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.

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.

Cost-effectiveness analysis of Smart Triage, a data-driven pediatric sepsis triage platform in Eastern Uganda.

BACKGROUND: Sepsis, characterized by organ dysfunction due to presumed or proven infection, has a case-fatality over 20% in severe cases in low-and-middle income countries. Early diagnosis and treatment have proven benefits, prompting our implementation of Smart Triage at Jinja Regional Referral Hospital in Uganda, a program that expedites treatment through a data-driven triage platform. We conducted a cost-effectiveness analysis of Smart Triage to explore its impact on patients and inform multicenter scale up. METHODS: The parent clinical trial for Smart Triage was pre-post in design, using the proportion of children receiving sepsis treatment within one hour as the primary outcome, a measure linked to mortality benefit in existing literature. We used a decision-analytic model with Monte Carlo simulation to calculate the cost per year-of-life-lost (YLL) averted of Smart Triage from societal, government, and patient perspectives. Healthcare utilization and lost work for seven days post-discharge were translated into costs and productivity losses via secondary linkage data. RESULTS: In 2021 United States dollars, Smart Triage requires an annuitized program cost of only $0.05 per child, but results in $15.32 saved per YLL averted. At a willingness-to-pay threshold of only $3 per YLL averted, well below published cost-effectiveness threshold estimates for Uganda, Smart Triage approaches 100% probability of cost-effectiveness over the baseline manual triage system. This cost-effectiveness was observed from societal, government, and patient perspectives. The cost-effectiveness observed was driven by a reduction in admission that, while explainable by an improved triage mechanism, may also be partially attributable to changes in healthcare utilization influenced by the coronavirus pandemic. However, Smart Triage remains cost-effective in sensitivity analyses introducing a penalty factor of up to 50% in the reduction in admission. CONCLUSION: Smart Triage's ability to both save costs and avert YLLs indicates that patients benefit both economically and clinically, while its high probability of cost-effectiveness strongly supports multicenter scale up. Areas for further research include the incorporation of years lived with disability when sepsis disability weights in low-resource settings become available and analyzing budget impact during multicenter scale up. TRIAL REGISTRATION: NCT04304235 (registered on 11/03/2020, clinicaltrials.gov).

Patulin Alters Insulin Signaling and Metabolic Flexibility in HepG2 and HEK293 Cells.

Non-communicable diseases (NCDs) have risen rapidly worldwide, sparking interest in causative agents and pathways. Patulin (PAT), a xenobiotic found in fruit products contaminated by molds, is postulated to be diabetogenic in animals, but little is known about these effects in humans. This study examined the effects of PAT on the insulin signaling pathway and the pyruvate dehydrogenase complex (PDH). HEK293 and HepG2 cells were exposed to normal (5 mM) or high (25 mM) glucose levels, insulin (1.7 nM) and PAT (0.2 µM; 2.0 µM) for 24 h. The qPCR determined gene expression of key enzymes involved in carbohydrate metabolism while Western blotting assessed the effects of PAT on the insulin signaling pathway and Pyruvate Dehydrogenase (PDH) axis. Under hyperglycemic conditions, PAT stimulated glucose production pathways, caused defects in the insulin signaling pathway and impaired PDH activity. These trends under hyperglycemic conditions remained consistent in the presence of insulin. These findings are of importance, given that PAT is ingested with fruit and fruit products. Results suggest PAT exposure may be an initiating event in insulin resistance, alluding to an etiological role in the pathogenesis of type 2 diabetes and disorders of metabolism. This highlights the importance of both diet and food quality in addressing the causes of NCDs.

Prognostic algorithms for post-discharge readmission and mortality among mother-infant dyads: an observational study protocol.

