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A Pediatric Asthma Risk Score to better predict asthma development in young children.

ABSTRACT: BACKGROUND:Asthma phenotypes are currently not amenable to primary prevention or early intervention because their natural history cannot be reliably predicted. Clinicians remain reliant on poorly predictive asthma outcome tools because of a lack of better alternatives. OBJECTIVE:We sought to develop a quantitative personalized tool to predict asthma development in young children. METHODS:Data from the Cincinnati Childhood Allergy and Air Pollution Study (n = 762) birth cohort were used to identify factors that predicted asthma development. The Pediatric Asthma Risk Score (PARS) was constructed by integrating demographic and clinical data. The sensitivity and specificity of PARS were compared with those of the Asthma Predictive Index (API) and replicated in the Isle of Wight birth cohort. RESULTS:PARS reliably predicted asthma development in the Cincinnati Childhood Allergy and Air Pollution Study (sensitivity = 0.68, specificity = 0.77). Although both the PARS and API predicted asthma in high-risk children, the PARS had improved ability to predict asthma in children with mild-to-moderate asthma risk. In addition to parental asthma, eczema, and wheezing apart from colds, variables that predicted asthma in the PARS included early wheezing (odds ratio [OR], 2.88; 95% CI, 1.52-5.37), sensitization to 2 or more food allergens and/or aeroallergens (OR, 2.44; 95% CI, 1.49-4.05), and African American race (OR, 2.04; 95% CI, 1.19-3.47). The PARS was replicated in the Isle of Wight birth cohort (sensitivity = 0.67, specificity = 0.79), demonstrating that it is a robust, valid, and generalizable asthma predictive tool. CONCLUSIONS:The PARS performed better than the API in children with mild-to-moderate asthma. This is significant because these children are the most common and most difficult to predict and might be the most amenable to prevention strategies.

SUBMITTER: Biagini Myers JM

PROVIDER: S-EPMC6504569 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

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A Pediatric Asthma Risk Score to better predict asthma development in young children.

Biagini Myers Jocelyn M JM Schauberger Eric E He Hua H Martin Lisa J LJ Kroner John J Hill Gregory M GM Ryan Patrick H PH LeMasters Grace K GK Bernstein David I DI Lockey James E JE Arshad S Hasan SH Kurukulaaratchy Ramesh R Khurana Hershey Gurjit K GK

The Journal of allergy and clinical immunology 20181213 5

<h4>Background</h4>Asthma phenotypes are currently not amenable to primary prevention or early intervention because their natural history cannot be reliably predicted. Clinicians remain reliant on poorly predictive asthma outcome tools because of a lack of better alternatives.<h4>Objective</h4>We sought to develop a quantitative personalized tool to predict asthma development in young children.<h4>Methods</h4>Data from the Cincinnati Childhood Allergy and Air Pollution Study (n = 762) birth coho ...[more]

PMID: 30554722

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Project description:BackgroundAsthma is the most common chronic condition in children and the third leading cause of hospitalization in pediatrics. The genome-wide association study catalog reports 140 studies with genome-wide significance. A polygenic risk score (PRS) with predictive value across ancestries has not been evaluated for this important trait.ObjectivesThis study aimed to train and validate a PRS relying on genetic determinants for asthma to provide predictions for disease occurrence in pediatric cohorts of diverse ancestries.MethodsThis study applied a Bayesian regression framework method using the Trans-National Asthma Genetic Consortium genome-wide association study summary statistics to derive a multiancestral PRS score, used one Electronic Medical Records and Genomics (eMERGE) cohort as a training set, used a second independent eMERGE cohort to validate the score, and used the UK Biobank data to replicate the findings. A phenome-wide association study was performed using the PRS to identify shared genetic etiology with other phenotypes.ResultsThe multiancestral asthma PRS was associated with asthma in the 2 pediatric validation datasets. Overall, the multiancestral asthma PRS has an area under the curve (AUC) of 0.70 (95% CI, 0.69-0.72) in the pediatric validation 1 and AUC of 0.66 (0.65-0.66) in the pediatric validation 2 datasets. We found significant discrimination across pediatric subcohorts of European (AUC, 95% CI, 0.60 and 0.66), African (AUC, 95% CI, 0.61 and 0.66), admixed American (AUC, 0.64 and 0.70), Southeast Asian (AUC, 0.65), and East Asian (AUC, 0.73) ancestry. Pediatric participants with the top 5% PRS had 2.80 to 5.82 increased odds of asthma compared to the bottom 5% across the training, validation 1, and validation 2 cohorts when adjusted for ancestry. Phenome-wide association study analysis confirmed the strong association of the identified PRS with asthma (odds ratio, 2.71, PFDR = 3.71 × 10-65) and related phenotypes.ConclusionsA multiancestral PRS for asthma based on Bayesian posterior genomic effect sizes identifies increased odds of pediatric asthma.

