Predicting need for hospitalization in acute pediatric asthma

Elevated Neutrophil-to-Lymphocyte Ratio Is Associated with Severe Asthma Exacerbation in Children

Asthma is the most common chronic respiratory disease in children. The neutrophil-to-lymphocyte ratio (NLR) is a marker of a chronic inflammatory state; however, data on the association of NLR with acute asthma exacerbations in children is lacking. In this cross-sectional study, between 2016 and 2021, children aged 2-18 years who were referred to the emergency department (ED) due to asthma exacerbation, were included. NLR, calculated from complete blood count upon arrival, was assessed as a continuous variable and was classified into four groups according to quartiles. The association between severity parameters and NLR quartiles was examined. A total of 831 ED visits for asthma exacerbation were included in the study. The median NLR was 1.6, 3.8, 6.7, and 12.9 in quartiles 1-4, respectively (p < 0.001). Demographic parameters, background diseases, and chronic medications were similar between the quartiles. Higher heart rate, body temperature, systolic blood pressure, and respiratory rate were observed in the higher NLR quartiles, as well as lower oxygen saturation. Higher urgency scale and higher rates of intravenous magnesium sulfate were observed in the higher NLR quartiles, with higher admission rates and prolonged hospitalizations. In summary, NLR upon admission is associated with the severity of asthma exacerbation and higher chances of hospitalization among children in the ED.

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.

Predicting hospitalization of pediatric asthma patients in emergency departments using machine learning

MOTIVATION

The timely identification of patients for hospitalization in emergency departments (EDs) can facilitate efficient use of hospital resources. Machine learning can help the early prediction of ED disposition; however, application of machine learning models requires both computer science skills and domain knowledge. This presents a barrier for those who want to apply machine learning to real-world settings.

OBJECTIVES

The objective of this study was to construct a competitive predictive model with a minimal amount of human effort to facilitate decisions regarding hospitalization of patients.

METHODS

This study used the electronic health record data from five EDs in a single healthcare system, including an academic urban children's hospital ED, from January 2009 to December 2013. We constructed two machine learning models by using automated machine learning algorithm (autoML) which allows non-experts to use machine learning model: one with data only available at ED triage, the other adding information available one hour into the ED visit. Random forest and logistic regression were employed as bench-marking models. The ratio of the training dataset to the test dataset was 8:2, and the area under the receiver operating characteristic curve (AUC), accuracy, and F1 were calculated to assess the quality of the models.

RESULTS

Of the 9,069 ED visits analyzed, the study population was made up of males (62.7 %), median (IQR) age was 6 (4, 10) years, and public insurance coverage (66.0 %). The majority had an Emergency Severity Index score of 3 (52.9 %). The prevalence of hospitalization was 22.5 %. The AUCs were 0.914 and 0.942. AUCs were 0.831 and 0.886 for random forests, and 0.795 and 0.823 for logistic regression. Among the predictors, an outcome at a prior visit, ESI level, time to first medication, and time to triage were the most important features for the prediction of the need for hospitalization.

CONCLUSIONS

In comparison with the conventional approaches, the use of autoML improved the predictive ability for the need for hospitalization. The findings can optimize ED management, hospital-level resource utilization and improve quality. Furthermore, this approach can support the design of a more effective patient ED flow for pediatric asthma care.

Predicting Admissions From a Paediatric Emergency Department - Protocol for Developing and Validating a Low-Dimensional Machine Learning Prediction Model

Introduction: Patients boarding in the Emergency Department can contribute to overcrowding, leading to longer waiting times and patients leaving without being seen or completing their treatment. The early identification of potential admissions could act as an additional decision support tool to alert clinicians that a patient needs to be reviewed for admission and would also be of benefit to bed managers in advance bed planning for the patient. We aim to create a low-dimensional model predicting admissions early from the paediatric Emergency Department.

