201,928 research outputs found

    Post-Intensive Care Unit Psychiatric Comorbidity and Quality of Life

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    The prevalence of psychiatric symptoms ranges from 17% to 44% in intensive care unit (ICU) survivors. The relationship between the comorbidity of psychiatric symptoms and quality of life (QoL) in ICU survivors has not been carefully examined. This study examined the relationship between psychiatric comorbidities and QoL in 58 survivors of ICU delirium. Patients completed 3 psychiatric screens at 3 months after discharge from the hospital, including the Patient Health Questionnaire-9 (PHQ-9) for depression, the Generalized Anxiety Disorder-7 (GAD-7) questionnaire for anxiety, and the Post-Traumatic Stress Syndrome (PTSS-10) questionnaire for posttraumatic stress disorder. Patients with 3 positive screens (PHQ-9 ≥ 10; GAD-7 ≥ 10; and PTSS-10 > 35) comprised the high psychiatric comorbidity group. Patients with 1 to 2 positive screens were labeled the low to moderate (low-moderate) psychiatric comorbidity group. Patients with 3 negative screens were labeled the no psychiatric morbidity group. Thirty-one percent of patients met the criteria for high psychiatric comorbidity. After adjusting for age, gender, Charlson Comorbidity Index, discharge status, and prior history of depression and anxiety, patients who had high psychiatric comorbidity were more likely to have a poorer QoL compared with the low-moderate comorbidity and no morbidity groups, as measured by a lower EuroQol 5 dimensions questionnaire 3-level Index (no, 0.69 ± 0.25; low-moderate, 0.70 ± 0.19; high, 0.48 ± 0.24; P = 0.017). Future studies should confirm these findings and examine whether survivors of ICU delirium with high psychiatric comorbidity have different treatment needs from survivors with lower psychiatric comorbidity

    Impact of metabolic comorbidity on the association between body mass index and heatlh-related quality of life: a Scotland-wide cross-sectional study of 5,608 participants

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    <p/>Background: The prevalence of obesity is rising in Scotland and globally. Overall, obesity is associated with increased morbidity, mortality and reduced health-related quality of life. Studies suggest that "healthy obesity" (obesity without metabolic comorbidity) may not be associated with morbidity or mortality. Its impact on health-related quality of life is unknown. <p/>Methods: We extracted data from the Scottish Health Survey on self-reported health-related quality of life, body mass index (BMI), demographic information and comorbidity. SF-12 responses were converted into an overall health utility score. Linear regression analyses were used to explore the association between BMI and health utility, stratified by the presence or absence of metabolic comorbidity (diabetes, hypertension, hypercholesterolemia or cardiovascular disease), and adjusted for potential confounders (age, sex and deprivation quintile). <p/>Results: Of the 5,608 individuals, 3,744 (66.8%) were either overweight or obese and 921 (16.4%) had metabolic comorbidity. There was an inverted U-shaped relationship whereby health utility was highest among overweight individuals and fell with increasing BMI. There was a significant interaction with metabolic comorbidity (p = 0.007). Individuals with metabolic comorbidty had lower utility scores and a steeper decline in utility with increasing BMI (morbidly obese, adjusted coefficient: -0.064, 95% CI -0.115, -0.012, p = 0.015 for metabolic comorbidity versus -0.042, 95% CI -0.067, -0.018, p = 0.001 for no metabolic comorbidity). <p/>Conclusions: The adverse impact of obesity on health-related quality of life is greater among individuals with metabolic comorbidity. However, increased BMI is associated with reduced health-related quality of life even in the absence of metabolic comorbidity, casting doubt on the notion of "healthy obesity"

    The Charlson comorbidity index in registry-based research : which version to use?

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    Background: Comorbidities may have an important impact on survival, and comorbidity scores are often implemented in studies assessing prognosis. The Charlson Comorbidity index is most widely used, yet several adaptations have been published, all using slightly different conversions of the International Classification of Diseases (ICD) coding. Objective: To evaluate which coding should be used to assess and quantify comorbidity for the Charlson Comorbidity Index for registry-based research, in particular if older ICD versions will be used. Methods: A systematic literature search was used to identify adaptations and modifications of the ICD-coding of the Charlson Comorbidity Index for general purpose in adults, published in English. Back-translation to ICD version 8 and version 9 was conducted by means of the ICD-code converter of Statistics Sweden. Results: In total, 16 studies were identified reporting ICD-adaptations of the Charlson Comorbidity Index. The Royal College of Surgeons in them United Kingdom combined 5 versions into, an adapted and updated version which appeared appropriate for research purposes. Their ICD-10 codes were back-translated into ICD-9 and ICD-8 according to their Proposed adaptations, and verified with previous versions of the Charlson Comorbidity Index. Conclusion: Many versions of the Charlson Comorbidity Index are used in parallel, so clear reporting of the version, exact ICD-coding and weighting is necessary to obtain transparency and reproducibility in research. Yet, the version of the Royal College of Surgeons is up-to-date and easy-to-use, and therefore an acceptable co-morbidity score to be used in registry-based research especially for surgical patients

    Secondary analysis of data on comorbidity/multimorbidity: a call for papers

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    Despite the high proportion and growing number of people with comorbidity/multimorbidity, clinical trials often exclude this group, leading to a limited evidence base to guide policy and practice for these individuals [1–5]. This evidence gap can potentially be addressed by secondary analysis of studies that were not originally designed to specifically examine comorbidity/multimorbidity, but have collected information from participants on co-occurring conditions. For example, secondary data analysis from randomized controlled trials may shed light on whether there is a differential impact of interventions on people with comorbidity/multimorbidity. Furthermore, data regarding comorbidity/multimorbidity can often be obtained from registration networks or administrative data sets

