Nebulized Terbutaline and Ipratropium Bromide Versus Terbutaline Alone in Acute Exacerbation of Chronic Obstructive Pulmonary Disease Requiring Noninvasive Ventilation: A Randomized Double-blind Controlled Trial.

ICU Admission Rate Among Patients With Acute Exacerbation of COPD: What Are Predictors for Ventilator Support?

Magnesium sulfate for acute exacerbations of chronic obstructive pulmonary disease.

Distributed lags play important roles in explaining the short-run dynamic and long-run cumulative effects of features on a response variable. Unlike the usual lag length selection, important lags with significant weights are selected in a distributed lag model (DLM). Inspired by the importance of distributed lags, this research focuses on the construction of distributed lag inspired machine learning (DLIML) for predicting vaccine-induced changes in COVID-19 hospitalization and intensive care unit (ICU) admission rates. Importance of a lagged feature in DLM is examined by hypothesis testing and a subset of important features are selected by evaluating an information criterion. Akin to the DLM, we demonstrate the selection of distributed lags in machine learning by evaluating importance scores and objective functions. Finally, we apply the DLIML with supervised learning for forecasting daily changes in COVID-19 hospitalization and ICU admission rates in United Kingdom (UK) and United States of America (USA). A sharp decline in hospitalization and ICU admission rates are observed when around 40% people are vaccinated. For one percent more vaccination, daily changes in hospitalization and ICU admission rates are expected to reduce by 4.05 and 0.74 per million after 14 days in UK, and 5.98 and 1.04 per million after 20 days in USA, respectively. Long-run cumulative effects in the DLM demonstrate that the daily changes in hospitalization and ICU admission rates are expected to jitter around the zero line in a long-run. Application of the DLIML selects fewer lagged features but provides qualitatively better forecasting outcome for data-driven healthcare service planning.

Background: In the present study, we aimed to evaluate the effects of medroxyprogesterone on hospital short clinical outcomes and ABG parameters in patients with chronic obstructive pulmonary disease (COPD) exacerbation under treatments with noninvasive ventilation (NIV) treated with progesterone 15 mg in comparison with placebo. Materials and Methods: This is a double-blinded clinical trial that was performed in 2020–2021 in Isfahan, Iran, on 60 patients with COPD exacerbation that require NIV. All patients received short-acting beta-agonists, short-acting anticholinergics, systemic corticosteroids, and NIV. Patients in the intervention group received tablets of progesterone 15 mg, every 6 h for 5 days and the control group received a placebo; patients in both groups received routine clinical cares. We collected data regarding the days requiring NIV, hospitalization duration, intubation, intensive care unit (ICU) admission, and death. Furthermore, blood pH, PCO2, O2 saturation, dyspnea score, and NIV hours usage per day were evaluated at the time of admission, 3 and 5 days during admission. Results: Hospital short clinical outcomes were not differently distributed between the two groups (P > 0.05). Comparing two groups during hospitalization in terms of short clinical outcomes including duration hospitalization, using NIV per day, ICU admission rate, and need to intubation showed that they are comparable (P > 0.05). PH in both groups improved during follow-up (P < 0.001) and patients in intervention groups showed higher improvement (P = 0.006). Mean PCO2 decreased significantly in the intervention group (P < 0.001) but not in the control group (P = 0.198) and totally intervention showed significant improvement in PCO2 compared with the control group (P = 0.047). Although mean O2 saturation was increased in both groups during follow-up period (P < 0.001, for both groups), two groups showed comparable (P = 0.910). Mean NIV using (hours/day) was decreased significantly in the intervention group (P = 0.023); however, it was not significantly higher than that was seen in the control group (P = 0.706). The mean dyspnea score was decreased in both groups (P < 0.001), although a greater decrease was seen in the intervention group (P < 0.001). Conclusion: Administration of medroxyprogesterone in patients with COPD exacerbation that required NIV was associated with significant improvements in blood pH, PCO2, dyspnea, and daily duration of NIV using after 3 and 5 days following hospitalization.

