Comparison of the effectiveness of delirium evaluation tools in intensive care patients: pre-deliric versions 1 and 2, E-pre-deliric and ICDSC.

OBJECTIVE: This study's objective was to compare the effectiveness of the delirium prediction model (pre-deliric) and the early prediction model (E-pre-deliric) in delirium prediction in an intensive care unit (ICU) according to the Intensive Care Delirium Screening Checklist (ICDSC). Our aim was to determine these models' usability and cut-off values for ICU patients. PATIENTS AND METHODS: We classified the studied patients based on their highest ICDSC scores (tested twice daily) during ICU hospitalization. ICDSC scores of 4 or higher indicated positive results for delirium, whereas a score of 0 represented a negative result. We recorded the patients' demographic and clinical details and characteristics and calculated their E-pre-deliric and pre-deliric version 1 and version 2 scores. To evaluate the effectiveness of the models, we used receiver operating characteristic (ROC) curve analysis. RESULTS: Two hundred fifty patients (55.6% males, mean age 60.6±18.7 years) participated in this study. Their mean Acute Physiology and Chronic Health Evaluation II (APACHE-II) score was 17.0±9.1. Delirium was more common in men, patients of older ages, those with high APACHE-II scores, those who had undergone urgent admissions, those with histories of trauma, those with high urea or creatinine values and those who had undergone sedation or mechanical ventilation. Compared to patients who did not develop delirium, those who did had longer ICU stays and hospital stays, as well as greater mortality risk. The cutoff values for the patients' pre-deliric version 1, pre-deliric version 2 and E-pre-deliric scores were 38% [area under ROC (AUROC)=1], 22% (AUROC=1) and 28% (AUROC=1), respectively. CONCLUSIONS: This study is the first to compare the pre-deliric and E-pre-deliric prediction models. These models' validity and reliability were acceptable. They were clinically useful, and we identified their cut-off values. These models provide options for early detection of delirium and are easily applicable in the ICU.

The effects of cognitive and emotional status on smoking cessation.

OBJECTIVE: Smoking cessation is affected by multiple factors including cognitive status of the patients. In this study, we aimed to investigate the effects of demographic, emotional and cognitive functions of 39 male and 42 female patients who applied to the smoking cessation outpatient clinic on smoking cessation. PATIENTS AND METHODS: This study recruited 81 healthy volunteers of equal age, gender, and educational level. Total Montreal Cognitive Assessment (MoCA) scores were compared according to age, gender, cessation methods, and Beck Depression Inventory and Beck Anxiety Inventory (BAI) scores in smoking cessation settings. RESULTS: In our study, there were 39 (48.1%) male patients and 42 (51.9%) female patients. While 36 patients were able to quit smoking, the remaining 38 were unable to do so. During follow-up, 7 patients had yet to be reached. Age, years of smoking, number of cigarettes smoked per day, education level, first reason for starting smoking, reasons for quitting smoking, quitting method, and medical drugs used were found to have no effect on smoking cessation; however, the MoCA total score, Beck depression scale, Beck anxiety scale, and smoking cessation scale score were found to have significant effects on smoking cessation. CONCLUSIONS: Various cognitive processes, particularly visuospatial and attention skills, have been found to be useful in quitting smoking. Furthermore, emotional states, such as depression and anxiety have a negative impact on quitting smoking. We believe that if it is provided to the patients in the smoking cessation outpatient clinic to boost cognitive capabilities and treat mood problems, the success of smoking cessation will increase.