Utilizing electronic health record data to understand comorbidity burden among people living with HIV: a machine learning approach

Yang, Xueying; Zhang, Jiajia; Chen, Shujie; Weissman, Sharon; Olatosi, Bankole; Li, Xiaoming

doi:10.1097/qad.0000000000002736

Cited by 17 publications

(7 citation statements)

References 98 publications

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“…In addition, closely comparable controls were not frequently included (Hasse et al 2011;Moore et al 2008;Rankgoane-Pono et al 2018). Moreover, some studies included relatively large numbers of people who are not on cART and/or virally suppressed (Worm et al 2010;Yang et al 2021). To understand whether PLHIV despite suppressive cART have higher risks of ARC compared to HIV-negative persons, longitudinal studies with virally suppressed PLHIV and age and lifestyle-comparable HIV-negative controls are typically needed (Aung et al 2021a).…”

Section: Introductionmentioning

confidence: 99%

Low incidence of advanced neurological burden but high incidence of age-related conditions that are dementia risk factors in aging people living with HIV: a data-linkage 10-year follow-up study

Aung

Bloch

Vincent³

et al. 2022

J. Neurovirol.

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Although increasing research is focusing on age-related comorbidities (ARC) among people living with HIV (PLHIV), no studies have concomitantly assessed non-HIV age-related neurological disorders (e.g., Alzheimer’s dementia). A total of 254 PLHIV and 69 HIV-negative controls completed baseline medical history and cognitive testing. ARC data were collected from medical records over the subsequent 9-10 years and included all types of strokes, all types of dementia, mild cognitive impairment, Parkinson's disease, motor neuron disease (grouped into a non-HIV age-related neurological category), cardiovascular disease, chronic kidney disease, chronic liver disease, chronic lung disease, non-AIDS cancers, osteoporosis, and diabetes. Kaplan–Meier curves assessed differences in the incident rates (per 1000 person year) of groups of ARC as defined above and combined ARC (i.e., development of any of the ARC) among younger (baseline age < 50) and older (baseline age ≥ 50) PLHIV and younger and older controls. Cox-proportional hazard models assessed the individual and interaction effects of HIV status and chronological age, in addition to a range of demographic and clinical variables including historical and baseline HIV brain involvement on the risk of developing combined ARC. Older PLHIV had a higher incidence of cardiovascular disease, osteoporosis, and combined ARC compared to other groups (p < 0.05). Incident rate of non-HIV age-related neurological disorders was 2.3 [0.93, 4.79] per 1000 person year. While this incident rate was higher in older PLHIV (5.37 [1.97, 11.92]) than older HIV-negative participants (3.58 [0.18-17.67]), this was not significant. In multivariate analyses, HIV status and chronological age, but not their interaction, and smoking were associated with higher risk of combined ARC (p < 0.05). In analyses focusing on PLHIV, older age and taking abacavir/efavirenz/atazanavir/darunavir containing antiretroviral treatments at the time of diagnosis were associated with greater ARC (p < 0.05). Non-HIV age-related neurological disorders are uncommon in older PLHIV, where the majority were < 70 years of age at the end of follow-up. However, the greater burden of ARC among older PLHIV, most of which are established dementia risk factors, warrants the establishment of commensurate prevention strategies and greater attention to neurocognitive screening.

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Section: Introductionmentioning

confidence: 99%

Low incidence of advanced neurological burden but high incidence of age-related conditions that are dementia risk factors in aging people living with HIV: a data-linkage 10-year follow-up study

Aung

Bloch

Vincent³

et al. 2022

J. Neurovirol.

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“…Clinical notes and electronic medical records were the most common primary data sources, used in 57 studies (69.5%). 21,23–27,29,30,33–36,40,42–46,48–52,54,55,57–64,66–68,70–73,75,79–93,95 Other primary sources included audio recordings ( n = 6, 7.3%), 6,28,32,38,39,65 administrative data ( n = 5, 6.1%), 37,47,53,77,...…”

