Medical Research Council of South Africa.

BACKGROUND Improved diagnostic tests for tuberculosis in children are needed. We hypothesized that transcriptional signatures of host blood could be used to distinguish tuberculosis from other diseases in African children who either were or were not infected with the human immunodeficiency virus (HIV). METHODS The study population comprised prospective cohorts of children who were undergoing evaluation for suspected tuberculosis in South Africa (655 children), Malawi (701 children), and Kenya (1599 children). Patients were assigned to groups according to whether the diagnosis was culture-confirmed tuberculosis, culture-negative tuberculosis, diseases other than tuberculosis, or latent tuberculosis infection. Diagnostic signatures distinguishing tuberculosis from other diseases and from latent tuberculosis infection were identified from genomewide analysis of RNA expression in host blood. RESULTS We identified a 51-transcript signature distinguishing tuberculosis from other diseases in the South African and Malawian children (the discovery cohort). In the Kenyan children (the validation cohort), a risk score based on the signature for tuberculosis and for diseases other than tuberculosis showed a sensitivity of 82.9% (95% confidence interval [CI], 68.6 to 94.3) and a specificity of 83.6% (95% CI, 74.6 to 92.7) for the diagnosis of culture-confirmed tuberculosis. Among patients with cultures negative for Mycobacterium tuberculosis who were treated for tuberculosis (those with highly probable, probable, or possible cases of tuberculosis), the estimated sensitivity was 62.5 to 82.3%, 42.1 to 80.8%, and 35.3 to 79.6%, respectively, for different estimates of actual tuberculosis in the groups. In comparison, the sensitivity of the Xpert MTB/RIF assay for molecular detection of M. tuberculosis DNA in cases of culture-confirmed tuberculosis was 54.3% (95% CI, 37.1 to 68.6), and the sensitivity in highly probable, probable, or possible cases was an estimated 25.0 to 35.7%, 5.3 to 13.3%, and 0%, respectively; the specificity of the assay was 100%. CONCLUSIONS RNA expression signatures provided data that helped distinguish tuberculosis from other diseases in African children with and those without HIV infection. (Funded by the European Union Action for Diseases of Poverty Program and others).

Pathogen DNA was isolated from roan antelope (Hippotragus equinus), sable antelope (Hippotragus niger), greater kudu (Tragelaphus strepsiceros), and common gray duiker (Sylvicapra grimmia) in South Africa whose deaths were attributed to either theileriosis or cytauxzoonosis. We developed Theileria species-specific probes used in combination with reverse line blot hybridization assays and identified three different species of Theileria in four African antelope species. The close phylogenetic relationship between members of the genera Theileria and Cytauxzoon, similarities in the morphologies of developmental stages, and confusion in the literature regarding theileriosis or cytauxzoonosis are discussed.

Background Maternal and neonatal mortality is high in Africa, but few large, prospective studies have been done to investigate the risk factors associated with these poor maternal and neonatal outcomes. Methods A 7-day, international, prospective, observational cohort study was done in patients having caesarean delivery in 183 hospitals across 22 countries in Africa. The inclusion criteria were all consecutive patients (aged ≥18 years) admitted to participating centres having elective and non-elective caesarean delivery during the 7-day study cohort period. To ensure a representative sample, each hospital had to provide data for 90% of the eligible patients during the recruitment week. The primary outcome was in-hospital maternal mortality and complications, which were assessed by local investigators. The study was registered on the South African National Health Research Database, number KZ_2015RP7_22, and on ClinicalTrials.gov, number NCT03044899. Findings Between February, 2016, and May, 2016, 3792 patients were recruited from hospitals across Africa. 3685 were included in the postoperative complications analysis (107 missing data) and 3684 were included in the maternal mortality analysis (108 missing data). These hospitals had a combined number of specialist surgeons, obstetricians, and anaesthetists totalling 0•7 per 100 000 population (IQR 0•2-2•0). Maternal mortality was 20 (0•5%) of 3684 patients (95% CI 0•3-0•8). Complications occurred in 633 (17•4%) of 3636 mothers (16•2-18•6), which were predominantly severe intraoperative and postoperative bleeding (136 [3•8%] of 3612 mothers). Maternal mortality was independently associated with a preoperative presentation of placenta praevia, placental abruption, ruptured uterus, antepartum haemorrhage (odds ratio 4•47 [95% CI 1•46-13•65]), and perioperative severe obstetric haemorrhage (5•87 [1•99-17•34]) or anaesthesia complications (11•47 (1•20-109•20]). Neonatal mortality was 153 (4•4%) of 3506 infants (95% CI 3•7-5•0). Interpretation Maternal mortality after caesarean delivery in Africa is 50 times higher than that of high-income countries and is driven by peripartum haemorrhage and anaesthesia complications. Neonatal mortality is double the global average. Early identification and appropriate management of mothers at risk of peripartum haemorrhage might improve maternal and neonatal outcomes in Africa.

Background: The African Surgical Outcomes Study (ASOS) showed that surgical patients in Africa have a mortality twice the global average. Existing risk assessment tools are not valid for use in this population because the pattern of risk for poor outcomes differs from high-income countries. The objective of this study was to derive and validate a simple, preoperative risk stratification tool to identify African surgical patients at risk for in-hospital postoperative mortality and severe complications. Methods: ASOS was a 7-day prospective cohort study of adult patients undergoing surgery in Africa. The ASOS Surgical Risk Calculator was constructed with a multivariable logistic regression model for the outcome of in-hospital mortality and severe postoperative complications. The following preoperative risk factors were entered into the model; age, sex, smoking status, ASA physical status, preoperative chronic comorbid conditions, indication for surgery, urgency, severity, and type of surgery. Results: The model was derived from 8799 patients from 168 African hospitals. The composite outcome of severe postoperative complications and death occurred in 423/8799 (4.8%) patients. The ASOS Surgical Risk Calculator includes the following risk factors: age, ASA physical status, indication for surgery, urgency, severity, and type of surgery. The model showed good discrimination with an area under the receiver operating characteristic curve of 0.805 and good calibration with c-statistic corrected for optimism of 0.784. Conclusions: This simple preoperative risk calculator could be used to identify high-risk surgical patients in African hospitals and facilitate increased postoperative surveillance. Clinical trial registration: NCT03044899.