SummaryBackgroundManagement of pneumonia in many low-income and middle-income countries is based on WHO guidelines that classify children according to clinical signs that define thresholds of risk. We aimed to establish whether some children categorised as eligible for outpatient treatment might have a risk of death warranting their treatment in hospital.MethodsWe did a retrospective cohort study of children aged 2–59 months admitted to one of 14 hospitals in Kenya with pneumonia between March 1, 2014, and Feb 29, 2016, before revised WHO pneumonia guidelines were adopted in the country. We modelled associations with inpatient mortality using logistic regression and calculated absolute risks of mortality for presenting clinical features among children who would, as part of revised WHO pneumonia guidelines, be eligible for outpatient treatment (non-severe pneumonia).FindingsWe assessed 16 162 children who were admitted to hospital in this period. 832 (5%) of 16 031 children died. Among groups defined according to new WHO guidelines, 321 (3%) of 11 788 patients with non-severe pneumonia died compared with 488 (14%) of 3434 patients with severe pneumonia. Three characteristics were strongly associated with death of children retrospectively classified as having non-severe pneumonia: severe pallor (adjusted risk ratio 5·9, 95% CI 5·1–6·8), mild to moderate pallor (3·4, 3·0–3·8), and weight-for-age Z score (WAZ) less than −3 SD (3·8, 3·4–4·3). Additional factors that were independently associated with death were: WAZ less than −2 to −3 SD, age younger than 12 months, lower chest wall indrawing, respiratory rate of 70 breaths per min or more, female sex, admission to hospital in a malaria endemic region, moderate dehydration, and an axillary temperature of 39°C or more.InterpretationIn settings of high mortality, WAZ less than −3 SD or any degree of pallor among children with non-severe pneumonia was associated with a clinically important risk of death. Our data suggest that admission to hospital should not be denied to children with these signs and we urge clinicians to consider these risk factors in addition to WHO criteria in their decision making.FundingWellcome Trust.

Background: Severe malnutrition contributes up to 50% of childhood mortality in developing countries is frequently characterised by electrolyte depletion, including low total body phosphate. During therapeutic re-feeding, electrolyte shift from extracellular to intra-cellular compartments may induce hypo-phosphataemia (hypo-P) with resultant increased morbidity and mortality. This biochemical imbalance is under-recognised, and the frequency of this problem among African malnourished children is unclear. Objectives: To determine the magnitude of hypo-phosphataemia in children under five years of age presenting to Kenyatta National Hospital with kwashiorkor and marasmic kwashiorkor and to evaluate the relationship between hypo-phosphataemia and nutritional intervention during the first five days of treatment. Design: Short longitudinal survey. Setting: The General Paediatric wards of the Kenyatta National Hospital (KNH), Nairobi. Subjects: Children under five years of age presenting with kwashiorkor or marasmic kwashiorkor at KNH were recruited into the study. Main outcome measures: Low serum phosphate level (<1.20mmol/l) and patient outcome (survival or death) during the first five days of treatment. Results: One hundred and sixty five children were enrolled between June 2005 and February 2006 of which 107 (64%) had kwashiorkor and 58 (36%) had marasmic kwashiorkor. They were of mean age 20 months (range 3-60), and 95 (58%) were male. The prevalence of hypo-phosphataemia was 86% on admission, increased to 90% and 93% on day one and two respectively, and then declined to 90% by the fourth day. At admission 6% were hypo-phosphataemic, increasing to 18% and 22% on day one and two respectively, and declining to 11% by day four. On admission mean serum phosphate was below normal at 0.91mmol/l, declined significantly to 0.67mmol/l and to a nadir of 0.63mmol/l after the first and second day of treatment respectively, then rose slightly to 0.75mmol/l on the fourth day (p<0.001 comparing each follow-up mean level with the admission level). There was a positive association between severity of nadir serum phosphate level and mortality (p=0.028). There were no deaths among children with normal nadir serum phosphate levels. However, among children with mild, moderate and severe nadir hypo-phosphataemia, 8, 14 and 21% died respectively. Children with dermatosis and hypomagnesaemia showed a trend for association with mortality (p=0.082 and 0.099 respectively). Conclusion: Hypo-phosphataemia is frequent among children with kwashiorkor and marasmic kwashiorkor presenting at KNH. Serum phosphate levels decline significantly during the first two days of nutritional intervention, and severity of hypo-phosphataemia is directly associated with mortality.

