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Objective:To evaluate the predictive value of mechanical power (MP) on the risk of in-hospital mortality in critical ill patients in emergency department.Methods:A total of 105 critical ill patients with invasive mechanical ventilation in the Department of Emergency of Second Affiliated Hospital of Guangzhou Medical University between December 1, 2017 and October 31, 2020 were retrospectively analyzed. Based on the clinical prognosis, the patients were divided into the in-hospital survival group (80 patients) and the in-hospital death group (25 patients). The clinical data and ventilator parameters were recorded, and the MP of the two groups was calculated in order to assess the predictive efficacy of MP on in-hospital death.Results:Compared to the in-hospital death group, the oxygenation index PaO 2/FiO 2 was significantly higher (271 mmHg vs. 217 mmHg, P=0.020) and blood lactate (1.59 mmol/L vs. 2.56 mmol/L, P<0.001) and procalcitonin (0.31 ng/mL vs. 3.55 ng/mL, P=0.028), minute ventilation (7.03 L/min vs.8.32 mmol/L, P=0.013), MP (14.37 J/min vs. 16.12 J/min, P=0.041), SOFA score (5 vs. 8, P=0.001) and APACHE II score (16 vs. 22, P=0.041) were significantly lower in the in-hospital survival group. Multivariate Logistic regression analysis showed that PaO 2/FiO 2( OR=1.015, P=0.044), MP ( OR=1.813, P=0.039) and SOFA score( OR=2.651, P=0.010) were independent risk factors for predicting hospital mortality in patients with mechanical ventilation. The areas under the ROC curves (AUC) were 0.62, 0.63 and 0.75, respectively. Moreover, the MP combined with SOFA score for predicting in-hospital death was significantly higher than that of MP alone (0.77 vs. 0.63, P<0.05). Conclusions:MP is associated with in-hospital death in patients with invasive mechanical ventilation in emergency department. MP combined with SOFA score can enhance its predictive efficacy
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OBJECTIVE@#To explore clinical rules based on the big data of the emergency department of the Second Affiliated Hospital of Guangzhou Medical University, and to establish an integrated platform for clinical research in emergency, which was finally applied to clinical practice.@*METHODS@#Based on the hospital information system (HIS), laboratory information system (LIS), emergency specialty system, picture archiving and communication systems (PACS) and electronic medical record system of the Second Affiliated Hospital of Guangzhou Medical University, the structural and unstructured information of patients in the emergency department from March 2019 to April 2022 was extracted. By means of extraction and fusion, normalization and desensitization quality control, the database was established. In addition, data were extracted from the database for adult patients with pre screening triage level III and below who underwent emergency visits from March 2019 to April 2022, such as demographic characteristics, vital signs during pre screening triage, diagnosis and treatment characteristics, diagnosis and grading, time indicators, and outcome indicators, independent risk factors for poor prognosis in patients were analyzed.@*RESULTS@#(1) The data of 338 681 patients in the emergency department of the Second Affiliated Hospital of Guangzhou Medical University from March 2019 to April 2022 were extracted, including 15 modules, such as demographic information, triage information, visit information, green pass and rescue information, diagnosis information, medical record information, laboratory examination overview, laboratory information, examination information, microbiological information, medication information, treatment information, hospitalization information, chest pain management and stroke management. The database ensured data visualization and operability. (2) Total 140 868 patients with pre-examination and triage level III and below were recruited from the emergency department database. The gender, age, type of admission to the hospital, pulse, blood pressure, Glasgow coma scale (GCS) and other indicators of the patients were included. Taking emergency admission to operating room, emergency admission to intervention room, emergency admission to intensive care unit (ICU) or emergency death as poor prognosis, the poor prognosis prediction model for patients with pre-examination and triage level III and below was constructed. The receiver operator characteristic curve and forest map results showed that the model had good predictive efficiency and could be used in clinical practice to reduce the risk of insufficient emergency pre-examination and triage.@*CONCLUSIONS@#The establishment of high-quality clinical database based on big data in emergency department is conducive to mining the clinical value of big data, assisting clinical decision-making, and improving the quality of clinical diagnosis and treatment.
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Adult , Humans , Big Data , Emergency Service, Hospital , Triage/methods , Intensive Care Units , Hospitalization , Retrospective StudiesABSTRACT
Objectives To investigate clinical features and the risk factors for 30-day death in elderly chest pain patients.Methods In the prospective study,514 patients with acute chest pain leading to emergency department visit were selected from March 2012-August 2010 and grouped into elderly group (aged≥65 years,n=309) and non-elderly group (aged< 65 years,n=205).The patient's clinical data during 30-day follow-up period were recorded for analysis and comparison.Multivariate regression analysis was used to investigate the risk factors of death.Results Among 514 cases with acute chest pain,30(5.8%)patients with all-cause death included 24 cases in group of 309 (7.8%) elderly patients and 6 (2.9%) cases in group of 205 non-elderly patients during 30 day follow-up period.Univariate regression analysis showed that female,low SBP,Killips' classification ≥ Ⅱ,high level of serum troponin T and creatinine,coronary artery ischemia were more likely to died during 30 day follow-up period.And female and Killips' classification ≥ Ⅱwere the independent factor for 30-day death in the elderly[OR:3.55 (95%CI:1.00-12.59) and 5.90 (95%CI:1.31-26.63)]respectively.Conclusions Elderly patients with acute chest pain for first emergency department visit are at high risk for 30-day death.Female and cardiac function Killips' classification ≥ Ⅱ,high levels of serum troponin T and creatinine and coronary artery ischemia are associated with 30-day death in patients with acute chest pain for first emergency visit.Female and Killips' classification ≥ Ⅱare the independent risk factor for 30-day death.
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Objective To observe the influence of hemoperfusion(HP) on microinflammatory state and atherosclerosis in uremic patients .Methods Thirty‐six patients with uremia were randomly assigned into 2 groups ,18 cases in eath group .The hemo‐dialysis(HD) group took hemodialysis for 3 times per week ,4 h per time;the HP+ HD group took once HP per week on the basis of HD .The levels of C‐reactive protein(CRP) ,total cholesterol(TC) ,triglyceride(TG) ,urea nitrogen(BUN) and serum creatinine (Cr) were measured before therapy and in six months after therapy .The atherosclerotic plaque size was detected by ultrasound with fine resolution .Results The levels of CRP ,TG and TC after treatment in the HP+ HD group were significantly decreased com‐pared with those before treatment and the HD group (P0 .05) .Conclusion HP can alleviate the inflam‐matory reaction and decrease the atherosclerosis occurrence .