    Lifetime Bipolar Disorder comorbidity and related clinical characteristics in patients with primary Obsessive Compulsive Disorder: a report from the International College of Obsessive-Compulsive Spectrum Disorders (ICOCS)

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    IntroductionBipolar disorder (BD) and obsessive compulsive disorder (OCD) are prevalent, comorbid, and disabling conditions, often characterized by early onset and chronic course. When comorbid, OCD and BD can determine a more pernicious course of illness, posing therapeutic challenges for clinicians. Available reports on prevalence and clinical characteristics of comorbidity between BD and OCD showed mixed results, likely depending on the primary diagnosis of analyzed samples.MethodsWe assessed prevalence and clinical characteristics of BD comorbidity in a large international sample of patients with primary OCD (n = 401), through the International College of Obsessive-Compulsive Spectrum Disorders (ICOCS) snapshot database, by comparing OCD subjects with vs without BD comorbidity.ResultsAmong primary OCD patients, 6.2% showed comorbidity with BD. OCD patients with vs without BD comorbidity more frequently had a previous hospitalization (p < 0.001) and current augmentation therapies (p < 0.001). They also showed greater severity of OCD (p < 0.001), as measured by the Yale-Brown Obsessive Compulsive Scale (Y-BOCS).ConclusionThese findings from a large international sample indicate that approximately 1 out of 16 patients with primary OCD may additionally have BD comorbidity along with other specific clinical characteristics, including more frequent previous hospitalizations, more complex therapeutic regimens, and a greater severity of OCD. Prospective international studies are needed to confirm our findings.Peer reviewe

    Inverse comorbidity: the power of paradox in the advancement of science

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    Research on comorbidity and multimorbidity is finally receiving the attention it deserves, particularly considering the magnitude and impact they have on health and the delivery of healthcare [1,2]. Numerous studies have demonstrated that individuals with Down’s syndrome, Parkinson’s disease, schizophrenia, diabetes, anorexia nervosa, Alzheimer’s disease, allergy related diseases, multiple sclerosis or Huntington’s disease (among other health problems) are protected against many forms of cancer, including solid tumors, smoking-related tumors and prostate cancer. This apparent anti-cancer effect, which we have termed inverse cancer comorbidity, has been observed in many serious CNS and immune disorders, and is the subject of active research [3–5].Journal of Comorbidity 2013;3(1):1–3

    The effects of latent variables in the development of comorbidity among common mental disorders

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    Background: Although numerous studies have examined the role of latent predispositions to internalizing and externalizing disorders in the structure of comorbidity among common mental disorders, none examined latent predispositions in predicting development of comorbidity. Methods: A novel method was used to study the role of latent variables in the development of comorbidity among lifetime DSM-IV disorders in the National Comorbidity Surveys. Broad preliminary findings are briefly presented to describe the method. The method used survival analysis to estimate time-lagged associations among 18 lifetime DSM-IV anxiety, mood, behavior, and substance disorders. A novel estimation approach examined the extent to which these predictive associations could be explained by latent canonical variables representing internalizing and externalizing disorders. Results: Consistently significant positive associations were found between temporally primary and secondary disorders. Within-domain time-lagged associations were generally stronger than between-domain associations. The vast majority of associations were explained by a model that assumed mediating effects of latent internalizing and externalizing variables, although the complexity of this model differed across samples. A number of intriguing residual associations emerged that warrant further investigation. Conclusions: The good fit of the canonical model suggests that common causal pathways account for most comorbidity among the disorders considered. These common pathways should be the focus of future research on the development of comorbidity. However, the existence of several important residual associations shows that more is involved than simple mediation. The method developed to carry out these analyses provides a unique way to pinpoint these significant residual associations for subsequent focused study. Depression and Anxiety, 2011. (c) 2011 Wiley-Liss, Inc

    The coexistence of terms to describe the presence of multiple concurrent diseases

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    Background: Consensus on terminology for multiple diseases is lacking. Because of the clinical relevance and social impact of multiple concurrent diseases, it is important that concepts are clear. Objective: To highlight the diversity of terms in the literature referring to the presence of multiple concurrent diseases/conditions and make recommendations. Design: A bibliometric analysis of English-language publications indexed in the MEDLINE database from 1970 to 2012 for the terms comorbidity, multimorbidity, polymorbidity, polypathology, pluripathology, multipathology, and multicondition, and a review of definitions of multimorbidity found in English-language publications indexed from 1970 to 2012 in the MEDLINE and SCOPUS databases. Results: Comorbidity was used in 67,557 publications, multimorbidity in 434, and the other terms in three to 31 publications. At least 144 publications used the term comorbidity without referring to an index disease. Thirteen general definitions of multimorbidity were identified, but only two were frequently used (91% of publications). The most frequently used definition (48% of publications) was “more than one or multiple chronic or long-term diseases/conditions”. Multimorbidity was not defined in 51% of the publications using the term. Conclusions: Comorbidity was overwhelmingly used to describe any clinical entity coexisting with an index disease under study. Multimorbidity was the term most frequently used when no index disease was designated. Several definitions of multimorbidity were found. However, most authors using the term did not define it. The use of clearly defined terms in the literature is recommended until a general consensus on the terminology of multiple coexistent diseases is reached.Journal of Comorbidity 2013;3(1):4–9 
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