Impact of preeclampsia on infant and maternal health among women with rheumatic diseases.

OBJECTIVES: We sought to identify the impact of preeclampsia on infant and maternal health among women with rheumatic diseases. METHODS: A retrospective single-center cohort study was conducted to describe pregnancy and infant outcomes among women with systemic lupus erythematosus (SLE) with and without preeclampsia as compared to women with other rheumatic diseases with and without preeclampsia. RESULTS: We identified 263 singleton deliveries born to 226 individual mothers (mean age 31 years, 35% non-Hispanic Black). Overall, 14% of women had preeclampsia; preeclampsia was more common among women with SLE than other rheumatic diseases (27% vs 8%). Women with preeclampsia had a longer hospital stay post-delivery. Infants born to mothers with preeclampsia were delivered an average of 3.3 weeks earlier than those without preeclampsia, were 4 times more likely to be born preterm, and twice as likely to be admitted to the neonatal intensive care unit. The large majority of women with SLE in this cohort were prescribed hydroxychloroquine and aspirin, with no clear association of these medications with preeclampsia. CONCLUSIONS: We found preeclampsia was an important driver of adverse infant and maternal outcomes. While preeclampsia was particularly common among women with SLE in this cohort, the impact of preeclampsia on the infants of all women with rheumatic diseases was similarly severe. In order to improve infant outcomes for women with rheumatic diseases, attention must be paid to preventing, identifying, and managing preeclampsia.

Comparison of methods that combine multiple randomized trials to estimate heterogeneous treatment effects.

Individualized treatment decisions can improve health outcomes, but using data to make these decisions in a reliable, precise, and generalizable way is challenging with a single dataset. Leveraging multiple randomized controlled trials allows for the combination of datasets with unconfounded treatment assignment to better estimate heterogeneous treatment effects. This article discusses several nonparametric approaches for estimating heterogeneous treatment effects using data from multiple trials. We extend single-study methods to a scenario with multiple trials and explore their performance through a simulation study, with data generation scenarios that have differing levels of cross-trial heterogeneity. The simulations demonstrate that methods that directly allow for heterogeneity of the treatment effect across trials perform better than methods that do not, and that the choice of single-study method matters based on the functional form of the treatment effect. Finally, we discuss which methods perform well in each setting and then apply them to four randomized controlled trials to examine effect heterogeneity of treatments for major depressive disorder.

Impact of state legislation and institutional protocols on opioid prescribing practices following pediatric tonsillectomy.

Objectives: Tonsillectomy is a common pediatric surgery, and pain is an important consideration in recovery. Due to the opioid epidemic, individual states, medical societies, and institutions have all taken steps to limit postoperative opioids, yet few studies have examined the effect of these interventions on pediatric otolaryngology practices. The primary aim of this study was to characterize opioid prescribing practices following North Carolina state opioid legislation and targeted institutional changes. Methods: This single center retrospective cohort study included 1552 pediatric tonsillectomy patient records from 2014 to 2021. The primary outcome was number of oxycodone doses per prescription. This outcome was assessed over three time periods: (1) Before 2018 North Carolina opioid legislation. (2) Following legislation, before institutional changes. (3) After institutional opioid-specific protocols. Results: The mean (± standard deviation) number of doses per prescription in Periods 1, 2, and 3 was: 58 ± 53, range 4-493; 28 ± 36, range 3-488; and 23 ± 17, range 1-139, respectively. In the adjusted model, Periods 2 and 3 had lower doses by -41% (95% CI -49%, -32%) and -40% (95% CI -55%, -19%) compared to Period 1. After 2018 North Carolina legislation, dosage decreased by -9% (95% CI -13%, -5%) per year. Despite interventions, ongoing variability in prescription regimens remained in all periods. Conclusion: Legislative and institution specific opioid interventions was associated with a 40% decrease in oxycodone doses per prescription following pediatric tonsillectomy. While variability in opioid practices decreased post-interventions, it was not eliminated. Level of evidence: 3.

