In order to identify the incidence of nosocomial pulmonary infection in surgical critical care patients in our hospital, we studied 800 patients discharged from surgical intensive care unit between May 1992 to Dec. 1994. One hundred and six episodes of pulmonary infection were found in 96 cases, in which 20 cases had been re-infected. The infection rate was 12.0%. The age of patients, APACHE- Ⅱ score and duration in ICU were closely related to the incidence of pulmonary infection. Tracheal intubation, tracheotomy and mechanical ventilation were the predisposing factors. The prevalent pathogens were pseudomonas aeruginosa, acinetobacter, staphylococcus aureus and candida albicans. 54.7% of cases were infected with more than one pathogens, and 36.8% of cases had fungal infection. The prevention and treatment are also discussed.
【Abstract】 Objective To analyze the risk factors for ventilator-associated pneumonia ( VAP) in respiratory intensive care unit ( RICU) , as well as the impact on mortality. Methods A retrospective cohort study was conducted in 105 patients who had received mechanical ventilation in RICUbetweenMay 2008 andJanuary 2010. The duration of intubation, vital signs, primary disease of respiratory failure and complications,blood biochemistry, blood routine tests, arterial blood gas analysis, APACHEⅡ score,medications, nutritional status, bronchoalveolar lavage ( BAL) , protected specimen brush ( PSB) quantitative culture, chest X-rayexamination were recorded and analyzed. Results The incidence rate of VAP was 32. 4% . Mortality in the VAP patients were significantly higher than those without VAP( 58. 8% vs. 28. 2% , P = 0. 007) . The duration of intubation, hypotension induced by intubation, cerebrovascular disease, and hypoalbuminemiawererisk factors for VAP in RICU. Conclusions Mortality of the patients with VAP increased obviously. The risk factors for VAP in RICU were the duration of intubation, hypotension after intubation, cerebrovascular disease, and hypoalbuminemia.
With the continuous development of critical care medicine, the survival rate of critical ill patients continues to increase. However, the residual dysfunction will have a far-reaching impact on the burden on patients, families, and health-care systems, and will significantly increase the demand of the follow-up rehabilitation treatment. Critical illness rehabilitation intervenes patients who are still in the intensive care unit (ICU). It can prevent complications, functional deterioration and dysfunction, improve functional activity and quality of life, shorten the time of mechanical ventilation, the length of ICU stay and hospital stay, and also reduce medical expenses. Experts at home and abroad believe that early rehabilitation of critical ill patients is safe and effective. So rehabilitation should be involved in critical ill patients as early as possible. However, the promotion of this model is still limited by the setting of safety parameters, the ICU culture, the lack of critical rehabilitation professionals, and the physiological and mental cognitive status of patients. Rehabilitation treatment in ICU is constantly being practiced at home and abroad.
Weaning difficulty is common in critically ill patients. Prolonged mechanical ventilation and weaning failure adversely affect the clinical outcome. How to better promote and achieve the early extubation is a very important subject. As a multi-dimensional monitoring method of important structure, function and morphology, critical care ultrasound which is helpful to improve our understanding and grasp of the core links in the respiratory circuit can comprehensively evaluate the state and reserve capacity of some important organs, such as the heart, lungs and diaphragm. It has great value in assessment of weaning and guided treatment. This paper will review the application of severe ultrasound in weaning.
Objective To analyze the hot spot and future application trend of artificial intelligence technology in the field of intensive care medicine. Methods The CNKI, WanFang Data, VIP and Web of Science core collection databases were electronically searched to collect the related literature about the application of artificial intelligence in the field of critical medicine from January 1, 2013 to December 31, 2022. Bibliometrics was used to visually analyze the author, country, research institution, co-cited literature and key words. Results A total of 986 Chinese articles and 4 016 English articles were included. The number of articles published had increased year by year in the past decade, and the top three countries in English literature were China, the United States and Germany. The predictive model and machine learning were the most frequent key words in Chinese and English literature, respectively. Predicting disease progression, mortality and prognosis were the research focus of artificial intelligence in the field of critical medicine. ConclusionThe application of artificial intelligence in the field of critical medicine is on the rise, and the research hotspots are mainly related to monitoring, predicting disease progression, mortality, disease prognosis and the classification of disease phenotypes or subtypes.