SARS-CoV-2 enters the cell through the binding of the S glycoprotein on the surface of the virus to the angiotensin- converting enzyme 2 (ACE-2) in the host cells and also SARS-CoV S protein binding to ACE-2 was inhibited by anti-A antibodies. The aim of the study was to investigate the relationship between blood groups and the course of COVID-19 in Turkey. Laboratory confirmed COVID-19 patients aged 18 and over (n = 39.850) were randomized in age and sex- matched groups according to blood groups. Advanced age, male sex and blood group A were found to be related with increased rate of intensive care unit (ICU) admission (OR = 1.089, 95% CI: 1.085–1.093 for age; OR = 1.963, 95% CI: 1.737–2.218 for male sex; OR = 1.216, 95% CI: 1.023–1.446 for blood group A). When blood group O individuals were compared to non-O individuals, no significant difference was observed regarding the rate of hospital and ICU admission, mechanical ventilation (MV) support, length of hospital and ICU stay, and case fatality rate (CFR). The CFR in patients with blood group A, B, O, and AB were 2.6%, 2.2%, 3.1%, and 2.3%, respectively. There were no significant differences between Rh-negative and positive patients regarding the rate of hospital and ICU admission (p = 0.280 and p = 0.741, respectively), also the rate of MV support and CFR was similar (p = 0.933 and p = 0.417). Our study revealed that ABO and Rh blood groups do not have any impact on the rate of hospital admission, hospital and ICU stay, MV support, and CFR.

Rationale: Intensive care unit (ICU) admissions are a key marker of asthma severity, and signal patients at highest risk for asthma-related morbidity and mortality. We aimed to assess trends in ICU admissions and use of invasive ventilation over the past 10 years in pediatric and adult patients with asthma in Ontario, Canada. Methods: We assembled a 10-year cross-sectional cohort of individuals with prevalent asthma (aged 0-59 years) using the OASIS (Ontario Asthma Surveillance Information System) population-based health administrative databases from 2013-2022. All-cause ICU admissions were identified for admissions where asthma was listed as the most responsible diagnosis. Yearly ICU admission rate was calculated per 1,000 asthma prevalence. Invasive ventilation (IV) rates per 1000 asthma patients who had at least one ICU admission were calculated. All analyses were stratified by age groups (children aged 0-18 and adults aged 19-39 and 40-59). Results: From 2013 to 2022, there were 7,870 ICU admissions among patients admitted to hospital with asthma or asthma-related conditions as the most responsible diagnosis. Over 1/3 (4318) of these ICU admissions were in children under 6 years old. Among adults aged 19-59, the ICU admission rates showed a 14% decline over time. In contrast, among children aged 0-18, a steady increasing trend in ICU admission was observed from 0.64 in 2013 to 1.67 per 1000 in 2022, a nearly 3-fold increase in 10 years (Rate Ratio=2.59, 95%CI: 2.28-2.94). The highest increase was in children aged 0-5 years with a rebound seen in the post COVID-pandemic years (Figure 1). There were 2,112 admissions requiring IV (295.2 per 1000 ICU asthma patients). A decrease in the IV rate (-64%) in children under 19 years of age was seen; whereas in adults 19-59 years old, there was a slight increase in IV rates (+25.6%). Conclusions: There has been an increase in ICU admission rates in pediatric patients with asthma, most notably in patients under 6 years of age, although rates of invasive ventilation have decreased in children. In adults, ICU admission and ventilation rates have been relatively stable. Further analysis is planned to identify trends in non-invasive ventilation, and risk factors for ICU admissions. Future studies are needed to assess the trend in ICU admissions in children, particularly in the post-COVID era.

