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Huxford, Charly; Dunsmuir, Dustin; Pillay, Yashodani; Ashebukara, Ivan Aye; Tusingwire, Fredson; Novakowski, Stefanie; Behan, Justine; Hwang, Bella; Ansermino, Mark; Lester, Deborah; Kissoon, Niranjan; Tagoola, Abner — 2023-11-15 <br /><strong>Objective(s):</strong> The Smart Triage Quality Improvement Training Program covers the basic concepts of the Quality Improvement process and provides a framework and tools that can be used to train staff on QI. Core learning components include: 1) understanding what QI is; 2) the QI model for improvement; and 3) QI methods and tools. <br /> <br /><strong>Data Description:</strong> This dataset includes the following materials for use in the Smart Triage Quality Improvement Training Program: 1) Quality Improvement Guide; 2) QI Activities Workbook. Materials were originally developed through a partnership with Walimu and the University of British Columbia. All materials are provided in the English language. <br /> <br /><strong>Data Limitations:</strong> These materials were designed for the Ugandan context and may not be generalizable to other settings. <br /> <br /><strong>Data Ethics Declaration:</strong> NA <br /> <br /><strong>Funding Source(s):</strong> BC Children's Hospital Foundation; Grand Challenges Canada; Mining4Life; Wellcome <br /><strong>NOTE for restricted files:</strong> If you are not yet a CoLab member, please complete our <a href = "https://rc.bcchr.ca/redcap/surveys/?s=EDCYL7AC79">membership application survey</a> to gain access to restricted files within 2 business days. <br />Some files may remain restricted to CoLab members. These files are deemed more sensitive by the file owner and are meant to be shared on a case-by-case basis. Please contact the CoLab coordinator at <a href = mailto:sepsiscolab@bccchr.ca>sepsiscolab@bcchr.ca</a> or visit our <a href = "https://wfpiccs.org/pediatric-sepsis-colab/">website</a>.

Kissoon, Niranjan; Fung, Jollee; Hwang, Bella; Trawin, Jessica; Symonds, Nicola; Knappett, Martina; Krepiakevich, Alexia; Liu, Christine; Businge, Stephen; Jabornisky, Roberto; Suiyven, Dzelamunyuy; Talla, Emmanuela; Nwankwor, Odiraa; Tagoola, Abner; Oguonu, Tagbo; Karlovich, Gabrielle; Kenechi, Onah Stanley; Dunsmuir, Dustin; Wiens, Matthew; Ansermino, J Mark — 2020-04-17 The purpose of this environmental scan is to support health facilities in identifying and assessing quality improvement (QI) priorities and initiatives to treat children with sepsis. <br /> <br /><strong><u>Tools Description:</strong></u> <br /><strong>Step 1 Environmental Scan</strong> - A health facility survey that gathers information regarding (1) the availability of resources and services in the health facility and (2) the readiness of the health facility to provide specific services to a defined minimum standard. <br /><strong>Step 2 Technology Readiness Scan</strong> - A short survey that aims to assess a facility’s level of technological preparedness for facilitating standard triage and discharge processes. Ultimately, it determines what technology is needed in order to effectively implement quality improvement intervention. <br /><strong>Step 3a-f Observational Scan</strong> - Assesses the quality and safety of care through observation of a health worker in suspected cases of pneumonia, diarrhea, and malaria, in order to assess adherence to standards in the patient care process. <br /><strong>Step 4 Caretaker Satisfaction Questionnaire</strong> - Assesses the patient-caretakers’ perspective of the quality of care they/their child received while at the facility. <br /><strong>Step 5 Health Worker Satisfaction Questionnaire</strong> - Assesses health workers’ perspectives of the quality of care provided at the facility. <br /><strong>Environmental Scan Feedback Survey</strong> - To be completed by data collector(s) and asks questions pertaining to the Scan’s relevancy and usability. The intention of this form is to collect suggestions on what elements of the Scan to add, eliminate, or modify to inform future module updates. <br /><strong>Written Report Of Results Feedback Survey</strong> - This survey asks questions pertaining to the presentation and value of the results report. <br /><strong>NOTE for restricted files:</strong> If you are not yet a CoLab member, please complete our <a href = "https://rc.bcchr.ca/redcap/surveys/?s=EDCYL7AC79">membership application survey</a> to gain access to restricted files within 2 business days. <br />Some files may remain restricted to CoLab members. These files are deemed more sensitive by the file owner and are meant to be shared on a case-by-case basis. Please contact the CoLab coordinator on <a href = "https://www.bcchr.ca/pediatric-sepsis-data-colab">this page</a> under "collaborate with the pediatric sepsis colab."