Introduction: In low-income country settings, the first six weeks after birth remain a critical period of vulnerability for both mother and newborn. Despite recommendations for routine follow-up after delivery and facility discharge, few mothers and newborns receive guideline recommended care during this period. Prediction modelling of post-delivery outcomes has the potential to improve outcomes for both mother and newborn by identifying high-risk dyads, improving risk communication, and informing a patient-centered approach to postnatal care interventions. This study aims to derive post-discharge risk prediction algorithms that identify mother-newborn dyads who are at risk of re-admission or death in the first six weeks after delivery at a health facility. Methods: This prospective observational study will enroll 7,000 mother-newborn dyads from two regional referral hospitals in southwestern and eastern Uganda. Women and adolescent girls aged 12 and above delivering singletons and twins at the study hospitals will be eligible to participate. Candidate predictor variables will be collected prospectively by research nurses. Outcomes will be captured six weeks following delivery through a follow-up phone call, or an in-person visit if not reachable by phone. Two separate sets of prediction models will be built, one set of models for newborn outcomes and one set for maternal outcomes. Derivation of models will be based on optimization of the area under the receiver operator curve (AUROC) and specificity using an elastic net regression modelling approach. Internal validation will be conducted using 10-fold cross-validation. Our focus will be on the development of parsimonious models (5-10 predictor variables) with high sensitivity (>80%). AUROC, sensitivity, and specificity will be reported for each model, along with positive and negative predictive values. Discussion: The current recommendations for routine postnatal care are largely absent of benefit to most mothers and newborns due to poor adherence. Data-driven improvements to postnatal care can facilitate a more patient-centered approach to such care. Increasing digitization of facility care across low-income settings can further facilitate the integration of prediction algorithms as decision support tools for routine care, leading to improved quality and efficiency. Such strategies are urgently required to improve newborn and maternal postnatal outcomes. Clinical trial registration: https://clinicaltrials.gov/, identifier (NCT05730387).

Health worker perspectives of Smart Triage, a digital triaging platform for quality improvement at a referral hospital in Uganda: a qualitative analysis.

BACKGROUND: Effective triage at hospitals can improve outcomes for children globally by helping identify and prioritize care for those most at-risk of death. Paper-based pediatric triage guidelines have been developed to support frontline health workers in low-resource settings, but these guidelines can be challenging to implement. Smart Triage is a digital triaging platform for quality improvement (QI) that aims to address this challenge. Smart Triage represents a major cultural and behavioural shift in terms of managing patients at health facilities in low-and middle-income countries. The purpose of this study is to understand user perspectives on the usability, feasibility, and acceptability of Smart Triage to inform ongoing and future implementation. METHODS: This was a descriptive qualitative study comprising of face-to-face interviews with health workers (n = 15) at a regional referral hospital in Eastern Uganda, conducted as a sub-study of a larger clinical trial to evaluate Smart Triage (NCT04304235). Thematic analysis was used to assess the usability, feasibility, and acceptability of the platform, focusing on its use in stratifying and prioritizing patients according to their risk and informing QI initiatives implemented by health workers. RESULTS: With appropriate training and experience, health workers found most features of Smart Triage usable and feasible to implement, and reported the platform was acceptable due to its positive impact on reducing the time to treatment for emergency pediatric cases and its use in informing QI initiatives within the pediatric ward. Several factors that reduced the feasibility and acceptability were identified, including high staff turnover, a lack of medical supplies at the hospital, and challenges with staff attitudes. CONCLUSION: Health workers can use the Smart Triage digital triaging platform to identify and prioritize care for severely ill children and improve quality of care at health facilities in low-resource settings. Future innovation is needed to address identified feasibility and acceptability challenges; however, this platform could potentially address some of the challenges to implementing current paper-based systems.

miR-27b inhibition contributes to cytotoxicity in patulin-exposed HEK293 cells.

