ArticleEGEMS (Washington, DC)2019
Validation of Algorithms Used to Identify Red Blood Cell Transfusion Related Admissions in Veteran Patients with End Stage Renal Disease.
Article in EGEMS (Washington, DC), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
1 citing paper in PubMed, 2 citations in OpenAlex.
- Analyzing real world data of blood transfusion adverse events: Opportunities and challenges.Transfusion · 2022Article
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe 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).
methodsHospitalizations 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.
resultsAfter 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).
conclusionsThe 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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