Methods and Analysis: The methodology Cross Industry Standard Process for Data Mining (CRISP-DM) will be followed. The dataset will comprise of 2 years of data, ~76,000 records. Potential predictors were identified from previous research, comprising of demographics, registration details, triage assessment, hospital usage and past medical history. Fifteen models will be developed comprised of 3 machine learning algorithms (Logistic regression, naïve Bayes and gradient boosting machine) and 5 sampling methods, 4 of which are aimed at addressing class imbalance (undersampling, oversampling, and synthetic oversampling techniques). The variables of importance will then be identified from the optimal model (selected based on the highest Area under the curve) and used to develop an additional low-dimensional model for deployment.

Discussion: A low-dimensional model comprised of routinely collected data, captured up to post triage assessment would benefit many hospitals without data rich platforms for the development of models with a high number of predictors. Novel to the planned study is the use of data from the Republic of Ireland and the application of sampling techniques aimed at improving model performance impacted by an imbalance between admissions and discharges in the outcome variable.

Predicting Severe Asthma Exacerbations in Children: Blueprint for Today and Tomorrow

Severe asthma exacerbations are the primary cause of morbidity and mortality in children with asthma. Accurate prediction of children at risk for severe exacerbations, defined as those requiring systemic corticosteroids, emergency department visit, and/or hospitalization, would considerably reduce health care utilization and improve symptoms and quality of life. Substantial progress has been made in identifying high-risk exacerbation-prone children. Known risk factors for exacerbations include demographic characteristics (ie, low income, minority race/ethnicity), poor asthma control, environmental exposures (ie, aeroallergen exposure/sensitization, concomitant viral infection), inflammatory biomarkers, genetic polymorphisms, and markers from other "omic" technologies. The strongest risk factor for a future severe exacerbation remains having had one in the previous year. Combining risk factors into composite scores and use of advanced predictive analytic techniques such as machine learning are recent methods used to achieve stronger prediction of severe exacerbations. However, these methods are limited in prediction efficiency and are currently unable to predict children at risk for impending (within days) severe exacerbations. Thus, we provide a commentary on strategies that have potential to allow for accurate and reliable prediction of children at risk for impending exacerbations. These approaches include implementation of passive, real-time monitoring of impending exacerbation predictors, use of population health strategies, prediction of severe exacerbation responders versus nonresponders to conventional exacerbation management, and considerations for preschool-age children who can be especially high risk. Rigorous prediction and prevention of severe asthma exacerbations is needed to advance asthma management and improve the associated morbidity and mortality.

Nurse-driven Clinical Pathway Based on an Innovative Asthma Score Reduces Admission Time for Children

We recently demonstrated that an innovative asthma score independent of auscultation could accurately predict the requirement for bronchodilator nebulization compared to the physician's routine clinical judgment to administer bronchodilators. We aimed to standardize inpatient care for children with acute asthma by implementing a clinical pathway based on this innovative asthma score.

Methods

We designed a nurse-driven clinical pathway. This pathway included standardized respiratory assessments and a protocol for the nursing staff to administer bronchodilators without a specific order from the physician. We compared the length of stay and the number of readmissions to a historical cohort.

Results

Seventy-nine patients with moderate acute asthma completed the pathway. We obtained a total of 858 Childhood asthma scores in these patients, with a median of 11 scores per patient (interquartile range 8-17). Patients treated according to the nurse-driven protocol were 3.3 times more likely to be discharged earlier (hazard ratio, 3.29; 95% confidence interval, 2.33-4.66; P < 0.05), and length of stay was significantly reduced (median 28 versus 53 h) compared to the historical standard practice. On request, the attending physician assessed the patient's respiratory status 42 times (4.9% of all childhood asthma score assessments). Patient safety was not compromised, and none of the patients were removed from the pathway. In each group, we readmitted two (2.5%) patients within 1 week after discharge.

Conclusion

This nurse-driven clinical pathway for children with acute asthma based on an asthma score independent of auscultation findings significantly decreased length of stay without compromising patient safety.

