ObjectiveTo identify the risk factors of Intensive Care Unit (ICU) nosocomial infection in ICU ward in a first-class hospital in Wuxi, and discuss the effective control measures, in order to provide evidence for making strategies in preventing and controlling nosocomial infection. MethodsAccording to the principle of random sampling and with the use of case-control study, a sample of 100 nosocomial infection patients were selected randomly from January 2012 to December 2014 as survey group, and another 100 patients without nosocomial infection as control group. The data were input using EpiData 2.0, and SPSS 13.0 was used for statistical analysis; t-test and χ2 test were conducted, and the risk factors were analyzed using multi-variate logistic regression model. The significant level of P-value was 0.05. ResultsBased on the results of univariate analysis, there were 13 risk factors for ICU nosocomial infection, including diabetes mellitus, hypoproteinemia, being bedridden, surgical operation, immunosuppression, glucocorticoids, organ transplantation, tracheal intubation, length of hospitalization, length of mechanical ventilation, length of central venous catheter, length of urinary catheter, and length of nasogastric tube indwelling. Multi-variate logistic analysis indicated that hospitalization of 7 days or longer[OR=1.106, 95%CI (1.025, 1.096), P=0.001], diabetes mellitus[OR=2.770, 95%CI (1.068, 7.186), P=0.036], surgical operation[OR=7.524, 95%CI (2.352, 24.063), P=0.001], mechanical ventilation of 7 days or longer[OR=1.222, 95%CI (1.116, 1.339), P<0.001], and nasogastric tube indwelling of 7 days or longer[OR=1.110, 95%CI (1.035, 1.190), P=0.003] were considered as independent risk factors for ICU nosocomial infection. ConclusionHospitalization of 7 days or longer, diabetes mellitus, surgical operation, tracheal intubation of 7 days or longer, and gastric intubation of 7 days or longer are the major risk factors for nosocomial infection in ICU ward. Advanced intervention and comprehensive prevention measures are helpful to reduce the nosocomial infection rate and ensure the safety of medical treatment.
Objective To evaluate systematically the effectiveness and safety of procalcitonin ( PCT) -guided therapy in comparison with standard therapy in patients with suspected or confirmed severe bacterial infections in intensive care unit ( ICU) . Methods Five randomized controlled trials ( 927 patients) were included for statistical analysis by the cochrane collaboration′s RevMan5. 0 software. Results PCT-guided therapy was associated with a significant reduction in duration of antibiotic therapy [ MD =- 2. 01, 95% CI ( - 2. 37, - 1. 64) , P lt;0. 00001] , but the mortality [ OR =1. 11, 95% CI ( 0. 83, 1. 49) ,P =0. 47] and length of ICU stay[ MD = 0. 49, 95% CI( - 1. 44, 2. 42) , P = 0. 62] were not significantly different. Conclusions An algorithmbased on serial PCT measurements would allow a more judicious use of antibiotics than currently traditional treatment of patients with severe infections in ICU. It can reduce the use of antibiotics and appears to be safe.
ObjectiveTo observe the effect of bundle interventions on ventilator-associated pneumonia (VAP) in Intensive Care Unit (ICU). MethodsBaseline survey among the patients undergoing mechanical ventilation was conducted during June 2011 to August 2011. During September 2011 to May 2012, the rate of VAP was monitored every three months after taking bundle measures, which included oral care, elevation of the head of the bed, daily assessment of readiness to extubation, optimizing process of devices disinfection and hand hygiene. ResultsThrough carrying out the bundle interventions, the VAP rate decreased from 61.2‰ to 34.9‰ after six months and 22.7‰ after nine months, and the ventilator utilization ratio decreased from 26.5% to 24.6% after six months and 22.6% after nine months. The alcohol-based hand disinfectant dosage was increased from 32.6 mL to 58.8 mL and 54.4 mL for each patient bed in ICU. ConclusionThe bundle intervention has been proved to be effective. Measures such as staff education, bedside supervision and monitoring data feedback can help implement bundle interventions.
