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      Prediction of disease severity in young children presenting with acute febrile illness in resource-limited settings: a protocol for a prospective observational study

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      BMJ Open
      BMJ Publishing Group
      paediatrics, primary care, public health, infectious diseases

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          Abstract

          Introduction

          In rural and difficult-to-access settings, early and accurate recognition of febrile children at risk of progressing to serious illness could contribute to improved patient outcomes and better resource allocation. This study aims to develop a prognostic clinical prediction tool to assist community healthcare providers identify febrile children who might benefit from referral or admission for facility-based medical care.

          Methods and analysis

          This prospective observational study will recruit at least 4900 paediatric inpatients and outpatients under the age of 5 years presenting with an acute febrile illness to seven hospitals in six countries across Asia. A venous blood sample and nasopharyngeal swab is collected from each participant and detailed clinical data recorded at presentation, and each day for the first 48 hours of admission for inpatients. Multianalyte assays are performed at reference laboratories to measure a panel of host biomarkers, as well as targeted aetiological investigations for common bacterial and viral pathogens. Clinical outcome is ascertained on day 2 and day 28.

          Presenting syndromes, clinical outcomes and aetiology of acute febrile illness will be described and compared across sites. Following the latest guidance in prediction model building, a prognostic clinical prediction model, combining simple clinical features and measurements of host biomarkers, will be derived and geographically externally validated. The performance of the model will be evaluated in specific presenting clinical syndromes and fever aetiologies.

          Ethics and dissemination

          The study has received approval from all relevant international, national and institutional ethics committees. Written informed consent is provided by the caretaker of all participants. Results will be shared with local and national stakeholders, and disseminated via peer-reviewed open-access journals and scientific meetings.

          Trial registration number

          NCT04285021.

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          Most cited references62

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          Global, regional, and national sepsis incidence and mortality, 1990–2017: analysis for the Global Burden of Disease Study

          Summary Background Sepsis is life-threatening organ dysfunction due to a dysregulated host response to infection. It is considered a major cause of health loss, but data for the global burden of sepsis are limited. As a syndrome caused by underlying infection, sepsis is not part of standard Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) estimates. Accurate estimates are important to inform and monitor health policy interventions, allocation of resources, and clinical treatment initiatives. We estimated the global, regional, and national incidence of sepsis and mortality from this disorder using data from GBD 2017. Methods We used multiple cause-of-death data from 109 million individual death records to calculate mortality related to sepsis among each of the 282 underlying causes of death in GBD 2017. The percentage of sepsis-related deaths by underlying GBD cause in each location worldwide was modelled using mixed-effects linear regression. Sepsis-related mortality for each age group, sex, location, GBD cause, and year (1990–2017) was estimated by applying modelled cause-specific fractions to GBD 2017 cause-of-death estimates. We used data for 8·7 million individual hospital records to calculate in-hospital sepsis-associated case-fatality, stratified by underlying GBD cause. In-hospital sepsis-associated case-fatality was modelled for each location using linear regression, and sepsis incidence was estimated by applying modelled case-fatality to sepsis-related mortality estimates. Findings In 2017, an estimated 48·9 million (95% uncertainty interval [UI] 38·9–62·9) incident cases of sepsis were recorded worldwide and 11·0 million (10·1–12·0) sepsis-related deaths were reported, representing 19·7% (18·2–21·4) of all global deaths. Age-standardised sepsis incidence fell by 37·0% (95% UI 11·8–54·5) and mortality decreased by 52·8% (47·7–57·5) from 1990 to 2017. Sepsis incidence and mortality varied substantially across regions, with the highest burden in sub-Saharan Africa, Oceania, south Asia, east Asia, and southeast Asia. Interpretation Despite declining age-standardised incidence and mortality, sepsis remains a major cause of health loss worldwide and has an especially high health-related burden in sub-Saharan Africa. Funding The Bill & Melinda Gates Foundation, the National Institutes of Health, the University of Pittsburgh, the British Columbia Children's Hospital Foundation, the Wellcome Trust, and the Fleming Fund.
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            Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): Explanation and Elaboration

