Going Viral: Assessing the Impact of Social Media on Enrollment in a Coronavirus Disease 2019 (COVID-19) Cohort Study.

Objective This study aimed to quantify the effect of social media posts on study enrollment among children with mild coronavirus disease 2019 (COVID-19). Methods The primary outcome was weekly study enrollments analyzed using a run chart. A secondary analysis used linear regression to assess study enrollments two days before and after a social media post, adjusted for the statewide pediatric seven-day-average severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) case rate, social media posting day, and the interaction of these two variables. Results In seven months before social media posting, only eight patients were enrolled. One week after social media posting began, the median weekly enrollment increased (0 to 3). In the regression model, neither social media post day nor the pediatric SARS-CoV-2 case rate was significantly associated with enrollment rate. However, the interaction of a post day and the pediatric case rate was significant. Conclusion Social media posts significantly increased enrollment among children with mild COVID-19 in a prospective study. This effect was amplified by the presence of high community case rates during the Omicron wave.

Institutional Variability in Respiratory Support Use for Pediatric Critical Asthma: A Multicenter Retrospective Study.

Rationale: Over 20,000 children are hospitalized in the United States for asthma every year. Although initial treatment guidelines are well established, there is a lack of high-quality evidence regarding the optimal respiratory support devices for these patients.Objectives: The objective of this study was to evaluate institutional and temporal variability in the use of respiratory support modalities for pediatric critical asthma.Methods: We conducted a retrospective cohort study using data from the Virtual Pediatrics Systems database. Our study population included children older than 2 years old admitted to a VPS contributing pediatric intensive care unit from January 2012 to December 2021 with a primary diagnosis of asthma or status asthmaticus. We evaluated the percentage of encounters using a high-flow nasal cannula (HFNC), continuous positive airway pressure (CPAP), noninvasive bilevel positive pressure ventilation (NIV), and invasive mechanical ventilation (IMV) for all institutions, then divided institutions into quintiles based on the volume of patients. We created logistic regression models to determine the influence of institutional volume and year of admission on respiratory support modality use. We also conducted time-series analyses using Kendall's tau.Results: Our population included 77,115 patient encounters from 163 separate institutions. Institutional use of respiratory modalities had significant variation in HFNC (28.3%, interquartile range [IQR], 11.0-49.0%; P < 0.01), CPAP (1.4%; IQR, 0.3-4.3%; P < 0.01), NIV (8.6%; IQR, 3.5-16.1%; P < 0.01), and IMV (5.1%; IQR, 3.1-8.2%; P < 0.01). Increased institutional patient volume was associated with significantly increased use of NIV (odds ratio [OR], 1.33; 1.29-1.36; P < 0.01) and CPAP (OR, 1.20; 1.15-1.25; P < 0.01), and significantly decreased use of HFNC (OR, 0.80; 0.79-0.81; P < 0.01) and IMV (OR, 0.82; 0.79-0.86; P < 0.01). Time was also associated with a significant increase in the use of HFNC (11.0-52.3%; P < 0.01), CPAP (1.6-5.4%; P < 0.01), and NIV (3.7-21.2%; P < 0.01), whereas there was no significant change in IMV use (6.1-4.0%; P = 0.11).Conclusions: Higher-volume centers are using noninvasive positive pressure ventilation more frequently for pediatric critical asthma and lower frequencies of HFNC and IMV. Treatment with HFNC, CPAP, and NIV for this population is increasing in the last decade.

Wide Institutional Variability in the Treatment of Pediatric Critical Asthma: A Multicenter Retrospective Study.

