Risk prediction to inform surveillance of chronic kidney disease in the US Healthcare Safety Net: a cohort study.
The capacity of electronic health record (EHR) data to guide targeted surveillance in chronic kidney disease (CKD) is unclear. We sought to leverage EHR data for predicting risk of progressing from CKD to end-stage renal disease (ESRD) to help inform surveillance of CKD among vulnerable patients from the healthcare safety-net.



We conducted a retrospective cohort study of adults (n = 28,779) with CKD who received care within 2 regional safety-net health systems during 1996-2009 in the Western United States. The primary outcomes were progression to ESRD and death as ascertained by linkage with United States Renal Data System and Social Security Administration Death Master files, respectively, through September 29, 2011. We evaluated the performance of 3 models which included demographic, comorbidity and laboratory data to predict progression of CKD to ESRD in conditions commonly targeted for disease management (hypertension, diabetes, chronic viral diseases and severe CKD) using traditional discriminatory criteria (AUC) and recent criteria intended to guide population health management strategies.



Overall, 1730 persons progressed to end-stage renal disease and 7628 died during median follow-up of 6.6 years. Performance of risk models incorporating common EHR variables was highest in hypertension, intermediate in diabetes and chronic viral diseases, and lowest in severe CKD. Surveillance of persons who were in the highest quintile of ESRD risk yielded 83-94 %, 74-95 %, and 75-82 % of cases who progressed to ESRD among patients with hypertension, diabetes and chronic viral diseases, respectively. Similar surveillance yielded 42-71 % of ESRD cases among those with severe CKD. Discrimination in all conditions was universally high (AUC ≥0.80) when evaluated using traditional criteria.



Recently proposed discriminatory criteria account for varying risk distribution and when applied to common clinical conditions may help to inform surveillance of CKD in diverse populations.
Kidney Research Institute, Department of Medicine, University of Washington, Seattle, WA, USA.|Department of Biostatistics, University of Washington, Seattle, WA, USA.
Yuxiang
Y
Xie
Kidney Research Institute, Department of Medicine, University of Washington, Seattle, WA, USA.|Department of Biostatistics, University of Washington, Seattle, WA, USA.
Marlena
M
Maziarz
Division of Nephrology, University of California San Francisco and San Francisco General Hospital, San Francisco, CA, USA.
Delphine S
DS
Tuot
Division of Nephrology, School of Medicine, Stanford University, Palo Alto, CA, USA.
Glenn M
GM
Chertow
Kidney Research Institute, Department of Medicine, University of Washington, Seattle, WA, USA.
Jonathan
J
Himmelfarb
Kidney Research Institute, Department of Medicine, University of Washington, Seattle, WA, USA. ynhall@uw.edu.|Kidney Research Institute, University of Washington, 325 9th Ave, Box 359606, Seattle, WA, 98104, USA. ynhall@uw.edu.
Yoshio N
YN
Hall
D000328:Adult; D000368:Aged; D019540:Area Under Curve; D002908:Chronic Disease; D015897:Comorbidity; D003920:Diabetes Mellitus / Q000453:epidemiology; D018450:Disease Progression; D057286:Electronic Health Records; D005260:Female; D006801:Humans; D006973:Hypertension / Q000453:epidemiology; D007676:Kidney Failure, Chronic / Q000453:epidemiology* / Q000401:mortality; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D011159:Population Surveillance / Q000379:methods*; D012372:ROC Curve; D006435:Renal Dialysis; D051436:Renal Insufficiency, Chronic / Q000401:mortality* / Q000503:physiopathology / Q000628:therapy; D012189:Retrospective Studies; D018570:Risk Assessment / Q000379:methods; D064876:Safety-net Providers; D014481:United States / Q000453:epidemiology; D014777:Virus Diseases / Q000453:epidemiology