Kyongtae T Bae1, Tiange Shi2, Cheng Tao3, Alan S L Yu4, Vicente E Torres5, Ronald D Perrone6, Arlene B Chapman7, Godela Brosnahan8, Theodore I Steinman9, William E Braun10, Avantika Srivastava2, Maria V Irazabal5, Kaleab Z Abebe11, Peter C Harris5, Douglas P Landsittel2. 1. Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania baek@upmc.edu. 2. Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania. 3. Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania. 4. Division of Nephrology and Hypertension, Department of Internal Medicine, and Jared Grantham Kidney Institute, Kansas University Medical Center, Kansas City, Kansas. 5. Division of Nephrology and Hypertension, Mayo Clinic College of Medicine, Rochester, Minnesota. 6. Division of Nephrology, Tufts University Medical Center, Boston, Massachusetts. 7. Section of Nephrology, University of Chicago School of Medicine, Chicago, Illinois. 8. Division of Renal Diseases and Hypertension, University of Colorado Denver Anschutz Medical Campus, Aurora, Colorado. 9. Renal Division, Beth Israel Deaconess Medical Center, Boston, Massachusetts. 10. Department of Nephrology and Hypertension, Cleveland Clinic, Cleveland, Ohio. 11. Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania.
Abstract
BACKGROUND: The Mayo Clinic imaging classification of autosomal dominant polycystic kidney disease (ADPKD) uses height-adjusted total kidney volume (htTKV) and age to identify patients at highest risk for disease progression. However, this classification applies only to patients with typical diffuse cystic disease (class 1). Because htTKV poorly predicts eGFR decline for the 5%-10% of patients with atypical morphology (class 2), imaging-based risk modeling remains unresolved. METHODS: Of 558 adults with ADPKD in the HALT-A study, we identified 25 patients of class 2A with prominent exophytic cysts (class 2Ae) and 43 patients of class 1 with prominent exophytic cysts; we recalculated their htTKVs to exclude exophytic cysts. Using original and recalculated htTKVs in association with imaging classification in logistic and mixed linear models, we compared predictions for developing CKD stage 3 and for eGFR trajectory. RESULTS: Using recalculated htTKVs increased specificity for developing CKD stage 3 in all participants from 82.6% to 84.2% after adjustment for baseline age, eGFR, BMI, sex, and race. The predicted proportion of class 2Ae patients developing CKD stage 3 using a cutoff of 0.5 for predicting case status was better calibrated to the observed value of 13.0% with recalculated htTKVs (45.5%) versus original htTKVs (63.6%). Using recalculated htTKVs reduced the mean paired difference between predicted and observed eGFR from 17.6 (using original htTKVs) to 4.0 ml/min per 1.73 m2 for class 2Ae, and from -1.7 (using original htTKVs) to 0.1 ml/min per 1.73 m2 for class 1. CONCLUSIONS: Use of a recalculated htTKV measure that excludes prominent exophytic cysts facilitates inclusion of class 2 patients and reclassification of class 1 patients in the Mayo classification model.
BACKGROUND: The Mayo Clinic imaging classification of autosomal dominant polycystic kidney disease (ADPKD) uses height-adjusted total kidney volume (htTKV) and age to identify patients at highest risk for disease progression. However, this classification applies only to patients with typical diffuse cystic disease (class 1). Because htTKV poorly predicts eGFR decline for the 5%-10% of patients with atypical morphology (class 2), imaging-based risk modeling remains unresolved. METHODS: Of 558 adults with ADPKD in the HALT-A study, we identified 25 patients of class 2A with prominent exophytic cysts (class 2Ae) and 43 patients of class 1 with prominent exophytic cysts; we recalculated their htTKVs to exclude exophytic cysts. Using original and recalculated htTKVs in association with imaging classification in logistic and mixed linear models, we compared predictions for developing CKD stage 3 and for eGFR trajectory. RESULTS: Using recalculated htTKVs increased specificity for developing CKD stage 3 in all participants from 82.6% to 84.2% after adjustment for baseline age, eGFR, BMI, sex, and race. The predicted proportion of class 2Ae patients developing CKD stage 3 using a cutoff of 0.5 for predicting case status was better calibrated to the observed value of 13.0% with recalculated htTKVs (45.5%) versus original htTKVs (63.6%). Using recalculated htTKVs reduced the mean paired difference between predicted and observed eGFR from 17.6 (using original htTKVs) to 4.0 ml/min per 1.73 m2 for class 2Ae, and from -1.7 (using original htTKVs) to 0.1 ml/min per 1.73 m2 for class 1. CONCLUSIONS: Use of a recalculated htTKV measure that excludes prominent exophytic cysts facilitates inclusion of class 2 patients and reclassification of class 1 patients in the Mayo classification model.
