Background: This systemic review aimed to explore the predictors of discharge and neurologic outcome of adult extracorporeal cardiopulmonary resuscitation (ECPR) to provide references for patient selection. Methods: Electronically searching of the Pubmed, Embase, Cochrane Library, and manual retrieval were done for clinical trials about predictors for adult ECPR which were published between January 2000 and January 2018 and included predictors for discharge and neurologic outcome. The literature was screened according to inclusion and exclusion criteria, the baseline information and interested outcomes were extracted. Two reviewers assessed the methodologic quality of the included studies and the quality of evidence for summary estimates independently. Pooled mean difference (MD) or odds ratio (OR) and 95% confidence interval (CI) were calculated by Review Manager Software 5.3. At last the quality of evidence for summary estimates was appraised according to Grading of Recommendations Assessment, Development, and Evaluation rating system. Results: In 16 studies, 1162 patients were enrolled. Out-of-hospital cardiac arrest (CA) (OR 0.58, 95% CI 0.36–0.93, P = .02), in-hospital CA (OR 1.73, 95% CI 1.08–2.77, P = .02), witnessed CA (OR 5.2, 95% CI 1.18–22.88, P = .01), bystander cardiopulmonary resuscitation (CPR) (OR 7.35, 95% CI 2.32–23.25, P < .01), initial shockable rhythm (OR 2.29, 95% CI 1.53–3.42, P < .01), 1st recorded nonshockable rhythm (OR 0.44, 95% CI 0.29–0.66, P < .01), CPR duration (MD −13.84 minutes, 95% CI −21 to −6.69, P < .0001), arrest-to-extracorporeal membrane oxygenation (ECMO) (MD −17.88 minutes, 95% CI −23.59 to −12.17, P < .01), PH (MD 0.14, 95% CI 0.08–0.21, P < .01), lactate (MD −3.66 mmol/L, 95% CI −7.15 to −0.17, P = .04), and percutaneous coronary intervention (PCI) (OR 1.63, 95% CI 1.02–2.58, P = .04)were identified as the survival predictors of ECPR. Shockable rhythm (OR 2.33, 95% CI 1.20–4.52, P = .01) and CPR duration (MD −9.85 minutes, 95% CI −15.71 to −3.99, P = .001) were identified as the neurologic outcome predictors of ECPR. Conclusion: Current evidence showed that in-hospital CA, witnessed CA, bystander CPR, initial shockable rhythm, shorter CPR duration and arrest-to-ECMO duration, higher baseline PH, lower baseline lactate and PCI were favourable survival predictors of adult ECPR, and shockable rhythm and shorter CPR duration were good neurological outcome predictors of adult ECPR.

The number of critically ill patients has increased globally along with the rise in emergency visits. Mortality prediction for critical patients is vital for emergency care, which affects the distribution of emergency resources. Traditional scoring systems are designed for all emergency patients using a classic mathematical method, but risk factors in critically ill patients have complex interactions, so traditional scoring cannot as readily apply to them. As an accurate model for predicting the mortality of emergency department critically ill patients is lacking, this study’s objective was to develop a scoring system using machine learning optimized for the unique case of critical patients in emergency departments. We conducted a retrospective cohort study in a tertiary medical center in Beijing, China. Patients over 16 years old were included if they were alive when they entered the emergency department intensive care unit system from February 2015 and December 2015. Mortality up to 7 days after admission into the emergency department was considered as the primary outcome, and 1624 cases were included to derive the models. Prospective factors included previous diseases, physiologic parameters, and laboratory results. Several machine learning tools were built for 7-day mortality using these factors, for which their predictive accuracy (sensitivity and specificity) was evaluated by area under the curve (AUC). The AUCs were 0.794, 0.840, 0.849 and 0.822 respectively, for the SVM, GBDT, XGBoost and logistic regression model. In comparison with the SAPS 3 model (AUC = 0.826), the discriminatory capability of the newer machine learning methods, XGBoost in particular, is demonstrated to be more reliable for predicting outcomes for emergency department intensive care unit patients.