Predictive Modeling to Identify Children With Complex Health Needs At Risk for Hospitalization.

BACKGROUND: Identifying children at high risk with complex health needs (CCHN) who have intersecting medical and social needs is challenging. This study's objectives were to (1) develop and evaluate an electronic health record (EHR)-based clinical predictive model ("model") for identifying high-risk CCHN and (2) compare the model's performance as a clinical decision support (CDS) to other CDS tools available for identifying high-risk CCHN. METHODS: This retrospective cohort study included children aged 0 to 20 years with established care within a single health system. The model development/validation cohort included 33 months (January 1, 2016-September 30, 2018) and the testing cohort included 18 months (October 1, 2018-March 31, 2020) of EHR data. Machine learning methods generated a model that predicted probability (0%-100%) for hospitalization within 6 months. Model performance measures included sensitivity, positive predictive value, area under receiver-operator curve, and area under precision-recall curve. Three CDS rules for identifying high-risk CCHN were compared: (1) hospitalization probability ≥10% (model-predicted); (2) complex chronic disease classification (using Pediatric Medical Complexity Algorithm [PMCA]); and (3) previous high hospital utilization. RESULTS: Model development and testing cohorts included 116 799 and 27 087 patients, respectively. The model demonstrated area under receiver-operator curve = 0.79 and area under precision-recall curve = 0.13. PMCA had the highest sensitivity (52.4%) and classified the most children as high risk (17.3%). Positive predictive value of the model-based CDS rule (19%) was higher than CDS based on the PMCA (1.9%) and previous hospital utilization (15%). CONCLUSIONS: A novel EHR-based predictive model was developed and validated as a population-level CDS tool for identifying CCHN at high risk for future hospitalization.

Immigration Policy and the Health of Latina Mothers and Their Infants.

Restrictive immigration policies may adversely affect the health of Latina mothers and their infants. We hypothesized that undocumented Latina mothers and their US born children would have worse birth outcomes and healthcare utilization following the November 2016 election. We used a controlled interrupted time series to estimate the impact of the 2016 presidential election on low birth weight (LBW), preterm birth, maternal depression, well child visit attendance, cancelled visits, and emergency department (ED) visits among infants born to Latina mothers on emergency Medicaid, a proxy for undocumented immigration status. There was a 5.8% (95% CI: -0.99%, 12.5%) increase in LBW and 4.6% (95% CI: -1.8%, 10.9%) increase in preterm births immediately after the 2016 election compared to controls. While these findings were not statistically significant at p < 0.05, the majority of our data suggest worsened birth outcomes among undocumented Latina mothers after the election, consistent with larger prior studies. There was no difference in well child or ED visits. While restrictive policies may have contributed to worse birth outcomes among undocumented Latina mothers, our findings suggest that Latino families still attend infants' scheduled visits.

Conflict resolution styles and skills and variation among medical students.

BACKGROUND: Conflict is inevitable on healthcare teams, yet few professional school curricula teach or assess conflict resolution skills. Little is known about the variation in conflict resolution styles across medical students and how these styles might impact conflict resolution skills. METHODS: This is a prospective, single blinded, group randomized quasi experimental trial to assess the impact of knowing one's own conflict resolution style on conflict resolution skills in a simulated encounter. Graduating medical students completed a mandatory conflict resolution session with standardized patients acting as nurses during a transition to residency course. Coaches reviewed videotapes of the simulation, focusing on students' skills with negotiation and emotional intelligence. Retrospectively, we assessed the impact of the students knowing their conflict resolution style prior to simulation, student gender, race, and intended field of practice on conflict resolution skills as judged by coaches. RESULTS: One hundred and eight students completed the simulated conflict session. Sixty-seven students completed the TKI before the simulated patient (SP) encounter and 41 after. The most common conflict resolution style was accommodating (n = 40). Knowing one's conflict resolution style in advance of the simulation and one's identified race/ethnicity did not impact skill as assessed by faculty coaches. Students pursuing diagnosis-based specialties had higher negotiation (p = 0.04) and emotional quotient (p = 0.006) scores than those pursuing procedural specialties. Females had higher emotional quotient scores (p = 0.02). CONCLUSIONS: Conflict resolution styles vary among medical students. Male gender and future practice in a procedural specialty impacted conflict resolution skills but knowing conflict resolution style did not.