Initial ED oxygen saturation ≤90% increases the risk of a complicated hospital course in pediatric asthmatics requiring admission

Emergency physicians are responsible for admitting children with asthma who do not respond to initial therapy. We examined the hypothesis that an initial room air pulse oximetry ≤90% elevates the risk of a complicated hospital course in children who require admission with acute asthma.

METHODS

Charts of all patients ages 2 years-17 years admitted for asthma from January 2017 to December 2017 were reviewed. An explicit chart review was performed by trained data extractors using a standardized form.

RESULTS

A total of 244 children meeting inclusion criteria were admitted for asthma from the ED during the study period. All patients had an initial room air pulse oximetry documented. Sixty-five were admitted to PICU status (27%), and 179 (73%) were admitted to floor status. The relative risk of a complicated course in those patients presenting with a saturation of ≤90% was 11.3 (95% CI 3.9-32.6). The mean initial pulse oximetry on patients with a complicated course was 85% versus 93% for those without a complicated course (p < 0.005).

CONCLUSION

Our data suggest that in pediatric asthmatics that require admission from the ED, those with pulse oximetry readings less than or equal to 90% on presentation are at higher risk of a complicated hospital course.

Racial/Ethnic Variation in Emergency Department Care for Children With Asthma

OBJECTIVE

To assess the variation between racial/ethnic groups in emergency department (ED) treatment of asthma for pediatric patients.

METHODS

This study was a cross-sectional analysis of pediatric (2-18 years) asthma visits among 6 EDs in the Upper Midwest between June 2011 and May 2012. We used mixed-effects logistic regression to assess the odds of receiving steroids, radiology tests, and returning to the ED within 30 days. We conducted a subanalysis of asthma visits where patients received at least 1 albuterol treatment in the ED.

RESULTS

The sample included 2909 asthma visits by 1755 patients who were discharged home from the ED. After adjusting for demographics, insurance type, and triage score, African American (adjusted odds ratio [aOR], 1.78; 95% confidence interval [CI], 1.40-2.26) and Hispanic (aOR, 1.64; 95% CI, 1.22-2.22) patients had higher odds of receiving steroids compared with whites. African Americans (aOR, 0.58; 95% CI, 0.46-0.74) also had lower odds of radiological testing compared with whites. Asians had the lowest odds of 30-day ED revisits (aOR, 0.26; 95% CI, 0.08-0.84), with no other significant differences detected between racial/ethnic groups. Subgroup analyses of asthma patients who received albuterol revealed similar results, with American Indians showing lower odds of radiological testing as well (aOR, 0.47; 95% CI, 0.22-1.01).

CONCLUSIONS

In this study, children from racial/ethnic minority groups had higher odds of steroid administration and lower odds of radiological testing compared with white children. The underlying reasons for these differences are likely multifactorial, including varying levels of disease severity, health literacy, and access to care.

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.

Optimizing community-level surveillance data for pediatric asthma management

Community-level approaches for pediatric asthma management rely on locally collected information derived primarily from two sources: claims records and school-based surveys. We combined claims and school-based surveillance data, and examined the asthma-related risk patterns among adolescent students. Symptom data collected from school-based asthma surveys conducted in Oakland, CA were used for case identification and determination of severity levels for students (high and low). Survey data were matched to Medicaid claims data for all asthma-related health care encounters for the year prior to the survey. We then employed recursive partitioning to develop classification trees that identified patterns of demographics and healthcare utilization associated with severity. A total of 561 students had complete matched data; 86.1% were classified as high-severity, and 13.9% as low-severity asthma. The classification tree consisted of eight subsets: three indicating high severity and five indicating low severity. The risk subsets highlighted varying combinations of non-specific demographic and socioeconomic predictors of asthma prevalence, morbidity and severity. For example, the subset with the highest class-prior probability (92.1%) predicted high-severity asthma and consisted of students without prescribed rescue medication, but with at least one in-clinic nebulizer treatment. The predictive accuracy of the tree-based model was approximately 66.7%, with an estimated 91.1% of high-severity cases and 42.3% of low-severity cases correctly predicted. Our analysis draws on the strengths of two complementary datasets to provide community-level information on children with asthma, and demonstrates the utility of recursive partitioning methods to explore a combination of features that convey asthma severity.

