Pediatric Asthma Exacerbations: 14-Day Emergency Department Return Visit Risk Factors.

BACKGROUND: Asthma, the most common chronic disease of childhood, can affect a child's physical and mental health and social and emotional development. OBJECTIVE: The aim of this study was to identify factors associated with emergency department (ED) return visits for asthma exacerbations within 14 days of an initial visit. METHODS: This was a retrospective review from Cerner Real-World Data for patients aged from 5 to 18 years and seen at an ED for an asthma exacerbation and discharged home at the index ED visit. Asthma visits were defined as encounters in which a patient was diagnosed with asthma and a beta agonist, anticholinergic, or systemic steroid was ordered or prescribed at that encounter. Return visits were ED visits for asthma within 14 days of an index ED visit. Data, including demographic characteristics, ED evaluation and treatment, health care utilization, and medical history, were collected. Data were analyzed via logistic regression mixed effects model. RESULTS: A total of 80,434 index visits and 17,443 return visits met inclusion criteria. Prior ED return visits in the past year were associated with increased odds of a return visit (odds ratio [OR] 2.12; 95% CI 2.07-2.16). History of pneumonia, a concomitant diagnosis of pneumonia, and fever were associated with increased odds of a return visit (OR 1.19; 95% CI 1.10-1.29; OR 1.15; 95% CI 1.04-1.28; OR 1.20; 95% CI 1.11-1.30, respectively). CONCLUSIONS: Several variables seem to be associated with statistically significant increased odds of ED return visits. These findings indicate a potentially identifiable population of at-risk patients who may benefit from additional evaluation, planning, or education prior to discharge.

Combined use of Donepezil and Memantine increases the probability of five-year survival of Alzheimer's disease patients.

BACKGROUND: Alzheimer's disease (AD) is the most common neurodegenerative disease. Studying the effects of drug treatments on multiple health outcomes related to AD could be beneficial in demonstrating which drugs reduce the disease burden and increase survival. METHODS: We conducted a comprehensive causal inference study implementing doubly robust estimators and using one of the largest high-quality medical databases, the Oracle Electronic Health Records (EHR) Real-World Data. Our work was focused on the estimation of the effects of the two common Alzheimer's disease drugs, Donepezil and Memantine, and their combined use on the five-year survival since initial diagnosis of AD patients. Also, we formally tested for the presence of interaction between these drugs. RESULTS: Here, we show that the combined use of Donepezil and Memantine significantly elevates the probability of five-year survival. In particular, their combined use increases the probability of five-year survival by 0.050 (0.021, 0.078) (6.4%), 0.049 (0.012, 0.085), (6.3%), 0.065 (0.035, 0.095) (8.3%) compared to no drug treatment, the Memantine monotherapy, and the Donepezil monotherapy respectively. We also identify a significant beneficial additive drug-drug interaction effect between Donepezil and Memantine of 0.064 (0.030, 0.098). CONCLUSIONS: Based on our findings, adopting combined treatment of Memantine and Donepezil could extend the lives of approximately 303,000 people with AD living in the USA to be beyond five-years from diagnosis. If these patients instead have no drug treatment, Memantine monotherapy or Donepezil monotherapy they would be expected to die within five years.

Alzheimer's disease is the most common type of dementia, affecting millions of people worldwide. In this study, we investigated the effects of two drugs commonly prescribed to people with Alzheimer's disease called Donepezil and Memantine to see whether they had an impact on when people died. We found that the combined use of Donepezil and Memantine significantly increased the probability of a person surviving five years compared to no drug treatment or treatment with Donepezil or Memantine alone. Our results suggest that the lives of many Alzheimer's patients in the USA who are currently on no drug treatment or just Donepezil or Memantine could be extended if they were treated with both drugs simultaneously.

Congenital anomalies and predisposition to severe COVID-19 among pediatric patients in the United States.

