Pediatric Training Crisis of Emergency Medicine Residency during the COVID-19 Pandemic

Coronavirus disease 2019 (COVID-19) is an emerging viral disease that has caused a global pandemic. Among emergency department (ED) patients, pediatric patient volume mostly and continuously decreased during the pandemic period. Decreased pediatric patient volume in a prolonged period could results in inadequate pediatric training of Emergency Medicine (EM) residents. We collected data regarding pediatric patients who were first seen by EM resident physicians between 1 February 2019, and 31 January 2021, which was divided into pre-epidemic and epidemic periods by 1 February 2020. A significant reduction in pediatric patients per hour (PPH) of EM residents was noted in the epidemic period (from 1.55 to 0.81, p < 0.001). The average patient number was reduced significantly in the classification of infection (from 9.50 to 4.00, p < 0.001), respiratory system (from 84.00 to 22.00, p < 0.001), gastrointestinal system (from 52.00 to 34.00, p = 0.007), otolaryngology (from 4.00 to 2.00, p = 0.022). Among the diagnoses of infectious disease, the most obvious drop was noted in the diagnosis of influenza and enterovirus infection. Reduced pediatric patient volume affected clinical exposure to pediatric EM training of EM residency. Changes in the proportion of pediatric diseases presented in the ED may induce inadequate experience with common and specific pediatric diseases.

Influence of Treatment Facility Type and Annual Patient Volume on Overall Survival in Patients with Mantle Cell Lymphoma: A National Cancer Database Analysis

Introduction: Mantle cell lymphoma (MCL) is an uncommon hematological malignancy with an estimated incidence of 1 per 100,000 persons per year in the United States and represents only about 5% of all non-Hodgkin lymphomas. Several studies have shown that treatment at academic centers and a higher hospital case volume are associated with improved outcomes for uncommon hematological malignancies, probably due to increased provider expertise and access to novel therapies. Treatment of MCL can be complex given the heterogenous nature of the disease and a frequent need for autologous stem cell transplantation in eligible patients. However, the impact of treatment at an academic center and facility patient volume on the survival of patients with MCL has not been well studied in large cohorts. In this study, we utilized the National Cancer Database (NCDB) to investigate the impact of treatment at an academic center and treatment facility volume on the overall survival (OS) of patients with MCL. Methods: The NCDB was used to identify adult patients (≥ 18 years) with newly diagnosed MCL from 2004 through 2017. For facility patient volume analysis, patients were divided into groups based on the average number of new MCL patients seen annually: Tercile 1 [T1] (1-3 patients/year), Tercile 2 [T2] (4-5 patients/year) and Tercile 3 [T3] (≥6 patients/year). Treating centers were divided into Academic and Non-academic using the NCDB definitions. Academic centers were defined as centers that accessions more than 500 newly diagnosed cancer cases per year, participate in postgraduate medical education in at least four program areas including internal medicine and surgery and participates in cancer-related clinical trials. The primary endpoint was overall survival (OS). Survival analysis was performed using the Kaplan-Meier method and Cox hazards proportional model. Statistical analysis was performed using SPSS version 25. Results: We identified 22,752 patients with MCL during the study period. 9,484 (42%) patients were treated at academic centers and 13,070 (57%) were treated at non-academic centers. In terms of facility patient volume 10,948 patients (48%) were in the T1 group, 4,637 (20%) were in the T2 group and 7,166 (31%) were in the T3 group. No significant differences were found in baseline demographics (age, gender, race/ethnicity, comorbidity scores), socioeconomical variables (insurance type, median income, area of residence) and disease-related factors (B-symptoms, Ann Arbor stage) between patients treated academic vs nonacademic centers, or between patients in T1 vs T2 vs T3 groups. Notably, compared to lower volume facilities, T3 facilities were more likely to be academic centers (T3: 81% vs T2: 42% vs T1: 16%, p&lt;0.001) . After a median follow-up of 3.4 years, the median overall survival (OS) was 5.6 years for the entire cohort. The median OS was inferior for patients treated at lower volume facilities (4.1 years for T1, 5.1 years for T2 and 9.0 years for T3, p&lt;0.001) (Figure 1A). Similarly, the median OS was shorter for patients treated at non-academic centers vs academic centers (4.3 years vs 7.5 years respectively, p&lt;0.001) (Figure 1B). In a multivariate analysis, treatment at a lower patient volume facility (Hazard ratio [HR] Q1= 1.26 [95%CI = 1.18-1.34]) and treatment at a non-academic center (HR = 1.1, 95%CI = 1.01-1.12) were both independent prognostic factors of inferior OS, after adjusting for demographics (age, gender, ethnicity, area of residence) and socioeconomic variables (income and insurance status). Conclusion: Patients with MCL treated at academic and higher volume facilities had a higher OS compared to patients treated at non-academic and lower volume facilities.. Additional research is needed to fully understand the mechanisms behind these differences. Patients with MCL may benefit from an early referral to academic and high-volume centers. Figure 1 Figure 1. Disclosures Munoz: Merck: Research Funding; Portola: Research Funding; Genentech: Research Funding; Incyte: Research Funding; Janssen: Research Funding; Seattle Genetics: Research Funding; Pharmacyclics/Abbvie, Bayer, Gilead/Kite Pharma, Pfizer, Janssen, Juno/Celgene, BMS, Kyowa, Alexion, Beigene, Fosunkite, Innovent, Seattle Genetics, Debiopharm, Karyopharm, Genmab, ADC Therapeutics, Epizyme, Beigene, Servier: Consultancy; Gilead/Kite Pharma, Kyowa, Bayer, Pharmacyclics/Janssen, Seattle Genetics, Acrotech/Aurobindo, Beigene, Verastem, AstraZeneca, Celgene/BMS, Genentech/Roche.: Speakers Bureau; Millennium: Research Funding; Pharmacyclics: Research Funding; Celgene: Research Funding; Physicians' Education Resource: Honoraria; Gilead/Kite Pharma: Research Funding; Kyowa: Honoraria; Bayer: Research Funding; Seattle Genetics: Honoraria; OncView: Honoraria; Targeted Oncology: Honoraria. Paludo: Karyopharm: Research Funding. Habermann: Seagen: Other: Data Monitoring Committee; Incyte: Other: Scientific Advisory Board; Tess Therapeutics: Other: Data Monitoring Committee; Morphosys: Other: Scientific Advisory Board; Loxo Oncology: Other: Scientific Advisory Board; Eli Lilly & Co.,: Other: Scientific Advisor. Nowakowski: Daiichi Sankyo: Consultancy; Zai Labolatory: Consultancy; TG Therapeutics: Consultancy; Blueprint Medicines: Consultancy; Nanostrings: Research Funding; MorphoSys: Consultancy; Kymera Therapeutics: Consultancy; Incyte: Consultancy; Ryvu Therapeutics: Consultancy; Kyte Pharma: Consultancy; Genentech: Consultancy, Research Funding; Roche: Consultancy, Research Funding; Celgene/Bristol Myers Squibb: Consultancy, Research Funding; Selvita: Consultancy; Curis: Consultancy; Karyopharm Therapeutics: Consultancy; Bantham Pharmaceutical: Consultancy. Wang: Novartis: Research Funding; LOXO Oncology: Membership on an entity's Board of Directors or advisory committees, Research Funding; TG Therapeutics: Membership on an entity's Board of Directors or advisory committees; Genentech: Research Funding; Eli Lilly: Membership on an entity's Board of Directors or advisory committees; MorphoSys: Research Funding; InnoCare: Research Funding; Incyte: Membership on an entity's Board of Directors or advisory committees, Research Funding.

