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

Effectiveness of a Vendor Predictive Model for the Risk of Pediatric Asthma Exacerbation: A Difference-in-Differences Analysis

BACKGROUND

Asthma is a common cause of morbidity and mortality in children. Predictive models may help providers tailor asthma therapies to an individual's exacerbation risk. The effectiveness of asthma risk scores on provider behavior and pediatric asthma outcomes remains unknown.

OBJECTIVE

Determine the impact of an electronic health record (EHR) vendor-released model on outcomes for children with asthma.

METHODS

The Epic Systems Risk of Pediatric Asthma Exacerbation model was implemented on February 24, 2021, for volunteer pediatric allergy and pulmonology providers as a noninterruptive risk score visible in the patient schedule view. Asthma hospitalizations, emergency department (ED) visits, or oral steroid courses within 90 days of the index visit were compared from February 24, 2019, to February 23, 2022, using a difference-in-differences design with a control group of visits to providers in the same departments. Volunteer providers were interviewed to identify barriers and facilitators to model use.

RESULTS

In the intervention group, asthma hospitalizations within 90 days decreased from 1.4% (54/3,842) to 0.7% (14/2,165) after implementation with no significant change in the control group (0.9% [171/19,865] preimplementation to 1.0% [105/10,743] post). ED visits in the intervention group decreased from 5.8% (222/3,842) to 5.5% (118/2,164) but increased from 5.5% (1,099/19,865) to 6.8% (727/10,743) in the control group. The adjusted difference-in-differences estimators for hospitalization, ED visit, and oral steroid outcomes were -0.9% (95% confidence interval [CI]: -1.6 to -0.3), -2.4% (-3.9 to -0.8), and -1.9% (-4.3 to 0.5). In qualitative analysis, providers understood the purpose of the model and felt it was useful to flag high exacerbation risk. Trust in the model was calibrated against providers' own clinical judgement.

CONCLUSION

This EHR vendor model implementation was associated with a significant decrease in asthma hospitalization and ED visits within 90 days of pediatric allergy and pulmonology clinic visits, but not oral steroid courses.

Predicting asthma-related crisis events using routine electronic healthcare data

BACKGROUND

There is no published algorithm predicting asthma crisis events (Accident and Emergency (A&E) attendance, hospitalisation or death) using routinely available electronic health record (EHR) data.

AIM

To develop an algorithm to identify individuals at high risk of an asthma crisis event.

DESIGN AND SETTING

Database analysis from primary care EHRs.

METHOD

Multivariable logistic regression was applied to a dataset of 61,861 people with asthma from England and Scotland using the Clinical Practice Research Datalink. External validation was performed using the Secure Anonymised Information Linkage databank of 174,240 patients from Wales. Outcomes were one or more hospitalisation (development dataset) and asthma-related hospitalisation, A&E attendance or death (validation dataset) within a 12-month period.

RESULTS

Risk factors for asthma-related crisis events included previous hospitalisation, older age, underweight, smoking and blood eosinophilia. The prediction algorithm had acceptable predictive ability with a Receiver Operating Characteristic (ROC) of 0.71 (0.70, 0.72) in the validation dataset. Using a cut-point based on the 7% of the population at greatest risk results in a positive predictive value of 5.7% (95% CI 5.3 - 6.1) and a negative predictive value of 98.9% (98.9 - 99.0), with sensitivity of 28.5% (26.7 - 30.3) and specificity of 93.3% (93.2 - 93.4); they had an event risk of 6.0% compared 1.1% for the remaining population. Eighteen people would be "needed to follow" to identify one admission.

CONCLUSIONS

This externally validated algorithm has acceptable predictive ability for identifying patients at high risk of asthma-related crisis events and excluding individuals not at high risk.

The Role of Home Environments in Allergic Disease

Allergic diseases are surprisingly common, chronic health conditions. The primary location where the vast majority of people are exposed to allergens and other substances is in their home. This means it is important to understand home environments and how a home's systems function and interact-and that how we occupy these spaces plays a crucial role in both environmental exposure and management of allergic disease. This review provides an overview of what is understood about home environmental exposure and its impact on our health, and proposes a systematic process for using a patient's environmental history to develop individualized, manageable and cost-effective recommendations. Once occupant-related information has been gathered, a home environmental exposure assessment should be performed focused on identifying the relationships between any identified sources of contaminants and the housing systems, and conditions that may be contributing to exposure. The results and recommendations from this assessment can then be used to guide exposure-reduction efforts by patients and/or their caregivers in an effort to improve disease management. In this review, we'll discuss three different types of home interventions-active, which must be routinely performed by the patient and/or caregiver, passive, which are interventions that work without routine, direct interaction from the homeowner, and behavioral changes in how the home environment is cleaned and maintained for long-term reduction of allergens. In this review, and others evaluated for this discussion, a significant number of home environmental assessment and intervention programs were shown to be cost effective, with the majority of programs showing a net positive return on investment. It is important to recognize that to be cost effective, the level and intensity of services offered through home visit programs need be stratified, based on the estimated health risks of the patient, in order to tailor the assessment and target the interventions to a patient's needs while maximizing cost effectiveness.