Ramandeep Singh AroraBackground Modern-day treatment of childhood cancer is punctuated by the necessary need for intensive care. This study was performed to understand the intensive care unit (ICU) admission rates and factors associated with ICU admission in a cohort of newly diagnosed childhood cancer patients in India. Materials and Methods All childhood (age <18 years) patients in the hospital-based cancer registry who had registered between March 1, 2013, and May 31, 2018, formed the cohort. ICU admissions were recorded and demographic and clinical factors associated with ICU admission were investigated. ICU admission rates were the primary outcome of interest and secondary outcomes were ICU admission rates for sick/supportive reasons, ICU admission rates for surgical/procedural reasons and mortality during ICU admission. Results In a cohort of 258 children (66% males, 61% from India, and median age 7 years), 149 (58%) patients needed one or more ICU admission (median one with range of one to five) with total 204 ICU admission episodes. While age group, gender, and nationality were not significantly associated with ICU admission, cancer type was (highest in neuroblastoma (82%) and central nervous system (CNS) tumors (71%)). Sick/supportive care ICU admissions were significantly higher in patients of younger age, Indian origin, and certain cancers (leukemias, lymphomas). Surgical/procedural ICU admissions were significantly higher in international patients and certain cancers (CNS tumors, neuroblastomas, and soft tissue sarcomas). There were 17 ICU deaths (11% of patients admitted to ICU) and all but one were from sick/supportive care ICU admissions. Conclusion Our study highlights higher than reported ICU admission rates and lower than reported mortality in children with cancer in low- and middle-income countries. We next plan to develop more specific ICU admission criteria, prospectively evaluating severity metrics in these patients, and explore the development of a high dependency unit.

Objective: To evaluate the efficacy of pneumococcal vaccines concerning hospital or intensive care unit (ICU) admissions due to pneumonia after vaccination. Setting: Inpatient and ICUs at Hamad General Hospital. Methods: The retrospective study included adults who were vaccinated between June 2012 and June 2013. Patient records were reviewed for hospital or ICU admissions due to pneumonia 2 years before and after vaccination. Main outcomes measures: The primary outcome was to compare the rates of hospital and ICU admissions for pneumonia 2 years before and after vaccination. The secondary outcome was to evaluate the efficacy of pneumococcal vaccines against different comorbidities. Key findings: One hundred sixty-one patients were included with a dominant age group of 64–85 (52%) years old. Comorbidities reported were hypertension (HTN), diabetes mellitus (DM), chronic obstructive pulmonary disease (COPD) and asthma. The rate of hospital admission due to pneumonia was significantly reduced within 2 years after vaccination, 71–39% (P < 0.001). There was a trend towards reduced ICU admission (12.4–10.6%), but the results did not achieve statistical significance (P > 0.72). In diabetic, hypertensive and COPD/Asthma patients, there was a statistically significant reduction in hospitalization. Although there was a reduction in ICU admission for both commodities, the results did not achieve statistical significance. Conclusion: Adults who received pneumococcal vaccines experienced reduced rates of hospital versus ICU admissions due to pneumonia infection.

BackgroundBased on the reports of low prevalence and severity of pediatric severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections, the Korean government has released new SARS-CoV-2 infection response and treatment guidelines for children under the age of 12 years. The government has further directed school reopening under strict preventive measures. However, there is still considerable concern on the impact of school reopening on community transmission of Coronavirus disease 2019 (COVID-19). In the present study, we aimed to evaluate the appropriateness of these directives and the severity of SARS-CoV-2 infections in children as compared to adults using sufficient national sample data.MethodsIn the present study, we evaluated the severity of SARS-CoV-2 infection in pediatric patients as compared to adults by analyzing the length of hospital stays (LOS), medical expenses, and hospital and intensive care unit (ICU) admission rates. A multivariate linear regression analysis was carried out to examine the effects of COVID-19 patients that the characteristics on the LOS and medical expenses, and multivariate logistic regression analysis were performed to identify COVID-19 characteristics that affect hospital and ICU admission rates and to prove the low SARS-CoV-2 infection severity in pediatric patients.ResultsThe hospitalization period for children aged 0–9 was 37% shorter and that of patients aged 10–19 years was 31% shorter than those of older age groups (P < 0.001). The analysis of the medical expenses by age showed that on average, medical expenses for children were approximately 4,900 USD lower for children than for patients over 80 years of age. The linear regression analysis also showed that patients who were 0–9 years old spent 87% and those aged 10–19 118% less on medical expenses than those aged 70 and over, even after the correction of other variables (P < 0.001). The probability of hospitalization was the lowest at 10–19 years old (odds ratio [OR], 0.05; 95% confidence interval [CI], 0.03–0.09), and their ICU admission rate was also the lowest at 0.14 (OR, 0.14; 95% CI, 0.08–0.24). On the other hand, the likelihood of hospitalization and ICU admission was the highest in children aged 0–9 years, and among patients under the age of 50 years in general.ConclusionThis study demonstrated the low severity of SARS-CoV-2 infection in younger patients (0–19 years) by analyzing the LOS, medical expenses, hospital, and intensive care unit admission rates as outcome variables. As the possibility to develop severe infection of coronavirus at the age of 10–19 was the lowest, a mitigation policy is also required for middle and high school students. In addition, children with underlying diseases need to be protected from high-risk infection environments.

