Boarding in the emergency department (ED) is a critical indicator of quality of care for hospitals. It is defined as the time between the admission decision and departure from the ED. As a result of ...boarding, patients stay in the ED until inpatient beds are available; moreover, boarding is associated with various adverse events.
The objective of our systematic review was to determine whether ED boarding (EDB) time is associated with in-hospital mortality (IHM).
A systematic search was conducted in academic databases to identify relevant studies. Medline, PubMed, Scopus, Embase, Cochrane, Web of Science, Cochrane, CINAHL and PsychInfo were searched. We included all peer-reviewed published studies from all previous years until November 2018. Studies performed in the ED and focused on the association between EDB and IHM as the primary objective were included. Extracted data included study characteristics, prognostic factors, outcomes, and IHM. A search update in PubMed was performed in May 2019 to ensure the inclusion of recent studies before publishing.
From the initial 4,321 references found through the systematic search, the manual screening of reference lists and the updated search in PubMed, a total of 12 studies were identified as eligible for a descriptive analysis. Overall, six studies found an association between EDB and IHM, while five studies showed no association. The last remaining study included both ICU and non-ICU subgroups and showed conflicting results, with a positive association for non-ICU patients but no association for ICU patients. Overall, a tendency toward an association between EDB and IHM using the pool random effect was observed.
Our systematic review did not find a strong evidence for the association between ED boarding and IHM but there is a tendency toward this association. Further well-controlled, international multicenter studies are needed to demonstrate whether this association exists and whether there is a specific EDB time cut-off that results in increased IHM.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Objectives To assess the overall effect of vitamin D supplementation on risk of acute respiratory tract infection, and to identify factors modifying this effect.Design Systematic review and ...meta-analysis of individual participant data (IPD) from randomised controlled trials.Data sources Medline, Embase, the Cochrane Central Register of Controlled Trials, Web of Science, ClinicalTrials.gov, and the International Standard Randomised Controlled Trials Number registry from inception to December 2015.Eligibility criteria for study selection Randomised, double blind, placebo controlled trials of supplementation with vitamin D3 or vitamin D2 of any duration were eligible for inclusion if they had been approved by a research ethics committee and if data on incidence of acute respiratory tract infection were collected prospectively and prespecified as an efficacy outcome.Results 25 eligible randomised controlled trials (total 11 321 participants, aged 0 to 95 years) were identified. IPD were obtained for 10 933 (96.6%) participants. Vitamin D supplementation reduced the risk of acute respiratory tract infection among all participants (adjusted odds ratio 0.88, 95% confidence interval 0.81 to 0.96; P for heterogeneity <0.001). In subgroup analysis, protective effects were seen in those receiving daily or weekly vitamin D without additional bolus doses (adjusted odds ratio 0.81, 0.72 to 0.91) but not in those receiving one or more bolus doses (adjusted odds ratio 0.97, 0.86 to 1.10; P for interaction=0.05). Among those receiving daily or weekly vitamin D, protective effects were stronger in those with baseline 25-hydroxyvitamin D levels <25 nmol/L (adjusted odds ratio 0.30, 0.17 to 0.53) than in those with baseline 25-hydroxyvitamin D levels ≥25 nmol/L (adjusted odds ratio 0.75, 0.60 to 0.95; P for interaction=0.006). Vitamin D did not influence the proportion of participants experiencing at least one serious adverse event (adjusted odds ratio 0.98, 0.80 to 1.20, P=0.83). The body of evidence contributing to these analyses was assessed as being of high quality.Conclusions Vitamin D supplementation was safe and it protected against acute respiratory tract infection overall. Patients who were very vitamin D deficient and those not receiving bolus doses experienced the most benefit.Systematic review registration PROSPERO CRD42014013953.
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BFBNIB, CMK, NMLJ, NUK, PNG, SAZU, UL, UM, UPUK
The prediction of emergency department (ED) disposition at triage remains challenging. Machine learning approaches may enhance prediction. We compared the performance of several machine learning ...approaches for predicting two clinical outcomes (critical care and hospitalization) among ED patients with asthma or COPD exacerbation.
Using the 2007–2015 National Hospital and Ambulatory Medical Care Survey (NHAMCS) ED data, we identified adults with asthma or COPD exacerbation. In the training set (70% random sample), using routinely-available triage data as predictors (e.g., demographics, arrival mode, vital signs, chief complaint, comorbidities), we derived four machine learning-based models: Lasso regression, random forest, boosting, and deep neural network. In the test set (the remaining 30% of sample), we compared their prediction ability against traditional logistic regression with Emergency Severity Index (ESI, reference model).
