Antiretroviral drug use by individuals living with HIV/AIDS and compliance with the Clinical Protocol and Therapy Guidelines

Brojan, Lucas Eduardo Fedaracz; Marca, L; Dias, Frederico Alves; Rattmann, Yanna Dantas

doi:10.31744/einstein_journal/2020ao4995

Cited by 9 publications

(8 citation statements)

References 18 publications

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“…Currently, the following methods are used in China and other countries to determine adherence in AIDS patients: (1) self-reported evaluation questionnaires [ 9 , 11 , 13 , 14 , 16 – 20 ], (2) tablet, prescription and pharmacy records for drug count [ 21 , 22 ], (3) plasma drug concentration monitoring [ 10 , 22 , 23 ] and (4) a mixture of self-reported questionnaires and plasma drug concentration monitoring. However, method (1) tends to be limited by patient-related subjective factors, and its reliability and validity tend to be questionable.…”

Section: Introductionmentioning

confidence: 99%

Adherence predictor variables in AIDS patients: an empirical study using the data mining-based RFM model

Wang

Shen

2021

AIDS Res Ther

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Background Highly active antiretroviral therapy (ART) is still the only effective method to stop the disease progression in acquired immunodeficiency syndrome (AIDS) patients. However, poor adherence to the therapy makes it ineffective. In this work, we construct an adherence prediction model of AIDS patients using the classical recency, frequency and monetary value (RFM) model in the data mining-based customer relationship management model to obtain adherence predictor variables. Methods We cleaned 257,305 diagnostic data elements of AIDS outpatients in Shanghai from August 2009 to December 2019 to obtain 16,440 elements. We tested the RFM and RFm (R: recent consultation month, F: consultation frequency, M/m: total/average medical costs per visit) models, three clustering methods (K-means, Kohonen and two-step clustering) and four decision algorithms (C5.0, the classification and regression tree, Chi-square Automatic Interaction Detector and Quick, Unbiased, Efficient, Statistical Tree) to select the optimal combination. The optimal model and clustering analysis were used to divide the patients into two groups (good and poor adherence), then the optimal decision algorithm was used to construct the prediction model of adherence and obtain its predictor variables. Results The results revealed that the RFm model, K-means clustering analysis and C5.0 algorithm were optimal. After three rounds of k-means clustering analysis, the optimal RFm clustering model quality was 0.8, 10,614 elements were obtained, including 9803 and 811 from patients with good or poor adherence, respectively, and five types of patients were identified. The prediction model had an accuracy of 100% with the recent consultation month as an important adherence predictor variable. Conclusions This work presented a prediction model for medication adherence in AIDS patients at the designated AIDS center in Shanghai, using the RFm model and the k-means and C5.0 algorithms. The model can be expanded to include patients from other centers in China and worldwide.

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Section: Introductionmentioning

confidence: 99%

Adherence predictor variables in AIDS patients: an empirical study using the data mining-based RFM model

Wang

Shen

2021

AIDS Res Ther

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“…Em 1996, foi promulgada a lei federal 9.313, onde certifica o acesso à terapia antirretroviral por meio do Sistema Único de Saúde (SUS). A política pública voltada ao controle da infecção pelo HIV apresenta resultados significativamente para a redução da mortalidade e dos internamentos por HIV/AIDS no Brasil (Brojan et al, 2020).…”

