Predictive accuracy of infant clinical sign algorithms for mortality in young infants aged 0 to 59 days: A systematic review
Document Type
Article
Department
Center of Excellence for Trauma and Emergencies
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.
Publication (Name of Journal)
Pediatrics
DOI
doi.org/10.1542/peds.2024-066588E
Recommended Citation
Shafiq, Y.,
Fung, A.,
Driker, S.,
Rees, C. A.,
Mediratta, R. P.,
Rosenberg, R.,
Hussaini, A. S.,
Adnan, J.,
Wade, C. G.,
Chou, R.,
Edmond, K. M.,
North, K.,
Lee, A. C.
(2024). Predictive accuracy of infant clinical sign algorithms for mortality in young infants aged 0 to 59 days: A systematic review. Pediatrics, 154(Suppl 1), S1-S20.
Available at:
https://ecommons.aku.edu/cete/2