Model-based prediction of CD4 cells counts in HIV-infected adults on antiretroviral therapy in Northwest Ethiopia: A flexible mixed effects approach

Designing a Predictive Model for Antiretroviral Regimen at the Antiretroviral Therapy Center in Chiro Hospital, Ethiopia

Nowadays, the huge amount of patient's data significantly increases with respect to the time in repositories and data mining is increasingly used as an emerging research area in medical fields for extracting useful and previously unknown insights/patterns from the repository data. These unknown patterns/hidden insights can help in discovering new knowledge hidden in these data repositories. From the observation, different ARV regimens were ordered for different patients. However, combination of these drugs causes different side effects on the patients. It has been observed that there was a lack of predictive studies and designed models available in hospitals specifically ART Centers that accurately determine or classify the patient's ARV regimen to TDF + 3TC + EFV, TDF + 3TC + NVP, AZT + 3TC + ATV/R, AZT + 3TC + LPV/R, TDF + 3TC + LVP/R, TDF + 3TC + ATV/R, 8888, and ABC + 3TC + LPV/R. In order to solve these kinds of problems, we built an accurate classifier system or model using parameters like Patient Age, Patient Encounter Day, Patient Encounter Month, Patient Encounter Year, Patient Weight, Patient CD4 Count Adult, Patient TB Screen, Patient Following WHO Stage, Patient CD4 Percent Child, Patient Regimen Specify, Patient Regimen, and so on. The general objective of this research was predictive modeling for the patient's ARV regimen class through data mining techniques so as to improve them. The study used the CRIPS-DM methodology to find and interpret patterns in repositories. A decision tree (J48 and Random Forest) algorithm was used for classification. Using all tested classifiers, the investigation of the study shows that the total accuracy was more than 60%. On the other hand, among different classifications, class H (ABC + 3TC + LPV/R) has shown the worst prediction. But it was revealed that the J48 classifier relatively produces higher classification accuracy for the D (AZT-3TC-NVP) regimen. Here, classification depended on the selected parameters, which revealed that prediction accuracy value differed among all classifiers and the selected attributes. Finally, the study concluded that data mining can be used as a significant technique to discover patient regimen based on salient affecting factors with 96.1% precision achieved. Ensemble learning resolves the categorizing models of greater anticipating performance with different learning algorithms. This model aligned with sentimental investigation to magnify the appearances of the dataset either from the social media or from primary data collection. The empirical investigation with different parameters shows the detailed improvement of their learning methods.

Joint and Separate Analysis for Longitudinal and Survival Data on Mother-to-Child Transmission of HIV Among Infected Mothers on Option B+ at Health Centers in North Shewa Zone, Ethiopia, 2017

BACKGROUND

Prevention of mother-to-child transmission of HIV (PMTCT) is a frequently used word for programs and intervention methods to decrease the risk of mother-to-child transmission of HIV. The aim of this study was to identify determinants of the reduction of CD4 count through time and the maternal transmission of HIV to their child on the PMTCT program at health centers in North Shewa Zone, Ethiopia.

METHODS

The cohort study design was conducted by using secondary data collected from the cohort register of PMTCT starting from September 1, 2014 to November 30, 2017. In this study, a longitudinal study was conducted for two types of result; these were longitudinal response measurements of HIV infected women CD4 count and the time to maternal transmission of HIV taken from 203 patients.

RESULTS

The prevalence rate of HIV infection among exposed infants was 5.58%. Baseline CD4 count, visiting times, weight, and interaction between visiting time and baseline CD4 count had a statistically significant effect on the longitudinal biomarker. From the Weibull AFT model, ART start, partner test, clinical stage, educational status, place of delivery, and MUAC were statistically significant. Hence, as a measurement unit decreased in square root CD4 cell count by 1.18 elevates the risk of maternal transmission of HIV.

CONCLUSION

In this study, the determinant of mother-to-child transmission of HIV including loss of weight, ART start (ANC), place of delivery at home, illiterate and mother with severe malnutrition, had a significant effect. The longitudinal biomarker also had a strong association with baseline CD4 and the risk of maternal transmission of HIV. Health education should be given about balanced diet, weight control, and take medication for HIV positive patients by the responsible bodies.