Use of Structured Electronic Health Records Data Elements for the Development of Computable Phenotypes to Identify Potential Adverse Events Associated with Intravenous Immunoglobulin Infusion.

INTRODUCTION: Detection of adverse reactions to drugs and biologic agents is an important component of regulatory approval and post-market safety evaluation. Real-world data, including insurance claims and electronic health records data, are increasingly used for the evaluation of potential safety outcomes; however, there are different types of data elements available within these data resources, impacting the development and performance of computable phenotypes for the identification of adverse events (AEs) associated with a given therapy. OBJECTIVE: To evaluate the utility of different types of data elements to the performance of computable phenotypes for AEs. METHODS: We used intravenous immunoglobulin (IVIG) as a model therapeutic agent and conducted a single-center, retrospective study of 3897 individuals who had at least one IVIG administration between 1 January 2014 and 31 December 2019. We identified the potential occurrence of four different AEs, including two proximal AEs (anaphylaxis and heart rate alterations) and two distal AEs (thrombosis and hemolysis). We considered three different computable phenotypes: (1) an International Classification of Disease (ICD)-based phenotype; (2) a phenotype-based on EHR-derived contextual information based on structured data elements, including laboratory values, medication administrations, or vital signs; and (3) a compound phenotype that required both an ICD code for the AE in combination with additional EHR-derived structured data elements. We evaluated the performance of each of these computable phenotypes compared with chart review-based identification of AEs, assessing the positive predictive value (PPV), specificity, and estimated sensitivity of each computable phenotype method. RESULTS: Compound computable phenotypes had a high positive predictive value for acute AEs such as anaphylaxis and bradycardia or tachycardia; however, few patients had both ICD codes and the relevant contextual data, which decreased the sensitivity of these computable phenotypes. In contrast, computable phenotypes for distal AEs (i.e., thrombotic events or hemolysis) frequently had ICD codes for these conditions in the absence of an AE due to a prior history of such events, suggesting that patient medical history of AEs negatively impacted the PPV of computable phenotypes based on ICD codes. CONCLUSIONS: These data provide evidence for the utility of different structured data elements in computable phenotypes for AEs. Such computable phenotypes can be used across different data sources for the detection of infusion-related adverse events.

Malnutrition and immune cell subsets in children undergoing kidney transplantation.

BACKGROUND: Malnutrition, including obesity and undernutrition, among children is increasing in prevalence and is common among children on renal replacement therapy. The effect of malnutrition on the pre-transplant immune system and how the pediatric immune system responds to the insult of both immunosuppression and allotransplantation is unknown. We examined the relationship of nutritional status with post-transplant outcomes and characterized the peripheral immune cell phenotypes of children from the Immune Development of Pediatric Transplant (IMPACT) study. METHODS: Ninety-eight patients from the IMPACT study were classified as having obesity, undernutrition, or normal nutrition-based pre-transplant measurements. Incidence of infectious and alloimmune outcomes at 1-year post-transplantation was compared between nutritional groups using Gray's test and Fine-Gray subdistribution hazards model. Event-free survival was estimated by Kaplan-Meier method and compared between groups. Differences in immune cell subsets between nutritional groups over time were determined using generalized estimating equations accounting for the correlation between repeated measurements. RESULTS: We did not observe that nutritional status was associated with infectious or alloimmune events or event-free survival post-transplant. We demonstrated that children with obesity had distinct T-and B-cell signatures relative to those with undernutrition and normal nutrition, even when controlling for immunosuppression. Children with obesity had a lower frequency of CD8 Tnaive cells 9-month post-transplant (p < .001), a higher frequency of CD4 CD57 + PD1- T cells, and lower frequencies of CD57-PD1+ CD8 and CD57-PD1- CD8 T cells at 12-month transplant (p < .05 for all). CONCLUSIONS: Children with obesity have distinct immunophenotypes that may influence the tailoring of immunosuppression.

