Distribution and drug resistance of pathogenic bacteria in emergency patients

BACKGROUND Antibiotic resistance has become a global threat for human health,calling for rational use of antibiotics.AIM To analyze the distribution and drug resistance of the bacteria,providing the prerequisite for use of antibiotics in emergency patients.METHODS A 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.RESULTS A 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.CONCLUSION The 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 drugresistant 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.

Correlation between the warning symptoms and prognosis of cardiac arrest

BACKGROUND A low survival rate in patients with cardiac arrest is associated with failure to recognize the condition in its initial stage.Therefore,recognizing the warning symptoms of cardiac arrest in the early stage may play an important role in survival.AIM To investigate the warning symptoms of cardiac arrest and to determine the correlation between the symptoms and outcomes.METHODS We included all adult patients with all-cause cardiac arrest who visited Peking University Third Hospital or Beijing Friendship Hospital between January 2012 and December 2014.Data on population,symptoms,resuscitation parameters,and outcomes were analysed.RESULTS Of the 1021 patients in the study,65.9%had symptoms that presented before cardiac arrest,25.2%achieved restoration of spontaneous circulation(ROSC),and 7.2%survived to discharge.The patients with symptoms had higher rates of an initial shockable rhythm(12.2%vs 7.5%,P=0.020),ROSC(29.1%vs 17.5%,P=0.001)and survival(9.2%vs 2.6%,P=0.001)than patients without symptoms.Compared with the out-of-hospital cardiac arrest(OHCA)without symptoms subgroup,the OHCA with symptoms subgroup had a higher rate of calls before arrest(81.6%vs 0.0%,P<0.001),health care provider-witnessed arrest(13.0%vs 1.4%,P=0.001)and bystander cardiopulmonary resuscitation(15.5%vs 4.9%,P=0.002);a shorter no flow time(11.7%vs 2.8%,P=0.002);and a higher ROSC rate(23.8%vs 13.2%,P=0.011).Compared to the in-hospital cardiac arrest(IHCA)without symptoms subgroup,the IHCA with symptoms subgroup had a higher mean age(66.2±15.2 vs 62.5±16.3 years,P=0.005),ROSC(32.0%vs 20.6%,P=0.003),and survival rates(10.6%vs 2.5%,P<0.001).The top five warning symptoms were dyspnea(48.7%),chest pain(18.3%),unconsciousness(15.2%),paralysis(4.3%),and vomiting(4.0%).Chest pain(20.9%vs 12.7%,P=0.011),cardiac etiology(44.3%vs 1.5%,P<0.001)and survival(33.9%vs 16.7%,P=0.001)were more common in males,whereas dyspnea(54.9%vs 45.9%,P=0.029)and a non-cardiac etiology(53.3%vs 41.7%,P=0.003)were more common in females.CONCLUSION Most patients had warning symptoms before cardiac arrest.Dyspnea,chest pain,and unconsciousness were the most common symptoms.Immediately recognizing these symptoms and activating the emergency medical system prevents resuscitation delay and improves the survival rate of OHCA patients in China.

Performance of Simplified Acute Physiology Score 3 in Predicting Hospital Mortality in Emergency Intensive Care Unit

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 11 （APACHE 11）, and predicted mortality were significantly higher in nonsurvivors than survivors （P 〈 0.05 or P 〈 0.01 ）. The AUC （95% confidence intervals [C/s]） 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 tbr 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 11 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.