Objective To investigate the antibiotic resistance distribution and profiles of multidrug resistant bacteria in respiratory intensive care unit ( RICU) , and to analyze the related risk factors for multidrug resistant bacterial infections. Methods Pathogens from79 patients in RICU from April 2008 to May 2009 were analyzed retrospectively. Meanwhile the risk factors were analyzed by multi-factor logistic analysis among three groups of patients with non-multidrug, multidrug and pandrug-resistant bacterialinfection. Results The top three in 129 isolated pathogenic bacteria were Pseudomonas aeruginosa ( 24. 0% ) , Staphylococcus aureus( 22. 5% ) , and Acinetobacter baumannii( 15. 5% ) . The top three in 76 isolated multidrug-resistant bacteria were Staphylococcus aureus ( 38. 9% ) , Pseudomonas aeruginosa ( 25. 0% ) , and Acinetobacter baumannii( 19. 4% ) . And the two main strains in 29 isolated pandrug-resistant bacteria were Pseudomonas aeruginosa ( 48. 3% ) and Acinetobacter baumannii ( 44. 8% ) . Multi-factor logistic analysis revealed that the frequency of admition to RICU, the use of carbapenem antibiotics, the time of mechanical ventilation, the time of urethral catheterization, and complicated diabetes mellitus were independent risk factors for multidrug-resistant bacterial infection( all P lt; 0. 05) . Conclusions There is a high frequency of multidrug-resistant bacterial infection in RICU. Frequency of admition in RICU, use of carbapenem antibiotics, time of mechanical ventilation, time of urethral catheterization, and complicated diabetes mellitus were closely related withmultidrug-resistant bacterial infection.
Objective To explore risk factors related to acute kidney injury (AKI) in children who underwent corrective surgery for tetralogy of Fallot (TOF). Methods We retrospectively analyzed the clinical data of 726 children with corrective procedures for TOF aged less than 3 years in our hospital from March 1st 2010 to March 1st 2013. Children with AKI were picked using Acute Kidney Injury Network criteria. Demographic and perioperative variables of the remaining patients were reviewed. Univariate analysis was performed to compare the AKI group (240 patients) with the non-AKI group (486 patients). Multivariable analysis was carried out to identify significant determinants of AKI. Results A total of 240 children were with AKI. The result of univariate analysis showed that there was a statistical difference in age, Nakata index, McGoon ratio, left ventricular end-diastolic volume index (LVEDVI), transannular right ventricular outflow tract (RVOT) patch, or fresh frozen plasma (FFP) in prime solution between the AKI group and the non-AKI group. Multivariable logistic regression showed that in older children (OR=1.425, 95% CI 1.071 to 1.983, P=0.011) with more transfusion of FFP in the priming solution (OR=1.486, 95% CI 1.325 to 2.674, P<0.001) led to higher morbidity of mild AKI. In addition, there was an increase in morbidity related to AKI when children had less Nakata index (OR=0.282, 95% CI 0.092 to 0.869, P=0.013). Conclusion Postoperative AKI increases in older children group. Infusion of more FFP in priming solution increases morbidity of AKI. The less Nakata index is significantly associated with severe AKI.
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.
Since the outbreak of coronavirus disease 2019 (COVID-19), there have been numerous studies confirming that physiotherapy is an essential part of the comprehensive treatment during hospitalization and can facilitate recovery in COVID-19 patients. However, physiotherapy protocols for COVID-19 patients in intensive care units are still lacking. This article reviews the literature and incorporates practical experience around recommendations for the safe protection during physiotherapy, recommendations for evaluation criteria and intervention of physiotherapy, and future work for COVID-19 patients, so as to provide a standardized recommendation for physiotherapists working in intensive care units.
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.
ObjectiveTo analyze targeted surveillance results of nosocomial infection in Neurosurgical Intensive Care Unit (ICU) and investigate the characteristics of nosocomial infection, in order to provide reference for constituting the intervention measures. MethodsWe monitored the incidence of nosocomial infection, the application and catheter-related infection of invasive operation, and the situation of multiple resistant bacteria screening and drug resistance characteristics of each patient who stayed more than two days in neurosurgical ICU during January to December 2013. ResultsThere were a total of 1 178 patients, and the total ICU stay was 4 144 days. The nosocomial infection rate was 4.92%, and the day incidence of nosocomial infection was 13.75‰. The nosocomial infection rate was significantly higher in January and between July and December compared with other months. Ventilator utilization rate was 9.75%; ventilator-associated pneumonia incidence density was 14.85 per 1 000 catheter-days; central line utilization rate was 28.40%; central line-associated bloodstream infection incidence density was 0.85 per 1 000 catheter-days; urinary catheter utilization rate was 97.90%; and the incidence density of catheter-associated urinary tract infection was 0.25 per 1 000 catheter-days. ConclusionThe nosocomial infection rate has an obvious seasonal characteristic in neurosurgical intensive care unit, so it is necessary to make sure that the hospital infection control full-time and part-time staff should be on alert, issue timely risk warning, and strengthen the risk management of hospital infection.
