Postoperatiivinen tehohoito ja tehovalvonta

Teema : tehohoitolääketiede. English summaryPeer reviewe

Predictors of hospital and one-year mortality in intensive care patients with refractory status epilepticus : a populationbased study

Background: The aim was to determine predictors of hospital and 1-year mortality in patients with intensive care unit (ICU)-treated refractory status epilepticus (RSE) in a population-based study. Methods: This was a retrospective study of the Finnish Intensive Care Consortium (FICC) database of adult patients (16 years of age or older) with ICU-treated RSE in Finland during a 3-year period (2010-2012). The database consists of admissions to all 20 Finnish hospitals treating RSE in the ICU. All five university hospitals and 11 out of 15 central hospitals participated in the present study. The total adult referral population in the study hospitals was 3.92 million, representing 91% of the adult population of Finland. Patients whose condition had a post-anoxic aetiological basis were excluded. Results: We identified 395 patients with ICU-treated RSE, corresponding to an annual incidence of 3.4/100,000 (95% confidence interval (CI) 3.04-3.71). Hospital mortality was 7.4% (95% CI 0-16.9%), and 1-year mortality was 25. 4% (95% CI 21.2-29.8%). Mortality at hospital discharge was associated with severity of organ dysfunction. Mortality at 1 year was associated with older age (adjusted odds ratio (aOR) 1.033, 95% CI 1.104-1.051, p = 0.001), sequential organ failure assessment (SOFA) score (aOR 1.156, CI 1.051-1.271, p = 0.003), super-refractory status epilepticus (SRSE) (aOR 2.215, 95% CI 1.20-3.84, p = 0.010) and dependence in activities of daily living (ADL) (aOR 2.553, 95% CI 1.537-4.243, p <0.0001). Conclusions: Despite low hospital mortality, 25% of ICU-treated RSE patients die within a year. Super-refractoriness, dependence in ADL functions, severity of organ dysfunction at ICU admission and older age predict long-term mortality.Peer reviewe

Using Hilbert-Huang Transform to Assess EEG Slow Wave Activity During Anesthesia in Post-Cardiac Arrest Patients

Proceeding volume: 38Hypoxic ischemic encephalopathy (HIE) is a severe consequence of cardiac arrest (CA) representing a substantial diagnostic challenge. We have recently designed a novel method for the assessment of HIE after CA. The method is based on estimating the severity of the brain injury by analyzing changes in the electroencephalogram (EEG) slow wave activity while the patient is exposed to an anesthetic drug propofol in a controlled manner. In this paper, Hilbert-Huang Transform (HHT) was used to analyze EEG slow wave activity during anesthesia in ten post-CA patients. The recordings were made in the intensive care unit 36-48 hours after the CA in an experiment, during which the propofol infusion rate was incrementally decreased to determine the drug-induced changes in the EEG at different anesthetic levels. HHT was shown to successfully capture the changes in the slow wave activity to the behavior of intrinsic mode functions (IMFs). While, in patients with good neurological outcome defined after a six-month control period, propofol induced a significant increase in the amplitude of IMFs representing the slow wave activity, the patients with poor neurological outcome were unable to produce such a response. Consequently, the proposed method offer substantial prognostic potential by providing a novel approach for early estimation of HIE after CA.Peer reviewe

Early recovery of frontal EEG slow wave activity during propofol sedation predicts outcome after cardiac arrest

