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

Inhaled Corticosteroids Rarely Prescribed at Emergency Department Discharge Despite Low Rates of Follow-Up Care

BACKGROUND

Guidelines recommend an inhaled corticosteroid (ICS) prescription on emergency department (ED) discharge after acute asthma exacerbations.

OBJECTIVE

We sought to identify rates and predictors of ICS prescription at ED discharge. Secondary outcomes included ICS prescription rates in a high-risk subgroup, outpatient follow-up rates within 30 days, and variation in ICS prescriptions among attending emergency physicians.

METHODS

This was a retrospective cohort study of adult asthma ED discharges for acute asthma exacerbation across 5 urban academic hospitals. We used multivariable logistic regression to evaluate predictors of ICS prescription after adjusting for patient characteristics and hospital-level clustering.

RESULTS

Among 3948 adult ED visits, an ICS was prescribed in 6% (n = 238) of visits. Only 14% (n = 552) completed an outpatient visit within 30 days. Among patients with 2 or more ED visits in 12 months, the ICS prescription rate was 6.7%. ICS administration in the ED (odds ratio [OR] 9.91; 95% CI 7.99-12.28) and prescribing a β-agonist on discharge (OR 2.67; 95% CI 2.08-3.44) were associated with higher odds of ICS prescription. Decreased odds of ICS prescription were associated with Hispanic ethnicity (OR 0.71; 95% CI 0.51-0.99) relative to Black race, and private (OR 0.75; 95% CI 0.62-0.91) or no insurance (OR 0.54; 95% CI 0.35-0.84) relative to Medicaid. One-third (36%, n = 66) of ED attendings prescribed 0 ICS prescriptions during the study period.

CONCLUSIONS

An ICS is infrequently prescribed on ED asthma discharge, and most patients do not have an outpatient follow-up within 30 days. Future studies should examine the extent to which ED ICS prescriptions improve outcomes for patients with barriers to accessing primary care.

Forecasting Future Asthma Hospital Encounters of Patients With Asthma in an Academic Health Care System: Predictive Model Development and Secondary Analysis Study

Background

Asthma affects a large proportion of the population and leads to many hospital encounters involving both hospitalizations and emergency department visits every year. To lower the number of such encounters, many health care systems and health plans deploy predictive models to prospectively identify patients at high risk and offer them care management services for preventive care. However, the previous models do not have sufficient accuracy for serving this purpose well. Embracing the modeling strategy of examining many candidate features, we built a new machine learning model to forecast future asthma hospital encounters of patients with asthma at Intermountain Healthcare, a nonacademic health care system. This model is more accurate than the previously published models. However, it is unclear how well our modeling strategy generalizes to academic health care systems, whose patient composition differs from that of Intermountain Healthcare.

Objective

This study aims to evaluate the generalizability of our modeling strategy to the University of Washington Medicine (UWM), an academic health care system.

Methods

All adult patients with asthma who visited UWM facilities between 2011 and 2018 served as the patient cohort. We considered 234 candidate features. Through a secondary analysis of 82,888 UWM data instances from 2011 to 2018, we built a machine learning model to forecast asthma hospital encounters of patients with asthma in the subsequent 12 months.

Results

Our UWM model yielded an area under the receiver operating characteristic curve (AUC) of 0.902. When placing the cutoff point for making binary classification at the top 10% (1464/14,644) of patients with asthma with the largest forecasted risk, our UWM model yielded an accuracy of 90.6% (13,268/14,644), a sensitivity of 70.2% (153/218), and a specificity of 90.91% (13,115/14,426).

Conclusions

Our modeling strategy showed excellent generalizability to the UWM, leading to a model with an AUC that is higher than all of the AUCs previously reported in the literature for forecasting asthma hospital encounters. After further optimization, our model could be used to facilitate the efficient and effective allocation of asthma care management resources to improve outcomes.

International Registered Report Identifier (IRRID)

RR2-10.2196/resprot.5039

Automatically Explaining Machine Learning Prediction Results on Asthma Hospital Visits in Patients With Asthma: Secondary Analysis

Background

Asthma is a major chronic disease that poses a heavy burden on health care. To facilitate the allocation of care management resources aimed at improving outcomes for high-risk patients with asthma, we recently built a machine learning model to predict asthma hospital visits in the subsequent year in patients with asthma. Our model is more accurate than previous models. However, like most machine learning models, it offers no explanation of its prediction results. This creates a barrier for use in care management, where interpretability is desired.

Objective

This study aims to develop a method to automatically explain the prediction results of the model and recommend tailored interventions without lowering the performance measures of the model.

Methods

Our data were imbalanced, with only a small portion of data instances linking to future asthma hospital visits. To handle imbalanced data, we extended our previous method of automatically offering rule-formed explanations for the prediction results of any machine learning model on tabular data without lowering the model’s performance measures. In a secondary analysis of the 334,564 data instances from Intermountain Healthcare between 2005 and 2018 used to form our model, we employed the extended method to automatically explain the prediction results of our model and recommend tailored interventions. The patient cohort consisted of all patients with asthma who received care at Intermountain Healthcare between 2005 and 2018, and resided in Utah or Idaho as recorded at the visit.

Results

Our method explained the prediction results for 89.7% (391/436) of the patients with asthma who, per our model’s correct prediction, were likely to incur asthma hospital visits in the subsequent year.

