Developing a risk stratification model for predicting future health care use in asthmatic children

Hanson, Jill; Lee, Brian; Williams, David D.; Murphy, Helen; Kennedy, Kevin F.; DeLurgio, Stephen A.; Portnoy, Jay M.; Reddy, Mamta

doi:10.1016/j.anai.2015.10.014

Cited by 8 publications

(8 citation statements)

References 8 publications

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“…Previously specified predictive models have reported a range of model performances from an AUC of 0.69, with sensitivity of 48% and specificity of 82% to sensitivity of 25% and specificity of 92% . Hanson et al . found that prior acute care visits for asthma predicted subsequent acute care visits with an AUC of 0.75, after adjustment for age, sex, race, and insurance status, and the association between prior ED visits and subsequent ED visits for asthma has been shown elsewhere as well .…”

Section: Discussionmentioning

confidence: 93%

“…Previously reported predictive models for asthma‐related health care utilization have used heterogeneous samples with varying amounts of clinical and administrative data. Multiple models have found that prior ED or hospital utilization is predictive of subsequent use . However, many models used information about previous prescriptions or detailed prior history and laboratory testing that are unlikely to be available to an ED provider, limiting their clinical utility.…”

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confidence: 99%

“…However, many models used information about previous prescriptions or detailed prior history and laboratory testing that are unlikely to be available to an ED provider, limiting their clinical utility. In addition, several studies were only able to ascertain return visits to a single center, potentially leading to ascertainment bias if patients visited a different hospital . This is of particular importance for emergency care as up to 32% of return visits could be to a different institution …”

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confidence: 99%

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Comparing Statewide and Single‐center Data to Predict High‐frequency Emergency Department Utilization Among Patients With Asthma Exacerbation

Samuels‐Kalow

Faridi

Espinola

et al. 2017

Academic Emergency Medicine

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Section: Discussionmentioning

confidence: 93%

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confidence: 99%

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confidence: 99%

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Comparing Statewide and Single‐center Data to Predict High‐frequency Emergency Department Utilization Among Patients With Asthma Exacerbation

Samuels‐Kalow

Faridi

Espinola

et al. 2017

Academic Emergency Medicine

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“…In a previous clinical study, combined serial measurements of FeNO and FEF 25%-75% , rather than a snapshot measurement, may be more useful in monitoring asthma control and may play a role in the early detection of asthma progression [27]. Several studies have stratified patients with pediatric asthma or risk of future AE [28][29][30].…”

Section: Predictive Value Of Feno and Spirometry For Childhood Asthmamentioning

confidence: 99%

Fractional exhaled nitric oxide and forced expiratory volume in 1 second/forced vital capacity have predictive value of asthma exacerbation in Korean school children

Kang

Yoon

Sim³

et al. 2020

Asia Pacific Allergy

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Background:The incidence of asthma exacerbation (AE) and the predictive value of spirometry and fractional exhaled nitric oxide (FeNO) in school children have not been evaluated. Objective: We sought to evaluate the efficacy of spirometry measurement and FeNO monitoring for predicting AE in school children in the Cheongju area in Korea. Methods: With parental agreement, we studied 170 students aged 7-12 years. Children were evaluated by an asthma specialist using baseline spirometry, skin prick test, seasonal FeNO measurement, and asthma control test. The study participants underwent a physical examination and their medical history was also evaluated by the specialist. They were assessed for asthma control status during regular doctor visits for 1 year. Results: In total, 160 children (94.1%) completed follow-up and FeNO monitoring. Of which, 26 children (16.3%) had AE. AE was associated with male children and children with allergic rhinitis (p < 0.05). While, children with AE tended to have higher FeNO than those without AE, no significant difference was found. The maximum value of FeNO ≥35 ppb was associated with AE (p < 0.05). Children with AE had a significantly decreased baseline forced expiratory volume in 1 second/forced vital capacity (FEV 1 /FVC), %predicted, forced expiratory flow at 25%-75% of FVC (FEF 25%-75% ). FEV 1 /FVC < 80% was associated with AE in children regardless of inhalant allergen sensitization (all p < 0.05). Conclusion: Baseline spirometry had a predictive value of AE in school children. Sensitive spirometric parameters such as FEV 1 /FVC and FEF 25%-75% can be used as prognostic factors to predict future childhood AE. FeNO value ≥ 35 ppb during monitoring was associated with AE in school children.

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“…7,89 In particular, among outpatients seen for asthma, the number of previous acute care visits (ED, urgent care or inpatient settings) predicted subsequent healthcare utilization. 10 Patients with 5 or more acute care visits accounted for only 1.1% of the sample, but had an adjusted odds ratio (aOR) of 59 (95% CI, 24-142) for a future acute care visit. 10 However, the Hanson model was limited to patients who were seen for asthma at an outpatient visit, and combined ED visits, urgent care and admission.…”

Section: Introductionmentioning

confidence: 99%

A Predictive Model for Identification of Children at Risk of Subsequent High-Frequency Utilization of the Emergency Department for Asthma

et al. 2019

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Background-Asthma is the most common chronic condition among children with highfrequency emergency department (ED) utilization. Previous research has shown in outpatients seen for asthma that acute care visits predict subsequent healthcare utilization. Among ED patients, however, the optimal method of predicting subsequent ED utilization remains to be described. The goal of this study was to create a predictive model to identify children in the ED who are at risk of subsequent high-frequency utilization of the ED for asthma.Methods-We used three years of data, 2013-2015, drawn from the electronic health records at a tertiary care, urban, children's hospital that is a high-volume center for asthma care. Data were split into a derivation (50%) and validation/test (50%) set, and three models were created for testing: (1) All index patients; (2) Removing patients with complex chronic conditions; (3) Subset of patients with in-network care on whom more clinical data were available. Each multivariable model was then tested in the validation set, and its performance evaluated by predicting error rate, calculation of a ROC curve and identification of the optimal cutpoint to maximize sensitivity and specificity.Results-There were 5535 patients with index ED visits of whom 2767 were in the derivation set and 2768 in the validation set. Of the 5535 patients, 125 patients (2.3%) had ≥4 visits for asthma in the outcome year. Significant predictors in models 1 and 2 were age and number of prior ED visits for asthma. For model 3 (additional clinical information available), the predictors were number of prior ED visits for asthma, number of primary care visits, and not having a controller

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Developing a risk stratification model for predicting future health care use in asthmatic children

Cited by 8 publications

References 8 publications

Comparing Statewide and Single‐center Data to Predict High‐frequency Emergency Department Utilization Among Patients With Asthma Exacerbation

Comparing Statewide and Single‐center Data to Predict High‐frequency Emergency Department Utilization Among Patients With Asthma Exacerbation

Fractional exhaled nitric oxide and forced expiratory volume in 1 second/forced vital capacity have predictive value of asthma exacerbation in Korean school children

A Predictive Model for Identification of Children at Risk of Subsequent High-Frequency Utilization of the Emergency Department for Asthma

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