Section: Resultsmentioning

confidence: 99%

The use of natural language processing in palliative care research: A scoping review

et al. 2022

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Background: Natural language processing has been increasingly used in palliative care research over the last 5 years for its versatility and accuracy. Aim: To evaluate and characterize natural language processing use in palliative care research, including the most commonly used natural language processing software and computational methods, data sources, trends in natural language processing use over time, and palliative care topics addressed. Design: A scoping review using the framework by Arksey and O’Malley and the updated recommendations proposed by Levac et al. was conducted. Sources: PubMed, Web of Science, Embase, Scopus, and IEEE Xplore databases were searched for palliative care studies that utilized natural language processing tools. Data on study characteristics and natural language processing instruments used were collected and relevant palliative care topics were identified. Results: 197 relevant references were identified. Of these, 82 were included after full-text review. Studies were published in 48 different journals from 2007 to 2022. The average sample size was 21,541 (median 435). Thirty-two different natural language processing software and 33 machine-learning methods were identified. Nine main sources for data processing and 15 main palliative care topics across the included studies were identified. The most frequent topic was mortality and prognosis prediction. We also identified a trend where natural language processing was frequently used in analyzing clinical serious illness conversations extracted from audio recordings. Conclusions: We found 82 papers on palliative care using natural language processing methods for a wide-range of topics and sources of data that could expand the use of this methodology. We encourage researchers to consider incorporating this cutting-edge research methodology in future studies to improve published palliative care data.

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“…The EHR data of PWH were extracted from the South Carolina Department of Health and Environmental Control (DHEC), including variables of HIV diagnosis date, HIV risk factors and laboratory results of CD4 + cell count and viral load. Details of the data sources were described elsewhere [ 8 , 9 ]. The research protocol received approval from the institutional review board in University of South Carolina and relevant South Carolina state agencies.…”

Section: Methodsmentioning

confidence: 99%

The impact of COVID-19 pandemic on the dynamic HIV care engagement among people with HIV: real-world evidence

Yang¹,

Zhang²,

Chen³

et al. 2023

Aids

Self Cite

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Introduction: Existing studies examining the impact of the COVID-19 pandemic on engagement in HIV care often capture cross-sectional status, while lacking longitudinal evaluations. This study examined the impact of the pandemic on the longitudinal dynamic change of retention in care and viral suppression status. Methods: The electronic health record (EHR) data of this population-level cohort study were retrieved from the statewide electronic HIV/AIDS reporting system in South Carolina. The study population was people with HIV (PWH) who had at least one year's symmetric follow-up observation record before and after the pandemic. Multivariable generalized linear mixed regression models were employed to analyze the impact of the pandemic on these outcomes, adjusting for socio-demographic characteristics and preexisting comorbidities. Results: In the adjusted models, PWH had a lower likelihood of retention in care (adjusted odds ratio [aOR]: 0.806, 95% confidence interval [CI]: 0.769, 0.844) and a higher probability of virological failure (aOR: 1.240, 95% CI: 1.169, 1.316) during the peri-pandemic period than pre-pandemic period. Results from interaction effect analysis from each cohort revealed that the negative effect of the pandemic on retention in care was more severe among PWH with high comorbidity burden than those without any comorbidity; meanwhile, a more striking virological failure was observed among PWH who reside in urban areas than in rural areas. Conclusion: The COVID-19 pandemic has a negative impact on retention in care and viral suppression among PWH in South Carolina, particularly for individuals with comorbidities and residing in urban areas.

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Utilizing electronic health record data to understand comorbidity burden among people living with HIV: a machine learning approach

Cited by 17 publications

References 98 publications

Low incidence of advanced neurological burden but high incidence of age-related conditions that are dementia risk factors in aging people living with HIV: a data-linkage 10-year follow-up study

Low incidence of advanced neurological burden but high incidence of age-related conditions that are dementia risk factors in aging people living with HIV: a data-linkage 10-year follow-up study

The use of natural language processing in palliative care research: A scoping review

The impact of COVID-19 pandemic on the dynamic HIV care engagement among people with HIV: real-world evidence

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