The coronavirus disease 2019 (COVID-19) pandemic is pushing health systems to, and possibly beyond, their limits.1 In Italy, the exponential rise in case numbers has caused a corresponding rise in demand for intensive care unit (ICU) beds.2 To determine how many ICU beds will be required in Australia, we propose a simple model of an uninterrupted pandemic process based on the local situation in late March 2020, and compare this model with recent data from the Lombardy.3 In queueing theory, Little's law4 describes the relationship between the number of patients in a system (L) and the mean arrival rate (λ) and length of time the patient remains in the system (W) as: L = λW If a tertiary hospital has a steady state rate of 20 new admissions of patients with confirmed COVID-19 per day, of whom one requires ICU admission5 (λ) for a mean 10 days (W), the hospital ICU will need at least 10 beds to accommodate these patients. If, however, the number of new confirmed cases increases by 20% each day (in late March 2020, the number was increasing in Australia by 23% each day6), and 100 cases are confirmed on one day, about 120 will be confirmed on the next. This increase in the daily rate of 20 new cases will mean one extra ICU admission per day, and the need for at least 10 further ICU beds. That is, the total number of ICU beds needed will be about 10% of the number of confirmed cases, or 50% of the number of new cases during the exponential growth phase of the epidemic. Approximately 2300 ICU beds are available in Australia;7 if public health measures fail to curb the rate of growth in case numbers, the national ICU capacity would be exceeded when the number of COVID-19 cases reaches 23 000. Other sources8 have estimated that Australia could cope with as many as 44 580 COVID-19 cases, but this would grant only a 3-day extension before ICU capacity was exceeded. In our exponential growth scenario, commencing with 100 confirmed cases on day 1, 31 ICU beds would be required by day 7 and 119 by day 14 (Box 1). In sensitivity analyses, ICU bed capacity is sufficient even after 30 days if the ICU admission rate is reduced to 2.5%, but would be exceeded by day 26 were the ICU admission rate as high as 10%. It is important to note that our model describes a particularly serious scenario, and that actual outcomes will be modified by parameters not included in the model, including potential lags between diagnosis, hospital admission, and transfer to intensive care, and the proportion of true positive results among people tested for infecton. To evaluate how realistic the uninterrupted exponential growth scenario is, we compared exponential and linear growth models with recent data for the Lombardy in Italy.9 Using piecewise regression models, the increase in the number of ICU patients during days 1–14 was exponential (R2 = 0.96); from day 15, ICU admissions continued to rise steeply, but the increase was linear (R2 = 0.99) (Box 2). To determine the reason for the change in growth rate at day 15, we compared the ICU admission and mortality rates for patients hospitalised with COVID-19. The mortality rate during days 1–14 was fairly constant at about 8.8%, but rose dramatically from day 15 to a mean 23%. Most deaths during the first 14 days were probably of patients in intensive care, but we suspect that from day 15 patients died partly because of the lack of access to ICU beds as demand exceeded the capacity of the system to provide them, as indicated by the fall in ICU admission rate (Box 3). While the assumptions of our model can be debated, the exponential increase in Australian cases until late March suggested that it described a realistic clinical scenario consistent with overseas data available at that time. The exponential increase in case numbers and subsequent demand for ICU beds could have overwhelmed the capacity of even the largest Australian hospitals if SARS-CoV-2 transmission had not been as drastically reduced as it appears to have been by the successful public health measures enacted by the federal and state governments and the adherence to these measures by the Australian public. The rate of ICU admissions per positive case may be lower in Australia than reported for Italy and China — because of healthier underlying demographic conditions, a greater number of detected milder cases, or both — but this would not change the overall implications of the model. Australia must maintain measures to strictly control the rate of new cases and continue to improve our ICU surge capacity, lest we squander the chance to avoid an Italian fate. No relevant disclosures.

