Factors affecting neurological outcome and the usefulness of neuron-specific enolase (NSE), S-100B, glial fibrillary acidic protein (GFAP), and procalcitonin (PCT) in predicting neurological outcomes were assessed in patients who survived at least 24 h after cardiopulmonary resuscitation (CPR). Thirty successfully resuscitated cardiac arrest patients were included in this prospective clinical study. The initial cardiac arrest rhythm, duration of CPR, return of spontaneous circulation time, administered doses of adrenaline, base excess, blood sugar, and hemodynamic parameters were recorded. Patients with Glasgow Outcome Scale (GOS) scores of 1-3 were defined as Group I and patients with GOS scores of 4-5 were defined as Group II. Serum NSE, GFAP, S-100B, and PCT levels were compared between the two groups shortly after CPR (hour 0) and at hours 12 and 24 of the postresuscitation period. Serum S-100B was significantly higher (P = 0.009) in Group II immediately after CPR. Serum S-100B and NSE after CPR at hours 0, 12, and 24 were significantly lower in patients who survived to hospital discharge. Serum PCT at hours 12 and 24 and serum S-100B after CPR at 0, 12, and 24 h reached 94.7% sensitivity. Serum NSE, GFAP, S-100B, and PCT specificities were lower than 50%. In predicting neurological outcomes, serum S-100B has high sensitivity and low specificity immediately after CPR.

A common cause of death in children and young adults, is important for injury management. Neuroinflammation is responsible for both primary and secondary brain injury, and C-reactive protein-albumin ratio (CAR) has allowed use of biomarkers such as procalcitonin (PCT) in predicting mortality. Here, we compared the performance of CAR and PCT in predicting ICU mortality in TBI.Methods: Adults with TBI were enrolled in our study. The medical records of 82 isolated TBI patients were reviewed retrospectively.Results: The mean patient age was 49.0 ± 22.69 years; 59 of all patients (72%) were discharged, and 23 (28%) died. There was a statistically significant difference between PCT and CAR values according to mortality (P<0.05). The area under the curve (AUC) was 0.646 with 0.071 standard error for PCT and 0.642 with 0.066 standard error for CAR. The PCT showed a similar AUC of the receiver operating characteristic to CAR.Conclusions: This study shows that CAR and PCT are usable biomarkers to predict ICU mortality in TBI. When the determined cut-off values are used to predict the course of the disease, the CAR and PCT biomarkers will provide more effective information for treatment planning and for preparation of the family for the treatment process and to manage their outcome expectations.

Some scoring systems, such as Acute Physiology and Chronic Health Evaluation II (APACHE II), are used to predict mortality, but they are not specialized for traumatic brain injury. INCNS is a new scoring system for traumatic brain injury developed by Goa et al. INCNS score evaluates inflammation, nutrition, consciousness, neurological function and systemic condition. The present study aims to evaluate performances of Acute Physiology and Chronic Health Evaluation II (APACHE II) and INCNS to predict mortality in traumatic brain injuries. In this study, 78 patients who were treated in anaesthesiology intensive care unit with the diagnosis of traumatic brain injury were included. Patients under the age of 18, foreigners, patients with incomplete data were excluded from this study. Medical records were examined retrospectively. APACHE II and INCNS scores in the first 24 hours were counted up. Of the 78 patients, 45 (57.7%) were males and 33 (42.3%) were females. The overall mortality was 34.6% (27/78). The mean APACHE II, INCNS score was 23.85±9.44 and 14.43±8.75, respectively. The area under the curve result of receiver operating characteristic curve analysis was 0.797 for the APACHE II and 0.847 for the INCNS. The INCNS scoring system had higher discriminatory power than the APACHE II in predicting the mortality of TBI in the ICU. INCNS can be considered as a usable prognostic model for Turkish people.

Objectives Malnutrition is a clinical condition that is frequently seen in critically ill patients in the intensive care unit (ICU). Although there are many scoring systems and tools used to determine nutritional risk, those that can be used in critically ill patients in the ICU are very few. The scoring systems used are insufficient to identify ICU patients with malnutrition or at risk. Malnutrition is generally presented with a decrease in skeletal muscle mass and muscle strength. Therefore, in many recent studies, attention has been drawn to the relationship between nutritional status and loss of muscle mass. Design A cohort study. Setting Forty-five patients hospitalised in an anaesthesia ICU in Turkey were included in the study. Participants Patients aged 18 years and older. Interventions Demographic data of patients included in the study, and Nutritional Risk Screening 2002 (NRS-2002) and Modified Nutrition Risk in Critically ill (mNUTRIC) scores in the first 24 hours of ICU admission were noted. Rectus abdominis muscle (RAM) and rectus femoris muscle (RFM) thicknesses were measured by the same person (intensive care specialist) with ultrasonography (USG). Outcome measures Finding a quantitative and practical evaluation method by determining the correlation of measurement of RAM and RFM thickness with USG with NRS-2002 and mNUTRIC score, which are scoring systems used to assess nutritional risk. Results The performance of RAM and RFM thickness in determining nutritional status was evaluated by receiver operating characteristic (ROC) analysis. Area under the ROC curves were calculated as >0.7 for RFM and RAM measurements (p<0.05). Specificity and sensitivity percentages of RAM were found to be higher than RFM in determining nutritional status. Conclusion This study showed that RAM and RFM thickness measured by USG can be a reliable and easily applicable quantitative method that can be used to determine nutritional risk in the ICU.

Introduction: Chest X-ray (CXR) is one of the routinely used radiological examinations in COVID-19. However, the lesion detectability level of CXR is low. To date, to the best of our knowledge, the visualization quality of X-ray in COVID-19 has not been specifically evaluated in different lesions. Our study aims to determine the visualization quality of CXR in COVID-19 patients according to elementary lesions. Material and Method: 52 COVID-positive patients (26 Males and 26 Females); 69,6346±15,14250 (32-89) years [mean±SD age (range)] were included in the study. 98 different elementary lesions of lung detected on CT were evaluated in six different groups (consolidation, indeterminate ground-glass opacity (IGGO), dense GGO (DGGO), reversed halo, parenchymal band and curvilinear band). Lesions were compared with CXR taken on the same day. The detectability rates of the lesions on CXR were evaluated. Results: The mean sizes of CXR negative and CXR positive lesions for every group (consolidations, IGGO, DGGO, reversed halo sign, parenchymal band, curvilinear band) were respectively 1.36 cm -5.75 cm, 3.44 cm -5.50 cm, 2.25 cm -5.06 cm, 2.5cm -4.09 cm, N/A -3.14 cm and 1 cm -4.5 cm. According to Mann-Whitney U analysis, p values were found as (respectively in consolidations, IGGO, DGGO, reversed halo sign, and curvilinear band) 0.0001p, 0.145, 0.0001 p, 0.143 and 0.286. Given consolidation and DGGO groups, there was a statistically significant difference between non-visualized and visualized groups. According to ROC analysis, cut-off values were respectively 3 cm and 3.5 cm for consolidation and DGGO. Conclusion: Our study showed that consolidations smaller than 3 cm and DGGO smaller than 3.5 cm are difficult to visualize with CXR. Although there is no definite cut-off value in other elementary lesions, the visualization ratio of parenchymal bands and curvilinear bants on chest X-rays is quite high. IGGOs may not be detected even at higher dimensions. Reversed halos less than 3 cm can rarely be detected on CXR.