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.

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.

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.

Long-term effects of malnutrition on severity of COVID-19.

The COVID-19 pandemic is a public health crisis that has the potential to exacerbate worldwide malnutrition. This study examines whether patients with a history of malnutrition are predisposed to severe COVID-19. To do so, data on 103,099 COVID-19 inpatient encounters from 56 hospitals in the United States between March 2020 and June 2020 were retrieved from the Cerner COVID-19 Dataset. Patients with a history of malnutrition between 2015 and 2019 were identified, and a random intercept logistic regression models for pediatric and adult patients were built controlling for patient demographics, socioeconomic status, admission vital signs, and related comorbidities. Statistical interactions between malnutrition and patient age were significant in both the pediatric [log-odds and 95% confidence interval: 0.094 (0.012, 0.175)] and adult [- 0.014 (- 0.021, - 0.006] models. These interactions, together with the main effect terms of malnutrition and age, imply higher odds for severe COVID-19 for children between 6 and 17 years with history of malnutrition. Even higher odds of severe COVID-19 exist for adults (with history of malnutrition) between 18 and 79 years. These results indicate that the long-term effect of malnutrition predisposes patients to severe COVID-19 in an age-dependent way.

Development and validation of an early warning tool for sepsis and decompensation in children during emergency department triage.

This study was designed to develop and validate an early warning system for sepsis based on a predictive model of critical decompensation. Data from the electronic medical records for 537,837 visits to a pediatric Emergency Department (ED) from March 2013 to December 2019 were collected. A multiclass stochastic gradient boosting model was built to identify early warning signs associated with death, severe sepsis, non-severe sepsis, and bacteremia. Model features included triage vital signs, previous diagnoses, medications, and healthcare utilizations within 6 months of the index ED visit. There were 483 patients who had severe sepsis and/or died, 1102 had non-severe sepsis, 1103 had positive bacteremia tests, and the remaining had none of the events. The most important predictors were age, heart rate, length of stay of previous hospitalizations, temperature, systolic blood pressure, and prior sepsis. The one-versus-all area under the receiver operator characteristic curve (AUROC) were 0.979 (0.967, 0.991), 0.990 (0.985, 0.995), 0.976 (0.972, 0.981), and 0.968 (0.962, 0.974) for death, severe sepsis, non-severe sepsis, and bacteremia without sepsis respectively. The multi-class macro average AUROC and area under the precision recall curve were 0.977 and 0.316 respectively. The study findings were used to develop an automated early warning decision tool for sepsis. Implementation of this model in pediatric EDs will allow sepsis-related critical decompensation to be predicted accurately after a few seconds of triage.

A super learner ensemble of 14 statistical learning models for predicting COVID-19 severity among patients with cardiovascular conditions.

BACKGROUND: Cardiovascular and other circulatory system diseases have been implicated in the severity of COVID-19 in adults. This study provides a super learner ensemble of models for predicting COVID-19 severity among these patients. METHOD: The COVID-19 Dataset of the Cerner Real-World Data was used for this study. Data on adult patients (18 years or older) with cardiovascular diseases between 2017 and 2019 were retrieved and a total of 13 of these conditions were identified. Among these patients, 33,042 admitted with positive diagnoses for COVID-19 between March 2020 and June 2020 (from 59 hospitals) were identified and selected for this study. A total of 14 statistical and machine learning models were developed and combined into a more powerful super learning model for predicting COVID-19 severity on admission to the hospital. RESULT: LASSO regression, a full extreme gradient boosting model with tree depth of 2, and a full logistic regression model were the most predictive with cross-validated AUROCs of 0.7964, 0.7961, and 0.7958 respectively. The resulting super learner ensemble model had a cross validated AUROC of 0.8006 (range: 0.7814, 0.8163). The unbiased AUROC of the super learner model on an independent test set was 0.8057 (95% CI: 0.7954, 0.8159). CONCLUSION: Highly predictive models can be built to predict COVID-19 severity of patients with cardiovascular and other circulatory conditions. Super learning ensembles will improve individual and classical ensemble models significantly.

Risk assessment models in genetics clinic for array comparative genomic hybridization: Clinical information can be used to predict the likelihood of an abnormal result in patients.

Array comparative genomic hybridization (aCGH) testing can diagnose chromosomal microdeletions and duplications too small to be detected by conventional cytogenetic techniques. We need to consider which patients are more likely to receive a diagnosis from aCGH testing versus patients that have lower likelihood and may benefit from broader genome wide scanning. We retrospectively reviewed charts of a population of 200 patients, 117 boys and 83 girls, who underwent aCGH testing in Genetics Clinic at Rhode Island hospital between 1 January/2008 and 31 December 2010. Data collected included sex, age at initial clinical presentation, aCGH result, history of seizures, autism, dysmorphic features, global developmental delay/intellectual disability, hypotonia and failure to thrive. aCGH analysis revealed abnormal results in 34 (17%) and variants of unknown significance in 24 (12%). Patients with three or more clinical diagnoses had a 25.0% incidence of abnormal aCGH findings, while patients with two or fewer clinical diagnoses had a 12.5% incidence of abnormal aCGH findings. Currently, we provide families with a range of 10-30% of a diagnosis with aCGH testing. With increased clinical complexity, patients have an increased probability of having an abnormal aCGH result. With this, we can provide individualized risk estimates for each patient.