Comparison between the Smart Triage model and the Emergency Triage Assessment and Treatment guidelines in triaging children presenting to the emergency departments of two public hospitals in Kenya.

Several triage systems have been developed, but little is known about their performance in low-resource settings. Evaluating and comparing novel triage systems to existing triage scales provides essential information about their added value, reliability, safety, and effectiveness before adoption. This study included children aged < 15 years who presented to the emergency departments of two public hospitals in Kenya between February and December 2021. We compared the performance of Emergency Triage Assessment and Treatment (ETAT) guidelines and Smart Triage (ST) models (ST model with independent triggers, and recalibrated ST model with independent triggers) in categorizing children into emergency, priority, and non-urgent triage categories. Sankey diagrams were used to visualize the distribution of children into similar or different triage categories by ETAT and ST models. Sensitivity, specificity, negative and positive predictive values for mortality and admission were calculated. 5618 children were enrolled, and the majority (3113, 55.4%) were aged between one and five years of age. Overall admission and mortality rates were 7% and 0.9%, respectively. ETAT classified 513 (9.2%) children into the emergency category compared to 1163 (20.8%) and 1161 (20.7%) by the ST model with independent triggers and recalibrated model with independent triggers, respectively. ETAT categorized 3089 (55.1%) children as non-urgent compared to 2097 (37.4%) and 2617 (46.7%) for the respective ST models. ETAT classified 191/395 (48.4%) admitted patients as emergencies compared to more than half by all the ST models. ETAT and ST models classified 25/49 (51%) and 39/49 (79.6%) deceased children as emergencies. Sensitivity for admission and mortality was 48.4% and 51% for ETAT and 74.9% and 79.6% for the ST models, respectively. Smart Triage shows potential for identifying critically ill children in low-resource settings, particularly when combined with independent triggers and performs comparably to ETAT. Evaluation of Smart Triage in other contexts and comparison to other triage systems is required.

Geographical validation of the Smart Triage Model by age group.

Infectious diseases in neonates account for half of the under-five mortality in low- and middle-income countries. Data-driven algorithms such as clinical prediction models can be used to efficiently detect critically ill children in order to optimize care and reduce mortality. Thus far, only a handful of prediction models have been externally validated and are limited to neonatal in-hospital mortality. The aim of this study is to externally validate a previously derived clinical prediction model (Smart Triage) using a combined prospective baseline cohort from Uganda and Kenya with a composite endpoint of hospital admission, mortality, and readmission. We evaluated model discrimination using area under the receiver-operator curve (AUROC) and visualized calibration plots with age subsets (< 30 days, ≤ 2 months, ≤ 6 months, and < 5 years). Due to reduced performance in neonates (< 1 month), we re-estimated the intercept and coefficients and selected new thresholds to maximize sensitivity and specificity. 11595 participants under the age of five (under-5) were included in the analysis. The proportion with an endpoint ranged from 8.9% in all children under-5 (including neonates) to 26% in the neonatal subset alone. The model achieved good discrimination for children under-5 with AUROC of 0.81 (95% CI: 0.79-0.82) but poor discrimination for neonates with AUROC of 0.62 (95% CI: 0.55-0.70). Sensitivity at the low-risk thresholds (CI) were 85% (83%-87%) and 68% (58%-76%) for children under-5 and neonates, respectively. After model revision for neonates, we achieved an AUROC of 0.83 (95% CI: 0.79-0.87) with 13% and 41% as the low- and high-risk thresholds, respectively. The updated Smart Triage performs well in its predictive ability across different age groups and can be incorporated into current triage guidelines at local healthcare facilities. Additional validation of the model is indicated, especially for the neonatal model.

External validation of a paediatric Smart triage model for use in resource limited facilities.

Models for digital triage of sick children at emergency departments of hospitals in resource poor settings have been developed. However, prior to their adoption, external validation should be performed to ensure their generalizability. We externally validated a previously published nine-predictor paediatric triage model (Smart Triage) developed in Uganda using data from two hospitals in Kenya. Both discrimination and calibration were assessed, and recalibration was performed by optimizing the intercept for classifying patients into emergency, priority, or non-urgent categories based on low-risk and high-risk thresholds. A total of 2539 patients were eligible at Hospital 1 and 2464 at Hospital 2, and 5003 for both hospitals combined; admission rates were 8.9%, 4.5%, and 6.8%, respectively. The model showed good discrimination, with area under the receiver-operator curve (AUC) of 0.826, 0.784 and 0.821, respectively. The pre-calibrated model at a low-risk threshold of 8% achieved a sensitivity of 93% (95% confidence interval, (CI):89%-96%), 81% (CI:74%-88%), and 89% (CI:85%-92%), respectively, and at a high-risk threshold of 40%, the model achieved a specificity of 86% (CI:84%-87%), 96% (CI:95%-97%), and 91% (CI:90%-92%), respectively. Recalibration improved the graphical fit, but new risk thresholds were required to optimize sensitivity and specificity.The Smart Triage model showed good discrimination on external validation but required recalibration to improve the graphical fit of the calibration plot. There was no change in the order of prioritization of patients following recalibration in the respective triage categories. Recalibration required new site-specific risk thresholds that may not be needed if prioritization based on rank is all that is required. The Smart Triage model shows promise for wider application for use in triage for sick children in different settings.