BACKGROUND AND OBJECTIVE: Congenital heart defects are known to be associated with increased odds of severe COVID-19. Congenital anomalies affecting other body systems may also be associated with poor outcomes. This study is an exhaustive assessment of congenital anomalies and odds of severe COVID-19 in pediatric patients. METHODS: Data were retrieved from the COVID-19 dataset of Cerner® Real-World Data for encounters from March 2020 to February 2022. Prior to matching, the data consisted of 664,523 patients less than 18 years old and 927,805 corresponding encounters with COVID-19 from 117 health systems across the United States. One-to-one propensity score matching was performed, and a cumulative link mixed-effects model with random intercepts for health system and patients was built to assess corresponding associations. RESULTS: All congenital anomalies were associated with worse COVID-19 outcomes, with the strongest association observed for cardiovascular anomalies (odds ratio [OR], 3.84; 95% CI, 3.63-4.06) and the weakest association observed for anomalies affecting the eye/ear/face/neck (OR, 1.16; 95% CI, 1.03-1.31). CONCLUSIONS AND RELEVANCE: Congenital anomalies are associated with greater odds of experiencing severe symptoms of COVID-19. In addition to congenital heart defects, all other birth defects may increase the odds for more severe COVID-19. IMPACT: All congenital anomalies are associated with increased odds of severe COVID-19. This study is the largest and among the first to investigate birth defects across all body systems. The multicenter large data and analysis demonstrate the increased odds of severe COVID19 in pediatric patients with congenital anomalies affecting any body system. These data demonstrate that all children with birth defects are at increased odds of more severe COVID-19, not only those with heart defects. This should be taken into consideration when optimizing prevention and intervention resources within a hospital.

Obesity Heterogeneity by Neighborhood Context in a Largely Latinx Sample.

Neighborhood socioeconomic context where Latinx children live may influence body weight status. Los Angeles County and Orange County of Southern California both are on the list of the top ten counties with the largest Latinx population in the USA. This heterogeneity allowed us to estimate differential impacts of neighborhood environment on children's body mass index z-scores by race/ethnicity using novel methods and a rich data source. We geocoded pediatric electronic medical record data from a predominantly Latinx sample and characterized neighborhoods into unique residential contexts using latent profile modeling techniques. We estimated multilevel linear regression models that adjust for comorbid conditions and found that a child's place of residence independently associates with higher body mass index z-scores. Interactions further reveal that Latinx children living in Middle-Class neighborhoods have higher BMI z-scores than Asian and Other Race children residing in the most disadvantaged communities. Our findings underscore the complex relationship between community racial/ethnic composition and neighborhood socioeconomic context on body weight status during childhood.

Artificial intelligence for nailfold capillaroscopy analyses - a proof of concept application in juvenile dermatomyositis.

BACKGROUND: Biomarkers for idiopathic inflammatory myopathies are difficult to identify and may involve expensive laboratory tests. We assess the potential for artificial intelligence (AI) to differentiate children with juvenile dermatomyositis (JDM) from healthy controls using nailfold capillaroscopy (NFC) images. We also assessed the potential of NFC images to reflect the range of disease activity with JDM. METHODS: A total of 1,120 NFC images from 111 children with active JDM, diagnosed between 1990 and 2020, and 321 NFC images from 31 healthy controls were retrieved from the CureJM JDM Registry. We built a lightweight and explainable deep neural network model called NFC-Net. Images were downscaled by interpolation techniques to reduce the computational cost. RESULTS: NFC-Net achieved high performance in differentiating patients with JDM from controls, with an area under the ROC curve (AUROC) of 0.93 (0.84, 0.99) and accuracy of 0.91 (0.82, 0.92). With sensitivity (0.85) and specificity (0.90) resulted in model precision of 0.95. The AUROC and accuracy for predicting clinical disease activity from inactivity were 0.75 (0.61, 0.81) and 0.74 (0.65, 0.79). CONCLUSION: The good performance of the NFC-Net demonstrates that NFC images are sufficient for detecting often unrecognized JDM disease activity, providing a reliable indicator of disease status. IMPACT: Proposed NFC-Net can accurately predict children with JDM from healthy controls using nailfold capillaroscopy (NFC) images. Additionally, it predicts the scores to JDM disease activity versus no activity. Equipped with gradients, NFC-Net is explainable and gives visual information beside the reported accuracies. NFC-Net is computationally efficient since it is applied to substantially downscaled NFC images. Furthermore, the model can be wrapped within an edge-based device like a mobile application that is accessible to both clinicians and patients.

A causal inference study: The impact of the combined administration of Donepezil and Memantine on decreasing hospital and emergency department visits of Alzheimer's disease patients.

