Comorbidities and treatment outcomes in multidrug resistant tuberculosis: a systematic review and meta-analysis
Bibliographic record
Abstract
Little is known about the impact of comorbidities on multidrug resistant (MDR) and extensively drug resistant (XDR) tuberculosis (TB) treatment outcomes. We aimed to examine the effect of human immunodeficiency virus (HIV), diabetes, chronic kidney disease (CKD), alcohol misuse, and smoking on MDR/XDRTB treatment outcomes. We searched MEDLINE, EMBASE, Cochrane Central Registrar and Cochrane Database of Systematic Reviews as per PRISMA guidelines. Eligible studies were identified and treatment outcome data were extracted. We performed a meta-analysis to generate a pooled relative risk (RR) for unsuccessful outcome in MDR/XDRTB treatment by co-morbidity. From 2457 studies identified, 48 reported on 18,257 participants, which were included in the final analysis. Median study population was 235 (range 60-1768). Pooled RR of unsuccessful outcome was higher in people living with HIV (RR = 1.41 [95%CI: 1.15-1.73]) and in people with alcohol misuse (RR = 1.45 [95%CI: 1.21-1.74]). Outcomes were similar in people with diabetes or in people that smoked. Data was insufficient to examine outcomes in exclusive XDRTB or CKD cohorts. In this systematic review and meta-analysis, alcohol misuse and HIV were associated with higher pooled OR of an unsuccessful outcome in MDR/XDRTB treatment. Further research is required to understand the role of comorbidities in driving unsuccessful treatment outcomes.
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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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.047 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".