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

Abstract

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 = 13). Eight hospital-based studies validated regression models (n = 13), which were administered as weighted scores (n = 8), regression formulas (n = 4), and a nomogram (n = 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 = 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.