OBJECTIVES: Children with status asthmaticus refractory to first-line therapies of systemic corticosteroids and inhaled beta-agonists often receive additional treatments. Because there are no national guidelines on the use of asthma therapies in the PICU, we sought to evaluate institutional variability in the use of adjunctive asthma treatments and associations with length of stay (LOS) and PICU use. DESIGN: Multicenter retrospective cohort study. SETTING: Administrative data from the Pediatric Health Information Systems (PHIS) database. PATIENTS: All inpatients 2-18 years old were admitted to a PHIS hospital between 2013 and 2021 with a diagnostic code for asthma. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: This study included 213,506 inpatient encounters for asthma, of which 29,026 patient encounters included care in a PICU from 39 institutions. Among these PICU encounters, large variability was seen across institutions in both the number of adjunctive asthma therapies used per encounter (min: 0.6, median: 1.7, max: 2.5, p < 0.01) and types of adjunctive asthma therapies (aminophylline, ipratropium, magnesium, epinephrine, and terbutaline) used. The center-level median hospital LOS ranged from 1 (interquartile range [IQR]: 1, 3) to 4 (3, 6) days. Among all the 213,506 inpatient encounters for asthma, the range of asthma admissions that resulted in PICU admission varied between centers from 5.2% to 47.3%. The average number of adjunctive therapies used per institution was not significantly associated with hospital LOS ( p = 0.81) nor the percentage of encounters with PICU admission ( p = 0.47). CONCLUSIONS: Use of adjunctive therapies for status asthmaticus varies widely among large children's hospitals and was not associated with hospital LOS or the percentage of encounters with PICU admission. Wide variance presents an opportunity for standardizing care with evidence-based guidelines to optimize outcomes and decrease adverse treatment effects and hospital costs.

Development of a biomarker signature using grating-coupled fluorescence plasmonic microarray for diagnosis of MIS-C.

Multisystem inflammatory syndrome in children (MIS-C) is a rare but serious condition that can develop 4-6 weeks after a school age child becomes infected by SARS-CoV-2. To date, in the United States more than 8,862 cases of MIS-C have been identified and 72 deaths have occurred. This syndrome typically affects children between the ages of 5-13; 57% are Hispanic/Latino/Black/non-Hispanic, 61% of patients are males and 100% have either tested positive for SARS-CoV-2 or had direct contact with someone with COVID-19. Unfortunately, diagnosis of MIS-C is difficult, and delayed diagnosis can lead to cardiogenic shock, intensive care admission, and prolonged hospitalization. There is no validated biomarker for the rapid diagnosis of MIS-C. In this study, we used Grating-coupled Fluorescence Plasmonic (GCFP) microarray technology to develop biomarker signatures in pediatric salvia and serum samples from patients with MIS-C in the United States and Colombia. GCFP measures antibody-antigen interactions at individual regions of interest (ROIs) on a gold-coated diffraction grating sensor chip in a sandwich immunoassay to generate a fluorescent signal based on analyte presence within a sample. Using a microarray printer, we designed a first-generation biosensor chip with the capability of capturing 33 different analytes from 80  µ L of sample (saliva or serum). Here, we show potential biomarker signatures in both saliva and serum samples in six patient cohorts. In saliva samples, we noted occasional analyte outliers on the chip within individual samples and were able to compare those samples to 16S RNA microbiome data. These comparisons indicate differences in relative abundance of oral pathogens within those patients. Microsphere Immunoassay (MIA) of immunoglobulin isotypes was also performed on serum samples and revealed MIS-C patients had several COVID antigen-specific immunoglobulins that were significantly higher than other cohorts, thus identifying potential new targets for the second-generation biosensor chip. MIA also identified additional biomarkers for our second-generation chip, verified biomarker signatures generated on the first-generation chip, and aided in second-generation chip optimization. Interestingly, MIS-C samples from the United States had a more diverse and robust signature than the Colombian samples, which was also illustrated in the MIA cytokine data. These observations identify new MIS-C biomarkers and biomarker signatures for each of the cohorts. Ultimately, these tools may represent a potential diagnostic tool for use in the rapid identification of MIS-C.

A machine-learning algorithm for diagnosis of multisystem inflammatory syndrome in children and Kawasaki disease in the USA: a retrospective model development and validation study.

