Development and internal validation of a pediatric acute asthma prediction rule for hospitalization

Pediatric Asthma Exacerbations: 14-Day Emergency Department Return Visit Risk Factors

BACKGROUND

Asthma, the most common chronic disease of childhood, can affect a child's physical and mental health and social and emotional development.

OBJECTIVE

The aim of this study was to identify factors associated with emergency department (ED) return visits for asthma exacerbations within 14 days of an initial visit.

METHODS

This was a retrospective review from Cerner Real-World Data for patients aged from 5 to 18 years and seen at an ED for an asthma exacerbation and discharged home at the index ED visit. Asthma visits were defined as encounters in which a patient was diagnosed with asthma and a beta agonist, anticholinergic, or systemic steroid was ordered or prescribed at that encounter. Return visits were ED visits for asthma within 14 days of an index ED visit. Data, including demographic characteristics, ED evaluation and treatment, health care utilization, and medical history, were collected. Data were analyzed via logistic regression mixed effects model.

RESULTS

A total of 80,434 index visits and 17,443 return visits met inclusion criteria. Prior ED return visits in the past year were associated with increased odds of a return visit (odds ratio [OR] 2.12; 95% CI 2.07-2.16). History of pneumonia, a concomitant diagnosis of pneumonia, and fever were associated with increased odds of a return visit (OR 1.19; 95% CI 1.10-1.29; OR 1.15; 95% CI 1.04-1.28; OR 1.20; 95% CI 1.11-1.30, respectively).

CONCLUSIONS

Several variables seem to be associated with statistically significant increased odds of ED return visits. These findings indicate a potentially identifiable population of at-risk patients who may benefit from additional evaluation, planning, or education prior to discharge.

Defining Treatment Response for Clinical Trials of Pediatric Acute Asthma

BACKGROUND

An agreed-upon definition of treatment response for clinical trials of pediatric acute asthma does not exist, limiting meaningful comparisons among therapeutic interventions and advances in asthma management.

OBJECTIVE

To develop a consensus definition of treatment response for clinical trials of pediatric acute asthma.

METHODS

A multidisciplinary panel of 22 experts participated in a Web-based modified Delphi process to achieve consensus on a definition of treatment response. Round 1 consisted of closed- and open-ended questions in which panelists ranked measures of treatment response developed by literature review, suggested additional measures, and explained their responses. In rounds 2 and 3, panelists reviewed summary statistics of the panel's rating from prior rounds and reconsidered their rankings. In round 3, pairwise ranking was performed to determine the ranked importance of components. Consensus was defined as 70% or greater agreement among panelists choosing Likert-scale values of 1 to 6 (extremely unimportant to extremely important) and an interquartile range less than 2.

RESULTS

Drawing on results from the expert panel, we developed a definition of treatment response that includes Clinical Severity Score, need for additional therapies, and hospitalization. Clinical Severity Score encompassed most ranked criteria (eg, respiratory distress, wheeze) for a treatment response definition. Panelists recommended that a valid and pragmatic severity score be used consistently across institutions. Panelists also achieved consensus on the top 10 criteria that appropriately classify need for hospitalization.

CONCLUSIONS

This consensus definition of treatment response can be used in clinical trials of children with acute asthma to standardize outcome measurement and report meaningful outcomes.

Challenges in evaluating the accuracy of AI-containing digital triage systems: A systematic review

INTRODUCTION

Patient-operated digital triage systems with AI components are becoming increasingly common. However, previous reviews have found a limited amount of research on such systems' accuracy. This systematic review of the literature aimed to identify the main challenges in determining the accuracy of patient-operated digital AI-based triage systems.

METHODS

A systematic review was designed and conducted in accordance with PRISMA guidelines in October 2021 using PubMed, Scopus and Web of Science. Articles were included if they assessed the accuracy of a patient-operated digital triage system that had an AI-component and could triage a general primary care population. Limitations and other pertinent data were extracted, synthesized and analysed. Risk of bias was not analysed as this review studied the included articles' limitations (rather than results). Results were synthesized qualitatively using a thematic analysis.

