BackgroundImproper use of antimicrobials can cause adverse drug events and high costs. The purpose of this study was to investigate the frequency and potential drug–drug interactions associated with antimicrobials among hospitalized patients.Material/MethodsThis study was conducted on the same day in 5 different hospitals in Turkey. We included patients aged ≥18 years who received at least 1 antimicrobial drug and at least 1 of any other drug. The Micromedex® online drug reference system was used to control and describe the interactions. Drug interactions were classified as contraindicated, major, moderate, and minor.ResultsPotential drug–drug interactions with antimicrobials were 26.4% of all interactions. Five (42%) of 12 contraindicated interactions and 61 (38%) of 159 major interactions were with antimicrobials. Quinolones, triazoles, metronidazole, linezolid, and clarithromycin accounted for 173 (25.7%) of 673 prescribed antimicrobials, but were responsible for 141 (92.1%) of 153 interactions. In multivariate analysis, number of prescribed antimicrobials (odds ratio: 2.3001, 95% CI: 1.6237–3.2582), number of prescribed drugs (odds ratio: 1.2008, 95% CI: 1.0943–1.3177), and hospitalization in the university hospital (odds ratio: 1.7798, 95% CI: 1.0035–3.1564) were independent risk factors for developing drug interactions.ConclusionsDue to risk of drug interactions, physicians should be more cautious when prescribing antimicrobials, particularly when prescribing quinolones, linezolid, azoles, metronidazole, and macrolides.

No abstract

Objective: We aimed to evaluate the effect of anaesthesia with thiopental (4 mg kg ) and ketamine− thiopental (1 mg kg −1 and 4 mg kg −1 , respectively) combination during electroconvulsive therapy (ECT) on the Hamilton Depression Rating Scale (HDRS) and Hamilton Anxiety Rating Scale (HAM-A) and haemodynamic variables in patients with resistant major depression. Methods:Patients with HDRS scores above 17 were included. The patients were randomly divided into three groups according to the anaesthesia used. Group 1 was given thiopental (4 mg kg ) was administered in all patients for muscle relaxation. HDRS and HAM-A scores were evaluated before ECT, after 3, 6. ECT and after the final ECT. Systolic and diastolic blood pressures, heart rates and oxygen saturations were recorded before and after anaesthesia induction and after the ECT procedure. Seizure duration was recorded.Results: Fifty-eight patients were included in the study. Thirty (52%) patients were male and 28 (48%) were female. The mean age was 42.7±15.8 years in Group 1, 44.8±11 years in Group 2 and 38.6±6.8 years in Group 3. In all groups, HDRS scores were reduced compared with the baseline values. There was no statistical significant difference between the groups regarding HDRS scores. HAM-A scores were higher in Group 2 and Group 3. Systolic and diastolic blood pressures and heart rate values were lower in Group 1 and the difference was statistically significant. Conclusion:In this study, anaesthesia induced with thiopental, ketamine and thiopental-ketamine combination was observed to not result in a difference in ECT for patients with treatment-resistant depression. Ketamine at a dose of 1 mg kg −1 given just before ECT did not enhance the antidepressant effect of ECT; however, anxiety scores were increased with ketamine application.Keywords: Depression, anxiety, thiopental, ketamine, electroconvulsive therapy Amaç: İlaç tedavisine dirençli major depresyon vakalarında elektrokonvülsif tedavide (EKT) anestezi indüksiyonunda tiyopental, ketamin ve ketamin-tiyopental kombinasyonunun Hamilton Depresyon Değerlendirme Ölçeği (HDDÖ), Hamilton Anksiyete Değerlendirme Ölçeği (HADÖ) ve hemodinami üzerine etkilerini değerlendirmeyi amaçladık.

Objective: Organ transplantation is important for patients with end-stage organ failure to survive. For this reason, detection of brain death cases and adequate number of donations are necessary. Methods: 31 cases diagnosed with brain death between 01.01.2018-01.01.2020 were evaluated retrospectively. Demographic characteristics, diagnoses causing brain death, time to detect brain death, additional tests applied for the diagnosis of brain death, time to diagnosis of brain death and cardiopulmonary arrest or donation, the proportion of families accepting organ donation, the proportion of donors, organ removed from donors the number and blood types of the cases were recorded Results: The number of cases diagnosed with brain death was 31, and the mean age of the cases was 46,71 (1-89) years. 71% (n=22) of the patients were admitted to the intensive care unit from the emergency department. The most common reason for admission to the intensive care unit 67.7% (n=21) was intracranial bleeding. While the family donation rate was 19% (n=5), three cases who accepted the donation could be donors. The mean age of the patients for whom organ donation was accepted was 35.80±11 years, while the mean age of the patients for whom organ donation was not accepted was 57.43±21.30 years (p=0.04). Conclusion: Due to the increasing number of end-stage organ failure patients awaiting transplantation, it is necessary to increase the number of cadaveric donors. Timely and sufficient detection of brain death cases, increasing the family donation rate and increasing the number of cadaveric donors will be contributed.

Objectives: In this study, we aimed to investigate whether quality of life (QoL) before intensive care unit (ICU) admission could predict ICU mortality in critically ill patients. Patients and methods: Between January 2019 and April 2019, a total of 105 ICU patients (54 males, 51 females; mean age: 58 years; range, 18 to 91 years) from two ICUs of a tertiary care hospital were included in this cross-sectional, prospective study. Pre-admission QoL was measured by the Short Form (SF)-12- Physical Component Scores (PCS) and Mental Component Scores (MCS) and EuroQoL five-dimension, five-level scale (EQ-5D-5L) within 24 h of ICU admission and mortality rates were estimated. Results: The overall mortality rate was 28.5%. Pre-admission QoL was worse in the non-survivors independent from age, sex, socioeconomic and education status, and comorbidities. During the hospitalization, the rate of sepsis and ventilator/hospital-acquired pneumonia were similar among the two groups (p>0.05). Logistic regression analysis adjusted for sex, age, education status, and Acute Physiology and Chronic Health Evaluation II (APACHE II) scores showed that pre-admission functional status as assessed by the SF-12 MCS (odds ratio [OR]: 14,2; 95% confidence interval [CI]: 2.5-79.0), SF-12 PCS (OR: 10.6; 95% CI: 1.8-62.7), and EQ-5D-5L (OR: 8.0; 95% CI: 1.5-44.5) were found to be independently associated with mortality. Conclusion: Worse pre-admission QoL is a strong predictor of mortality in critically ill patients. The SF-12 and EQ-5D-5L scores are both valuable tools for this assessment. Not only the physical status, but also the mental status before ICU admission should be evaluated in terms of QoL to better utilize ICU resources.