Authors: Robert W Schrier; Kaleab Z Abebe; Ronald D Perrone; Vicente E Torres; William E Braun; Theodore I Steinman; Franz T Winklhofer; Godela Brosnahan; Peter G Czarnecki; Marie C Hogan; Dana C Miskulin; Frederic F Rahbari-Oskoui; Jared J Grantham; Peter C Harris; Michael F Flessner; Kyongtae T Bae; Charity G Moore; Arlene B Chapman Journal: N Engl J Med Date: 2014-11-15 Impact factor: 91.245
Authors: Alan S L Yu; Chengli Shen; Douglas P Landsittel; Peter C Harris; Vicente E Torres; Michal Mrug; Kyongtae T Bae; Jared J Grantham; Frederic F Rahbari-Oskoui; Michael F Flessner; William M Bennett; Arlene B Chapman Journal: Kidney Int Date: 2017-12-28 Impact factor: 10.612
Authors: María V Irazabal; Laureano J Rangel; Eric J Bergstralh; Sara L Osborn; Amber J Harmon; Jamie L Sundsbak; Kyongtae T Bae; Arlene B Chapman; Jared J Grantham; Michal Mrug; Marie C Hogan; Ziad M El-Zoghby; Peter C Harris; Bradley J Erickson; Bernard F King; Vicente E Torres Journal: J Am Soc Nephrol Date: 2014-06-05 Impact factor: 10.121
Authors: Ahsan Alam; Neera K Dahl; Joshua H Lipschutz; Sandro Rossetti; Patricia Smith; Daniel Sapir; Jordan Weinstein; Philip McFarlane; Daniel G Bichet Journal: Am J Kidney Dis Date: 2015-05-07 Impact factor: 8.860
Authors: Jared J Grantham; Vicente E Torres; Arlene B Chapman; Lisa M Guay-Woodford; Kyongtae T Bae; Bernard F King; Louis H Wetzel; Deborah A Baumgarten; Phillip J Kenney; Peter C Harris; Saulo Klahr; William M Bennett; Gladys N Hirschman; Catherine M Meyers; Xiaoling Zhang; Fang Zhu; John P Miller Journal: N Engl J Med Date: 2006-05-18 Impact factor: 91.245
Authors: Arlene B Chapman; Vicente E Torres; Ronald D Perrone; Theodore I Steinman; Kyongtae T Bae; J Philip Miller; Dana C Miskulin; Frederic Rahbari Oskoui; Amirali Masoumi; Marie C Hogan; Franz T Winklhofer; William Braun; Paul A Thompson; Catherine M Meyers; Cass Kelleher; Robert W Schrier Journal: Clin J Am Soc Nephrol Date: 2010-01 Impact factor: 8.237
Authors: Kyongtae T Bae; Fang Zhu; Arlene B Chapman; Vicente E Torres; Jared J Grantham; Lisa M Guay-Woodford; Deborah A Baumgarten; Bernard F King; Louis H Wetzel; Philip J Kenney; Marijn E Brummer; William M Bennett; Saulo Klahr; Catherine M Meyers; Xiaoling Zhang; Paul A Thompson; J Philip Miller Journal: Clin J Am Soc Nephrol Date: 2005-10-26 Impact factor: 8.237
Authors: Kyongtae T Bae; Cheng Tao; Robert Feldman; Alan S L Yu; Vicente E Torres; Ronald D Perrone; Arlene B Chapman; Godela Brosnahan; Theodore I Steinman; William E Braun; Michal Mrug; William M Bennett; Peter C Harris; Avantika Srivastava; Douglas P Landsittel; Kaleab Z Abebe Journal: Clin J Am Soc Nephrol Date: 2022-02-25 Impact factor: 10.614
Authors: Stephen L Seliger; Terry Watnick; Andrew D Althouse; Ronald D Perrone; Kaleab Z Abebe; Kenneth R Hallows; Dana C Miskulin; Kyongtae T Bae Journal: Kidney360 Date: 2020-12-31
Authors: Adriana V Gregory; Deema A Anaam; Andrew J Vercnocke; Marie E Edwards; Vicente E Torres; Peter C Harris; Bradley J Erickson; Timothy L Kline Journal: J Digit Imaging Date: 2021-04-05 Impact factor: 4.056