Alzheimer's disease is the most common type of dementia that currently affects over 6.5 million people in the U.S. Currently there is no cure and the existing drug therapies attempt to delay the mental decline and improve cognitive abilities. Two of the most commonly prescribed such drugs are Donepezil and Memantine. We formally tested and confirmed the presence of a beneficial drug-drug interaction of Donepezil and Memantine using a causal inference analysis. We applied doubly robust estimators to one of the largest and high-quality medical databases to estimate the effect of two commonly prescribed Alzheimer's disease (AD) medications, Donepezil and Memantine, on the average number of hospital or emergency department visits per year among patients diagnosed with AD. Our results show that, compared to the absence of medication scenario, the Memantine monotherapy, and the Donepezil monotherapy, the combined use of Donepezil and Memantine treatment significantly reduces the average number of hospital or emergency department visits per year by 0.078 (13.8%), 0.144 (25.5%), and 0.132 days (23.4%), respectively. The assessed decline in the average number of hospital or emergency department visits per year is consequently associated with a substantial reduction in medical costs. As of 2022, according to the Alzheimer's Disease Association, there were over 6.5 million individuals aged 65 and older living with AD in the US alone. If patients who are currently on no drug treatment or using either Donepezil or Memantine alone were switched to the combined used of Donepezil and Memantine therapy, the average number of hospital or emergency department visits could decrease by over 613 thousand visits per year. This, in turn, would lead to a remarkable reduction in medical expenses associated with hospitalization of AD patients in the US, totaling over 940 million dollars per year.

Chronic Neurological Disorders and Predisposition to Severe COVID-19 in Pediatric Patients in the United States.

BACKGROUND: We investigated the association between chronic pediatric neurological conditions and the severity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). METHODS: This matched retrospective case-control study includes patients (n = 71,656) with chronic complex neurological disorders under 18 years of age, with laboratory-confirmed diagnosis of COVID-19 or a diagnostic code indicating infection or exposure to SARS-CoV-2, from 103 health systems in the United States. The primary outcome was the severity of coronavirus disease 2019 (COVID-19), which was classified as severe (invasive oxygen therapy or death), moderate (noninvasive oxygen therapy), or mild/asymptomatic (no oxygen therapy). A cumulative link mixed effects model was used for this study. RESULTS: In this study, a cumulative link mixed effects model (random intercepts for health systems and patients) showed that the following classes of chronic neurological disorders were associated with higher odds of severe COVID-19: muscular dystrophies and myopathies (OR = 3.22; 95% confidence interval [CI]: 2.73 to 3.84), chronic central nervous system disorders (OR = 2.82; 95% CI: 2.67 to 2.97), cerebral palsy (OR = 1.97; 95% CI: 1.85 to 2.10), congenital neurological disorders (OR = 1.86; 95% CI: 1.75 to 1.96), epilepsy (OR = 1.35; 95% CI: 1.26 to 1.44), and intellectual developmental disorders (OR = 1.09; 95% CI: 1.003 to 1.19). Movement disorders were associated with lower odds of severe COVID-19 (OR = 0.90; 95% CI: 0.81 to 0.99). CONCLUSIONS: Pediatric patients with chronic neurological disorders are at higher odds of severe COVID-19. Movement disorders were associated with lower odds of severe COVID-19.

A pilot study to improve provider adherence to NAEPP guidelines.

The prevalence and morbidity of Asthma in the United States has increased since the 1991 National Asthma Education and Prevention Program (NAEPP) and updated Expert Panel Report -3 (EPR-3) guidelines in 2007 were published. To improve provider adherence to the NAEPP EPR-3 guidelines Children's Hospital of Orange County (CHOC) in California integrated the HealtheIntentSM Pediatric Asthma Registry (PAR) into the electronic medical record (EMR) in 2015. METHODS: A serial cross-sectional design was used to compare provider management of CHOC MediCal asthma patients before 2014 (N = 6606) and after 2018 (N = 6945) integration of the Registry with NAEPP guidelines into the EMR. Four provider adherence measures (Asthma Control Test [ACT], Asthma Action Plan [AAP], inhaled corticosteroids [ICS] and spacers) were evaluated using General Linear Mixed Models and Chi square. FINDINGS: In 2018, patients were more likely to receive an ACT, (OR = 14.95, 95% CI 12.67, 17.65, p < .001), AAP (OR = 12.70, 95% CI 11.10, 14.54, p < .001), ICS (OR = 1.85, 95% CI 8.52, 14.54, p < .001) and spacer (OR = 1.45, 95% CI 1.31, 1.6, p < .001) compared to those in 2014. DISCUSSION: The pilot study showed integration of the Pediatric Asthma Registry into the EMR, as a computer decision support tool that was an effective intervention to increase provider adherence to NAEPP guidelines. Ongoing monitoring and education are needed to promote and sustain provider behavioral change. Additional research to include multi-sites and decreased time between evaluation years is recommended. APPLICATION TO PRACTICE: Can be used for excellent health policy decision making as a direct impact on patient care and outcomes, by improving provider adherence to the NAEPP guidelines.