<b>Aims and objectives:</b> The recent corona virus disease 2019 (COVID-19) pandemic produced high and excessive demands for hospitalization. Vaccination is an efficient strategy to reduce hospitalization and intensive care unit (ICU) admission. With this study our aim is to present efficacy of vaccination on oxygen demand, ICU admission and mortality rate. <b>Methods:</b> In this retrospective study, we surveyed COVID-19 positive patients hospitalised from October 2021 to January 2022, in North Cyprus. Among these 189 patients 93 needed supplemental oxygen therapy. These patients were divided into subgroups according to their vaccination status, and the vaccinated patients were further classified under vaccine types and booster numbers. <b>Results:</b> Of the 93 patients, 40% (n=38) experienced severe symptoms leading to ICU admission and their mortality rate is 15% (n=14). ICU admission and mortality rate is found to be 38% (n=20) and 19% (n=10) respectively in the unvaccinated group (n=52). 41 vaccinated patients were admitted to the ICU. 44%(n=8) were injected with booster, 22% (n=4) with inactive vaccine, 22%(n=4), with mRNA and 11% (n=2) with vector vaccine. Mortality rate among vaccinated patients is 9.8%(n=4). <b>Conclusions:</b> The study have shown that there is no difference between the mortality rates (p=0.52) of vaccinated and unvaccinated patients who needed supplementary oxygen therapy.&nbsp;Their ICU admission rate (p=0.67) does not show a significant difference as well. The results demonstrate that even though ICU admission does not differ in vaccinated and unvaccinated groups, mortality rate is double in the latter (p=0.25).

