Risk prediction to inform surveillance of chronic kidney disease in the US Healthcare Safety Net : A cohort study
(2016) In BMC Nephrology 17(1).- Abstract
Background: 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. Methods: 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,... (More)
Background: 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. Methods: 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. Results: 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. Conclusions: 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.
(Less)
- author
- Xie, Yuxiang ; Maziarz, Marlena LU ; Tuot, Delphine S. ; Chertow, Glenn M. ; Himmelfarb, Jonathan and Hall, Yoshio N.
- publishing date
- 2016-06-08
- type
- Contribution to journal
- publication status
- published
- in
- BMC Nephrology
- volume
- 17
- issue
- 1
- article number
- 57
- publisher
- BioMed Central (BMC)
- external identifiers
-
- scopus:84975842826
- pmid:27276913
- ISSN
- 1471-2369
- DOI
- 10.1186/s12882-016-0272-0
- language
- English
- LU publication?
- no
- id
- 24ef40a7-583d-465b-8322-ff3eb9d4a350
- date added to LUP
- 2019-08-05 11:52:08
- date last changed
- 2024-02-15 18:24:51
@article{24ef40a7-583d-465b-8322-ff3eb9d4a350,
abstract = {{<p>Background: 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. Methods: 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. Results: 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. Conclusions: 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.</p>}},
author = {{Xie, Yuxiang and Maziarz, Marlena and Tuot, Delphine S. and Chertow, Glenn M. and Himmelfarb, Jonathan and Hall, Yoshio N.}},
issn = {{1471-2369}},
language = {{eng}},
month = {{06}},
number = {{1}},
publisher = {{BioMed Central (BMC)}},
series = {{BMC Nephrology}},
title = {{Risk prediction to inform surveillance of chronic kidney disease in the US Healthcare Safety Net : A cohort study}},
url = {{http://dx.doi.org/10.1186/s12882-016-0272-0}},
doi = {{10.1186/s12882-016-0272-0}},
volume = {{17}},
year = {{2016}},
}