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.

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..

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.

Multivariable Analysis of Patient Satisfaction in the Pediatric Emergency Department.

BACKGROUND: Published data on predictive factors associated with parent satisfaction from care in a pediatric emergency department (ED) visit are limited to be descriptive and obtained from small data sets. Accordingly, the purpose of this study was to determine both modifiable and nonmodifiable demographic and operational factors that influence parental satisfaction using a large and ethnically diverse site data set. METHODS: Data consist of responses to the National Research Council (NRC) survey questionnaires and electronic medical records of 15,895 pediatric patients seen in a pediatric ED between the ages of 0 and 17 years discharged from May 2018 to September 2019. Bivariate, χ2, and multivariable logistic regression analyses were carried out using the NRC item on rating the ED between 0 and 10 as the primary outcome. Responses were coded using a top-box approach, a response of "9" or "10" represented satisfaction with the facility, and every other response was indicated as undesirable. Demographic data and NRC questionnaire were used as potential predictors. RESULTS: Multivariable regression analysis found the following variables as independent predictors for positive parental rating of the ED: Hispanic race/ethnicity (odds ratio [OR], 1.285), primary language Spanish (OR, 2.399), and patients who had government-sponsored insurance (OR, 1.470). Those survey items with the largest effect size were timeliness of care (OR, 0.188) and managing discomfort (OR, 0.412). CONCLUSIONS: Parental rating of an ED is associated with nonmodifiable variables such as ethnicity and modifiable variables such as timeliness of care and managing discomfort.

Prolonged hospital length of stay in pediatric trauma: a model for targeted interventions.

BACKGROUND: In this study, trauma-specific risk factors of prolonged length of stay (LOS) in pediatric trauma were examined. Statistical and machine learning models were used to proffer ways to improve the quality of care of patients at risk of prolonged length of stay and reduce cost. METHODS: Data from 27 hospitals were retrieved on 81,929 hospitalizations of pediatric patients with a primary diagnosis of trauma, and for which the LOS was >24 h. Nested mixed effects model was used for simplified statistical inference, while a stochastic gradient boosting model, considering high-order statistical interactions, was built for prediction. RESULTS: Over 18.7% of the encounters had LOS >1 week. Burns and corrosion and suspected and confirmed child abuse are the strongest drivers of prolonged LOS. Several other trauma-specific and general pediatric clinical variables were also predictors of prolonged LOS. The stochastic gradient model obtained an area under the receiver operator characteristic curve of 0.912 (0.907, 0.917). CONCLUSIONS: The high performance of the machine learning model coupled with statistical inference from the mixed effects model provide an opportunity for targeted interventions to improve quality of care of trauma patients likely to require long length of stay. IMPACT: Targeted interventions on high-risk patients would improve the quality of care of pediatric trauma patients and reduce the length of stay. This comprehensive study includes data from multiple hospitals analyzed with advanced statistical and machine learning models. The statistical and machine learning models provide opportunities for targeted interventions and reduction in prolonged length of stay reducing the burden of hospitalization on families.

A Nested Mixed Effects Multicenter Model Examining the Risk Factors for Pediatric Trauma Return Visits Within 72 Hours.

BACKGROUND: Return visits within 72 h are an important metric in evaluating the performance of emergency rooms. This has not been well studied in the pediatric trauma population. We sought to determine novel risk factors for return visits to the emergency department (ED) after trauma that may assist in identifying patients most at risk of revisit. METHODS: We used the Cerner Health Facts Database to retrieve data from 34 EDs across the United States that care for pediatric trauma patients aged <15 y. The data consist of 610,845 patients and 816,571 ED encounters. We retrieved variables encompassing demographics, payor, current and past health care resource utilization, trauma diagnoses, other diagnoses/comorbidities, medications, and surgical procedures. We built a nested mixed effects logistic regression model to provide statistical inference on the return visits. RESULTS: Traumas resulting from burns and corrosion, injuries to the shoulder and arms, injuries to the hip and legs, and trauma to the head and neck are all associated with increased odds of returning to the ED. Patients suffering from poisoning relating to drugs and other biological substances and patients with trauma to multiple body regions have reduced odds of returning to the ED. Longer ED length of stay and prior health care utilization (ED or inpatient) are associated with increased odds of a return visit. The sex of the patient and payor had a statistically significant effect on the risk of a return visit to the ED within 72 h of discharge. CONCLUSIONS: Certain traumas expose patients to an increased risk for return visits to the ED and, as a result, provide opportunity for improved quality of care. Targeted interventions that include education, observation holds, or a decision to hospitalize instead of discharge home may help improve patient outcomes and decrease the rate of ED returns. LEVEL OF EVIDENCE: III (Prognostic and Epidemiology).

