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.

The State of Surgical Research in Sub-Saharan Africa: An Urgent Call for Surgical Research Trainers.

Background: Surgery in low- and middle-income countries (LMICs) is poorly developed because of years of neglect. Sustained research on global surgery led to its recognition as an indivisible and indispensable part of primary healthcare in 2015. However, this has had little visible effect on surgical ecosystems within LMICs, especially in sub-Saharan Africa (SSA). SSA surgical research systems strengthening, which includes skills transfer, with local priority setting driving the research agenda, is needed to propel global surgery into the future. Methods: The authors performed a literature review of the state of surgical research within SSA and also report the initial efforts of two research training nonprofits to empower young African surgeons with research skills. Results: Surgical research in SSA is disadvantaged even before it is birthed, facing monumental challenges at every stage of development, from research agenda determination to funding, study execution, and publication. Compared with a global output of 17.49 publications per 100,000 population, SSA produces 0.9 (P < 0.0001). The Surgeons in Humanitarian Alliance for Reconstructive, Research, and Education and Enabling Africa Clinical Health Research programs are involved in the longitudinal research mentorship of surgical residents within SSA; the improved quality of research and successful publications by participants suggest nascent steps in growing young surgical scientists. Conclusions: In the absence of an existing surgical research infrastructure within LMICs, global surgery research trainers should link up and collaborate to help develop a surgical research community that will provide the local data required to help transform the SSA surgical ecosystem.

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.

Repeatability of Pulse Oximetry Measurements in Children During Triage in 2 Ugandan Hospitals.

BACKGROUND: In low- and middle-income countries, health workers use pulse oximeters for intermittent spot measurements of oxygen saturation (SpO2). However, the accuracy and reliability of pulse oximeters for spot measurements have not been determined. We evaluated the repeatability of spot measurements and the ideal observation time to guide recommendations during spot check measurements. METHODS: Two 1-minute measurements were taken for the 3,903 subjects enrolled in the study conducted April 2020-January 2022 in Uganda, collecting 1 Hz SpO2 and signal quality index (SQI) data. The repeatability between the 2 measurements was assessed using an intraclass correlation coefficient (ICC), calculated using a median of all seconds of non-zero SpO2 values for each recording (any quality, Q1) and again with a quality filter only using seconds with SQI 90% or higher (good quality, Q2). The ICC was also recalculated for both conditions of Q1 and Q2 using the initial 5 seconds, then the initial 10 seconds, and continuing with 5-second increments up to the full 60 seconds. Lastly, the whole minute ICC was calculated with good quality (Q2), including only records where both measurements had a mean SQI of more than 70% (Q3). RESULTS: The repeatability ICC with condition Q1 was 0.591 (95% confidence interval [CI]=0.570, 0.611). Using only the first 5 seconds of each measurement reduced the repeatability to 0.200 (95% CI=0.169, 0.230). Filtering with Q2, the whole-minute ICC was 0.855 (95% CI=0.847, 0.864). The ICC did not improve beyond the first 35 seconds. For Q3, the repeatability rose to 0.908 (95% CI=0.901, 0.914). CONCLUSIONS: Training guidelines must emphasize the importance of signal quality and duration of measurement, targeting a minimum of 35 seconds of adequate-quality, stable data. In addition, the design of new devices should incorporate user prompts and force quality checks to encourage more accurate pulse oximetry measurements.

A route to decreasing N pollution from livestock: Use of Festulolium hybrids improves efficiency of N flows in rumen simulation fermenters.

