Purpose: The Chulalongkorn-Hasanuddin Rifampicin-Resistant Tuberculosis Screening Tool (CUHAS-ROBUST) is an artificial intelligence–based (AI–based) application for rifampicin-resistant tuberculosis (RR-TB) screening. This study aims to elaborate on the drug-resistant TB (DR-TB) problem and the impact of CUHAS-ROBUST implementation on RR-TB screening.Design/methodology/approach: A qualitative approach with content analysis was performed from September 2020 to October 2020. Medical staff from the primary care center were invited online for application trials and in-depth video call interviews. Transcripts were derived as a data source. An inductive thematic data saturation technique was conducted. Descriptive data of participants, user experience and the impact on the health service were summarizedFindings: A total of 33 participants were selected from eight major islands in Indonesia. The findings show that DR-TB is a new threat, and its diagnosis faces obstacles particularly prolonged waiting time and inevitable delayed treatment. Despite overcoming the RR-TB screening problems with fast prediction, the dubious screening performance, and the reliability of data collection for input parameters were the main concerns of CUHAS-ROBUST. Nevertheless, this application increases the confidence in decision-making, promotes medical procedure compliance, active surveillance and enhancing a low-cost screening approach.Originality/value: The CUHAS-ROBUST achieved its purpose as a tool for clinical decision-making in RR-TB screening. Moreover, this study demonstrates AI roles in enhancing health-care quality and boost public health efforts against tuberculosis.

Background and objectives: Diagnosis of Pulmonary Rifampicin Resistant Tuberculosis (RR-TB) with the Drug-Susceptibility Test (DST) is costly and time-consuming. Furthermore, GeneXpert for rapid diagnosis is not widely available in Indonesia. This study aims to develop and evaluate the CUHAS-ROBUST model performance, an artificial-intelligence-based RR-TB screening tool. Methods: A cross-sectional study involved suspected all type of RR-TB patients with complete sputum Lowenstein Jensen DST (reference) and 19 clinical, laboratory, and radiology parameter results, retrieved from medical records in hospitals under the Faculty of Medicine, Hasanuddin University Indonesia, from January 2015-December 2019. The Artificial Neural Network (ANN) models were built along with other classifiers. The model was tested on participants recruited from January 2020-October 2020 and deployed into CUHAS-ROBUST (index test) application. Sensitivity, specificity, and accuracy were obtained for assessment. Results: A total of 487 participants (32 Multidrug-Resistant/MDR 57 RR-TB, 398 drug-sensitive) were recruited for model building and 157 participants (23 MDR and 21 RR) in prospective testing. The ANN full model yields the highest values of accuracy (88% (95% CI 85–91)), and sensitivity (84% (95% CI 76–89)) compare to other models that show sensitivity below 80% (Logistic Regression 32%, Decision Tree 44%, Random Forest 25%, Extreme Gradient Boost 25%). However, this ANN has lower specificity among other models (90% (95% CI 86–93)) where Logistic Regression demonstrates the highest (99% (95% CI 97–99)). This ANN model was selected for the CUHAS-ROBUST application, although still lower than the sensitivity of global GeneXpert results (87.5%). Conclusion: The ANN-CUHAS ROBUST outperforms other AI classifiers model in detecting all type of RR-TB, and by deploying into the application, the health staff can utilize the tool for screening purposes particularly at the primary care level where the GeneXpert examination is not available. Trial registration: NCT04208789.