Wiens, Matthew O; Bone, Jeffrey N; Kumbakumba, Elias; Businge, Stephen; Tagoola, Abner; Sherine, Sheila Oyella; Byaruhanga, Emmanuel; Ssemwanga, Edward; Barigye, Celestine; Nsungwa, Jesca; Olaro, Charles; Ansermino, J Mark; Kissoon, Niranjan; Singer, Joel; Larson, Charles P; Lavoie, Pascal M; Dunsmuir, Dustin; Moschovis, Peter P; Novakowski, Stefanie; Komugisha, Clare; Tayebwa, Mellon; Mwesignwa, Douglas; Knappett, Martina; West, Nicholas; Nguyen, Vuong; Mugisha, Nathan-Kenya; Kabakyenga, Jerome — 2022-12-06 <br /><strong>Background:</strong> Substantial mortality occurs after hospital discharge in children younger than 5 years with suspected sepsis, especially in low-income countries. A better understanding of its epidemiology is needed for effective interventions to reduce child mortality in these countries. We evaluated risk factors for death after discharge in children admitted to hospital for suspected sepsis in Uganda, and assessed how these differed by age, time of death, and location of death. <br /> <br /><strong>Methods:</strong> In this prospective observational cohort study, we recruited 0-60-month-old children admitted with suspected sepsis from the community to the paediatric wards of six Ugandan hospitals. The primary outcome was six-month post-discharge mortality among those discharged alive. We evaluated the interactive impact of age, time of death, and location of death on risk factors for mortality.<br /> <br /><strong>Findings:</strong> 6,545 children were enrolled, with 6,191 discharged alive. The median (interquartile range) time from discharge to death was 28 (9-74) days, with a six-month post-discharge mortality rate of 5·5%, constituting 51% of total mortality. Deaths occurred at home (45%), in-transit to care (18%), or in hospital (37%) during a subsequent readmission. Post-discharge death was strongly associated with weight-for-age z-scores < -3 (adjusted risk ratio [aRR] 4·7, 95% CI 3·7–5·8 vs a Z score of >–2), referral for further care (7·3, 5·6–9·5), and unplanned discharge (3·2, 2·5–4·0). The hazard ratio of those with severe anaemia increased with time since discharge, while the hazard ratios of discharge vulnerabilities (unplanned, poor feeding) decreased with time. Age influenced the effect of several variables, including anthropometric indices (less impact with increasing age), anaemia (greater impact), and admission temperature (greater impact).<br /> <br /><strong>Data Collection Methods:</strong> All data were collected at the point of care using encrypted study tablets and these data were then uploaded to a Research Electronic Data Capture (REDCap) database hosted at the BC Children’s Hospital Research Institute (Vancouver, Canada). At admission, trained study nurses systematically collected data on clinical, social and demographic variables. Following discharge, field officers contacted caregivers at 2 and 4 months by phone, and in-person at 6 months, to determine vital status, post-discharge health-seeking, and readmission details. Verbal autopsies were conducted for children who had died following discharge.<br /> <br /><strong>Data Processing Methods:</strong> For this analysis, data from both cohorts (0-6 months and 6-60 months) were combined and analysed as a single dataset. We used periods of overlapping enrolment (72% of total enrolment months) between the two cohorts to determine site-specific proportions of children who were 0-6 and 6-60 months of age. These proportions were used to weight the cohorts for the calculation of overall mortality rate. Z-scores were calculated using height and weight. Hematocrit was converted to hemoglobin. Distance to hospital was calculated using latitude and longitude. Extra symptom and diagnosis categories were created based on text field in these two variables. BCS score was created by summing all individual components.