ObjectiveTo analyze the trend of disease spectrum and main diagnosis and therapeutic technologies in respiratory intensive care unit (RICU) in recent years, and find out the trend of change of patient’s characteristics and commonly used interventions in order to provide evidence for planning discipline development and improving personnel training program.MethodsPatients information and main diagnosis and therapeutic technologies of 1503 inpatients in RICU of Shenzhen People's Hospital from January, 2017 to December, 2020 were collected. The changes of disease spectrum and diagnosis and treatment technologies in different years were compared and analyzed.ResultsAmong all the patients, 66.3% were directly admitted into RICU, 12.1% were transferred from respiratory department, and 21.6% were transferred from other departments. The proportion of patients with non-respiratory diseases as principal diagnosis had an increasing trend, from 18.8% in 2017 to 37.3% in 2020 (P<0.05). The diseases with most obvious increasing trend were sepsis, nervous system diseases, circulatory system diseases and extra-pulmonary malignancies (P<0.05). The use of respiratory related diagnosis and therapeutic technologies was gradually increasing, meanwhile, the use of non-traditional respiratory related technologies, especially continuous renal replacement therapy, was also increasing. There was no significant difference in fatality rate among different years (P>0.05).ConclusionsThe number of patients with extra-pulmonary diseases and the use of non-traditional respiratory related diagnosis and therapeutic technologies in RICU were increasing. The development of RICU and the allocation of technical personnel needed to be improved accordingly.
Objective To analyze risk factors for prolonged stay in intensive care unit (ICU) after cardiac valvular surgery. Methods Between January 2005 and May 2005, five hundred and seven consecutive patients undergone cardiac valvular surgery were divided into two groups based on if their length of ICU stay more than 5 days (prolonged stay in ICU was defined as 5 days or more). Group Ⅰ: 75 patients required prolonged ICU stay. Group Ⅱ: 432 patients did not require prolonged ICU stay. Univariate and multivariate analysis (logistic regression) were used to identify the risk factors. Results Seventyfive patients required prolonged ICU stay. Univariate risk factors showed that age, the proportion of previous heart surgery, smoking history and repeat cardiopulmonary bypass (CPB) support, cardiothoracicratio, the CPB time and aortic crossclamping time of group Ⅰ were higher or longer than those of group Ⅱ. The heart function, left ventricular ejection fraction (LVEF), pulmonary function of group Ⅰwere worse than those of group Ⅱ(Plt;0.05, 0.01). Logistic regression identified that preoperative age≥65 years (OR=4.399), LVEF≤0.50(OR=2.788),cardiothoracic ratio≥0.68(OR=2.411), maximal voluntary ventilation observed value/predicted value %lt;71%(OR=4.872), previous heart surgery (OR=3.241) and repeat CPB support during surgery (OR=18.656) were final risk factors for prolonged ICU stay. Conclusion Prolonged ICU stay after cardiac valvular surgery can be predicted through age, LVEF, cardiothoracic ratio, maximal voluntary ventilation, previous heart surgery and repeat CPB support during surgery. The patients with these risk factors need more preoperative care and postoperative care to reduce mortality, morbidity and avoid prolonged ICU stay after cardiac valvular surgery.
ObjectiveTo evaluate the diagnostic value of various severity assessment scoring systems for sepsis after cardiac surgery and the predictive value for long-term prognosis.MethodsThe clinical data of patients who underwent cardiac sugeries including coronary artery bypass grafting (CABG) and (or) valve reconstruction/valve replacement were extracted from Medical Information Mark for Intensive Care-Ⅲ (MIMIC-Ⅲ). A total of 6 638 patients were enrolled in this study, including 4 558 males and 2 080 females, with an average age of 67.0±12.2 years. Discriminatory power was determined by comparing the area under the receiver operating characteristic (ROC) curve (AUC) for each scoring system individually using the method of DeLong. An X-tile analysis was used to determine the optimal cut-off point for each scoring system, and the patients were grouped by the cut-off point, and Kaplan-Meier curves and log-rank test were applied to analyze their long-term survival.ResultsCompared with the sequential organ failure assessment (SOFA) score, acute physiology score-Ⅲ (APS-Ⅲ, P<0.001), the simplified acute physiology score-Ⅱ (SAPS-Ⅱ, P<0.001) and logistic organ dysfunction score (LODS, P<0.001) were more accurate in distinguishing sepsis. Compared with the non-septic group, the 10-year overall survival rate of the septic group was lower (P<0.001). Except for the systemic inflammation response score (SIRS) system, the 10-year overall survival rates of patients in the high risk layers of SOFA (HR=2.50, 95%CI 2.23-2.80, P<0.001), SAPS (HR=2.93, 95%CI 2.64-3.26, P<0.001), SAPS-Ⅱ (HR=2.77, 95%CI 2.51-3.04, P<0.001), APS-Ⅲ (HR=2.90, 95%CI 2.63-3.20, P<0.001), LODS (HR=2.17, 95%CI 1.97-2.38, P<0.001), modified logistic organ dysfunction score (MLODS, HR=2.04, 95%CI 1.86-2.25, P<0.001) and the Oxford acute severity of illness score (OASIS, HR=2.37, 95%CI 2.16-2.60, P<0.001) systems were lower than those in the low risk layers.ConclusionCompared with SOFA score, APS-Ⅲ score may have higher value in the diagnosis of sepsis in patients who undergo isolated CABG, a valve procedure or a combination of both. Except for SIRS scoring system, SOFA, APS-Ⅲ, SAPS, SAPS-Ⅱ, LODS, MLODS and OASIS scoring systems can be applied to predict the long-term outcome of patients after cardiac surgery.