Project description:ObjectiveOur objective was to develop and validate a prognostic score for predicting mortality at the time of extracorporeal membrane oxygenation initiation for children with respiratory failure. Preextracorporeal membrane oxygenation mortality prediction is important for determining center-specific risk-adjusted outcomes and counseling families.DesignMultivariable logistic regression of a large international cohort of pediatric extracorporeal membrane oxygenation patients.SettingMulti-institutional data.PatientsPrognostic score development: A total of 4,352 children more than 7 days to less than 18 years old, with an initial extracorporeal membrane oxygenation run for respiratory failure reported to the Extracorporeal Life Support Organization's data registry during 2001-2013 were used for derivation (70%) and validation (30%). Bidirectional stepwise logistic regression was used to identify factors associated with mortality. Retained variables were assigned a score based on the odds of mortality with higher scores indicating greater mortality. External validation was accomplished using 2,007 patients from the Pediatric Health Information System dataset.InterventionsNone.Measurements and main resultsThe Pediatric Pulmonary Rescue with Extracorporeal Membrane Oxygenation Prediction score included mode of extracorporeal membrane oxygenation; preextracorporeal membrane oxygenation mechanical ventilation more than 14 days; preextracorporeal membrane oxygenation severity of hypoxia; primary pulmonary diagnostic categories including, asthma, aspiration, respiratory syncytial virus, sepsis-induced respiratory failure, pertussis, and "other"; and preextracorporeal membrane oxygenation comorbid conditions of cardiac arrest, cancer, renal and liver dysfunction. The area under the receiver operating characteristic curve for internal and external validation datasets were 0.69 (95% CI, 0.67-0.71) and 0.66 (95% CI, 0.63-0.69).ConclusionsPediatric Pulmonary Rescue with Extracorporeal Membrane Oxygenation Prediction is a validated tool for predicting in-hospital mortality among children with respiratory failure receiving extracorporeal membrane oxygenation support.

Project description:Mortality rates among hospitalized children in many government hospitals in sub-Saharan Africa are high. Pediatric emergency services in these hospitals are often sub-optimal. Timely recognition of critically ill children on arrival is key to improving service delivery. We present a simple risk score to predict inpatient mortality among hospitalized children. Between April 2010 and June 2011, the Uganda Malaria Surveillance Project (UMSP), in collaboration with the National Malaria Control Program (NMCP), set up an enhanced sentinel site malaria surveillance program for children hospitalized at four public hospitals in different districts: Tororo, Apac, Jinja and Mubende. Clinical data collected through March 2013, representing 50249 admissions were used to develop a mortality risk score (derivation data set). One year of data collected subsequently from the same hospitals, representing 20406 admissions, were used to prospectively validate the performance of the risk score (validation data set). Using a backward selection approach, 13 out of 25 clinical parameters recognizable on initial presentation, were selected for inclusion in a final logistic regression prediction model. The presence of individual parameters was awarded a score of either 1 or 2 based on regression coefficients. For each individual patient, a composite risk score was generated. The risk score was further categorized into three categories; low, medium, and high. Patient characteristics were comparable in both data sets. Measures of performance for the risk score included the receiver operating characteristics curves and the area under the curve (AUC), both demonstrating good and comparable ability to predict deathusing both the derivation (AUC =0.76) and validation dataset (AUC =0.74). Using the derivation and validation datasets, the mortality rates in each risk category were as follows: low risk (0.8% vs. 0.7%), moderate risk (3.5% vs. 3.2%), and high risk (16.5% vs. 12.6%), respectively. Our analysis resulted in development of a risk score that ably predicted mortality risk among hospitalized children. While validation studies are needed, this approach could be used to improve existing triage systems.

Dataset Information

A Pediatric Asthma Risk Score to better predict asthma development in young children.

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A Pediatric Asthma Risk Score to better predict asthma development in young children.

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