04.2024 published: Print-Electronic Citation Status Publisher |
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| doi: |
10.2215/CJN.0000000000000427 |
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| Förderinstitution / Projekttitel: |
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| 245 | 1 | 0 | |a Validation of the Mayo Imaging Classification System for Predicting Kidney Outcomes in ADPKD |
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| 520 | |a Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Society of Nephrology. | ||
| 520 | |a BACKGROUND: The Mayo Imaging Classification was developed to predict the rate of disease progression in patients with autosomal dominant polycystic kidney disease. This study aimed to validate its ability to predict kidney outcomes in a large multicenter autosomal dominant polycystic kidney disease cohort | ||
| 520 | |a METHODS: Included were patients with ≥1 height-adjusted total kidney volume (HtTKV) measurement and ≥3 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 with predictions from the Mayo Clinic future eGFR equation. The future eGFR prediction equation was also tested for nonlinear 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 | ||
| 520 | |a RESULTS: We analyzed 618 patients with a mean age of 47±11 years and mean eGFR of 64±25 ml/min per 1.73 m 2 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 total kidney volume growth rates and eGFR decline were 5.33%±3.90%/yr and -3.31±2.53 ml/min per 1.73 m 2 per 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 younger than 40 years and older than 60 years and those with PKD2 mutations. A polynomial model allowing nonlinear 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 | ||
| 520 | |a CONCLUSIONS: The Mayo Imaging Classification 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 | ||
| 650 | 4 | |a Journal Article | |
| 700 | 1 | |a Geertsema, Paul |e verfasserin |4 aut | |
| 700 | 1 | |a Knol, Martine G E |e verfasserin |4 aut | |
| 700 | 1 | |a van Gastel, Maatje D A |e verfasserin |4 aut | |
| 700 | 1 | |a de Haas, Robbert J |e verfasserin |4 aut | |
| 700 | 1 | |a Meijer, Esther |e verfasserin |4 aut | |
| 700 | 1 | |a Gansevoort, Ron T |e verfasserin |4 aut | |
| 700 | 0 | |a DIPAK Consortium |e verfasserin |4 aut | |
| 700 | 1 | |a Gansevoort, R T |e investigator |4 oth | |
| 700 | 1 | |a Drenth, J P H |e investigator |4 oth | |
| 700 | 1 | |a Peters, D J M |e investigator |4 oth | |
| 700 | 1 | |a Zietse, R |e investigator |4 oth | |
| 700 | 1 | |a Salih, M |e investigator |4 oth | |
| 700 | 1 | |a de Fijter, J W |e investigator |4 oth | |
| 700 | 1 | |a Nijenhuis, T |e investigator |4 oth | |
| 700 | 1 | |a Meijer, E |e investigator |4 oth | |
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