MATERIALS AND METHODS: The development of the prognostic model utilized prospectively collected longitudinal data of adult TB patients who smoked in the state of Selangor between 2013 until 2017, which were obtained from the Malaysian Tuberculosis Information System (MyTB) database. Data were randomly split into development and internal validation cohorts. A simple prognostic score (T-BACCO SCORE) was constructed based on the regression coefficients of predictors in the final logistic model of the development cohort. Estimated missing data was 2.8% from the development cohort and was completely at random. Model discrimination was determined using c-statistics (AUCs), and calibration was based on the Hosmer and Lemeshow goodness of fit test and calibration plot.
RESULTS: The model highlights several variables with different T-BACCO SCORE values as predictors for LTFU among TB patients who smoke (e.g., age group, ethnicity, locality, nationality, educational level, monthly income level, employment status, TB case category, TB detection methods, X-ray categories, HIV status, and sputum status). The prognostic scores were categorized into three groups that predict the risk for LTFU: low-risk (<15 points), medium-risk (15 to 25 points) and high-risk (> 25 points). The model exhibited fair discrimination with a c-statistic of 0.681 (95% CI 0.627-0.710) and good calibration with a nonsignificant chi-square Hosmer‒Lemeshow's goodness of fit test χ2 = 4.893 and accompanying p value of 0.769.
CONCLUSION: Predicting LTFU among TB patients who smoke in the early phase of TB treatment is achievable using this simple T-BACCO SCORE. The applicability of the tool in clinical settings helps health care professionals manage TB smokers based on their risk scores. Further external validation should be carried out prior to use.
METHODS: Secondary data from MyTB version 2.1, a national database, were analysed using R version 3.6.1. Descriptive analysis and multivariable logistic regression were conducted to identify treatment success and its determinants.
RESULTS: In total, 3630 cases of TB cases were registered among children in Malaysia between 2013 and 2017. The overall treatment success rate was 87.1% in 2013 and plateaued between 90.1 and 91.4% from 2014 to 2017. TB treatment success was positively associated with being a Malaysian citizen (aOR = 3.43; 95% CI = 2.47, 4.75), being a child with BCG scars (aOR = 1.93; 95% CI = 1.39, 2.68), and being in the older age group (aOR = 1.06; 95% CI = 1.03, 1.09). Having HIV co-infection (aOR = 0.31; 95% CI = 0.16, 0.63), undergoing treatment in public hospitals (aOR = 0.38; 95% CI =0.25, 0.58), having chest X-ray findings of advanced lesion (aOR = 0.48; 95% CI = 0.33, 0.69), having EPTB (aOR = 0.58; 95% CI = 0.41, 0.82) and having sputum-positive PTB (aOR = 0.58; 95% CI = 0.43, 0.79) were negatively associated with TB treatment success among children.
CONCLUSIONS: The overall success rate of treatment among children with TB in Malaysia has achieved the target of 90% since 2014 and remained plateaued until 2017. The socio-demographic characteristics of children, place of treatment, and TB disease profile were associated with the likelihood of TB treatment success among children. The treatment success rate can be increased by strengthening contact tracing activities and promoting early identification targeting the youngest children and non-Malaysian children.
MATERIALS AND METHODS: Using registry-based secondary data, a retrospective cohort study was conducted. TB patients' sociodemographic characteristics, clinical disease data and treatment outcomes at one-year surveillance were extracted from the database and analyzed. Logistic regression analysis was used to determine factors associated with unsuccessful treatment outcomes and all-cause mortality.
RESULTS: A total of 97,505 TB cases (64.3% males) were included in this study. TB treatment success (cases categorized as cured and completed treatment) was observed in 80.7% of the patients. Among the 19.3% patients with unsuccessful treatment outcomes, 10.2% died, 5.3% were lost to follow-up, 3.6% had outcomes not evaluated while the remaining failed treatment. Unsuccessful TB treatment outcomes were found to be associated with older age, males, foreign nationality, urban dwellers, lower education levels, passive detection of TB cases, absence of bacille Calmette-Guerin (BCG) scar, underlying diabetes mellitus, smoking, extrapulmonary TB, history of previous TB treatment, advanced chest radiography findings and human immunodeficiency virus (HIV) infection. Factors found associated with all-cause mortality were similar except for nationality (higher among Malaysians) and place of residence (higher among rural dwellers), while smoking and history of previous TB treatment were not found to be associated with all-cause mortality.
CONCLUSIONS: This study identified various sociodemographic characteristics and TB disease-related variables which were associated with unsuccessful TB treatment outcomes and mortality; these can be used to guide measures for risk assessment and stratification of TB patients in future.
METHODS: This was a prospective observational study carried out at a tertiary referral centre. POAG patients on topical antiglaucoma medications and planned for phaco-ECP were recruited. WDT was performed before surgery and 6 weeks postoperatively by drinking 10 mL/kg of water in 5 min followed by serial IOP by Goldmann applanation tonometry measurements at 15, 30, 45, and 60 min. Mean IOP, IOP fluctuation (difference between highest and lowest IOP), IOP reduction, and factors affecting IOP fluctuation were analysed.
RESULTS: Twenty eyes from 17 patients were included. Baseline IOP was similar before (14.7 ± 2.7 mm Hg) and after (14.8 ± 3.4 mm Hg, p = 0.90) surgery. There was no difference in mean IOP (17.6 ± 3.4 mm Hg vs. 19.3 ± 4.7 mm Hg pre- and postoperative, respectively, p = 0.26) or peak IOP (19.37 ± 3.74 mm Hg vs. 21.23 ± 5.29 mm Hg, p = 0.25), albeit a significant reduction in IOP-lowering medications (2.2 ± 1.15 vs. 0.35 ± 0.93, p < 0.001) postoperatively. IOP fluctuation was significantly greater (6.4 ± 3.2 mm Hg vs. 4.6 ± 2.1 mm Hg, p = 0.015) with more eyes having significant IOP fluctuation of ≥6 mm Hg (11 eyes [55%] vs. 4 eyes [20%], p < 0.001) postoperatively. Factors that were significantly associated with increased postoperative IOP fluctuations were higher preoperative IOP fluctuation (β = 0.69, 95% CI 0.379-1.582, p = 0.004) and more number of postoperative antiglaucoma medications (β = 0.627, 95% CI 0.614-3.322, p = 0.008).
CONCLUSION: Reducing aqueous production with phaco-ECP does not eliminate IOP fluctuation in POAG patients. The increase in postoperative IOP fluctuation suggests increased outflow resistance after phaco-ECP.
METHODS: 71 patients from 18 facilities participated in the 8-week single-arm intervention study. GRVOTS mobile apps were installed in their mobile apps, and patients were expected to fulfill tasks such as providing Video Direct Observe Therapy (VDOTS) daily as well as side effect reporting. At 3-time intervals of baseline,1-month, and 2-month intervals, the number of VDOT taken, the Malaysian Medication Adherence Assessment Tool (MyMAAT), and the Intrinsic Motivation Inventory (IMI) questionnaire were collected. One-sample t-test was conducted comparing the VDOT video adherence to the standard rate of 80%. RM ANOVA was used to analyze any significant differences in MyMAAT and IMI scores across three-time intervals.
RESULTS: This study involved 71 numbers of patients from 18 healthcare facilities who showed a significantly higher treatment adherence score of 90.87% than a standard score of 80% with a mean difference of 10.87(95% CI: 7.29,14.46; p