POS1162 PREDICTORS OF HOSPITALISATION IN PATIENTS WITH RHEUMATIC DISEASE AND COVID-19 IN IRELAND: DATA FROM THE COVID-19 GLOBAL RHEUMATOLOGY ALLIANCE PHYSICIAN-REPORTED REGISTRY
Richard ConwayElena NikiphorouChristiana A. DemetriouCandice LowKelly LeamyJoseph RyanRichard G. KavanaghAbigail FraserJohn CareyPaul G. O’ConnellRachael FloodRonan MullanDavid KanePhilip C. RobinsonJean W. LiewRebecca GraingerGéraldine McCarthy
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Background: There is limited data regarding the risk of hospitalisation in patients with rheumatic disease and COVID-19 in Ireland. Objectives: We used the COVID-19 Global Rheumatology Alliance (GRA) registry data to study outcomes and their predictors. Methods: We examined data on patients and their disease-related characteristics entered into the COVID-19 GRA provider registry from Ireland (24th March 2020 to 31st August 2020). Multivariable logistic regression was used to assess the association of demographic and clinical characteristics with hospitalisation. Results: Of 105 patients, 47 (45.6%) were hospitalised and 10 (9.5%) died. Multivariable logistic regression analysis showed age (OR=1.06, 95%CI 1.01 to 1.10), number of comorbidities (OR=1.93, 95%CI 1.11 to 3.35), and glucocorticoid use (OR=15.01, 95%CI 1.77 to 127.16) were significantly associated with hospitalisation. A diagnosis of inflammatory arthritis was associated with a lower odds of hospitalisation (OR=0.09, 95%CI 0.02 to 0.32). All significant variable model Most parsimonious model Unadjusted OR (95% CI) Adjusted OR (95%CI)* Adjusted p-value* Adjusted OR (95%CI) & Adjusted p-value & Female 0.45 (0.20-1.02) 0.33 (0.05-2.23) 0.34 (0.09-1.36) 0.128 Age (years) 1.08 (1.05-1.11) 1.04 (0.97-1.10) 0.224 1.06 (1.01-1.10) 0.010 Inflammatory arthritis 0.11 (0.05-0.28) 0.14 (0.02-0.95) 0.044 0.09 (0.02-0.32) <0.001 Connective Tissue Disease and Other 1.56 (0.62 - 3.92) No comorbidities 0.11 (0.04-0.30) 0.76 (0.09-6.58) 0.802 Most common comorbidities COPD / asthma 4.77 (1.23-18.54) 3.09 (0.16-60.07) 0.456 CVD 3.40 (1.31-8.85) 0.11 (0.01-1.88) 0.129 Hypertension 3.71 (1.52-9.08) 0.56 (0.04-7.94) 0.668 Obesity 0.58 (0.10-3.30) Number of comorbidities (Median, IQR) 3.01 (1.92-4.72) 2.99 (0.59-15.02) 0.184 1.93 (1.11-3.35) 0.020 Never Smoker ref. 0.889 Ever Smoker 3.17 (1.18-8.89) 1.19 (0.10-13.68) Medication prior to COVID-19 diagnosis Glucocorticoids 9.26 (1.95-43.89) 18.14 (1.13-290.81) 0.041 15.01 (1.77-127.16) 0.013 csDMARD monotherapy 0.42 (0.17-1.00) b/tsDMARD (monotherapy or in combination with csDMARD) 0.24 (0.10-0.58) 1.36 (0.19-9.72) 0.557 Conclusion: Increasing age, comorbidity burden, and glucocorticoid use were associated with hospitalisation, while a diagnosis of inflammatory arthritis was associated with lower odds of hospitalization. Disclosure of Interests: Richard Conway Speakers bureau: Janssen, Roche, Sanofi, Abbvie, Elena Nikiphorou Speakers bureau: AbbVie, Eli-Lilly, Gilead, Celltrion, Pfizer, Sanofi, Christiana Demetriou: None declared, Candice Low: None declared, Kelly Leamy: None declared, John Ryan: None declared, Ronan Kavanagh: None declared, Alexander Fraser: None declared, John Carey: None declared, Paul O’Connell: None declared, Rachael Flood: None declared, Ronan Mullan: None declared, David Kane: None declared, Philip Robinson Speakers bureau: UCB, Roche, Pfizer, Gilead, Janssen, Novartis, Eli Lilly, Abbvie, Grant/research support from: Abbvie, UCB, Novartis, Janssen, Pfizer, Jean Liew Grant/research support from: Pfizer, Rebecca Grainger Speakers bureau: Pfizer, Cornerstones, Janssen, Novartis, Abbvie, Geraldine McCarthy: None declared.Cite
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This is the first report of a projected series regarding the comorbidity of cardiovascular disease (CVD), diabetes and chronic kidney disease (CKD) in Australia. Comorbidity refers to any two or more of these diseases that occur in one person at the same time. The questions to be answered in this report include: 1. How many Australians have comorbidity of CVD, diabetes and CKD? 2. What is the proportion of hospitalisations with these comorbidities? 3. How much do these comorbidities contribute to deaths? 4. What is the magnitude of comorbidity in the context of each individual disease? 5. Are there differences in the distribution of these comorbidities among age groups and sexes?