Of 3206 eligible ED visits, corresponding to weighted estimates of 13.9 million visits, 4% had critical care outcome and 26% had hospitalization outcome. For the critical care prediction, the best performing approach– boosting – achieved the highest discriminative ability (C-statistics 0.80 vs. 0.68), reclassification improvement (net reclassification improvement NRI 53%, P = 0.002), and sensitivity (0.79 vs. 0.53) over the reference model. For the hospitalization prediction, random forest provided the highest discriminative ability (C-statistics 0.83 vs. 0.64) reclassification improvement (NRI 92%, P < 0.001), and sensitivity (0.75 vs. 0.33). Results were generally consistent across the asthma and COPD subgroups.
Based on nationally-representative ED data, machine learning approaches improved the ability to predict disposition of patients with asthma or COPD exacerbation.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
To investigate the temporal trend in the national incidence of bronchiolitis hospitalizations, their characteristics, inpatient resource use, and hospital cost from 2000 through 2016.
We performed a ...serial, cross-sectional analysis of nationally representative samples (the 2000, 2003, 2006, 2009, 2012, and 2016 Kids' Inpatient Databases) of children (age <2 years) hospitalized for bronchiolitis. We identified all children hospitalized with bronchiolitis by using
466.1 and
J21. Complex chronic conditions were defined by the pediatric complex chronic conditions classification by using inpatient data. The primary outcomes were the incidence of bronchiolitis hospitalizations, mechanical ventilation use, and hospital direct cost. We examined the trends accounting for sampling weights.
From 2000 to 2016, the incidence of bronchiolitis hospitalization decreased from 17.9 to 13.5 per 1000 person-years in US children (25% decrease;
< .001). In contrast, the proportion of bronchiolitis hospitalizations among overall hospitalizations increased from 16% to 18% (
< .001). There was an increase in the proportion of children with a complex chronic condition (6%-13%; 117% increase), hospitalization to children's hospital (15%-29%; 93% increase), and mechanical ventilation use (2%-5%; 184% increase; all
< .001). Likewise, the hospital cost increased from $449 million to $734 million (63% increase) nationally (with an increase in geometric mean of cost per hospitalization from $3267 to $4086; 25% increase;
< .001 adjusted for inflation) from 2003 to 2016.
From 2000 through 2016, the incidence of bronchiolitis hospitalizations among US children declined. In contrast, mechanical ventilation use and nationwide hospital direct cost substantially increased.
Asthma is a heterogeneous respiratory disease reflecting distinct pathobiologic mechanisms. These mechanisms are based, at least partly, on different genetic factors shared by many other conditions, ...such as allergic diseases and obesity. Investigating the shared genetic effects enables better understanding of the mechanisms of phenotypic correlations and is less subject to confounding by environmental factors. The increasing availability of large-scale genome-wide association study (GWAS) for asthma has enabled researchers to examine the genetic contributions to the epidemiologic associations between asthma subtypes and those between coexisting diseases and/or traits and asthma. Studies have found not only shared but also distinct genetic components between asthma subtypes, indicating that the heterogeneity is related to distinct genetics. This review summarizes a recently compiled analytic approach—genome-wide cross-trait analysis—to determine shared and distinct genetic architecture. The genome-wide cross-trait analysis features in several analytic aspects: genetic correlation, cross-trait meta-analysis, Mendelian randomization, polygenic risk score, and functional analysis. In this article, we discuss in detail the scientific goals that can be achieved by these analyses, their advantages, and their limitations. We also make recommendations for future directions: (1) ethnicity-specific asthma GWASs and (2) application of cross-trait methods to multiomics data to dissect the heritability found in GWASs. Finally, these analytic approaches are also applicable to complex and heterogeneous traits beyond asthma.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Pregnant women and newborns are at increased risk of vitamin D deficiency. Our objective was to create a global summary of maternal and newborn vitamin D status. We completed a systematic review ...