Background Symptoms after COVID-19 infection affect the quality of life of its survivor especially to the special senses including olfactory function. It is important to prevent the disability at an earlier stage. Vaccination as key prevention has been proven to be effective in reducing symptomatic disease and severity. However, the effects of vaccination on post COVID symptoms have not been evaluated. This study aimed to evaluate the possible protection of full vaccination and the occurrence of post-COVID olfactory dysfunction, specifically anosmia and hyposmia in patients who were diagnosed with COVID19. Method A longitudinal analysis using the retrospective cohort of the Indonesian patient-based Post-COVID survey collected from July 2021 until December 2021, involving COVID-19 Patients confirmed by RT-PCR and/or Antigen test. Variables including demography, comorbidities, health behavior, type of vaccine, symptoms, and treatment were collected through an online questionnaire based on the American Academy of Otolaryngology-Head and Neck Surgery (AAO-HNS). Participants were matched (1:1) using propensity matching score into two exposure statuses, infected 1)>14 days of full vaccination and 2)14 days after being fully vaccinated had a 69% (adjusted OR 0.31 95% CI 0.102-0.941) probability of developing olfactory dysfunction. Longer days of fully vaccinated to infection associated with increased risk (adjusted OR 1.012 95% CI 1.002-1.022 p-value 0.015). A cut-off of 88 days of full vaccination-to-diagnosis duration has Area Under Curve (AUC) of 0.693 (p=0.002), the sensitivity of 73.9%, and specificity of 63.3% in differentiating the olfactory dysfunction event in two weeks after COVID with a crude odds ratio of 4.852 (95% CI 1.831-12.855 p=0.001) Conclusion After 14 days of full vaccination, the protective effect could reduce the chance of post-COVID olfactory dysfunction although a longer full vaccination-to-diagnosis duration increases the risk. It is important to consider a booster shot starting from 89 days after the last dose in those who received the inactivated viral regimen.

Background: Coronavirus disease (COVID-19) not only has a long-term effect on its survivors, it also affects their quality of life, including inducing depression as a possible manifestation of central nervous system disruption. Favipiravir shows promising efficacy as an antiviral drug for the treatment of COVID-19. However, its effect on the sequelae of COVID-19 has not been explored. Therefore, this study aims to assess the impact of Favipiravir and address the factors associated with post-COVID depression in Indonesia.Method: This cohort study conducted a post-COVID-19 survey on Indonesian patients who were diagnosed by using real-time polymerase chain reaction (RT-PCR) and antigen tests until January 2022. An online questionnaire was distributed to obtain information on demographics, comorbidities, health behavior, symptoms, and treatment. The propensity technique was used to allocate the participants into the favipiravir and nonrecipient groups (1:1). The Patient Health Questionnaire-9 (PHQ-9) was used for outcome measurement. The cohort was followed up biweekly for 60 days after onset/diagnosis to determine the occurrence of depression. Cox regression analysis with an adjusted odds ratio and 95% confidence interval was used to estimate the effect of favipiravir on post-COVID-19 depression.Results: The data included the information of 712 participants, of whom 18.54% had depression within 60 days after onset/diagnosis. Depression was higher in the nonrecipient group (21.06%) than in the favipiravir group (16.01%). After adjustment by other factors, favipiravir prescription was found to be associated with depression (aOR 0.488, 95% CI 0.339–0.701 p < 0.001). In accordance with the PHQ-9 subset, favipiravir exerted a significant protective effect against depressive mood and loss of interest. However, patients living alone were prone to experiencing loss of interest (aOR 2.253, 95% CI 1.329–3.818, p = 0.003).Conclusion: The data obtained in this preliminary survey suggested that favipiravir may be useful for preventing post-COVID depression. However, further study is needed. Moreover, the provision of mental health support, particularly to those who live alone, must be ensured.Trial registration: RegistryNCT05060562.

The post COVID-19 symptoms affect the productivity and the quality of life among survivors. It is imperative to identify the effect of virus variants and the vaccination against post-COVID-19 symptoms. There were 242 participants from the eastern part of Indonesia diagnosed with COVID-19 during July 2021-July to 2022 were recruited online. The participants underwent data collection and semi-clinical follow-up for post-COVID-19 symptoms within 30 days after the first symptoms or from the diagnosis day using a validated clinical questionnaire and physician confirmation. Fatigue was the most reported post-COVID-19 symptom (27.7%), followed by chronic cough (21.5%) and headache (15.3%). Adjusted by confounding factors in hierarchical logistic regression, the differences in post-COVID-19 symptoms were insignificant across different variants. Regarding vaccine efficacy against three post-COVID-19 symptoms, people with two-dose vaccination significantly reported lower post-COVID-19 chronic cough (adjusted Odds Ratio 0.244 95% CI OR 0.071-0.838), but the protection against fatigue and the chronic headache was insignificant. There is an indication that vaccine efficacy may be waning along with the emerging new variants.