Are trauma research programs in academic and non-academic centers measured by equal standards? A survey of 137 level I trauma centers in the United States

Background American College of Surgeons level I trauma center verification requires an active research program. This study investigated differences in the research programs of academic and non-academic trauma centers. Methods A 28-question survey was administered to ACS-verified level I trauma centers in 11/12/2020–1/7/2021. The survey included questions on center characteristics (patient volume, staff size), peer-reviewed publications, staff and resources dedicated to research, and funding sources. Results The survey had a 31% response rate: 137 invitations were successfully delivered via email, and 42 centers completed at least part of the survey. Responding level I trauma centers included 36 (86%) self-identified academic and 6 (14%) self-identified non-academic centers. Academic and non-academic centers reported similar annual trauma patient volume (2190 vs. 2450), number of beds (545 vs. 440), and years of ACS verification (20 vs. 14), respectively. Academic centers had more full-time trauma surgeons (median 8 vs 6 for non-academic centers) and general surgery residents (median 30 vs 7) than non-academic centers. Non-academic centers more frequently ranked trauma surgery (100% vs. 36% academic), basic science (50% vs. 6% academic), neurosurgery (50% vs. 14% academic), and nursing (33% vs. 0% academic) in the top three types of studies conducted. Academic centers were more likely to report non-profit status (86% academic, 50% non-academic) and utilized research funding from external governmental or non-profit grants more often (76% vs 17%). Conclusions Survey results suggest that academic centers may have more physician, resident, and financial resources available to dedicate to trauma research, which may make fulfillment of ACS level I research requirements easier. Structural and institutional changes at non-academic centers, such as expansion of general surgery resident programs and increased pursuit of external grant funding, may help ensure that academic and non-academic sites are equally equipped to fulfill ACS research criteria.