In-center hemodialysis (HD) patients face greater communicable disease risks, including drug-resistant bacterial colonization and viral hepatitis, compared with home dialysis patients, who limit these risks, avoiding three-times weekly travel to dialysis clinics for treatments (1,2). Minimizing the high coronavirus disease 2019 (COVID-19) morbidity in dialysis patients is essential (3). We explored severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) positive rates, COVID-19–related hospitalization, mortality, and intensive care unit (ICU) admission by dialysis modality in Ontario, Canada. Data collection was in accordance with Ontario Health's legislative authority under the Ontario Personal Health Information Protection Act of 2004. We linked seven administrative health databases including the Ontario Renal Reporting System, which captures the modality and treatment changes of all adult (>18 years) home dialysis (peritoneal dialysis or home HD) or center-based HD patients across Ontario. Our observation period was between March 1, 2020 and November 20, 2020. For individuals, demographic data, neighborhood income quintile, location, residence in long-term care, comorbidity, hospitalization, ICU admission, deaths, and SARS-CoV-2 provincial RT-PCR testing and results information was obtained. Assuming a 14-day SARS-CoV-2 incubation period, COVID-19 infection was ascribed to the dialysis modality after 14 consecutive days of a home or in-center modality. Patients were followed until death, kidney transplantation, or study end. Follow-up SARS-CoV-2 tests after initial positive results were excluded in ascertaining testing rates. Long-term care residents and regional programs with no COVID-19 infections during the study period were excluded. Event rates for the following were defined as: (1) SARS-CoV-2 tests; (2) positive SARS-CoV-2 tests; (3) initial COVID-19 hospitalization (hospitalization within 1 week of SARS-CoV-2 positivity and/or hospitalization with an international classification of diseases-10 diagnosis of COVID-19); (4) COVID-19 mortality or ICU admission (admission to an ICU with COVID-19 during the initial COVID-19 hospitalization or death within 30 days of COVID-19 admission or SARS-CoV-2 positivity); (5) non–COVID-19 mortality (deaths during the study period not occurring within 30 days of SARS-CoV-2 positivity or COVID-19 admission; and (6) all-cause mortality. Unadjusted and adjusted rate ratios (ARR) for these events were calculated by dialysis modality using a log-binomial model. Logistic regression was used to calculate the adjusted odds ratio (AOR) of hospitalization and ICU admission and/or 30-day mortality by dialysis modality among those with COVID-19 infection. All models were adjusted for factors as indicated in Table 1. Marginal generalized estimating equations and a working dependence correlation structure were used to account for patients contributing patient-times to both modalities. Analyses were performed using SAS Version 9.4 SAS Institute, Cary, NC). Table 1. - Adjusted rate ratios for SARS-CoV-2 and COVID-19 outcomes by dialysis modality Outcome Number with Outcome/ Person Days Rate (per 100,000 person-days) Unadjusted Rate Ratio(95% CI) Adjusted Rate Ratio(95% CI) SARS-CoV-2 tests In-center HD 20804/2,084,665 998 1.0 (Ref) 1.0 (Ref) Home dialysis 2317/761,059 304 0.35 (0.34 to 0.38) 0.37 (0.35 to 0.38) SARS-CoV-2 positive In-center HD 182/2,084,665 8.7 1.0 (Ref) 1.0 (Ref) Home dialysis 34/761,059 4.5 0.59 (0.41 to 0.86) 0.57 (0.39 to 0.83) COVID-19 Hospitalization In-center HD 177/2,084,665 8.5 1.0 (Ref) 1.0 (Ref) Home dialysis 29/761,059 3.8 0.53 (0.38 to 0.75) 0.57 (0.40 to 0.81) COVID-19 ICU admission 30-day mortality In-center HD 45/2,084,665 2.2 1.0 (Ref) 1.0 (Ref) Home dialysis 6/761,059 0.8 0.37 (0.16 to 0.85) 0.44 (0.18 to 1.09) Overall Mortality In-center HD 1030/2,084,665 49.4 1.0 (Ref) 1.0 (Ref) Home dialysis 283/761,059 37.2 0.75 (0.66 to 0.86) 0.94 (0.82 to 1.08) Non-COVID-19 mortality In-center HD 996/2,084,665 47.8 1.0 (Ref) 1.0 (Ref) Home dialysis 278/761,059 36.5 0.76 (0.67 to 0.87) 0.96 (0.83 to 1.09) Mortality reported over the study period. All models adjusted for age, gender, diabetes, length of time on dialysis, race, neighborhood income quintile, geographic location, and prior kidney transplantation. A total of 587 patients contributed to both modalities during the study period. Linked databases included: The Ontario Renal Reporting System, The Registered Persons Database, The Ontario Laboratory Information System COVID-19 database, The Ontario Renal Network COVID-19 data collection tool, The Canadian Institute for Health Information Discharge Abstract Database, The Ontario Health Insurance Plan (was used to determined residency in long-term care), and the Postal Code Conversion File (Statistics Canada; was linked via postal codes to determine neighborhood income quintiles and geographic location). 95% CI, 95% confidence interval; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2; COVID-19, coronavirus disease; HD-hemodialysis; ICU, intensive care unit. We identified 3622 home dialysis (n=2853 peritoneal dialysis, n=769 home HD) and 9890 center-based HD patients (761,059 and 2,084,665 person-days, respectively). In-center patients were older (66.0±15 versus 63±15 years) and of longer dialysis vintage (median interquartile range, 2 [1–4] versus 2 [1–3] years), and a greater proportion had diabetes (53% versus 41%) compared with home dialysis patients. Fifty-three percent and 49% of home dialysis and in-center patients were in the greater Toronto area, respectively. SARS-CoV-2 testing rates were lower in home versus in-center dialysis patients (ARR, 0.37; 95% confidence interval [95% CI], 0.35 to 0.38). Six positive SARS-COV-2 episodes occurring within 14 days of dialysis initiation/modality transition were excluded. Positive SARS-CoV-2 tests and COVID-19 hospitalization rates were lower in home compared with in-center dialysis patients. COVID-19–related ICU admission and mortality (ARR, 0.44; 95% CI, 0.18 to 1.09) was lower in home versus in-center patients but did not reach statistical significance (Table 1). Among COVID-19–infected individuals, hospitalization occurred in 85% (29 of 34) and 86% (177 of 205) of home and in-center patients, respectively. Median length of hospitalization (interquartile range; days) was 13 (3–25) for in-center dialysis patients compared with 12 (11–27) for home dialysis patients. Mortality and/or ICU admission occurred in 18% (6 of 34) and 21% (45 of 205) of infected home and in-center patients, respectively. There were no differences in hospitalization (AOR, 1.3; 95% CI, 0.38 to 4.8) or death and/or ICU admission risks (AOR, 1.3; 95% CI, 0.37 to 4.8) in home compared with in-center COVID-19–infected patients. Non-COVID-19–related and overall mortality rates were similar in home versus center-based patients over the study period. We found a lower burden of COVID-19 infection, hospitalization, mortality, and ICU admission in community-dwelling home dialysis versus in-center patients. Our findings may relate to greater case finding in the in-center population, more frequent health care encounters, and routine screening/outbreak surveillance, which was at the program's discretion. If increased testing among in-center HD patients was the only explanation for the higher rates of COVID-19, one would have expected a disproportionate excess of milder cases (i.e., SARS-CoV-2 positivity not requiring hospital admission) among in-center compared with home dialysis patients. However, among in-center HD patients, we also observed higher rates of COVID-19 hospitalization, mortality, and ICU admission that may have been due to a higher infection rate rather than greater case-associated morbidity. Among COVID-19–infected individuals, we found no differences in the adjusted odds of hospitalization and ICU admission or 30-day mortality by home versus in-center treatment (albeit with limited power owing to low event rates). Our study captured over 90% of SARS-CoV-2 provincial tests. A limitation of this study is residual confounding based on unmeasured differences between in-center and home dialysis patients. One cannot exclude case-mix differences in the in-center versus home patients that may have accounted for the differences in COVID-19–related adverse event risks. We also could not distinguish between asymptomatic and symptomatic outpatient cases. Asymptomatic cases may have not been identified in the absence of mass screening. As community transmission of SARS-CoV-2 increased, and as HD facilities intensified infection prevention and control measures, differences in COVID-19 infection rates by dialysis modality may be attenuated compared with our observed findings over the early pandemic period. In the United States, rates of home dialysis continue to increase following the introduction of favorable reimbursement and policy reform (4,5). In addition to other purported benefits, a major shift to home-based dialysis care could render the ESKD population more resilient to the effects of COVID-19, reducing exposure episodes and total exposure time to SARS-CoV-2 while conferring lower future exposure risks to highly transmissible infections. Disclosures P.G. Blake is a contracted Medical Lead and Medical Director at Ontario Renal Network, Ontario Health, has received honoraria from Baxter Global for speaking engagements, and is on the Editorial Board of American Journal of Nephrology. J. Ip, Y. Tang, D. Thomas, and A. Yeung are salaried employees of Ontario Renal Network, Ontario Health. M. Oliver is a contracted Medical Lead at Ontario Renal Network, Ontario Health and is owner of Oliver Medical Management Inc., which licenses Dialysis Management Analysis and Reporting System software. He has received honoraria for speaking from Baxter Healthcare and participated on Advisory Boards for Janssen and Amgen. J. Perl reports grants from the Agency for Healthcare Research and Quality during the conduct of the study; personal fees from AstraZeneca Canada, Baxter Healthcare, DaVita Healthcare Partners, DCI, Fresenius Medical Care, LiberDi, Otsuka, and US Renal Care; research funding and salary support from Arbor Research Collaborative For Health and Agency for Healthcare Research and Quality; speakers bureau for Baxter Healthcare and Fresenius Medical Care; and is on the advisory board for Liberdi, outside of the submitted work. Funding None.

