To investigate the efficacy of amiodarone and lidocaine in cardiac arrest patients.

Objective: To investigate the trends in incidence and mortality of esophageal cancer in cancer registration areas of Anhui Province from 2014 to 2020, so as to provide the basis for formulating prevention and control measures. Methods: The incidence and mortality data of esophageal cancer in Anhui Province from 2014 to 2020 was collected through the Cancer Registry in Anhui Province. The crude incidence and crude mortality were calculated. The Chinese population-standardized rate was standardized using the age structure of the standard population from the Fifth National Population Census in 2000. The trends in incidence and mortality of esophageal cancer were analyzed using the average annual percent change (APPC), stratified by genders, urban/rural areas, and ages. Results: In Anhui Province, the rank of esophageal cancer incidence dropped from the third in 2014 to the sixth in 2020. Concurrently, the crude incidence and Chinese population-standardized incidence declined from 28.74/100 000 and 20.74/100 000 to 19.23/100 000 and 10.59/100 000, respectively (AAPC=-5.846%, -9.658%, both P<0.05). The mortality rank remained stable at the fourth in 2014 and 2020, while the crude mortality and Chinese population-standardized mortality decreased from 19.96/100 000 and 14.09/100 000 to 16.00/100 000 and 8.41/100 000, respectively (AAPC=-3.542%, -7.784%, both P<0.05). The Chinese population-standardized incidence (AAPC=-9.682%, -9.188%, -6.175% and -12.575%, all P<0.05) and Chinese population-standardized mortality (AAPC=-7.734%. -7.447%. -5.366% and -10.209%, all P<0.05) showed declining trends in males, females, urban, and rural areas, respectively. From 2014 to 2020 in Anhui Province, the crude incidence and mortality of esophageal cancer generally increased with age. However, significant declining trends were observed in crude incidence (AAPC=-12.779%, -11.701%, -11.955% and -5.751%, all P<0.05) and crude mortality (AAPC=-12.255%, -11.120%, -10.985% and -5.751%, all P<0.05) for the age groups of 40-<50, 50-<60, 60-<70, 70-<80 years. A significant declining trend in crude incidence was also seen in the ≥80 years group (APPC=-6.334%, P<0.05), but the trend in crude mortality was no statistically significant (P>0.05). Conclusion In registration areas of Anhui Province, the incidence and mortality of esophageal cancer exhibited a declining trend from 2014 to 2020, calling for focused attention on the middle-aged and elderly population and enhanced health behaviors such as tobacco and alcohol control.

Objective:Metabolic alkalosis has raised concerns in patients receiving continuous renal replacement therapy (CRRT) via regional citrate anticoagulation (RCA). This study searched for alkalosis-related factors and mechanisms.Methods:It's a retrospective study of alkalosis in patients who received CRRT for at least 12 hours with RCA in a tertiary hospital between April 2017 and April 2020. Demographic features, baseline laboratory results, CRRT metrics and laboratory results at 12h after CRRT was recorded. Patients was grouped based on whether alkalosis exist at 12h after CRRT, and multivariable logistic regression analysis was used to identify risk factors for alkalosis during CRRT with citrate anticoagulation.Results:The 59 patients meeting the inclusion criteria were 49% male, with a mean age of (55±18) years old, and 42% had alkalosis by 12 hours after CRRT. No significant differences in demographic features or laboratory results were observed patients with or without alkalosis. CRRT metrics, including blood flow rate, citrate rate, replacement fluid rate and total effluent rate, were significantly different among groups ( P<0.01). Multivariable Logistic regression analysis indicated that the citrate rate was a risk factor for alkalosis ( OR=1.088, 95% CI 1.020-1.161, P =0.010). In patients receiving no NaHCO 3 and without alkalosis, the linear regression analysis described the relationships of citrate with replacement fluid rate (citrate rate = 0.090 × replacement fluid rate + 56.581; R2 = 0.6918) and total effluent rate (citrate rate = 0.099 × total effluent rate + 2.449). Conclusions:This retrospective observational study demonstrated that CRRT metrics are highly associated with alkalosis after 12 hours of CRRT. Without NaHCO3 infusion, a 10-fold linear correlation was observed between citrate and total effluent rate in patients without metabolic alkalosis.

Objective:The research group had previously developed an artificial intelligence algorithm to predict sepsis within 24 hours at the triage stage in emergency departments. This research studied the doctors’ response to algorithm-generated risk alerts and designs appropriate physician-algorithm collaboration strategies to further enhance sepsis risk identification capabilities.Methods:The research collected 40 cases of sepsis in the emergency departments from the open medical database MIMIC-IV (Medical Information Mart for Intensive Care) for a collaboration test. The cases were selected according to their typicality and classified according to the model’s confidence in its prediction. A total of 165 emergency doctors from 58 hospitals in China, stratified by professional rank, participated in the study. Four collaboration modes were designed with different information volumes and reading costs using the information offered by the algorithm. During the test, before and after the model presented its results and interpretive information according to the collaboration mode, the doctors were asked to rate the sepsis risk for each sample and record their confidence.Results:Analysis of the 4 704 valid evaluations done by 147 doctors showed that different collaboration modes caused no significant difference on doctors’ detection of sepsis risk. For cases with high model confidence, physicians’ diagnostic accuracy improved by 2.6%±0.6% ( P=0.02) post-algorithm input, with increased confidence in correct judgments. Conversely, for low-confidence model predictions, diagnostic accuracy decreased by 2.6%±1.4% ( P=0.06), accompanied by reduced clinician confidence in accurate assessments. Conclusions:The collaboration effect is mostly determined by the model’s confidence in its prediction. Different collaboration modes cause no significant difference, and doctors of different titles are influenced consistently with the same model confidence. Suggestions for collaboration design are as follow. When the model has low confidence in its own assessment of a patient’s sepsis risk, it should not directly demonstrate its assessment. When the model has high confidence, its assessment can be offered to the doctors as a reference. When predicting sepsis at the triage stage in the emergency departments, no extra interpretive information is needed.

