A Roadmap for Using Causal Inference and Machine Learning to Personalize Asthma Medication Selection.

Inhaled corticosteroid (ICS) is a mainstay treatment for controlling asthma and preventing exacerbations in patients with persistent asthma. Many types of ICS drugs are used, either alone or in combination with other controller medications. Despite the widespread use of ICSs, asthma control remains suboptimal in many people with asthma. Suboptimal control leads to recurrent exacerbations, causes frequent ER visits and inpatient stays, and is due to multiple factors. One such factor is the inappropriate ICS choice for the patient. While many interventions targeting other factors exist, less attention is given to inappropriate ICS choice. Asthma is a heterogeneous disease with variable underlying inflammations and biomarkers. Up to 50% of people with asthma exhibit some degree of resistance or insensitivity to certain ICSs due to genetic variations in ICS metabolizing enzymes, leading to variable responses to ICSs. Yet, ICS choice, especially in the primary care setting, is often not tailored to the patient's characteristics. Instead, ICS choice is largely by trial and error and often dictated by insurance reimbursement, organizational prescribing policies, or cost, leading to a one-size-fits-all approach with many patients not achieving optimal control. There is a pressing need for a decision support tool that can predict an effective ICS at the point of care and guide providers to select the ICS that will most likely and quickly ease patient symptoms and improve asthma control. To date, no such tool exists. Predicting which patient will respond well to which ICS is the first step toward developing such a tool. However, no study has predicted ICS response, forming a gap. While the biologic heterogeneity of asthma is vast, few, if any, biomarkers and genotypes can be used to systematically profile all patients with asthma and predict ICS response. As endotyping or genotyping all patients is infeasible, readily available electronic health record data collected during clinical care offer a low-cost, reliable, and more holistic way to profile all patients. In this paper, we point out the need for developing a decision support tool to guide ICS selection and the gap in fulfilling the need. Then we outline an approach to close this gap via creating a machine learning model and applying causal inference to predict a patient's ICS response in the next year based on the patient's characteristics. The model uses electronic health record data to characterize all patients and extract patterns that could mirror endotype or genotype. This paper supplies a roadmap for future research, with the eventual goal of shifting asthma care from one-size-fits-all to personalized care, improve outcomes, and save health care resources.

Disparities in Pediatric Epilepsy Remission Are Associated With Race and Ethnicity.

OBJECTIVE: The purpose of our study was to assess whether race/ethnicity was associated with seizure remission in pediatric epilepsy. METHODS: This was a retrospective population-based cohort study of children who were evaluated for new-onset epilepsy in the clinic, emergency department, and/or hospital by a pediatric neurologist in an integrated health care delivery system. Children were between ages 6 months and 15 years at their initial presentation of epilepsy. The cohort, identified through an electronic database, was assembled over 6 years, with no less than 5 years of follow-up. All children were evaluated for race, ethnicity, insurance type, and socioeconomic background. Patient outcome was determined at the conclusion of the study period and categorized according to their epilepsy control as either drug resistant (pharmacoresistant and intractable) or drug responsive (controlled, probable remission, and terminal remission). RESULTS: In the final cohort of 776 patients, 63% were drug responsive (control or seizure remission). After controlling for confounding socioeconomic and demographic factors, children of Hispanic ethnicity experienced reduced likelihood (hazard) of drug-responsive epilepsy (hazard ratio 0.6, P < .001), and had longer median time to remission (8 years; 95% CI 5.9-9.6 years) compared to white non-Hispanic patients (5.6 years; 95% CI 4.9-6.1 years). Among Hispanic patients, higher health care costs were associated with reduced likelihood of drug responsiveness. SIGNIFICANCE: We found that Hispanic ethnicity is associated with a reduced likelihood of achieving seizure control and remission. This study suggests that factors associated with the race/ethnicity of patients contributes to their likelihood of achieving seizure freedom.