Risk of obstetric anal sphincter injuries at the time of admission for delivery: A clinical prediction model.

OBJECTIVE: To develop and validate a model to predict obstetric anal sphincter injuries (OASIS) using only information available at the time of admission for labour. DESIGN: A clinical predictive model using a retrospective cohort. SETTING: A US health system containing one community and one tertiary hospital. SAMPLE: A total of 22 873 pregnancy episodes with in-hospital delivery at or beyond 21 weeks of gestation. METHODS: Thirty antepartum risk factors were identified as candidate variables, and a prediction model was built using logistic regression predicting OASIS versus no OASIS. Models were fit using the overall study population and separately using hospital-specific cohorts. Bootstrapping was used for internal validation and external cross-validation was performed between the two hospital cohorts. MAIN OUTCOME MEASURES: Model performance was estimated using the bias-corrected concordance index (c-index), calibration plots and decision curves. RESULTS: Fifteen risk factors were retained in the final model. Decreasing parity, previous caesarean birth and cardiovascular disease increased risk of OASIS, whereas tobacco use and black race decreased risk. The final model from the total study population had good discrimination (c-index 0.77, 95% confidence interval [CI] 0.75-0.78) and was able to accurately predict risks between 0 and 35%, where average risk for OASIS was 3%. The site-specific model fit using patients only from the tertiary hospital had c-stat 0.74 (95% CI 0.72-0.77) on community hospital patients, and the community hospital model was 0.77 (95%CI 0.76-0.80) on the tertiary hospital patients. CONCLUSIONS: OASIS can be accurately predicted based on variables known at the time of admission for labour. These predictions could be useful for selectively implementing OASIS prevention strategies.

Predicting in-hospital length of stay: a two-stage modeling approach to account for highly skewed data.

BACKGROUND: In the early stages of the COVID-19 pandemic our institution was interested in forecasting how long surgical patients receiving elective procedures would spend in the hospital. Initial examination of our models indicated that, due to the skewed nature of the length of stay, accurate prediction was challenging and we instead opted for a simpler classification model. In this work we perform a deeper examination of predicting in-hospital length of stay. METHODS: We used electronic health record data on length of stay from 42,209 elective surgeries. We compare different loss-functions (mean squared error, mean absolute error, mean relative error), algorithms (LASSO, Random Forests, multilayer perceptron) and data transformations (log and truncation). We also assess the performance of two stage hybrid classification-regression approach. RESULTS: Our results show that while it is possible to accurately predict short length of stays, predicting longer length of stay is extremely challenging. As such, we opt for a two-stage model that first classifies patients into long versus short length of stays and then a second stage that fits a regresssor among those predicted to have a short length of stay. DISCUSSION: The results indicate both the challenges and considerations necessary to applying machine-learning methods to skewed outcomes. CONCLUSIONS: Two-stage models allow those developing clinical decision support tools to explicitly acknowledge where they can and cannot make accurate predictions.

Environmental and clinical data utility in pediatric asthma exacerbation risk prediction models.

