Application of Artificial Intelligence of Machine learning in Assessing Stroke Among HIV Patients on Protease Inhibitors-ART: A Bayesian Network Approach

Background: In our investigation, we aim to utilize Bayesian network models in the field of machine learning to assess the likelihood of CVD in adults who are HIV positive and receiving protease inhibitors-antiretroviral therapy (PIs-ART). It is imperative to comprehend the risk factors and prognosis of stroke in order to effectively manage individuals infected with HIV, particularly those who have not yet initiated HAART. Methods: This retrospective cohort study investigates stroke prevalence among HIV patients on Protease Inhibitors-ART at Zambia's Adult Infectious Disease Center from 2009 to 2019. Data from 2867 patients' EHRs were analyzed for demographic, clinical, and mortality information. Demographic and clinical data were obtained from an anonymous electronic case system. We utilized descriptive analysis along with logistic regression and Bayesian Network Model models to elucidate the characteristics and predictors of stroke among HAART-naive PLWH. Results: This study analyzed data from 2867 HIV patients on Protease Inhibitors-ART to assess stroke prevalence and associated risk factors. Of the cohort, 105 individuals had stroke (prevalence: 3.7%), primarily ischemic infarction (56.2%). Most patients were aged 30-55 years (64.4%) and male (90.2%). Common comorbidities included diabetes (3.8%), hypertension (12.2%), and opportunistic infections like CMV (27.9%) and PCP (36.1%). Mortality rate was 6.6%. Bayesian network modeling predicted post-stroke outcomes, identifying age, CD4 count, lipid profile, comorbidities, and previous cardiovascular events as significant predictors. These findings highlight the complex interplay of risk factors in stroke occurrence among HIV patients on ART. Conclusions: Our findings highlight the significance of early screening for stroke, timely intervention for risk factors across various age groups, and management of CD4 count among HAART-naive PLWH in order to alleviate the burden of stroke. These insights are crucial for informing targeted interventions aimed at reducing the occurrence and mortality associated with stroke in this population. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study did not receive any funding ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Ethical approval was obtained from the University of Zambia Biomedical Research Ethics Committee and Permission from University Teaching Hospital- Adult Infectious Disease Center in Zambia. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors