Objective To evaluate the sedative and analgesic efficacy and adverse effect of dexmedetomidine versus propofol on the postoperative patients in intensive care unit (ICU). Methods The relevant randomized controlled trials (RCTs) were searched in The Cochrane Library, MEDLINE, PubMed, SCI, SpringerLinker, ScinceDirect, CNKI, VIP, WanFang Data and CBM from the date of their establishment to November 2011. The quality of the included studies was evaluated after the data were extracted by two reviewers independently, and then the meta-analysis was performed by using RevMan 5.1. Results Ten RCTs involoving 793 cases were included. The qualitative analysis results showed: within a certain range of dosage as dexmedetomidine: 0.2-2.5 μg/(kg·h), and propofol: 0.8-4 mg/(kg·h), dexmedetomidine was similar to propofol in sedative effect, but dexmedetomidine group needed smaller dosage of supplemental analgesics during the period of sedative therapy. The results of meta-analysis showed: the percentage of patients needing supplemental analgesics in dexmedetomidine group was less than that in propofol group during the period of sedative therapy (OR=0.24, 95%CI 0.08 to 0.68, P=0.008). Compared with the propofol group, the duration of ICU stay was significantly shorter in the dexmedetomidine group (WMD= –1.10, 95%CI –1.88 to –0.32, P=0.006), but the mechanical ventilated time was comparable between the two groups (WMD=0.89, 95%CI –1.15 to 2.93, P=0.39); the incidence of adverse effects had no significant difference between two groups (bradycardia: OR=3.57, 95%CI 0.86 to 14.75, P=0.08; hypotension: OR=1.00, 95%CI 0.30 to 3.32, P=1.00); respiratory depression seemed to be more frequently in propofol group, which however needed further study. Mortalities were similar in both groups after the sedative therapy (OR=1.03, 95%CI 0.54 to 1.99, P=0.92). Conclusion Within an exact range of dosage, dexmedetomidine is comparable with propofol in sedative effect. Besides, it has analgesic effect, fewer adverse effects and fewer occurrences of respiratory depression, and it can save the extra dosage of analgesics and shorten ICU stay. Still, more larger-sample, multi-center RCTs are needed to provide more evidence to support this outcome.
Objective To analyze the policy and guideline, the institutional management and the operation mechanism of ICU medical risk management in the United Kingdom, the United States, Australia, Canada and Taiwan, so as to provide evidence and recommendations for health care risk management policy in China. Methods Such databases as PubMed, EMBASE, The Cochrane Library were searched to include the literatures such as the guideline documents and the research reports on ICU medical risk management in the United Kingdom, the United States, Australia, Canada and Taiwan; the institutional management and the operation mechanism of the risk management in the above four countries and one area were comprehensively analyzed, and especially the UK model was highly emphasized. Results A total of 31 literatures were included, including 1 guideline, 5 reviews, 2 investigative reports and 23 research documents. The United Kingdom guided the ICU risk management in forms of the standard and the guideline, formulated a clear tool of event classification and corresponding response mechanism. The United States learned from Australia’s experience and established the ICU safety reporting system; both of them regarded ICU as one part of the medical risk management and set up a special management column. Conclusion The ICU risk management with the independent report system in the United Kingdom is brought into the scope of national patient safety management, and is regarded as the relative complete system at present. In Australia and the USA, the national institutions are in charge of setting up the research projects of ICU risk management; the industry associations and the non-governmental organizations lead the risk research; and the experimental units popularize gradually after self-application.
Objectives To describe the attitude , subjective norm and behavioral intention of ICU nurses toward mechanically ventilated patients in Chengdu. Methods The modified version of Attitude, Subjective Norm and Behavioral Intention of Nurses Toward Mechanically Ventilated Patients (ASIMP) was used to investigate ICU nurses in three tertiary-level hospitals in Chengdu. Results The attitude, subjective norm and behavioral intention among ICT nurses respectively toward mechanically ventilated patients were 69.1%, 91.3%, and 95.9%. Conclusion The attitude, subjective norm and behavioral intention of most ICT nurses toward mechanically ventilated patients were positive.