Predicting risk of hospitalisation: a retrospective population-based analysis in a paediatric population in Emilia-Romagna, Italy

OBJECTIVES

Develop predictive models for a paediatric population that provide information for paediatricians and health authorities to identify children at risk of hospitalisation for conditions that may be impacted through improved patient care.

DESIGN

Retrospective healthcare utilisation analysis with multivariable logistic regression models.

DATA

Demographic information linked with utilisation of health services in the years 2006-2014 was used to predict risk of hospitalisation or death in 2015 using a longitudinal administrative database of 527 458 children aged 1-13 years residing in the Regione Emilia-Romagna (RER), Italy, in 2014.

OUTCOME MEASURES

Models designed to predict risk of hospitalisation or death in 2015 for problems that are potentially avoidable were developed and evaluated using the C-statistic, for calibration to assess performance across levels of predicted risk, and in terms of their sensitivity, specificity and positive predictive value.

RESULTS

Of the 527 458 children residing in RER in 2014, 6391 children (1.21%) were hospitalised for selected conditions or died in 2015. 49 486 children (9.4%) of the population were classified in the 'At Higher Risk' group using a threshold of predicted risk >2.5%. The observed risk of hospitalisation (5%) for the 'At Higher Risk' group was more than four times higher than the overall population. We observed a C-statistic of 0.78 indicating good model performance. The model was well calibrated across categories of predicted risk.

CONCLUSIONS

It is feasible to develop a population-based model using a longitudinal administrative database that identifies the risk of hospitalisation for a paediatric population. The results of this model, along with profiles of children identified as high risk, are being provided to the paediatricians and other healthcare professionals providing care to this population to aid in planning for care management and interventions that may reduce their patients' likelihood of a preventable, high-cost hospitalisation.

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.

Initial Asthma Severity Assessment Tools as Predictors of Hospitalization

BACKGROUND

Assessment tools to classify and prioritize patients, such as systems of triage, and indicators of severity, such as clinical respiratory scores, are helpful in guiding the flow of asthmatic patients in the emergency department.

OBJECTIVE

Our aim was to assess the performance of the Pediatric Assessment Triangle (PAT), triage level (TL), Pulmonary Score (PS), and initial O₂ saturation (O₂ sat), in predicting hospitalization in pediatric acute asthma exacerbations.

STUDY DESIGN

Retrospective study evaluating PAT, TL, and PS at presentation, and initial O₂ sat of asthmatic children in the pediatric emergency department (PED). The primary outcome measure was the rate of hospitalization. Secondary outcomes were length of stay (LOS) in the PED and admission to the pediatric intensive care unit (PICU).

RESULTS

PAT, TL, PS, and initial O₂ sat were recorded in 14,953 asthmatic children. Multivariate analysis yielded the following results: Abnormal PAT and more severe TLs (I-II) were independent risk factors for hospitalization (odds ratio [OR] 1.6, 95% confidence interval [CI] 1.4-1.8; OR 3.4, 95% CI 2.6-4.3, respectively) and longer LOS (OR 1.5, 95% CI 1.3-1.7; OR 2.6, 95% CI 2-3.3, respectively). PS > 3 showed a strong association with hospitalization (OR 8.1, 95% CI 7-9.4), PICU admission (OR 9.6, 95% CI 3-30.9) and longer LOS (OR 6.2, 95% CI 5.6-6.9). O₂ sat < 94% was an independent predictor of admission (OR 5.2, 95% CI 4.6-5.9), PICU admission (OR 4.6, 95% CI 4.5-4.6), and longer LOS (OR 4.6, 95% CI 4.1-5.2).