Neighborhood disadvantage and pediatric inpatient opioid prescription patterns.

BACKGROUND: To explore the role of children's residential environment on opioid prescribing patterns in a predominantly Latinx sample. METHODS: We connected geocoded data from electronic medical records in a diverse sample of pediatric patients to neighborhood environments constructed using latent profile modeling techniques. We then estimated a series of multilevel models to determine whether opioid prescribing patterns vary by residential context. RESULTS: A stepwise pattern exists between neighborhood disadvantage and pediatric opioid prescription patterns, such that higher levels of disadvantage associate with a greater likelihood of opioid prescription, independent of the patient's individual profile. CONCLUSION: In a largely Latinx sample of children, the neighborhood in which a child lives influences whether or not they will receive opioids. Considering the differences in patient residential environment may reduce variation in opioid dispensing rates among pediatric patients.

Caregiver Satisfaction with Emergency Department Care for Pediatric Patients with Neurodevelopmental Disorders.

OBJECTIVE: Children with neurodevelopmental disorders (NDDs) often encounter increased adversity when navigating the health care system. In this study, we explored the pediatric emergency department (PED) experience for patients with NDDs and their caregivers compared with that of patients without NDDs. METHODS: Data for this study were obtained from National Research Corporation patient experience survey questionnaires and electronic medical record (EMR) data for patients presenting to a PED between May 2018 and September 2019. ED satisfaction was determined by the top-box approach; ED ratings of 9/10 or 10/10 were considered to reflect high ED satisfaction. Demographics, Emergency Severity Index, ED length of stay, time from arrival to triage, time to provider assessment, and diagnoses were extracted from the EMR. Patients with NDDs were identified based on International Classification of Diseases, Tenth Revision codes; patients with intellectual disabilities, pervasive and specific developmental disorders, or attention-deficit/hyperactivity disorders were included in the NDD cohort. One-to-one propensity score matching between patients with and without NDDs was performed, and a multivariable logistic regression model was built on the matched cohort. RESULTS: Patients with NDDs represented over 7% of survey respondents. Matching was successful for 1162 patients with NDDs (99.5%), resulting in a matched cohort sample size of 2324. Caregivers of patients with NDDs had 25% lower odds of reporting high ED satisfaction (95% confidence interval [CI], 0.62-0.91, p = 0.004). CONCLUSION: Caregivers of patients with NDDs make up a significant proportion of survey respondents and are more likely to rate the ED poorly than caregivers of patients without NDDs. This suggests an opportunity for targeted interventions in this population to improve patient care and experience.

Birth Weight and Gestational Age as Modifiers of Rehospitalization after Neonatal Intensive Care Unit Admission.

OBJECTIVE: This study aimed to assess interaction effects between gestational age and birth weight on 30-day unplanned hospital readmission following discharge from the neonatal intensive care unit (NICU). STUDY DESIGN: This is a retrospective study that uses the study site's Children's Hospitals Neonatal Database and electronic health records. Population included patients discharged from a NICU between January 2017 and March 2020. Variables encompassing demographics, gestational age, birth weight, medications, maternal data, and surgical procedures were controlled for. A statistical interaction between gestational age and birth weight was tested for statistical significance. RESULTS: A total of 2,307 neonates were included, with 7.2% readmitted within 30 days of discharge. Statistical interaction between birth weight and gestational age was statistically significant, indicating that the odds of readmission among low birthweight premature patients increase with increasing gestational age, whereas decrease with increasing gestational age among their normal or high birth weight peers. CONCLUSION: The effect of gestational age on odds of hospital readmission is dependent on birth weight. KEY POINTS: · Population included patients discharged from a NICU between January 2017 and March 2020.. · A total of 2,307 neonates were included, with 7.2% readmitted within 30 days of discharge.. · The effect of gestational age on odds of hospital readmission is dependent on birth weight..

The von Willebrand Factor Antigen Reflects the Juvenile Dermatomyositis Disease Activity Score.

