Performance of prediction rules and guidelines in detecting serious bacterial infections among Tanzanian febrile children

Kristina Keitel,Mary Kilowoko,Blaise Genton,Valérie D’Acremont,Esther Kyungu

doi:10.1186/s12879-019-4371-y

Abstract

BackgroundHealth-workers in developing countries rely on clinical algorithms, such as the Integrated Management of Childhood Illnesses (IMCI), for the management of patients, including diagnosis of serious bacterial infections (SBI). The diagnostic accuracy of IMCI in detecting children with SBI is unknown. Prediction rules and guidelines for SBI from well-resourced countries at outpatient level may help to improve current guidelines; however, their diagnostic performance has not been evaluated in resource-limited countries, where clinical conditions, access to care, and diagnostic capacity differ. The aim of this study was to estimate the diagnostic accuracy of existing prediction rules and clinical guidelines in identifying children with SBI in a cohort of febrile children attending outpatient health facilities in Tanzania.MethodsStructured literature review to identify available prediction rules and guidelines aimed at detecting SBI and retrospective, external validation on a dataset containing 1005 febrile Tanzanian children with acute infections. The reference standard, SBI, was established based on rigorous clinical and microbiological criteria.ResultsFour prediction rules and five guidelines, including IMCI, could be validated. All examined rules and guidelines had insufficient diagnostic accuracy for ruling-in or ruling-out SBI with positive and negative likelihood ratios ranging from 1.04–1.87 to 0.47–0.92, respectively. IMCI had a sensitivity of 36.7% (95% CI 29.4–44.6%) at a specificity of 70.3% (67.1–73.4%). Rules that use a combination of clinical and laboratory testing had better performance compared to rules and guidelines using only clinical and or laboratory elements.ConclusionsCurrently applied guidelines for managing children with febrile illness have insufficient diagnostic accuracy in detecting children with SBI. Revised clinical algorithms including simple point-of-care tests with improved accuracy for detecting SBI targeting in tropical resource-poor settings are needed. They should undergo careful external validation against clinical outcome before implementation, given the inherent limitations of gold standards for SBI.

Highlights

Health-workers in developing countries rely on clinical algorithms, such as the Integrated Management of Childhood Illnesses (IMCI), for the management of patients, including diagnosis of serious bacterial infections (SBI)
Health workers rely on the World Health Organization (WHO) Integrated Management of Childhood Illnesses (IMCI) algorithm, which recommends presumptive treatment based on clinical signs and symptoms
Though IMCI and Integrated Community Case Management guidelines (iCCM) have been implemented globally, their performance in detecting children with SBI has not been validated to date using stringent microbiological methods, instead of expert clinical diagnosis (and chest radiograph (CXR) in some studies) [11, 12]

Summary

Introduction

Health-workers in developing countries rely on clinical algorithms, such as the Integrated Management of Childhood Illnesses (IMCI), for the management of patients, including diagnosis of serious bacterial infections (SBI). Health workers rely on the World Health Organization (WHO) Integrated Management of Childhood Illnesses (IMCI) algorithm, which recommends presumptive treatment based on clinical signs and symptoms (besides the rapid diagnostic test for malaria that was introduced in the 2014 version, [7]). External validation to support their use in resource-poor settings is lacking This is especially important because of differences in clinical presentations (e.g. malaria co-infection), the health care system (e.g. access to care, the possibility of safety netting, the level of training of primary care providers)

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