Subdividing ART patients and analyzing the medical burden by modeling of CD4 cell count
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 r...
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| Format: | Article |
| Language: | English |
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Wolters Kluwer Medknow Publications
2023-02-01
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| Series: | Journal of Family Medicine and Primary Care |
| Subjects: | |
| Online Access: | https://journals.lww.com/10.4103/jfmpc.jfmpc_1765_22 |
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| _version_ | 1846170920397307904 |
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| author | Li Min Wang Qunwei |
| author_facet | Li Min Wang Qunwei |
| author_sort | Li Min |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-a2c347b8601944ee850f6c78edb3a3b4 |
| institution | Kabale University |
| issn | 2249-4863 2278-7135 |
| language | English |
| publishDate | 2023-02-01 |
| publisher | Wolters Kluwer Medknow Publications |
| record_format | Article |
| series | Journal of Family Medicine and Primary Care |
| spelling | doaj-art-a2c347b8601944ee850f6c78edb3a3b42024-11-11T11:04:04ZengWolters Kluwer Medknow PublicationsJournal of Family Medicine and Primary Care2249-48632278-71352023-02-0112235235910.4103/jfmpc.jfmpc_1765_22Subdividing ART patients and analyzing the medical burden by modeling of CD4 cell countLi MinWang QunweiObjective: 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.https://journals.lww.com/10.4103/jfmpc.jfmpc_1765_22antiretroviral therapycd4 cell countcommon factor analysisdata miningmedical burdenrecency-frequency model |
| spellingShingle | Li Min Wang Qunwei Subdividing ART patients and analyzing the medical burden by modeling of CD4 cell count Journal of Family Medicine and Primary Care antiretroviral therapy cd4 cell count common factor analysis data mining medical burden recency-frequency model |
| title | Subdividing ART patients and analyzing the medical burden by modeling of CD4 cell count |
| title_full | Subdividing ART patients and analyzing the medical burden by modeling of CD4 cell count |
| title_fullStr | Subdividing ART patients and analyzing the medical burden by modeling of CD4 cell count |
| title_full_unstemmed | Subdividing ART patients and analyzing the medical burden by modeling of CD4 cell count |
| title_short | Subdividing ART patients and analyzing the medical burden by modeling of CD4 cell count |
| title_sort | subdividing art patients and analyzing the medical burden by modeling of cd4 cell count |
| topic | antiretroviral therapy cd4 cell count common factor analysis data mining medical burden recency-frequency model |
| url | https://journals.lww.com/10.4103/jfmpc.jfmpc_1765_22 |
| work_keys_str_mv | AT limin subdividingartpatientsandanalyzingthemedicalburdenbymodelingofcd4cellcount AT wangqunwei subdividingartpatientsandanalyzingthemedicalburdenbymodelingofcd4cellcount |