RESULTS

The search generated 76 articles and following exclusion 8 articles (6 primary articles and 2 reviews) were included in the analysis. Articles' limitations were synthesized into three groups: epistemological, ontological and methodological limitations. Limitations varied with regards to intractability and the level to which they can be addressed through methodological choices. Certain methodological limitations related to testing triage systems using vignettes can be addressed through methodological adjustments, whereas epistemological and ontological limitations require that readers of such studies appraise the studies with limitations in mind.

DISCUSSION

The reviewed literature highlights recurring limitations and challenges in studying the accuracy of patient-operated digital triage systems with AI components. Some of these challenges can be addressed through methodology whereas others are intrinsic to the area of inquiry and involve unavoidable trade-offs. Future studies should take these limitations in consideration in order to better address the current knowledge gaps in the literature.

Prospective Observational Study of Clinical Outcomes After Intravenous Magnesium for Moderate and Severe Acute Asthma Exacerbations in Children

BACKGROUND

There is limited knowledge regarding whether intravenous magnesium (IV-Mg) improves outcomes in children with acute asthma exacerbations.

OBJECTIVE

To examine whether IV-Mg improves outcomes in children with moderate and severe exacerbations.

METHODS

We performed a secondary analysis using data from a prospective observational cohort of children aged 5 to 17 years with moderate and severe exacerbations. Standardized treatment included systemic corticosteroid and inhaled albuterol, with consideration of IV-Mg (75 mg/kg) for patients with insufficient response after 20 minutes. Propensity score (PS) models were used to examine associations of IV-Mg treatment with change in the validated Acute Asthma Intensity Research Score, hospitalization rate, and time to spacing of inhaled albuterol of 4 hours or more among hospitalized participants.

RESULTS

Among 301 children, median (interquartile range) age was 8.1 (6.4-10.2) years, 170 were Black (57%), 201 were male (67%), and 84 received IV-Mg (28%). In a PS covariate-adjusted multivariable linear regression model, IV-Mg treatment was associated with a 2-hour increase in the Acute Asthma Intensity Research Score (β-coefficient = 0.98; 95% confidence interval [CI], 0.20-1.77), indicating increased exacerbation severity. Three additional PS-based models yielded similar results. Participants receiving IV-Mg had 5.8-fold (95% CI, 2.8-11.9) and 6.8-fold (95% CI, 3.6-12.9) greater odds of hospitalization in PS-based multivariable regression models. Among hospitalized participants, there was no difference in time to albuterol of every 4 hours or more in a PS covariate-adjusted Cox proportional hazards model (hazard ratio = 1.2; 95% CI, 0.8-1.8).

CONCLUSIONS

Among children with moderate and severe exacerbations, IV-Mg is associated with increased exacerbation severity, increased risk for hospitalization, and no acceleration in exacerbation resolution among hospitalized participants.

Machine learning methods applied to triage in emergency services: A systematic review

BACKGROUND

In emergency services is important to accurately assess and classify symptoms, which may be improved with the help of technology. One mechanism that could help and improve predictions from health records or patient flow is machine learning (ML).

AIM

To analyse the effectiveness of ML systems in triage for making predictions at the emergency department in comparison with other triage scales/scores.

METHODS

Following the PRISMA recommendations, a systematic review was conducted using CINAHL, Cochrane, Cuiden, Medline and Scopus databases with the search equation "Machine learning AND triage AND emergency".

RESULTS

Eleven studies were identified. The studies show that the use of ML methods consistently predict important outcomes like mortality, critical care outcomes and admission, and the need for hospitalization in comparison with scales like Emergency Severity Index or others. Among the ML models considered, XGBoost and Deep Neural Networks obtained the highest levels of prediction accuracy, while Logistic Regression performed obtained the worst values.

CONCLUSIONS

Machine learning methods can be a good instrument for helping triage process with the prediction of important emergency variables like mortality or the need for critical care or hospitalization.