Epidemiology and ecology of the sylvatic cycle of African Swine Fever Virus in Kenya.

African Swine Fever (ASF) is caused by a DNA virus (AFSV) maintained and transmitted by the Argasid ticks. The re-emergence of the disease in Africa coupled with its rapid spread globally is a threat to the pig industry, food security and livelihoods. The ecology and epidemiology of the ASFV sylvatic cycle, especially in the face of changing land use and land cover, further compounds the menace and impacts of this disease in Kenya. The study aimed to determine the occurrence and distribution of ASFV seroprevalence in warthog populations, the tick vectors and extent of tick infestation of warthog burrows, and the genotypes of ASFV in soft ticks in Kenya. Warthogs from different parts of Kenya were captured and venous blood was centrifuged to harvest sera. Warthog burrows were examined for their conditions and to extract ticks. Sera were analyzed for antibodies against ASFV using a commercial ELISA kit coated with p32 ASFV recombinant protein. Ticks were pooled, DNA extracted and the p72 gene of the ASFV was amplified by qPCR and conventional PCR. The overall seroprevalence of ASFV in warthogs was 87.5 %. A total of 228 warthog burrows were examined and 2154 argasid ticks were extracted from the burrows. Tick pools from Kigio Farm and Lewa Wildlife Conservancies were ASFV-positive by qPCR and conventional PCR. ASFV was further confirmed by the Twist Comprehensive Viral Research Panel (TCVRP), which also identified the argasid ticks as Ornithodoros porcinus. The ticks were infected with virus genotype IX, and their occurrence overlaps with regions of previous ASF outbreaks in domestic pigs. Further, Viruses that could be tick endosymbionts/commensals or due to bloodmeal were detected in ticks by TCVRP; Porcine type-C oncovirus; Pandoravirus neocaledonia; Choristoneura fumiferana granulovirus; Enterobacteria phage p7; Leporid herpesvirus 4 isolate; 5; Human Lymphotropic virus; Human herpesvirus 5. In conclusion, our results suggest that infected Ornithodoros spp. seems to have a rich virome, which has not been explored but could be exploited to inform ASF control in Kenya. Further, the ecology of Ornithodoros spp. and burrow-use dynamics are complex and more studies are needed to understand these dynamics, specifically in the spread of ASFV at the interface of wild and domestic pigs. Further, our results provide evidence of genotype IX ASFV sylvatic cycle which through O. porcinus tick transmission has resulted in high exposure of adult common warthogs. Finally, the co-circulation of ASFV genotype IX in the same location with past ASF outbreaks in domestic pigs and presently in ticks brings to focus the role of the interface and ticks on virus transmission to pigs and warthogs.

A Novel Coronavirus and a Broad Range of Viruses in Kenyan Cave Bats.

BACKGROUND AND METHODS: To investigate virus diversity in hot zones of probable pathogen spillover, 54 oral-fecal swabs were processed from five bat species collected from three cave systems in Kenya, using metagenome sequencing. RESULTS: Viruses belonging to the Astroviridae, Circoviridae, Coronaviridae, Dicistroviridae, Herpesviridae and Retroviridae were detected, with unclassified viruses. Retroviral sequences were prevalent; 74.1% of all samples were positive, with distinct correlations between virus, site and host bat species. Detected retroviruses comprised Myotis myotis, Myotis ricketti, Myotis daubentonii and Galidia endogenous retroviruses, murine leukemia virus-related virus and Rhinolophus ferrumequinum retrovirus (RFRV). A near-complete genome of a local RFRV strain with identical genome organization and 2.8% nucleotide divergence from the prototype isolate was characterized. Bat coronavirus sequences were detected with a prevalence of 24.1%, where analyses on the ORF1ab region revealed a novel alphacoronavirus lineage. Astrovirus sequences were detected in 25.9%of all samples, with considerable diversity. In 9.2% of the samples, other viruses including Actinidia yellowing virus 2, bat betaherpesvirus, Bole tick virus 4, Cyclovirus and Rhopalosiphum padi virus were identified. CONCLUSIONS: Further monitoring of bats across Kenya is essential to facilitate early recognition of possibly emergent zoonotic viruses.

Complete Reference Genome Assembly for Commensalibacter sp. Strain AMU001, an Acetic Acid Bacterium Isolated from the Gut of Honey Bees.

We report here the genome sequence of a Commensalibacter sp. strain (AMU001) isolated from honey bees (Apis mellifera) from Seychelles. By combining long- and short-read sequencing technologies, we produced the first complete reference genome assembly for the Commensalibacter genus. We anticipate that this will aid future comparative and functional genomic studies.