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. 2023 Feb;12(2):352-359.
doi: 10.4103/jfmpc.jfmpc_1765_22. Epub 2023 Feb 28.

Subdividing ART patients and analyzing the medical burden by modeling of CD4 cell count

Li Min  1   2 , Wang Qunwei  1
Affiliations

Subdividing ART patients and analyzing the medical burden by modeling of CD4 cell count

Li Min et al. J Family Med Prim Care. 2023 Feb.

Abstract

Objective: To subdivide the antiretroviral therapy (ART) human immunodeficiency virus (HIV)/acquired immune deficiency syndrome (AIDS) patients by modeling the CD4 cell count variable, with an aim to reduce the medical burden from lifelong ART.

Materials and methods: The data of outpatients at the research unit between August 2009 and December 2020 were exported and mined. A recency-frequency (RF) model was established for data subdivision, and data of non-churn ART patients were preserved. Common factor analysis (CFA) was conducted on the three indicators of the baseline/mean/last CD4 cell counts to obtain critical variables; then, k-means modeling was used to subdivide ART patients and their medical burden was analyzed.

Results: A total of 12,106 samples of non-churn ART patients were preserved by RF modeling. The baseline/mean/last CD4 cell counts served as important variables employed for modeling. The patients were divided into 15 types, including two types with poor compliance and poor immune reconstitution, two types with good compliance but poor immune reconstitution, four types with poor compliance but good immune reconstitution, and seven types with good compliance and good immune reconstitution. The frequency of visits was 5.25-9.95 visits/person/year, and the percentage of examination fees was 44.24%-59.05%, with a medical burden of 4114.24-12,676.66 yuan/person/year, of which 42.62%-70.09% was reduced.

Conclusion: The CD4 cell count is not only an important indicator for judging post-ART immune recovery, but also a major modeling variable in subdividing ART patients with varying medical burdens. Poor compliance and poor immune reconstitution lead to excessive visits and frequent examinations, which were the leading causes of the heavy medical burden of ART.

Keywords: Antiretroviral therapy; CD4 cell count; common factor analysis; data mining; medical burden; recency-frequency model.

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Conflict of interest statement

There are no conflicts of interest.

Figures

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Figure 1
Subdividing ART Patients by Modeling of CD4 Cell Count and Analysis of the Medical Burden
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