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

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 5 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 5 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 5 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.