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.

Designing an Implementable Clinical Prediction Model for Near-Term Mortality and Long-Term Survival in Patients on Maintenance Hemodialysis

RATIONALE & OBJECTIVE

The life expectancy of patients treated with maintenance hemodialysis (MHD) is heterogeneous. Knowledge of life-expectancy may focus care decisions on near-term versus long-term goals. The current tools are limited and focus on near-term mortality. Here, we develop and assess potential utility for predicting near-term mortality and long-term survival on MHD.

STUDY DESIGN

Predictive modeling study.

SETTING & PARTICIPANTS

42,351 patients contributing 997,381 patient months over 11 years, abstracted from the electronic health record (EHR) system of midsize, nonprofit dialysis providers.

NEW PREDICTORS & ESTABLISHED PREDICTORS

Demographics, laboratory results, vital signs, and service utilization data available within dialysis EHR.

OUTCOME

For each patient month, we ascertained death within the next 6 months (ie, near-term mortality) and survival over more than 5 years during receipt of MHD or after kidney transplantation (ie, long-term survival).

ANALYTICAL APPROACH

We used least absolute shrinkage and selection operator logistic regression and gradient-boosting machines to predict each outcome. We compared these to time-to-event models spanning both time horizons. We explored the performance of decision rules at different cut points.

RESULTS

All models achieved an area under the receiver operator characteristic curve of≥0.80 and optimal calibration metrics in the test set. The long-term survival models had significantly better performance than the near-term mortality models. The time-to-event models performed similarly to binary models. Applying different cut points spanning from the 1st to 90th percentile of the predictions, a positive predictive value (PPV) of 54% could be achieved for near-term mortality, but with poor sensitivity of 6%. A PPV of 71% could be achieved for long-term survival with a sensitivity of 67%.

LIMITATIONS

The retrospective models would need to be prospectively validated before they could be appropriately used as clinical decision aids.

CONCLUSIONS

A model built with readily available clinical variables to support easy implementation can predict clinically important life expectancy thresholds and shows promise as a clinical decision support tool for patients on MHD. Predicting long-term survival has better decision rule performance than predicting near-term mortality.

PLAIN-LANGUAGE SUMMARY

Clinical prediction models (CPMs) are not widely used for patients undergoing maintenance hemodialysis (MHD). Although a variety of CPMs have been reported in the literature, many of these were not well-designed to be easily implementable. We consider the performance of an implementable CPM for both near-term mortality and long-term survival for patients undergoing MHD. Both near-term and long-term models have similar predictive performance, but the long-term models have greater clinical utility. We further consider how the differential performance of predicting over different time horizons may be used to impact clinical decision making. Although predictive modeling is not regularly used for MHD patients, such tools may help promote individualized care planning and foster shared decision making.

Translating ethical and quality principles for the effective, safe and fair development, deployment and use of artificial intelligence technologies in healthcare

OBJECTIVE

The complexity and rapid pace of development of algorithmic technologies pose challenges for their regulation and oversight in healthcare settings. We sought to improve our institution's approach to evaluation and governance of algorithmic technologies used in clinical care and operations by creating an Implementation Guide that standardizes evaluation criteria so that local oversight is performed in an objective fashion.

MATERIALS AND METHODS

Building on a framework that applies key ethical and quality principles (clinical value and safety, fairness and equity, usability and adoption, transparency and accountability, and regulatory compliance), we created concrete guidelines for evaluating algorithmic technologies at our institution.

RESULTS

An Implementation Guide articulates evaluation criteria used during review of algorithmic technologies and details what evidence supports the implementation of ethical and quality principles for trustworthy health AI. Application of the processes described in the Implementation Guide can lead to algorithms that are safer as well as more effective, fair, and equitable upon implementation, as illustrated through 4 examples of technologies at different phases of the algorithmic lifecycle that underwent evaluation at our academic medical center.

DISCUSSION

By providing clear descriptions/definitions of evaluation criteria and embedding them within standardized processes, we streamlined oversight processes and educated communities using and developing algorithmic technologies within our institution.

CONCLUSIONS

We developed a scalable, adaptable framework for translating principles into evaluation criteria and specific requirements that support trustworthy implementation of algorithmic technologies in patient care and healthcare operations.

Comparing Natural Language Processing and Structured Medical Data to Develop a Computable Phenotype for Patients Hospitalized Due to COVID-19: Retrospective Analysis

Background

Throughout the COVID-19 pandemic, many hospitals conducted routine testing of hospitalized patients for SARS-CoV-2 infection upon admission. Some of these patients are admitted for reasons unrelated to COVID-19 and incidentally test positive for the virus. Because COVID-19-related hospitalizations have become a critical public health indicator, it is important to identify patients who are hospitalized because of COVID-19 as opposed to those who are admitted for other indications.

Objective

We compared the performance of different computable phenotype definitions for COVID-19 hospitalizations that use different types of data from electronic health records (EHRs), including structured EHR data elements, clinical notes, or a combination of both data types.

Methods

We conducted a retrospective data analysis, using clinician chart review-based validation at a large academic medical center. We reviewed and analyzed the charts of 586 hospitalized individuals who tested positive for SARS-CoV-2 in January 2022. We used LASSO (least absolute shrinkage and selection operator) regression and random forests to fit classification algorithms that incorporated structured EHR data elements, clinical notes, or a combination of structured data and clinical notes. We used natural language processing to incorporate data from clinical notes. The performance of each model was evaluated based on the area under the receiver operator characteristic curve (AUROC) and an associated decision rule based on sensitivity and positive predictive value. We also identified top words and clinical indicators of COVID-19-specific hospitalization and assessed the impact of different phenotyping strategies on estimated hospital outcome metrics.