Ruminant agriculture suffers from inefficient capture of forage protein and consequential release of N pollutants to land. This is due to proteolysis in the rumen catalyzed by both microbial but initially endogenous plant proteases. Plant breeding-based solutions are sought to minimize these negative environmental impacts. The aim of this study was to perform an integrated study of rumen N metabolism using semi-continuous rumen simulation fermenters (Rusitec) to explore the extent to which swards containing Festulolium populations (interspecific hybrids between Lolium and Festuca grass species) with decreased rates of endogenous protein degradation conferred advantageous protein utilization in comparison with a National Listed perennial ryegrass. An in vitro experiment was conducted using three Festulolium hybrids (Lolium perenne × Festuca arundinacea var. glaucescens, LpFg; Lolium perenne × Festuca mairei, LpFm; and Lolium multiflorum × Festuca arundinacea var. glaucescens, LmFg) and a Lolium perenne, Lp control. LpFm and LmFg demonstrated significantly lower plant-mediated proteolysis than the control. Fresh forage was incubated in Rusitec with rumen fluid from four donor cows. Feed disappearance and production of gas, methane, and volatile fatty acids were similar across cultivars. Whereas no differences in microbial protein synthesis were noted across treatments during early fermentation (0-6 hr after feeding), an increased microbial N flow in LpFm (+30%) and LmFg hybrids (+41%) was observed during late fermentation (6-24 hr after feeding), with higher overall microbial N flows (+13.5% and + 20.2%, respectively) compared with the control (Lp). We propose an underpinning mechanism involving the partitioning of amino acid catabolism toward branched-chain amino acids and microbial protein synthesis in grasses with slow plant-mediated proteolysis instead of accumulation of rumen ammonia in grasses with fast plant-mediated proteolysis. These observations indicate the potential of Festulolium hybrids with a slow plant-mediated proteolysis trait to improve the efficiency of capture of forage protein and decrease the release of N pollutants onto the land.

Measuring movement towards improved emergency obstetric care in rural Kenya with implementation of the PRONTO simulation and team training program.

As the proportion of facility-based births increases, so does the need to ensure that mothers and their newborns receive quality care. Developing facility-oriented obstetric and neonatal training programs grounded in principles of teamwork utilizing simulation-based training for emergency response is an important strategy for improving the quality care. This study uses 3 dimensions of the Kirkpatrick Model to measure the impact of PRONTO International (PRONTO) simulation-based training as part of the Linda Afya ya Mama na Mtoto (LAMMP, Protect the Health of mother and child) in Kenya. Changes in knowledge of obstetric and neonatal emergency response, self-efficacy, and teamwork were analyzed using longitudinal, fixed-effects, linear regression models. Participants from 26 facilities participated in the training between 2013 and 2014. The results demonstrate improvements in knowledge, self-efficacy, and teamwork self-assessment. When comparing pre-Module I scores with post-training scores, improvements range from 9 to 24 percentage points (p values < .0001 to .026). Compared to baseline, post-Module I and post-Module II (3 months later) scores in these domains were similar. The intervention not only improved participant teamwork skills, obstetric and neonatal knowledge, and self-efficacy but also fostered sustained changes at 3 months. The proportion of facilities achieving self-defined strategic goals was high: 95.8% of the 192 strategic goals. Participants rated the PRONTO intervention as extremely useful, with an overall score of 1.4 out of 5 (1, extremely useful; 5, not at all useful). Evaluation of how these improvements affect maternal and perinatal clinical outcomes is forthcoming.

High throughput synthesis of polyesters using entropically driven ring-opening polymerizations.

Copolyesters were synthesized in a high throughput (HT) manner and in high yield on ca. a 90 mg scale using entropically driven ring-opening polymerizations (ED-ROPs). This synthetic approach is a valuable addition to the HT polymer synthesis arsenal in that it allows condensation-type polymers with relatively large repeat units, such as those in poly(ethylene terephthalate) and poly(butylene terephthalate), to be obtained easily. The synthetic procedure involved taking mixtures of the appropriate macrocyclic oligoesters and heating them together under neat conditions at 250-300 degrees C for 2 h in the presence of 0.1 mol % of di- n-butyltin oxide or tetra- n-butylammonium tetrafluoroborate. In most cases Mw values were >25,000 and, as expected for ED-ROPs, the polydispersity indices were close to 2.0. Higher molecular weights could be obtained by using longer reaction times, but this might lead to product decomposition. The method worked well for esters formally derived from aliphatic or aromatic acids and alcohols, but less well for esters derived from phenols. Attempts were also made to synthesize copolymers by mixing together the two homopolymers and heating with a catalyst. These reactions were successful in a few instances, but generally, they were not. This is probably because the homopolymers did not mix well. An aluminum reaction block with 36 wells lined with Teflon cups, that fitted snugly in a cylindrical Buchi oven, was the most successful method for carrying out syntheses in an HT manner.