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Considering that specific genetic profiles, psychopathological conditions and neurobiological systems underlie human behaviours, the phenotypic differentiation of obese patients according to eating behaviours should be investigated. The aim of this study was to classify obese patients according to their eating behaviours and to compare these clusters in regard to psychopathology, personality traits, neurocognitive patterns and genetic profiles.
A total of 201 obese outpatients seeking weight reduction treatment underwent a dietetic visit, psychological and psychiatric assessment and genotyping for SCL6A2 polymorphisms. Eating behaviours were clustered through two-step cluster analysis, and these clusters were subsequently compared.
Two groups emerged: cluster 1 contained patients with predominantly prandial hyperphagia, social eating, an increased frequency of the long allele of the 5-HTTLPR and low scores in all tests; and cluster 2 included patients with more emotionally related eating behaviours (emotional eating, grazing, binge eating, night eating, post-dinner eating, craving for carbohydrates), dysfunctional personality traits, neurocognitive impairment, affective disorders and increased frequencies of the short (S) allele and the S/S genotype.
Aside from binge eating, dysfunctional eating behaviours were useful symptoms to identify two different phenotypes of obese patients from a comprehensive set of parameters (genetic, clinical, personality and neuropsychology) in this sample. Grazing and emotional eating were the most important predictors for classifying obese patients, followed by binge eating. This clustering overcomes the idea that ‘binging’ is the predominant altered eating behaviour, and could help physicians other than psychiatrists to identify whether an obese patient has an eating disorder. Finally, recognising different types of obesity may not only allow a more comprehensive understanding of this illness, but also make it possible to tailor patient-specific treatment pathways.
The importance of the proper identification of delirium, with its high incidence and adversities in the intensive care setting, has been widely recognized. One common screening instrument is the Intensive Care Delirium Screening Checklist (ICDSC); however, the symptom profile and key features of delirium dependent on the level of sedation have not yet been evaluated.
In this prospective cohort study, the ICDSC was evaluated versus the Diagnostic and Statistical Manual, 4th edition, text revision, diagnosis of delirium set as standard with respect to the symptom profile, and correct identification of delirium. The aim of this study was to identify key features of delirium in the intensive care setting dependent on the Richmond Agitation and Sedation Scale levels of sedation: drowsiness versus alert and calmness.
The 88 delirious patients of 225 were older, had more severe disease, and prolonged hospitalization. Irrespective of the level of sedation, delirium was correctly classified by items related to inattention, disorientation, psychomotor alterations, inappropriate speech or mood, and symptom fluctuation. In the drowsy patients, inattention reached substantial sensitivity and specificity, whereas psychomotor alterations and sleep-wake cycle disturbances were sensitive lacked specificity. The positive prediction was substantial across items, whereas the negative prediction was only moderate. In the alert and calm patient, the sensitivities were substantial for psychomotor alterations, sleep-wake cycle disturbances, and symptom fluctuations; however, these fluctuations were not specific. The positive prediction was moderate and the negative prediction substantial. Between the nondelirious drowsy and alert, the symptom profile was similar; however, drowsiness was associated with alterations in consciousness.
Significance of results
In the clinical routine, irrespective of the level of sedation, delirium was characterized by the ICDSC items for inattention, disorientation, psychomotor alterations, inappropriate speech or mood and symptom fluctuation. Further, drowsiness caused altered levels of consciousness.
In the intensive care setting, delirium is a common occurrence that comes with subsequent adversities. Therefore, several instruments have been developed to screen for and detect delirium. Their validity and psychometric properties, however, remain controversial.
In this prospective cohort study, the Confusion Assessment Method for the Intensive Care Unit (CAM–ICU) and the Intensive Care Delirium Screening Checklist (ICDSC) were evaluated versus the DSM–IV–TR in the diagnosis of delirium with respect to their validity and psychometric properties.
Out of some 289 patients, 210 with matching CAM–ICU, ICDSC, and DSM–IV–TR diagnoses were included. Between the scales, the prevalence of delirium ranged from 23.3% with the CAM–ICU, to 30.5% with the ICDSC, to 43.8% with the DSM–IV–TR criteria. The CAM–ICU showed only moderate concurrent validity (Cohen's κ = 0.44) and sensitivity (50%), but high specificity (95%). The ICDSC also reached moderate agreement (Cohen's κ = 0.60) and sensitivity (63%) while being very specific (95%). Between the CAM–ICU and the ICDSC, the concurrent validity was again only moderate (Cohen's κ = 0.56); however, the ICDSC yielded higher sensitivity and specificity (78 and 83%, respectively).
Significance of Results:
In the daily clinical routine, neither the CAM–ICU nor the ICDSC, common tools used in screening and detecting delirium in the intensive care setting, reached sufficient concurrent validity; nor did they outperform the DSM–IV–TR diagnostic criteria with respect to sensitivity or positive prediction, but they were very specific. Thus, the non-prediction by the CAM–ICU or ICDSC did not refute the presence of delirium. Between the CAM–ICU and ICDSC, the ICDSC proved to be the more accurate instrument.
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