PERFORMANCE OF PEDIATRIC MORTALITY PREDICTION MODELS IN LOW- AND MIDDLE-INCOME COUNTRIES: A SYSTEMATIC REVIEW AND META-ANALYSIS
CCCF ePoster library. Muttalib F. 11/12/19; 283353; EP82
Dr. Fiona Muttalib
Dr. Fiona Muttalib
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Abstract
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ePoster
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.
 


Image

1. United Nations Inter-agency Group for Child Mortality Estimation. Levels and Trends in Child Mortality: Report 2018, Estimates developed by the United Nations Inter-agency Group for Child Mortality Estimation. New           York2018.
2. English M. Child survival: district hospitals and paediatricians. Arch Dis Child 2005;90:974-8.
3. Campbell H, Duke T, Weber M, et al. Global initiatives for improving hospital care for children: state of the art and future prospects. Pediatrics 2008;121:e984-92.
4. Li MY, Kelly J, Subhi R, Were W, Duke T. Global use of the WHO Pocket Book of Hospital Care for Children. Paediatrics and International Child Health 2013;33:4-11.
5. Nariadhara MR, Sawe HR, Runyon MS, Mwafongo V, Murray BL. Modified systemic inflammatory response syndrome and provider gestalt predicting adverse outcomes in children under 5 years presenting to an
         urban emergency department of a tertiary hospital in Tanzania. Trop Med Health 2019;47:13.
6. Mpimbaza A, Sears D, Sserwanga A, et al. Admission Risk Score to Predict Inpatient Pediatric Mortality at Four Public Hospitals in Uganda. 2015:0133950-.
7. English M, Esamai F, Wasunna A, et al. Assessment of inpatient paediatric care in first referral level hospitals in 13 districts in Kenya. Lancet 2004;363:1948-53.
8. Olson et al. Task shifting an inpatient triage, assessment and treatment programme improves the quality of care for hospitalised Malawian children. Tropical Medicine and International Health 2013;18:879-86.
9. Parshuram CS, Dryden-Palmer K, Farrell C, et al. Effect of a Pediatric Early Warning System on All-Cause Mortality in Hospitalized Pediatric Patients: The EPOCH Randomized Clinical Trial. JAMA 2018;319:1002-12.
10.    Feudtner C, Berry JG, Parry G, et al. Statistical uncertainty of mortality rates and rankings for children's hospitals. Pediatrics 2011;128:e966-72.
11.    Molyneux E. Paediatric emergency care in developing countries. Lancet 2001;357:86-7.
12.    Berkley JA, Ross A, Mwangi I, et al. Prognostic indicators of early and late death in children admitted to district hospital in Kenya: cohort study. BMJ 2003;326:361.
13.    Ayieko P, Ogero M, Makone B, et al. Characteristics of admissions and variations in the use of basic investigations, treatments and outcomes in Kenyan hospitals within a new Clinical Information Network. Arch Dis
         Child 2016;101:223-9.
14.    Molyneux E, Ahmad S, Robertson A. Improved triage and emergency care for children reduces inpatient mortality in a resource-constrained setting. Bull World Health Organ 2006;84:314-9.
15.    Arifeen SE, Hoque DM, Akter T, et al. Effect of the Integrated Management of Childhood Illness strategy on childhood mortality and nutrition in a rural area in Bangladesh: a cluster randomised trial. Lancet
         2009;374:393-403.
16.    Armstrong Schellenberg JR, Adam T, Mshinda H, et al. Effectiveness and cost of facility-based Integrated Management of Childhood Illness (IMCI) in Tanzania. Lancet 2004;364:1583-94.
17.    Crouse HL, Torres F, Vaides H, et al. Impact of an Emergency Triage Assessment and Treatment (ETAT)-based triage process in the paediatric emergency department of a Guatemalan public hospital. Paediatr Int
         Child Health 2016;36:219-24.