CONCLUSIONS

PAT, TL, PS, and initial O₂ sat are good predictors of hospitalization in pediatric acute asthma exacerbations.

Pediatric asthma severity score is associated with critical care interventions

AIM

To determine if a standardized asthma severity scoring system (PASS) was associated with the time spent on continuous albuterol and length of stay in the pediatric intensive care unit (PICU).

METHODS

This is a single center, retrospective chart review study at a major children’s hospital in an urban location. To qualify for this study, participants must have been admitted to the PICU with a diagnosis of status asthmaticus. There were a total of 188 participants between the ages of two and nineteen, excluding patients receiving antibiotics for pneumonia. PASS was calculated upon PICU admission. Subjects were put into one of three categories based on PASS: ≤ 7 (mild), 8-11 (moderate), and ≥ 12 (severe). The groups were compared based on different variables, including length of continuous albuterol and PICU stay.

RESULTS

The age distribution across all groups was similar. The median length of continuous albuterol was longest in the severe group with a duration of 21.5 h (11.5-27.5), compared to 15 (7.75-23.75) and 10 (5-15) in the moderate and mild groups, respectively (P = 0.001). The length of stay was longest in the severe group, with a stay of 35.6 h (22-49) compared to 26.5 (17-30) and 17.6 (12-29) in the moderate and mild groups, respectively (P = 0.001).

CONCLUSION

A higher PASS is associated with a longer time on continuous albuterol, an increased likelihood to require noninvasive ventilation, and a longer stay in the ICU. This may help safely distribute asthmatics to lower and higher levels of care in the future.

Improving Guideline-Based Care of Acute Asthma in a Pediatric Emergency Department

BACKGROUND AND OBJECTIVE

Rapid repetitive administration of short-acting β-agonists (SABA) is the most effective means of reducing acute airflow obstruction in asthma. Little evidence exists that assesses process measures (ie, timeliness) and outcomes for asthma. We used quality improvement (QI) methods to improve emergency department care in accordance with national guidelines including timely SABA administration and use of asthma severity scores.

METHODS

The Model for Improvement was used and interventions were targeted at 4 key drivers: knowledge, engagement, decision support, and workflow enhancement. Time series analysis was performed and outcomes assessed on statistical process control charts.

RESULTS

Asthma severity scoring increased from 0% to >95% in triage and to >75% for repeat scores. Time to first SABA (T1) improved by 32.8 minutes (47%). T1 for low severity patients improved by 17.6 minutes (28%). T1 for high severity patients improved by 3.1 minutes to 18.1 minutes (15%). Time to third SABA (T3) improved by 30 minutes (24%). T3 for low severity patients improved by 42.5 minutes (29%) and T3 for high severity patients improved by 21 minutes (23%). Emergency department length of stay for low severity patients discharged to home improved by 29.3 minutes (15%). The number of asthma-related visits between 48-hour return hospitalizations increased from 114 to 261. The admission rate decreased 6.0%.

CONCLUSIONS

We implemented standardized asthma severity scoring with high rates of compliance, improved timely administration of β-agonist treatments, demonstrated early improvements in Emergency department length of stay, and reduced admission rates without increasing unplanned return admissions.

Standardized Asthma Admission Criteria Reduce Length of Stay in a Pediatric Emergency Department

OBJECTIVES

Asthma is the most common chronic illness in children and accounts for > 600,000 emergency department (ED) visits each year. Reducing ED length of stay (LOS) for moderate to severe asthmatics improves ED throughput and patient care for this high-risk population. The objective of this study was to determine the impact of adding standardized, respiratory score-based admission criteria to an asthma pathway on ED LOS for admitted patients, time to bed request, overall percentage of admitted asthmatics, inpatient LOS, and percentage of pediatric intensive care unit (PICU) admissions.

