1.A clinical study of mechanical ventilation in the treatment of acute respiratory failure following abdominal surgery
Shifang DING ; Wei ZHOU ; Qian ZHAI ; Xiaomei CHEN ; Kefu WANG ; Chen LI
Chinese Journal of General Surgery 2001;0(08):-
Objective To explore the predisposing factors in the development of acute respiratory failure after abdominal surgery and the factors affecting the therapeutic effect of mechanical ventilation. Methods A (retrospective) study was undertaken for acute respiratory failure after abdominal surgery in 91 patients. The (underline) diseases, introducing causes and efficacy of mechanical ventilation were retrospectively analysed. (Results) Postoperative pneumonia was the cause of acute respiratory failure in 53 cases and ARDS caused by severe abdominal infection and severe acute pancreatitis in 38 cases. Of the 91 cases, complicated with COPD in 38 cases, severe malnutrion 32 cases, and hypokalemia 14 cases. Respiratory failure occurred at(4.08?2.45)days after operation. The duration of mechanical ventilation was(21.66?21.42)days; 33 cases died, and 58 cases were successfully recovered with mechanical ventilation.Conclusions The (management) of acute respiratory failure after abdominal asurgery should be rational use of mechanical (ventilation), adjustment of weaning strategy and avoidance of dependance on mechanical ventilation. Timely treatment of the primary disease, effective control of abdominal infection and aggressive symptomatic and (supportive) treatment are factors that affect the success or failure of mechanical ventilation.
2.Analysis of central venous catheter related sepsis
Shifang DING ; Wei ZHOU ; Enhua SUN ; Xiaojun SUI ; Xiaomei CHEN ; Kefu WANG ; Shen LI
Parenteral & Enteral Nutrition 1997;0(04):-
Objectives: To investigate the distribution of common pathogens and their antibiotic resistance from patiens with catheter related sepsis (CRS).Methods: Catheter bacteria cultrure and antibiotic sensitivity test were performed from 69 patiens with CRS.Results: The common pathogens in CRS were fungi (41.1%),Gram-positive cocci (35.6%)and Gram-negtive bacilli (23.3%). Non-C. albicans species were major pathogen (19/30 stranins).The most strains were staphylococcus epidermidis in Gram-positive cocci and the most of them were Methicillin resistant.No vancomycin resistant strains were found. The Gram negative bacilli were often resistant to third generation cephalosporens.Conclusions: The dorminant pathogens of CRS are fungi and gram positive cocci and we should pay more attention to pathogens of resistence to antibiotics. In order to control CRS, CVC must be used reasonably and shorten the duration of retention.
3. Observation of preliminary efficacy and nursing care of skin relief cream and Biafine for radiation-induced skin damage in patients with nasopharyngeal carcinoma
Kefu SHI ; Liping QI ; Dan ZHOU ; Huixia FENG
Chinese Journal of Radiation Oncology 2019;28(10):728-730
Objective:
To compare the preliminary efficacy and nursing care of radioprotective agent skin relief cream and Biafine for the prevention and treatment of neck radiation-induced skin damage in patients with nasopharyngeal carcinoma.
