Undernutrition and Treatment Success in Drug-Resistant Tuberculosis in Uganda

Baluku, Joseph Baruch; Namiiro, Sharon; Nabwana, Martin; Muttamba, Winters; Kirenga, Bruce

doi:10.2147/idr.s332148

Cited by 16 publications

(7 citation statements)

References 46 publications

(57 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…MUAC measurement, an indicator of patient nutrition status was also found to be predictive of non-adherence. This concurred with two different studies (72,73) both conducted in Uganda, among tuberculosis patients, that found that undernutrition leads to unfavorable treatment outcomes. This finding also resonated with the fact that tuberculosis drugs are administered according to patient weight at diagnosis.…”

Section: (Which Was Not Certified By Peer Review)supporting

confidence: 87%

A machine learning model to explore individual risk factors for tuberculosis treatment non-adherence in Mukono district

Gichuhi

Magumba

Kumar

et al. 2022

Preprint

View full text Add to dashboard Cite

Despite the availability and implementation of well-known efficacious interventions for Tuberculosis (TB) prevention and treatment by the Ministry of Health, Uganda (MoH), treatment non-adherence persists. Moreover, identifying a specific patient at risk of non-adherence is still a challenge. Thus, this study set out to utilize machine learning modeling to explore individual risk factors predictive of tuberculosis treatment non-adherence in the Mukono district. This was a retrospective study based on a record review of 838 TB patients enrolled in six health facilities (3 government, 3 private-not-for-profit) in the Mukono district. We developed five machine learning algorithms (Logistic regression (LR), artificial neural networks (ANN), support vector machines (SVM), Random Forest (RF), and AdaBoost) to explore the individual risk factors for tuberculosis treatment non-adherence. Also, we evaluated their accuracy by computing the positive predictive value (PPV) and negative predictive value (NPV) through the aid of a confusion matrix. Of the five developed and evaluated models, SVM performed the best with an accuracy of 91.28 % compared to RF (89.97%), LR (88.30%), ANN (88.30%), and AdaBoost (91.05%) respectively. Individual risk factors predictive of non-adherence included; TB type, GeneXpert results, sub-country, ART status, contacts below 5 years, health facility ownership, sputum test results at 2 months, treatment supporter, CPT Dapson status, risk group, patient age, gender, middle and upper arm circumference, referral, positive sputum test at 5 months and 6 months. This study shows that classification machine learning techniques can identify patient factors predictive of treatment non-adherence and accurately differentiate between adherent and non-adherent patients. Thus, TB program management should consider adopting the machine learning techniques evaluated in this study as a screening tool for both identity and target-suited interventions for these patients.

show abstract

Section: (Which Was Not Certified By Peer Review)supporting

confidence: 87%

A machine learning model to explore individual risk factors for tuberculosis treatment non-adherence in Mukono district

Gichuhi

Magumba

Kumar

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…It was interesting to observe a disconnect in the prevalence of obesity as measured by the BMI (4.0%) and waist-hip ratio/waist circumference (39.3%) in our study. In fact, undernutrition, as measured by the BMI is very prevalent among people with DRTB in Uganda [ 49 ]. However, similar to our findings, central obesity was prevalent in 33% of people with TB and it correlated with DM better than the BMI in a study from the Philippines [ 50 ].…”

Section: Discussionmentioning

confidence: 99%

Cardiovascular risk factors among people with drug-resistant tuberculosis in Uganda