OBJECTIVE: This study determined if an accessible, serologic indicator of vascular disease activity, the von Willebrand factor antigen (vWF:Ag), was useful to assess disease activity in children with juvenile dermatomyositis (JDM), a rare disease, but the most common of the pediatric inflammatory myopathies. METHODS: A total of 305 children, median age 10 years, 72.5% female, 76.5% white, with definite/probable JDM at diagnosis, were enrolled in the Ann & Robert H. Lurie Cure JM Juvenile Myositis Repository, a longitudinal database. Disease Activity Score (DAS) and vWF:Ag data were obtained at each visit. These data were analyzed using generalized estimating equation (GEE) models (both linear and logistic) to determine if vWF:Ag reflects disease severity in children with JDM. A secondary analysis was performed for untreated active JDM to exclude the effect of medications on vWF:Ag. RESULT: The vWF:Ag test was elevated in 25% of untreated JDM. We found that patients with elevated vWF:Ag had a 2.55-fold higher DAS total (CI95: 1.83-3.27, p < 0.001). Patients with difficulty swallowing had 2.57 higher odds of elevated vWF:Ag (CI95: 1.5-4.38, p < 0.001); those with more generalized skin involvement had 2.58-fold higher odds of elevated vWF:Ag (CI95: 1.27-5.23, p = 0.006); and those with eyelid peripheral blood vessel dilation had 1.32-fold higher odds of elevated vWF:Ag (CI95: 1.01-1.72, p = 0.036). Untreated JDM with elevated vWF:Ag had more muscle weakness and higher muscle enzymes, neopterin and erythrocyte sedimentation rate compared to JDM patients with a normal vWF:Ag. CONCLUSION: vWF:Ag elevation is a widely accessible concomitant of active disease in 25% of JDM.

An Introduction to Artificial Intelligence in Developmental and Behavioral Pediatrics.

ABSTRACT: Technological breakthroughs, together with the rapid growth of medical information and improved data connectivity, are creating dramatic shifts in the health care landscape, including the field of developmental and behavioral pediatrics. While medical information took an estimated 50 years to double in 1950, by 2020, it was projected to double every 73 days. Artificial intelligence (AI)-powered health technologies, once considered theoretical or research-exclusive concepts, are increasingly being granted regulatory approval and integrated into clinical care. In the United States, the Food and Drug Administration has cleared or approved over 160 health-related AI-based devices to date. These trends are only likely to accelerate as economic investment in AI health care outstrips investment in other sectors. The exponential increase in peer-reviewed AI-focused health care publications year over year highlights the speed of growth in this sector. As health care moves toward an era of intelligent technology powered by rich medical information, pediatricians will increasingly be asked to engage with tools and systems underpinned by AI. However, medical students and practicing clinicians receive insufficient training and lack preparedness for transitioning into a more AI-informed future. This article provides a brief primer on AI in health care. Underlying AI principles and key performance metrics are described, and the clinical potential of AI-driven technology together with potential pitfalls is explored within the developmental and behavioral pediatric health context.

Epidemiology of Neonatal COVID-19 in the United States.

OBJECTIVES: Data on coronavirus disease 2019 (COVID-19) infections in neonates are limited. We aimed to identify and describe the incidence, presentation, and clinical outcomes of neonatal COVID-19. METHODS: Over 1 million neonatal encounters at 109 United States health systems, from March 2020 to February 2021, were extracted from the Cerner Real World Database. COVID-19 diagnosis was assessed using severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) laboratory tests and diagnosis codes. Incidence of COVID-19 per 100 000 encounters was estimated. RESULTS: COVID-19 was diagnosed in 918 (0.1%) neonates (91.1 per 100 000 encounters [95% confidence interval 85.3-97.2]). Of these, 71 (7.7%) had severe infection (7 per 100 000 [95% confidence interval 5.5-8.9]). Median time to diagnosis was 14.5 days from birth (interquartile range 3.1-24.2). Common signs of infection were tachypnea and fever. Those with severe infection were more likely to receive respiratory support (50.7% vs 5.2%, P < .001). Severely ill neonates received analgesia (38%), antibiotics (33.8%), anticoagulants (32.4%), corticosteroids (26.8%), remdesivir (2.8%), and COVID-19 convalescent plasma (1.4%). A total of 93.6% neonates were discharged home after care, 1.1% were transferred to another hospital, and discharge disposition was unknown for 5.2%. One neonate (0.1%) with presentation suggestive of multisystem inflammatory syndrome in children died after 11 days of hospitalization. CONCLUSIONS: Most neonates infected with SARS-CoV-2 were asymptomatic or developed mild illness without need for respiratory support. Some had severe illness requiring treatment of COVID-19 with remdesivir and COVID-19 convalescent plasma. SARS-CoV-2 infection in neonates, though rare, may result in severe disease.