BACKGROUND: Asthma exacerbations are triggered by a variety of clinical and environmental factors, but their relative impacts on exacerbation risk are unclear. There is a critical need to develop methods to identify children at high-risk for future exacerbation to allow targeted prevention measures. We sought to evaluate the utility of models using spatiotemporally resolved climatic data and individual electronic health records (EHR) in predicting pediatric asthma exacerbations. METHODS: We extracted retrospective EHR data for 5982 children with asthma who had an encounter within the Duke University Health System between January 1, 2014 and December 31, 2019. EHR data were linked to spatially resolved environmental data, and temporally resolved climate, pollution, allergen, and influenza case data. We used xgBoost to build predictive models of asthma exacerbation over 30-180 day time horizons, and evaluated the contributions of different data types to model performance. RESULTS: Models using readily available EHR data performed moderately well, as measured by the area under the receiver operating characteristic curve (AUC 0.730-0.742) over all three time horizons. Inclusion of spatial and temporal data did not significantly improve model performance. Generating a decision rule with a sensitivity of 70% produced a positive predictive value of 13.8% for 180 day outcomes but only 2.9% for 30 day outcomes. CONCLUSIONS: EHR data-based models perform moderately wellover a 30-180 day time horizon to identify children who would benefit from asthma exacerbation prevention measures. Due to the low rate of exacerbations, longer-term models are likely to be most clinically useful. TRIAL REGISTRATION: Not applicable.

Asthma and the Risk of SARS-CoV-2 Infection Among Children and Adolescents.

OBJECTIVES: Over 6 million pediatric severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections have occurred in the United States, but risk factors for infection remain poorly defined. We sought to evaluate the association between asthma and SARS-CoV-2 infection risk among children. METHODS: We conducted a retrospective cohort study of children 5 to 17 years of age receiving care through the Duke University Health System and who had a Durham County, North Carolina residential address. Children were classified as having asthma using previously validated electronic health record-based definitions. SARS-CoV-2 infections were identified based on positive polymerase chain reaction testing of respiratory samples collected between March 1, 2020, and September 30, 2021. We matched children with asthma 1:1 to children without asthma, using propensity scores and used Poisson regression to evaluate the association between asthma and SARS-CoV-2 infection risk. RESULTS: Of 46 900 children, 6324 (13.5%) met criteria for asthma. Children with asthma were more likely to be tested for SARS-CoV-2 infection than children without asthma (33.0% vs 20.9%, P < .0001). In a propensity score-matched cohort of 12 648 children, 706 (5.6%) children tested positive for SARS-CoV-2 infection, including 350 (2.8%) children with asthma and 356 (2.8%) children without asthma (risk ratio: 0.98, 95% confidence interval: 0.85-1.13. There was no evidence of effect modification of this association by inhaled corticosteroid prescription, history of severe exacerbation, or comorbid atopic diseases. Only 1 child with asthma required hospitalization for SARS-CoV-2 infection. CONCLUSIONS: After controlling for factors associated with SARS-CoV-2 testing, we found that children with asthma have a similar SARS-CoV-2 infection risk as children without asthma.

Inpatient pharmacists using a readmission risk model in supporting discharge medication reconciliation to reduce unplanned hospital readmissions: a quality improvement intervention.

INTRODUCTION: Reducing unplanned hospital readmissions is an important priority for all hospitals and health systems. Hospital discharge can be complicated by discrepancies in the medication reconciliation and/or prescribing processes. Clinical pharmacist involvement in the medication reconciliation process at discharge can help prevent these discrepancies and possibly reduce unplanned hospital readmissions. METHODS: We report the results of our quality improvement intervention at Duke University Hospital, in which pharmacists were involved in the discharge medication reconciliation process on select high-risk general medicine patients over 2 years (2018-2020). Pharmacists performed traditional discharge medication reconciliation which included a review of medications for clinical appropriateness and affordability. A total of 1569 patients were identified as high risk for hospital readmission using the Epic readmission risk model and had a clinical pharmacist review the discharge medication reconciliation. RESULTS: This intervention was associated with a significantly lower 7-day readmission rate in patients who scored high risk for readmission and received pharmacist support in discharge medication reconciliation versus those patients who did not receive pharmacist support (5.8% vs 7.6%). There was no effect on readmission rates of 14 or 30 days. The clinical pharmacists had at least one intervention on 67% of patients reviewed and averaged 1.75 interventions per patient. CONCLUSION: This quality improvement study showed that having clinical pharmacists intervene in the discharge medication reconciliation process in patients identified as high risk for readmission is associated with lower unplanned readmission rates at 7 days. The interventions by pharmacists were significant and well received by ordering providers. This study highlights the important role of a clinical pharmacist in the discharge medication reconciliation process.