Predictors of pediatric readmissions among patients with neurological conditions.

BACKGROUND: Unplanned readmission is one of many measures of the quality of care of pediatric patients with neurological conditions. In this multicenter study, we searched for novel risk factors of readmission of patients with neurological conditions. METHODS: We retrieved hospitalization data of patients less than 18 years with one or more neurological conditions. This resulted in a total of 105,834 encounters from 18 hospitals. We included data on patient demographics, prior healthcare resource utilization, neurological conditions, number of other conditions/diagnoses, number of medications, and number of surgical procedures performed. We developed a random intercept logistic regression model using stepwise minimization of Akaike Information Criteria for variable selection. RESULTS: The most important neurological conditions associated with unplanned pediatric readmissions include hydrocephalus, inflammatory diseases of the central nervous system, sleep disorders, disease of myoneural junction and muscle, other central nervous system disorder, other spinal cord conditions (such as vascular myelopathies, and cord compression), and nerve, nerve root and plexus disorders. Current and prior healthcare resource utilization variables, number of medications, other diagnoses, and certain inpatient surgical procedures were associated with changes in odds of readmission. The area under the receiver operator characteristic curve (AUROC) on the independent test set is 0.733 (0.722, 0.743). CONCLUSIONS: Pediatric patients with certain neurological conditions are more likely to be readmitted than others. However, current and prior healthcare resource utilization remain some of the strongest indicators of readmission within this population as in the general pediatric population.

Criticality index conducted in pediatric emergency department triage.

OBJECTIVE: To develop and analyze the performance of a machine learning model capable of predicting the disposition of patients presenting to a pediatric emergency department (ED) based on triage assessment and historical information mined from electronic health records. METHODS: We retrospectively reviewed data from 585,142 ED visits at a pediatric quaternary care institution between 2013 and 2020. An extreme gradient boosting machine learning model was trained on a randomly selected training data set (50%) to stratify patients into 3 classes: (1) high criticality (patients requiring intensive care unit [ICU] care within 4 h of hospital admission, patients who died within 4 h of admission, and patients who died in the ED); (2) moderate criticality (patients requiring hospitalization without the need for ICU care); and (3) low criticality (patients discharged home). Variables considered during model development included triage vital signs, aspects of triage nursing assessment, demographics, and historical information (diagnoses, medication use, and healthcare utilization). Historical factors were limited to the 6 months preceding the index ED visit. The model was tested on a previously withheld test data set (40%), and its performance analyzed. RESULTS: The distribution of criticality among high, moderate, and low was 1.5%, 7.1%, and 91.4%, respectively. The one-versus-all area under the receiver operating characteristic (AUROC) curve for high and moderate criticality was 0.982 (95% CI 0.980, 0.983) and 0.968 (0.967, 0.969). The multi-class macro average AUROC and area under the receiver operating characteristic curve were 0.976 and 0.754. The features most integral to model performance included history of intravenous medications, capillary refill, emergency severity index level, history of hospitalization, use of a supplemental oxygen device, age, and history of admission to the ICU. CONCLUSION: Pediatric ED disposition can be accurately predicted using information available at triage, providing an opportunity to improve quality of care and patient outcomes.