Timely orchiopexy by 18 months of age: Are we meeting the standards defined by the 2014 AUA guidelines?

BACKGROUND: Cryptorchidism is one of the most common reasons for pediatric urology referral and one of the few pediatric urologic conditions in which there are established AUA guidelines that recommend orchiopexy be performed before 18 months of age. While access to timely orchiopexy has been studied previously, there is no current study with data from a national clinical database evaluating timely orchiopexy after the AUA guidelines were published. Additionally, prior studies on delayed orchiopexy may have included patients with an ascended testis, which is a distinct population from those with true undescended testicles. OBJECTIVES: To evaluate in a national, clinical database if timely orchiopexy improved after the AUA guidelines were published in 2014. In particular, we aim to evaluate a younger group of patients, 0-5 years of age, in an effort to account for potential ascending testes. STUDY DESIGN: Using Cerner Real-World Data™, a national, de-identified database of 153 million individuals, we analyzed pediatric patients undergoing orchiopexy in the United States from 2000 to 2021. We included males 0-18 years old and further focused on the subset 0-5 years. Primary outcome was timely orchiopexy, defined as age at orchiopexy less than 18 months. Predictor variables included race, ethnicity and insurance status. Statistical analyses were performed using logistic regression. RESULTS: Of the total 17,012 individuals identified as undergoing orchiopexy, 9274 were ages 0-5 at the time of surgery. Comparing time periods pre and post AUA guidelines (2000-2014 versus 2015-2021), we found a significant difference in the proportion of timely orchiopexy (51% versus 56%, respectively; p < 0.0001) (Figure). In multivariable analyses, Hispanic (OR = 0.65, p < 0.0001), African American (OR = 0.74, p < 0.0001), and Native American males (OR = 0.66, p = 0.008) were less likely to have timely orchiopexy compared to non-Hispanic White males. Individuals without insurance (OR = 0.81, p = 0.03) or with public insurance (OR = 0.88, p = 0.02) were less likely to have timely orchiopexy as compared to those with private insurance. CONCLUSIONS: Nearly a decade after publication of the AUA cryptorchidism guidelines, a large proportion of patients are still not undergoing orchiopexy by 18 months of age. This is the first study to show that timely orchiopexy has improved among patients 0-5 years, but the majority of patients are still not undergoing timely orchiopexy. Health disparities were apparent among Hispanic, African American, Native American, and uninsured males, highlighting the need for further progress in access to pediatric surgical care.

A comparative study on deep learning models for text classification of unstructured medical notes with various levels of class imbalance.

BACKGROUND: Discharge medical notes written by physicians contain important information about the health condition of patients. Many deep learning algorithms have been successfully applied to extract important information from unstructured medical notes data that can entail subsequent actionable results in the medical domain. This study aims to explore the model performance of various deep learning algorithms in text classification tasks on medical notes with respect to different disease class imbalance scenarios. METHODS: In this study, we employed seven artificial intelligence models, a CNN (Convolutional Neural Network), a Transformer encoder, a pretrained BERT (Bidirectional Encoder Representations from Transformers), and four typical sequence neural networks models, namely, RNN (Recurrent Neural Network), GRU (Gated Recurrent Unit), LSTM (Long Short-Term Memory), and Bi-LSTM (Bi-directional Long Short-Term Memory) to classify the presence or absence of 16 disease conditions from patients' discharge summary notes. We analyzed this question as a composition of 16 binary separate classification problems. The model performance of the seven models on each of the 16 datasets with various levels of imbalance between classes were compared in terms of AUC-ROC (Area Under the Curve of the Receiver Operating Characteristic), AUC-PR (Area Under the Curve of Precision and Recall), F1 Score, and Balanced Accuracy as well as the training time. The model performances were also compared in combination with different word embedding approaches (GloVe, BioWordVec, and no pre-trained word embeddings). RESULTS: The analyses of these 16 binary classification problems showed that the Transformer encoder model performs the best in nearly all scenarios. In addition, when the disease prevalence is close to or greater than 50%, the Convolutional Neural Network model achieved a comparable performance to the Transformer encoder, and its training time was 17.6% shorter than the second fastest model, 91.3% shorter than the Transformer encoder, and 94.7% shorter than the pre-trained BERT-Base model. The BioWordVec embeddings slightly improved the performance of the Bi-LSTM model in most disease prevalence scenarios, while the CNN model performed better without pre-trained word embeddings. In addition, the training time was significantly reduced with the GloVe embeddings for all models. CONCLUSIONS: For classification tasks on medical notes, Transformer encoders are the best choice if the computation resource is not an issue. Otherwise, when the classes are relatively balanced, CNNs are a leading candidate because of their competitive performance and computational efficiency.

