Prevalence of microalbuminuria and associated factors among HIV - infected ART naïve patients at Mulago hospital: a cross-sectional study in Uganda

High prevalence of albuminuria among adult males living with HIV in Botswana

Chronic HIV disease is associated with a fivefold increase in albuminuria outside of sub-Saharan Africa. However, very little is known about albuminuria risk among people living with HIV (PLWH) in sub-Saharan Africa. Therefore, we conducted a cross-sectional observational HIV clinic-based study of albuminuria among 1533 adults aged 21 years or older between January 2020 and January 2021 in Gaborone, Botswana. Clinical albuminuria was defined using a sex-based albumin‒creatinine ratio (ACR) of 25-355 mg/g for females and 17-250 mg/g for males. The study population mean age was 48.5 (SD 10.3) years, and 764/1533 (49.7%) were female. The overall prevalence of albuminuria was 20.7% (95% CI 18.7%, 22.8%). A higher proportion of males were more likely to be categorized as having albuminuria than females, 25% (95% CI 22.0, 28.2) versus 16.4% (95% CI 13.8,19.2), P value < 0.001. In the final multivariate models, predictors of albuminuria differed by sex group. Larger longitudinal studies are required to evaluate the impact of albuminuria among PLWH with particular emphasis on the effect of sex on the risk of albuminuria.

Development of a Risk Model for Predicting Microalbuminuria in the Chinese Population Using Machine Learning Algorithms

Objective

Microalbuminuria (MAU) occurs due to universal endothelial damage, which is strongly associated with kidney disease, stroke, myocardial infarction, and coronary artery disease. Screening patients at high risk for MAU may aid in the early identification of individuals with an increased risk of cardiovascular events and mortality. Hence, the present study aimed to establish a risk model for MAU by applying machine learning algorithms.

Methods

This cross-sectional study included 3,294 participants ranging in age from 16 to 93 years. R software was used to analyze missing values and to perform multiple imputation. The observed population was divided into a training set and a validation set according to a ratio of 7:3. The first risk model was constructed using the prepared data, following which variables with P <0.1 were extracted to build the second risk model. The second-stage model was then analyzed using a chi-square test, in which a P ≥ 0.05 was considered to indicate no difference in the fit of the models. Variables with P <0.05 in the second-stage model were considered important features related to the prevalence of MAU. A confusion matrix and calibration curve were used to evaluate the validity and reliability of the model. A series of risk prediction scores were established based on machine learning algorithms.

Results

Systolic blood pressure (SBP), diastolic blood pressure (DBP), fasting blood glucose (FBG), triglyceride (TG) levels, sex, age, and smoking were identified as predictors of MAU prevalence. Verification using a chi-square test, confusion matrix, and calibration curve indicated that the risk of MAU could be predicted based on the risk score.

Conclusion

Based on the ability of our machine learning algorithm to establish an effective risk score, we propose that comprehensive assessments of SBP, DBP, FBG, TG, gender, age, and smoking should be included in the screening process for MAU.