Development and Validation of Prediction Models of Adverse Kidney Outcomes in the Population With and Without Diabetes Mellitus

Morgan E Grams; Nigel J Brunskill; Shoshana H Ballew; Yingying Sang; Josef Coresh; Kunihiro Matsushita; Aditya Surapaneni; Samira Bell; Juan J Carrero; Gabriel Chodick; Marie Evans; Hiddo J L Heerspink; Lesley A Inker; Kunitoshi Iseki; Philip A Kalra; H Lester Kirchner; Brian J Lee; Adeera Levin; Rupert W Major; James Medcalf; Girish N Nadkarni; David M J Naimark; Ana C Ricardo; Simon Sawhney; Manish M Sood; Natalie Staplin; Nikita Stempniewicz; Benedicte Stengel; Keiichi Sumida; Jamie P Traynor; Jan van den Brand; Chi-Pang Wen; Mark Woodward; Jae Won Yang; Angela Yee-Moon Wang; Navdeep Tangri; CKD Prognosis Consortium

doi:10.2337/dc22-0698

Development and Validation of Prediction Models of Adverse Kidney Outcomes in the Population With and Without Diabetes Mellitus

Morgan E Grams^* (Corresponding Author), Nigel J Brunskill, Shoshana H Ballew, Yingying Sang, Josef Coresh^* (Corresponding Author), Kunihiro Matsushita, Aditya Surapaneni, Samira Bell, Juan J Carrero, Gabriel Chodick, Marie Evans, Hiddo J L Heerspink, Lesley A Inker, Kunitoshi Iseki, Philip A Kalra, H Lester Kirchner, Brian J Lee, Adeera Levin, Rupert W Major, James MedcalfGirish N Nadkarni, David M J Naimark, Ana C Ricardo, Simon Sawhney, Manish M Sood, Natalie Staplin, Nikita Stempniewicz, Benedicte Stengel, Keiichi Sumida, Jamie P Traynor, Jan van den Brand, Chi-Pang Wen, Mark Woodward, Jae Won Yang, Angela Yee-Moon Wang, Navdeep Tangri, CKD Prognosis Consortium

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

Abstract

OBJECTIVE: To predict adverse kidney outcomes for use in optimizing medical management and clinical trial design.

RESEARCH DESIGN AND METHODS: In this meta-analysis of individual participant data, 43 cohorts (N = 1,621,817) from research studies, electronic medical records, and clinical trials with global representation were separated into development and validation cohorts. Models were developed and validated within strata of diabetes mellitus (presence or absence) and estimated glomerular filtration rate (eGFR; ≥60 or <60 mL/min/1.73 m2) to predict a composite of ≥40% decline in eGFR or kidney failure (i.e., receipt of kidney replacement therapy) over 2-3 years.

RESULTS: There were 17,399 and 24,591 events in development and validation cohorts, respectively. Models predicting ≥40% eGFR decline or kidney failure incorporated age, sex, eGFR, albuminuria, systolic blood pressure, antihypertensive medication use, history of heart failure, coronary heart disease, atrial fibrillation, smoking status, and BMI, and, in those with diabetes, hemoglobin A1c, insulin use, and oral diabetes medication use. The median C-statistic was 0.774 (interquartile range [IQR] = 0.753, 0.782) in the diabetes and higher-eGFR validation cohorts; 0.769 (IQR = 0.758, 0.808) in the diabetes and lower-eGFR validation cohorts; 0.740 (IQR = 0.717, 0.763) in the no diabetes and higher-eGFR validation cohorts; and 0.750 (IQR = 0.731, 0.785) in the no diabetes and lower-eGFR validation cohorts. Incorporating the previous 2-year eGFR slope minimally improved model performance, and then only in the higher-eGFR cohorts.

CONCLUSIONS: Novel prediction equations for a decline of ≥40% in eGFR can be applied successfully for use in the general population in persons with and without diabetes with higher or lower eGFR.

Original language	English
Pages (from-to)	2055-2063
Number of pages	8
Journal	Diabetes Care
Volume	45
Issue number	9
Early online date	20 Jul 2022
DOIs	https://doi.org/10.2337/dc22-0698
Publication status	Published - 1 Sep 2022

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Data from: Development and validation of prediction models of adverse kidney outcomes in the population with and without diabetes mellitus
Grams, M. E. (Creator), Brunskill, N. J. (Creator), Ballew, S. H. (Creator), Sang, Y. (Creator), Coresh, J. (Creator), Matsushita, K. (Creator) & Sawhney, S. (Creator), University of Aberdeen, 2022
DOI: https://doi.org/10.2337/figshare.20061143.
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Grams, M. E., Brunskill, N. J., Ballew, S. H., Sang, Y., Coresh, J., Matsushita, K., Surapaneni, A., Bell, S., Carrero, J. J., Chodick, G., Evans, M., Heerspink, H. J. L., Inker, L. A., Iseki, K., Kalra, P. A., Kirchner, H. L., Lee, B. J., Levin, A., Major, R. W., ... CKD Prognosis Consortium (2022). Development and Validation of Prediction Models of Adverse Kidney Outcomes in the Population With and Without Diabetes Mellitus. Diabetes Care, 45(9), 2055-2063. https://doi.org/10.2337/dc22-0698

