Accuracy of Infant Clinical Signs to Predict Young Infant Mortality

ABSTRACT 1: Accuracy of Tachypnea to Predict Mortality in Young Infants: A Systematic Review and Meta-analysis Context: Tachypnea in young infants is commonly used in clinical assessment to identify high-risk infants, however the optimal respiratory rate threshold is unclear. Objective: To systematically review the evidence on the accuracy of different thresholds of tachypnea to predict mortality in infants 0-59 days. Data Sources: MEDLINE, Embase, CINAHL, Global Index Medicus, and CENTRAL. Study Selection: Studies reporting the accuracy of tachypnea (≥60, ≥70, or ≥80 breaths per minute (bpm)) to predict mortality in infants 0-59 days. Data Extraction: We followed Cochrane methods for study screening, data extraction, and quality assessment using the Newcastle-Ottawa and QUAPAS scales. Results: Of 7641 studies identified, 8 were included. Tachypnea with threshold of ≥60 bpm had an overall sensitivity of 57% (95% CI: 23%-86%) and specificity of 54% (95% CI: 28%-79%) for predicting future mortality in infants by 59 days of age (6 studies, N = 3819 infants). The pooled odds ratio for the association between tachypnea ≥60 bpm and mortality was 2.00 (95% CI: 1.39-2.87, 7 studies, N = 7122 infants). Tachypnea ≥70 bpm had a 4.6-fold higher odds of mortality (95% CI: 1.60-13.00, 1 study, N = 6924 infants). There was limited data on other thresholds and timing of mortality (early versus late neonatal periods). Limitations: Heterogeneity and the low number of studies limited the evidence. Conclusions: Tachypnea was associated with significantly higher odds of mortality in young infants. Overall sensitivity and specificity were low. Further research is needed to determine the optimal threshold and the accuracy for different postnatal ages. ABSTRACT 2: Accuracy of Infant Clinical Signs to Predict Neonatal Mortality in Rural Bangladesh Background: Clinical signs provide early warning of illness in neonates at high risk of dying in community settings in low- and middle- income countries (LMICs). There is limited validation of current clinical signs and/or algorithms to predict neonatal mortality in LMICs. Objectives: 1) To examine the diagnostic accuracy of existing clinical sign algorithms to predict neonatal mortality, and 2) To determine the association of individual clinical signs with neonatal mortality and their diagnostic accuracy in predicting neonatal death. Methods: We conducted a secondary analysis of a birth cohort in Sylhet, Bangladesh (NCT01572532). Of 7788 live births, 6251 newborns had a clinical examination during a community health worker home visit within 3 days of birth, and 5289 were followed up until 28 days of age with available vital status. We validated existing newborn clinical sign algorithms identified from our prior systematic review (Shafiq 2024), including four Integrated Management of Childhood Illness (IMCI)-like checklist algorithms and the Score of Essential Neonatal Symptoms and Signs (SENSS). We also estimated the odds ratios (ORs) for neonatal mortality associated with individual clinical signs exploring optimal thresholds using univariable logistic regression; and calculated sensitivity and specificity of individual clinical signs for identifying neonatal death. Results: The WHO Young Infant Study 7-sign modification Z checklist algorithm had the sensitivity of 66.7% (95% CI: 56.6% to 75.7%) and specificity of 68.6% (95% CI: 67.3% to 69.9%) for predicting future mortality. The SENSS algorithm had an Area Under the Curve (AUC) of 75.9% (95% CI: 69.9% to 82.0%), calibration intercept of -1.21 (95% CI: -1.69 to -0.72), and calibration slope of 0.94 (95% CI: 0.78 to 1.11). Among individual clinical signs at birth, fast breathing ≥80 breaths per minute (bpm), fever ≥38.5˚C, and hypothermia .5˚C showed the strongest association with neonatal death, with ORs were 44.4 (95% CI: 13.8 to 142.6) for fast breathing ≥80 bpm, 39.3 (95% CI: 3.5 to 441.3) for fever ≥38.5˚C, and 30.3 (95% CI: 17.1 to 53.9) for hypothermia .5˚C. Conclusions: Four IMCI-like checklist algorithms had low sensitivity and high specificity to predict neonatal mortality. The Score of Essential Neonatal Symptoms and Signs (SENSS) algorithm reported fair discrimination to identifying neonates at high risk of dying. Further research is needed to optimize clinical sign algorithms to identify high-risk infants in different age groups and settings.

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