ObjectiveTo analyze the clinical significance of a heart rate (HR) or respiratory rate (RR) higher or lower than the normal in pediatric triage.MethodsA retrospective observational study was conducted with data from the Korean National Emergency Department Information System. The subjects were children <15 years of age in 2016. Reported HRs and RRs were divided into seven groups: grade -3 (3 or more standard deviations [SDs]<normal), grade -2 (2 SDs<normal), grade -1 (1 SD<normal), grade 0 (normal), grade 1 (1 SD>normal), grade 2 (2 SDs>normal), and grade 3 (3 or more SDs>normal). The main outcomes were hospitalization and intensive care unit (ICU) admission rates. Logistic regression analysis was used to analyze the relationship of the outcomes according to grade in each group.ResultsData for 981,297 patients were analyzed. Hospitalization and ICU admission rates increased significantly in the higher HR group (grades 1 to 3; odds ratio [OR], 1.353; P<0.001; OR, 1.747; P<0.001; respectively) and in the higher RR group (OR, 1.144; P<0.001; OR, 1.396; P<0.001; respectively), compared with grade 0 group. In the lower HR group (grades -1 to -3), the hospitalization rate decreased (OR, 0.928; P<0.001), whereas the ICU admission rate increased (OR, 1.207; P=0.001). Although the hospitalization rate increased. In the lower RR group (OR, 1.016; P=0.008), the ICU admission rate did not increase (OR, 0.973; P=0.338).ConclusionDeviations in HR and RR above normal are related to increased risks of hospitalization and ICU admission. However, this association may not apply to deviations below normal.