Results

Based on a chart review, 38.2% (224/586) of patients were determined to have been hospitalized for reasons other than COVID-19, despite having tested positive for SARS-CoV-2. A computable phenotype that used clinical notes had significantly better discrimination than one that used structured EHR data elements (AUROC: 0.894 vs 0.841; P<.001) and performed similarly to a model that combined clinical notes with structured data elements (AUROC: 0.894 vs 0.893; P=.91). Assessments of hospital outcome metrics significantly differed based on whether the population included all hospitalized patients who tested positive for SARS-CoV-2 or those who were determined to have been hospitalized due to COVID-19.

Conclusions

These findings highlight the importance of cause-specific phenotyping for COVID-19 hospitalizations. More generally, this work demonstrates the utility of natural language processing approaches for deriving information related to patient hospitalizations in cases where there may be multiple conditions that could serve as the primary indication for hospitalization.

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.

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.

Association between Gentrification and Health and Healthcare Utilization

There is tremendous interest in understanding how neighborhoods impact health by linking extant social and environmental drivers of health (SDOH) data with electronic health record (EHR) data. Studies quantifying such associations often use static neighborhood measures. Little research examines the impact of gentrification-a measure of neighborhood change-on the health of long-term neighborhood residents using EHR data, which may have a more generalizable population than traditional approaches. We quantified associations between gentrification and health and healthcare utilization by linking longitudinal socioeconomic data from the American Community Survey with EHR data across two health systems accessed by long-term residents of Durham County, NC, from 2007 to 2017. Census block group-level neighborhoods were eligible to be gentrified if they had low socioeconomic status relative to the county average. Gentrification was defined using socioeconomic data from 2006 to 2010 and 2011-2015, with the Steinmetz-Wood definition. Multivariable logistic and Poisson regression models estimated associations between gentrification and development of health indicators (cardiovascular disease, hypertension, diabetes, obesity, asthma, depression) or healthcare encounters (emergency department [ED], inpatient, or outpatient). Sensitivity analyses examined two alternative gentrification measures. Of the 99 block groups within the city of Durham, 28 were eligible (N = 10,807; median age = 42; 83% Black; 55% female) and 5 gentrified. Individuals in gentrifying neighborhoods had lower odds of obesity (odds ratio [OR] = 0.89; 95% confidence interval [CI]: 0.81-0.99), higher odds of an ED encounter (OR = 1.10; 95% CI: 1.01-1.20), and lower risk for outpatient encounters (incidence rate ratio = 0.93; 95% CI: 0.87-1.00) compared with non-gentrifying neighborhoods. The association between gentrification and health and healthcare utilization was sensitive to gentrification definition.

Utilization and Outcomes of Extracorporeal Membrane Oxygenation Following Traumatic Brain Injury in the United States

Objectives: Describe contemporary ECMO utilization patterns among patients with traumatic brain injury (TBI) and examine clinical outcomes among TBI patients requiring ECMO. Design: Retrospective cohort study. Setting: Premier Healthcare Database (PHD) between January 2016 to June 2020. Subjects: Adult patients with TBI who were mechanically ventilated and stratified by exposure to ECMO. Results: Among patients exposed to ECMO, we examined the following clinical outcomes: hospital LOS, ICU LOS, duration of mechanical ventilation, and hospital mortality. Of our initial cohort (n = 59,612), 118 patients (0.2%) were placed on ECMO during hospitalization. Most patients were placed on ECMO within the first 2 days of admission (54.3%). Factors associated with ECMO utilization included younger age (OR 0.96, 95% CI (0.95–0.97)), higher injury severity score (ISS) (OR 1.03, 95% CI (1.01–1.04)), vasopressor utilization (2.92, 95% CI (1.90–4.48)), tranexamic acid utilization (OR 1.84, 95% CI (1.12–3.04)), baseline comorbidities (OR 1.06, 95% CI (1.03–1.09)), and care in a teaching hospital (OR 3.04, 95% CI 1.31–7.05). A moderate degree (ICC = 19.5%) of variation in ECMO use was explained at the individual hospital level. Patients exposed to ECMO had longer median (IQR) hospital and ICU length of stay (LOS) [26 days (11–36) versus 9 days (4–8) and 19.5 days (8–32) versus 5 days (2–11), respectively] and a longer median (IQR) duration of mechanical ventilation [18 days (8–31) versus 3 days (2–8)]. Patients exposed to ECMO experienced a hospital mortality rate of 33.9%, compared to 21.2% of TBI patients unexposed to ECMO. Conclusions: ECMO utilization in mechanically ventilated patients with TBI is rare, with significant variation across hospitals. The impact of ECMO on healthcare utilization and hospital mortality following TBI is comparable to non-TBI conditions requiring ECMO. Further research is necessary to better understand the role of ECMO following TBI and identify patients who may benefit from this therapy.

Transition from pediatric to adult care in type 1 diabetes mellitus: a longitudinal analysis of age at transfer and gap in care

INTRODUCTION

Adolescents and young adults (AYAs) with type 1 diabetes (T1D) are at risk of suboptimal glycemic control and high acute care utilization. Little is known about the optimal age to transfer people with T1D to adult care, or time gap between completing pediatric care and beginning adult endocrinology care.