BACKGROUND: Multisystem inflammatory syndrome in children (MIS-C) is a novel disease that was identified during the COVID-19 pandemic and is characterised by systemic inflammation following SARS-CoV-2 infection. Early detection of MIS-C is a challenge given its clinical similarities to Kawasaki disease and other acute febrile childhood illnesses. We aimed to develop and validate an artificial intelligence algorithm that can distinguish among MIS-C, Kawasaki disease, and other similar febrile illnesses and aid in the diagnosis of patients in the emergency department and acute care setting. METHODS: In this retrospective model development and validation study, we developed a deep-learning algorithm called KIDMATCH (Kawasaki Disease vs Multisystem Inflammatory Syndrome in Children) using patient age, the five classic clinical Kawasaki disease signs, and 17 laboratory measurements. All features were prospectively collected at the time of initial evaluation from patients diagnosed with Kawasaki disease or other febrile illness between Jan 1, 2009, and Dec 31, 2019, at Rady Children's Hospital in San Diego (CA, USA). For patients with MIS-C, the same data were collected from patients between May 7, 2020, and July 20, 2021, at Rady Children's Hospital, Connecticut Children's Medical Center in Hartford (CT, USA), and Children's Hospital Los Angeles (CA, USA). We trained a two-stage model consisting of feedforward neural networks to distinguish between patients with MIS-C and those without and then those with Kawasaki disease and other febrile illnesses. After internally validating the algorithm using stratified tenfold cross-validation, we incorporated a conformal prediction framework to tag patients with erroneous data or distribution shifts. We finally externally validated KIDMATCH on patients with MIS-C enrolled between April 22, 2020, and July 21, 2021, from Boston Children's Hospital (MA, USA), Children's National Hospital (Washington, DC, USA), and the CHARMS Study Group consortium of 14 US hospitals. FINDINGS: 1517 patients diagnosed at Rady Children's Hospital between Jan 1, 2009, and June 7, 2021, with MIS-C (n=69), Kawasaki disease (n=775), or other febrile illnesses (n=673) were identified for internal validation, with an additional 16 patients with MIS-C included from Connecticut Children's Medical Center and 50 from Children's Hospital Los Angeles between May 7, 2020, and July 20, 2021. KIDMATCH achieved a median area under the receiver operating characteristic curve during internal validation of 98·8% (IQR 98·0-99·3) in the first stage and 96·0% (95·6-97·2) in the second stage. We externally validated KIDMATCH on 175 patients with MIS-C from Boston Children's Hospital (n=50), Children's National Hospital (n=42), and the CHARMS Study Group consortium of 14 US hospitals (n=83). External validation of KIDMATCH on patients with MIS-C correctly classified 76 of 81 patients (94% accuracy, two rejected by conformal prediction) from 14 hospitals in the CHARMS Study Group consortium, 47 of 49 patients (96% accuracy, one rejected by conformal prediction) from Boston Children's Hospital, and 36 of 40 patients (90% accuracy, two rejected by conformal prediction) from Children's National Hospital. INTERPRETATION: KIDMATCH has the potential to aid front-line clinicians to distinguish between MIS-C, Kawasaki disease, and other similar febrile illnesses to allow prompt treatment and prevent severe complications. FUNDING: US Eunice Kennedy Shriver National Institute of Child Health and Human Development, US National Heart, Lung, and Blood Institute, US Patient-Centered Outcomes Research Institute, US National Library of Medicine, the McCance Foundation, and the Gordon and Marilyn Macklin Foundation.

Assessing the Effect of Electronic Medical Record Note Template on Firearm Access Screening in High-Risk Children.

BACKGROUND: Children who survive a suicide attempt are at greater risk of later dying by suicide. Firearm screening and provision of lethal means restriction counseling may improve the safety of this high-risk cohort. Our objective was to determine firearm screening rates among children hospitalized after suicide attempts. We also assessed the effects of templating firearm screening questions into the psychiatric consultation note. METHODS: This retrospective pre- and postintervention study identified children <19 years old admitted after a suicide attempt from January 1, 2016 to March 1, 2020. In mid-2017, the psychiatry consult note incorporated a previously available optional firearm screening tool as an embedded field (intervention). The intervention effect on proportion of children at high risk screened for firearm access was assessed by interrupted time series analysis. RESULTS: Of 26 088 total admissions, 357 met inclusion criteria. The majority were teenagers (15 years old, interquartile range 14 to 16), 275 were female (77%), and 167 were White (47%). Overall, 286 (80%) of patients had firearm access screening documentation. Of the 71 (20%) without screening, 21 (30%) were discharged from the hospital; 50 (70%) were transferred to psychiatric facilities. There was no significant difference in screening rates after the intervention (Intervention shift P = .74, slope P = .85). CONCLUSIONS: Many children were not screened for firearm access after a suicide attempt requiring hospitalization despite the screening tool integration. Multidisciplinary quality improvement efforts are needed to ensure that this critical risk reduction intervention is implemented for all patients after a suicide attempt.

Multicenter Validation of a Machine Learning Algorithm for Diagnosing Pediatric Patients with Multisystem Inflammatory Syndrome and Kawasaki Disease.