Management of Children with Acute Asthma Attack: A RAND/UCLA Appropriateness Approach

Bronchial asthma is the most frequent chronic disease in children and affects up to 20% of the pediatric population, depending on the geographical area. Asthma symptoms vary over time and in intensity, and acute asthma attack can resolve spontaneously or in response to therapy. The aim of this project was to define the care pathway for pediatric patients who come to the primary care pediatrician or Emergency Room with acute asthmatic access. The project was developed in the awareness that for the management of these patients, broad coordination of interventions in the pre-hospital phase and the promotion of timely and appropriate assistance modalities with the involvement of all health professionals involved are important. Through the application of the RAND method, which obliges to discuss the statements derived from the guidelines, there was a clear increase in the concordance in the behavior on the management of acute asthma between primary care pediatricians and hospital pediatricians. The RAND method was found to be useful for the selection of good practices forming the basis of an evidence-based approach, and the results obtained form the basis for further interventions that allow optimizing the care of the child with acute asthma attack at the family and pediatric level. An important point of union between the primary care pediatrician and the specialist hospital pediatrician was the need to share spirometric data, also including the use of new technologies such as teleconsultation. Monitoring the progress of asthma through spirometry could allow the pediatrician in the area to intervene early by modifying the maintenance therapy and help the patient to achieve good control of the disease.

A NICE combination for predicting hospitalisation at the Emergency Department: a European multicentre observational study of febrile children

Background

Prolonged Emergency Department (ED) stay causes crowding and negatively impacts quality of care. We developed and validated a prediction model for early identification of febrile children with a high risk of hospitalisation in order to improve ED flow.

Methods

The MOFICHE study prospectively collected data on febrile children (0-18 years) presenting to 12 European EDs. A prediction models was constructed using multivariable logistic regression and included patient characteristics available at triage. We determined the discriminative values of the model by calculating the area under the receiver operating curve (AUC).

Findings

Of 38,424 paediatric encounters, 9,735 children were admitted to the ward and 157 to the PICU. The prediction model, combining patient characteristics and NICE alarming, yielded an AUC of 0.84 (95%CI 0.83-0.84).The model performed well for a rule-in threshold of 75% (specificity 99.0% (95%CI 98.9-99.1%, positive likelihood ratio 15.1 (95%CI 13.4-17.1), positive predictive value 0.84 (95%CI 0.82-0.86)) and a rule-out threshold of 7.5% (sensitivity 95.4% (95%CI 95.0-95.8), negative likelihood ratio 0.15 (95%CI 0.14-0.16), negative predictive value 0..95 (95%CI 0.95-9.96)). Validation in a separate dataset showed an excellent AUC of 0.91 (95%CI 0.90- 0.93). The model performed well for identifying children needing PICU admission (AUC 0.95, 95%CI 0.93-0.97). A digital calculator was developed to facilitate clinical use.

Interpretation

Patient characteristics and NICE alarming signs available at triage can be used to identify febrile children at high risk for hospitalisation and can be used to improve ED flow.

Funding

European Union, NIHR, NHS.

Predicting severe pneumonia in the emergency department: a global study of the Pediatric Emergency Research Networks (PERN)-study protocol

INTRODUCTION

Pneumonia is a frequent and costly cause of emergency department (ED) visits and hospitalisations in children. There are no evidence-based, validated tools to assist physicians in management and disposition decisions for children presenting to the ED with community-acquired pneumonia (CAP). The objective of this study is to develop a clinical prediction model to accurately stratify children with CAP who are at risk for low, moderate and severe disease across a global network of EDs.

METHODS AND ANALYSIS

This study is a prospective cohort study enrolling up to 4700 children with CAP at EDs at ~80 member sites of the Pediatric Emergency Research Networks (PERN; https://pern-global.com/). We will include children aged 3 months to <14 years with a clinical diagnosis of CAP. We will exclude children with hospital admissions within 7 days prior to the study visit, hospital-acquired pneumonias or chronic complex conditions. Clinical, laboratory and imaging data from the ED visit and hospitalisations within 7 days will be collected. A follow-up telephone or text survey will be completed 7-14 days after the visit. The primary outcome is a three-tier composite of disease severity. Ordinal logistic regression, assuming a partial proportional odds specification, and recursive partitioning will be used to develop the risk stratification models.

