The Experts below are selected from a list of 588813 Experts worldwide ranked by ideXlab platform
Vladan Starcevic - One of the best experts on this subject based on the ideXlab platform.
-
Comorbidity models of panic disorder agoraphobia and personality disturbance
Journal of Personality Disorders, 1992Co-Authors: Vladan StarcevicAbstract:This article presents and critically examines the models of Comorbidity of panic disorder/agoraphobia and personality disturbance. The research findings indicate that the DSM-III “anxious cluster” of personality disorders and dependent and avoidant personality disorders, in particular, are significantly associated with panic disorder/agoraphobia. However, the nature of this Comorbidity is subject to various interpretations, put forward as Comorbidity models. No Comorbidity model has thus far been established as clearly and universally superior to others, and the models' value and range of applicability remain to be determined through prospective studies. The article examines some of the methodological issues in Comorbidity research, particularly as they pertain to the presented Comorbidity models.
-
Comorbidity Models of Panic Disorder/Agoraphobia and Personality Disturbance
Journal of Personality Disorders, 1992Co-Authors: Vladan StarcevicAbstract:This article presents and critically examines the models of Comorbidity of panic disorder/agoraphobia and personality disturbance. The research findings indicate that the DSM-III “anxious cluster” of personality disorders and dependent and avoidant personality disorders, in particular, are significantly associated with panic disorder/agoraphobia. However, the nature of this Comorbidity is subject to various interpretations, put forward as Comorbidity models. No Comorbidity model has thus far been established as clearly and universally superior to others, and the models' value and range of applicability remain to be determined through prospective studies. The article examines some of the methodological issues in Comorbidity research, particularly as they pertain to the presented Comorbidity models.
Anne Elixhauser - One of the best experts on this subject based on the ideXlab platform.
-
identifying increased risk of readmission and in hospital mortality using hospital administrative data the ahrq elixhauser Comorbidity index
Medical Care, 2017Co-Authors: Brian J Moore, Susan White, Raynard Washington, Natalia Coenen, Anne ElixhauserAbstract:Objective:We extend the literature on Comorbidity measurement by developing 2 indices, based on the Elixhauser Comorbidity measures, designed to predict 2 frequently reported health outcomes: in-hospital mortality and 30-day readmission in administrative data. The Elixhauser measures are commonly us
-
Comorbidity measures for use with administrative data
Medical Care, 1998Co-Authors: Anne Elixhauser, Claudia Steiner, D R Harris, Rosanna M CoffeyAbstract:Objectives.This study attempts to develop a comprehensive set of Comorbidity measures for use with large administrative inpatient datasets.Methods.The study involved clinical and empirical review of Comorbidity measures, development of a framework that attempts to segregate comorbidities from other
Bogda Koczwara - One of the best experts on this subject based on the ideXlab platform.
-
The role of Comorbidity assessment in guiding treatment decision-making for women with early breast cancer: a systematic literature review
Supportive Care in Cancer, 2019Co-Authors: Stephanie Webster, Raymond Chan, Sharon Lawn, Bogda KoczwaraAbstract:Purpose Comorbidity in breast cancer patients impacts treatment choice, toxicity, and outcomes. While Comorbidity measurement tools are frequently used by researchers, little is known about their use in clinical practice. The aim of this review was to examine the use of Comorbidity measurement tools in clinical practice and their role in treatment decision-making in breast cancer. Methods Six electronic databases were searched from inception to 21 March 2019. Quantitative or mixed methods studies addressing primary treatment of breast cancer and identifying a Comorbidity measurement tool used in clinical practice treatment decision-making were included. Data was extracted on tool utilized, impact on treatment decisions or outcomes, pattern of use, and psychometric properties. Results A systematic search of literature yielded 752 studies. Of the four studies that met inclusion criteria, each utilized a comprehensive geriatric assessment tool, though only in a subset of patients. No studies found direct Comorbidity measurement tools utilized independently of geriatric assessment. Assessment results had variable impact on treatment decisions. Impacts on patient mortality and treatment toxicity, cost-effectiveness, and psychometric characteristics of the tools were not identified. Conclusions There is little evidence on use of specific Comorbidity tools in clinical decision-making in breast cancer outside of Comorbidity assessment as part of geriatric assessment tools. There was limited impact on decision-making or patient outcomes when these were utilized. Further research is needed to identify barriers to Comorbidity assessment in clinical practice and identify Comorbidity tools that have the potential to improve patient outcomes.
C Cunningham - One of the best experts on this subject based on the ideXlab platform.
-
consideration of Comorbidity in treatment decision making in multidisciplinary cancer team meetings a systematic review
Annals of Oncology, 2015Co-Authors: Jeannine Stairmand, Lesley Batten, Maureen Holdaway, Louise Signal, Christopher Jackson, Diana Sarfati, C CunninghamAbstract:ABSTRACT Background Comorbidity is very common among patients with cancer. Multidisciplinary team meetings (MDTs) are increasingly the context within which cancer treatment decisions are made internationally. Little is known about how Comorbidity is considered, or impacts decisions, in MDTs. Methods A systematic literature review was conducted to evaluate previous evidence on consideration, and impact, of Comorbidity in cancer MDT treatment decision making. Twenty-one original studies were included. Results Lack of information on Comorbidity in MDTs impedes the ability of MDT members to make treatment recommendations, and for those recommendations to be implemented among patients with Comorbidity. Where treatment is different from that recommended due to Comorbidity, it is more conservative, despite evidence that such treatment may be tolerated and effective. MDT members are likely to be unaware of the extent to which issues such as Comorbidity are ignored. Conclusions MDTs should systematically consider treatment of patients with Comorbidity. Further research is needed to assist clinicians to undertake MDT decision making that appropriately addresses Comorbidity. If this were to occur, it would likely contribute to improved outcomes for cancer patients with comorbidities.
Mario Lazzarino - One of the best experts on this subject based on the ideXlab platform.
-
Risk stratification based on both disease status and extra-hematologic comorbidities in patients with myelodysplastic syndrome
Haematologica, 2011Co-Authors: Matteo G Della Porta, Ilaria Ambaglio, Esther Zipperer, Corinna Strupp, Cristiana Pascutto, Erica Travaglino, Luca Malcovati, Rosangela Invernizzi, Andrea Kuendgen, Mario LazzarinoAbstract:The incidence of myelodysplastic syndromes increases with age and a high prevalence of co-morbid conditions has been reported in these patients. So far, risk assessment in myelodysplastic syndromes has been mainly based on disease status. We studied the prognostic impact of Comorbidity on the natural history of myelodysplastic syndrome with the aim of developing novel tools for risk assessment. The study population included a learning cohort of 840 patients diagnosed with myelodysplastic syndrome in Pavia, Italy, and a validation cohort of 504 patients followed in Duesseldorf, Germany. Information on Comorbidity was extracted from detailed review of the patients' medical charts and laboratory values at diagnosis and during the course of the disease. Univariable and multivariable survival analyses with both fixed and time-dependent covariates were performed using Cox's proportional hazards regression models. Comorbidity was present in 54% of patients in the learning cohort. Cardiac disease was the most frequent Comorbidity and the main cause of non-leukemic death. In multivariable analysis, Comorbidity had a significant impact on both non-leukemic death (P=0.01) and overall survival (P=0.02). Cardiac, liver, renal, pulmonary disease and solid tumors were found to independently affect the risk of non-leukemic death. A time-dependent myelodysplastic syndrome-specific Comorbidity index (MDS-CI) was developed for predicting the effect of Comorbidity on outcome. This identified three groups of patients which showed significantly different probabilities of non-leukemic death (P
