Predictive variables for peripheral neuropathy in treated HIV type 1 infection revealed by machine learning

Abstract

Objective: Peripheral neuropathies (PNPs) in HIV-infected patients are highly debilitating because of neuropathic pain and physical disabilities. We defined prevalence and associated predictive variables for PNP subtypes in a cohort of persons living with HIV.Design: Adult persons living with HIV in clinical care were recruited to a longitudinal study examining neurological complications.Methods: Each patient was assessed for symptoms and signs of PNP with demographic, laboratory, and clinical variables. Univariate, multiple logistic regression and machine learning analyses were performed by comparing patients with and without PNP.Results: Three patient groups were identified: PNP (n ¼ 111) that included HIVassociated distal sensory polyneuropathy (n ¼ 90) or mononeuropathy (n ¼ 21), and non-neuropathy (n ¼ 408). Univariate analyses showed multiple variables differed significantly between the non-neuropathy and PNP groups including age, estimated HIV type 1 (HIV-1) duration, education, employment, neuropathic pain, peak viral load, polypharmacy, diabetes, cardiovascular disorders, AIDS, and prior neurotoxic nucleoside antiretroviral drug exposure. Classification algorithms distinguished those with PNP, all with area under the receiver operating characteristic curve values of more than 0.80. Random forest models showed greater accuracy and area under the receiver operating characteristic curve values compared with the multiple logistic regression analysis. Relative importance plots showed that the foremost predictive variables of PNP were HIV-1 duration, peak plasma viral load, age, and low CD4 þ T-cell levels.Conclusion: PNP in HIV-1 infection remains common affecting 21.4% of patients in care. Machine-learning models uncovered variables related to PNP that were undetected by conventional analyses, emphasizing the importance of statistical algorithmic approaches to understanding complex neurological syndromes.

Elevated Biomarkers of Inflammation and Vascular Dysfunction Are Associated with Distal Sensory Polyneuropathy in People with HIV

“…Potential confounders known to be risk factors for DSP were considered and assessed. These included a history of diabetes mellitus (self-reported or antidiabetic medications) [44], metabolic syndrome according to AHA criteria [45], sex-specific cardiovascular disease (CVD) risk determined by the modified Framingham risk score [46,47], as well as anthropometric and demographic characteristics, such as age, gender, ethnicity, and height [36,44,48]. Information regarding the history of methamphetamine and other substance abuse and dependency was gathered using the Composite International Diagnostic Interview (CIDI version 2.1) [49,50].…”

Artificial Intelligence and Machine Learning Based Prediction of Viral Load and CD4 Status of People Living with HIV (PLWH) on Anti-Retroviral Treatment in Gedeo Zone Public Hospitals

“…Furthermore, understanding the conditionally dependent clinical features that drive these outcomes is a major challenge. 10,[18][19][20] The use of predictive modeling to identify patients who are most at risk of treatment failure shows promise. Machine learning (ML) and Artificial Intelligence (AI) algorithms are important in healthcare because they analyze medical data and offer some striking potential in generating useful evidence that can play a significant role in healthcare decision-making.…”