Baluku

Nabwana

Nalunjogi

et al. 2022

BMC Cardiovasc Disord

Self Cite

View full text Add to dashboard Cite

Background Tuberculosis (TB) and its risk factors are independently associated with cardiovascular disease (CVD). We determined the prevalence and associations of CVD risk factors among people with drug-resistant tuberculosis (DRTB) in Uganda. Methods In this cross-sectional study, we enrolled people with microbiologically confirmed DRTB at four treatment sites in Uganda between July to December 2021. The studied CVD risk factors were any history of cigarette smoking, diabetes mellitus (DM) hypertension, high body mass index (BMI), central obesity and dyslipidaemia. We used modified Poisson regression models with robust standard errors to determine factors independently associated with each of dyslipidaemia, hypertension, and central obesity. Results Among 212 participants, 118 (55.7%) had HIV. Overall, 196 (92.5%, 95% confidence interval (CI) 88.0-95.3) had ≥ 1 CVD risk factor. The prevalence; 95% CI of individual CVD risk factors was: dyslipidaemia (62.5%; 55.4–69.1), hypertension (40.6%; 33.8–47.9), central obesity (39.3%; 32.9–46.1), smoking (36.3%; 30.1–43.1), high BMI (8.0%; 5.0–12.8) and DM (6.5%; 3.7–11.1). Dyslipidaemia was associated with an increase in glycated haemoglobin (adjusted prevalence ratio (aPR) 1.14, 95%CI 1.06–1.22). Hypertension was associated with rural residence (aPR 1.89, 95% CI 1.14–3.14) and previous history of smoking (aPR 0.46, 95% CI 0.21–0.98). Central obesity was associated with increasing age (aPR 1.02, 95%CI 1.00–1.03), and elevated diastolic blood pressure (aPR 1.03 95%CI 1.00–1.06). Conclusion There is a high prevalence of CVD risk factors among people with DRTB in Uganda, of which dyslipidaemia is the commonest. We recommend integrated services for identification and management of CVD risk factors in DRTB.

show abstract

“…In addition, GNRI seems to have a better ability to predict the risk of all-cause death than BMI, but this difference has not reached statistical significance. Many studies 3,[17][18][19] showed a close relationship between malnutrition and the occurrence of pulmonary TB, but it has not been clarified whether malnutrition increases the short-term mortality risk of pulmonary TB, the results of univariate analysis generally tended that malnutrition was a factor affecting the mortality risk of TB patients. However, the results of the multivariate analysis were different.…”

Section: Discussionmentioning

confidence: 99%

Malnutrition is Associated with an Increased Risk of Death in Hospitalized Patients with Active Pulmonary Tuberculosis: A Propensity Score Matched Retrospective Cohort Study

Xu¹,

Zhu²,

Cai³

et al. 2022

IDR

View full text Add to dashboard Cite

Background This study aimed to investigate whether nutrition levels in patients with active pulmonary tuberculosis (TB) affect their risk of all-cause mortality during hospitalization and to further evaluate the predictive ability of Geriatric Nutritional Risk Index (GNRI) and Body Mass Index (BMI) for risk of all-cause mortality. Methods Patients from January 1, 2020 to December 31, 2021 were retrieved, and a total of 1847 were included. The primary outcome was all-cause mortality. Propensity score matching (PSM) was performed for risk adjustment, and receiver operating characteristic (ROC) curve analysis was performed to assess the predictive ability of GNRI and BMI for all-cause mortality. Results Malnourished TB patients were older, had more congestive heart failure, and had more chronic obstructive pulmonary disease or asthma. Under the nutrition level grouping defined by GNRI, the all-cause mortality in the malnourished group did not appear to reach a statistical difference compared with the nonmalnourished group (P = 0.078). When grouped by level of nutrition as defined by BMI, the all-cause mortality was higher in the malnourished group (P = 0.009), and multivariate logistic regression analysis revealed that malnutrition was an independent risk factor for all-cause mortality. After propensity score matching, the results showed that the all-cause mortality was higher in the malnutrition group, regardless of BMI or GNRI defined nutrition level grouping, compared with the control group (both P < 0.001). The ROC curve analysis revealed that the area under the curve (AUC) was 0.811 ([95% confidence interval (CI) 0.701–0.922], P < 0.001) for GNRI and 0.728 ([95% CI 0.588–0.869], P = 0.001) for BMI. Conclusion In the clinical treatment of patients with active TB, more attention should be paid to the management of nutritional risk. GNRI may be a highly effective and easy method for predicting short-term outcomes in patients with active pulmonary TB.

show abstract

Undernutrition and Treatment Success in Drug-Resistant Tuberculosis in Uganda

Cited by 16 publications

References 46 publications

A machine learning model to explore individual risk factors for tuberculosis treatment non-adherence in Mukono district

A machine learning model to explore individual risk factors for tuberculosis treatment non-adherence in Mukono district

Cardiovascular risk factors among people with drug-resistant tuberculosis in Uganda

Malnutrition is Associated with an Increased Risk of Death in Hospitalized Patients with Active Pulmonary Tuberculosis: A Propensity Score Matched Retrospective Cohort Study

Contact Info

Product

Resources

About