<br /> <br /><strong>Abbreviations:</strong><br /> MUAC -mid upper arm circumference<br /> wfa – weight for age<br /> wfl – weight for length<br /> bmi – body mass index<br /> lfa – length for age<br /> abx - antibiotics<br /> hr – heart rate<br /> rr – respiratory rate<br /> antimal - antimalarial<br /> sysbp – systolic blood pressure<br /> diasbp – diastolic blood pressure<br /> resp – respiratory<br /> cap - capillary<br /> BCS - Blantyre Coma Scale<br /> dist- distance<br /> hos - hospital<br /> ed - education<br /> disch - discharge<br /> dis -discharge<br /> fu – follow-up<br /> pd – post-discharge<br /> loc - location<br /> materl - maternal<br /> <br /><strong>Ethics Declaration:</strong> This study was approved by the Mbarara University of Science and Technology Research Ethics Committee (No. 15/10-16), the Uganda National Institute of Science and Technology (HS 2207), and the University of British Columbia / Children & Women’s Health Centre of British Columbia Research Ethics Board (H16-02679). This manuscript adheres to the guidelines for STrengthening the Reporting of OBservational studies in Epidemiology (STROBE).<br /> <br /><strong>Study Protocol & Supplementary Materials:</strong> <br /> <a href = "https://borealisdata.ca/dataset.xhtml?persistentId=doi%3A10.5683%2FSP3%2FQRUMNQ&version=1.0">Smart Discharges to improve post-discharge health outcomes in children: A prospective before-after study with staggered implementation </a><br /> <br /><strong>NOTE for restricted files:</strong> If you are not yet a CoLab member, please complete our <a href = "https://rc.bcchr.ca/redcap/surveys/?s=EDCYL7AC79">membership application survey</a> to gain access to restricted files within 2 business days. <br />Some files may remain restricted to CoLab members. These files are deemed more sensitive by the file owner and are meant to be shared on a case-by-case basis. Please contact the CoLab coordinator at <a href = mailto:sepsiscolab@bccchr.ca>sepsiscolab@bcchr.ca</a> or visit our <a href = "https://wfpiccs.org/pediatric-sepsis-colab/">website</a>.

Kissoon, Niranjan; Fung, Jollee; Hwang, Bella; Trawin, Jessica; Krepiakevich, Alexia; Symonds, Nicola; Knappett, Martina; Liu, Christine; Businge, Stephen; Jabornisky, Roberto; Suiyven, Dzelamunyuy; Talla, Emmanuela; Nwankwor, Odiraa; Tagoola, Abner; Oguonu, Tagbo; Karlovich, Gabrielle; Kenechi, Onah Stanley; Dunsmuir, Dustin; Wiens, Matthew; Ansermino, J Mark — 2021-06-24 The purpose of this environmental scan is to support health facilities in identifying and assessing quality improvement (QI) priorities and initiatives to treat children with sepsis. This dataset contains training materials for project setup and data collection. <br /><strong>NOTE for restricted files:</strong> If you are not yet a CoLab member, please complete our <a href = "https://rc.bcchr.ca/redcap/surveys/?s=EDCYL7AC79">membership application survey</a> to gain access to restricted files within 2 business days. <br />Some files may remain restricted to CoLab members. These files are deemed more sensitive by the file owner and are meant to be shared on a case-by-case basis. Please contact the CoLab coordinator on <a href = "https://www.bcchr.ca/pediatric-sepsis-data-colab">this page</a> under "collaborate with the pediatric sepsis colab."