Patulin (PAT) is a mycotoxin produced by Penicillium and other fungi that contaminate fruit. PAT targets the kidney and is associated with nephrotoxicity. Micro-RNAs (miRNA) may offer new insights into PAT-induced nephrotoxicity. Cytochrome P450 family 1, subfamily B, polypeptide 1 (CYP1B1), involved in metabolism of dietary toxins is negatively regulated by miR-27b and linked with the nuclear factor kappa B (NF-κB) pathway and peroxisome proliferator activated receptor gamma (PPARÉ£) in renal fibrosis. This study investigated the effects of PAT on miR-27b, CYP1B1, PPARÉ£ and cytotoxicity in human kidney (HEK293) cells. HEK293 cells were exposed to PAT (2.5 µM, 24h). Protein expression of CYP1B1, PPARÉ£, NF-κB (p65), pNF-κB (p65) (phospho-Ser563) and cleaved PARP-1 was quantified using western blotting. QPCR evaluated mRNA levels of CYP1B1, IL-6, miR-27b, OGG1, mtDNA, TFAM and UCP2. Mitochondrial membrane potential and phosphatidylserine (PS) externalization was evaluated by flow cytometry while levels of ATP and caspase -9, -8, -3/7 activity was measured using luminometry. PAT significantly decreased miR-27b levels (p = 0.0014) and increased CYP1B1 mRNA (p = 0.0015) and protein (p = 0.0013) levels. PPARÉ£ protein expression was significantly increased (p = 0.0002) and associated with decreased NF-κB activation (p = 0.0273) and IL-6 mRNA levels (p = 0.0265). Finally, PAT significantly compromised mitochondrial repair mechanisms and increased apoptotic biomarkers. PAT altered miR-27b levels and PPARÉ£, with associated changes to NF-κB activation, downstream IL-6 and CYP1B1 expression. These results show that PAT impairs detoxification mechanisms leading to mitochondrial damage and apoptosis. In conclusion, PAT altered the epigenetic environment and impaired detoxification processes, supporting a mechanism for nephrotoxic outcomes.

Patulin activates the NRF2 pathway by modulation of miR-144 expression in HEK293 cells.

Patulin (PAT) is a mycotoxin produced by various fungal species that commonly contaminate apples and other fruit products. PAT is associated with glutathione (GSH) depletion and oxidative stress. Cytoprotective and antioxidant (AO) enzymes limit toxic outcomes and confer resistance to oxidative stress by influencing the expression of cytoprotective genes. The induction of these genes is tightly regulated by transcription factor nuclear factor erythroid 2 p45-related factor 2 (NRF2), a potential target of microRNA (miR)-144. This study aims to determine a possible role for miR-144 in NRF2 pathway activation following PAT exposure in human embryonic kidney (HEK293) cells. HEK293 cells were exposed to varying PAT concentrations (0, 0.2, 0.5, 1 µmol/L; 24 h). Protein expression of Keap1, NRF2, and phosphorylated (p) NRF2 (ser40) was quantified using western blotting. Gene expression of NRF2, SOD2, CAT, GPx, NQO1, GSTA1, HMOX, and miR-144 were evaluated by qPCR. PAT significantly decreased miR-144 (p = 0.0249) and concomitantly increased NRF2 protein expression, stability, and activation as evidenced by increased pNRF2 (p = 0.0216) expression and decreased total NRF2 (p = 0.0237). This was consistent with qPCR data which showed increased transcript levels of NRF2 (p = 0.0378) as well as the target genes CAT (p = 0.0273), NQO1 (p = 0.0156), HMOX (p = 0.0249), and GSTA1 (p = 0.0237). No changes were observed in Keap1 expression (p = 0.6444). This study implicates microRNAs in a mechanistic role in PAT-induced toxicity. PAT decreased miR-144 expression leading to NRF2 pathway activation and elevated AO gene expression.

Patulin suppresses α1-adrenergic receptor expression in HEK293 cells.

Patulin (PAT) is a common mycotoxin contaminant of apple products linked to impaired metabolic and kidney function. Adenosine monophosphate activated protein kinase (AMPK), abundantly expressed in the kidney, intercedes metabolic changes and renal injury. The alpha-1-adrenergic receptors (α1-AR) facilitate Epinephrine (Epi)-mediated AMPK activation, linking metabolism and kidney function. Preliminary molecular docking experiments examined potential interactions and AMPK-gamma subunit 3 (PRKAG3). The effect of PAT exposure (0.2-2.5 µM; 24 h) on the AMPK pathway and α1-AR was then investigated in HEK293 human kidney cells. AMPK agonist Epi determined direct effects on the α1-AR, metformin was used as an activator for AMPK, while buthionine sulphoximine (BSO) and N-acetyl cysteine (NAC) assessed GSH inhibition and supplementation respectively. ADRA1A and ADRA1D expression was determined by qPCR. α1-AR, ERK1/2/MAPK and PI3K/Akt protein expression was assessed using western blotting. PAT (1 µM) decreased α1-AR protein and mRNA and altered downstream signalling. This was consistent in cells stimulated with Epi and metformin. BSO potentiated the observed effect on α1-AR while NAC ameliorated these effects. Molecular docking studies performed on Human ADRA1A and PRKAG3 indicated direct interactions with PAT. This study is the first to show PAT modulates the AMPK pathway and α1-AR, supporting a mechanism of kidney injury.