RESEARCH DESIGN AND METHODS

This retrospective, longitudinal study examined the transition of AYAs with T1D who received endocrinology care within Duke University Health System. We used linear multivariable or Poisson regression modeling to assess the association of (1) sociodemographic and clinical factors associated with gap in care and age at transfer among AYAs and (2) the impact of gap in care and age at transfer on subsequent glycemic control and acute care utilization.

RESULTS

There were 214 subjects included in the analysis (54.2% female, 72.8% white). The median time to transition and age at transition were 8.0 months and 21.5 years old, respectively. The median gap in care was extended by a factor of 3.39 (95% CI=1.25 to 9.22, p=0.02) for those who did not see a mental health provider pre-transfer. Individuals who did not see a diabetes educator in pediatrics had an increase in mean age at transition of 2.62 years (95% CI=0.93 to 4.32, p&lt;0.01). The post-transfer emergency department visit rate was increased for every month increase in gap in care by a relative factor of 1.07 (95% CI=1.03 to 1.11, p&lt;0.01). For every year increase in age at transition, post-transfer hospitalization rate was associated with a reduction of a relative factor of 0.62 (95% CI=0.45 to 0.85, p&lt;0.01) and emergency department visit rate by 0.58 (95% CI=0.45 to 0.76, p&lt;0.01).

CONCLUSIONS

Most AYAs with T1D have a prolonged gap in care. When designing interventions to improve health outcomes for AYAs transitioning from pediatric to adult-based care, we should aim to minimize gaps in care.

Risk Factors and Neurological Outcomes Associated With Circulatory Shock After Moderate-Severe Traumatic Brain Injury: A TRACK-TBI Study

BACKGROUND

Extracranial multisystem organ failure is a common sequela of severe traumatic brain injury (TBI). Risk factors for developing circulatory shock and long-term functional outcomes of this patient subset are poorly understood.

OBJECTIVE

To identify emergency department predictors of circulatory shock after moderate-severe TBI and examine long-term functional outcomes in patients with moderate-severe TBI who developed circulatory shock.

METHODS

We conducted a retrospective cohort study using the Transforming Clinical Research and Knowledge in TBI database for adult patients with moderate-severe TBI, defined as a Glasgow Coma Scale (GCS) score of <13 and stratified by the development of circulatory shock within 72 hours of hospital admission (Sequential Organ Failure Assessment score ≥2). Demographic and clinical data were assessed with descriptive statistics. A forward selection regression model examined risk factors for the development of circulatory shock. Functional outcomes were examined using multivariable regression models.

RESULTS

Of our moderate-severe TBI population (n = 407), 168 (41.2%) developed circulatory shock. Our predictive model suggested that race, computed tomography Rotterdam scores <3, GCS in the emergency department, and development of hypotension in the emergency department were associated with developing circulatory shock. Those who developed shock had less favorable 6-month functional outcomes measured by the 6-month GCS-Extended (odds ratio 0.36, P = .002) and 6-month Disability Rating Scale score (Diff. in means 3.86, P = .002) and a longer length of hospital stay (Diff. in means 11.0 days, P < .001).

CONCLUSION

We report potential risk factors for circulatory shock after moderate-severe TBI. Our study suggests that developing circulatory shock after moderate-severe TBI is associated with poor long-term functional outcomes.

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.

A framework for the oversight and local deployment of safe and high-quality prediction models

Artificial intelligence/machine learning models are being rapidly developed and used in clinical practice. However, many models are deployed without a clear understanding of clinical or operational impact and frequently lack monitoring plans that can detect potential safety signals. There is a lack of consensus in establishing governance to deploy, pilot, and monitor algorithms within operational healthcare delivery workflows. Here, we describe a governance framework that combines current regulatory best practices and lifecycle management of predictive models being used for clinical care. Since January 2021, we have successfully added models to our governance portfolio and are currently managing 52 models.

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.

Combining adult with pediatric patient data to develop a clinical decision support tool intended for children: leveraging machine learning to model heterogeneity

Background

Clinical decision support (CDS) tools built using adult data do not typically perform well for children. We explored how best to leverage adult data to improve the performance of such tools. This study assesses whether it is better to build CDS tools for children using data from children alone or to use combined data from both adults and children.

Methods

Retrospective cohort using data from 2017 to 2020. Participants include all individuals (adults and children) receiving an elective surgery at a large academic medical center that provides adult and pediatric services. We predicted need for mechanical ventilation or admission to the intensive care unit (ICU). Predictor variables included demographic, clinical, and service utilization factors known prior to surgery. We compared predictive models built using machine learning to regression-based methods that used a pediatric or combined adult-pediatric cohort. We compared model performance based on Area Under the Receiver Operator Characteristic.

Results

While we found that adults and children have different risk factors, machine learning methods are able to appropriately model the underlying heterogeneity of each population and produce equally accurate predictive models whether using data only from pediatric patients or combined data from both children and adults. Results from regression-based methods were improved by the use of pediatric-specific data.

Conclusions

CDS tools for children can successfully use combined data from adults and children if the model accounts for underlying heterogeneity, as in machine learning models.

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.

Feasibility of Post-hospitalization Telemedicine Video Visits for Children With Medical Complexity

OBJECTIVES

To evaluate feasibility and acceptability of post-hospitalization telemedicine video visits (TMVV) during hospital-to-home transitions for children with medical complexity (CMC); and explore associations with hospital utilization, caregiver self-efficacy (CSE), and family self-management (FSM).