Aim of the study: EEG slow wave activity (SWA) has shown prognostic potential in post-resuscitation care. In this prospective study, we investigated the accuracy of continuously measured early SWA for prediction of the outcome in comatose cardiac arrest (CA) survivors. Methods: We recorded EEG with a disposable self-adhesive frontal electrode and wireless device continuously starting from ICU admission until 48 h from return of spontaneous circulation (ROSC) in comatose CA survivors sedated with propofol. We determined SWA by offline calculation of C-Trend (R) Index describing SWA as a score ranging from 0 to 100. The functional outcome was defined based on Cerebral Performance Category (CPC) at 6 months after the CA to either good (CPC 1-2) or poor (CPC 3-5). Results: Outcome at six months was good in 67 of the 93 patients. During the first 12 h after ROSC, the median C-Trend Index value was 38.8 (interquartile range 28.0-56.1) in patients with good outcome and 6.49 (3.01-18.2) in those with poor outcome showing significant difference (p < 0.001) at every hour between the groups. The index values of the first 12h predicted poor outcome with an area under curve of 0.86 (95% CI0.61-0.99). With a cutoff value of 20, the sensitivity was 83.3% (69.6%-92.3%) and specificity 94.7% (83.4%-99.7%) for categorization of outcome. Conclusion: EEG SWA measured with C-Trend Index during propofol sedation offers a promising practical approach for early bedside evaluation of recovery of brain function and prediction of outcome after CA.Peer reviewe

Variation in the use of renal replacement therapy in patients with septic shock : a substudy of the prospective multicenter observational FINNAKI study

Peer reviewe

Myocardial ischemia-reperfusion injury and systemic inflammatory response in high-risk cardiac surgery:a clinical study of the effects of high-dose glucose-insulin treatment and the use of leukocyte-depleting filter

Abstract Cardiac surgery with cardiopulmonary bypass induces the activation of systemic inflammatory response syndrome (SIRS) and results in at least some degree of global myocardial ischemia. Although these responses are usually short-lived, they may lead to serious complications and organ system failures. The present study evaluated the effects of high-dose glucose-insulin (1IU/kg/h) treatment (GIK) administered with the hyperinsulinemic normoglycemic clamp technique and a leukocyte-depleting filter on markers of systemic inflammatory response and myocardial ischemia-reperfusion injury in certain cardiac surgical risk groups. The study involved four prospective randomized controlled clinical trials and 119 patients. Cardioprotective effects were measured as myocardial enzyme release, recovery of contractile function and incidence of arrhythmias in all studies. The hemodynamic and metabolic effects of high-dose glucose-insulin treatment were evaluated in patients admitted for combined aortic valve (AS) and coronary surgery (40) and for urgent coronary surgery (39), and the latter study also involved proinflammatory cytokine and C-reactive protein analyses. The impacts of leukocyte filter on the expression of neutrophil adhesion molecules along with proinflammatory cytokines were evaluated in patients admitted for combined aortic valve (AS) and coronary surgery (20) and for solitary coronary surgery (20). The high-dose glucose-insulin treatment was associated with better preserved myocardial contractile function and less need for inotropic support after combined aortic valve and coronary surgery (I) and attenuation of postoperative CRP release after urgent coronary surgery (II). No effects on postoperative myocardial enzyme release (I, II) or on proinflammatory cytokine responses (II) were detected. The number of hypoglycemic events was low. The use of a leukocyte filter throughout the cardiopulmonary bypass period increased the neutrophil adhesion molecule CD11b expression in patients with both normal and prolonged CPB times and was associated with an enhanced proinflammatory cytokine response (III, IV). In conclusion, high-dose glucose-insulin treatment is safe, but requires strict control of blood glucose level. It reduces the need for inotropic support in patients with compromised cardiac status. The use of leukocyte filter leads to increased leukocyte activation and proinflammatory reaction

Monitoring of nighttime EEG slow-wave activity during dexmedetomidine infusion in patients with hyperactive ICU delirium:an observational pilot study