ObjectiveTo investigate the incidence and trendency of healthcare-associated infections (HAIs) in a pediatric intensive care unit (ICU) of a hospital, identify the main objectives of infection control, and formulate corresponding preventive and control measures.MethodsA prospective targeted monitoring method was adopted to investigate HAIs in the pediatric ICU of a hospital from January 2013 to December 2018.ResultsFrom January 2013 to December 2018, the number of target ICU patients was 11 898, the number of patient-days was 55 159; 226 HAIs occurred, the HAI case rate was 1.90%, the incidence of HAI per 1 000 patient-days was 4.10‰, and the adjusted incidence of HAI per 1 000 patient-days was 1.21‰. The main infection site was respiratory tract [83 cases (36.7%)], with ventilator-associated pneumonia in 73 cases (32.3%); secondly, 69 patients (30.5%) had bloodstream infection, among which 48 (21.2%) had non-catheter-related bloodstream infection.ConclusionHospital targeted monitoring is helpful to grasp the situation and trend of HAIs, define the main target of infection control, and formulate corresponding preventive and control measures, which can effectively reduce the incidence of HAIs.
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.
ObjectiveTo systematically review the risk factors associated with sleep disorders in ICU patients.MethodsWe searched The Cochrane Library, PubMed, EMbase, Web of Science, CNKI, Wanfang Data, VIP and CBM databases to collect cohort studies, case-control studies and cross-sectional studies on the risk factors associated with sleep disorders in ICU patients from inception to October, 2018. Two reviewers independently screened literature, extracted data and evaluated the bias risk of included studies. Then, meta-analysis was performed by using RevMan 5.3 software.ResultsA total of 9 articles were included, with a total of 1 068 patients, including 12 risk factors. The results of meta-analysis showed that the combined effect of equipment noise (OR=0.42, 95%CI 0.26 to 0.68, P=0.000 4), patients’ talk (OR=0.53, 95%CI 0.42 to 0.66, P<0.000 01), patients’ noise (OR=0.39, 95%CI 0.21 to 0.74, P=0.004), light (OR=0.29, 95%CI 0.18 to 0.45, P<0.000 01), night treatment (OR=0.36, 95%CI 0.26 to 0.50, P<0.000 01), diseases and drug effects (OR=0.17,95%CI 0.08 to 0.36, P<0.000 01), pain (OR=0.37, 95%CI 0.17 to 0.82, P=0.01), comfort changes (OR=0.34,95%CI 0.17 to 0.67,P=0.002), anxiety (OR=0.31,95%CI 0.12 to 0.78, P=0.01), visit time (OR=0.72, 95%CI 0.53 to 0.98, P=0.04), economic burden (OR=0.63, 95%CI 0.48 to 0.82, P=0.000 5) were statistically significant risk factors for sleep disorders in ICU patients.ConclusionCurrent evidence shows that the risk factors for sleep disorders in ICU patients are environmental factors (talking voices of nurses, patient noise, and light), treatment factors (night treatment), disease factors (disease itself and drug effects, pain,) and psychological factors (visiting time, economic burden). Due to the limited quality and quantity of included studies, more high quality studies are needed to verify the above conclusions.
ObjectiveTo analyze and discuss the importance of non-catheter-related hospital infection in intensive care unit (ICU). MethodA prospective target monitoring of all the patients in the general ICU was carried out from January 2011 to December 2013. The hospital infection cases grouped by infection types were analyzed with SPSS 17.0. ResultsA total of 5 364 patients were monitored, 455 of whom had hospital infections totaled 616 times. The hospital infection rate was 11.5%. The amount and constituent ratio of the catheter-related infections showed a declining trend year by year, while the non-catheter-related infections revealed an escalating trend year by year. In these 455 patients, the mixed infection group had the longest hospital stay, followed by the catheter-related infection group and the non-catheter-related infection group (P<0.05). The catheter-related infection group had higher crude mortality rate than both of the mixed infection group and the non-catheter-related infection group (P<0.017). ConclusionsNon-catheter-related infections which get higher and higher proportion in ICU hospital infections should be paid more attention to, while catheter-related infections which could prolong hospitalization and increase the risk of death in ICU patients, remain the focus of the target monitoring of hospital infection in ICU.
Objective To explore the nurses’ cognition of busyness in intensive care unit (ICU), summarize the main busy scenes, and provide strategies for solving problems of busyness. Methods Nurses in three ICU departments of Shanghai Oriental Hospital were selected by purpose sampling method from September 2020 to January 2021. Face-to-face semi-structured in-depth interviews were conducted with nurses. The interview data were analyzed and thematically refined using the method of Colaizzi data analysis. Results A total of 10 nurses were interviewed, including 8 general nurses and 2 head nurses, all of whom were women. The cognition of busyness covered three elements: explosively increased workload, time pressure, and overwhelming information from multiple sources. Busy scenes included four themes: large amount of patients, critical conditions of patients, unstable conditions of patients, and frequent service transfer among different medical divisions. Conclusions According to the three elements of nurses’ cognition of busyness and scenes of it, nursing managers can put forward corresponding solutions. This can retain or attract more nurses to work in ICU and provide better services for patients.
