目的:比较早期预警评分(EWS)和改良早期预警评分(MEWS)预测急诊住院患者死亡风险的能力。方法:随机抽取409名四川大学华西医院急诊住院患者,采用EWS和MEWS对患者进行评分,使用ROC曲线比较两者预测急诊住院患者死亡风险的能力。结果:EWS预测患者住院的曲线面积为0.849±0.132,其最佳截断值为4分;MEWS预测急诊患者住院的曲线下面积为0.876±0.124,其最佳截断值为5分。结论:MEWS较EWS对于预测急诊住院患者死亡风险有较高的效能,还可以进一步改进提高其预测能力。
ObjectiveTo evaluate the prognosis factors for early death (within 60 days) of acute myocardial infarction (AMI) patients for early identification and prevention of the disease. MethodsWe analyzed the information of AML patients who were admitted to the emergency department between May 2009 and July 2010, and analyzed their clinical data, such as gender, age, prehospital time, myocardial enzyme, electrocardiogram, complications, whether the patients had thrombolysis therapy, time of thrombolysis, end point observation and time of death, ect. Cox multivariate survival analysis was performed with the use of SPSS 18.0 software. ResultsSeventy-one cases were collected with one of them excluded for fragmented data. After analysing, we found that patients' age and isoenzymes of creatine kinase (CK-MB) level were prognosis factors for early death. Further analysis showed that the relative risk (RR) of age was 1.166 (P=0.023), and the RR of CK-MB was 1.001 (P=0.004). ConclusionPatients' age has predictive value for early death of AML. More attention should be paid to AML patients with advanced age. Detecting myocardial enzymes levels, especially the CK-MB level, is significant for predicting early death. Other indicators need to be further explored due to the possible limitation of our study.
Objective To explore the association between plasma IgG and acute exacerbation (AE) or death risk in patients with chronic obstructive pulmonary disease (COPD). Methods A total of 262 COPD patients treated in our hospital from February 2018 to February 2020 were recruited in our study. All patients were divided into AE≥2 group and AE≤1 group according to AE frequency during follow-up of 1 year. Basic data and laboratory data such as IgG, IgA and IgM of two groups were comparatively analyzed. Univariate analysis and COX regression were performed to analyze the related factors of frequency of AE≥2 times in 1 year. Depicting restricted cubic spline was performed to analyze the relation between IgG and AE by R software. All patients were also divided into high IgG group, low IgG group, high IgA group and low IgA group based on median of patients’ baseline plasma IgG and IgA level, depicting survival curve by Kaplan-Meier to analyse differences between the groups with different IgG or IgA level in the risk of AE and death respectively. ResultsFinally, there were 14 patients lost to follow-up and 248 cases were included (AE≤1 group contained 154 cases, AE≥2 group contained 94 cases) until February 28, 2021. Age and COPD Assessment Test (CAT) scores in the AE≥2 group were higher than those in the AE≤1 group; Albumin, IgG and IgA level in the AE≥2 group were lower than those in the AE≤1 group; Neutrophil to lymphocyte ratio (NLR) in the AE≥2 group was higher than that in the AE≤1 group (all P<0.05). There were 99 and 114 cases of AE in the high IgG and low IgG groups respectively within 1 year. Kaplan Meier survival analysis showed that risk of AE in the high IgG group and high IgA group were lower than that in the low IgG group and the low IgA group (log rank χ2=23.791, 67.153, both P=0.000). Risk of death in the high IgG group was lower than that in the low IgG group (log rank χ2=6.214, P=0.013), there was no statistically difference in the risk of death in the high IgA group compared to the low IgA group (log rank χ2=2.400, P=0.121). Multivariate Cox regression analysis showed that CAT score (HR=1.096, P=0.001) and NLR (HR=2.061, P=0.000) were independent risk factors of frequency of AE≥2 times in 1 year for COPD patients, albumin (HR=0.921, P=0.006) and IgG (HR=0.572, P=0.000）were the independent protective factors. Restricted cubic spline analysis showed that combining the COX regression model, after adjusting for IgA, albumin, NLR and other variables, there was non-linear relationship between IgG level and AE (P=0.000).Conclusion Plasma IgG level is related to AE in COPD patients, and may become a reliable predictor of acute exacerbation risk in the future.