Methods:
Sixty-seven nasopharyngeal carcinoma patients initially treated with intensity-modulated radiotherapy in Sun Yat-sen University Cancer Prevention and Control Center from 2016 to 2017 were recruited and assigned into the control (
4.The predictive value of estimated renal perfusion pressure in acute kidney injury of severe multiple trauma patients
Jing QI ; Chuanzheng SUN ; Huaizheng LIU ; Kefu ZHOU ; Zheren DAI ; Yishu TANG
Chinese Journal of Emergency Medicine 2021;30(8):968-972
Objective:To investigate the predictive value of estimated renal perfusion pressure (eRPP) for acute kidney injury (AKI) in severe multiple trauma patients.Methods:Severe multiple trauma patients were collected based on the inclusion criteria and exclusion criteria from the Trauma Center, the Third Xiangya Hospital, Central South University. Subsequently, patients were divided into the AKI group and non-AKI group according to the occurrence of AKI during 72 h admission to hospital. Further clinical information, ISS score, SOFA score, APACHE Ⅱ score, mean arterial pressure (MAP), central venous pressure (CVP) and intra-abdominal pressure (IAP) were collected, and eRPP were calculated. Additionally, the differences of parameters in the AKI group and non-AKI group were analyzed and logistic regression analysis was performed to identify the independent predicted risk factors for AKI. Finally, ROC curve was conducted to identify specificity, sensibility and best cut-off point.Results:A total of 173 severe multiple trauma patients were finally analyzed. Compared with the non-AKI group, the serum albumin [(32.21±5.20)g/L vs. (34.83±4.20)g/L, P =0.001] and 24 h urine output [(711.90±241.38)mL vs. (1 101.21±509.86)mL, P =0.001] were significantly lower and serum lactate [(2.80±0.96)mmol/L vs. (1.89±0.63)mmol/L, P<0.001], ISS score [(29.05±5.91) vs. (22.17±4.02), P <0.001], APACHEⅡ score [(38.84±21.47) vs. (31.45±18.24), P <0.001] and SOFA score [(5.26±2.08) vs. (3.14±1.34), P <0.001], in-hospital mortality (9.52% vs. 2.29%, P=0.038), and ICU stay [(8.43±6.46)d vs. (6.42±3.78) d, P =0.01) were significantly higher in the AKI group. Moreover, 6, 12 and 24 h of CVP and eRPP after admission were associated with the incidence of AKI. Logistic regression analysis showed that 24 h urine output, CVP and eRPP were the independent predictive factors (P <0.05) and 24 h of eRPP after admission applied a better predictive value of the incidence in AKI. Conclusions:24 h of eRPP might be the most suitable independent predictive factor for AKI in severe multiple trauma patients.
5.A polit study of using CT-radiomics based machine learning model in predicting immune cells infiltrating and prognosis of pancreatic cancer
Tiansong XIE ; Weiwei WENG ; Wei LIU ; Kefu LIU ; Weiqi SHENG ; Zhengrong ZHOU
Chinese Journal of Radiology 2022;56(4):425-430
Objective:To investigate the value of CT-radiomics based machine learning model in predicting the abundance of tumor infiltrating CD8 +T cells and the prognosis of pancreatic cancer patients. Methods:A total of 150 pancreatic cancer patients who underwent surgical excision and confirmed by pathology from Fudan University Shanghai Cancer Center between December 2011 and January 2017 were retrospectively enrolled. The patients were randomly divided into the training set ( n=105) and the validation set ( n=45) in a 7∶3 ratio with simple random sampling. The immunohistochemical method was used to assess the abundance of tumor infiltrating CD8 +T cells, and the patients were then divided into high infiltrating group ( n=75) and low infiltrating group ( n=75) according to the median. The prognosis between the 2 groups was evaluated using Kaplan-Meier method and log-rank test. Radiomic features were extracted from preoperative venous-phase enhanced CT images in the training set. The Wilcoxon test, the max-relevance and min-redundancy algorithm were used to select the optimal feature set. Three supervised machine learning models (decision tree, random forest and extra tree) were established based on the optimal feature set to predict the abundance of tumor infiltrating CD8 +T cells. Performance of above-mentioned models to predict the abundance of tumor infiltrating CD8 +T cells in pancreatic cancer was tested in the validation set. The evaluation parameters included area under the receiver operating characteristic curve (AUC), F1-score, accuracy, precision and recall. Results:The median overall survival time of patients in high infiltrating group and low infiltrating group were 875 days and 529 days, respectively (χ2=11.53, P<0.001). The optimal feature set consisted of 10 radiomic features in training set. In the validation set, the decision tree, random forest and extra tree model showed the AUC of 0.620, 0.704 and 0.745, respectively; corresponding to a F1-score of 0.457, 0.667 and 0.744, the accuracy of 57.8%, 68.9% and 75.6%, the precision of 66.7%, 73.7% and 80.0%, the recall of 34.8%, 60.9% and 69.6%. Conclusions:Pancreatic cancer patients with high tumor infiltrating CD8 +T cells have better prognosis than those with low tumor infiltrating CD8 +T cells. The radiomics-based extra tree model is valuable in predicting the CD8 +T cells infiltrating level in pancreatic cancer.