Background: Sepsis is the leading cause of death and disability in children. Every hour of delay in treatment is associated with an escalating risk of morbidity and mortality. The burden of sepsis is greatest in low-and middleincome countries where timely treatment may not occur due to delays in diagnosis and prioritization of critically ill children. To circumvent these challenges, we propose the development and clinical evaluation of a digital triage tool that will identify high risk children and reduce time to treatment. We will also implement and clinically validate a Radio-Frequency Identification system to automate tracking of patients. The mobile platform (mobile device and dashboard) and automated patient tracking system will create a low cost, highly scalable solution for critically ill children, including those with sepsis. Methods: This is pre-post intervention study consisting of three phases. Phase I will be a baseline period where data is collected on key predictors and outcomes before implementation of the digital triage tool. In Phase I, there will be no changes to healthcare delivery processes in place at the study hospitals. Phase II will involve model derivation, technology development, and usability testing. Phase III will be the intervention period where data is collected on key predictors and outcomes after implementation of the digital triage tool. The primary outcome, time to treatment initiation, will be compared to assess effectiveness of the digital health intervention. Discussion: Smart technology has the potential to overcome the barrier of limited clinical expertise in the identification of the child at risk. This mobile health platform, with sensors and data-driven applications, will provide real-time individualized risk prediction to rapidly triage patients and facilitate timely access to life-saving treatments for children in low-and middleincome countries, where specialists are not regularly available and deaths from sepsis are common.

Background: Many hospitalized children in developing countries die from infectious diseases. Early recognition of those who are critically ill coupled with timely treatment can prevent many deaths. A data-driven, electronic triage system to assist frontline health workers in categorizing illness severity is lacking. This study aimed to develop a data-driven parsimonious triage algorithm for children under five years of age. Methods: This was a prospective observational study of children under-five years of age presenting to the outpatient department of Mbagathi Hospital in Nairobi, Kenya between January and June 2018. A study nurse examined participants and recorded history and clinical signs and symptoms using a mobile device with an attached low-cost pulse oximeter sensor. The need for hospital admission was determined independently by the facility clinician and used as the primary outcome in a logistic predictive model. We focused on the selection of variables that could be quickly and easily assessed by low skilled health workers. Results: The admission rate (for more than 24 hours) was 12% (N=138/1,132). We identified an eight-predictor logistic regression model including continuous variables of weight, mid-upper arm circumference, temperature, pulse rate, and transformed oxygen saturation, combined with dichotomous signs of difficulty breathing, lethargy, and inability to drink or breastfeed. This model predicts overnight hospital admission with an area under the receiver operating characteristic curve of 0.88 (95% CI 0.82 to 0.94). Low- and high-risk thresholds of 5% and 25%, respectively were selected to categorize participants into three triage groups for implementation.  Conclusion: A logistic regression model comprised of eight easily understood variables may be useful for triage of children under the age of five based on the probability of need for admission. This model could be used by frontline workers with limited skills in assessing children. External validation is needed before adoption in clinical practice.

Background: Many hospitalized children in developing countries die from infectious diseases. Early recognition of those who are critically ill coupled with timely treatment can prevent many deaths. A data-driven, electronic triage system to assist frontline health workers in categorizing illness severity is lacking. This study aimed to develop a data-driven parsimonious triage algorithm for children under five years of age. Methods: This was a prospective observational study of children under-five years of age presenting to the outpatient department of Mbagathi Hospital in Nairobi, Kenya between January and June 2018. A study nurse examined participants and recorded history and clinical signs and symptoms using a mobile device with an attached low-cost pulse oximeter sensor. The need for hospital admission was determined independently by the facility clinician and used as the primary outcome in a logistic predictive model. We focused on the selection of variables that could be quickly and easily assessed by low skilled health workers. Results: The admission rate (for more than 24 hours) was 12% (N=138/1,132). We identified an eight-predictor logistic regression model including continuous variables of weight, mid-upper arm circumference, temperature, pulse rate, and transformed oxygen saturation, combined with dichotomous signs of difficulty breathing, lethargy, and inability to drink or breastfeed. This model predicts overnight hospital admission with an area under the receiver operating characteristic curve of 0.88 (95% CI 0.82 to 0.94). Low- and high-risk thresholds of 5% and 25%, respectively were selected to categorize participants into three triage groups for implementation.  Conclusion: A logistic regression model comprised of eight easily understood variables may be useful for triage of children under the age of five based on the probability of need for admission. This model could be used by frontline workers with limited skills in assessing children. External validation is needed before adoption in clinical practice.