Kissoon, Niranjan; Fung, Jollee; Hwang, Bella; Trawin, Jessica; Symonds, Nicola; Knappett, Martina; Krepiakevich, Alexia; Liu, Christine; Businge, Stephen; Jabornisky, Roberto; Suiyven, Dzelamunyuy; Talla, Emmanuela; Nwankwor, Odiraa; Tagoola, Abner; Oguonu, Tagbo; Karlovich, Gabrielle; Kenechi, Onah Stanley; Dunsmuir, Dustin; Wiens, Matthew; Ansermino, J Mark — 2021-08-03 The purpose of this environmental scan is to support health facilities in identifying and assessing quality improvement (QI) priorities and initiatives to treat children with sepsis. This dataset contains generic protocol and templates, written consent form templates, and verbal consent scripts to assist project teams in the ethics application process. <br /><strong>NOTE for restricted files:</strong> If you are not yet a CoLab member, please complete our <a href = "https://rc.bcchr.ca/redcap/surveys/?s=EDCYL7AC79">membership application survey</a> to gain access to restricted files within 2 business days. <br />Some files may remain restricted to CoLab members. These files are deemed more sensitive by the file owner and are meant to be shared on a case-by-case basis. Please contact the CoLab coordinator on <a href = "https://www.bcchr.ca/pediatric-sepsis-data-colab">this page</a> under "collaborate with the pediatric sepsis colab."

Kuhn, Sarah; Song, Andrew; Novakowski, Stefanie; Johnson, Teresa; Dunsmuir, Dustin; Knappett, Martina; Trawin, Jessica; Ansermino, J Mark — 2021-10-28 <br /><strong>NOTE for restricted files:</strong> If you are not yet a CoLab member, please complete our <a href = "https://rc.bcchr.ca/redcap/surveys/?s=EDCYL7AC79">membership application survey</a> to gain access to restricted files within 2 business days. <br />Some files may remain restricted to CoLab members. These files are deemed more sensitive by the file owner and are meant to be shared on a case-by-case basis. Please contact the CoLab coordinator on <a href = "https://www.bcchr.ca/pediatric-sepsis-data-colab">this page</a> under "collaborate with the pediatric sepsis colab." Video tutorials on open data and how to navigate and use the Sepsis CoLab's Dataverse.

Dunsmuir, Dustin — 2020-08-05 This is the data collection Android app for the Improving the Early Diagnosis of Neonatal Sepsis in Malawi study. The app is used to collect vital signs data from neonates at multiple time points. Survey field values can be entered via onscreen keypad and respiratory rate (via tapping while watching breaths) and pulse oximetry (via a connected pulse oximeter) are integrated into the app. <br /><strong>NOTE for restricted files:</strong> If you are not yet a CoLab member, please complete our <a href = "https://rc.bcchr.ca/redcap/surveys/?s=EDCYL7AC79">membership application survey</a> to gain access to restricted files within 2 business days. <br />Some files may remain restricted to CoLab members. These files are deemed more sensitive by the file owner and are meant to be shared on a case-by-case basis. Please contact the CoLab coordinator on <a href = "https://www.bcchr.ca/pediatric-sepsis-data-colab">this page</a> under "collaborate with the pediatric sepsis colab."

Huxford, Charly; Dunsmuir, Dustin; Pillay, Yashodani; Ashebukara, Ivan Aye; Tusingwire, Fredson; Novakowski, Stefanie; Behan, Justine; Pallot, Katija; Hwang, Bella; Ansermino, Mark — 2023-11-08 <br /><strong>Objective(s):</strong> The Smart Triage Health Worker Training Program uses a train-the-trainer model to improve the quality of triage care. Core learning components include: 1) understanding what triage is; 2) effective triaging using the Smart Triage platform; and 3) best practices for health workers. <br /> <br /><strong>Data Description:</strong> This dataset includes the following materials for use in the Smart Triage Training Program: 1) Health Workers Guide; 2) Smart Triage Handbook; 3) Caregivers counselling card Materials were originally developed through a partnership with Walimu and the University of British Columbia. All materials are provided in the English language. <br /> <br /><strong>Data Limitations:</strong> These materials were designed for the Ugandan context and may not be generalizable to other settings. <br /> <br /><strong>Data Ethics Declaration:</strong> NA <br /> <br /><strong>Funding Source(s):</strong> BC Children's Hospital Foundation; Grand Challenges Canada; Mining4Life; Wellcome <br /><strong>NOTE for restricted files:</strong> If you are not yet a CoLab member, please complete our <a href = "https://rc.bcchr.ca/redcap/surveys/?s=EDCYL7AC79">membership application survey</a> to gain access to restricted files within 2 business days. <br />Some files may remain restricted to CoLab members. These files are deemed more sensitive by the file owner and are meant to be shared on a case-by-case basis. Please contact the CoLab coordinator at <a href = mailto:sepsiscolab@bccchr.ca>sepsiscolab@bcchr.ca</a> or visit our <a href = "https://wfpiccs.org/pediatric-sepsis-colab/">website</a>.