METHOD

This non-randomized pilot study assigned CMC (n=28) to weekly TMVV for four weeks post-hospitalization; control CMC (n=20) received usual care without telemedicine. Feasibility was measured by time to connection and proportion of TMVV completed; acceptability was measured by parent-reported surveys. Pre/post-discharge changes in CSE, FSM, and hospital utilization were assessed.

RESULTS

64 TMVV were completed; 82 % of patients completed 1 TMVV; 54 % completed four TMVV. Median time to TMVV connection was 1 minute (IQR=2.5). Parents reported high acceptability of TMVV (mean 6.42; 1 -7 scale). CSE and FSM pre/post-discharge were similar for both groups; utilization declined in both groups post-discharge.

DISCUSSION

Post-hospitalization TMVV for CMC were feasible and acceptable during hospital-to-home transitions.

Using Electronic Health Records to understand the population of local children captured in a large health system in Durham County, NC, USA, and implications for population health research

Although local policies aimed at reducing childhood health inequities can benefit from local data, sample size constraints in population representative health surveys often prevent rigorous evaluations of child health disparities and health care patterns at local levels. Electronic Health Records (EHRs) offer a possible solution as they contain large amounts of information on pediatric patients within a health system. In this paper, we consider the suitability of using EHRs from a large health system to study local children's health by evaluating the extent to which the EHRs capture the county's child population. First, we compare the demographic characteristics of Duke University Health System pediatric patients who live in Durham County, NC (USA) to the child population estimates in the 2015-2019 American Community Survey. We then examine geographic variation in census tract rates of children captured in the EHR data and estimate negative binomial models to assess how tract characteristics are associated with these rates. We also perform these analyses for the subset of pediatric patients who have a well-child encounter. We find that the demographic characteristics of pediatric patients captured by the EHRs are similar to those of the county's child population. Although the county rate of children captured in the EHRs is high, there is variation across census tracts. On average, census tracts with higher concentrations of non-Hispanic Black residents have lower capture rates and tracts with higher concentrations of poverty have higher capture rates, with the poorest tracts showing the largest racial gap in rates of children captured by EHRs. Our findings suggest that EHRs from a large health system can be used to assess children's population health, but that EHR-based evaluations of children's health disparities and health care patterns should account for differences in who is captured by the EHRs based on census tract characteristics.

Observability and its impact on differential bias for clinical prediction models

OBJECTIVE

Electronic health records have incomplete capture of patient outcomes. We consider the case when observability is differential across a predictor. Including such a predictor (sensitive variable) can lead to algorithmic bias, potentially exacerbating health inequities.

MATERIALS AND METHODS

We define bias for a clinical prediction model (CPM) as the difference between the true and estimated risk, and differential bias as bias that differs across a sensitive variable. We illustrate the genesis of differential bias via a 2-stage process, where conditional on having the outcome of interest, the outcome is differentially observed. We use simulations and a real-data example to demonstrate the possible impact of including a sensitive variable in a CPM.

RESULTS

If there is differential observability based on a sensitive variable, including it in a CPM can induce differential bias. However, if the sensitive variable impacts the outcome but not observability, it is better to include it. When a sensitive variable impacts both observability and the outcome no simple recommendation can be provided. We show that one cannot use observed data to detect differential bias.

DISCUSSION

Our study furthers the literature on observability, showing that differential observability can lead to algorithmic bias. This highlights the importance of considering whether to include sensitive variables in CPMs.

CONCLUSION

Including a sensitive variable in a CPM depends on whether it truly affects the outcome or just the observability of the outcome. Since this cannot be distinguished with observed data, observability is an implicit assumption of CPMs.

Supercharging Imbalanced Data Learning With Energy-based Contrastive Representation Transfer

Dealing with severe class imbalance poses a major challenge for many real-world applications, especially when the accurate classification and generalization of minority classes are of primary interest. In computer vision and NLP, learning from datasets with long-tail behavior is a recurring theme, especially for naturally occurring labels. Existing solutions mostly appeal to sampling or weighting adjustments to alleviate the extreme imbalance, or impose inductive bias to prioritize generalizable associations. Here we take a novel perspective to promote sample efficiency and model generalization based on the invariance principles of causality. Our contribution posits a meta-distributional scenario, where the causal generating mechanism for label-conditional features is invariant across different labels. Such causal assumption enables efficient knowledge transfer from the dominant classes to their under-represented counterparts, even if their feature distributions show apparent disparities. This allows us to leverage a causal data augmentation procedure to enlarge the representation of minority classes. Our development is orthogonal to the existing imbalanced data learning techniques thus can be seamlessly integrated. The proposed approach is validated on an extensive set of synthetic and real-world tasks against state-of-the-art solutions.

Impact of a clinical pharmacist on provider prescribing patterns in a primary care clinic

BACKGROUND

Sodium-glucose transporter 2 (SGLT2) inhibitors and glucagon-like peptide-1 (GLP-1) agonists have demonstrated beneficial outcomes in patients with type 2 diabetes at high cardiovascular risk. Unfortunately, these agents are still underutilized in primary care practice. A clinical pharmacist was embedded at a primary care clinic to provide diabetes and hypertension management under a collaborative practice agreement with a supervising physician.

OBJECTIVES

This study will evaluate whether the presence of an embedded pharmacist in a primary care clinic affects prescribing patterns of novel, evidence-based diabetes therapies.

METHODS

We abstracted information on SGLT2 inhibitor and GLP-1 agonist prescribing patterns from 3 primary care clinics across 2 time periods as a single-center, retrospective cohort study. We used a difference-in-difference analysis to compare prescription rates and assess the impact of embedding the pharmacist into clinical practice. Prescriptions written by the pharmacist were excluded.