Objective:Cardiac arrest (CA) represents a significant public health challenge, posing a substantial threat to individual health and survival. To enhance the survival rates of patients experiencing out-of-hospital cardiac arrest (OHCA), Baoan District in Shenzhen City has undertaken exploratory initiatives and practical interventions, yielding promising preliminary outcomes.Methods:1.Innovate emergency medical services by developing a "four-circle integration" system that connects to the hospital. This system encompasses the social emergency medical system, the out-of-hospital emergency medical system, the in-hospital emergency medical service system, and the intensive care treatment system. 2.Develop a comprehensive model for the construction of a social emergency medical training system, characterized by party leadership, government oversight, departmental coordination, professional guidance, technological support, and community involvement, termed the "Baonan Model." Additionally, establish evaluation criteria to assess the effectiveness of the social emergency medical training system in Baonan District; 3. Develop a cardiac arrest registration system and a social emergency medical training management system for Baonan District; 4. Enhance the proficiency in treatment techniques and the quality of cardiopulmonary resuscitation among emergency medical professionals; 5. Strengthen and advance the development of a "five-minute social rescue network" to address the critical "emergency window period." .Result:In Baonan District, 9.18% of the public is trained in emergency medical skills. The bystander CPR rate for OHCA is 26.11%, AED use is at 4.78%, the 30-day survival rate is 6.31%, and the discharge survival rate is 4.44%.Conclusion:The implementation of the aforementioned measures can substantially enhance the survival rate of patients experiencing OHCA at the time of discharge.

We report a case of a male patient who developed persistent fever and central nervous system symptoms after eating raw snails for 10 days. The patient was diagnosed with Angiostrongyliasis depended on the clinical presentation, epidemiological history, and etiological results. The patient recovered after receiving albendazole anthelmintic and dexamethasone anti-inflammatory therapy. This article incorporates literature review to sort out the diagnosis and treatment of this patient, in order to provide feasible reference for clinicians.

Atypical hemolytic uremic syndrome (aHUS), a rare disease caused by complement abnormalities, is characterized by microangiopathic hemolytic anemia, thrombocytopenia, and acute kidney injury. In this paper, we report a patient with severe renal insufficiency with rapidly progressive decline in binocular visual acuity, who developed eosinophilia during the course of the disease, and was diagnosed with aHUS after excluding other diseases. After glucocorticoid treatment, eosinophils decreased to normal, and after treatment with plasmapheresis combined with eculizumab, renal function tended to be stable, platelets returned to normal, but visual acuity did not improve significantly. This article reviews the diagnosis and treatment process of this patient and incorporates the review of literature, in the hope of providing reference for clinicians.

We report a case of a male patient who developed persistent fever and central nervous system symptoms after eating raw snails for 10 days. The patient was diagnosed with Angiostrongyliasis depended on the clinical presentation, epidemiological history, and etiological results. The patient recovered after receiving albendazole anthelmintic and dexamethasone anti-inflammatory therapy. This article incorporates literature review to sort out the diagnosis and treatment of this patient, in order to provide feasible reference for clinicians.

In 2009,the World Health Organization included snakebite on the list of neglected tropical diseases,acknowledging it as a common occupational hazard for farmers,plantation workers,and others,causing tens of thousands of deaths and chronic physical disabilities every year.This guideline aims to provide practical information to help clinical professionals evaluate and treat snakebite victims.These recommendations are based on clinical experience and clinical research evidence.This guideline focuses on the following topics:snake venom,clinical manifestations,auxiliary examination,diagnosis,treatments,and prevention.

BACKGROUND:Sepsis is one of the main causes of mortality in intensive care units(ICUs).Early prediction is critical for reducing injury.As approximately 36%of sepsis occur within 24 h after emergency department(ED)admission in Medical Information Mart for Intensive Care(MIMIC-IV),a prediction system for the ED triage stage would be helpful.Previous methods such as the quick Sequential Organ Failure Assessment(qSOFA)are more suitable for screening than for prediction in the ED,and we aimed to find a light-weight,convenient prediction method through machine learning. METHODS:We accessed the MIMIC-IV for sepsis patient data in the EDs.Our dataset comprised demographic information,vital signs,and synthetic features.Extreme Gradient Boosting(XGBoost)was used to predict the risk of developing sepsis within 24 h after ED admission.Additionally,SHapley Additive exPlanations(SHAP)was employed to provide a comprehensive interpretation of the model's results.Ten percent of the patients were randomly selected as the testing set,while the remaining patients were used for training with 10-fold cross-validation. RESULTS:For 10-fold cross-validation on 14,957 samples,we reached an accuracy of 84.1%±0.3%and an area under the receiver operating characteristic(ROC)curve of 0.92±0.02.The model achieved similar performance on the testing set of 1,662 patients.SHAP values showed that the five most important features were acuity,arrival transportation,age,shock index,and respiratory rate. CONCLUSION:Machine learning models such as XGBoost may be used for sepsis prediction using only a small amount of data conveniently collected in the ED triage stage.This may help reduce workload in the ED and warn medical workers against the risk of sepsis in advance.