Fumonisin B1-induced oxidative stress triggers Nrf2-mediated antioxidant response in human hepatocellular carcinoma (HepG2) cells.

Fumonisin B1 (FB1), a causative agent for animal-related mycotoxicoses, has been implicated in human and animal cancer. FB1 induces oxidative stress but the related survival responses are not well established. Central to this response is the transcription factor, nuclear factor erythroid 2 p45-related factor 2 (Nrf2). The effects of FB1 on Nrf2-related survival responses in human hepatoma (HepG2) cells were investigated. HepG2 cells were treated with 200 µmol/l FB1 (IC50-24 h). Cellular redox status was assessed via the quantification of intracellular reactive oxygen species (ROS), lipid peroxidation, protein oxidation and the antioxidant glutathione (GSH). The protein expression of oxidative stress and mitochondrial stress response proteins [Nrf2, phosphorylated-Nrf2 (pNrf2), superoxide dismutase 2 (SOD2), catalase (CAT), sirtuin 3 (Sirt 3) and Lon-protease 1 (Lon-P1)] were quantified by western blotting, while gene expression levels of SOD2, CAT and GPx were assessed using quantitative polymerase chain reaction (qPCR). Lastly, the fluorometric, JC-1 assay was used to determine mitochondrial polarisation. FB1 significantly increased ROS (p ≤ 0.001), and induced lipid peroxidation (p < 0.05) and protein carbonylation (p ≤ 0.001), which corresponded with the increase in GSH levels (p < 0.05). A significant increase in pNrf2, SOD2, SOD2, CAT (p < 0.05), CAT (p ≤ 0.01) and GPx (p ≤ 0.001) expression was observed; however, total Nrf2 (p > 0.05) was reduced. There was also a minor reduction in the mitochondrial membrane potential of HepG2 cells (p < 0.05); however, the expression of Sirt 3 and Lon-P1 (p ≤ 0.001) were upregulated. Exposure to FB1 induced oxidative stress in HepG2 cells and initiated Nrf2-regulated transcription of antioxidants.

HIV induced nitric oxide and lipid peroxidation, influences neonatal birthweight in a South African population.

HIV has been implicated in adverse birth outcomes, due to increased oxidative stress and inflammation. In addition, HIV has been reported to increase nitric oxide levels. Therefore the combined exposures to HIV and traffic-related air pollution, within South Durban, South Africa (SA), may lead to adverse birth outcomes. However, the exact mechanism is still unknown; this study aimed to identify a potential mechanism. First, the influence of HIV on oxidative and nitrosative stress markers in pregnant women was assessed. Secondly, the effect of these stress makers and exposure to oxides of nitrogen (NOx) on neonatal birthweight (BW) was evaluated. Finally, the effect HIV and traffic-related pollution exposure has on the oxidative and endoplasmic profile and epigenetic regulation of Nrf2-Keap1 pathway by miR-144 and miR-28 in pregnant women was determined. Women, in their third trimester with singleton pregnancies, who were HIV+ and HIV-, were recruited from Durban, SA. Biomarker levels of serum nitrites/nitrates (NO) and malondialdehyde (MDA) were analysed and mRNA expression levels of oxidative and endoplasmic stress response genes were assessed. Land regression modelling was performed to determine NOx exposure levels. HIV exposure during pregnancy was associated with increased NO levels. NO was shown to reduce neonatal BW. NO and MDA was found to reciprocally increase each other, with HIV differentially influencing MDA's effect on BW. HIV down-regulated miR-144 which was negatively associated with Nrf2, suggesting a potential mechanism for HIV associated chronic oxidative stress. This study proposes that NO plays a key role in neonatal BW reduction in response to HIV and traffic-related air pollution.