18.    Robison J AZ, Durand C, et al. Implementation of ETAT (Emergency Triage Assessment And Treatment) in a central hospital in malawi. Archives of Disease in Childhood 2011;96:74-5.
19.    Olson et al. Development of a severity of illness scoring system (inpatient triage, assessment and treatment) for resource-constrained hospitals in developing countries. Tropical Medicine and International Health
         2013;18:871-8.
20.    George EC, Walker AS, Kiguli S, et al. Predicting mortality in sick African children: the FEAST Paediatric Emergency Triage (PET) Score. BMC Med 2015;13:174.
21.    Mpimbaza A, Sears D, Sserwanga A, et al. Admission Risk Score to Predict Inpatient Pediatric Mortality at Four Public Hospitals in Uganda. PLoS One 2015;10:e0133950.
22.    Conroy et al. Prospective validation of pediatric disease severity scores to predict mortality in Ugandan children presenting with malaria and non-malaria febrile illness. Critical Care 2015;19.
23.    Brown et al. Scoping Review of Pediatric Early Warning Systems (PEWS) in Resource-Limited and Humanitarian Settings. Frontiers in Pediatrics 2018;6:410.
24.    World Bank Country and Lending Groups. 2018. (Accessed January 29, 2019, at https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups.)
25.    Agulnik A, Mora Robles LN, Forbes PW, et al. Improved outcomes after successful implementation of a pediatric early warning system (PEWS) in a resource-limited pediatric oncology hospital. Cancer
         2017;123:2965-74.
26.    Moons KG, de Groot JA, Bouwmeester W, et al. Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS checklist. PLoS Med 2014;11:e1001744.
27.    Wolff RF, Moons KGM, Riley RD, et al. PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies. Ann Intern Med 2019;170:51-8.
28.    Debray TP, Damen JA, Riley RD, et al. A framework for meta-analysis of prediction model studies with binary and time-to-event outcomes. Stat Methods Med Res 2018:962280218785504.
29.    Alba AC, Agoritsas T, Walsh M, et al. Discrimination and Calibration of Clinical Prediction Models: Users' Guides to the Medical Literature. JAMA 2017;318:1377-84.
30.    Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. BMJ 2003;327:557-60.
31.    Huguet A, Hayden JA, Stinson J, et al. Judging the quality of evidence in reviews of prognostic factor research: adapting the GRADE framework. Syst Rev 2013;2:71.
32.    Iorio A, Spencer, F.; Falavigna, M.; Alba, C.; Lang, E.; Burnand, B. et al. Use of GRADE for assessment of evidence about prognosis: rating confidence in estimates of event rates in broad categories of patients BMJ
         2015: 350 :h870.
33.    Harmesh S B, Ravinder Kumar S. simple clinical score "TOPRS" to predict outcome in pediatric emergency department in a teaching hospital in India. Iran J Pediatr 2012;22:97-101.
34.    Kumar N, Thomas N, Singhal D, Puliyel JM, Sreenivas V. Triage score for severity of illness. Indian pediatr 2003.
35.    Lowlaavar N, Larson CP, Kumbakumba E, et al. Pediatric in-Hospital Death from Infectious Disease in Uganda: Derivation of Clinical Prediction Models. PLoS One 2016;11:e0150683.
36.    Gupta et al. Validation of 'Signs of Inflammation in Children that Kill' (SICK) score for immediate non-invasive assessment of severity of illness. Italian Journal of Pediatrics 2010;36:35.
37.    Gérardin et al. Evaluation of Pediatric Risk of Mortality (PRISM) scoring in African children with falciparum malaria. Pediatric Critical Care Medicine 2006;7:45-7.
38.    Agulnik A, Mendez Aceituno A, Mora Robles LN, et al. Validation of a pediatric early warning system for hospitalized pediatric oncology patients in a resource-limited setting. Cancer 2017;123:4903-13.
39.    Chaiyakulsil CPU. Validation of pediatric early warning score in pediatric emergency department. Pediatrics International 2015;57:694-8.