Anxiety and Mood Disorders Impacting Physician Opioid Prescribing in the Pediatric Hospital Setting.

This research examined whether pediatric inpatients without an anxiety/mood disorder are more likely to receive opioids in response to pain compared to patients diagnosed with a mental health condition. Research questions were tested using cross-sectional inpatient electronic medical record data. Propensity score matching was used to match patients with a disorder with patients without the disorder (anxiety analyses: N = 2892; mood analyses: N = 1042). Although patients with anxiety and mood disorders experienced greater pain, physicians were less likely to order opioids for these patients. Analyses also disclosed an interaction of anxiety with pain-the pain-opioid relation was stronger for patients without an anxiety disorder than for patients with an anxiety diagnosis. Instead, physicians were more likely to place non-opioid analgesic orders to manage the pain of patients with anxiety disorders. Findings imply that pain management decisions might be influenced by patient's mental health.

Prediction of 7-Day Readmission Risk for Pediatric Trauma Patients.

BACKGROUND: Pediatric patients admitted for trauma may have unique risk factors of unplanned readmission and require condition-specific models to maximize accuracy of prediction. We used a multicenter data set on trauma admissions to study risk factors and predict unplanned 7-day readmissions with comparison to the 30-day metric. METHODS: Data from 28 hospitals in the United States consisting of 82,532 patients (95,158 encounters) were retrieved, and 75% of the data were used for building a random intercept, mixed-effects regression model, whereas the remaining were used for evaluating model performance. The variables included were demographics, payer, current and past health care utilization, trauma-related and other diagnoses, medications, and surgical procedures. RESULTS: Certain conditions such as poisoning and medical/surgical complications during treatment of traumatic injuries are associated with increased odds of unplanned readmission. Conversely, trauma-related conditions, such as trauma to the thorax, knee, lower leg, hip/thigh, elbow/forearm, and shoulder/upper arm, are associated with reduced odds of readmission. Additional predictors include the current and past health care utilization and the number of medications. The corresponding 7-day model achieved an area under the receiver operator characteristic curve of 0.737 (0.716, 0.757) on an independent test set and shared similar risk factors with the 30-day version. CONCLUSIONS: Patients with trauma-related conditions have risk of readmission modified by the type of trauma. As a result, additional quality of care measures may be required for patients with trauma-related conditions that elevate their risk of readmission.

HealtheDataLab - a cloud computing solution for data science and advanced analytics in healthcare with application to predicting multi-center pediatric readmissions.

BACKGROUND: There is a shortage of medical informatics and data science platforms using cloud computing on electronic medical record (EMR) data, and with computing capacity for analyzing big data. We implemented, described, and applied a cloud computing solution utilizing the fast health interoperability resources (FHIR) standardization and state-of-the-art parallel distributed computing platform for advanced analytics. METHODS: We utilized the architecture of the modern predictive analytics platform called Cerner® HealtheDataLab and described the suite of cloud computing services and Apache Projects that it relies on. We validated the platform by replicating and improving on a previous single pediatric institution study/model on readmission and developing a multi-center model of all-cause readmission for pediatric-age patients using the Cerner® Health Facts Deidentified Database (now updated and referred to as the Cerner Real World Data). We retrieved a subset of 1.4 million pediatric encounters consisting of 48 hospitals' data on pediatric encounters in the database based on a priori inclusion criteria. We built and analyzed corresponding random forest and multilayer perceptron (MLP) neural network models using HealtheDataLab. RESULTS: Using the HealtheDataLab platform, we developed a random forest model and multi-layer perceptron model with AUC of 0.8446 (0.8444, 0.8447) and 0.8451 (0.8449, 0.8453) respectively. We showed the distribution in model performance across hospitals and identified a set of novel variables under previous resource utilization and generic medications that may be used to improve existing readmission models. CONCLUSION: Our results suggest that high performance, elastic cloud computing infrastructures such as the platform presented here can be used for the development of highly predictive models on EMR data in a secure and robust environment. This in turn can lead to new clinical insights/discoveries.