Grams, ME, Brunskill, NJ, Ballew, SH, Sang, Y, Coresh, J, Matsushita, K, Surapaneni, A, Bell, S, Carrero, JJ, Chodick, G, Evans, M, Heerspink, HJL, Inker, LA, Iseki, K, Kalra, PA, Kirchner, HL, Lee, BJ, Levin, A, Major, RW, Medcalf, J, Nadkarni, GN, Naimark, DMJ, Ricardo, AC, Sawhney, S, Sood, MM, Staplin, N, Stempniewicz, N, Stengel, B, Sumida, K, Traynor, JP, van den Brand, J, Wen, C-P, Woodward, M, Yang, JW, Wang, AY-M, Tangri, N & CKD Prognosis Consortium 2022, 'Development and Validation of Prediction Models of Adverse Kidney Outcomes in the Population With and Without Diabetes Mellitus', Diabetes Care, vol. 45, no. 9, pp. 2055-2063. https://doi.org/10.2337/dc22-0698

@article{db12ceb0fffd476eb7c4a53ff10e99cf,

title = "Development and Validation of Prediction Models of Adverse Kidney Outcomes in the Population With and Without Diabetes Mellitus",

abstract = "OBJECTIVE: To predict adverse kidney outcomes for use in optimizing medical management and clinical trial design.RESEARCH DESIGN AND METHODS: In this meta-analysis of individual participant data, 43 cohorts (N = 1,621,817) from research studies, electronic medical records, and clinical trials with global representation were separated into development and validation cohorts. Models were developed and validated within strata of diabetes mellitus (presence or absence) and estimated glomerular filtration rate (eGFR; ≥60 or <60 mL/min/1.73 m2) to predict a composite of ≥40% decline in eGFR or kidney failure (i.e., receipt of kidney replacement therapy) over 2-3 years.RESULTS: There were 17,399 and 24,591 events in development and validation cohorts, respectively. Models predicting ≥40% eGFR decline or kidney failure incorporated age, sex, eGFR, albuminuria, systolic blood pressure, antihypertensive medication use, history of heart failure, coronary heart disease, atrial fibrillation, smoking status, and BMI, and, in those with diabetes, hemoglobin A1c, insulin use, and oral diabetes medication use. The median C-statistic was 0.774 (interquartile range [IQR] = 0.753, 0.782) in the diabetes and higher-eGFR validation cohorts; 0.769 (IQR = 0.758, 0.808) in the diabetes and lower-eGFR validation cohorts; 0.740 (IQR = 0.717, 0.763) in the no diabetes and higher-eGFR validation cohorts; and 0.750 (IQR = 0.731, 0.785) in the no diabetes and lower-eGFR validation cohorts. Incorporating the previous 2-year eGFR slope minimally improved model performance, and then only in the higher-eGFR cohorts.CONCLUSIONS: Novel prediction equations for a decline of ≥40% in eGFR can be applied successfully for use in the general population in persons with and without diabetes with higher or lower eGFR.",

author = "Grams, {Morgan E} and Brunskill, {Nigel J} and Ballew, {Shoshana H} and Yingying Sang and Josef Coresh and Kunihiro Matsushita and Aditya Surapaneni and Samira Bell and Carrero, {Juan J} and Gabriel Chodick and Marie Evans and Heerspink, {Hiddo J L} and Inker, {Lesley A} and Kunitoshi Iseki and Kalra, {Philip A} and Kirchner, {H Lester} and Lee, {Brian J} and Adeera Levin and Major, {Rupert W} and James Medcalf and Nadkarni, {Girish N} and Naimark, {David M J} and Ricardo, {Ana C} and Simon Sawhney and Sood, {Manish M} and Natalie Staplin and Nikita Stempniewicz and Benedicte Stengel and Keiichi Sumida and Traynor, {Jamie P} and {van den Brand}, Jan and Chi-Pang Wen and Mark Woodward and Yang, {Jae Won} and Wang, {Angela Yee-Moon} and Navdeep Tangri and {CKD Prognosis Consortium}",

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doi = "10.2337/dc22-0698",

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T1 - Development and Validation of Prediction Models of Adverse Kidney Outcomes in the Population With and Without Diabetes Mellitus

