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Validation of Algorithms Used to Identify Red Blood Cell Transfusion Related Admissions in Veteran Patients with End Stage Renal Disease

BACKGROUND: The goal of this study was to compare the performance of several database algorithms designed to identify red blood cell (RBC) Transfusion Related hospital Admissions (TRAs) in Veterans with end stage renal disease (ESRD). METHODS: Hospitalizations in Veterans with ESRD and evidence of d...

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Autores principales: Peters, Celena B., Hansen, Jared L., Halwani, Ahmad, Cho, Monique E., Leng, Jianwei, Huynh, Tina, Burningham, Zachary, Caloyeras, John, Matsuda, Tara, Sauer, Brian C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Ubiquity Press 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6611485/
https://www.ncbi.nlm.nih.gov/pubmed/31304183
http://dx.doi.org/10.5334/egems.257
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author Peters, Celena B.
Hansen, Jared L.
Halwani, Ahmad
Cho, Monique E.
Leng, Jianwei
Huynh, Tina
Burningham, Zachary
Caloyeras, John
Matsuda, Tara
Sauer, Brian C.
author_facet Peters, Celena B.
Hansen, Jared L.
Halwani, Ahmad
Cho, Monique E.
Leng, Jianwei
Huynh, Tina
Burningham, Zachary
Caloyeras, John
Matsuda, Tara
Sauer, Brian C.
author_sort Peters, Celena B.
collection PubMed
description BACKGROUND: The goal of this study was to compare the performance of several database algorithms designed to identify red blood cell (RBC) Transfusion Related hospital Admissions (TRAs) in Veterans with end stage renal disease (ESRD). METHODS: Hospitalizations in Veterans with ESRD and evidence of dialysis between 01/01/2008 and 12/31/2013 were screened for TRAs using a clinical algorithm (CA) and four variations of claims-based algorithms (CBA 1–4). Criteria were implemented to exclude patients with non-ESRD-related anemia (e.g., injury, surgery, bleeding, medications known to produce anemia). Diagnostic performance of each algorithm was delineated based on two clinical representations of a TRA: RBC transfusion required to treat ESRD-related anemia on admission regardless of the reason for admission (labeled as TRA) and hospitalization for the primary purpose of treating ESRD-related anemia (labeled TRA-Primary). The performance of all algorithms was determined by comparing each to a reference standard established by medical records review. Population-level estimates of classification agreement statistics were calculated for each algorithm using inverse probability weights and bootstrapping procedures. Due to the low prevalence of TRAs, the geometric mean was considered the primary measure of algorithm performance. RESULTS: After application of exclusion criteria, the study consisted of 12,388 Veterans with 26,672 admissions. The CA had a geometric mean of 90.8% (95% Confidence Interval: 81.8, 95.6) and 94.7% (95% CI: 80.5, 98.7) for TRA and TRA-Primary, respectively. The geometric mean for the CBAs ranged from 60.3% (95% CI: 53.2, 66.9) to 91.8% (95% CI: 86.9, 95) for TRA, and from 80.7% (95% CI: 72.9, 86.7) to 96.7% (95% CI: 94.1, 98.2) for TRA-Primary. The adjusted proportions of admissions classified as TRAs was 3.2% (95% CI: 2.8, 3.8) and TRA-Primary was 1.3% (95% CI: 1.1, 1.7). CONCLUSIONS: The CA and select CBAs were able to identify TRAs and TRA-primary with high levels of accuracy and can be used to examine anemia management practices in ESRD patients.
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spelling pubmed-66114852019-07-14 Validation of Algorithms Used to Identify Red Blood Cell Transfusion Related Admissions in Veteran Patients with End Stage Renal Disease Peters, Celena B. Hansen, Jared L. Halwani, Ahmad Cho, Monique E. Leng, Jianwei Huynh, Tina Burningham, Zachary Caloyeras, John Matsuda, Tara Sauer, Brian C. EGEMS (Wash DC) Empirical Research BACKGROUND: The goal of this study was to compare the performance of several database algorithms designed to identify red blood cell (RBC) Transfusion Related hospital Admissions (TRAs) in Veterans with end stage renal disease (ESRD). METHODS: Hospitalizations in Veterans with ESRD and evidence of dialysis between 01/01/2008 and 12/31/2013 were screened for TRAs using a clinical algorithm (CA) and four variations of claims-based algorithms (CBA 1–4). Criteria were implemented to exclude patients with non-ESRD-related anemia (e.g., injury, surgery, bleeding, medications known to produce anemia). Diagnostic performance of each algorithm was delineated based on two clinical representations of a TRA: RBC transfusion required to treat ESRD-related anemia on admission regardless of the reason for admission (labeled as TRA) and hospitalization for the primary purpose of treating ESRD-related anemia (labeled TRA-Primary). The performance of all algorithms was determined by comparing each to a reference standard established by medical records review. Population-level estimates of classification agreement statistics were calculated for each algorithm using inverse probability weights and bootstrapping procedures. Due to the low prevalence of TRAs, the geometric mean was considered the primary measure of algorithm performance. RESULTS: After application of exclusion criteria, the study consisted of 12,388 Veterans with 26,672 admissions. The CA had a geometric mean of 90.8% (95% Confidence Interval: 81.8, 95.6) and 94.7% (95% CI: 80.5, 98.7) for TRA and TRA-Primary, respectively. The geometric mean for the CBAs ranged from 60.3% (95% CI: 53.2, 66.9) to 91.8% (95% CI: 86.9, 95) for TRA, and from 80.7% (95% CI: 72.9, 86.7) to 96.7% (95% CI: 94.1, 98.2) for TRA-Primary. The adjusted proportions of admissions classified as TRAs was 3.2% (95% CI: 2.8, 3.8) and TRA-Primary was 1.3% (95% CI: 1.1, 1.7). CONCLUSIONS: The CA and select CBAs were able to identify TRAs and TRA-primary with high levels of accuracy and can be used to examine anemia management practices in ESRD patients. Ubiquity Press 2019-07-03 /pmc/articles/PMC6611485/ /pubmed/31304183 http://dx.doi.org/10.5334/egems.257 Text en Copyright: © 2019 The Author(s) http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. See http://creativecommons.org/licenses/by/4.0/.
spellingShingle Empirical Research
Peters, Celena B.
Hansen, Jared L.
Halwani, Ahmad
Cho, Monique E.
Leng, Jianwei
Huynh, Tina
Burningham, Zachary
Caloyeras, John
Matsuda, Tara
Sauer, Brian C.
Validation of Algorithms Used to Identify Red Blood Cell Transfusion Related Admissions in Veteran Patients with End Stage Renal Disease
title Validation of Algorithms Used to Identify Red Blood Cell Transfusion Related Admissions in Veteran Patients with End Stage Renal Disease
title_full Validation of Algorithms Used to Identify Red Blood Cell Transfusion Related Admissions in Veteran Patients with End Stage Renal Disease
title_fullStr Validation of Algorithms Used to Identify Red Blood Cell Transfusion Related Admissions in Veteran Patients with End Stage Renal Disease
title_full_unstemmed Validation of Algorithms Used to Identify Red Blood Cell Transfusion Related Admissions in Veteran Patients with End Stage Renal Disease
title_short Validation of Algorithms Used to Identify Red Blood Cell Transfusion Related Admissions in Veteran Patients with End Stage Renal Disease
title_sort validation of algorithms used to identify red blood cell transfusion related admissions in veteran patients with end stage renal disease
topic Empirical Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6611485/
https://www.ncbi.nlm.nih.gov/pubmed/31304183
http://dx.doi.org/10.5334/egems.257
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