Prevalence and etiologies of pulmonary hypertension in Africa: a systematic review and meta-analysis
Bibliographic record
Abstract
Despite the recent increasing worldwide attention towards pulmonary hypertension (PH), its epidemiology remains poorly described in Africa. Accordingly, we performed a systematic review and meta-analysis of PH prevalence, incidence and etiologies in Africa. We searched PubMed, EMBASE, African Journals Online, and Africa Index Medicus. Published observational studies until September 20, 2017, including adult participants residing in Africa were considered. Two review authors independently selected studies, assessed included studies for methodological quality, and extracted data. A random-effects model was used for meta-analysis. Heterogeneity was evaluated by the χ 2 test on Cochrane’s Q statistic which is quantified by I2 values. Using Newcastle-Ottawa Scale, we considered a score of 0–4, 5–7, and 8–10 as indicative of high, moderate, and low risk of bias in included studies, respectively. Of 1611 entries, 25 studies were retained. Twelve (48%), seven (28%), and six (24%) papers had respectively a low, moderate and high risk of bias. The prevalence of PH widely varied across different populations: 9.8% (95% confidence interval: 3.2–19.3; I2 = 99.4%; 6 studies) in 11,163 people presenting with cardiac complaints; 10.6% (4.3–19.1; I2 = 90.3%; 4 studies) in 937 HIV-infected people; 32.9% (17.6–50.4; I2 = 97.2%; 3 studies) in 2077 patients with heart failure; 23.2% (15.2–32.2; I2 = 59.4%; 3 studies) in 248 patients on hemodialysis; 12.9% (11.8–14.0; I2 = 79.7%; 2 studies) in 3750 patients with rheumatic heart disease; 36.9% (29.7–44.3; I2 = 79.7; 2 studies) in 79 patients with sickle cell disease; 62.7% (49.0–74.7; 1 study) in 51 patients with chronic obstructive pulmonary disease; 25.4% (16.3–37.3; 1 study) in 63 patients with systemic lupus erythematous; 68.7% (62.8–74.1; 1 study) in 259 patients with cardiac surgery; and 7.4% (4.6–11.9; 1 study) in 202 patients with systemic sclerosis. No study reported PH incidence. From one international study (n = 209), PH etiologies were: left heart disease (68.9%), pulmonary arterial hypertension (15.8%), lung disease and/or hypoxia (12.0%), chronic thromboembolic PH (1.9%) and unclear/multifactorial PH (15.8%). The prevalence of PH is relatively high in some populations in Africa, perhaps mainly driven by left heart diseases, highlighting the need for context-specific interventions.
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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 | 0.017 | 0.034 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.049 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 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".