An unsupervised learning approach to identify immunoglobulin utilization patterns using electronic health records

Author:

Riazi Kiarash12ORCID,Ly Mark2,Barty Rebecca34ORCID,Callum Jeannie567,Arnold Donald M.489ORCID,Heddle Nancy M.48ORCID,Down Douglas G.10,Sidhu Davinder11,Li Na12410ORCID

Affiliation:

1. Department of Community Health Sciences, Cumming School of Medicine University of Calgary Calgary Alberta Canada

2. Centre for Health Informatics, Cumming School of Medicine University of Calgary Calgary Canada

3. Ontario Regional Blood Coordinating Network Hamilton Ontario Canada

4. Michael G. DeGroote Centre for Transfusion Research, Department of Medicine McMaster University Hamilton Ontario Canada

5. Department of Pathology and Molecular Medicine Kingston Health Sciences Centre and Queen's University Kingston Ontario Canada

6. Department of Laboratory Medicine and Molecular Diagnostics Sunnybrook Health Sciences Centre Toronto Ontario Canada

7. Department of Laboratory Medicine and Pathobiology University of Toronto Toronto Ontario Canada

8. Centre for Innovation Canadian Blood Services Ottawa Ontario Canada

9. Department of Medicine, Michael G. DeGroote School of Medicine McMaster University Hamilton Ontario Canada

10. Department of Computing and Software McMaster University Hamilton Ontario Canada

11. Cumming School of Medicine University of Calgary Calgary Alberta Canada

Abstract

AbstractBackgroundManaging Canada's immunoglobulin (Ig) product resource allocation is challenging due to increasing demand, high expenditure, and global shortages. Detection of groups with high utilization rates can help with resource planning for Ig products. This study aims to uncover utilization subgroups among the Ig recipients using electronic health records (EHRs).MethodsThe study included all Ig recipients (intravenous or subcutaneous) in Calgary from 2014 to 2020, and their EHR data, including blood inventory, recipient demographics, and laboratory test results, were analyzed. Patient clusters were derived based on patient characteristics and laboratory test data using K‐means clustering. Clusters were interpreted using descriptive analyses and visualization techniques.ResultsAmong 4112 recipients, six clusters were identified. Clusters 1 and 2 comprised 408 (9.9%) and 1272 (30.9%) patients, respectively, contributing to 62.2% and 27.1% of total Ig utilization. Cluster 3 included 1253 (30.5%) patients, with 86.4% of infusions administered in an inpatient setting. Cluster 4, comprising 1034 (25.1%) patients, had a median age of 4 years, while clusters 2–6 were adults with median ages of 46–60. Cluster 5 had 62 (1.5%) patients, with 77.3% infusions occurring in emergency departments. Cluster 6 contained 83 (2.0%) patients receiving subcutaneous Ig treatments.ConclusionThe results identified data‐driven segmentations of patients with high Ig utilization rates and patients with high risk for short‐term inpatient use. Our report is the first on EHR data‐driven clustering of Ig utilization patterns. The findings hold the potential to inform demand forecasting and resource allocation decisions during shortages of Ig products.

Funder

Calgary Foundation

Canadian Blood Services

Mitacs

Natural Sciences and Engineering Research Council of Canada

Publisher

Wiley

Subject

Hematology,Immunology,Immunology and Allergy

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