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Background and purpose — Using patient-reported health-related quality of life (HRQoL), approximately 10% of patients report some degree of dissatisfaction after a total hip arthroplasty (THA). The preoperative comorbidity burden may play a role in predicting which patients may have limited benefit from a THA. Therefore, we examined whether gain in HRQoL measured with the EuroQol-5D (EQ-5D) at 3 and 12 months of follow-up depended on the comorbidity burden in THA patients Patients and methods — 1,582 THA patients treated at the Regional Hospital West Jutland from 2008 to 2013 were included. The comorbidity burden was collected from an administrative database and assessed with the Charlson Comorbidity Index (CCI). The CCI was divided into 3 levels: no comorbidity burden, low, and high comorbidity burden. HRQoL was measured using the EQ-5D preoperatively and at 3 and 12 months' follow-up. Association between low and high comorbidity burden compared with no comorbidity burden and gain in HRQoL was analyzed with multiple linear regression. Results — All patients, regardless of comorbidity burden, gained significantly in HRQoL. A positive association between comorbidity burden and gain in HRQoL was found at 3-month follow-up for THA patients with a high comorbidity burden (coeff: 0.09 (95% CI 0.02 – 0.16)) compared with patients with no comorbidity burden. Interpretation — A comorbidity burden prior to THA does not preclude a gain in HRQoL up to 1 year after THA.
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There are several different definitions of the combination of multiple nosology within one organism: comorbidity, multimorbidity, syntropy, and dystropy, etc. Comorbidity is an important component of pathophysiological processes, which has a significant impact on the course and outcome of cardiac diseases in patients. Therefore, in recent decades, researchers have been actively engaged in the problem of assessing the degree of contribution of comorbidity to the overall state of the body. For this purpose, a number of scales and indices of comorbidity have been developed, which allow estimating the burden of comorbidity on the underlying disease within certain groups of diseases. Consideration of comorbidity in routine clinical practice allows to increase reliable prognostic assumptions and correctly build a therapeutic strategy. As a result, it improves patients’ quality of life, allows them to achieve favorable outcomes, and most effectively prevents complications in patients with comorbidity. The assessment of comorbidity in cardiological, endocrinological, oncological, and neurological pathologies is particularly important, since they have the most general negative effect on the entire patient’s body.
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在治疗的选择和老病人的幸存上识别 comorbidity 的影响的目的(≥ 70 年) 与先进非小的房间肺癌症(NSCLC ) 。方法临床的特征和 177 个老病人，有好表演地位，的治疗的选择 PS ≤ 1 ) 回顾地在肿瘤学部门被分析，上海肺的医院，在到 2005 年 12 月的 2005 年 1 月之间。幸存数据仅仅在收到了化疗的那些被分析。所有病人被 comorbidity 的数字作为没有(0 ) 成层，温和(1 2 ) 并且严重(≥ 3 ) 组。结果病人，收到了化疗，的比例温和、严重的 comorbidity 是显著地不同的(79.3%，76.2%和57.4%， P = 0.038 )，并且也有关于在三个组之中的辩解的放射疗法率显著地不同(21.7%，11.7%和37.0%， P = 0.014 )。中部的幸存和 1 年的幸存在没有评价，温和、严重的 comorbidity 组，是 13.6 对 10.2 对 7.6 个月并且 53.5% 对 41.3% 对 20.8% 分别地(木头等级， P = 0.071 ) 。在 univariate 并且多，变量考克斯为分析建模，仅仅严重的 comorbidity 是有 NSCLC 的老病人的幸存的一个独立危险因素。相对比率(RR， 95% CI ) ：(2.09， 1.06 4.15 ) ， P = 0.034。结论 Comorbidity 可以稍微与先进 NSCLC 影响老病人的治疗的选择，但是仅仅严重的 comorbidity 是幸存的一个独立预示的因素。
Univariate analysis
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Comorbidity may be an important reason for head and neck surgeons to treat elderly patients less intensively. This article provides an overview of the influence of age and comorbidity on choice of therapy, postoperative complications, and survival.Several retrospective studies show that elderly patients can undergo surgery if they do not have severe comorbid disorders. Severe comorbidity influences the rate of postoperative complications, and the higher complication rate in older patients reported in some studies is probably due to a higher level of comorbidity. Comorbidity also affects the survival of cancer patients, but several studies have failed to detect a relation between age and survival after correction for comorbidity. Thus, although severe comorbidity may influence the choice of treatment, patient age as such should not be a reason to exclude patients from intensive therapy.If severe comorbidity is not present, elderly patients should receive standard treatment for head and neck cancer. Treatment choice should be based on medical findings and patient preference, not on chronologic age.
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