ETHICS AND DISSEMINATION

This study will result in a clinical prediction model to accurately identify risk of severe disease on presentation to the ED. Ethics approval was obtained for all sites included in the study. Cincinnati Children's Hospital Institutional Review Board (IRB) serves as the central IRB for most US sites. Informed consent will be obtained from all participants. Results will be disseminated through international conferences and peer-reviewed publications. This study overcomes limitations of prior pneumonia severity scores by allowing for broad generalisability of findings, which can be actively implemented after model development and validation.

Emergency department treatment of asthma in children: A review

Asthma is the most common chronic illness in children, with >700,000 emergency department (ED) visits each year. Asthma is a respiratory disease characterized by a combination of airway inflammation, bronchoconstriction, bronchial hyperresponsiveness, and variable outflow obstruction, with clinical presentations ranging from mild to life-threatening. Standardized ED treatment can improve patient outcomes, including fewer hospital admissions. Informed by the most recent guidelines, this review focuses on the optimal approach to diagnosis and treatment of children with acute asthma exacerbations who present to the ED.

Machine Learning-Based Prediction of Clinical Outcomes for Children During Emergency Department Triage

IMPORTANCE

While machine learning approaches may enhance prediction ability, little is known about their utility in emergency department (ED) triage.

OBJECTIVES

To examine the performance of machine learning approaches to predict clinical outcomes and disposition in children in the ED and to compare their performance with conventional triage approaches.

DESIGN, SETTING, AND PARTICIPANTS

Prognostic study of ED data from the National Hospital Ambulatory Medical Care Survey from January 1, 2007, through December 31, 2015. A nationally representative sample of 52 037 children aged 18 years or younger who presented to the ED were included. Data analysis was performed in August 2018.

MAIN OUTCOMES AND MEASURES

The outcomes were critical care (admission to an intensive care unit and/or in-hospital death) and hospitalization (direct hospital admission or transfer). In the training set (70% random sample), using routinely available triage data as predictors (eg, demographic characteristics and vital signs), we derived 4 machine learning-based models: lasso regression, random forest, gradient-boosted decision tree, and deep neural network. In the test set (the remaining 30% of the sample), we measured the models' prediction performance by computing C statistics, prospective prediction results, and decision curves. These machine learning models were built for each outcome and compared with the reference model using the conventional triage classification information.

RESULTS

Of 52 037 eligible ED visits by children (median [interquartile range] age, 6 [2-14] years; 24 929 [48.0%] female), 163 (0.3%) had the critical care outcome and 2352 (4.5%) had the hospitalization outcome. For the critical care prediction, all machine learning approaches had higher discriminative ability compared with the reference model, although the difference was not statistically significant (eg, C statistics of 0.85 [95% CI, 0.78-0.92] for the deep neural network vs 0.78 [95% CI, 0.71-0.85] for the reference; P = .16), and lower number of undertriaged critically ill children in the conventional triage levels 3 to 5 (urgent to nonurgent). For the hospitalization prediction, all machine learning approaches had significantly higher discrimination ability (eg, C statistic, 0.80 [95% CI, 0.78-0.81] for the deep neural network vs 0.73 [95% CI, 0.71-0.75] for the reference; P < .001) and fewer overtriaged children who did not require inpatient management in the conventional triage levels 1 to 3 (immediate to urgent). The decision curve analysis demonstrated a greater net benefit of machine learning models over ranges of clinical thresholds.

CONCLUSIONS AND RELEVANCE

Machine learning-based triage had better discrimination ability to predict clinical outcomes and disposition, with reduction in undertriaging critically ill children and overtriaging children who are less ill.

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

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 (±SD) age of 7.0 (±4.2) 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.

Development of a pediatric asthma predictive index for hospitalization

BACKGROUND

Variation in emergency department (ED) management for asthma exacerbation leads to disparities in care. Current asthma severity scores are insufficient to be used for hospitalization decisions.

OBJECTIVE

To develop and internally validate an asthma predictive index for hospitalization (APIH) to guide practitioners in their admission decision for children with asthma exacerbations.