Section: Introductionunclassified

Relação entre a infecção pela COVID-19 em pacientes com HIV

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Nascimento²,

Sousa³

et al. 2020

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Sabe-se que HIV destrói progressivamente certos glóbulos brancos do sangue chamados linfócitos CD4+. Os linfócitos ajudam a defender o corpo contra células estranhas, organismos infecciosos e câncer. Assim, as pessoas ficam vulneráveis ao ataque por muitos outros organismos infecciosos. Muitas das complicações da infecção por HIV são geralmente resultado de outras infecções e não da infecção por HIV diretamente. Com isso, a presente pesquisa teve como objetivo descrever as principais relações entre a infecção pela covid-19 em pacientes com HIV. Trata-se de uma revisão integrativa, método de pesquisa criterioso empregado para demonstrar o conhecimento produzido sobre um dado assunto ou temática. Para a realização desse estudo, foram consultadas as bases de dados: MEDLINE, LILACS, Scholar google e SCIELO. Utilizou-se os Descritores em Ciências da Saúde (DeCS) de forma associada: HIV, AIDS, COVID-19, complicação e Infecções por vírus. Após a aplicação dos critérios de inclusão, restaram 8 estudos. Os estudos incluídos foram tabelados e foram feitas revisões críticas e corroborativas entre cada um deles. A COVD-19 é uma doença grave e todas as pessoas que vivem com HIV devem tomar todas as medidas de prevenção recomendadas para minimizar a exposição e prevenir a infecção pelo vírus SARS-CoV-2. Como na população em geral, pessoas idosas vivendo com HIV ou pessoas vivendo com HIV com problemas cardíacos ou pulmonares podem estar em maior risco de serem infectadas pelo vírus e de apresentarem sintomas mais graves.

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“…Currently, the following methods are used in China and other countries to determine adherence in AIDS patients: (1) self-reported evaluation questionnaires [9, 11, 13-14, 16, 17-20], (2) tablet, prescription and pharmacy records for drug count [21,22], (3) plasma drug concentration monitoring [10,[22][23] and (4) a mixture of self-reported questionnaires and plasma drug concentration monitoring. However, method (1) tends to be limited by patient-related subjective factors, and its reliability and validity tend to be questionable.…”

Section: Introductionmentioning

confidence: 99%

Adherence predictor variables in AIDS patients: An empirical study using the data mining-based RFM model

Li¹,

Wang²,

Shen³

2020

Preprint

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Background Highly active antiretroviral therapy (ART) is still the only effective method to stop the disease progression in acquired immunodeficiency syndrome (AIDS) patients. However, poor adherence to the therapy makes it ineffective. In this work, we construct an adherence prediction model of AIDS patients using the classical recency, frequency and monetary value (RFM) model in the data mining-based customer relationship management model to obtain adherence predictor variables. Methods We cleaned 257305 diagnostic data elements of AIDS outpatients in Shanghai from August 2009 to December 2019 to obtain 16440 elements. We tested the RFM and RFm (R: recent consultation month, F: consultation frequency, M/m: total/average medical costs per visit) models, three clustering methods (K-means, Kohonen and two-step clustering) and four decision algorithms (C5.0, the classification and regression tree, Chi-square Automatic Interaction Detector and Quick, Unbiased, Efficient, Statistical Tree) to select the optimal combination. The optimal model and clustering analysis were used to divide the patients into two groups (good and poor adherence), then the optimal decision algorithm was used to construct the prediction model of adherence and obtain its predictor variables. Results The results revealed that the RFm model, K-means clustering analysis and C5.0 algorithm were optimal. After three rounds of k-means clustering analysis, the optimal RFm clustering model quality was 0.8, 10614 elements were obtained, including 9803 and 811 from patients with good or poor adherence, respectively, and five types of patients were identified. The prediction model had an accuracy of 100% with the recent consultation month as an important adherence predictor variable. Conclusions This work presented a prediction model for medication adherence in AIDS patients at the designated AIDS center in Shanghai, using the RFm model and the k-means and C5.0 algorithms. The model can be expanded to include patients from other centers in China and worldwide.

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Antiretroviral drug use by individuals living with HIV/AIDS and compliance with the Clinical Protocol and Therapy Guidelines

Cited by 9 publications

References 18 publications

Adherence predictor variables in AIDS patients: an empirical study using the data mining-based RFM model

Adherence predictor variables in AIDS patients: an empirical study using the data mining-based RFM model

Relação entre a infecção pela COVID-19 em pacientes com HIV

Adherence predictor variables in AIDS patients: An empirical study using the data mining-based RFM model

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