Abstract Background: The disturbance of sleep has been associated with intensive care unit (ICU) delirium. Monitoring of EEG slow-wave activity (SWA) has potential in measuring sleep quality and quantity. We investigated the quantitative monitoring of night-time SWA and its association with the clinical evaluation of sleep in patients with hyperactive ICU delirium treated with dexmedetomidine. Methods: We performed overnight EEG recordings in 15 patients diagnosed with hyperactive delirium during moderate dexmedetomidine sedation. SWA was evaluated by offline calculation of the C-Trend Index, describing SWA in one parameter ranging 0 to 100 in values. Average and percentage of SWA values &lt;50 were categorized as poor. The sleep quality and depth was clinically evaluated by the bedside nurse using the Richards-Campbell Sleep Questionnaire (RCSQ) with scores &lt;70 categorized as poor. Results: Nighttime SWA revealed individual sleep structures and fundamental variation between patients. SWA was poor in 67%, sleep quality (RCSQ) in 67%, and sleep depth (RCSQ) in 60% of the patients. The category of SWA aligned with that of RCSQ-based sleep quality in 87% and RCSQ-based sleep depth in 67% of the patients. Conclusions: Both, SWA and clinical evaluation suggested that the quality and depth of nighttime sleep were poor in most patients with hyperactive delirium despite dexmedetomidine infusion. Furthermore, the SWA and clinical evaluation classifications were not uniformly in agreement. An objective mode such as practical EEG-based solution for sleep evaluation and individual drug dosing in the ICU setting could offer potential in improving sleep for patients with delirium

A correlation-driven mapping for deep learning application in detecting artifacts within the EEG

Abstract Objective: When developing approaches for automatic preprocessing of electroencephalogram (EEG) signals in non-isolated demanding environment such as intensive care unit (ICU) or even outdoor environment, one of the major concerns is varying nature of characteristics of different artifacts in time, frequency and spatial domains, which in turn causes a simple approach to be not enough for reliable artifact removal. Considering this, current study aims to use correlation-driven mapping to improve artifact detection performance. Approach: A framework is proposed here for mapping signals from multichannel space (regardless of the number of EEG channels) into two-dimensional RGB space, in which the correlation of all EEG channels is simultaneously taken into account, and a deep convolutional neural network (CNN) model can then learn specific patterns in generated 2D representation related to specific artifact. Main results: The method with a classification accuracy of 92.30% (AUC = 0.96) in a leave-three-subjects-out cross-validation procedure was evaluated using data including 2310 EEG sequences contaminated by artifacts and 2285 artifact-free EEG sequences collected with BrainStatus self-adhesive electrode and wireless amplifier from 15 intensive care patients. For further assessment, several scenarios were also tested including performance variation of proposed method under different segment lengths, different numbers of isoline and different numbers of channel. The results showed outperformance of CNN fed by correlation coefficients data over both spectrogram-based CNN and EEGNet on the same dataset. Significance: This study showed the feasibility of utilizing correlation image of EEG channels coupled with deep learning as a promising tool for dimensionality reduction, channels fusion and capturing various artifacts patterns in temporal-spatial domains. A simplified version of proposed approach was also shown to be feasible in real-time application with latency of 0.0181 s for making real-time decision

Forehead electrodes sufficiently detect propofol-induced slow waves for the assessment of brain function after cardiac arrest

Abstract In a recent study, we proposed a novel method to evaluate hypoxic ischemic encephalopathy (HIE) by assessing propofol-induced changes in the 19-channel electroencephalogram (EEG). The study suggested that patients with HIE are unable to generate EEG slow waves during propofol anesthesia 48 h after cardiac arrest (CA). Since a low number of electrodes would make the method clinically more practical, we now investigated whether our results received with a full EEG cap could be reproduced using only forehead electrodes. Experimental data from comatose post-CA patients (N = 10) were used. EEG was recorded approximately 48 h after CA using 19-channel EEG cap during a controlled propofol exposure. The slow wave activity was calculated separately for all electrodes and four forehead electrodes (Fp1, Fp2, F7, and F8) by determining the low-frequency (< 1 Hz) power of the EEG. HIE was defined by following the patients’ recovery for six months. In patients without HIE (N = 6), propofol substantially increased (244 ± 91%, mean ± SD) the slow wave activity in forehead electrodes, whereas the patients with HIE (N = 4) were unable to produce such activity. The results received with forehead electrodes were similar to those of the full EEG cap. With the experimental pilot study data, the forehead electrodes were as capable as the full EEG cap in capturing the effect of HIE on propofol-induced slow wave activity. The finding offers potential in developing a clinically practical method for the early detection of HIE