(1959–2014) and meta‐analysis of studies reporting serum 25‐hydroxyvitamin D 25(OH)D concentration in maternal and newborn populations. The 95 identified studies were unevenly distributed by World Health Organization (WHO) region: Americas (24), European (33), Eastern Mediterranean (13), South‐East Asian (7), Western Pacific (16) and African (2). Average maternal 25(OH)D concentrations (nmol L−1) by region were 47–65 (Americas), 15–72 (European), 13–60 (Eastern Mediterranean), 20–52 (South‐East Asian), 42–72 (Western Pacific) and 92 (African). Average newborn 25(OH)D concentrations (nmol L−1) were 35–77 (Americas), 20–50 (European), 5–50 (Eastern Mediterranean), 20–22 (South‐East Asian), 32–67 (Western Pacific) and 27–35 (African). The prevalences of 25(OH)D <50 and <25 nmol L−1 by WHO region in pregnant women were: Americas (64%, 9%), European (57%, 23%), Eastern Mediterranean (46%, 79%), South‐East Asian (87%, not available) and Western Pacific (83%, 13%). Among newborns these values were: Americas (30%, 14%), European (73%, 39%), Eastern Mediterranean (60%, not available), South‐East Asian (96%, 45%) and Western Pacific (54%, 14%). By global region, average 25(OH)D concentration varies threefold in pregnant women and newborns, and prevalence of 25(OH)D <25 nmol L−1 varies eightfold in pregnant women and threefold in newborns. Maternal and newborn 25(OH)D concentrations are highly correlated. Addressing vitamin D deficiency in pregnant women and newborns should be a global priority. To protect children from the adverse effects of vitamin D deficiency requires appropriate interventions during both pregnancy and childhood.
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FZAB, GIS, IJS, IZUM, KILJ, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBMB, UL, UM, UPUK
Development of emergency department (ED) triage systems that accurately differentiate and prioritize critically ill from stable patients remains challenging. We used machine learning models to ...predict clinical outcomes, and then compared their performance with that of a conventional approach-the Emergency Severity Index (ESI).
Using National Hospital and Ambulatory Medical Care Survey (NHAMCS) ED data, from 2007 through 2015, we identified all adult patients (aged ≥ 18 years). In the randomly sampled training set (70%), using routinely available triage data as predictors (e.g., demographics, triage vital signs, chief complaints, comorbidities), we developed four machine learning models: Lasso regression, random forest, gradient boosted decision tree, and deep neural network. As the reference model, we constructed a logistic regression model using the five-level ESI data. The clinical outcomes were critical care (admission to intensive care unit or in-hospital death) and hospitalization (direct hospital admission or transfer). In the test set (the remaining 30%), we measured the predictive performance, including area under the receiver-operating-characteristics curve (AUC) and net benefit (decision curves) for each model.
Of 135,470 eligible ED visits, 2.1% had critical care outcome and 16.2% had hospitalization outcome. In the critical care outcome prediction, all four machine learning models outperformed the reference model (e.g., AUC, 0.86 95%CI 0.85-0.87 in the deep neural network vs 0.74 95%CI 0.72-0.75 in the reference model), with less under-triaged patients in ESI triage levels 3 to 5 (urgent to non-urgent). Likewise, in the hospitalization outcome prediction, all machine learning models outperformed the reference model (e.g., AUC, 0.82 95%CI 0.82-0.83 in the deep neural network vs 0.69 95%CI 0.68-0.69 in the reference model) with less over-triages in ESI triage levels 1 to 3 (immediate to urgent). In the decision curve analysis, all machine learning models consistently achieved a greater net benefit-a larger number of appropriate triages considering a trade-off with over-triages-across the range of clinical thresholds.
Compared to the conventional approach, the machine learning models demonstrated a superior performance to predict critical care and hospitalization outcomes. The application of modern machine learning models may enhance clinicians' triage decision making, thereby achieving better clinical care and optimal resource utilization.
OBJECTIVES: To evaluate the association between serum 25‐hydroxyvitamin D (25(OH)D) levels and mortality in a representative U.S. sample of older adults.
DESIGN: Prospective cohort from the Third ...National Health and Nutrition Examination Survey (NHANES III) and linked mortality files.
SETTING: Noninstitutionalized U.S. civilian population.
PARTICIPANTS: Three thousand four hundred eight NHANES III participants aged 65 and older enrolled from 1988 to 1994 and followed for mortality through 2000.
MEASUREMENTS: Primary exposure was serum 25(OH)D level at enrollment. Primary and secondary outcomes were all‐cause and cardiovascular disease (CVD) mortality, respectively.
