Risk prediction to inform surveillance of chronic kidney disease in the US Healthcare Safety Net: a cohort study. Issue 1 (December 2016)
- Record Type:
- Journal Article
- Title:
- Risk prediction to inform surveillance of chronic kidney disease in the US Healthcare Safety Net: a cohort study. Issue 1 (December 2016)
- Main Title:
- Risk prediction to inform surveillance of chronic kidney disease in the US Healthcare Safety Net: a cohort study
- Authors:
- Xie, Yuxiang
Maziarz, Marlena
Tuot, Delphine
Chertow, Glenn
Himmelfarb, Jonathan
Hall, Yoshio - Abstract:
- 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, 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 ESRDAbstract 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. … (more)
- Is Part Of:
- BMC nephrology. Volume 17:Issue 1(2016)
- Journal:
- BMC nephrology
- Issue:
- Volume 17:Issue 1(2016)
- Issue Display:
- Volume 17, Issue 1 (2016)
- Year:
- 2016
- Volume:
- 17
- Issue:
- 1
- Issue Sort Value:
- 2016-0017-0001-0000
- Page Start:
- 1
- Page End:
- 10
- Publication Date:
- 2016-12
- Subjects:
- Kidneys -- Diseases -- Periodicals
616.61005 - Journal URLs:
- http://www.biomedcentral.com/bmcnephrol/ ↗
http://www.pubmedcentral.nih.gov/tocrender.fcgi?journal=47 ↗
http://link.springer.com/ ↗ - DOI:
- 10.1186/s12882-016-0272-0 ↗
- Languages:
- English
- ISSNs:
- 1471-2369
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - BLDSS-3PM
British Library STI - ELD Digital store - Ingest File:
- 10042.xml