No abstract

Background Sepsis is the leading cause of death in children under five in low-and middle-income countries. The rapid identification of the sickest children and timely antibiotic administration may improve outcomes. We developed and implemented a digital triage platform to rapidly identify critically ill children to facilitate timely intravenous antibiotic administration. Objective This quality improvement initiative sought to reduce the time to antibiotic administration at a dedicated children's hospital outpatient department in Mbarara, Uganda. Intervention and study design The digital platform consisted of a mobile application that collects clinical signs, symptoms, and vital signs to prioritize children through a combination of emergency triggers and predictive risk algorithms. A computer-based dashboard enabled the prioritization of children by displaying an overview of all children and their triage categories. We evaluated the impact of the digital triage platform over an 11-week pre-implementation phase and an 11-week postimplementation phase. The time from the end of triage to antibiotic administration was compared to evaluate the quality improvement initiative. Results There was a difference of-11 minutes (95% CI,-16.0 to-6.0; p < 0.001; Mann-Whitney U test) in time to antibiotics, from 51 minutes (IQR, 27.0-94.0) pre-implementation to 44 minutes (IQR, 19.0-74.0) post-implementation. Children prioritized as emergency received the greatest time benefit (-34 minutes; 95% CI,-9.0 to-58.0; p < 0.001; Mann-Whitney U test).

Background A timely differential diagnostic is essential to identify the etiology of central nervous system (CNS) infections in children, in order to facilitate targeted treatment, manage patients, and improve clinical outcome. Objective The Pediatric Infection-Point-of-Care (PI-POC) trial is investigating novel methods to improve and strengthen the differential diagnostics of suspected childhood CNS infections in low-income health systems such as those in Southwestern Uganda. This will be achieved by evaluating (1) a novel DNA-based diagnostic assay for CNS infections, (2) a commercially available multiplex PCR-based meningitis/encephalitis (ME) panel for clinical use in a facility-limited laboratory setting, (3) proteomics profiling of blood from children with severe CNS infection as compared to outpatient controls with fever yet not severely ill, and (4) Myxovirus resistance protein A (MxA) as a biomarker in blood for viral CNS infection. Further changes in the etiology of childhood CNS infections after the introduction of the pneumococcal conjugate vaccine against Streptococcus pneumoniae will be investigated. In addition, the carriage and invasive rate of Neisseria meningitidis will be recorded and serotyped, and the expression of its major virulence factor (polysaccharide capsule) will be investigated. Methods The PI-POC trial is a prospective observational study of children including newborns up to 12 years of age with clinical features of CNS infection, and age-/sex-matched outpatient controls with fever yet not severely ill. Participants are recruited at 2 Pediatric clinics in Mbarara, Uganda. Cerebrospinal fluid (for cases only), blood, and nasopharyngeal (NP) swabs (for both cases and controls) sampled at both clinics are analyzed at the Epicentre Research Laboratory through gold-standard methods for CNS infection diagnosis (microscopy, biochemistry, and culture) and a commercially available ME panel for multiplex PCR analyses of the cerebrospinal fluid. An additional blood sample from cases is collected on day 3 after admission. After initial clinical analyses in Mbarara, samples will be transported to Stockholm, Sweden for (1) validation analyses of a novel nucleic acid–based POC test, (2) biomarker research, and (3) serotyping and molecular characterization of S. pneumoniae and N. meningitidis. Results A pilot study was performed from January to April 2019. The PI-POC trial enrollment of patients begun in April 2019 and will continue until September 2020, to include up to 300 cases and controls. Preliminary results from the PI-POC study are expected by the end of 2020. Conclusions The findings from the PI-POC study can potentially facilitate rapid etiological diagnosis of CNS infections in low-resource settings and allow for novel methods for determination of the severity of CNS infection in such environment. Trial Registration ClinicalTrials.gov NCT03900091; https://clinicaltrials.gov/ct2/show/NCT03900091 International Registered Report Identifier (IRRID) DERR1-10.2196/21430

Background Leptospirosis is an emerging neglected zoonotic disease that presents with nonspecific signs/symptoms and it can be mistaken for other diseases. Owing to limited diagnostic capacity and unawareness, the data on human leptospirosis particularly in neonates are scarce in many sub-Saharan countries. It has been underreported hindering preventive and control measures in place. The study aimed at determining prevalence of leptospirosis as a cause of febrile illness in neonates using IgM ELISA and a quantitative real-time PCR (qPCR). Methods This was a descriptive cross-sectional study that included 103 neonatal sepsis cases whose parents/legal guardians gave informed consent. The data on demographic and clinical characteristics were collected using structured data collection form. EDTA whole blood sample was collected from the neonates by trained study nurses. From the samples, IgM ELISA was done using automated analyzers, DNA extracted and qPCR was performed using primers for LipL32, specific for the pathogenic leptospires. Results The prevalence of anti-leptospiral IgM among the neonates as determined by ELISA was 4.3%, where all of them presented with lethargy and poor feeding. No pathogenic Leptospira species DNA was amplified by qPCR. Conclusions Evidence of leptospirosis was demonstrated in neonatal sepsis cases in this study. The findings suggest considerations of leptospirosis in the differential diagnosis of neonates with sepsis. More data are needed on the real epidemiology, clinical features, and burden of leptospirosis in neonates. There is need to include intermediate pathogenic species of Leptospira in the diagnostic qPCR assays.

Background: In many low-income countries, more than five percent of hospitalized children die following hospital discharge. The identification of those at risk has limited progress to improve outcomes. We aimed to develop algorithms to predict post-discharge mortality among children admitted with suspected sepsis. Methods: Four prospective cohort studies were conducted at six hospitals in Uganda between 2012 and 2021. Death occurring within six months of discharge was the primary outcome. Separate models were developed for children 0-6 months of age and for those 6-60 months of age, based on candidate predictors collected at admission. Within each age group, three models were derived, each with a maximum of eight variables based on variable importance. Deriving parsimonious models with different sets of predictors was prioritized to improve usability and support implementation in settings where some data elements are unavailable. All models were internally validated using 10-fold cross validation. Findings: 8,810 children were prospectively enrolled, of whom 470 died in hospital and 161 (1.9%) were lost to follow-up; 257 (7.7%) and 233 (4.8%) post-discharge deaths occurred in the 0-6-month and 6-60-month age groups, respectively. The primary models had an area under the receiver operating characteristic curve (AUROC) of 0.77 (95%CI 0.74-0.80) for 0-6-month-olds and 0.75 (95%CI 0.72-0.79) for 6-60-month-olds; mean AUROCs among the 10 cross-validation folds were 0.75 and 0.73, respectively. Calibration across risk strata were good with Brier scores of 0.07 and 0.04, respectively. The most important variables included anthropometry and oxygen saturation. Additional variables included duration of illness, jaundice-age interaction, and a bulging fontanelle among 0-6-month-olds; and prior admissions, coma score, temperature, age-respiratory rate interaction, and HIV status among 6-60-month-olds. Interpretation: Simple prediction models at admission with suspected sepsis can identify children at risk of post-discharge mortality. Further external validation is recommended for different contexts. Models can be integrated into existing processes to improve peri-discharge care as children transition from the hospital to the community.