Reduced pediatric urgent asthma utilization and exacerbations during the COVID-19 pandemic.

The COVID-19 pandemic has had a profound impact on healthcare access and utilization, which could have important implications for children with chronic diseases, including asthma. We sought to evaluate changes in healthcare utilization and outcomes in children with asthma during the COVID-19 pandemic. We used electronic health records data to evaluate healthcare use and asthma outcomes in 3959 children and adolescents, 5-17 years of age, with a prior diagnosis of asthma who had a history of well-child visits and encounters within the healthcare system. We assessed all-cause healthcare encounters and asthma exacerbations in the 12-months preceding the start of the COVID-19 pandemic (March 1, 2019-February 29, 2020) and the first 12 months of the pandemic (March 1, 2020-February 28, 2021). All-cause healthcare encounters decreased significantly during the pandemic compared to the preceding year, including well-child visits (48.1% during the pandemic vs. 66.6% in the prior year; p < .01), emergency department visits (9.7% vs. 21.0%; p < .01), and inpatient admissions (1.6% vs. 2.5%; p < .01), though there was over a 100-fold increase in telehealth encounters. Asthma exacerbations that required treatment with systemic steroids also decreased (127 vs. 504 exacerbations; p < .01). Race/ethnicity was not associated with changes in healthcare utilization or asthma outcomes. The COVID-19 pandemic corresponded to dramatic shifts in healthcare utilization, including increased telehealth use and improved outcomes among children with asthma. Social distancing measures may have also reduced asthma trigger exposure.

Relationship between antithymocyte globulin, T cell phenotypes, and clinical outcomes in pediatric kidney transplantation.

Depletional induction using antithymocyte globulin (ATG) reduces rates of acute rejection in adult kidney transplant recipients, yet little is known about its effects in children. Using a longitudinal cohort of 103 patients in the Immune Development in Pediatric Transplant (IMPACT) study, we compared T cell phenotypes after ATG or non-ATG induction. We examined the effects of ATG on the early clinical outcomes of alloimmune events (development of de novo donor specific antibody and/or biopsy proven rejection) and infection events (viremia/viral infections). Long-term patient and graft outcomes were examined using the Scientific Registry of Transplant Recipients. After ATG induction, although absolute counts of CD4 and CD8 T cells were lower, patients had higher percentages of CD4 and CD8 memory T cells with a concomitant decrease in frequency of naïve T cells compared to non-ATG induction. In adjusted and unadjusted models, ATG induction was associated with increased early event-free survival, with no difference in long-term patient or allograft survival. Decreased CD4+ naïve and increased CD4+ effector memory T cell frequencies were associated with improved clinical outcomes. Though immunologic parameters are drastically altered with ATG induction, long-term clinical benefits remain unclear in pediatric patients.

Severe Acute Respiratory Syndrome Coronavirus 2 Infections Among Children in the Biospecimens from Respiratory Virus-Exposed Kids (BRAVE Kids) Study.