            The TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) Statement includes a 22-item checklist, which aims to improve the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. This explanation and elaboration document describes the rationale; clarifies the meaning of each item; and discusses why transparent reporting is important, with a view to assessing risk of bias and clinical usefulness of the prediction model. Each checklist item of the TRIPOD Statement is explained in detail and accompanied by published examples of good reporting. The document also provides a valuable reference of issues to consider when designing, conducting, and analyzing prediction model studies. To aid the editorial process and help peer reviewers and, ultimately, readers and systematic reviewers of prediction model studies, it is recommended that authors include a completed checklist in their submission. The TRIPOD checklist can also be downloaded from www.tripod-statement.org.
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              Calculating the sample size required for developing a clinical prediction model

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                Author and article information

                Journal
                BMJ Open
                BMJ Open
                bmjopen
                bmjopen
                BMJ Open
                BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )
                2044-6055
                2021
                25 January 2021
                : 11
                : 1
                : e045826
                Affiliations
                [1 ]departmentAngkor Hospital for Children , Cambodia Oxford Medical Research Unit , Siem Reap, Cambodia
                [2 ]departmentCentre for Tropical Medicine and Global Health , University of Oxford , Oxford, Oxfordshire, UK
                [3 ]Médecins Sans Frontières Operational Centre Barcelona , Barcelona, Spain
                [4 ]departmentCentre for Tropical Medicine , Universitas Gadjah Mada , Yogyakarta, Daerah Istimewa Yogyakart, Indonesia
                [5 ]departmentMicrobiology Department , Lao-Oxford-Mahosot Hospital-Wellcome Trust Research Unit , Vientiane, Vientiane, Lao People's Democratic Republic
                [6 ]departmentFaculty of Tropical Medicine , Mahidol-Oxford Tropical Medicine Research Unit , Bangkok, Thailand
                [7 ]Hanoi Medical University , Hanoi, Viet Nam
                [8 ]departmentClinical Trials, Epidemiology and Biostatistics , Research Institute for Tropical Medicine , Muntinlupa City, Philippines
                [9 ]departmentLaboratory Medicine & Pathobiology , University of Toronto , Toronto, Ontario, Canada
                [10 ]Angkor Hospital for Children , Siem Reap, Siem Reap, Cambodia
                [11 ]departmentDepartment of Primary Care Health Sciences , University of Oxford , Oxford, UK
                [12 ]Faculty of Postgraduate Studies, University of Health Sciences , Vientiane, Lao People's Democratic Republic
                [13 ]departmentCentre for Nutrition and Food Security (CNFS) , icddr, b , Dhaka, Dhaka, Bangladesh
                [14 ]Savannakhet Provincial Health Department , Savannakhet, Lao People's Democratic Republic
                [15 ]University of Pennsylvania , Philadelphia, Pennsylvania, USA
                [16 ]Salavan Provincial Hospital , Salavan, Lao People's Democratic Republic
                Author notes
                [Correspondence to ] Dr Arjun Chandna; arjun@ 123456tropmedres.ac ; arjunchandna@ 123456gmail.com
                Author information
                http://orcid.org/0000-0003-1313-7922
                http://orcid.org/0000-0002-5904-5970
                http://orcid.org/0000-0003-2418-2091
                http://orcid.org/0000-0001-5524-0325
                http://orcid.org/0000-0002-3410-8541
                Article
                bmjopen-2020-045826
                10.1136/bmjopen-2020-045826
                7839891
                33495264
                7db9d619-7e9b-457d-bb2f-e0c36c75f761
                © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY. Published by BMJ.

                This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See:  https://creativecommons.org/licenses/by/4.0/.

                History
                : 13 October 2020
                : 03 December 2020
                : 11 January 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100004440, Wellcome Trust;
                Award ID: 219644/Z/19/Z
                Funded by: MSF-OCBA;
                Categories
                Global Health
                1506
                1699
                Protocol
                Custom metadata
                unlocked

                Medicine
                paediatrics,primary care,public health,infectious diseases
                Medicine
                paediatrics, primary care, public health, infectious diseases

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