Objective To investigate the pathogen distribution and drug resistance in ICU patients, provide reference for prevention of severe infection and empirical antibacterial treatment. Methods The patients admitted in ICU between January 2013 and December 2014 were retrospectively analyzed. The pathogenic data were collected including bacterial and fungal culture results, the flora distribution and drug resistance of pathogenic bacteria. Results A total of 2088 non-repeated strains were isolated, including 1403 (67.2%) strains of Gram-positive bacteria, 496 (23.8%) strains of Gram-negative bacteria, and 189 (9.0%) strains of fungus. There were 1324 (63.42%) strains isolated from sputum or other respiratory specimens, 487 (23.33%) strains from blood specimens, 277 (13.27%) strains from other specimens. The bacteria included Acinetobacter baumannii (17.2%), Klebsiella pneumoniae (14.8%), Pseudomonas aeruginosa (9.9%), C. albicans (6.3%), E. coli (5.6%), E. cloacae (5.4%), Epidermis staphylococcus (5.0%) and Staphylococcus aureus (4.7%). There were 15 strains of penicillium carbon resistant enterobacteriaceae bacteria (CRE) accounting for 2.3%, including 5 strains of Pneumonia klebsiella, 4 strains of E. cloacae. In 117 strains of E. coli, drug-resistant strains accounted for 86.4% including 85.5% of multiple drug-resistant strains (MDR) and 0.9% of extremely-drug resistant (XDR) strains. In 359 strains of Acinetobacter baumannii, drug-resistant strains accounted for 75.2% including 72.1% of XDR strains and 3.1% of MDR strains. MDR strains accounted for 10.6% in Pseudomonas aeruginosa. Detection rate of methicillin resistant Staphylococcus aureus (MRSA) and methicillin resistant coagulase-negative Staphylococci (MRCNS) was 49.0% and 95.5%, respectively. There were 4 strains of vancomycin resistant Enterococcus faecalis. There were 131 (69.3%) strains of C. albicans, 23 (12.2%) strains of smooth candida. C. albicans was sensitive to amphotericin and 5-fluorine cytosine, and the resistance rate was less than 1% to other antifungle agents. The resistance rate of smooth ball candida was higher than C. albicans and nearly smooth candida, but still less than 15%. Conclusions The predominant pathogens in ICU was gram-negative bacteria. The top eight pathogenic bacteria were Acinetobacter baumanni, Klebsiella pneumoniae, Pseudomonas aeruginosa, C. albicans, E. coli, E. cloacae, Epidermis staphylococcus and S. aureus. Sputum and blood are common specimens. CRE accounts for 2.3%. Drug-resistant strains are most common in E. coli mainly by MDR, followed by Acinetobacter baumannii mainly by XDR, and least in Pseudomonas aeruginosa. C. albicans is the most common fungus with low drug resitance.
ObjectiveTo explore the development and application of a novel ventilator alarm management model in critically ill patients receiving invasive mechanical ventilation (MV) in the intensive care unit (ICU) using machine learning (ML) and Internet of Medical Things (IoMT). The study aims to identify alarms’ intervention requirements. MethodsA retrospective cohort study and ML analysis were conducted, including adult patients receiving invasive MV in the ICU at West China Hospital from February 10, 2024, to July 22, 2024. A total of 76 ventilator alarm-related parameters were collected through the IoMT system. Feature selection was performed using a stratified approach, and six ML algorithms were applied: Gaussian Naive Bayes, K-Nearest Neighbors, Linear Discriminant Analysis, Support Vector Machine, Categorical Boosting (CatBoost), and Logistic Regression. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC-ROC). ResultsA total of 107 patients and their associated ventilator alarm records were included. Thirteen highly relevant features were selected from the 76 parameters for model training through stratified feature selection. The CatBoost model demonstrated the best predictive performance, with an AUC-ROC of 0.984 7 and an accuracy of 0.912 3 in the training set. External validation of the CatBoost model yielded an AUC-ROC of 0.805 4. ConclusionThe CatBoost-based ML model successfully constructed in this study has high accuracy and reliability in predicting the ventilator alarms in ICU patients, providing an effective tool for ventilator alarm management. The CatBoost-based ML method exhibited remarkable efficacy in predicting the necessity of ventilator intervention in critically ill ICU patients. Further large-scale multicenter studies are recommended to validate its clinical application value and promote model optimization and implementation.