Objective To identify the predictors for readmission in the ICU among cardiac surgery patients. Methods We conducted a retrospective cohort study of 2 799 consecutive patients under cardiac surgery, who were divided into two groups including a readmission group (47 patients, 27 males and 20 females at age of 62.0±14.4 years) and a non readmission group (2 752 patients, 1 478 males and 1 274 females at age of 55.0±13.9 years) in our hospital between January 2014 and October 2016. Results The incidence of ICU readmission was 1.68% (47/2 799). Respiratory disorders were the main reason for readmission (38.3%).Readmitted patients had a significantly higher in-hospital mortality compared to those requiring no readmission (23.4% vs. 4.6%, P<0.001). Logistic regression analysis revealed that pre-operative renal dysfunction (OR=5.243, 95%CI 1.190 to 23.093, P=0.029), the length of stay in the ICU (OR=1.002, 95%CI 1.001 to 1.004, P=0.049), B-type natriuretic peptide (BNP) in the first postoperative day (OR=1.000, 95%CI 1.000 to 1.001, P=0.038), acute physiology and chronic health evaluationⅡ (APACHEⅡ) score in the first 24 hours of admission to the ICU (OR=1.171, 95%CI 1.088 to1.259, P<0.001), and the drainage on the day of surgery (OR=1.001, 95%CI1.001 to 1.002, P<0.001) were the independent risk factors for readmission to the cardiac surgery ICU. Conclusion The early identification of high risk patients for readmission in the cardiac surgery ICU could encourage both more efficient healthcare planning and resources allocation.
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 explore factors affecting the shunt safety of patients in emergency intensive care unit (EICU), construct a shunt safety evaluation model, and evaluate its prediction effectiveness, so as to provide a theoretical basis for the decision-making of shunt safety in EICU. Methods The demographic data, vital signs, laboratory examinations and other indicators of patients transferred to the general ward from the EICU of West China Hospital of Sichuan University from 0:00 on August 1, 2019 to 23:59 on May 31, 2021 were collected and analyzed. The short-term poor prognosis after being transferred out of the EICU was regarded as the end-point event. Of the patients, 70% were randomly selected as the model construction cohort, and 30% were the model validation cohort. In the model construction cohort, multivariate logistic regression analysis was used to screen the influencing factors affecting shunt safety, and the shunt safety evaluation model of patients in EICU was constructed. In the validation cohort, receiver operating characteristic curve was used to evaluate the effectiveness of the model in evaluating the shunt safety of patients in EICU. Results A total of 582 patients were included, of whom 59 patients (10.1%) had a poor short-term prognosis. Multivariate logistic regression analysis showed that the patients’ respiratory rate when leaving the EICU [odds ratio (OR)=0.863, 95% confidence interval (CI) (0.794, 0.938), P=0.001], Glasgow Coma Scale scores [OR=1.575, 95%CI (1.348, 1.841), P<0.001], albumin [OR=1.137, 95%CI (1.008, 1.282), P=0.036], prothrombin time [OR=0.956, 95%CI (0.914, 1.000), P=0.048] were the influencing factors of shunt safety. Based on the above indicators, a shunt safety evaluation model for patients in EICU was created. The area under the curve for the shunt safety assessment model to predict poor short-term prognosis was 0.815, the best cut-off value was 4 points, the sensitivity was 93.3%, and the specificity was 61.5%. Conclusions The patients’ respiratory rate when leaving EICU, Glasgow Coma Scale scores, albumin and prothrombin time are factors affecting the shunt safety for patients in EICU. The shunt safety assessment model can better predict the short-term poor prognosis of patients transferred from EICU to general ward.
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.
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.
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.
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.