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.