Targeted Monitoring And Influencing Factors Analysis Of Hospital Infection In Patients Undergoing Esophageal Cancer Surgery

ObjectivesTo understand the current situation of hospital infection in patients with esophageal cancer undergoing surgery,to explore the pathogenic characteristics and independent risk factors of postoperative hospital infection,so as to provide a theoretical basis for clinical doctors and nurses to take timely preventive and control measures,reasonably apply antibiotics,and reduce the occurrence of hospital infection;To provide theoretical support for relevant hospital infection management departments to formulate prevention and control measures;In order to make full and effective use of medical resources,and optimize relevant infection control measures.MethodsA total of 500 patients with esophageal cancer who met the inclusion criteria from January 1,2016 to December 31,2022 were selected from a Grade Ⅲ Grade A hospital.The pre-investigation of 100 cases in the early stage of this study showed that the probability of hospital infection after resection of esophageal cancer was 13%,The specified allowable error is 3%,the confidence is 0.95,and the sample size calculated by PASS21 software is 405.Assuming that the incomplete rate of case information is 10%,the sample size is 450;Assuming that the qualified rate of the questionnaire is 90%,the total sample size is at least 500.By using the method of target monitoring,the patients’ relevant data were filled in the self-designed《Target Monitoring Questionnaire for Esophageal Cancer Surgery Patients》,and the patients with hospital infection were then filled in the《Hospital Infection Questionnaire for Esophageal Cancer Surgery Patients》.The research contents mainly include:the current situation of nosocomial infection in patients undergoing esophageal cancer surgery,the characteristics of pathogen distribution,The factors of postoperative hospital infection of esophageal cancer were analyzed by multivariate logistic regression,and the factors were assigned according to the regression coefficient to establish the predictive scoring model of postoperative hospital infection risk.The predictive effectiveness(discrimination and calibration)of the model was evaluated by using the subject working characteristic curve(ROC)and the Hosmer-Lemeshow test.The multi-layer perceptron module in the neural network section of SPSS25.0 statistical software is used to establish a neural network prediction model for hospital infection in surgical patients.The variables affecting hospital infection are screened out through multifactor logistic regression analysis in advance as the input variables of the neural network model,and the neural network prediction model is established and verified.Results1.A total of 500 patients with esophageal cancer were included,including 467 males(93.4%)and 33 females(6.6%),with an average age of 64.9±4.7 years.2.There were 50 cases of hospital infection,55 cases of infection,the rate of hospital infection was 10.00%,and the rate of hospital infection was 11.00%.Infection site:30 cases of lung infection(54.50%),15 cases of superficial wound infection(27.30%),5 cases of thoracic infection(9.10%),5 cases of deep wound infection(9.10%).3.Samples were submitted 66 times after operation,and 55 strains of pathogenic bacteria were detected.Gram negative bacteria were the most common pathogens,accounting for 65.50%,and Klebsiella pneumoniae and Escherichia coli were the most common;15 strains of Gram positive bacteria(27.20%),among which Staphylococcus aureus was the most common;4 fungi(7.30%).4.Logistic regression analysis was used to screen six independent factors for nosocomial infection in patients undergoing esophageal cancer surgery:history of diabetes,preoperative leukocyte level,preoperative hospital stay,operation time,and indwelling gastric tube time;Preoperative albumin is the protective factor.5.Establish and verify the risk prediction scoring model of postoperative hospital infection in patients with esophageal cancer,The model equation was constructed according to the independent influencing factors of the multivariate regression analysis.The prediction efficiency(discrimination and calibration)of the scoring model was evaluated using the receiver operating characteristic curve(ROC)and the Hosmer-Lemeshow test.The area under the ROC curve(AUC)is 0.990,indicating good discrimination.The Hosmer-Lemeshow test P=0.993,indicating high calibration(good prediction efficiency).To use spss 25.0 construct a neural network prediction model.The neural network prediction model shows that the correct rate of samples is 97.60%.The variables of preoperative hospitalization time,operative time,and indwelling gastric tube time are more important in predicting hospital infection.The area under the working characteristic curve(ROC)of the model is 0.993.ConclusionsThe incidence of hospital infection in patients undergoing esophageal cancer surgery is high,especially in lung infection.Therefore,patients undergoing esophageal cancer surgery should be considered as high-risk groups of hospital infection..History of diabetes,preoperative leukocyte level,preoperative hospital stay,operation time,time of indwelling gastric tube are risk factors of hospital infection after esophageal cancer surgery.Corresponding prevention and control measures should be taken against the above risk factors to reduce the incidence of hospital infection in this population.This study uses logistic regression analysis to build a risk prediction model of hospital infection after esophageal cancer surgery,which is used to guide the prevention and treatment of hospital infection after esophageal cancer surgery,reduce the incidence of infection,improve the prognosis of patients,and also provide reference for the application of logistic regression analysis model in clinical practice.The neural network prediction model is established to intuitively analyze the importance of influencing factors on hospital infection..In view of the risk factors of hospital infection,clinical medical staff should identify high-risk patients who are easy to be infected at an early stage,so as to actively carry out infection prevention and control work.To provide theoretical support for relevant hospital infection management departments to formulate prevention and control measures,make full and effective use of medical resources,and optimize relevant infection control measures.