CCCF ePoster library. Muttalib F. 11/12/19; 283353; EP82
Dr. Fiona Muttalib
Dr. Fiona Muttalib
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Topic: Systematic Review, Meta-Analysis, or Meta-Synthesis

Muttalib, F1,2, Clavel V3, Shah, V1, 4, Adhikari, NK1, 5
Affiliations 1 Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Canada 2 Center for Global Child Health, Hospital for Sick Children, Toronto, Canada 3 Department of Pediatrics, Faculty of Medicine, McGill University, Montreal, Canada, 4 Department of Paediatrics, Mount Sinai Hospital, University of Toronto, Toronto, Canada,  5 Department of Critical Care Medicine, Sunnybrook Health Sciences Centre, Toronto, Canada

Introduction: The majority of the world's childhood mortality occurs in low- and middle-income countries (LMICs). Improved identification of children at risk for hospital death is needed. Objectives: We conducted a systematic review to evaluate the performance of prognostic models to predict hospital mortality and clinical deterioration events among acutely ill children in LMICs. Methods: We systematically searched Embase, Ovid Medline, Scopus, CDSR, CENTRAL, CINAHL, Ebsco Global Health, African Index Medicus, African Journals Online, African Healthline, Med-Carib, and Global index medicus in March 2019. Citations were included if they described the development or validation of a pediatric prognostic model for hospital mortality or clinical deterioration events in LMICs. Data extraction and risk of bias assessment were conducted in duplicate (VC, FM) and disagreements resolved by consensus with adjudication by a third reviewer as needed (NA). Results: Of 56906 citations, 15 full-text articles (106 392 included children) met inclusion criteria. Meta-analysis of six validated scores with random effects models demonstrated good discrimination for hospital mortality (concordance statistic 0.69 – 0.86). The Lambarene Organ Dysfunction Score (LODS) and Signs of Inflammation in Children that Kill (SICK) had the best discrimination, with C-statistics respectively of 0.85 (95% CI 0.78–0.91) and 0.82 (95% CI 0.81–0.86). Calibration and classification measures were not compared as they were poorly reported. All studies were at high risk of bias due to unclear or inappropriate selection of predictor variables and handling of missing data and incomplete reporting of performance measures. Conclusion: Several prognostic models have been validated in single patient cohorts with reasonable discrimination. However, clarifying missing data and further validation would be required prior to recommending the use of a specific prognostic model in research or in clinical practice.


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