RESULTS

Across all 3 clinics, 1309 and 1489 patients were included in the pre-intervention and postintervention periods, respectively. The percentage of patients prescribed either an SGLT2 inhibitor or GLP-1 agonist, similar between both groups at baseline, rose to 11.6% in the nonintervention clinics and 15.0% in the intervention clinic. There was a statistically significant increase in SGLT2 inhibitor and GLP-1 agonist prescribing in the intervention clinic compared with nonintervention clinics (P = 0.034). This change in prescribing patterns appeared even greater when excluding prescribers who were not present during both pre-intervention and postintervention periods (P = 0.009).

CONCLUSION

The presence of a pharmacist is associated with increased SGLT2 inhibitor and GLP-1 agonist prescribing within a clinic, even in patients not seen directly by the pharmacist. These results suggest that an on-site clinical pharmacist providing care for patients with diabetes may indirectly influence the prescribing behavior of co-located primary care providers, increasing the adoption of novel noninsulin diabetic medications.

Association of Early Multiple Organ Dysfunction With Clinical and Functional Outcomes Over the Year Following Traumatic Brain Injury: A Transforming Research and Clinical Knowledge in Traumatic Brain Injury Study

OBJECTIVES

Traumatic brain injury is a leading cause of death and disability in the United States. While the impact of early multiple organ dysfunction syndrome has been studied in many critical care paradigms, the clinical impact of early multiple organ dysfunction syndrome in traumatic brain injury is poorly understood. We examined the incidence and impact of early multiple organ dysfunction syndrome on clinical, functional, and disability outcomes over the year following traumatic brain injury.

DESIGN

Retrospective cohort study.

SETTING

Patients enrolled in the Transforming Clinical Research and Knowledge in Traumatic Brain Injury study, an 18-center prospective cohort study of traumatic brain injury patients evaluated in participating level 1 trauma centers.

SUBJECTS

Adult (age > 17 yr) patients with moderate-severe traumatic brain injury (Glasgow Coma Scale < 13). We excluded patients with major extracranial injury (Abbreviated Injury Scale score ≥ 3).

INTERVENTIONS

Development of early multiple organ dysfunction syndrome, defined as a maximum modified Sequential Organ Failure Assessment score greater than 7 during the initial 72 hours following admission.

MEASUREMENTS AND MAIN RESULTS

The main outcomes were: hospital mortality, length of stay, 6-month functional and disability domains (Glasgow Outcome Scale-Extended and Disability Rating Scale), and 1-year mortality. Secondary outcomes included: ICU length of stay, 3-month Glasgow Outcome Scale-Extended, 3-month Disability Rating Scale, 1-year Glasgow Outcome Scale-Extended, and 1-year Disability Rating Scale. We examined 373 subjects with moderate-severe traumatic brain injury. The mean (sd) Glasgow Coma Scale in the emergency department was 5.8 (3.2), with 280 subjects (75%) classified as severe traumatic brain injury (Glasgow Coma Scale 3-8). Among subjects with moderate-severe traumatic brain injury, 252 (68%) developed early multiple organ dysfunction syndrome. Subjects that developed early multiple organ dysfunction syndrome had a 75% decreased odds of a favorable outcome (Glasgow Outcome Scale-Extended 5-8) at 6 months (adjusted odds ratio, 0.25; 95% CI, 0.12-0.51) and increased disability (higher Disability Rating Scale score) at 6 months (adjusted mean difference, 2.04; 95% CI, 0.92-3.17). Subjects that developed early multiple organ dysfunction syndrome experienced an increased hospital length of stay (adjusted mean difference, 11.4 d; 95% CI, 7.1-15.8), with a nonsignificantly decreased survival to hospital discharge (odds ratio, 0.47; 95% CI, 0.18-1.2).

CONCLUSIONS

Early multiple organ dysfunction following moderate-severe traumatic brain injury is common and independently impacts multiple domains (mortality, function, and disability) over the year following injury. Further research is necessary to understand underlying mechanisms, improve early recognition, and optimize management strategies.

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.

Association of Vasopressor Choice with Clinical and Functional Outcomes Following Moderate to Severe Traumatic Brain Injury: A TRACK-TBI Study

BACKGROUND

Early hypotension following moderate to severe traumatic brain injury (TBI) is associated with increased mortality and poor long-term outcomes. Current guidelines suggest the use of intravenous vasopressors to support blood pressure following TBI; however, guidelines do not specify vasopressor type, resulting in variation in clinical practice. Minimal data are available to guide clinicians on optimal early vasopressor choice to support blood pressure following TBI. Therefore, we conducted a multicenter study to examine initial vasopressor choice for the support of blood pressure following TBI and its association with clinical and functional outcomes after injury.

METHODS

We conducted a retrospective cohort study of patients enrolled in the transforming research and clinical knowledge in traumatic brain injury (TRACK-TBI) study, an 18-center prospective cohort study of patients with TBI evaluated in participating level I trauma centers. We examined adults with moderate to severe TBI (defined as Glasgow Coma Scale score < 13) who were admitted to the intensive care unit and received an intravenous vasopressor within 48 h of admission. The primary exposure was initial vasopressor choice (phenylephrine versus norepinephrine), and the primary outcome was 6-month Glasgow Outcomes Scale Extended (GOSE), with the following secondary outcomes: length of hospital stay, length of intensive care unit stay, in-hospital mortality, new requirement for dialysis, and 6-month Disability Rating Scale. Regression analysis was used to assess differences in outcomes between patients exposed to norepinephrine versus phenylephrine, with propensity weighting to address selection bias due to the nonrandom allocation of the treatment groups and patient dropout.