40.    Debray TP, Damen JA, Snell KI, et al. A guide to systematic review and meta-analysis of prediction model performance. BMJ 2017;356:i6460.
 

ePoster
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.
 


Image

1. United Nations Inter-agency Group for Child Mortality Estimation. Levels and Trends in Child Mortality: Report 2018, Estimates developed by the United Nations Inter-agency Group for Child Mortality Estimation. New           York2018.
2. English M. Child survival: district hospitals and paediatricians. Arch Dis Child 2005;90:974-8.
3. Campbell H, Duke T, Weber M, et al. Global initiatives for improving hospital care for children: state of the art and future prospects. Pediatrics 2008;121:e984-92.
4. Li MY, Kelly J, Subhi R, Were W, Duke T. Global use of the WHO Pocket Book of Hospital Care for Children. Paediatrics and International Child Health 2013;33:4-11.
5. Nariadhara MR, Sawe HR, Runyon MS, Mwafongo V, Murray BL. Modified systemic inflammatory response syndrome and provider gestalt predicting adverse outcomes in children under 5 years presenting to an
         urban emergency department of a tertiary hospital in Tanzania. Trop Med Health 2019;47:13.
6. Mpimbaza A, Sears D, Sserwanga A, et al. Admission Risk Score to Predict Inpatient Pediatric Mortality at Four Public Hospitals in Uganda. 2015:0133950-.
7. English M, Esamai F, Wasunna A, et al. Assessment of inpatient paediatric care in first referral level hospitals in 13 districts in Kenya. Lancet 2004;363:1948-53.
8. Olson et al. Task shifting an inpatient triage, assessment and treatment programme improves the quality of care for hospitalised Malawian children. Tropical Medicine and International Health 2013;18:879-86.
9. Parshuram CS, Dryden-Palmer K, Farrell C, et al. Effect of a Pediatric Early Warning System on All-Cause Mortality in Hospitalized Pediatric Patients: The EPOCH Randomized Clinical Trial. JAMA 2018;319:1002-12.
10.    Feudtner C, Berry JG, Parry G, et al. Statistical uncertainty of mortality rates and rankings for children's hospitals. Pediatrics 2011;128:e966-72.
11.    Molyneux E. Paediatric emergency care in developing countries. Lancet 2001;357:86-7.
12.    Berkley JA, Ross A, Mwangi I, et al. Prognostic indicators of early and late death in children admitted to district hospital in Kenya: cohort study. BMJ 2003;326:361.
13.    Ayieko P, Ogero M, Makone B, et al. Characteristics of admissions and variations in the use of basic investigations, treatments and outcomes in Kenyan hospitals within a new Clinical Information Network. Arch Dis
         Child 2016;101:223-9.
14.    Molyneux E, Ahmad S, Robertson A. Improved triage and emergency care for children reduces inpatient mortality in a resource-constrained setting. Bull World Health Organ 2006;84:314-9.
15.    Arifeen SE, Hoque DM, Akter T, et al. Effect of the Integrated Management of Childhood Illness strategy on childhood mortality and nutrition in a rural area in Bangladesh: a cluster randomised trial. Lancet
         2009;374:393-403.
16.    Armstrong Schellenberg JR, Adam T, Mshinda H, et al. Effectiveness and cost of facility-based Integrated Management of Childhood Illness (IMCI) in Tanzania. Lancet 2004;364:1583-94.
17.    Crouse HL, Torres F, Vaides H, et al. Impact of an Emergency Triage Assessment and Treatment (ETAT)-based triage process in the paediatric emergency department of a Guatemalan public hospital. Paediatr Int
         Child Health 2016;36:219-24.
18.    Robison J AZ, Durand C, et al. Implementation of ETAT (Emergency Triage Assessment And Treatment) in a central hospital in malawi. Archives of Disease in Childhood 2011;96:74-5.
