The Clinical and Laboratory Efficacy of HA 330 Treatment Combined with Continuous Renal Replacement Therapy in Septic Shock Patients: A Case Series.

INTRODUCTION: Blood purification therapy is a method used to enable cytokine removal and to improve disturbed immune homeostasis in patients with sepsis or septic shock. This study aimed to evaluate the impact of HA 330 treatment on biochemical and hemodynamic parameters and cytokine levels in adult patients with septic shock. METHODS: Critically ill patients with septic shock who received continuous veno-venous hemodiafiltration and HA 330 treatment were included in this prospective observational study. Biochemical and hemodynamic parameters were followed throughout HA 330 treatment. Serum interleukin (IL)-1ß, IL-6, IL-8, tumor necrosis factor (TNF)-α, high-mobility group box1 (HMGB-1) protein, IL-10 levels were analyzed by ELISA method, before and after each HA 330 session. RESULTS: A total of 18 critically ill patients were included in this study. The median APACHE 2 score was 22.2 ± 7.49 and median SOFA score 9.6 ± 5.44 on intensive care unit admission. SOFA scores were significantly decreased on the 3rd day of HA 330 treatment, compared to 2nd day scores (p = 0.017). Median leukocyte value was significantly decreased (p = 0.027 and p = 0.024), while hemodynamic parameters remained unchanged throughout the HA 330 treatment. Median CRP and procalcitonin levels were significantly reduced at day 3 of HA 330 treatment compared to the baseline (p = 0.015 and p = 0.033, respectively). Serum IL-1 ß, IL-6, IL-8, TNF-a, HMGB-1, and IL-10 levels decreased insignificantly by 11.5%, 26.4%, 11.4%, 37.9%, 0.02%, and 35.5%, respectively, at the end of the hemoperfusion treatment compared to the pre-treatment. CONCLUSION: The administration of HA 330-based hemoperfusion in septic shock patients revealed improvements in SOFA scores, leukocyte count, and CRP and procalcitonin levels. However, there was no statistically significant change in concentrations of inflammatory cytokines and hemodynamic parameters during HA 330 treatment.

Analysis of Consciousness Level Using Galvanic Skin Response during Therapeutic Effect.

The neurological status of patients in the Intensive Care Units (ICU) is determined by the Glasgow Coma Scale (GCS). Patients in coma are thought to be unaware of what is happening around them. However, many studies show that the family plays an important role in the recovery of the patient and is a great emotional resource. In this study, Galvanic Skin Response (GSR) signals were analyzed from 31 patients with low consciousness levels between GCS 3 and 8 to determine relationship between consciousness level and GSR signals as a new approach. The effect of family and nurse on unconscious patients was investigated by GSR signals recorded with a new proposed protocol. The signals were recorded during conversation and touching of the patient by the nurse and their families. According to numerical results, the level of consciousness can be separated using GSR signals. Also, it was found that family and nurse had statistically significant effects on the patient. Patients with GCS 3,4, and 5 were considered to have low level of consciousness, while patients with GCS 6,7, and 8 were considered to have high level of consciousness. According to our results, it is obtained lower GSR amplitude in low GCS (3, 4, 5) compared to high GCS (7, 8). It was concluded that these patients were aware of therapeutic affect although they were unconscious. During the classification stage of this study, the class imbalance problem, which is common in medical diagnosis, was solved using Synthetic Minority Over-Sampling Technique (SMOTE), Adaptive Synthetic Sampling (ADASYN) and random oversampling methods. In addition, level of consciousness was classified with 92.7% success using various decision tree algorithms. Random Forest was the method which provides higher accuracy compared to all other methods. The obtained results showed that GSR signal analysis recorded in different stages gives very successful GCS score classification performance according to literature studies.

Quantitative Electroencephalography Analysis for Improved Assessment of Consciousness Levels in Deep Coma Patients Using a Proposed Stimulus Stage.

"Coma" is defined as an inability to obey commands, to speak, or to open the eyes. So, a coma is a state of unarousable unconsciousness. In a clinical setting, the ability to respond to a command is often used to infer consciousness. Evaluation of the patient's level of consciousness (LeOC) is important for neurological evaluation. The Glasgow Coma Scale (GCS) is the most widely used and popular scoring system for neurological evaluation and is used to assess a patient's level of consciousness. The aim of this study is the evaluation of GCSs with an objective approach based on numerical results. So, EEG signals were recorded from 39 patients in a coma state with a new procedure proposed by us in a deep coma state (GCS: between 3 and 8). The EEG signals were divided into four sub-bands as alpha, beta, delta, and theta, and their power spectral density was calculated. As a result of power spectral analysis, 10 different features were extracted from EEG signals in the time and frequency domains. The features were statistically analyzed to differentiate the different LeOC and to relate with the GCS. Additionally, some machine learning algorithms have been used to measure the performance of the features for distinguishing patients with different GCSs in a deep coma. This study demonstrated that GCS 3 and GCS 8 patients were classified from other levels of consciousness in terms of decreased theta activity. To the best of our knowledge, this is the first study to classify patients in a deep coma (GCS between 3 and 8) with 96.44% classification performance.

Classification of Depth of Coma Using Complexity Measures and Nonlinear Features of Electroencephalogram Signals.

In recent years, some electrophysiological analysis methods of consciousness have been proposed. Most of these studies are based on visual interpretation or statistical analysis, and there is hardly any work classifying the level of consciousness in a deep coma. In this study, we perform an analysis of electroencephalography complexity measures by quantifying features efficiency in differentiating patients in different consciousness levels. Several measures of complexity have been proposed to quantify the complexity of signals. Our aim is to lay the foundation of a system that will objectively define the level of consciousness by performing a complexity analysis of Electroencephalogram (EEG) signals. Therefore, a nonlinear analysis of EEG signals obtained with a recording scheme proposed by us from 39 patients with Glasgow Coma Scale (GCS) between 3 and 8 was performed. Various entropy values (approximate entropy, permutation entropy, etc.) obtained from different algorithms, Hjorth parameters, Lempel-Ziv complexity and Kolmogorov complexity values were extracted from the signals as features. The features were analyzed statistically and the success of features in classifying different levels of consciousness was measured by various classifiers. Consequently, levels of consciousness in deep coma (GCS between 3 and 8) were classified with an accuracy of 90.3%. To the authors' best knowledge, this is the first demonstration of the discriminative nonlinear features extracted from tactile and auditory stimuli EEG signals in distinguishing different GCSs of comatose patients.