Risk factors of mortality in pulmonary TB patients in Yerevan, Armenia
Bibliographic record
Abstract
Background TB is one of the ten leading causes of death worldwide. Comorbidities significantly contribute to TB related mortality. In many countries, including Armenia, the successful treatment rate among pulmonary TB (PTB) patients is below the WHO target of 85%. The mortality rate among TB patients was 5.7/100 000 people in 2013 in Armenia. The objective of the study was to identify the risk factors for death among PTB patients in Yerevan, Armenia. Methods We used a retrospective cohort study design. The study population included all adult patients registered in Yerevan outpatient TB facilities whose treatment outcomes were recorded in the database of the Armenian National Tuberculosis Control Center for the period January 1, 2013 to December 31, 2014. The electronic database and medical records were reviewed to obtain information on all the necessary variables. To identify risk factors associated with death outcome we used multiple logistic regression models to compare the variables between TB patients who died and those who survived. Results The study included 621 patients. After adjusting for potential confounders for each risk factor, the odds of death outcome was statistically significantly associated with having CVDs (OR = 14.74; CI:5.02-43.23; P < 0.001), cancer (OR = 9.29; CI:2.17-39.7; P = 0.003), hepatitis C (OR = 6.27; CI:1.88-20.91; P = 0.003), age (OR = 1.03; CI:1.00-1.06; P = 0.029), having DR TB (OR = 2.88; CI:1.05-7.92; P = 0.041) and combined form of TB (pulmonary and extrapulmonary) (OR = 4.57; CI: 1.05-7.92; p = 0.041). Higher weight was a protective factor from death (OR = 0.95 (0.92-0.99) 0.016). Conclusions Our study demonstrated that comorbidities, such as hepatitis C, CVD and cancer, as well as, higher age, DR type of TB and combined form of TB are risk factors for mortality in patients with TB. Key messages: TB patients with serious comorbidities are more likely to have a death outcome during the treatment TB treatment should be more carefully managed among older patients
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".