Conclusions

This study is the first to demonstrate the feasibility of automatically offering rule-formed explanations for the prediction results of any machine learning model on imbalanced tabular data without lowering the performance measures of the model. After further improvement, our asthma outcome prediction model coupled with the automatic explanation function could be used by clinicians to guide the allocation of limited asthma care management resources and the identification of appropriate interventions.

Developing a Model to Predict Hospital Encounters for Asthma in Asthmatic Patients: Secondary Analysis

Background

As a major chronic disease, asthma causes many emergency department (ED) visits and hospitalizations each year. Predictive modeling is a key technology to prospectively identify high-risk asthmatic patients and enroll them in care management for preventive care to reduce future hospital encounters, including inpatient stays and ED visits. However, existing models for predicting hospital encounters in asthmatic patients are inaccurate. Usually, they miss over half of the patients who will incur future hospital encounters and incorrectly classify many others who will not. This makes it difficult to match the limited resources of care management to the patients who will incur future hospital encounters, increasing health care costs and degrading patient outcomes.

Objective

The goal of this study was to develop a more accurate model for predicting hospital encounters in asthmatic patients.

Methods

Secondary analysis of 334,564 data instances from Intermountain Healthcare from 2005 to 2018 was conducted to build a machine learning classification model to predict the hospital encounters for asthma in the following year in asthmatic patients. The patient cohort included all asthmatic patients who resided in Utah or Idaho and visited Intermountain Healthcare facilities during 2005 to 2018. A total of 235 candidate features were considered for model building.

Results

The model achieved an area under the receiver operating characteristic curve of 0.859 (95% CI 0.846-0.871). When the cutoff threshold for conducting binary classification was set at the top 10.00% (1926/19,256) of asthmatic patients with the highest predicted risk, the model reached an accuracy of 90.31% (17,391/19,256; 95% CI 89.86-90.70), a sensitivity of 53.7% (436/812; 95% CI 50.12-57.18), and a specificity of 91.93% (16,955/18,444; 95% CI 91.54-92.31). To steer future research on this topic, we pinpointed several potential improvements to our model.

Conclusions

Our model improves the state of the art for predicting hospital encounters for asthma in asthmatic patients. After further refinement, the model could be integrated into a decision support tool to guide asthma care management allocation.

International Registered Report Identifier (IRRID)

RR2-10.2196/resprot.5039

Psychiatric-related Revisits to the Emergency Department Following Rapid Expansion of Community Mental Health Services

OBJECTIVES

Repeat visits (revisits) to emergency departments (EDs) for psychiatric care reflect poor continuity of care and impose a high financial cost. We test whether rapid expansion of community health centers (CHCs)-which provide regional, low-cost primary care-correspond with fewer repeat psychiatric-related ED visits (PREDVs).

METHODS

We obtained repeated cross-sectional time-series data for 7.8 million PREDVs from the State Emergency Department Database for four populous U.S. states (California, Florida, North Carolina, and New York) from 2006 to 2011. We specified as the outcome variable the count of repeat visits per ED visitor with a psychiatric diagnosis. We retrieved aggregate-level mental health visits at CHCs from the Uniform Data System. Negative binomial regression methods controlled for individual-level confounders, county health system and sociodemographic attributes, year fixed effects, and county fixed effects.

RESULTS

The risk of a repeat PREDV decreased with a county-level increase in mental health patients seen at CHCs (incidence rate ratio = 0.986, 95% confidence interval = 0.98 to 0.99). Conversion of this rate ratio to the number of revisits averted indicated 34,000 fewer repeat PREDVs in these four states statistically associated with a 1% expansion in CHC mental health visits. Exploratory analyses found that revisits decline for relatively mild/moderate illnesses (e.g., mood, anxiety disorders) but not for severe illnesses (e.g., schizophrenia/psychoses).

CONCLUSION

An increase in mental health services at CHCs corresponds with a modest decline in repeat PREDVs. This decline concentrates among those with less severe mental illnesses.

Statistical tools used for analyses of frequent users of emergency department: a scoping review

OBJECTIVE

Frequent users represent a small proportion of emergency department users, but they account for a disproportionately large number of visits. Their use of emergency departments is often considered suboptimal. It would be more efficient to identify and treat those patients earlier in their health problem trajectory. It is therefore essential to describe their characteristics and to predict their emergency department use. In order to do so, adequate statistical tools are needed. The objective of this study was to determine the statistical tools used in identifying variables associated with frequent use or predicting the risk of becoming a frequent user.

METHODS

We performed a scoping review following an established 5-stage methodological framework. We searched PubMed, Scopus and CINAHL databases in February 2019 using search strategies defined with the help of an information specialist. Out of 4534 potential abstracts, we selected 114 articles based on defined criteria and presented in a content analysis.

RESULTS

We identified four classes of statistical tools. Regression models were found to be the most common practice, followed by hypothesis testing. The logistic regression was found to be the most used statistical tool, followed by χ2 test and t-test of associations between variables. Other tools were marginally used.

CONCLUSIONS

This scoping review lists common statistical tools used for analysing frequent users in emergency departments. It highlights the fact that some are well established while others are much less so. More research is needed to apply appropriate techniques to health data or to diversify statistical point of views.