Oxygen saturation as a predictor of prolonged, frequent bronchodilator therapy in children with acute asthma

Mehta, Sanjay V.; Parkin, Patricia C.; Stephens, Derek; Schuh, Suzanne

doi:10.1016/j.jpeds.2004.06.072

Cited by 20 publications

(5 citation statements)

References 22 publications

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“…In addition to being accurate, the model is clinically sensible, incorporating, in descending order of importance, oxygen saturation, respiratory rate, triage acuity, pulse rate, weight‐for‐age z‐score, and age, followed by SES and weather variables. Consistent with prior studies, initial oxygen saturation was the most important predictor of hospitalization . Weight as a risk factor for poor asthma control and severity in children is well studied; however, it has not been used to predict need for hospital‐level care in the pediatric ED setting .…”

Section: Discussionsupporting

confidence: 54%

“…Several asthma severity scores have attempted to predict hospitalization at the time of triage . However, most are better at predicting safe discharge from the hospital, rather than making an early determination of need for hospital‐level care . Traditional prediction models and asthma scores have relied solely on clinical data and response to treatment .…”

mentioning

confidence: 99%

“…However, most are better at predicting safe discharge from the hospital, rather than making an early determination of need for hospital‐level care . Traditional prediction models and asthma scores have relied solely on clinical data and response to treatment . Asthma, however, is a heterogeneous disease with many phenotypes, each with a different set of environmental, genetic, and clinical factors .…”

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confidence: 99%

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A Machine Learning Approach to Predicting Need for Hospitalization for Pediatric Asthma Exacerbation at the Time of Emergency Department Triage

Patel

Chamberlain²,

Chamberlain

2018

Academic Emergency Medicine

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Objectives: Pediatric asthma is a leading cause of emergency department (ED) utilization and hospitalization.Earlier identification of need for hospital-level care could triage patients more efficiently to high-or low-resource ED tracks. Existing tools to predict disposition for pediatric asthma use only clinical data, perform best several hours into the ED stay, and are static or score-based. Machine learning offers a population-specific, dynamic option that allows real-time integration of available nonclinical data at triage. Our objective was to compare the performance of four common machine learning approaches, incorporating clinical data available at the time of triage with information about weather, neighborhood characteristics, and community viral load for early prediction of the need for hospital-level care in pediatric asthma.Methods: Retrospective analysis of patients ages 2 to 18 years seen at two urban pediatric EDs with asthma exacerbation over 4 years. Asthma exacerbation was defined as receiving both albuterol and systemic corticosteroids. We included patient features, measures of illness severity available in triage, weather features, and Centers for Disease Control and Prevention influenza patterns. We tested four models: decision trees, LASSO logistic regression, random forests, and gradient boosting machines. For each model, 80% of the data set was used for training and 20% was used to validate the models. The area under the receiver operating characteristic (AUC) curve was calculated for each model.Results: There were 29,392 patients included in the analyses: mean (AESD) age of 7.0 (AE4.2) years, 42% female, 77% non-Hispanic black, and 76% public insurance. The AUCs for each model were: decision tree 0.72 (95% confidence interval [CI] = 0.66-0.77), logistic regression 0.83 (95% CI = 0.82-0.83), random forests 0.82 (95% CI = 0.81-0.83), and gradient boosting machines 0.84 (95% CI = 0.83-0.85). In the lowest decile of risk, only 3% of patients required hospitalization; in the highest decile this rate was 100%. After patient vital signs and acuity, age and weight, followed by socioeconomic status (SES) and weather-related features, were the most important for predicting hospitalization.Conclusions: Three of the four machine learning models performed well with decision trees preforming the worst. The gradient boosting machines model demonstrated a slight advantage over other approaches at predicting need for hospital-level care at the time of triage in pediatric patients presenting with asthma exacerbation. The addition of weight, SES, and weather data improved the performance of this model.

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Section: Discussionsupporting

confidence: 54%

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confidence: 99%

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confidence: 99%

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A Machine Learning Approach to Predicting Need for Hospitalization for Pediatric Asthma Exacerbation at the Time of Emergency Department Triage

Patel

Chamberlain²,

Chamberlain

2018

Academic Emergency Medicine

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“…Baseline saturations of <92% have been correlated with AAE severity and prolonged frequent bronchodilator therapy. 11 , 47 Hypoxemia after 1 hour of bronchodilator treatment is a risk factor for hospitalization. 7 , 11…”

Section: Resultsmentioning

confidence: 99%

Paediatrics: how to manage acute asthma exacerbations

Leung¹

2021

DIC

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Background Asthma is the most common chronic disease of childhood and a major source of childhood health burden worldwide. These burdens are particularly marked when children experience characteristic ‘symptom flare-ups’ or acute asthma exacerbations (AAEs). AAE are associated with significant health and economic impacts, including acute Emergency Department visits, occasional hospitalizations, and rarely, death. To treat children with AAE, several medications have been studied and used. Methods We conducted a narrative review of the literature with the primary objective of understanding the evidence of their efficacy. We present this efficacy evidence in the context of a general stepwise management pathway for paediatric AAEs. This framework is developed from the combined recommendations of eight established (inter)national paediatric guidelines. Discussion Management of paediatric AAE centres around four major care goals: (1) immediate and objective assessment of AAE severity; (2) prompt and effective medical interventions to decrease respiratory distress and improve oxygenation; (3) appropriate disposition of patient; and (4) safe discharge plans. Several medications are currently recommended with varying efficacies, including heliox, systemic corticosteroids, first-line bronchodilators (salbutamol/albuterol), adjunctive bronchodilators (ipratropium bromide, magnesium sulfate) and second-line bronchodilators (aminophylline, i.v. salbutamol, i.v. terbutaline, epinephrine, ketamine). Care of children with AAE is further enhanced using clinical severity scoring, pathway-driven care and after-event discharge planning. Conclusions AAEs in children are primarily managed by medications supported by a growing body of literature. Continued efforts to study the efficacy of second-line bronchodilators, integrate AAE management with long-term asthma control and provide fair/equitable care are required.

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“…The CAS was adapted from the physical findings in pediatric asthma scores validated previously by Parkin et al 1 and Liu et al 3 Oxygen saturation was included in the CAS because this value at presentation in the emergency department has been shown to predict the need for intensive bronchodilator therapy. 10,11 The total score ranges from 2 to 12, with a higher score indicating more respiratory distress.…”

Section: Methodsmentioning

confidence: 99%

An Innovative Childhood Asthma Score Predicts the Need for Bronchodilator Nebulization in Children With Acute Asthma Independent of Auscultative Findings

Kamps¹,

Veeger

Heijsman³

2014

Respir Care

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Oxygen saturation as a predictor of prolonged, frequent bronchodilator therapy in children with acute asthma

Cited by 20 publications

References 22 publications

A Machine Learning Approach to Predicting Need for Hospitalization for Pediatric Asthma Exacerbation at the Time of Emergency Department Triage

A Machine Learning Approach to Predicting Need for Hospitalization for Pediatric Asthma Exacerbation at the Time of Emergency Department Triage

Paediatrics: how to manage acute asthma exacerbations

An Innovative Childhood Asthma Score Predicts the Need for Bronchodilator Nebulization in Children With Acute Asthma Independent of Auscultative Findings

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