Changes in Physician Telemedicine Use during COVID-19: Effects of Practice Setting, Demographics, Training, and Organizational Policies

Telemedicine use increased during the COVID-19 pandemic, but uptake was uneven and future use is uncertain. This study, then, examined the ability of personal and environmental variables to predict telemedicine adoption during the COVID-19 pandemic. A total of 230 physicians practicing in the U.S. completed questions concerning personal and environmental characteristics, as well as telemedicine use at three time points: pre-pandemic, during the pandemic, and anticipated future use. Associations between use and characteristics were determined to identify factors important for telemedicine use. Physicians reported that telemedicine accounted for 3.72% of clinical work prior to the pandemic, 46.03% during the pandemic, and predicted 25.44% after the pandemic ends. Physicians within hospitals reported less increase in telemedicine use during the pandemic than within group practice (p = 0.016) and less increase in use at hospitals compared to academic medical centers (p = 0.027) and group practice (p = 0.008). Greater telemedicine use was associated with more years in practice (p = 0.009), supportive organizational policies (p = 0.001), organizational encouragement (p = 0.003), expectations of greater patient volume (p = 0.003), and perceived higher quality of patient care (p = 0.032). Characteristics such as gender, number of physicians, and level of telemedicine training were not significant predictors. Organizations interested in supporting physicians to adopt telemedicine should encourage its use and create policies supporting its use. More senior physicians had a greater degree of telemedicine uptake, while training programs did not predict use, suggesting that efforts to develop telemedicine competency in younger physicians may be ineffective and should be re-examined.

Leveraging Emergency Department Information Systems to Address Palliative Care Needs of ED Patients During the COVID Pandemic

The arrival of the COVID-19 pandemic to hospitals in New York City stressed our emergency departments (ED) with high patient volume, stresses on hospital resources and the arrival of numerous high acuity, critically ill patients. Amid this time, we sought to leverage the ED Information Systems (EDIS), to assist in connecting critically ill patients, their families, and providers in the ED with palliative care resources. We discuss 4 innovative, thoughtful solutions to assist ED providers in identifying and addressing the acute and unique palliative care needs of COVID patients.

Changes in Cardiac Index Induced by Unilateral Passive Leg Raising: a Novel Way for Assessing Fluid Responsiveness

Background: To evaluate the fluid responsiveness of patients, we examined the change in cardiac index (CI) during a unilateral passive leg raising (PLR) test using the ProAQT/Pulsioflex. In addition, we compared the change of CI triggered by bilateral PLR test and unilateral PLR test, and the ability to estimate volume responsiveness in patients.Methods: This was a prospective, observational study, and we enrolled 40 individuals thought of volume expansion. The data of cardiac index, stroke variation in volume, stroke volume index, along with variation in pulse pressure were obtained with ProAQT/Pulsioflex at a semi-recumbent position, during unilateral PLR, bilateral PLR, as well as after expansion of volume (500 ml saline over 15 min). If CI improved more than 15% to the expansion of volume, patients were defined as responders.Results: We excluded three patients. We found that a unilateral PLR-triggered CI increment ≥7.455% forecasted a fluid-triggered CI increment ≥15% with 77.27% sensitivity and 83.33% specificity. Meanwhile, bilateral PLR-triggered increases in CI ≥9.8% forecasted a fluid-triggered CI increment ≥15% with 95.45% sensitivity and 77.78% specificity. The area under the ROC curves constructed for unilateral and bilateral PLR-triggered changes in CI was not significantly different (p=0.1544).Conclusions: The change of CI induced by unilateral PLR may estimate volume responsiveness in patients.Trial registration: Unilateral passive leg raising test to assess patient volume responsiveness: Single-Center Observational Clinical Study, ChiCTR2100046762. Registered 28 May 2021, https://www.chictr.org.cn/edit.aspx?pid=127104&htm=4

Determinants and perception of health insurance participation among healthcare providers in Nigeria: A mixed-methods study

Background To accelerate universal health coverage, Nigeria’s National Health Insurance Scheme (NHIS) decentralized the implementation of government health insurance to the individual states in 2014. Lagos is one of the states that passed a State Health Insurance Scheme into law, in order to expand the benefits of health insurance beyond the few residents enrolled in community-based health insurance programs, commercial private health insurance plans or the NHIS. Public and private healthcare providers are a critical component of the Lagos State Health Scheme (LSHS) rollout. This study explored the determinants and perception of provider participation in health insurance programs including the LSHS. Methods This study used a mixed-methods cross sectional design. Quantitative data were collected from 60 healthcare facilities representatively sampled from 6 Local Government Areas in Lagos state. For the qualitative data, providers were interviewed using structured questionnaires on selected characteristics of each health facility in addition to the managers’ opinions about the challenges and benefits of insurance participation, capacity pressure, resource availability and financial management consequences. Results A higher proportion of provider facilities participating in insurance relative to non-participating facilities were larger with mid to (very) high patient volume, workforce, and longer years of operation. In addition, a greater proportion of private facilities compared to public facilities participated in insurance. Furthermore, a higher proportion of secondary and tertiary facilities relative to primary facilities participated in insurance. Lastly, increase in patient volume and revenue were motivating factors for provider facilities to participate in insurance, while low tariffs, delay and denial of payments, and patients’ unrealistic expectations were mentioned as inhibiting factors. Conclusion For the Lagos state and other government insurance schemes in developing countries to be successful, effective contracting and quality assurance of healthcare providers are essential. The health facilities indicated that these would require adequate and regular provider payment, investments in infrastructure upgrades and educating the public about insurance benefit plans and service expectations.