Outcome prediction model and prognostic biomarkers for COVID-19 patients in Vietnam.

Background: Accurate prognosis is important either after acute infection or during long-term follow-up of patients infected by severe acute respiratory syndrome coronavirus 2. This study aims to predict coronavirus disease 2019 (COVID-19) severity based on clinical and biological indicators, and to identify biomarkers for prognostic assessment. Methods: We included 261 Vietnamese COVID-19 patients, who were classified into moderate and severe groups. Disease severity prediction based on biomarkers and clinical parameters was performed by applying machine learning and statistical methods using the combination of clinical and biological data. Results: The random forest model could predict with 97% accuracy the likelihood of COVID-19 patients who subsequently worsened to the severe condition. The most important indicators were interleukin (IL)-6, ferritin and D-dimer. The model could still predict with 92% accuracy after removing IL-6 from the analysis to generalise the applicability of the model to hospitals with limited capacity for IL-6 testing. The five most effective indicators were C-reactive protein (CRP), D-dimer, IL-6, ferritin and dyspnoea. Two different sets of biomarkers (D-dimer, IL-6 and ferritin, and CRP, D-dimer and IL-6) are applicable for the assessment of disease severity and prognosis. The two biomarker sets were further tested through machine learning algorithms and relatively validated on two Danish COVID-19 patient groups (n=32 and n=100). The results indicated that various biomarker sets combined with clinical data can be used for detection of the potential to develop the severe condition. Conclusion: This study provided a simple and reliable model using two different sets of biomarkers to assess disease severity and predict clinical outcomes in COVID-19 patients in Vietnam.

Effectiveness and safety of bedaquiline-based, modified all-oral 9-11-month treatment regimen for rifampicin-resistant tuberculosis in Vietnam.

OBJECTIVES: World Health Organization recommends a 7-drug 9-11-month rifampicin-resistant tuberculosis (RR-TB) short treatment regimen (STR). To reduce the pill burden, we assessed the safety and effectiveness of a 5-drug 9-11-month modified STR (mSTR). METHODS: Prospective cohort study of an all-oral mSTR (comprising bedaquiline, levofloxacin, linezolid [LZD], clofazimine, and/or pyrazinamide) for patients with RR-TB without confirmed fluoroquinolone resistance, enrolled in Vietnam between 2020-2021. RESULTS: A total of 108 patients were enrolled in this study. Overall, 63 of 74 (85%) achieved culture conversion at 2 months. Of 106 evaluated, 95 (90%) were successfully treated, six (6%) were lost-to-follow-up, one (1%) died, and four (4%) had treatment failure, including three with permanent regimen change owing to adverse events (AE) and one with culture reversion. Of 108, 32 (30%) patients encountered at least one AE. Of 45 AEs recorded, 13 (29%) were serious (hospitalization, life threatening, or death). The median time to AE was 3 months (IQR: 2-5). A total of 26 AEs led to regimen adaptation: either dose reduction (N = 1), drug temporary interruption (N = 19), or drug permanent discontinuation (N = 6, 4 attributed to LZD). CONCLUSION: The high treatment success of 5-drug mSTR might replace the 7-drug regimen in routine care. AEs were frequent, but manageable in most patients. Active AEs monitoring is essential, particularly when using LZD throughout.

The impact of digital health technologies on tuberculosis treatment: a systematic review.

Digital technologies are increasingly harnessed to support treatment of persons with tuberculosis (TB). Since in-person directly observed treatment (DOT) can be resource intensive and challenging to implement, these technologies may have the potential to improve adherence and clinical outcomes. We reviewed the effect of these technologies on TB treatment adherence and patient outcomes.We searched several bibliographical databases for studies reporting the effect of digital interventions, including short message service (SMS), video-observed therapy (VOT) and medication monitors (MMs), to support treatment for active TB. Only studies with a control group and which reported effect estimates were included.Four trials showed no statistically significant effect on treatment completion when SMS was added to standard care. Two observational studies of VOT reported comparable treatment completion rates when compared with in-person DOT. MMs increased the probability of cure (RR 2.3, 95% CI 1.6-3.4) in one observational study, and one trial reported a statistically significant reduction in missed treatment doses relative to standard care (adjusted means ratio 0.58, 95% CI 0.42-0.79).Evidence of the effect of digital technologies to improve TB care remains limited. More studies of better quality are needed to determine how such technologies can enhance programme performance.