Predictive Accuracy of Infant Clinical Sign Algorithms for Mortality in Young Infants Aged 0 to 59 Days: A Systematic Review

CONTEXT: Clinical sign algorithms are a key strategy to identify young infants at risk of mortality. OBJECTIVE: Synthesize the evidence on the accuracy of clinical sign algorithms to predict all-cause mortality in young infants 0–59 days. DATA SOURCES: MEDLINE, Embase, CINAHL, Global Index Medicus, and Cochrane CENTRAL Registry of Trials. STUDY SELECTION: Studies evaluating the accuracy of infant clinical sign algorithms to predict mortality. DATA EXTRACTION: We used Cochrane methods for study screening, data extraction, and risk of bias assessment. We determined certainty of evidence using Grading of Recommendations Assessment Development and Evaluation. RESULTS: We included 11 studies examining 26 algorithms. Three studies from non-hospital/ community settings examined sign-based checklists (n 5 13). Eight hospital-based studies validated regression models (n 5 13), which were administered as weighted scores (n 5 8), regression formulas (n 5 4), and a nomogram (n 5 1). One checklist from India had a sensitivity of 98% (95% CI: 88%–100%) and specificity of 94% (93%–95%) for predicting sepsis-related deaths. However, external validation in Bangladesh showed very low sensitivity of 3% (0%–10%) with specificity of 99% (99%–99%) for all-cause mortality (ages 0–9 days). For hospital-based prediction models, area under the curve (AUC) ranged from 0.76–0.93 (n 5 13). The Score for Essential Neonatal Symptoms and Signs had an AUC of 0.89 (0.84–0.93) in the derivation cohort for mortality, and external validation showed an AUC of 0.83 (0.83–0.84). LIMITATIONS: Heterogeneity of algorithms and lack of external validation limited the evidence. CONCLUSIONS: Clinical sign algorithms may help identify at-risk young infants, particularly in hospital settings; however, overall certainty of evidence is low with limited external validation.