METHODS

Data were collected from 12,066 children between 5 and 18 years old diagnosed with asthma exacerbation in the ED. Epidemiologic findings, number of inhaled corticosteroid canisters, short-acting β-blocker canisters, oral steroids, coexisting atopy, family history of atopy, insurance, and prior asthma ED visits or hospitalizations were compared between patients hospitalized and discharged. We used univariate analysis and multivariate analysis to determine the best predictor variables for hospitalization. Our study internally validated the prediction index to estimate future performance of the prediction rule.

RESULTS

The highest risk factors associated with asthma hospitalization from the ED are oxygen saturation less than 94%, respiratory rate greater than 31/min, history of pneumonia, and asthma ED visits in past 12 months. With a reduced predictive model that combined these risk factors, the odds ratio was 44.9 (95% CI, 32.8-61.4), which is extremely significant. Our C index of discrimination of 0.77 was similar to the validation C index of 0.78, which confirms a solid prediction model.

CONCLUSION

We have developed and internally validated a pediatric hospitalization prediction index for acute asthma exacerbation in the ED. Further studies are needed to externally validate the APIH before its implementation into clinical practice.

Risk Prediction Score for HIV Infection: Development and Internal Validation with Cross-Sectional Data from Men Who Have Sex with Men in China

Receptive anal intercourse, multiple partners, condomless sex, sexually transmitted infections (STIs), and drug/alcohol addiction are familiar factors that correlate with increased human immunodeficiency virus (HIV) risk among men who have sex with men (MSM). To improve estimation to HIV acquisition, we created a composite score using questions from routine survey of 3588 MSM in Beijing, China. The HIV prevalence was 13.4%. A risk scoring tool using penalized maximum likelihood multivariable logistic regression modeling was developed, deploying backward step-down variable selection to obtain a reduced-form model. The full penalized model included 19 sexual predictors, while the reduced-form model had 12 predictors. Both models calibrated well; bootstrap-corrected c-indices were 0.70 (full model) and 0.71 (reduced-form model). Non-Beijing residence, short-term living in Beijing, illegal drug use, multiple male sexual partners, receptive anal sex, inconsistent condom use, alcohol consumption before sex, and syphilis infection were the strongest predictors of HIV infection. Discriminating higher-risk MSM for targeted HIV prevention programming using a validated risk score could improve the efficiency of resource deployment for educational and risk reduction programs. A valid risk score can also identify higher risk persons into prevention and vaccine clinical trials, which would improve trial cost-efficiency.

Machine learning approaches for predicting disposition of asthma and COPD exacerbations in the ED

OBJECTIVE

The prediction of emergency department (ED) disposition at triage remains challenging. Machine learning approaches may enhance prediction. We compared the performance of several machine learning approaches for predicting two clinical outcomes (critical care and hospitalization) among ED patients with asthma or COPD exacerbation.

METHODS

Using the 2007-2015 National Hospital and Ambulatory Medical Care Survey (NHAMCS) ED data, we identified adults with asthma or COPD exacerbation. In the training set (70% random sample), using routinely-available triage data as predictors (e.g., demographics, arrival mode, vital signs, chief complaint, comorbidities), we derived four machine learning-based models: Lasso regression, random forest, boosting, and deep neural network. In the test set (the remaining 30% of sample), we compared their prediction ability against traditional logistic regression with Emergency Severity Index (ESI, reference model).

RESULTS

Of 3206 eligible ED visits, corresponding to weighted estimates of 13.9 million visits, 4% had critical care outcome and 26% had hospitalization outcome. For the critical care prediction, the best performing approach- boosting - achieved the highest discriminative ability (C-statistics 0.80 vs. 0.68), reclassification improvement (net reclassification improvement [NRI] 53%, P = 0.002), and sensitivity (0.79 vs. 0.53) over the reference model. For the hospitalization prediction, random forest provided the highest discriminative ability (C-statistics 0.83 vs. 0.64) reclassification improvement (NRI 92%, P < 0.001), and sensitivity (0.75 vs. 0.33). Results were generally consistent across the asthma and COPD subgroups.