Asdo, Ahmad; Mawji, Alishah; Omara, Isaac; Aye Ishebukara, Ivan Aine; Komugisha, Clare; Novakowski, Stefanie; Pillay, Yashodani; Wiens, Matthew O; Akech, Samuel; Oyella, Florence; Tagoola, Abner; Kissoon, Niranjan; Ansermino, J Mark; Dunsmuir, Dustin — 2024-03-14 <br /><strong>Background:</strong> Pneumonia is the leading cause of death in children globally. In low- and middle-income countries the diagnosis of pneumonia relies heavily on an accurate assessment of respiratory rate, which can be unreliable in nurses and clinicians with less advanced training. In order to inform more accurate measurements, we investigate the repeatability of the RRate app used by nurses in district hospitals in Uganda. <br/> <br /><strong>Methods:</strong> This planned secondary analysis included 3679 children aged 0-5 years. The dataset had two sequential measurements of respiratory rate using the RRate app. We measured the agreement between respiratory rate observations and clustering around fixed thresholds defined by WHO for fast breathing, which are 60 breaths per minute (bpm) for under two months (Age-1), 50 bpm for two to 12 months (Age-2), and 40 bpm for 12.1 to 60 months (Age-3). We then assessed the repeatability of the paired measurements using the Intraclass Correlation Coefficient (ICC). <br/> <br /><strong>Results:</strong> The respiratory rate measurement took less than 15 seconds for 7,277 (98.9%) of the measurements. Despite respiratory rates clustering around the WHO fast-breathing thresholds, the breathing classification based on the thresholds was changed in only 12.6% of children. The mean (SD) respiratory rate by age group was 60 (13.1) bpm for Age-1, 49 (11.9) bpm for Age-2, and 38 (10.1) for Age-3, and the bias (Limits of Agreements) were 0.3 (-10.8 – 11.3), 0.4 (-8.5 – 9.3), and 0.1 (-6.8, 7.0) for Age-1, Age-2, and Age-3 respectively. Most importantly, the repeatability of the two respiratory rate measurements for the 3,679 children was high, with an ICC value (95% CI) of 0.95 (0.94 – 0.95). <br/> <br /><strong>Discussion:</strong> The RRate measurements were both efficient and repeatable. The simplicity, repeatability, and efficiency of the RRate app used by healthcare workers in LMICs supports more widespread adoption for clinical use. <br/> <br /><strong>NOTE for restricted files:</strong> If you are not yet a CoLab member, please complete our <a href = "https://rc.bcchr.ca/redcap/surveys/?s=EDCYL7AC79">membership application survey</a> to gain access to restricted files within 2 business days. <br />Some files may remain restricted to CoLab members. These files are deemed more sensitive by the file owner and are meant to be shared on a case-by-case basis. Please contact the CoLab coordinator at <a href = mailto:sepsiscolab@bccchr.ca>sepsiscolab@bcchr.ca</a> or visit our <a href = "https://wfpiccs.org/pediatric-sepsis-colab/">website</a>.