Racial Differences in Mortality Among Veterans Hospitalized for Exacerbation of Chronic Obstructive Pulmonary Disease

Intelligent volume-assured pressure support (iVAPS) is a relatively new hybrid mode of non-invasive ventilation (NIV). There is still limited evidence for iVAPS. The aim of this study was to compare the effectiveness of iVAPS to that of bi-level positive airway pressure spontaneous/timed (BPAP S/T) in patients with acute hypercapnic respiratory failure or acute-on-chronic hypercapnic respiratory failure caused by acute exacerbation of chronic obstructive pulmonary disease (AECOPD) in the emergency department. This was an observational, retrospective study. Eighty-two patients with hypercapnic respiratory failure caused by AECOPD, who were admitted to our emergency department, were analysed. Arterial blood gas (ABG) parameters, length of hospital stay and rate of intensive care unit (ICU) admission were compared between iVAPS and BPAP S/T. A total of 82 patients (26 females, 56 males, mean age 68.26±11.63years) who were treated with iVAPS (N=26) or BPAP S/T (N=56) were enrolled. There were no significant differences between two modes with respect to demographics such as age, gender, presence of comorbidity, usage of long-term oxygen therapy or NIV, and the baseline ABG parameters. The presence of pneumonia was significantly higher in BPAP S/T (P =.01). The rate of ICU admission was 26.9% in iVAPS vs 25% in BPAP S/T. The mean length of hospital stay was 11.5±12.3days in iVAPS and 9.7±7.4days in BPAP S/T (P=.53). The mean values of ABG parameters at the 1st and 24th hours of NIV therapy did not differ in both groups. Both modes were similarly effective in the management of appropriately selected patients with hypercapnic respiratory failure caused by AECOPD. Hence, we underline that NIV mode selection in the emergency department should be performed in line with experiences of clinicians/institutions and accessibility of ventilator devices/modes.