Common Data Model-based Analysis of Selective Leukoreduction Protocol Compliance at Three Hospitals
Background: The selective leukoreduction protocol (SLP) is limited in that patients who require it can be overlooked. We estimated SLP compliance (SLPC) using the Observational Medical Outcomes Partnership common data model (CDM).
Methods: Patients were classified into eight groups: pre- and post-hematology disease (A and B), pre- and post-solid organ transplantation (C and D), solid cancer (E), immunodeficiency (F), anticancer therapy (G), and cardiovascular surgery (H). We examined the red blood cell (RBC) transfusion history from three hospital datasets comprising approximately three million patients over 20 years using CDM-based analysis. SLPC was calculated as the percentage of patients who received only leukoreduced RBCs in total patients transfused RBCs.
Results: In total, 166,641 patients from three hospitals were enrolled in this study. From 2001 to 2021, SLPC in all groups, except H, tended to increase, although there were differences among the hospitals. Based on the most recent values (2017-2021), the SLPC in groups A, B, D, and G was maintained at ≥75% until 1,095 days before or after diagnosis or treatment. Groups E, F, and H had < 50% SLPC one day after diagnosis and treatment.
Conclusions: CDM analysis supports the review of large datasets for SLPC evaluation. Although SLPC tended to improve in most patient groups, additional education and monitoring are needed for groups that continue to show low SLPC.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:43 |
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| Enthalten in: |
Annals of laboratory medicine - 43(2023), 2 vom: 01. März, Seite 187-195 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Choi, Sooin [VerfasserIn] |
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| Links: |
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| Themen: |
Dataset |
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| Anmerkungen: |
Date Completed 26.10.2022 Date Revised 27.12.2022 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.3343/alm.2023.43.2.187 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM347960839 |
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| 500 | |a Date Revised 27.12.2022 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Background: The selective leukoreduction protocol (SLP) is limited in that patients who require it can be overlooked. We estimated SLP compliance (SLPC) using the Observational Medical Outcomes Partnership common data model (CDM) | ||
| 520 | |a Methods: Patients were classified into eight groups: pre- and post-hematology disease (A and B), pre- and post-solid organ transplantation (C and D), solid cancer (E), immunodeficiency (F), anticancer therapy (G), and cardiovascular surgery (H). We examined the red blood cell (RBC) transfusion history from three hospital datasets comprising approximately three million patients over 20 years using CDM-based analysis. SLPC was calculated as the percentage of patients who received only leukoreduced RBCs in total patients transfused RBCs | ||
| 520 | |a Results: In total, 166,641 patients from three hospitals were enrolled in this study. From 2001 to 2021, SLPC in all groups, except H, tended to increase, although there were differences among the hospitals. Based on the most recent values (2017-2021), the SLPC in groups A, B, D, and G was maintained at ≥75% until 1,095 days before or after diagnosis or treatment. Groups E, F, and H had < 50% SLPC one day after diagnosis and treatment | ||
| 520 | |a Conclusions: CDM analysis supports the review of large datasets for SLPC evaluation. Although SLPC tended to improve in most patient groups, additional education and monitoring are needed for groups that continue to show low SLPC | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Dataset | |
| 650 | 4 | |a Hematologic disease | |
| 650 | 4 | |a Immunodeficiency disease | |
| 650 | 4 | |a Leukocyte reduction | |
| 650 | 4 | |a Red blood cell transfusion | |
| 650 | 4 | |a Transplantation | |
| 700 | 1 | |a Choi, Soo Jeong |e verfasserin |4 aut | |
| 700 | 1 | |a Shin, Jeong Won |e verfasserin |4 aut | |
| 700 | 1 | |a Yoon, Young Ahn |e verfasserin |4 aut | |
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