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BACKGROUNDAntibiotic resistance has become a global threat for human health, calling for rational use of antibiotics.AIMTo analyze the distribution and drug resistance of the bacteria, providing the prerequisite for use of antibiotics in emergency patients.METHODSA total of 2048 emergency patients from 2013 to 2017 were enrolled. Their clinical examination specimens were collected, followed by isolation of bacteria. The bacterial identification and drug susceptibility testing were carried out.RESULTSA total of 3387 pathogens were isolated. The top six pathogens were Acinetobacter baumannii (660 strains), Staphylococcus aureus (436 strains), Klebsiella pneumoniae (347 strains), Pseudomonas aeruginosa (338 strains), Escherichia coli (237 strains), and Candida albicans (207 strains). The isolation rates of these pathogens decreased year by year except Klebsiella pneumoniae, which increased from 7.1% to 12.1%. Acinetobacter baumannii is a widely-resistant strain, with multiple resistances to imipenem, ciprofloxacin, minocycline and tigecycline. The Staphylococcus aureus had high resistance rates to levofloxacin, penicillin G, and tetracycline. But the susceptibility of it to vancomycin and tigecycline were 100%. Klebsiella pneumoniae had high resistance rates to imipenem, cefoperazone/sulbactam, amikacin, and ciprofloxacin, with the lowest resistance rate to tigecycline. The resistance rates of Pseudomonas aeruginosa to cefoperazone/sulbactam and imipenem were higher, with the resistance rate to amikacin below 10%. Besides, Escherichia coli had high resistance rates to ciprofloxacin and cefoperazone/sulbactam and low resistance rates to imipenem, amikacin, and tigecycline.CONCLUSIONThe pathogenic bacteria isolated from the emergency patients were mainly Acinetobacter baumannii, Staphylococcus aureus, Klebsiella pneumoniae, Pseudomonas aeruginosa, Escherichia coli, and Candida albicans. The detection rates of drug-resistant bacteria were high, with different bacteria having multiple drug resistances to commonly used antimicrobial agents, guiding the rational use of drugs and reducing the production of multidrug-resistant bacteria.

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:Since the 1980s, severity of illness scoring systems has gained increasing popularity in Intensive Care Units (ICUs). Physicians used them for predicting mortality and assessing illness severity in clinical trials. The objective of this study was to assess the performance of Simplified Acute Physiology Score 3 (SAPS 3) and its customized equation for Australasia (Australasia SAPS 3, SAPS 3 [AUS]) in predicting clinical prognosis and hospital mortality in emergency ICU (EICU).Methods:A retrospective analysis of the EICU including 463 patients was conducted between January 2013 and December 2015 in the EICU of Peking University Third Hospital. The worst physiological data of enrolled patients were collected within 24 h after admission to calculate SAPS 3 score and predicted mortality by regression equation. Discrimination between survivals and deaths was assessed by the area under the receiver operator characteristic curve (AUC). Calibration was evaluated by Hosmer-Lemeshow goodness-of-fit test through calculating the ratio of observed-to-expected numbers of deaths which is known as the standardized mortality ratio (SMR).Results:A total of 463 patients were enrolled in the study, and the observed hospital mortality was 26.1% (121/463). The patients enrolled were divided into survivors and nonsurvivors. Age, SAPS 3 score, Acute Physiology and Chronic Health Evaluation Score II (APACHE II), and predicted mortality were significantly higher in nonsurvivors than survivors (P < 0.05 or P < 0.01). The AUC (95% confidence intervals [CIs]) for SAPS 3 score was 0.836 (0.796–0.876). The maximum of Youden's index, cutoff, sensitivity, and specificity of SAPS 3 score were 0.526%, 70.5 points, 66.9%, and 85.7%, respectively. The Hosmer-Lemeshow goodness-of-fit test for SAPS 3 demonstrated a Chi-square test score of 10.25, P = 0.33, SMR (95% CI) = 0.63 (0.52–0.76). The Hosmer-Lemeshow goodness-of-fit test for SAPS 3 (AUS) demonstrated a Chi-square test score of 9.55, P = 0.38, SMR (95% CI) = 0.68 (0.57–0.81). Univariate and multivariate analyses were conducted for biochemical variables that were probably correlated to prognosis. Eventually, blood urea nitrogen (BUN), albumin, lactate and free triiodothyronine (FT3) were selected as independent risk factors for predicting prognosis.Conclusions:The SAPS 3 score system exhibited satisfactory performance even superior to APACHE II in discrimination. In predicting hospital mortality, SAPS 3 did not exhibit good calibration and overestimated hospital mortality, which demonstrated that SAPS 3 needs improvement in the future.

Background: Both the American Heart Association (AHA) and European Resuscitation Council (ERC) have strongly recommended targeted temperature management (TTM) for patients who remain in coma after return of spontaneous circulation (ROSC). However, the role of TTM, especially hypothermia, in cardiac arrest patients after TTM2 trials has become much uncertain.Methods: We searched four online databases (PubMed, Embase, CENTRAL, and Web of Science) and conducted a Bayesian network meta-analysis. Based on the time of collapse to ROSC and whether the patient received TTM or not, we divided this analysis into eight groups (&lt;20 min + TTM, &lt;20 min, 20–39 min + TTM, 20–39 min, 40–59 min + TTM, 40–59 min, ≥60 min + TTM and ≥60 min) to compare their 30-day and at-discharge survival and neurologic outcomes.Results: From an initial search of 3,023 articles, a total of 9,005 patients from 42 trials were eligible and were included in this network meta-analysis. Compared with other groups, patients in the &lt;20 min + TTM group were more likely to have better survival and good neurologic outcomes (probability = 46.1 and 52.5%, respectively). In comparing the same time groups with and without TTM, only the survival and neurologic outcome of the 20–39 min + TTM group was significantly better than that of the 20–39 min group [odds ratio = 1.41, 95% confidence interval (1.04–1.91); OR = 1.46, 95% CI (1.07–2.00) respectively]. Applying TTM with &lt;20 min or more than 40 min of collapse to ROSC did not improve survival or neurologic outcome [ &lt;20 min vs. &lt;20 min + TTM: OR = 1.02, 95% CI (0.61–1.71)/OR = 1.03, 95% CI (0.61–1.75); 40–59 min vs. 40–59 min + TTM: OR = 1.50, 95% CI (0.97–2.32)/OR = 1.40, 95% CI (0.81–2.44); ≧60 min vs. ≧60 min + TTM: OR = 2.09, 95% CI (0.70–6.24)/OR = 4.14, 95% CI (0.91–18.74), respectively]. Both survival and good neurologic outcome were closely related to the time from collapse to ROSC.Conclusion: Survival and good neurologic outcome are closely associated with the time of collapse to ROSC. These findings supported that 20–40 min of collapse to ROSC should be a more suitable indication for TTM for cardiac arrest patients. Moreover, the future trials should pay more attention to these patients who suffer from moderate injury.Systematic Review Registration: [https://inplasy.com/?s=202180027], identifier [INPLASY202180027]