ObjectiveTo systematically review the effects of ulinastatin on postoperative intensive care unit (ICU) stay time and mechanical ventilation time in patients with cardiopulmonary bypass (CPB). MethodsWe searched databases including MEDLINE, EMbase, Web of Science, The Cochrane Library (Issue 5, 2014), CBM, CNKI, WanFang Data and VIP from inception to May, 2014, to collect randomized controlled trials (RCTs) of ulinastatin for patients with CPB. Meanwhile, conference papers, dissertation and references of included studies were also retrieved manually to collect additional studies. Two reviewers independently screened literature, extracted data and assessed the risk of bias of included studies. Then, meta-analysis was performed using RevMan 5.2.0 software. ResultsA total of 7 RCTs involving 299 patients were included. The results of meta-analysis showed that:(1) There was no difference between two groups in ICU stay time (MD=-5.40, 95%CI -17.75 to 6.94, P=0.39); (2) The time of mechanical ventilation of the urinastatin group was significantly shorter than that of the saline group (MD=-6.58, 95%CI -10.61 to -2.56, P=0.000 1). The results of subgroup analysis showed that:in the CPB time >100 min subgroup, the time of mechanical ventilation of the urinastatin group was significantly shorter than that of the saline group (MD=-13.85, 95%CI -21.28 to -6.42, P=0.000 3); however, in the CPB time <100 min subgroup, there was no significant difference between two groups in the time of mechanical ventilation (MD=-1.39, 95%CI -3.22 to 0.45, P=0.14). ConclusionCurrent evidence shows, compared with saline, the administration of urinastatin during CPB can reduce postoperative mechanical ventilation time, but cannot reduce ICU stay time. Due to limited quality and quantity of the included studies, more high quality studies are needed to verify the above conclusion.
ObjectiveTo evaluate four triage methods including START, Care-Flight, rapid emergency medicinescore (REMS) and Sacco score for the length of hospital stay, length of ICU stay and the severity of injury in Lushan earthquake victims.MethodsA retrospective analysis was performed in 41 cases of critical earthquake victims in the emergency department of West China Hospital from April 20th to April 26th, 2013 in Lushan earthquake. The length of hospital stay and length of ICU stay were compared for four triage methods. The correlation between four triage methods and length of hospital stay, length of ICU stay and injury severity score (ISS) were also analyzed.ResultsThe length of ICU stay for victims whose triage level were red by START triage method or Care-Flight triage method was longer than whose triage levels were yellow. But the length of hospital stay for victims between the two triage levels had no significant difference. In addition, there was a correlation between critical victims and the length of ICU stay in the classification of START triage method and Care-Flight triage method.ConclusionThe length of ICU stay of the victims, whose triage level are red by START triage or Care-Flight triage methods, are longer than whose triage level are yellow. The levels of START and Care-Flight triage are correlated to length of ICU stay.
ObjectiveTo systematically evaluate the efficacy of high-flow nasal cannula oxygen therapy (HFNC) in post-extubation intensive care unit (ICU) patients.MethodsThe PubMed, Embase, Cochrane Library, CNKI, WanFang, VIP Databases were searched for all published available randomized controlled trials (RCTs) or cohort studies about HFNC therapy in post-extubation ICU patients. The control group was treated with conventional oxygen therapy (COT) or non-invasive positive pressure ventilation (NIPPV), while the experimental group was treated with HFNC. Two reviewers separately searched the articles, evaluated the quality of the literatures, extracted data according to the inclusion and exclusion criteria. RevMan5.3 was used for meta-analysis. The main outcome measurements included reintubation rate and length of ICU stay. The secondary outcomes included ICU mortality and hospital acquired pneumonia (HAP) rate.ResultsA total of 20 articles were enrolled. There were 3 583 patients enrolled, with 1 727 patients in HFNC group, and 1 856 patients in control group (841 patients with COT, and 1 015 with NIPPV). Meta-analysis showed that HFNC had a significant advantage over COT in reducing the reintubation rate of patients with postextubation (P<0.000 01), but there was no significant difference as compared with that of NIPPV (P=0.21). It was shown by pooled analysis of two subgroups that compared with COT/NIPPV, HFNC had a significant advantage in reducing reintubation rate in patients of postextubation (P<0.000 01). There was no significant difference in ICU mortality between HFNC and COT (P=0.38) or NIPPV (P=0.36). There was no significant difference in length of ICU stay between HFNC and COT (P=0.30), but there had a significant advantage in length of ICU stay between HFNC and NIPPV (P<0.000 01). It was shown by pooled analysis of two subgroups that compared with COT/NIPPV, HFNC had a significant advantage in length of ICU stay (P=0.04). There was no significant difference in HAP rate between HFNC and COT (P=0.61) or NIPPV (P=0.23).ConclusionsThere is a significant advantage to decrease reintubation rate between HFNC and COT, but there is no significant difference in ICU mortality, length of ICU stay or HAP rate. There is a significant advantage to decrease length of ICU stay between HFNC and NIPPV, but there is no significant difference in ICU mortality, reintubation rate or HAP rate.