METHODS

This was a retrospective study of a quality improvement intervention. Statistical process control methodologies were used to analyze measures 15 months before and after implementation of a modified asthma pathway (June 2010 to December 2012; pathway modification September 2011).

RESULTS

A total of 3,688 patients aged 1 through 18 years who presented to the ED with an asthma exacerbation during the study period were included. Patients were excluded if they were not eligible for the asthma pathway. Patient characteristics were similar before and after the intervention. Mean ED LOS and time to bed request for admitted asthmatics both decreased by 30 minutes. There was no change in percentage of asthma admissions (34%), mean inpatient LOS (1.4 days), or percentage of PICU admissions (2%).

CONCLUSIONS

Standardizing care for asthma patients to include objective admission criteria early in the ED course may optimize patient care and improve ED flow.

Initial oxygen saturation values can predict the need to hospitalise children with mild wheezing

AIM

Mild wheezing during respiratory infections is a common cause of paediatric hospital admissions. This study aimed to identify factors predicting this condition in children over six months of age.

METHODS

We reviewed the medical records of 539 children, aged 6 months to 16 years, who visited the emergency department because of wheezing during respiratory infection. Mild disease was defined as hospital stays of less than 48 h and severe disease was staying at least 48 h or being treated in intensive care. Patients with an initial oxygen saturation value (SaO2 ) below 90% were analysed separately.

RESULTS

Most (87%) of the 539 patients had mild disease, 6% had a severe disease and 7% had an initial SaO2 below 90%. The area under the receiver operating characteristic (ROC) curve for the initial SaO2 predicting mild disease was 0.75 (95% CI 0.53-0.97), and the optimal cut-off value was 93%. An initial SaO2 >93% had a negative predictive value of 93%. Although 270 patients (50%) were hospitalised, only 140 (26%) would have been admitted using an optimal cut-off of SaO2 ≤93%.

CONCLUSION

An initial SaO2 >93% reflects a mild course of acute wheezing and using this cut-off point could have almost halved hospital admissions.

PRAM score as predictor of pediatric asthma hospitalization

OBJECTIVES

The objective was to determine the association between asthma severity as measured by the Pediatric Respiratory Assessment Measure (PRAM) score and the likelihood of admission for pediatric patients who present to the emergency department (ED) with moderate-to-severe asthma exacerbations and who receive intensive asthma therapy.

METHODS

This was a secondary analysis of a prospective study of triage nurse-initiated steroid therapy in pediatric asthma. Children aged 2 to 17 years inclusive, presenting with moderate-to-severe acute asthma exacerbations (defined as PRAM ≥ 4), were included. To be eligible for inclusion in the study, children must have received "intensive asthma therapy," defined as nurse-initiated initial bronchodilator and oral steroid therapy at arrival to triage. PRAM scores were measured hourly as per ED protocol. The primary outcome was inpatient hospitalization; secondary outcome was ED stay greater than 8 hours. Logistic regression models were used to predict admission based on PRAM score at triage and then hourly thereafter. The area under the receiver operating characteristic curve (AUC) was calculated for each hour.

RESULTS

A total of 297 patients were included in the analysis, with an admission rate of 11.4% for patients receiving intensive therapy. The 3-hour PRAM (AUC = 0.85) significantly improved prediction of admission compared to PRAM at triage (p = 0.04).

CONCLUSIONS

The 3-hour PRAM scores best predicts the need for hospitalization. These results may be applied in clinical settings to facilitate the decision to admit or initiate more aggressive adjunctive therapy to decrease the need for hospitalization.

Predictive factors of hospitalization in children with acute asthma at a university emergency care unit

OBJECTIVE

This study analyzed the factors that predicted the hospitalization of children with asthma following standardized treatment in emergency care unit (ECU).

