Reading: Validation of Algorithms Used to Identify Red Blood Cell Transfusion Related Admissions in V...

Download

A- A+
Alt. Display
  • Login has been disabled for this journal while it is transferred to a new platform. Please try again in 48 hours.

Empirical research

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

Authors:

Celena B. Peters ,

Informatics, Decision-Enhancement, and Analytic Sciences Center (IDEAS), VA Salt Lake City Health Care System, Salt Lake City, UT; University of Utah, Department of Internal Medicine, Salt Lake City, UT, US
X close

Jared L. Hansen,

Informatics, Decision-Enhancement, and Analytic Sciences Center (IDEAS), VA Salt Lake City Health Care System, Salt Lake City, UT; University of Utah, Department of Internal Medicine, Salt Lake City, UT, US
X close

Ahmad Halwani,

Informatics, Decision-Enhancement, and Analytic Sciences Center (IDEAS), VA Salt Lake City Health Care System, Salt Lake City, UT; University of Utah, Department of Internal Medicine, Salt Lake City, UT, US
X close

Monique E. Cho,

Informatics, Decision-Enhancement, and Analytic Sciences Center (IDEAS), VA Salt Lake City Health Care System, Salt Lake City, UT; University of Utah, Department of Internal Medicine, Salt Lake City, UT, US
X close

Jianwei Leng,

Informatics, Decision-Enhancement, and Analytic Sciences Center (IDEAS), VA Salt Lake City Health Care System, Salt Lake City, UT; University of Utah, Department of Internal Medicine, Salt Lake City, UT, US
X close

Tina Huynh,

Informatics, Decision-Enhancement, and Analytic Sciences Center (IDEAS), VA Salt Lake City Health Care System, Salt Lake City, UT; University of Utah, Department of Internal Medicine, Salt Lake City, UT, US
X close

Zachary Burningham,

Informatics, Decision-Enhancement, and Analytic Sciences Center (IDEAS), VA Salt Lake City Health Care System, Salt Lake City, UT; University of Utah, Department of Internal Medicine, Salt Lake City, UT, US
X close

John Caloyeras,

Amgen Inc., US
X close

Tara Matsuda,

Amgen Inc., US
X close

Brian C. Sauer

Informatics, Decision-Enhancement, and Analytic Sciences Center (IDEAS), VA Salt Lake City Health Care System, Salt Lake City, UT; University of Utah, Department of Internal Medicine, Salt Lake City, UT, US
X close

Abstract

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.

How to Cite: Peters CB, Hansen JL, Halwani A, Cho ME, Leng J, Huynh T, et al.. Validation of Algorithms Used to Identify Red Blood Cell Transfusion Related Admissions in Veteran Patients with End Stage Renal Disease. eGEMs (Generating Evidence & Methods to improve patient outcomes). 2019;7(1):23. DOI: http://doi.org/10.5334/egems.257
196
Views
31
Downloads
2
Citations
  Published on 03 Jul 2019

Galley file missing.

Please contact support [at] ubiquitypress.com

comments powered by Disqus