Predicting Appropriate Admission of Bronchiolitis Patients in the Emergency Department: Rationale and Methods.

BACKGROUND: In young children, bronchiolitis is the most common illness resulting in hospitalization. For children less than age 2, bronchiolitis incurs an annual total inpatient cost of $1.73 billion. Each year in the United States, 287,000 emergency department (ED) visits occur because of bronchiolitis, with a hospital admission rate of 32%-40%. Due to a lack of evidence and objective criteria for managing bronchiolitis, ED disposition decisions (hospital admission or discharge to home) are often made subjectively, resulting in significant practice variation. Studies reviewing admission need suggest that up to 29% of admissions from the ED are unnecessary. About 6% of ED discharges for bronchiolitis result in ED returns with admission. These inappropriate dispositions waste limited health care resources, increase patient and parental distress, expose patients to iatrogenic risks, and worsen outcomes. Existing clinical guidelines for bronchiolitis offer limited improvement in patient outcomes. Methodological shortcomings include that the guidelines provide no specific thresholds for ED decisions to admit or to discharge, have an insufficient level of detail, and do not account for differences in patient and illness characteristics including co-morbidities. Predictive models are frequently used to complement clinical guidelines, reduce practice variation, and improve clinicians' decision making. Used in real time, predictive models can present objective criteria supported by historical data for an individualized disease management plan and guide admission decisions. However, existing predictive models for ED patients with bronchiolitis have limitations, including low accuracy and the assumption that the actual ED disposition decision was appropriate. To date, no operational definition of appropriate admission exists. No model has been built based on appropriate admissions, which include both actual admissions that were necessary and actual ED discharges that were unsafe. OBJECTIVE: The goal of this study is to develop a predictive model to guide appropriate hospital admission for ED patients with bronchiolitis. METHODS: This study will: (1) develop an operational definition of appropriate hospital admission for ED patients with bronchiolitis, (2) develop and test the accuracy of a new model to predict appropriate hospital admission for an ED patient with bronchiolitis, and (3) conduct simulations to estimate the impact of using the model on bronchiolitis outcomes. RESULTS: We are currently extracting administrative and clinical data from the enterprise data warehouse of an integrated health care system. Our goal is to finish this study by the end of 2019. CONCLUSIONS: This study will produce a new predictive model that can be operationalized to guide and improve disposition decisions for ED patients with bronchiolitis. Broad use of the model would reduce iatrogenic risk, patient and parental distress, health care use, and costs and improve outcomes for bronchiolitis patients.

Pregnancy and Village Outreach Tibet: a descriptive report of a community- and home-based maternal-newborn outreach program in rural Tibet.

OBJECTIVE: The Pregnancy and Village Outreach Tibet (PAVOT) program, a model for community- and home-based maternal-newborn outreach in rural Tibet, is presented. METHODS: This article describes PAVOT, including the history, structure, content, and activities of the program, as well as selected program outcome measures and demographic characteristics, health behaviors, and pregnancy outcomes of women who recently participated in the program. RESULTS: The PAVOT program was developed to provide health-related services to pregnant rural Tibetan women at risk of having an unattended home birth. The program involves training local healthcare workers and laypersons to outreach pregnant women and family members. Outreach includes basic maternal-newborn health education and simple obstetric and neonatal life-saving skills training. In addition, the program distributes safe and clean birth kits, newborn hats, blankets, and maternal micronutrient supplements (eg, prenatal vitamins and minerals). More than 980 pregnant women received outreach during the study period. More than 92% of outreach recipients reported receiving safe pregnancy and birth education, clean birthing and uterine massage skills instruction, and clean umbilical cord care training. Nearly 80% reported basic newborn resuscitation skills training. Finally, nearly 100% of outreach recipients received maternal micronutrient supplements and safe and clean birth kits. CONCLUSION: The PAVOT program is a model program that has been proven to successfully provide outreach to rural-living Tibetans by delivering maternal-newborn health education, skills training, and resources to the home.