METHODS

This retrospective study examined data collected from the clinical records of children, 14 years or younger, who were diagnosed with asthma (often with bronchopneumonia, pneumonia, or other illnesses) and treated at the ECU of Santo André from January 2005 to December 2009. The following data were analyzed: month and year of care, child's age and sex, period of observation, and need for hospitalization. A pediatrician confirmed the clinical diagnoses of all participants. The children were first given clinical treatments and were then admitted to ECU for follow-up assessment.

RESULTS

The number of hospital admissions was analyzed, and correlations were found with regard to this variable and child age (χ(2) = 166.9; P = 0.00001), the presence of associated illnesses (χ(2) = 63.8; P < 0.00001), and the observation period length (χ(2) = 11.4; P = 0.009). The number of hospital admissions was not correlated with child sex (χ(2) = 0.013; P = 0.9) or time of year (χ(2) = 15.8; P = 0.1). The 3-day observation period was not significant (P = 0.4) with regard to the remainder of the variables in the multiple logistic regression analysis.

CONCLUSIONS

Age, mainly children younger than 1 year, the presence of associated illnesses, and the observation period length predicted the hospitalization of children with asthma following treatment in ECU. Sex and seasonality did not affect the need for hospitalization.

Acute asthma exacerbations in childhood: risk factors, prevention and treatment

Asthma is a heterogeneous disease more appropriately seen as a syndrome rather than a single pathologic entity. Although it can remain quiescent for extended time periods, the inflammatory and remodeling processes affect the bronchial milieu and predispose to acute and occasionally severe clinical manifestations. The complexity underlying these episodes is enhanced during childhood, an era of ongoing alterations and maturation of key biological systems. In this review, the authors focus on such sudden-onset events, emphasizing on their diversity on the basis of the numerous asthma phenotypes.

Asthma vital signs at triage: home or admission (ASTHmA)

OBJECTIVES

Commonly used acute asthma scoring systems assess severity of symptoms, whereas other clinical models aim to predict hospitalization; all rely on a measure of response to treatment and use the same criteria across age ranges. This may not reflect a child's changing physiology and response to illness as he or she grows older.This study aimed to find age-specific objective predictors of hospitalization readily known at triage. The goal is to identify rapidly those who will likely need admission regardless of treatment administered or response to aggressive treatment in the emergency department (ED).

METHODS

Children between 1 and 18 years of age with a final primary ED International Classification of Diseases, Ninth Revision, diagnosis of asthma or asthma-related spectrum of disease were studied using data from the National Hospital Ambulatory Medical Care Survey. The primary outcome was hospital admission (observation unit, ward, monitored, or pediatric intensive care unit).Triage vital signs, mode of arrival, recent visits, emergency severity index score, as well as demographic and socioeconomic factors were incorporated into age-specific forward-selection multiple logistic regression models.

RESULTS

In 2,454,983 ED visits for asthma or reactive airway disease among children 1 to 18 years of age, patterns of vital sign predictors for admission varied by age group. Across all ages, diastolic hypotension at triage was an early, consistent, independent predictor of admission, especially in 1- to 3-year-olds (odds ratio, 6.27; 95% confidence interval, 6.01-6.54) and 3- to 6-year-olds (odds ratio, 17.95; 95% confidence interval, 16.80-19.17).

CONCLUSIONS

Age-specific assessment is important in the evaluation of acute asthma or reactive airway exacerbation. Diastolic hypotension may serve as an early warning indicator of severity of disease and need for hospitalization. Variability by age group in vital sign predictor for admission calls for further development or refinement of age-specific asthma assessment tools.

Multicenter analysis of quality indicators for children treated in the emergency department for asthma

OBJECTIVE

To test the hypothesis that an association exists between process and outcome measures of the quality of acute asthma care provided to children in the emergency department.

METHODS

Investigators at 14 US sites prospectively enrolled consecutive children 2 to 17 years of age presenting to the emergency department with acute asthma. In models adjusted for variables commonly associated with the quality of acute asthma care, we measured the association between 7 measures of concordance with national asthma guideline-recommended processes and 2 outcomes. Specifically, we modeled the association between 5 receipt/nonreceipt process measures and successful discharge and the association between 2 timeliness measures and admission.

