Diagnostic Accuracy of Clinical Sign Algorithms to Identify Sepsis in Young Infants Aged 0 to 59 Days: A Systematic Review and Meta-analysis

Abstract

CONTEXT Accurate identification of possible sepsis in young infants is needed to effectively manage and reduce sepsis-related morbidity and mortality. OBJECTIVE Synthesize evidence on the diagnostic accuracy of clinical sign algorithms to identify young infants (aged 0–59 days) with suspected sepsis. DATA SOURCES MEDLINE, Embase, CINAHL, Global Index Medicus, and Cochrane CENTRAL Registry of Trials. STUDY SELECTION Studies reporting diagnostic accuracy measures of algorithms including infant clinical signs to identify young infants with suspected sepsis. DATA EXTRACTION We used Cochrane methods for study screening, data extraction, risk of bias assessment, and determining certainty of evidence using Grading of Recommendations Assessment Development and Evaluation. RESULTS We included 19 studies (12 Integrated Management of Childhood Illness [IMCI] and 7 non-IMCI studies). The current World Health Organization (WHO) 7-sign IMCI algorithm had a sensitivity of 79% (95% CI 77%–82%) and specificity of 77% (95% CI 76%–78%) for identifying sick infants aged 0–59 days requiring hospitalization/antibiotics (1 study, N = 8889). Any IMCI algorithm had a pooled sensitivity of 84% (95% CI 75%–90%) and specificity of 80% (95% CI 64%–90%) for identifying suspected sepsis (11 studies, N = 15523). When restricting the reference standard to laboratory-supported sepsis, any IMCI algorithm had a pooled sensitivity of 86% (95% CI 82%–90%) and lower specificity of 61% (95% CI 49%–72%) (6 studies, N = 14278). LIMITATIONS Heterogeneity of algorithms and reference standards limited the evidence. CONCLUSIONS IMCI algorithms had acceptable sensitivity for identifying young infants with suspected sepsis. Specificity was lower using a reference standard of laboratory-supported sepsis diagnosis.