Objective To analyze the clinical characteristics, mortality risk and risk factors of patients with carbapenem resistant Acinetobacter baumannii (CRAB), so as to provide references for the prevention and control of CRAB. Methods Inpatients with Acinetobacter baumannii were selected from the clinical samples in the intensive care unit of Sichuan Academy of Medical Sciences and Sichuan Provincial People’s Hospital between January 2018 and December 2021. The patients were divided into CRAB infection group, carbapenem-sensitive Acinetobacter baumannii (CSAB) infection group and CRAB colonization group. Survival analysis was used to analyze the mortality risk and its influencing factors in patients with CRAB infection. Results A total of 696 patients were included. Among them, there were 392 cases of CRAB infection, 267 cases of CRAB colonization, and 37 cases of CSAB infection. The factors that increased the 30-day mortality risk of CRAB mainly included blood transfusion or use of blood products, mechanical ventilation, respiratory failure, maximum procalcitonin and age. Kaplan-Meier analysis showed that the 30-day mortality risk of CRAB infection group was higher than that of CSAB infection group（χ2=4.837, P=0.028), there was no significant difference between CRAB infection group and CRAB colonization group in 30-day mortality risk（χ2=0.219, P=0.640). Conclusions The mortality risk of CRAB infected patients is higher. Compared with the infection status, the 30-day mortality risk of patients is more attributed to drug resistance status. The effective method to control the mortality rate of CRAB should focus on reducing the hospital acquisition rate of CRAB.
Objective To evaluate the risk factors for postoperative in-hospital mortality in elderly patients receiving cardiac valvular surgery, and develop a new prediction models using the least absolute shrinkage and selection operator (LASSO)-logistic regression. Methods The patients≥65 years who underwent cardiac valvular surgery from 2016 to 2018 were collected from the Chinese Cardiac Surgery Registry (CCSR). The patients who received the surgery from January 2016 to June 2018 were allocated to a training set, and the patients who received the surgery from July to December 2018 were allocated to a testing set. The risk factors for postoperative mortality were analyzed and a LASSO-logistic regression prediction model was developed and compared with the EuroSCOREⅡ. Results A total of 7 163 patients were collected in this study, including 3 939 males and 3 224 females, with a mean age of 69.8±4.5 years. There were 5 774 patients in the training set and 1389 patients in the testing set. Overall, the in-hospital mortality was 4.0% (290/7163). The final LASSO-logistic regression model included 7 risk factors: age, preoperative left ventricular ejection fraction, combined coronary artery bypass grafting, creatinine clearance rate, cardiopulmonary bypass time, New York Heart Association cardiac classification. LASSO-logistic regression had a satisfying discrimination and calibration in both training [area under the curve (AUC)=0.785, 0.627] and testing cohorts (AUC=0.739, 0.642), which was superior to EuroSCOREⅡ. Conclusion The mortality rate for elderly patients undergoing cardiac valvular surgery is relatively high. LASSO-logistic regression model can predict the risk of in-hospital mortality in elderly patients receiving cardiac valvular surgery.
ObjectiveTo investigate the value of preoperative clinical data and computed tomography angiography (CTA) data in predicting perioperative mortality risk in patients with acute aortic dissection (AAD), and to construct a Nomogram prediction model. MethodsA retrospective study was conducted on AAD patients treated at Affiliated Hospital of Zunyi Medical University from February 2013 to July 2023. Patients who died during the perioperative period were included in the death group, and those who improved during the same period were randomly selected as the non-death group. The first CTA data and preoperative clinical data within the perioperative period of the two groups were collected, and related risk factors were analyzed to screen out independent predictive factors for perioperative death. The Nomogram prediction model for perioperative mortality risk in AAD patients was constructed using the screened independent predictive factors, and the effect of the Nomogram was evaluated by calibration curves and area under the curve (AUC). ResultsA total of 270 AAD patients were included. There were 60 patients in the death group, including 42 males and 18 females with an average age of 56.89±13.42 years. There were 210 patients in the non-death group, including 163 males and 47 females with an average age of 56.15±13.77 years. Multivariate logistic regression analysis showed that type A AAD [OR=0.218, 95%CI (0.108, 0.440), P<0.001], irregular tear morphology [OR=2.054, 95%CI (1.025, 4.117), P=0.042], decreased hemoglobin [OR=0.983, 95%CI (0.971, 0.995), P=0.007], increased uric acid [OR=1.003, 95%CI (1.001, 1.005), P=0.004], and increased aspartate aminotransferase [OR=1.003, 95%CI (1.000, 1.006), P=0.035] were independent risk factors for perioperative death in AAD patients. The Nomogram prediction model constructed using the above risk factors had an AUC of 0.790 for predicting perioperative death, indicating good predictive performance. ConclusionType A AAD, irregular tear morphology, decreased hemoglobin, increased uric acid, and increased aspartate aminotransferase are independent predictive factors for perioperative death in AAD patients. The Nomogram prediction model constructed using these factors can help assess the perioperative mortality risk of AAD patients.