Zhang, Cherri; Wiens, Matthew O; Dunsmuir, Dustin; Pillay, Yashodani; Huxford, Charly; Kimutai, David; Tenywa, Emmanuel; Ouma, Mary; Kigo, Joyce; Kamau, Stephen; Chege, Mary; Kenya-Mugisha, Nathan; Mwaka, Savio; Dumont, Guy A; Kisson, Niranjan; Akech, Samuel; Ansermino, J Mark — 2024-06-12 <br /><strong>Background:</strong> Age is an important risk factor among critically ill children with neonates being the most vulnerable. Clinical prediction models need to account for age differences and must be externally validated and updated, if necessary, to enhance reliability, reproducibility, and generalizability. We externally validated the Smart Triage model using a combined prospective baseline cohort from three hospitals in Uganda and two in Kenya using admission, mortality, and readmission. <br/> <br /><strong>Methods:</strong> We evaluated model discrimination using area under the receiver-operator curve (AUROC) and visualized calibration plots. In addition, we performed subsetting analysis based on age groups (< 30 days, ≤ 2 months, ≤ 6 months, and < 5 years). We revised the model for neonates (< 1 month) by re-estimating 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. <br/> <br /><strong>Results:</strong> The proportion with an outcome 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 0.85 (0.83-0.87) and 0.68 (0.58-0.76) for children under-5 and neonates, respectively. Specificity at the high-risk thresholds were 0.93 (0.93-0.94) and 0.96 (0.94-0.98) for children under-5 and neonates, respectively. After model revision for neonates, we achieved an AUROC of 0.83 (0.79-0.87) with 13% and 41% as the low- and high-risk thresholds, respectively. <br/> <br /><strong>Discussion:</strong> The Smart Triage model showed good discrimination for children under-5. However, a revised model is recommended for neonates due to their uniqueness in disease susceptibly, host response, and underlying physiological reserve. External validation of the neonatal model and additional external validation of the under-5 model in different contexts is required. <br/> <br /><strong>NOTE for restricted files:</strong> If you are not yet a CoLab member, please complete our <a href = "https://rc.bcchr.ca/redcap/surveys/?s=EDCYL7AC79">membership application survey</a> to gain access to restricted files within 2 business days. <br />Some files may remain restricted to CoLab members. These files are deemed more sensitive by the file owner and are meant to be shared on a case-by-case basis. Please contact the CoLab coordinator at <a href = mailto:sepsiscolab@bccchr.ca>sepsiscolab@bcchr.ca</a> or visit our <a href = "https://wfpiccs.org/pediatric-sepsis-colab/">website</a>.

Wiens, Matthew O; Nguyen, Vuong; Bone, Jeffrey N; Kumbakumba, Elias; Businge, Stephen; Tagoola, Abner; Sherine, Sheila Oyella; Byaruhanga, Emmanuel; Ssemwanga, Edward; Barigye, Celestine; Nsungwa, Jesca; Olaro,Charles; Ansermino, J Mark; Kissoon, Niranjan; Singer, Joel; Larson, Charles P; Lavoie, Pascal M; Dunsmuir, Dustin; Moschovis, Peter P; Novakowski, Stefanie; Komugisha, Clare; Tayebwa, Mellon; Mwesigwa, Douglas; Knappett, Martina; West, Nicholas; Kenya-Mugisha, Nathan; Kabakyenga, Jerome — 2024-07-16 <br/><strong>Background:</strong> In many low-income countries, over five percent of hospitalized children die following hospital discharge. The lack of available tools to identify those at risk of post-discharge mortality has limited the ability to make progress towards improving outcomes. We aimed to develop algorithms designed to predict post-discharge mortality among children admitted with suspected sepsis.<br /> <br /><strong>Methods:</strong> Four prospective cohort studies of children in two age groups (0–6 and 6–60 months) were conducted between 2012–2021 in six Ugandan hospitals. Prediction models were derived for six-months post-discharge mortality, based on candidate predictors collected at admission, each with a maximum of eight variables, and internally validated using 10-fold cross-validation.<br /> <br /><strong>Findings:</strong> 8,810 children were enrolled: 470 (5.3%) died in hospital; 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 was good: Brier scores were 0.07 and 0.04, respectively. The most important variables included anthropometry and oxygen saturation. Additional variables included: illness duration, 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.<br /> <br /><strong>Data Processing Methods:</strong> The post-processed data files were created using R version 4.2.2. (R Foundation for Statistical Computing, Vienna, Austria) and briefly involved renaming columns from the different datasets so that they are consistent, converting categories coded as “unknown”, “don’t know”, or “missing” to NA, creating new columns, calculating z-scored variables, and converting relevant columns to factors or dates. <br /> <br /><strong>Ethics Declaration:</strong> These studies were approved by the Mbarara University of Science and Technology (No. 15/10-16), the Uganda National Council for Science and Technology (HS 2207), and the University of British Columbia (H16-02679).<br />