Objective To compare the bacterial spectrums of respiratory intensive care unit (RICU) patients derived from traditional bacterial culture and loop-mediated isothermal amplification (LAMP) assay. To analyze the relationship between clinical factors and clinical outcome of patients. Methods Data of patients in RICU with lower respiratory tract infection from October 2018 to December 2020 was collected. The bacterial spectrums obtained by traditional culture method and LAMP-based method were compared. Clinical factors were divided into two categories and taken into analysis of variance for assessing their relevance with clinical outcomes. Those with significances in analysis of variance were taken into binary logistic regression. Results A total of 117 patients were included. The ratio of patients with positive bacterial culture results was 39.13% (n=115), and that with positive LAMP assay results was 72.65% (n=117). The ratios of patients with at least two positive results for culture and LAMP were 8.70% (n=115) and 36.75% (n=117), respectively. According to chi-squared test, mechanical ventilation (χ2=5.260, P=0.022), and patients with two or more bacteria positive for LAMP assay (χ2=8.227, P=0.004) were related to higher risk of death. Mechanical ventilation and patients with two bacteria positive for LAMP assay were included in binary logistic regression. The odds ratio for death was 4.789 in patients with two or more bacteria positive by LAMP assay (95% confidence interval 1.198 - 19.144, P=0.027). Conclusions LAMP-based method is helpful in detecting more bacteria from respiratory tract specimens of RICU patients, which will be a contributor to precision medicine. Patients with at least two bacteria positive based on LAMP assay have higher risk of death.
ObjectiveTo explore the infection condition of Acinetobacter baumannii at the Neurosurgery Intensive Care Unit (NICU), and analyze the possible risk factors. MethodsWe retrospectively analyzed the clinical data of Acinetobacter baumannii infection patients with craniocerebral injury treated at the NICU between January 2011 and June 2013. We collected such information as infection patients' population distribution, infection site, invasive operations and patients' nurse-in-charge level and so on, and analyzed the possible risk factors for the infection. ResultsThirty-one patients were infected with Acinetobacter baumannii, and they were mainly distributed between 60 and 80 years old. The main infection site was lower respiratory tract, followed in order by urinary tract, gastrointestinal tract, skin and soft tissue. The risk factors might be related to age, invasive operation, nurse working ability, etc. ConclusionThe patients at the NICU are vulnerable to infection of Acinetobacter baumannii. Reducing invasive diagnosis and nursing procedures, providing optimal care, and carrying out specialized nurse standardization training may be the important means to effectively reduce the infection.
Objective To discuss the effect of monitoring-training-planning (MTP) intervention model on the prevention and control of catheter–associated urinary tract infection (CAUTI) in Intensive Care Unit (ICU). Methods Patients with indwelling catheter from departments with ICU (ICU, ICU of the Department of Neurosurgery, ICU of the Department of Neurologic Medicine) between 2014 and 2015 were included in this study. Based on the inclusion criteria, target monitoring indicators were set in accordance with Hospital Infection Monitoring Norms. A total of 493 patients with indwelling catheters from January to December 2014 were subjected to target surveillance, and were used as baseline for the study. A total of 529 patients with indwelling catheters from January to December 2015 were treated with MTP intervention. The occurrence of indwelling catheter–associated urinary tract infections in the intensive care unit was compared before and after intervention. Results The incidence of indwelling catheter-associated urinary tract infections before and after MTP intervention were different, and the difference was statistically significant (P<0.05). Conclusion MTP intervention model can effectively prevent and reduce indwelling catheter-associated urinary tract infections 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.