Distinguishing Among Causes of Death for Patients with Kidney Failure on Hemodialysis

Key Points We found poor ability to distinguish among different causes of death using clinical information in the 30 days before death for patients on hemodialysis. We found that models of different causes of death identified similar associated clinical factors. Given the lack of clear phenotypic differences, this study questions the usefulness of cause of death in research in dialysis. Background Patients treated with maintenance hemodialysis are at high risk of death from a variety of causes. Methods To identify markers ( i.e ., risk phenotypes) that distinguish among causes of death, we used dialysis electronic health record data for a cohort of adults treated with maintenance in-center hemodialysis who died between 2003 and 2016 ( n =19,793). Patients were linked to the United States Renal Data System (USRDS) Files. We classified USRDS-reported causes of death into five categories: sudden cardiac death, nonsudden cardiac death cardiovascular death, infection, others, and unknown. A subcohort was linked to the National Death Index with similar categories defined. We used ensemble classification trees to discriminate among causes using demographics, vital signs, laboratory measures, health service utilization, and comorbidity claims from 30 days before death. We then created nested case-control populations for each cause of death and used ridge logistic regression to evaluate clinical risk markers that associate with distinct causes. Results The area under the receiver operating characteristic curves from ensemble classification trees were all between 0.59 and 0.70, suggesting minimal ability to distinguish among causes using clinical risk markers. Model coefficients were similar and highly correlated across different cause of death models ( i.e ., 0.87–0.94). This suggests that most clinical risk markers are shared across causes without distinct risk phenotypes. Conclusions We conclude that different causes of death may share similar clinical risk markers in the setting of kidney failure or that the causes of death attributed on USRDS or National Death Index forms are not precise.

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Publication Details

Journal
Kidney360
Published
2024-12-16
DOI
https://doi.org/10.34067/kid.0000000681
Citations
5
Primary Topic
Dialysis and Renal Disease Management
Type
article
Field-Weighted Citation Impact
1.70

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article

Distinguishing Among Causes of Death for Patients with Kidney Failure on Hemodialysis

Daniel E. Weiner, Julia J. Scialla, Michelle Tran, Tariq Shafi et al.
5 citations
Kidney360
Dialysis and Renal Disease Management
1.70
article

Distinguishing Among Causes of Death for Patients with Kidney Failure on Hemodialysis

Daniel E. Weiner, Julia J. Scialla, Michelle Tran, Tariq Shafi, Benjamin Alan Goldstein, Patti L. Ephraim, Jonathan Wilson, Chun Anna Xu
article en
5 citations

Abstract

Key Points We found poor ability to distinguish among different causes of death using clinical information in the 30 days before death for patients on hemodialysis. We found that models of different causes of death identified similar associated clinical factors. Given the lack of clear phenotypic differences, this study questions the usefulness of cause of death in research in dialysis. Background Patients treated with maintenance hemodialysis are at high risk of death from a variety of causes. Methods To identify markers ( i.e ., risk phenotypes) that distinguish among causes of death, we used dialysis electronic health record data for a cohort of adults treated with maintenance in-center hemodialysis who died between 2003 and 2016 ( n =19,793). Patients were linked to the United States Renal Data System (USRDS) Files. We classified USRDS-reported causes of death into five categories: sudden cardiac death, nonsudden cardiac death cardiovascular death, infection, others, and unknown. A subcohort was linked to the National Death Index with similar categories defined. We used ensemble classification trees to discriminate among causes using demographics, vital signs, laboratory measures, health service utilization, and comorbidity claims from 30 days before death. We then created nested case-control populations for each cause of death and used ridge logistic regression to evaluate clinical risk markers that associate with distinct causes. Results The area under the receiver operating characteristic curves from ensemble classification trees were all between 0.59 and 0.70, suggesting minimal ability to distinguish among causes using clinical risk markers. Model coefficients were similar and highly correlated across different cause of death models ( i.e ., 0.87–0.94). This suggests that most clinical risk markers are shared across causes without distinct risk phenotypes. Conclusions We conclude that different causes of death may share similar clinical risk markers in the setting of kidney failure or that the causes of death attributed on USRDS or National Death Index forms are not precise.

Kidney360Vol. 6(3)
Tufts Medical Center (US), Northwell Health (US), Feinstein Institute for Medical Research (US), Duke University (US), Methodist Hospital (US), University of Virginia (US)
American Society of Nephrology, National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases
Reduced inequalities, Peace, Justice and strong institutions
Openalex Percentile: Top 13%
Dialysis and Renal Disease Management
1.70
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