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

BACKGROUND

Asthma is the most common chronic condition among children with high-frequency emergency department (ED) utilization. Previous research has shown in outpatients seen for asthma that acute care visits predict subsequent health care 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 3 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 3 models were created for testing: (1) all index patients; (2) removing patients with complex chronic conditions; and (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 receiver operating characteristic (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 or more 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 medication. Areas under the ROC curve were 0.77 for model 1, 0.80 for model 2, and 0.77 for model 3.

CONCLUSIONS

Administrative data available at the time of ED triage can predict subsequent high utilization of the ED, with areas under the ROC curve of 0.77 to 0.80. The addition of clinical variables did not improve the model performance. These models provide useful tools for researchers interested in examining intervention efficacy by predicted risk group.

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

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 (FEV₁/FVC), %predicted, forced expiratory flow at 25%–75% of FVC (FEF_25%–75%). FEV₁/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 FEV₁/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.

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

BACKGROUND

Previous studies examining high-frequency emergency department (ED) utilization have primarily used single-center data, potentially leading to ascertainment bias if patients visit multiple centers. The goals of this study were 1) to create a predictive model to prospectively identify patients at risk of high-frequency ED utilization for asthma and 2) to examine how that model differed using statewide versus single-center data.

METHODS

To track ED visits within a state, we analyzed 2011 to 2013 data from the New York State Healthcare Cost and Utilization Project State Emergency Department Databases. The first year of data (2011) was used to determine prior utilization, 2012 was used to identify index ED visits for asthma and for demographics, and 2013 was used for outcome ascertainment. High-frequency utilization was defined as 4+ ED visits for asthma within 1 year after the index visit. We performed analyses separately for children (age < 21 years) and adults and constructed two models: one included all statewide (multicenter) visits and the other was restricted to index hospital (single-center) visits. Multivariable logistic regression models were developed from potential predictors selected a priori. The final model was chosen by evaluating model performance using Akaike's Information Criterion scores, 10-fold cross-validation, and receiver operating characteristic curves.

RESULTS

Among children, high-frequency ED utilization for asthma was observed in 2,417 of 94,258 (2.56%) using all statewide visits, compared to 1,853 of 94,258 (1.97%) for index hospital visits only. Among adults, the corresponding results were 7,779 of 159,874 (4.87%) and 5,053 of 159,874 (3.16%), respectively. In the multicenter visit model, the area under the curve (AUC) from 10-fold cross-validation for children was 0.70 (95% confidence interval [CI] = 0.69-0.72), compared to 0.71 (95% CI = 0.69-0.72) in the single-center visit model. The corresponding AUC results for adults were 0.76 (95% CI = 0.76-0.77) and 0.76 (95% CI = 0.75-0.77), respectively.

CONCLUSION

Data available at the index ED visit can predict subsequent high-frequency utilization for asthma with AUC ranging from 0.70 to 0.76. Model accuracy was similar regardless of whether outcome ascertainment included all statewide visits (multicenter) or was limited to the index hospital (single-center).

Predicting Subsequent High-Frequency, Low-Acuity Utilization of the Pediatric Emergency Department

OBJECTIVE

To derive and test a predictive model for high-frequency (4 or more visits per year), low-acuity (emergency severity index 4 or 5) utilization of the pediatric emergency department.

METHODS

The study sample used 3 years of data (2012-2014) from a single tertiary-care children's hospital for patients <21 years of age. Utilization in 2013 defined the index visit; prior utilization was drawn from 2012; and 2014 was used for outcome measurement. Candidate predictor variables were those that would be available at the time of triage. Data were split into derivation and test sets randomly; variables with a significant univariate association in the derivation set were included for multivariable modeling. The final model from the derivation set was then tested in the validation set, with calculation of a receiver operating characteristic curve.

RESULTS

There were 90,972 visits in 2013, of which 61,430 were first (index) visits. A total of 590 (1%) had 4 or more triage level 4 or 5 visits in the following year (2014). The final model included site of primary care, age, acuity, previous utilization, race, and insurance, and had an area under the receiver operating characteristic curve of 0.84.

CONCLUSIONS

Data available to the emergency department provider at the time of initial visit triage can predict utilization for low-acuity complaints in the subsequent year. Future work should focus on validation and refinement of the model in additional settings, and electronic calculation of risk status for targeted intervention to improve appropriate utilization of health care services.