RESULTS

The final study sample included 156 patients, of whom 79 (51%) received norepinephrine, 69 (44%) received phenylephrine, and 8 (5%) received an alternate drug as their initial vasopressor. 121 (77%) of patients were men, with a mean age of 43.1 years. Of patients receiving norepinephrine as their initial vasopressor, 32% had a favorable outcome (GOSE 5-8), whereas 40% of patients receiving phenylephrine as their initial vasopressor had a favorable outcome. Compared with phenylephrine, exposure to norepinephrine was not significantly associated with improved 6-month GOSE (weighted odds ratio 1.40, 95% confidence interval 0.66-2.96, p = 0.37) or any secondary outcome.

CONCLUSIONS

The majority of patients with moderate to severe TBI received either phenylephrine or norepinephrine as first-line agents for blood pressure support following brain injury. Initial choice of norepinephrine, compared with phenylephrine, was not associated with improved clinical or functional outcomes.

Variational Disentanglement for Rare Event Modeling

Combining the increasing availability and abundance of healthcare data and the current advances in machine learning methods have created renewed opportunities to improve clinical decision support systems. However, in healthcare risk prediction applications, the proportion of cases with the condition (label) of interest is often very low relative to the available sample size. Though very prevalent in healthcare, such imbalanced classification settings are also common and challenging in many other scenarios. So motivated, we propose a variational disentanglement approach to semi-parametrically learn from rare events in heavily imbalanced classification problems. Specifically, we leverage the imposed extreme-distribution behavior on a latent space to extract information from low-prevalence events, and develop a robust prediction arm that joins the merits of the generalized additive model and isotonic neural nets. Results on synthetic studies and diverse real-world datasets, including mortality prediction on a COVID-19 cohort, demonstrate that the proposed approach outperforms existing alternatives.

Clinician Burnout Associated With Sex, Clinician Type, Work Culture, and Use of Electronic Health Records

Importance

Electronic health records (EHRs) are considered a potentially significant contributor to clinician burnout.

Objective

To describe the association of EHR usage, sex, and work culture with burnout for 3 types of clinicians at an academic medical institution.

Design, Setting, and Participants

This cross-sectional study of 1310 clinicians at a large tertiary care academic medical center analyzed EHR usage metrics for the month of April 2019 with results from a well-being survey from May 2019. Participants included attending physicians, advanced practice providers (APPs), and house staff from various specialties. Data were analyzed between March 2020 and February 2021.

Exposures

Clinician demographic characteristics, EHR metadata, and an institution-wide survey.

Main Outcomes and Measures

Study metrics included clinician demographic data, burnout score, well-being measures, and EHR usage metadata.

Results

Of the 1310 clinicians analyzed, 542 (41.4%) were men (mean [SD] age, 47.3 [11.6] years; 448 [82.7%] White clinicians, 52 [9.6%] Asian clinicians, and 21 [3.9%] Black clinicians) and 768 (58.6%) were women (mean [SD] age, 42.6 [10.3] years; 573 [74.6%] White clinicians, 105 [13.7%] Asian clinicians, and 50 [6.5%] Black clinicians). Women reported more burnout (survey score ≥50: women, 423 [52.0%] vs men, 258 [47.6%]; P = .008) overall. No significant differences in EHR usage were found by sex for multiple metrics of time in the EHR, metrics of volume of clinical encounters, or differences in products of clinical care. Multivariate analysis of burnout revealed that work culture domains were significantly associated with self-reported results for commitment (odds ratio [OR], 0.542; 95% CI, 0.427-0.688; P < .001) and work-life balance (OR, 0.643; 95% CI, 0.559-0.739; P < .001). Clinician sex significantly contributed to burnout, with women having a greater likelihood of burnout compared with men (OR, 1.33; 95% CI, 1.01-1.75; P = .04). An increased number of days spent using the EHR system was associated with less likelihood of burnout (OR, 0.966; 95% CI, 0.937-0.996; P = .03). Overall, EHR metrics accounted for 1.3% of model variance (P = .001) compared with work culture accounting for 17.6% of variance (P < .001).

Conclusions and Relevance

In this cross-sectional study, sex-based differences in EHR usage and burnout were found in clinicians. These results also suggest that local work culture factors may contribute more to burnout than metrics of EHR usage.

Identified themes of interactive visualizations overlayed onto EHR data: an example of improving birth center operating room efficiency

OBJECTIVE

While electronic health record (EHR) systems store copious amounts of patient data, aggregating those data across patients can be challenging. Visual analytic tools that integrate with EHR systems allow clinicians to gain better insight and understanding into clinical care and management. We report on our experience building Tableau-based visualizations and integrating them into our EHR system.

MATERIALS AND METHODS

Visual analytic tools were created as part of 12 clinician-initiated quality improvement projects. We built the visual analytic tools in Tableau and linked it within our EPIC environment. We identified 5 visual themes that spanned the various projects. To illustrate these themes, we choose 1 exemplary project which aimed to improve obstetric operating room efficiency.

RESULTS

Across our 12 projects, we identified 5 visual themes that are integral to project success: scheduling & optimization (in 11/12 projects); provider assessment (10/12); executive assessment (8/12); patient outcomes (7/12); and control and goal charts (2/12).

DISCUSSION

Many visualizations share common themes. Identification of these themes has allowed our internal team to be more efficient and directed in developing visualizations for future projects.