Impact of anesthesiologists on parental perioperative satisfaction scores.

BACKGROUND: While the focus on patient experience as an important outcome has significantly increased over the past decade, there is paucity of data on predictive factors associated with parental recommendation of a surgical facility to friends and family. METHODS: Data for this report were obtained from a Hospital Information System and Picker Health validated surveys completed by 538 parents whose children underwent outpatient surgery from July 2014 to March 2016. Bivariate, chi-squared, and multivariate logistic regression analysis were carried out using the Picker Health item "Would you recommend this outpatient surgical facility to your friends and family?" as the primary outcome. Demographic data and 53 Picker Health items were used as potential predictors. RESULTS: Multivariate logistic regression analysis found the following variables as independent predictors for parental recommendation: quality of perioperative communication by anesthesiologists (odds ratio [95% confidence interval]=0.23 [0.09, 0.58]); provision of information on whom to call for help after discharge (0.22 [0.07, 0.64]); child's perceived baseline health (0.37 [0.15, 0.90]); and ill-informed staff about child's procedure (0.30 [0.21, 0.79]). Variables such as child's pain and child's nausea and vomiting were not predictive for referral pattern. CONCLUSION: Parental recommendation of a surgical facility to friends and family depends on a number of variables with the quality of perioperative communication with the anesthesiologist being the most predictive item.

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.

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.

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.

Standardized Health data and Research Exchange (SHaRE): promoting a learning health system.

Aggregate de-identified data from electronic health records (EHRs) provide a valuable resource for research. The Standardized Health data and Research Exchange (SHaRE) is a diverse group of US healthcare organizations contributing to the Cerner Health Facts (HF) and Cerner Real-World Data (CRWD) initiatives. The 51 facilities at the 7 founding organizations have provided data about more than 4.8 million patients with 63 million encounters to HF and 7.4 million patients and 119 million encounters to CRWD. SHaRE organizations unmask their organization IDs and provide 3-digit zip code (zip3) data to support epidemiology and disparity research. SHaRE enables communication between members, facilitating data validation and collaboration as we demonstrate by comparing imputed EHR module usage to actual usage. Unlike other data sharing initiatives, no additional technology installation is required. SHaRE establishes a foundation for members to engage in discussions that bridge data science research and patient care, promoting the learning health system.

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.

Targeted Clinical Interventions for Reducing Pediatric Readmissions.

BACKGROUND: In this interventional study, we addressed the selection and application of clinical interventions on pediatric patients identified as at risk by a predictive model for readmissions. METHODS: A predictive model for readmissions was implemented, and a team of providers expanded corresponding clinical interventions for at-risk patients at a freestanding children's hospital. Interventions encompassed social determinants of health, outpatient care, medication reconciliation, inpatient and discharge planning, and postdischarge calls and/or follow-up. Statistical process control charts were used to compare readmission rates for the 3-year period preceding adoption of the model and clinical interventions with those for the 2-year period after adoption of the model and clinical interventions. Potential financial savings were estimated by using national estimates of the cost of pediatric inpatient readmissions. RESULTS: The 30-day all-cause readmission rates during the periods before and after predictive modeling (and corresponding 95% confidence intervals [CI]) were 12.5% (95% CI: 12.2%-12.8%) and 11.1% (95% CI: 10.8%-11.5%), respectively. More modest but similar improvements were observed for 7-day readmissions. Statistical process control charts indicated nonrandom reductions in readmissions after predictive model adoption. The national estimate of the cost of pediatric readmissions indicates an associated health care savings due to reduced 30-day readmission during the 2-year predictive modeling period at $2 673 264 (95% CI: $2 612 431-$2 735 364). CONCLUSIONS: A combination of predictive modeling and targeted clinical interventions to improve the management of pediatric patients at high risk for readmission was successful in reducing the rate of readmission and reducing overall health care costs. The continued prioritization of patients with potentially modifiable outcomes is key to improving patient outcomes.

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.