BACKGROUND: Multisystem inflammatory syndrome in children (MIS-C) is a novel disease identified during the COVID-19 pandemic characterized by systemic inflammation following SARS-CoV-2 infection. Delays in diagnosing MIS-C may lead to more severe disease with cardiac dysfunction or death. Most pediatric patients recover fully with anti-inflammatory treatments, but early detection of MIS-C remains a challenge given its clinical similarities to Kawasaki disease (KD) and other acute childhood illnesses. METHODS: We developed KIDMATCH ( K awasak I D isease vs M ultisystem Infl A mma T ory syndrome in CH ildren), a deep learning algorithm for screening patients for MIS-C, KD, or other febrile illness, using age, the five classical clinical KD signs, and 17 laboratory measurements prospectively collected within 24 hours of admission to the emergency department from 1448 patients diagnosed with KD or other febrile illness between January 1, 2009 and December 31, 2019 at Rady Children's Hospital. For MIS-C patients, the same data was collected from 131 patients between May 14, 2020 to June 18, 2021 at Rady Children's Hospital, Connecticut Children's Hospital, and Children's Hospital Los Angeles. We trained a two-stage model consisting of feedforward neural networks to distinguish between MIS-C and non MIS-C patients and then KD and other febrile illness. After internally validating the algorithm using 10-fold cross validation, we incorporated a conformal prediction framework to tag patients with erroneous data or distribution shifts, enhancing the model generalizability and confidence by flagging unfamiliar cases as indeterminate instead of making spurious predictions. We externally validated KIDMATCH on 175 MIS-C patients from 16 hospitals across the United States. FINDINGS: KIDMATCH achieved a high median area under the curve in the 10-fold cross validation of 0.988 [IQR: 0.98-0.993] in the first stage and 0.96 [IQR: 0.956-0.972] in the second stage using thresholds set at 95% sensitivity to detect positive MIS-C and KD cases respectively during training. External validation of KIDMATCH on MIS-C patients correctly classified 76/83 (2 rejected) patients from the CHARMS consortium, 47/50 (1 rejected) patients from Boston Children's Hospital, and 36/42 (2 rejected) patients from Children's National Hospital. INTERPRETATION: KIDMATCH has the potential to aid frontline clinicians with distinguishing between MIS-C, KD, and similar febrile illnesses in a timely manner to allow prompt treatment and prevent severe complications. FUNDING: Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Heart, Lung, and Blood Institute, Patient-Centered Outcomes Research Institute, National Library of Medicine.

Comparing Artificial Intelligence and Traditional Methods to Identify Factors Associated With Pediatric Asthma Readmission.

OBJECTIVE: To identify and contrast risk factors for six-month pediatric asthma readmissions using traditional models (Cox proportional-hazards and logistic regression) and artificial neural-network modeling. METHODS: This retrospective cohort study of the 2013 Nationwide Readmissions Database included children 5 to 18 years old with a primary diagnosis of asthma. The primary outcome was time to asthma readmission in the Cox model, and readmission within 180 days in logistic regression. A basic neural network construction with 2 hidden layers and multiple replications considered all dataset variables and potential variable interactions to predict 180-day readmissions. Logistic regression and neural-network models were compared on area-under-the receiver-operating curve. RESULTS: Of 18,489 pediatric asthma hospitalizations, 1858 were readmitted within 180 days. In Cox and logistic models, longer index length of stay, public insurance, and nonwinter index admission seasons were associated with readmission risk, whereas micropolitan county was protective. In neural-network modeling, 9 factors were significantly associated with readmissions. Four overlapped with the Cox model (nonwinter-month admission, long length of stay, public insurance, and micropolitan hospitals), whereas 5 were unique (age, hospital bed number, teaching-hospital status, weekend index admission, and complex chronic conditions). The area under the curve was 0.592 for logistic regression and 0.637 for the neural network. CONCLUSIONS: Different methods can produce different readmission models. Relying on traditional modeling alone overlooks key readmission risk factors and complex factor interactions identified by neural networks.

A Case Study of a Rapidly Progressive Cellulitis of the Penis and Scrotum in a Two-Month-Old Infant.