CONCLUSION

Organizing visual analytics into themes can allow informatics teams to more efficiently provide visual products to clinical collaborators.

Development of a Machine Learning Model Using Electronic Health Record Data to Identify Antibiotic Use Among Hospitalized Patients

IMPORTANCE

Comparisons of antimicrobial use among hospitals are difficult to interpret owing to variations in patient case mix. Risk-adjustment strategies incorporating larger numbers of variables haves been proposed as a method to improve comparisons for antimicrobial stewardship assessments.

OBJECTIVE

To evaluate whether variables of varying complexity and feasibility of measurement, derived retrospectively from the electronic health records, accurately identify inpatient antimicrobial use.

DESIGN, SETTING, AND PARTICIPANTS

Retrospective cohort study, using a 2-stage random forests machine learning modeling analysis of electronic health record data. Data were split into training and testing sets to measure model performance using area under the curve and absolute error. All adult and pediatric inpatient encounters from October 1, 2015, to September 30, 2017, at 2 community hospitals and 1 academic medical center in the Duke University Health System were analyzed. A total of 204 candidate variables were categorized into 4 tiers based on feasibility of measurement from the electronic health records.

MAIN OUTCOMES AND MEASURES

Antimicrobial exposure was measured at the encounter level in 2 ways: binary (ever or never) and number of days of therapy. Analyses were stratified by age (pediatric or adult), unit type, and antibiotic group.

RESULTS

The data set included 170 294 encounters and 204 candidate variables from 3 hospitals during the 3-year study period. Antimicrobial exposure occurred in 80 190 encounters (47%); 64 998 (38%) received 1 to 6 days of therapy, and 15 192 (9%) received 7 or more days of therapy. Two-stage models identified antimicrobial use with high fidelity (mean area under the curve, 0.85; mean absolute error, 1.0 days of therapy). Addition of more complex variables increased accuracy, with largest improvements occurring with inclusion of diagnosis information. Accuracy varied based on location and antibiotic group. Models underestimated the number of days of therapy of encounters with long lengths of stay.

CONCLUSIONS AND RELEVANCE

Models using variables derived from electronic health records identified antimicrobial exposure accurately. Future risk-adjustment strategies incorporating encounter-level information may make comparisons of antimicrobial use more meaningful for hospital antimicrobial stewardship assessments.

How and when informative visit processes can bias inference when using electronic health records data for clinical research

OBJECTIVE

Electronic health records (EHR) data have become a central data source for clinical research. One concern for using EHR data is that the process through which individuals engage with the health system, and find themselves within EHR data, can be informative. We have termed this process informed presence. In this study we use simulation and real data to assess how the informed presence can impact inference.

MATERIALS AND METHODS

We first simulated a visit process where a series of biomarkers were observed informatively and uninformatively over time. We further compared inference derived from a randomized control trial (ie, uninformative visits) and EHR data (ie, potentially informative visits).

RESULTS

We find that only when there is both a strong association between the biomarker and the outcome as well as the biomarker and the visit process is there bias. Moreover, once there are some uninformative visits this bias is mitigated. In the data example we find, that when the "true" associations are null, there is no observed bias.

DISCUSSION

These results suggest that an informative visit process can exaggerate an association but cannot induce one. Furthermore, careful study design can, mitigate the potential bias when some noninformative visits are included.

CONCLUSIONS

While there are legitimate concerns regarding biases that "messy" EHR data may induce, the conditions for such biases are extreme and can be accounted for.

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.

Development and Performance of a Clinical Decision Support Tool to Inform Resource Utilization for Elective Operations

IMPORTANCE

Hospitals ceased most elective procedures during the height of coronavirus disease 2019 (COVID-19) infections. As hospitals begin to recommence elective procedures, it is necessary to have a means to assess how resource intensive a given case may be.

OBJECTIVE

To evaluate the development and performance of a clinical decision support tool to inform resource utilization for elective procedures.

DESIGN, SETTING, AND PARTICIPANTS

In this prognostic study, predictive modeling was used on retrospective electronic health records data from a large academic health system comprising 1 tertiary care hospital and 2 community hospitals of patients undergoing scheduled elective procedures from January 1, 2017, to March 1, 2020. Electronic health records data on case type, patient demographic characteristics, service utilization history, comorbidities, and medications were and abstracted and analyzed. Data were analyzed from April to June 2020.

MAIN OUTCOMES AND MEASURES

Predicitons of hospital length of stay, intensive care unit length of stay, need for mechanical ventilation, and need to be discharged to a skilled nursing facility. These predictions were generated using the random forests algorithm. Predicted probabilities were turned into risk classifications designed to give assessments of resource utilization risk.

RESULTS

Data from the electronic health records of 42 199 patients from 3 hospitals were abstracted for analysis. The median length of stay was 2.3 days (range, 1.3-4.2 days), 6416 patients (15.2%) were admitted to the intensive care unit, 1624 (3.8%) received mechanical ventilation, and 2843 (6.7%) were discharged to a skilled nursing facility. Predictive performance was strong with an area under the receiver operator characteristic ranging from 0.76 to 0.93. Sensitivity of the high-risk and medium-risk groupings was set at 95%. The negative predictive value of the low-risk grouping was 99%. We integrated the models into a daily refreshing Tableau dashboard to guide decision-making.

CONCLUSIONS AND RELEVANCE

The clinical decision support tool is currently being used by surgical leadership to inform case scheduling. This work shows the importance of a learning health care environment in surgical care, using quantitative modeling to guide decision-making.