RESULTS

In this cohort of 1426 patients, 62% were discharged without relapse or ongoing symptoms (successful discharge), 15% were discharged with relapse or ongoing symptoms, and 24% were admitted. The composite score for receipt of all 5 receipt/nonreceipt process measures was 84%, and for timeliness measures, 57% receive a timely corticosteroid and 92% a timely β-agonist. Our adjusted models showed no association between process and outcome measures, with 1 exception: timely β-agonist administration was associated with admission, likely reflecting confounding by severity rather than a true process-outcome association.

CONCLUSIONS

We found no clinically significant association between process and outcome quality measures in the delivery of asthma-related care to children in a multicenter study. Although the quality of emergency department care does not predict successful discharge, other factors, such as outpatient care, may better predict outcomes.

Critical pathways for post-emergency outpatient diagnosis and treatment: tools to improve the value of emergency care

The decision to admit a patient to the hospital after an emergency department (ED) visit is expensive, frequently not evidence-based, and variable. Outpatient critical pathways are a promising approach to reduce hospital admission after emergency care. Critical pathways exist to risk stratify patients for potentially serious diagnoses (e.g., acute myocardial infarction [AMI]) or evaluate response to therapy (e.g., community-acquired pneumonia) within a short time period (i.e., less than 36 hours), to determine if further hospital-based acute care is needed. Yet, such pathways are variably used while many patients are admitted for conditions for which they could be treated as outpatients. In this article, the authors propose a model of post-ED critical pathways, describe their role in emergency care, list common diagnoses that are amenable to critical pathways in the outpatient setting, and propose a research agenda to address barriers and solutions to increase the use of outpatient critical pathways. If emergency providers are to routinely conduct rapid evaluations in outpatient or observation settings, they must have several conditions at their disposal: 1) evidence-based tools to accurately risk stratify patients for protocolized care, 2) systems of care that reliably facilitate workup in the outpatient setting, and 3) a medical environment conducive to noninpatient pathways, with aligned risks and incentives among patients, providers, and payers. Increased use of critical pathways after emergency care is a potential way to improve the value of emergency care.

Emergency department crowding is associated with decreased quality of care for children with acute asthma

STUDY OBJECTIVE

We seek to determine which dimensions of quality of care are most influenced by emergency department (ED) crowding for patients with acute asthma exacerbations.

METHODS

This cross-sectional study with retrospective data collection included patients aged 2 to 21 years treated for acute asthma during November 2007 to October 2008 at a children's hospital ED. We studied 3 processes of care-asthma score, β-agonist, and corticosteroid administration-and 9 quality measures representing 3 quality dimensions: timeliness (1-hour receipt of each process), effectiveness (receipt/nonreceipt of each process), and equity (language, identified primary care provider, and insurance). Primary independent variables were 2 crowding measures: ED occupancy and number waiting to see an attending-level physician. Models were adjusted for age, language, insurance, primary care access, triage level, ambulance arrival, oximetry, smoke exposure, and time of day. For timeliness and effectiveness quality measures, we calculated the adjusted risk of each quality measure at 5 percentiles of crowding for each crowding measure and assessed the significance of the adjusted relative interquartile risk ratios. For equity measures, we tested their role as moderators of the crowding-quality models.

RESULTS

The asthma population included 927 patients. Timeliness and effectiveness quality measures showed an inverse, dose-related association with crowding, an effect not moderated by equity measures. Patients were 52% to 74% less likely to receive timely care and were 9% to 14% less likely to receive effective care when each crowding measure was at the 75th rather than at the 25th percentile (P<.05).

CONCLUSION

ED crowding is associated with decreased timeliness and effectiveness-but not equity-of care for children with acute asthma.