Raihana, Shahreen; Dunsmuir, Dustin; Huda, Tanvir; Zhou, Guohai; Sadeq-Ur Rahman, Qazi; Garde, Ainara; Moinuddin, Md; Karlen, Walter; Dumont, Guy A; Kissoon, Niranjan; Arifeen, Sharms El; Larson, Charles; Ansermino, J Mark — 2024-11-19 <br/><strong>Background:</strong> The reduction in the deaths of millions of children who die from infectious diseases requires early initiation of treatment and improved access to care available in health facilities. A major challenge is the lack of objective evidence to guide front line health workers in the community to recognize critical illness in children earlier in their course. <br> <br /><strong>Methods:</strong> We undertook a prospective observational study of children less than 5 years of age presenting at the outpatient or emergency department of a rural tertiary care hospital between October 2012 and April 2013. Study physicians collected clinical signs and symptoms from the facility records, and with a mobile application performed recordings of oxygen saturation, heart rate and respiratory rate. Facility physicians decided the need for hospital admission without knowledge of the oxygen saturation. Multiple logistic predictive models were tested. <br> <br /><strong>Findings:</strong> Twenty-five percent of the 3374 assessed children, with a median (interquartile range) age of 1.02 (0.42–2.24), were admitted to hospital. We were unable to contact 20% of subjects after their visit. A logistic regression model using continuous oxygen saturation, respiratory rate, temperature and age combined with dichotomous signs of chest indrawing, lethargy, irritability and symptoms of cough, diarrhea and fast or difficult breathing predicted admission to hospital with an area under the receiver operating characteristic curve of 0.89 (95% confidence interval -CI: 0.87 to 0.90). At a risk threshold of 25% for admission, the sensitivity was 77% (95% CI: 74% to 80%), specificity was 87% (95% CI: 86% to 88%), positive predictive value was 70% (95% CI: 67% to 73%) and negative predictive value was 91% (95% CI: 90% to 92%). <br> <br /><strong>Conclusion:</strong> A model using oxygen saturation, respiratory rate and temperature in combination with readily obtained clinical signs and symptoms predicted the need for hospitalization of critically ill children. External validation of this model in a community setting will be required before adoption into clinical practice. <br> <br /><strong>NOTE for restricted files:</strong> If you are not yet a CoLab member, please complete our <a href = "https://rc.bcchr.ca/redcap/surveys/?s=EDCYL7AC79">membership application survey</a> to gain access to restricted files within 2 business days. <br />Some files may remain restricted to CoLab members. These files are deemed more sensitive by the file owner and are meant to be shared on a case-by-case basis. Please contact the CoLab coordinator at <a href = mailto:sepsiscolab@bccchr.ca>sepsiscolab@bcchr.ca</a> or visit our <a href = "https://wfpiccs.org/pediatric-sepsis-colab/">website</a>.

Mawji, Alishah; Akech, Samuel; Mwaniki, Paul; Dunsmuir, Dustin; Bone, Jeffrey; Wiens, Matthew O; Gorges, Matthias; Kimutai, David; Kissoon, Niranjan; English, Mike; Ansermino, J Mark — 2024-11-19 <br/><strong>Background:</strong> 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. <br> <br /><strong>Methods:</strong> 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. <br> <br /><strong>Results:</strong> 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. <br> <br /><strong>Conclusion:</strong> 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. <br> <br /><strong>NOTE for restricted files:</strong> If you are not yet a CoLab member, please complete our <a href = "https://rc.bcchr.ca/redcap/surveys/?s=EDCYL7AC79">membership application survey</a> to gain access to restricted files within 2 business days. <br />Some files may remain restricted to CoLab members. These files are deemed more sensitive by the file owner and are meant to be shared on a case-by-case basis. Please contact the CoLab coordinator at <a href = mailto:sepsiscolab@bccchr.ca>sepsiscolab@bcchr.ca</a> or visit our <a href = "https://wfpiccs.org/pediatric-sepsis-colab/">website</a>.