Background: Ozone (O₃) and nitrogen dioxide (NO₂) are the two main gaseous pollutants in the atmosphere that act as oxidants. Their short-term effects and interaction on emergency room visits (ERVs) for respiratory diseases remain unclear. Methods: We conducted a time-series study based on 144,326 ERVs for respiratory diseases of Peking University Third Hospital from 2014 to 2019 in Beijing, China. Generalized additive models with quasi-Poisson regression were performed to analyze the association of O₃, NO₂ and their composite indicators (O_x and O_x^wt) with ERVs for respiratory diseases. An interaction model was further performed to evaluate the interaction between O₃ and NO₂. Results: Exposure to O₃, NO₂, O_x and O_x^wt was positively associated with ERVs for total respiratory diseases and acute upper respiratory infection (AURI). For instance, a 10 μg/m³ increase in O₃ and NO₂ were associated with 0.93% (95%CI: 0.05%, 1.81%) and 5.87% (95%CI: 3.92%, 7.85%) increase in AURI at lag0-5 days, respectively. Significant linear exposure–response relationships were observed in O_x and O_x^wt over the entire concentration range. In stratification analysis, stronger associations were observed in the group aged < 18 years for both O₃ and NO₂, in the warm season for O₃, but in the cold season for NO₂. In interaction analysis, the effect of O₃ on total respiratory emergency room visits and AURI visits was the strongest at high levels (> 75% quantile) of NO₂ in the < 18 years group. Conclusions: Short-term exposure to O₃ and NO₂ was positively associated with ERVs for respiratory diseases, particularly in younger people (< 18 years). This study for the first time demonstrated the synergistic effect of O₃ and NO₂ on respiratory ERVs, and O_x and O_x^wt may be potential proxies.

Aim: To investigate (1) the association between pre-hospital emergency medical resources and pre-hospital emergency medical system (EMS) response time among patients with Out-of-hospital cardiac arrest (OHCA); (2) whether the association differs between urban and suburbs.Methods: Densities of ambulances and physicians were independent variables, respectively. Pre-hospital emergency medical system response time was dependent variable. Multivariate linear regression was used to investigate the roles of ambulance density and physician density in pre-hospital EMS response time. Qualitative data were collected and analyzed to explore reasons for the disparities in pre-hospital resources between urban areas and suburbs.Results: Ambulance density and physician density were both negatively associated with call to ambulance dispatch time, with odds ratios (ORs) 0.98 (95% confidence interval [CI] 0.96–0.99; P = 0.001) and 0.97 (95% CI; 0.93–0.99; P < 0.001), respectively. ORs of ambulance density and physician density in association with total response time were 0.99 (95% CI: 0.97–0.99; P = 0.013) and 0.90 (95% CI: 0.86–0.99; P = 0.048). The effect of ambulance density on call to ambulance dispatch time in urban areas was 14% smaller than that in suburb areas and that on total response time in urban areas was 3% smaller than the effect in suburbs. Similar effects were identified for physician density on urban–suburb disparities in call to ambulance dispatch time and total response time. The main reasons summarized from stakeholders for a lack of physicians and ambulances in suburbs included low income, poor personal incentive mechanisms, and inequality in financial distribution of the healthcare system.Conclusion: Improving pre-hospital emergency medical resources allocation can reduce system delay and narrow urban-suburb disparity in EMS response time for OHCA patients.

Skin diseases have become a global concern. This study aims to evaluate the associations between ambient air pollution and emergency room visits for skin diseases under the background of improving air quality in China. Based on 45,094 cases from a general hospital and fixed-site monitoring environmental data from 2014–2019 in Beijing, China, this study used generalized additive models with quasi-Poisson regression to estimate the exposure–health associations at lag 0–1 to lag 0–7. PM_2.5 and NO₂ exposure were associated with increased emergency room visits for total skin diseases (ICD10: L00-L99). Positive associations of PM_2.5, PM₁₀, O₃ and NO₂ with dermatitis/eczema (ICD-10: L20–30), as well as SO₂ and NO₂ with urticaria (ICD-10: L50) visits were also found. For instance, a 10 μg/m³ increase in PM_2.5 was associated with increases of 0.7% (95%CI: 0.2%, 1.2%) in total skin diseases visits at lag 0–5 and 1.1% (95%CI: 0.6%, 1.7%) in dermatitis/eczema visits at lag 0–1, respectively. For PM_2.5, PM₁₀ and CO, stronger annual associations were typically observed in the high-pollution (2014) and low-pollution (2018/2019) years. For instance, a 10 μg/m³ increase in PM_2.5 at lag 0–5 was associated with increases of 1.8% (95%CI: 1.0%, 2.6%) and 2.3% (95%CI: 0.4%, 4.3%) in total skin disease visits in 2014 and 2018, respectively. Our study emphasizes the necessity of controlling the potential health hazard of air pollutants on skin, although significant achievements in air quality control have been made in China.