BACKGROUND: Child with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection typically have mild symptoms that do not require medical attention, leaving a gap in our understanding of the spectrum of SARS-CoV-2-related illnesses that the viruses causes in children. METHODS: We conducted a prospective cohort study of children and adolescents (aged <21 years) with a SARS-CoV-2-infected close contact. We collected nasopharyngeal or nasal swabs at enrollment and tested for SARS-CoV-2 using a real-time polymerase chain reaction assay. RESULTS: Of 382 children, 293 (77%) were SARS-CoV-2-infected. SARS-CoV-2-infected children were more likely to be Hispanic (P < .0001), less likely to have asthma (P = .005), and more likely to have an infected sibling contact (P = .001) than uninfected children. Children aged 6-13 years were frequently asymptomatic (39%) and had respiratory symptoms less often than younger children (29% vs 48%; P = .01) or adolescents (29% vs 60%; P < .001). Compared with children aged 6-13 years, adolescents more frequently reported influenza-like (61% vs 39%; P < .001) , and gastrointestinal (27% vs 9%; P = .002), and sensory symptoms (42% vs 9%; P < .0001) and had more prolonged illnesses (median [interquartile range] duration: 7 [4-12] vs 4 [3-8] days; P = 0.01). Despite the age-related variability in symptoms, wWe found no difference in nasopharyngeal viral load by age or between symptomatic and asymptomatic children. CONCLUSIONS: Hispanic ethnicity and an infected sibling close contact are associated with increased SARS-CoV-2 infection risk among children, while asthma is associated with decreased risk. Age-related differences in clinical manifestations of SARS-CoV-2 infection must be considered when evaluating children for coronavirus disease 2019 and in developing screening strategies for schools and childcare settings.

Well-Child Care Attendance and Risk of Asthma Exacerbations.

BACKGROUND: Asthma remains a leading cause of hospitalization in US children. Well-child care (WCC) visits are routinely recommended, but how WCC adherence relates to asthma outcomes is poorly described. METHODS: We conducted a retrospective longitudinal cohort study using electronic health records among 5 to 17 year old children residing in Durham County with confirmed asthma and receiving primary care within a single health system, to compare the association between asthma exacerbations and previous WCC exposure. Exacerbations included any International Classification of Diseases, Ninth Revision, or International Classification of Diseases, 10th Revision, coded asthma exacerbation encounter with an accompanying systemic glucocorticoid prescription. Exacerbations were grouped by severity: ambulatory encounter only, urgent care, emergency department, hospital encounters <24 hours, and hospital admissions ≥24 hours. In the primary analysis, we assessed time to asthma exacerbation based on the presence or absence of a WCC visit in the preceding year using a time-varying covariate Cox model. RESULTS: A total of 5656 children met eligibility criteria and were included in the primary analysis. Patients with the highest WCC visit attendance tended to be younger, had a higher prevalence of private insurance, had greater asthma medication usage, and were less likely to be obese. The presence of a WCC visit in the previous 12 months was associated with a reduced risk of all-cause exacerbations (hazard ratio: 0.90; 95% confidence interval: 0.83-0.98) and severe exacerbations requiring hospital admission (hazard ratio: 0.53; 95% confidence interval: 0.39-0.71). CONCLUSIONS: WCC visits were associated with a lower risk of subsequent severe exacerbations, including asthma-related emergency department visits and hospitalizations. Poor WCC visit adherence predicts pediatric asthma morbidity, especially exacerbations requiring hospitalization.

Implementation and Continuous Monitoring of an Electronic Health Record Embedded Readmissions Clinical Decision Support Tool.

Unplanned hospital readmissions represent a significant health care value problem with high costs and poor quality of care. A significant percentage of readmissions could be prevented if clinical inpatient teams were better able to predict which patients were at higher risk for readmission. Many of the current clinical decision support models that predict readmissions are not configured to integrate closely with the electronic health record or alert providers in real-time prior to discharge about a patient's risk for readmission. We report on the implementation and monitoring of the Epic electronic health record-"Unplanned readmission model version 1"-over 2 years from 1/1/2018-12/31/2019. For patients discharged during this time, the predictive capability to discern high risk discharges was reflected in an AUC/C-statistic at our three hospitals of 0.716-0.760 for all patients and 0.676-0.695 for general medicine patients. The model had a positive predictive value ranging from 0.217-0.248 for all patients. We also present our methods in monitoring the model over time for trend changes, as well as common readmissions reduction strategies triggered by the score.