Development and validation of a diagnostic aid for convulsive epilepsy in sub-Saharan Africa: a retrospective case-control study

Background: Identification of convulsive epilepsy in sub-Saharan Africa relies on access to resources that are often unavailable. Infrastructure and resource requirements can further complicate case verification. Using machine-learning techniques, we have developed and tested a region-specific questionnaire panel and predictive model to identify people who have had a convulsive seizure. These findings have been implemented into a free app for health-care workers in Kenya, Uganda, Ghana, Tanzania, and South Africa. Methods: In this retrospective case-control study, we used data from the Studies of the Epidemiology of Epilepsy in Demographic Sites in Kenya, Uganda, Ghana, Tanzania, and South Africa. We randomly split these individuals using a 7:3 ratio into a training dataset and a validation dataset. We used information gain and correlation-based feature selection to identify eight binary features to predict convulsive seizures. We then assessed several machine-learning algorithms to create a multivariate prediction model. We validated the best-performing model with the internal dataset and a prospectively collected external-validation dataset. We additionally evaluated a leave-one-site-out model (LOSO), in which the model was trained on data from all sites except one that, in turn, formed the validation dataset. We used these features to develop a questionnaire-based predictive panel that we implemented into a multilingual app (the Epilepsy Diagnostic Companion) for health-care workers in each geographical region. Findings: We analysed epilepsy-specific data from 4097 people, of whom 1985 (48·5%) had convulsive epilepsy, and 2112 were controls. From 170 clinical variables, we initially identified 20 candidate predictor features. Eight features were removed, six because of negligible information gain and two following review by a panel of qualified neurologists. Correlation-based feature selection identified eight variables that demonstrated predictive value; all were associated with an increased risk of an epileptic convulsion except one. The logistic regression, support vector, and naive Bayes models performed similarly, outperforming the decision-tree model. We chose the logistic regression model for its interpretability and implementability. The area under the receiver operator curve (AUC) was 0·92 (95% CI 0·91–0·94, sensitivity 85·0%, specificity 93·7%) in the internal-validation dataset and 0·95 (0·92–0·98, sensitivity 97·5%, specificity 82·4%) in the external-validation dataset. Similar results were observed for the LOSO model (AUC 0·94, 0·93–0·96, sensitivity 88·2%, specificity 95·3%). Interpretation: On the basis of these findings, we developed the Epilepsy Diagnostic Companion as a predictive model and app offering a validated culture-specific and region-specific solution to confirm the diagnosis of a convulsive epileptic seizure in people with suspected epilepsy. The questionnaire panel is simple and accessible for health-care workers without specialist knowledge to administer. This tool can be iteratively updated and could lead to earlier, more accurate diagnosis of seizures and improve care for people with epilepsy

Fire and Herbivory Interactively Suppress the Survival and Growth of Trees in an African Semiarid Savanna

There has been a long-standing interest in understanding how interactions between fire and herbivory influence woody vegetation dynamics in savanna ecosystems. However, controlled, replicated experiments examining how different fire regimes interact with different herbivore groups are rare. We tested the effects of single and repeated burns, crossed with six replicated herbivore treatments, on the mortality and growth of woody vegetation in the Kenya Long-term Exclosure Experiment plots located in a semi-arid savanna system in central Kenya. Burned plots experienced higher tree mortality overall, but differences between burns and non-burns were only significant in plots excluding all wild herbivores and in plots accessible to megaherbivores. Cattle ameliorated the negative effects of repeat burns on tree mortality, perhaps by suppressing fuel load accumulation. Across all herbivore treatments, trees experienced a significant reduction in height within the first two years after fire (top-kill), which was followed by a gradual recovery. Saplings and coppices subjected to repeated burns regrew faster than those that were burned once, except in the presence of megaherbivores. This study highlights strong context-dependent interactions between fire and different herbivore groups, and extends previous approaches to understanding fire–herbivory interactions, which have tended to lump the effects of different herbivore groups, or study them separately

Utilization of dried blood spot specimens can expedite nationwide surveillance of HIV drug resistance in resource-limited settings.

INTRODUCTION:Surveillance of HIV drug resistance (HIVDR) is crucial to ensuring the continued success of antiretroviral therapy (ART) programs. With the concern of reduced genotyping sensitivity of HIV on dried blood spots (DBS), DBS for HIVDR surveillance have been limited to ART-naïve populations. To investigate if DBS under certain conditions may also be a feasible sample type for HIVDR testing in ART patients, we piloted nationwide surveys for HIVDR among ART patients using DBS in two African countries with rapid scale-up of ART. METHODS:EDTA-venous blood was collected to prepare DBS from adult and pediatric ART patients receiving treatment during the previous 12-36 months. DBS were stored at ambient temperature for two weeks and then at -80°C until shipment at ambient temperature to the WHO-designated Specialized HIVDR Laboratory at CDC in Atlanta. Viral load (VL) was determined using NucliSENS EasyQ® HIV-1 v2.0 kits; HIVDR genotyping was performed using the ATCC HIV-1 Drug Resistance Genotyping kits. RESULTS:DBS were collected from 1,368 and 1,202 ART patients; 244 and 255 these specimens had VL ≥1,000 copies/mL in Kenya and Tanzania, respectively. The overall genotyping rate of those DBS with VL ≥1,000 copies/mL was 93.0% (95% CI: 89.1%-95.6%) in Kenya and 91.8% (87.7%-94.6%) in Tanzania. The turnaround times for the HIVDR surveys from the time of collecting DBS to completing laboratory testing were 6.5 months and 9.3 months for the Kenya and Tanzania surveys, respectively. CONCLUSIONS:The study demonstrates a favorable outcome of using DBS for nationwide surveillance of HIVDR in ART patients. Our results confirm that DBS collected and stored at ambient temperature for two weeks, and shipped with routine courier services are a reliable sample type for large-scale surveillance of acquired HIVDR