Evaluation of associations between asthma exacerbations and distance to roadways using geocoded electronic health records data

Background

Asthma exacerbations in children often require medications, urgent care, and hospitalization. Multiple environmental triggers have been associated with asthma exacerbations, including particulate matter 2.5 (PM2.5) and ozone, which are primarily generated by motor vehicle exhaust. There is mixed evidence as to whether proximity to highways increases risk of asthma exacerbations.

Methods

To evaluate the impact of highway proximity, we assessed the association between asthma exacerbations and the distance of child’s primary residence to two types of roadways in Durham County, North Carolina, accounting for other patient-level factors. We abstracted data from the Duke University Health System electronic health record (EHR), identifying 6208 children with asthma between 2014 and 2019. We geocoded each child’s distance to roadways (both 35 MPH+ and 55 MPH+). We classified asthma exacerbation severity into four tiers and fitted a recurrent event survival model to account for multiple exacerbations.

Results

There was a no observed effect of residential distance from 55+ MPH highway (Hazard Ratio: 0.98 (95% confidence interval: 0.94, 1.01)) and distance to 35+ MPH roadway (Hazard Ratio: 0.98 (95% confidence interval: 0.83, 1.15)) and any asthma exacerbation. Even those children living closest to highways (less 0.25 miles) had no increased risk of exacerbation. These results were consistent across different demographic strata.

Conclusions

While the results were non-significant, the characteristics of the study sample – namely farther distance to roadways and generally good ambient environmental pollution may contribute to the lack of effect. Compared to previous studies, which often relied on self-reported measures, we were able to obtain a more objective assessment of outcomes. Overall, this work highlights the opportunity to use EHR data to study environmental impacts on disease.

Supplementary Information

Supplementary information accompanies this paper at 10.1186/s12889-020-09731-0.

Unanticipated Respiratory Compromise and Unplanned Intubations on General Medical and Surgical Floors

BACKGROUND

Unanticipated respiratory compromise that lead to unplanned intubations is a known phenomenon in hospitalized patients. Most events occur in patients at high risk in well-monitored units; less is known about the incidence, risk factors, and trajectory of patients thought at low risk on lightly monitored general care wards. The aims of our study were to quantify demographic and clinical characteristics associated with unplanned intubations on general care floors and to analyze the medications administered, monitoring strategies, and vital-sign trajectories before the event.

METHODS

We performed a multicenter retrospective cohort study of hospitalized subjects on the general floor who had unanticipated, unplanned intubations on general care floors from August 2014 to February 2018.

RESULTS

We identified 448 unplanned intubations. The incidence rate was 0.420 per 1,000 bed-days (95% CI 0.374-0.470) in the academic hospital and was 0.430 (95% CI 0.352-0.520) and 0.394 per 1,000 bed-days (95% CI 0.301-0.506) at our community hospitals. Extrapolating these rates to total hospital admissions in the United States, we estimate 64,000 events annually. The mortality rate was 49.1%. Within 12 h preceding the event, 35.3% of the subjects received opiates. All received vital-sign assessments. Most were monitored with pulse oximetry. In contrast, 2.5% were on cardiac telemetry, and only 4 subjects used capnography; 53.7% showed significant vital-sign changes in the 24 h before the event. However, 46.3% had no significant change in any vital signs.

CONCLUSIONS

Our study showed unanticipated respiratory compromise that required an unplanned intubation of subjects on the general care floor, although not common, carried a high mortality. Besides pulse oximetry and routine vital-sign assessments, very little monitoring was in use. A significant portion of the subjects had no vital-sign abnormalities leading up to the event. Further research is needed to determine the phenotype of the different etiologies of unexpected acute respiratory failure to identify better risk stratification and monitoring strategies.

Evaluation of ML-Based Clinical Decision Support Tool to Replace an Existing Tool in an Academic Health System: Lessons Learned

There is increasing application of machine learning tools to problems in healthcare, with an ultimate goal to improve patient safety and health outcomes. When applied appropriately, machine learning tools can augment clinical care provided to patients. However, even if a model has impressive performance characteristics, prospectively evaluating and effectively implementing models into clinical care remains difficult. The primary objective of this paper is to recount our experiences and challenges in comparing a novel machine learning-based clinical decision support tool to legacy, non-machine learning tools addressing potential safety events in the hospitals and to summarize the obstacles which prevented evaluation of clinical efficacy of tools prior to widespread institutional use. We collected and compared safety events data, specifically patient falls and pressure injuries, between the standard of care approach and machine learning (ML)-based clinical decision support (CDS). Our assessment was limited to performance of the model rather than the workflow due to challenges in directly comparing both approaches. We did note a modest improvement in falls with ML-based CDS; however, it was not possible to determine that overall improvement was due to model characteristics.

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.

Five analytic challenges in working with electronic health records data to support clinical trials with some solutions

Electronic health records data are becoming a key data resource in clinical research. Owing to issues of data efficiency, electronic health records data are being used for clinical trials. This includes both large-scale pragmatic trails and smaller—more focused—point-of-care trials. While electronic health records data open up a number of scientific opportunities, they also present a number of analytic challenges. This article discusses five particular challenges related to organizing electronic health records data for analytic purposes. These are as follows: (1) data are not organized for research purposes, (2) data are both densely and irregularly observed, (3) we don’t have all data elements we may want or need, (4) data are both cross-sectional and longitudinal, and (5) data may be informatively observed. While laying out these challenges, the article notes how many of these challenges can be addressed by careful and thoughtful study design as well as by integration of clinicians and informaticians into the analytic team.