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 analyze epidemic characteristics of multidrug-resistant organism (MDRO) in Neurosurgical Intensive Care Unit (NSICU), and to analyze the status of infection and colonization, in order to provide reference for constituting intervention measures. MethodsPatients who stayed in NSICU during January 2014 to April 2015 were actively monitored for the MDRO situation. ResultsA total of 218 MDRO pathogens were isolated from 159 patients, and 42 cases were healthcare-associated infections (HAI) among 159 patients. The Acinetobacter baumannii was the most common one in the isolated acinetobacter. Colonization rate was positively correlated with the incidence of HAI. From January to December, there was a significantly increase in the colonization rate, but not in the incidence of HAI. ConclusionThe main MDRO situation is colonization in NSICU. The obvious seasonal variation makes the HAI risk at different levels. So it is necessary that full-time and part-time HAI control staff be on alert, issue timely risk warning, and strengthen risk management. The Acinetobacter baumannii has become the number one target for HAI prevention and control in NSICU, so their apparent seasonal distribution is worthy of more attention, and strict implementation of HAI prevention and control measures should be carried out.
Objective To investigate the predictors for carbapenem-resistant Acinetobacter baumannii, Enterobacteriaceae and Pseudomonas aeruginosa (CR-AEP) as the pathogens of bloodstream infection (BSI) for intensive care unit (ICU) patients. Methods A retrospective case-control study based on ICU- healthcare-associated infection (HAI) research database was carried out. The patients who have been admitted to the central ICU between 2015 and 2019 in the ICU-HAI research database of West China Hospital of Sichuan University were selected. The included patients were divided into two groups, of which the patients with ICU-acquired BSI due to CR-AEP were the case group and the patients with BSI due to the pathogens other than CR-AEP were the control group. The clinical features of the two groups of patients were compared. Logistic regression model was used to identify the predictors of BSI due to CR-AEP.ResultsA total of 197 patients with BSI were included, including 83 cases in the case group and 114 cases in the control group. A total of 214 strains of pathogenic bacteria were isolated from the 197 BSI cases, including 86 CR-AEP strains. The results of multivariate logistic regression analysis showed that previous use of tigecycline [odds ratio (OR)=2.490, 95% confidence interval (CI) (1.141, 5.436), P=0.022] was associated with higher possibility for CR-AEP as the pathogens of BSI in ICU patients with BSI, while previous use of antipseudomonal penicillin [OR=0.497, 95%CI (0.256, 0.964), P=0.039] was associated with lower possibility for that. Conclusion Previous use of tigecycline or antipseudomonal penicillin is the predictor for CR-AEP as the pathogens of BSI in ICU patients with BSI.
Objective To explore the effect of “net bottom” management in the control of device-associated infections (DAIs) in elderly patients by setting infection monitoring doctors and nurses in the emergency intensive care unit (EICU). Methods Elderly patients who aged≥60 years old admitted to the EICU of the First People’s Hospital of Lianyungang between April 2018 and March 2021 were selected as the research subjects. A “net bottom” management mode was established and implemented for the purpose of infection prevention and control, taking medical and other departments as the coordination and management subjects, and infection monitoring doctors and nurses as the core. The effectiveness of the management intervention was evaluated by comparing the incidences of DAIs in elderly patients, the compliance rates of medical staff in hand hygiene, and the consumption of hand sanitizer per bed day in EICU among the primary stage (from April 2018 to March 2019), intermediate stage (from April 2019 to March 2020), and later stage (from April 2020 to March 2021). Results During the primary stage, intermediate stage, and later stage, there were 540, 497, and 507 elderly inpatients in EICU monitored, respectively, and the incidences of nosocomial infections were 7.22% (39/540), 5.84% (29/497), and 4.14% (21/507), respectively, showing a decreasing trend (χ2trend=4.557, P=0.033). The incidences of ventilator-associated pneumonia, central line-associated bloodstream infections, and catheter-associated urinary tract infections decreased from 4.82‰, 2.53‰, and 0.95‰, respectively in the primary stage, to 0.51‰, 1.01‰, and 0.53‰, respectively in the later stage, among which the difference in the incidence of ventilator-associated pneumonia was statistically significant (P<0.05). The hand hygiene compliance rate of EICU medical staff increased from 70.39% to 86.67% (P<0.05), and the consumption of hand sanitizer per bed day increased from 33.70 mL to 67.27 mL. The quarterly hand hygiene compliance rate was positively correlated with the quarterly consumption of hand sanitizer per bed day (rs=0.846, P=0.001), and negatively correlated with the quarterly incidence of nosocomial infections (rs=–0.769, P=0.003). Conclusion The “net bottom” management by setting up infection monitoring doctors and nurses in the EICU and multi-department collaboration can reduce the incidence of DAIs in elderly patients in EICU, which plays a positive role in promoting the hospital infection management and improving the quality of hospital infection management.