A two-month-old infant presented with rapidly progressive cellulitis of the penis and scrotum without a history of trauma, circumcision, or previous infection. After multiple failed antibiotic regimens covering common pathogens associated with cellulitis, a combination of ceftazidime and clindamycin was used to treat his infection. The previous evidence of anaerobic bacteria being implicated in male genitourinary infections and the antibiotic course necessary in this patient's treatment indicate that infantile scrotal cellulitis could require a distinct approach from typical skin and soft tissue infections.

Kawasaki Disease Hospitalizations in the United States 2016-2020: A Comparison of Before and During the Coronavirus Disease 2019 Era.

BACKGROUND: Kawasaki disease (KD) is an acute vasculitis of young children. A comparison of US hospitalization rates and epidemiologic features of KD in 2020 to those of precoronavirus disease years has yet to be reported. METHODS: Using a large, inpatient database, we conducted a retrospective cohort study and analyzed data for patients with (1) diagnosis coding for KD, (2) IV immunoglobulin treatment administered during hospitalization and (3) discharge date between January 1, 2016, and December 30, 2020. Severe cases were defined as those requiring adjunctive therapy or IV immunoglobulin-resistant therapy. RESULTS: The annual number of KD hospitalizations were stable from 2016 to 2019 (n = 1652, 1796, 1748, 1692, respectively) but decreased in 2020 (n = 1383). KD hospitalizations demonstrated seasonal variation with an annual peak between December and April. A second peak of KD admissions was observed in May 2020. The proportion of KD cases classified as severe increased to 40% in 2020 from 33% during the years 2016-2019 (P < 0.01). Median age in years increased from 2.9 in subjects hospitalized from 2016 to 2019 to 3.2 in 2020 (P = 0.002). CONCLUSIONS: Compared with the previous 4 years, the annual number of pediatric KD admissions decreased, and children discharged with diagnostic codes for KD in 2020 were generally older and more likely to have severe morbidity possibly reflective of misdiagnosed multisystem inflammatory syndrome in children. Clinicians should be wary of a possible rise in KD rates in the postcoronavirus disease 2019 era as social distancing policies are lifted and other viruses associated with KD return.

Intravenous Magnesium and Hospital Outcomes in Children Hospitalized With Asthma.

BACKGROUND: Use of intravenous magnesium (IVMg) for childhood asthma exacerbations has increased significantly in the last decade. Emergency department administration of IVMg has been shown to reduce asthma hospitalization, yet most children receiving IVMg in the emergency department are subsequently hospitalized. Our objective with the study was to examine hospital outcomes of children given IVMg for asthma exacerbations. METHODS: We conducted a retrospective cohort study using data from the Pediatric Health Information System. We used propensity score matching to compare children who received IVMg on the first day of hospitalization with those who did not. Primary outcomes were initiation and duration of noninvasive positive pressure ventilation. Secondary outcomes included mechanical ventilation (MV) initiation, duration of MV, length of stay, and subsequent tertiary medication use. Primary analysis was restricted to children admitted to nonintensive care inpatient units. RESULTS: Overall, 91 309 hospitalizations met inclusion criteria. IVMg was administered in 25 882 (28.4%) children. After propensity score matching, IVMg was not significantly associated with lower initiation (adjusted odds ratio 0.88; 95% confidence interval [CI] 0.74-1.05) or shorter duration of noninvasive positive pressure ventilation (rate ratio 0.94; 95% CI 0.87-1.02). Similarly, no significant associations were seen for MV initiation, MV duration, or length of stay. IVMg was associated with lower subsequent tertiary medication use (adjusted odds ratio 0.66; 95% CI 0.60-0.72). However, the association was lost when ipratropium was removed from the tertiary medication definition. CONCLUSIONS: IVMg administration was not significantly associated with improved hospital outcomes. Further study is needed to inform the optimal indications and timing of magnesium use during hospitalization.

Development of a Health Disparities Index: Proof of Concept with Chest Radiography in Asthma.