RESULTS: During the median 7.3 years of follow‐up, there were 1,493 (44%) deaths, including 767 CVD‐related deaths. Median 25(OH)D level was 66 nmol/L. Adjusting for demographics, season, and cardiovascular risk factors, baseline 25(OH)D levels were inversely associated with all‐cause mortality risk (adjusted hazard ratio (HR)=0.95, 95% confidence interval (CI)=0.92–0.98, per 10 nmol/L 25OHD). Compared with subjects with 25(OH)D levels of 100 nmol/L or higher, the adjusted HR for subjects with levels less than 25.0 nmol/L was 1.83 (95% CI=1.14–2.94) and for levels of 25.0 to 49.9 nmol/L was 1.47 (95% CI=1.09–1.97). The association appeared stronger for CVD mortality (adjusted HR=2.36, 95% CI=1.17–4.75, for subjects with 25OHD levels<25.0 nmol/L vs those ≥100.0 nmol/L) than for non‐CVD mortality (adjusted HR=1.42, 95% CI=0.73–2.79, for subjects with 25OHD levels<25.0 nmol/L vs those ≥100.0 nmol/L).
CONCLUSION: In noninstitutionalized older adults, a group at high risk for all‐cause mortality, serum 25(OH)D levels had an independent, inverse association with CVD and all‐cause mortality. Randomized controlled trials of vitamin D supplementation in older adults are warranted to determine whether this association is causal and reversible.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
9.
Acute Allergic Reactions to mRNA COVID-19 Vaccines Blumenthal, Kimberly G; Robinson, Lacey B; Camargo, Carlos A ...
JAMA : the journal of the American Medical Association,
04/2021, Volume:
325, Issue:
15
Journal Article
Peer reviewed
Open access
Of 64900 employees who received their first dose of a COVID-19 vaccine, 25929 (40%) received the Pfizer-BioNTech vaccine and 38971 (60%) received the Moderna vaccine. At least 1 symptom survey was ...completed by 52 805 (81%). Acute allergic reactions were reported by 1365 employees overall (2.10% 95% CI, 1.99%-2.22%), more frequently with the Moderna vaccine compared with Pfizer-BioNTech (2.20% 95% CI, 2.06%-2.35% vs 1.95% 95% CI, 1.79%-2.13%; P= .03). Anaphylaxis was confirmed in 16 employees (0.025% 95% CI, 0.014%-0.040%): 7 cases from the Pfizer-BioNTech vaccine (0.027% 95% CI, O.011%-0.056%) and 9 cases from the Moderna vaccine (0.023% 95% CI, 0.011%-0.044%) (P=.76). Individuals with anaphylaxis were a mean age of 41 (SD, 13) years, and 15 (94%) were female (Table 2): 10 (63%) had an allerav historv and 5 (31%) had an anaphylaxis history. Mean time to anaphylaxis onset was 17 (SD, 28; range, 1-120) minutes.
Clinical and epidemiologic studies have shown that obesity is associated with asthma and that these associations differ by asthma subtype. Little is known about the shared genetic components between ...obesity and asthma.
We sought to identify shared genetic associations between obesity-related traits and asthma subtypes in adults.
A cross-trait genome-wide association study (GWAS) was performed using 457,822 subjects of European ancestry from the UK Biobank. Experimental evidence to support the role of genes significantly associated with both obesity-related traits and asthma through a GWAS was sought by using results from obese versus lean mouse RNA sequencing and RT-PCR experiments.
We found a substantial positive genetic correlation between body mass index and later-onset asthma defined by asthma age of onset at 16 years or greater (Rg = 0.25, P = 9.56 × 10−22). Mendelian randomization analysis provided strong evidence in support of body mass index causally increasing asthma risk. Cross-trait meta-analysis identified 34 shared loci among 3 obesity-related traits and 2 asthma subtypes. GWAS functional analyses identified potential causal relationships between the shared loci and Genotype-Tissue Expression (GTEx) quantitative trait loci and shared immune- and cell differentiation–related pathways between obesity and asthma. Finally, RNA sequencing data from lungs of obese versus control mice found that 2 genes (acyl-coenzyme A oxidase-like ACOXL and myosin light chain 6 MYL6) from the cross-trait meta-analysis were differentially expressed, and these findings were validated by using RT-PCR in an independent set of mice.
Our work identified shared genetic components between obesity-related traits and specific asthma subtypes, reinforcing the hypothesis that obesity causally increases the risk of asthma and identifying molecular pathways that might underlie both obesity and asthma.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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