CONCLUSIONS

Based on nationally-representative ED data, machine learning approaches improved the ability to predict disposition of patients with asthma or COPD exacerbation.

Guideline on management of the acute asthma attack in children by Italian Society of Pediatrics

BACKGROUND

Acute asthma attack is a frequent condition in children. It is one of the most common reasons for emergency department (ED) visit and hospitalization. Appropriate care is fundamental, considering both the high prevalence of asthma in children, and its life-threatening risks. Italian Society of Pediatrics recently issued a guideline on the management of acute asthma attack in children over age 2, in ambulatory and emergency department settings.

METHODS

The Grading of Recommendations Assessment, Development, and Evaluation (GRADE) methodology was adopted. A literature search was performed using the Cochrane Library and Medline/PubMed databases, retrieving studies in English or Italian and including children over age 2 year.

RESULTS

Inhaled ß2 agonists are the first line drugs for acute asthma attack in children. Ipratropium bromide should be added in moderate/severe attacks. Early use of systemic steroids is associated with reduced risk of ED visits and hospitalization. High doses of inhaled steroids should not replace systemic steroids. Aminophylline use should be avoided in mild/moderate attacks. Weak evidence supports its use in life-threatening attacks. Epinephrine should not be used in the treatment of acute asthma for its lower cost / benefit ratio, compared to β2 agonists. Intravenous magnesium solphate could be used in children with severe attacks and/or forced expiratory volume1 (FEV1) lower than 60% predicted, unresponsive to initial inhaled therapy. Heliox could be administered in life-threatening attacks. Leukotriene receptor antagonists are not recommended.

CONCLUSIONS

This Guideline is expected to be a useful resource in managing acute asthma attacks in children over age 2.

Adapting an Evidence-Based Pediatric Acute Asthma Exacerbation Severity Assessment Tool for Pediatric Primary Care

INTRODUCTION

The purposes of this project were (a) to examine criteria derived from evidence-based pediatric acute asthma exacerbation assessment tools, asthma scores, and the acute asthma prediction rule validated and used in the emergency department and (b) to adapt these criteria for pediatric primary care.

METHOD

The three stages of the project included (a) identification of criteria in a literature review, (b) validation of the criteria by an expert panel, and (c) adaptation of the criteria in the design of an assessment tool.

RESULTS

The criteria were validated and adapted in the design of The Pediatric Acute Asthma Exacerbation Severity Assessment and Disposition Decision-Making Tool for Pediatric Primary Care.

DISCUSSION

The adaptation of criteria derived from the evidence and validated by an expert panel will inform and guide clinicians in assessing severity and support decision making in determining disposition of pediatric patients presenting with an acute asthma exacerbation in primary care.

Admission predictability of children with acute asthma

OBJECTIVES

We aimed to evaluate the seasonal variations of acute asthma presentation in children and the utility of the pediatric asthma score (PAS) and its components in early admission prediction.

METHODS

As part of a randomized controlled trial addressing the clinical efficacy of budesonide nebulization in the treatment of acute asthma in children, the PAS was measured at baseline, 1^st, 2^nd, 3^rd, and 4^th h from the start of medications. Decision of admission was taken at or beyond the 2^nd h.

RESULTS

Out of a total 906 emergency department (ED) visits with moderate-to-severe acute asthma, 157 children were admitted. June to September had the lowest number of visits. The admission-to-discharge ratio varied throughout the year. During the ED stay, between baseline and 3^rd h, admission predictability of the total score improved progressively with a small difference between the 2^nd and 3^rd h. The total score remained the strongest predictor of admission at every time point compared to its individual components. The drop of PAS from baseline to the 2^nd h was not a good predictor of admission. Oxygen saturation (OS) and respiratory rate (RR) had relatively higher predictability than other components.

CONCLUSIONS

Decision of admission could be made to many children with moderate-to-severe acute asthma at the 2^nd h of ED stay based on their total PAS. OS and RR should be part of any scoring system to evaluate acute asthma in children.

Clinical respiratory scales: which one should we use?