OBJECTIVES: To develop a tool for quantifying health disparity (Health Disparity Index[HDI]) and explore hospital variation measured by this index using chest radiography (CXR) in asthma as the proof of concept. STUDY DESIGN: This was a retrospective cohort study using the Pediatric Health Information System database including children with asthma between 5 and 18 years old. Inpatient and emergency department (ED) encounters from January 1, 2017, to December 31, 2018, with low or moderate severity were included. Exclusions included hospitals with <10 cases in any racial/ethnic group. The HDI measured variation in CXR use among children with asthma based on race/ethnicity. The HDI was calculated as the absolute difference between maximum and minimum percentages of CXR use (range = 0-100) when there was statistical evidence that the percentages were different. RESULTS: Data from 36 hospitals included 16 744 inpatient and 75 805 ED encounters. Overall, 19.7% of encounters had a CXR (34.3% for inpatient; 16.5% for ED). In inpatient encounters, 47.2% (17/36) of hospitals had a significant difference in imaging across racial/ethnic groups. Of these, the median hospital-level HDI was 19.4% (IQR 13.5-20.1). In ED encounters, 78.8% (28/36) of hospitals had a statistically significant difference in imaging across racial/ethnic groups, with a median hospital-level HDI of 10.2% (IQR 8.3-14.1). There was no significant association between the inpatient HDI and ED HDI (P = .46). CONCLUSIONS: The HDI provides a practical measure of disparity. To improve equity in healthcare, metrics are needed that are intuitive, accurate, usable, and actionable. Next steps include application of this index to other conditions.

Risk Factors for Pediatric Asthma Readmissions: A Systematic Review.

OBJECTIVE: To systematically review the literature on pediatric asthma readmission risk factors. STUDY DESIGN: We searched PubMed/MEDLINE, CINAHL, Scopus, PsycINFO, and Cochrane Central Register of Controlled Trials for published articles (through November 2019) on pediatric asthma readmission risk factors. Two authors independently screened titles and abstracts and consensus was reached on disagreements. Full-text articles were reviewed and inclusion criteria applied. For articles meeting inclusion criteria, authors abstracted data on study design, patient characteristics, and outcomes, and 4 authors assessed bias risk. RESULTS: Of 5749 abstracts, 74 met inclusion criteria. Study designs, patient populations, and outcome measures were highly heterogeneous. Risk factors consistently associated with early readmissions (≤30 days) included prolonged length of stay (OR range, 1.1-1.6) and chronic comorbidities (1.7-3.2). Risk factors associated with late readmissions (>30 days) included female sex (1.1-1.6), chronic comorbidities (1.5-2), summer discharge (1.5-1.8), and prolonged length of stay (1.04-1.7). Across both readmission intervals, prior asthma admission was the most consistent readmission predictor (1.3-5.4). CONCLUSIONS: Pediatric asthma readmission risk factors depend on the readmission interval chosen. Prior hospitalization, length of stay, sex, and chronic comorbidities were consistently associated with both early and late readmissions. TRIAL REGISTRATION: CRD42018107601.

Safety Events in Children's Hospitals During the COVID-19 Pandemic.

BACKGROUND AND OBJECTIVES: The coronavirus disease 2019 (COVID-19) pandemic has impacted hospitals, potentially affecting quality and safety. Our objective was to compare pediatric hospitalization safety events during the pandemic versus previous years. METHODS: In this retrospective cohort study of hospitalizations in the Pediatric Health Information System, we compared Pediatric Quality Indicator (PDI) rates from March 15 to May 31, 2017-2019 (pre-COVID-19), with those from March 15 to May 31, 2020 (during COVID-19). Generalized linear mixed-effects models with adjustment for patient characteristics (eg, diagnosis, clinical severity) were used. RESULTS: There were 399 113 discharges pre-COVID-19 and 88 140 during COVID-19. Unadjusted PDI rates were higher during versus pre-COVID-19 for overall PDIs (6.39 vs 5.05; P < .001). In adjusted analyses, odds of postoperative sepsis were higher during COVID-19 versus pre-COVID-19 (adjusted odds ratio 1.28 [95% confidence interval 1.04-1.56]). The remainder of the PDIs did not have increased adjusted odds during compared with pre-COVID-19. CONCLUSIONS: Postoperative sepsis rates increased among children hospitalized during COVID-19. Efforts are needed to improve safety of postoperative care for hospitalized children.

Engagement and Leadership in Firearm-Related Violence Prevention: The Role of the Pediatric Hospitalist.

Gun violence is a US public health crisis. Approximately 7000 children are hospitalized each year because of firearm-related injuries. As pediatric hospitalists, we are poised to address this crisis, whether we care directly for patients who are victims of gun violence. In this article, we aim to provide practical tools and opportunities for pediatric hospitalists to address the epidemic of gun safety and gun violence prevention, including specifics related to the inpatient setting. We provide a framework to act within 4 domains: clinical care, advocacy, education and research.