AU - Grams, Morgan E

AU - Brunskill, Nigel J

AU - Ballew, Shoshana H

AU - Sang, Yingying

AU - Coresh, Josef

AU - Matsushita, Kunihiro

AU - Surapaneni, Aditya

AU - Bell, Samira

AU - Carrero, Juan J

AU - Chodick, Gabriel

AU - Evans, Marie

AU - Heerspink, Hiddo J L

AU - Inker, Lesley A

AU - Iseki, Kunitoshi

AU - Kalra, Philip A

AU - Kirchner, H Lester

AU - Lee, Brian J

AU - Levin, Adeera

AU - Major, Rupert W

AU - Medcalf, James

AU - Nadkarni, Girish N

AU - Naimark, David M J

AU - Ricardo, Ana C

AU - Sawhney, Simon

AU - Sood, Manish M

AU - Staplin, Natalie

AU - Stempniewicz, Nikita

AU - Stengel, Benedicte

AU - Sumida, Keiichi

AU - Traynor, Jamie P

AU - van den Brand, Jan

AU - Wen, Chi-Pang

AU - Woodward, Mark

AU - Yang, Jae Won

AU - Wang, Angela Yee-Moon

AU - Tangri, Navdeep

AU - CKD Prognosis Consortium

PY - 2022/9/1

Y1 - 2022/9/1

N2 - OBJECTIVE: To predict adverse kidney outcomes for use in optimizing medical management and clinical trial design.RESEARCH DESIGN AND METHODS: In this meta-analysis of individual participant data, 43 cohorts (N = 1,621,817) from research studies, electronic medical records, and clinical trials with global representation were separated into development and validation cohorts. Models were developed and validated within strata of diabetes mellitus (presence or absence) and estimated glomerular filtration rate (eGFR; ≥60 or <60 mL/min/1.73 m2) to predict a composite of ≥40% decline in eGFR or kidney failure (i.e., receipt of kidney replacement therapy) over 2-3 years.RESULTS: There were 17,399 and 24,591 events in development and validation cohorts, respectively. Models predicting ≥40% eGFR decline or kidney failure incorporated age, sex, eGFR, albuminuria, systolic blood pressure, antihypertensive medication use, history of heart failure, coronary heart disease, atrial fibrillation, smoking status, and BMI, and, in those with diabetes, hemoglobin A1c, insulin use, and oral diabetes medication use. The median C-statistic was 0.774 (interquartile range [IQR] = 0.753, 0.782) in the diabetes and higher-eGFR validation cohorts; 0.769 (IQR = 0.758, 0.808) in the diabetes and lower-eGFR validation cohorts; 0.740 (IQR = 0.717, 0.763) in the no diabetes and higher-eGFR validation cohorts; and 0.750 (IQR = 0.731, 0.785) in the no diabetes and lower-eGFR validation cohorts. Incorporating the previous 2-year eGFR slope minimally improved model performance, and then only in the higher-eGFR cohorts.CONCLUSIONS: Novel prediction equations for a decline of ≥40% in eGFR can be applied successfully for use in the general population in persons with and without diabetes with higher or lower eGFR.

AB - OBJECTIVE: To predict adverse kidney outcomes for use in optimizing medical management and clinical trial design.RESEARCH DESIGN AND METHODS: In this meta-analysis of individual participant data, 43 cohorts (N = 1,621,817) from research studies, electronic medical records, and clinical trials with global representation were separated into development and validation cohorts. Models were developed and validated within strata of diabetes mellitus (presence or absence) and estimated glomerular filtration rate (eGFR; ≥60 or <60 mL/min/1.73 m2) to predict a composite of ≥40% decline in eGFR or kidney failure (i.e., receipt of kidney replacement therapy) over 2-3 years.RESULTS: There were 17,399 and 24,591 events in development and validation cohorts, respectively. Models predicting ≥40% eGFR decline or kidney failure incorporated age, sex, eGFR, albuminuria, systolic blood pressure, antihypertensive medication use, history of heart failure, coronary heart disease, atrial fibrillation, smoking status, and BMI, and, in those with diabetes, hemoglobin A1c, insulin use, and oral diabetes medication use. The median C-statistic was 0.774 (interquartile range [IQR] = 0.753, 0.782) in the diabetes and higher-eGFR validation cohorts; 0.769 (IQR = 0.758, 0.808) in the diabetes and lower-eGFR validation cohorts; 0.740 (IQR = 0.717, 0.763) in the no diabetes and higher-eGFR validation cohorts; and 0.750 (IQR = 0.731, 0.785) in the no diabetes and lower-eGFR validation cohorts. Incorporating the previous 2-year eGFR slope minimally improved model performance, and then only in the higher-eGFR cohorts.CONCLUSIONS: Novel prediction equations for a decline of ≥40% in eGFR can be applied successfully for use in the general population in persons with and without diabetes with higher or lower eGFR.

U2 - 10.2337/dc22-0698

DO - 10.2337/dc22-0698

M3 - Article

C2 - 35856507

VL - 45

SP - 2055

EP - 2063

JO - Diabetes Care

JF - Diabetes Care

SN - 0149-5992

IS - 9

ER -

Development and Validation of Prediction Models of Adverse Kidney Outcomes in the Population With and Without Diabetes Mellitus

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Data from: Development and validation of prediction models of adverse kidney outcomes in the population with and without diabetes mellitus

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