Causes of HIV-related CNS infection in Cameroon, Malawi, and Tanzania: epidemiological findings from the DREAMM HIV-related CNS implementation study
Bibliographic record
Abstract
Background CNS infections cause approximately a third of HIV-related deaths. The Driving Reduced AIDS-Associated Meningo-encephalitis Mortality DREAMM study aimed to prospectively diagnose the aetiology of HIV-related CNS infection in five public hospitals in Cameroon, Malawi, and Tanzania. Methods DREAMM was a multicentre, hybrid type-2 implementation science project. Adults (aged ≥18 years) presenting with a first episode of suspected CNS infection, who were HIV seropositive or willing to have an HIV test, were eligible for recruitment. Following implementation of the DREAMM model of care, we measured the prevalence of cryptococcal meningitis, tuberculous meningitis, bacterial meningitis, and cerebral toxoplasmosis and did a χ 2 test to assess whether prevalence differed between countries. We also reported disease-specific mortality and Toxoplasma gondii seroprevalence. Findings Of 356 participants with suspected CNS infection analysed at baseline, 269 (76%) were diagnosed as having a CNS infection. Of these, 202 (75%) had a confirmed diagnosis. Between Cameroon, Malawi, and Tanzania, the prevalence of the four main types of CNS infection differed (cryptococcal meningitis p=0·0014, bacterial meningitis p=0·0043, CNS tuberculosis p<0·0001, and toxoplasmosis p<0·0001). Cryptococcal meningitis (148 [55%] of 269) was the leading cause overall. The next most common causes were CNS tuberculosis in Tanzania (29 [29%] of 99) and bacterial meningitis in Malawi (15 [19%] of 80). In Cameroon, cerebral toxoplasmosis (39 [43%] of 90) was the leading cause followed by cryptococcal meningitis (36 [40%] of 90). For cryptococcal meningitis, all-cause 2-week mortality was 23% (34 of 147) and all-cause 10-week mortality was 45% (66 of 146). Interpretation Within the study population, the aetiology of HIV-related CNS infection varied substantially between Malawi, Cameroon, and Tanzania. Additional prospective epidemiological data are needed to inform HIV programmes. 2-week cryptococcal meningitis mortality outcomes were similar to those of clinical trials. However, new interventions are urgently needed to sustain mortality reductions following hospital discharge. Funding European and Developing Countries Clinical Trials Partnership and French Agency for Research on AIDS and Viral Hepatitis. Translations For the French and Portuguese translations of the abstract see Supplementary Materials section.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".