ObjectivesTo systematically review the delirium risk prediction models in intensive care unit (ICU) patients.MethodsThe Cochrane Library, PubMed, Web of Science, Ovid, VIP, WanFang Date and CNKI databases were electronically searched to collect studies on delirium risk prediction models in intensive care unit patients from inception to December, 2018. Two reviewers independently screened literature, extracted data, evaluated the included studies according to the CHARMS checklist, and then systematic review was performed to evaluate the risk prediction models.ResultsA total of 9 studies were included, of which 7 were prospective studies. Six models were internally validated. All studies reported the area under receiver operating characteristic curve (AUROC) over 0.7 (0.739-0.926). The reduction of cognitive reserve and increased blood urea nitrogen were the most commonly reported predisposing and precipitating factors of delirium among all prediction models. Methodologically, the absence or unreported of the blind method, to a certain extent, partially increase the risk of bias.ConclusionsNine prediction models all have great power in early identifying and screening patients who are at high risk of developing ICU delirium. On the basis of judiciously selecting a practical prediction model for clinical practice or carrying out a large sample-size prospective cohort study to construct the localized prediction model, stratified prevention strategies should be formulated and implemented according to the risk stratification results to reduce the incidence of ICU delirium and accelerate the rational allocation of medical resources for delirium prevention.
ObjectiveTo systematically review the risk prediction model of intensive care unit (ICU) readmissions. MethodsCNKI, WanFang Data, VIP, CBM, PubMed, EMbase, Web of Science and The Cochrane Library databases were electronically searched to collect the related studies on risk prediction models of ICU readmissions from inception to June 12th, 2022. Two reviewers independently screened literature, extracted data and assessed the risk of bias of included studies; then, the qualitative systematic review was performed. ResultsA total of 15 studies involving 23 risk prediction models were included. The area under the ROC curve of the models was 0.609-0.924. The most common five predictors of the included model were age, length of ICU hospitalization, heart rate, respiration, and admission diagnosis. ConclusionThe overall prediction performance of the risk prediction model of ICU readmissions is good; however, there are differences in research types and outcomes, and the clinical value of the model needs to be further studied.
Objective To evaluate the effects of intensive care unit (ICU)-acquired hypernatremia (IAH) on the outcome of septic shock patients. Methods This retrospective study analyzed 116 septic shock patients admitted to the ICU of the First Affiliated Hospital of Soochow University from August 2018 to December 2022. Patients were divided into two groups: IAH group and normonatremia group. χ2 test, t test and the Mann-Whitney U test of the non-parametric test were used to compare the differences in clinical data between the two groups. Independent risk factors for IAH were identified by unconditioned Logistic regression analysis, and receiver operating characteristic (ROC) curves were constructed to determine their role in predicting IAH. The Kaplan-Meier curve was used to evaluate the effects of IAH and its duration on 28-day survival. Results Renal insufficiency, K+ concentration, body temperature max, mechanical ventilation, chronic critical illness, rapid recovery, sepsis-associated encephalopathy, persistent inflammation, immunosuppression and catabolism syndrome, and the length of stay in ICU had significant differences (P<0.05). Multivariate logistic regression analysis showed: total urine volume in the previous 3 days [odds ratio (OR) 1.09, 95% confidence interval (CI) 1.01 - 1.16, P=0.019] and sodium content in enteral nutrition preparations (670 mg) (OR 6.00, 95%CI 1.61 - 22.42, P=0.006) were independent risk factors for IAH. In addition, the area under the ROC curve of total urine output in the first 3 days was 0.800 (95%CI 0.678 - 0.922, P=0.001). Finally, the duration of IAH was significantly correlated with 28-day survival rate (P=0.020). Conclusions IAH is a common and serious complication in septic shock, and is the main cause of poor prognosis. Sodium status may act as an ideal screening tool for patients with septic shock.
Objective To develop a machine learning (ML) model to predict the risk of death in intensive care unit (ICU) patients with chronic obstructive pulmonary disease (COPD), explain the factors related to the risk of death in COPD patients, and solve the "black box" problem of ML model. Methods A total of 8088 patients with severe COPD were selected from the eICU Collaborative Research Database (eICU-CRD). Data within the initial 24 hours of each ICU stay were extracted and randomly divided, with 70% for model training and 30% for model validation. The LASSO regression was deployed for predictor variable selection to avoid overfitting. Five ML models were employed to predict in-hospital mortality. The prediction performance of the ML models was compared with alternative models using the area under curve (AUC), while SHAP (SHapley Additive exPlanations) method was used to explain this random forest (RF) model. Results The RF model performed best among the APACHE IVa scoring system and five ML models with the AUC of 0.830 (95%CI 0.806 - 0.855). The SHAP method detects the top 10 predictors according to the importance ranking and the minimum of non-invasive systolic blood pressure was recognized as the most significant predictor variable. Conclusion Leveraging ML model to capture risk factors and using the SHAP method to interpret the prediction outcome can predict the risk of death of patients early, which helps clinicians make accurate treatment plans and allocate medical resources rationally.