Association of Congenital and Acquired Cardiovascular Conditions With COVID-19 Severity Among Pediatric Patients in the US.

Importance: Identifying the associations between severe COVID-19 and individual cardiovascular conditions in pediatric patients may inform treatment. Objective: To assess the association between previous or preexisting cardiovascular conditions and severity of COVID-19 in pediatric patients. Design, Setting, and Participants: This retrospective cohort study used data from a large, multicenter, electronic health records database in the US. The cohort included patients aged 2 months to 17 years with a laboratory-confirmed diagnosis of COVID-19 or a diagnosis code indicating infection or exposure to SARS-CoV-2 at 85 health systems between March 1, 2020, and January 31, 2021. Exposures: Diagnoses for 26 cardiovascular conditions between January 1, 2015, and December 31, 2019 (before infection with SARS-CoV-2). Main Outcomes and Measures: The main outcome was severe COVID-19, defined as need for supplemental oxygen or in-hospital death. Mixed-effects, random intercept logistic regression modeling assessed the significance and magnitude of associations between 26 cardiovascular conditions and COVID-19 severity. Multiple comparison adjustment was performed using the Benjamini-Hochberg false discovery rate procedure. Results: The study comprised 171 416 pediatric patients; the median age was 8 years (IQR, 2-14 years), and 50.28% were male. Of these patients, 17 065 (9.96%) had severe COVID-19. The random intercept model showed that the following cardiovascular conditions were associated with severe COVID-19: cardiac arrest (odds ratio [OR], 9.92; 95% CI, 6.93-14.20), cardiogenic shock (OR, 3.07; 95% CI, 1.90-4.96), heart surgery (OR, 3.04; 95% CI, 2.26-4.08), cardiopulmonary disease (OR, 1.91; 95% CI, 1.56-2.34), heart failure (OR, 1.82; 95% CI, 1.46-2.26), hypotension (OR, 1.57; 95% CI, 1.38-1.79), nontraumatic cerebral hemorrhage (OR, 1.54; 95% CI, 1.24-1.91), pericarditis (OR, 1.50; 95% CI, 1.17-1.94), simple biventricular defects (OR, 1.45; 95% CI, 1.29-1.62), venous embolism and thrombosis (OR, 1.39; 95% CI, 1.11-1.73), other hypertensive disorders (OR, 1.34; 95% CI, 1.09-1.63), complex biventricular defects (OR, 1.33; 95% CI, 1.14-1.54), and essential primary hypertension (OR, 1.22; 95% CI, 1.08-1.38). Furthermore, 194 of 258 patients (75.19%) with a history of cardiac arrest were younger than 12 years. Conclusions and Relevance: The findings suggest that some previous or preexisting cardiovascular conditions are associated with increased severity of COVID-19 among pediatric patients in the US and that morbidity may be increased among individuals children younger than 12 years with previous cardiac arrest.

Cerner real-world data (CRWD) - A de-identified multicenter electronic health records database.

Cerner Real-World Data TM (CRWD) is a de-identified big data source of multicenter electronic health records. Cerner Corporation secured appropriate data use agreements and permissions from more than 100 health systems in the United States contributing to the database as of March 2022. A subset of the database was extracted to include data from only patients with SARS-CoV-2 infections and is referred to as the Cerner COVID-19 Dataset. The December 2021 version of CRWD consists of 100 million patients and 1.5 billion encounters across all care settings. There are 2.3 billion, 2.9 billion, 486 million, and 11.5 billion records in the condition, medication, procedure, and lab (laboratory test) tables respectively. The 2021 Q3 COVID-19 Dataset consists of 130.1 million encounters from 3.8 million patients. The size and longitudinal nature of CRWD can be leveraged for advanced analytics and artificial intelligence in medical research across all specialties and is a rich source of novel discoveries on a wide range of conditions including but not limited to COVID-19.