19.    Olson et al. Development of a severity of illness scoring system (inpatient triage, assessment and treatment) for resource-constrained hospitals in developing countries. Tropical Medicine and International Health
         2013;18:871-8.
20.    George EC, Walker AS, Kiguli S, et al. Predicting mortality in sick African children: the FEAST Paediatric Emergency Triage (PET) Score. BMC Med 2015;13:174.
21.    Mpimbaza A, Sears D, Sserwanga A, et al. Admission Risk Score to Predict Inpatient Pediatric Mortality at Four Public Hospitals in Uganda. PLoS One 2015;10:e0133950.
22.    Conroy et al. Prospective validation of pediatric disease severity scores to predict mortality in Ugandan children presenting with malaria and non-malaria febrile illness. Critical Care 2015;19.
23.    Brown et al. Scoping Review of Pediatric Early Warning Systems (PEWS) in Resource-Limited and Humanitarian Settings. Frontiers in Pediatrics 2018;6:410.
24.    World Bank Country and Lending Groups. 2018. (Accessed January 29, 2019, at https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups.)
25.    Agulnik A, Mora Robles LN, Forbes PW, et al. Improved outcomes after successful implementation of a pediatric early warning system (PEWS) in a resource-limited pediatric oncology hospital. Cancer
         2017;123:2965-74.
26.    Moons KG, de Groot JA, Bouwmeester W, et al. Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS checklist. PLoS Med 2014;11:e1001744.
27.    Wolff RF, Moons KGM, Riley RD, et al. PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies. Ann Intern Med 2019;170:51-8.
28.    Debray TP, Damen JA, Riley RD, et al. A framework for meta-analysis of prediction model studies with binary and time-to-event outcomes. Stat Methods Med Res 2018:962280218785504.
29.    Alba AC, Agoritsas T, Walsh M, et al. Discrimination and Calibration of Clinical Prediction Models: Users' Guides to the Medical Literature. JAMA 2017;318:1377-84.
30.    Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. BMJ 2003;327:557-60.
31.    Huguet A, Hayden JA, Stinson J, et al. Judging the quality of evidence in reviews of prognostic factor research: adapting the GRADE framework. Syst Rev 2013;2:71.
32.    Iorio A, Spencer, F.; Falavigna, M.; Alba, C.; Lang, E.; Burnand, B. et al. Use of GRADE for assessment of evidence about prognosis: rating confidence in estimates of event rates in broad categories of patients BMJ
         2015: 350 :h870.
33.    Harmesh S B, Ravinder Kumar S. simple clinical score "TOPRS" to predict outcome in pediatric emergency department in a teaching hospital in India. Iran J Pediatr 2012;22:97-101.
34.    Kumar N, Thomas N, Singhal D, Puliyel JM, Sreenivas V. Triage score for severity of illness. Indian pediatr 2003.
35.    Lowlaavar N, Larson CP, Kumbakumba E, et al. Pediatric in-Hospital Death from Infectious Disease in Uganda: Derivation of Clinical Prediction Models. PLoS One 2016;11:e0150683.
36.    Gupta et al. Validation of 'Signs of Inflammation in Children that Kill' (SICK) score for immediate non-invasive assessment of severity of illness. Italian Journal of Pediatrics 2010;36:35.
37.    Gérardin et al. Evaluation of Pediatric Risk of Mortality (PRISM) scoring in African children with falciparum malaria. Pediatric Critical Care Medicine 2006;7:45-7.
38.    Agulnik A, Mendez Aceituno A, Mora Robles LN, et al. Validation of a pediatric early warning system for hospitalized pediatric oncology patients in a resource-limited setting. Cancer 2017;123:4903-13.
39.    Chaiyakulsil CPU. Validation of pediatric early warning score in pediatric emergency department. Pediatrics International 2015;57:694-8.
40.    Debray TP, Damen JA, Snell KI, et al. A guide to systematic review and meta-analysis of prediction model performance. BMJ 2017;356:i6460.
 

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