INTRODUCTION

There are countless clinical respiratory scales for acute dyspnoea. Most healthcare professionals choose one based on previous personal experience or following local practice, unaware of the implications of their choice. The lack of critical comparisons between those different tools has been a widespread problem that only recently has begun to be addressed via score validation studies. Here we try to assess and compare the quality criteria of measurement properties of acute dyspnoea scores. Areas covered: A literature review was conducted by searching the PubMed database. Forty-five documents were deemed eligible as they reported the use or building of clinical scales, using at least two parameters, and applied these to an acute episode of respiratory dyspnoea. Our primary focus was the description of the validity, reliability and utility of 41 suitable scoring instruments. Differences in sample selection, study design, rater profiles and potential methodological shortcomings were also addressed. Expert commentary: All acute dyspnoea scores lack complete validation. In particular, the areas of measurement error and interpretability have not been addressed correctly by any of the tools reviewed. Frequent modification of pre-existing scores (in items composition and/or name), differences in study design and discrepancies in reviewed sources also hinder the search for an adequate tool.

The asthma prediction rule to decrease hospitalizations for children with asthma

PURPOSE OF REVIEW

The aim of the present review was to discuss the challenges around clinical decision-making for hospitalization of children with acute asthma exacerbations and the development, internal validation, and future potential of the asthma prediction rule (APR) to provide meaningful clinical decision-support that might decrease unnecessary hospitalizations.

RECENT FINDINGS

The APR was developed and internally validated using predictor variables available before treatment in the emergency department, and performed well to predict 'need-for-hospitalization.' Oxygen saturation on room air and expiratory phase prolongation were most strongly associated with need-for-hospitalization.

SUMMARY

Research on prediction rules in pediatric asthma is rare. We developed and internally validated the APR using clinically intuitive predictor variables that are available at the bedside. Before incorporation into electronic decision-support the APR must undergo external validation and an impact analysis to determine if use of this tool will change clinician behavior and improve patient outcomes.

Pulse Oximeter Plethysmograph Estimate of Pulsus Paradoxus as a Measure of Acute Asthma Exacerbation Severity and Response to Treatment

OBJECTIVES

Pulsus paradoxus is one of the few objective bedside measures of acute asthma exacerbation severity but is difficult to measure in tachypneic and tachycardic patients and in noisy clinical environments. Our primary objective was to examine whether pulse oximeter plethysmograph estimate of pulsus paradoxus (PEP) is associated with physiologic and symptom measures of acute exacerbation severity (airway resistance by impulse oscillometry [%IOS] and the Acute Asthma Intensity Research Score [AAIRS]). Secondary objectives were to validate the previous association of PEP with percent predicted forced expiratory volume in 1 second (%FEV1 ) and to examine associations of change of PEP with change of these outcomes after 2 hours of treatment.

METHODS

This was a secondary analysis of data from a prospective observational study of patients aged 5-17 years with acute asthma exacerbations. The predictor variable, PEP, was measured using a dedicated pulse oximeter and waveform analysis program. Outcome measures included the AAIRS, %IOS, and %FEV1 at baseline and after 2 hours of treatment. We examined associations of PEP with %IOS and the AAIRS at baseline using multiple linear regression models adjusted for age, sex, and race. As secondary analyses we similarly examined the association of PEP with %FEV1 at baseline and change of PEP with change of %IOS, the AAIRS, and %FEV1 after 2 hours of treatment using multiple linear regression models adjusted for the baseline value of the outcome measure and the AAIRS.

RESULTS

Among 684 participants (61% males; 61% African American) there were associations of baseline PEP with %IOS, the AAIRS, and %FEV1 (p < 0.001). Change of PEP after 2 hours of treatment was associated with change of %FEV1 (p < 0.001) and change of the AAIRS (p = 0.01) but not with change of %IOS (p = 0.60).

CONCLUSIONS

PEP demonstrates criterion validity in predicting baseline %IOS, the AAIRS, and %FEV1 , and responsiveness to change of the AAIRS and %FEV1 . Data contained in the oximeter plethysmograph waveform might be utilized as a continuous, objective measure of acute asthma exacerbation severity and real-time response to treatment.