Anaemia in heart failure patients: the prevalence of haematinic deficiencies and the role of ACE inhibitors and aspirin doses as risk factors

Background: Patients with heart failure often have comorbidities that alter the progression of heart failure and impact on prognosis. One such comorbidity is anaemia, and clinicians have started to appreciate the full gravity of its impact on heart failure patients. Yet, the extent of the problem is not fully understood, particularly the role of heart failure therapy itself as a risk factor for developing anaemia. Objective: This study aimed to investigate the prevalence of anaemia in a cohort of heart failure patients. The impact of using different ACEIs and different doses of aspirin was also explored, together with the prevalence of haematinic deficiencies. Methods: Medication lists and pathology results were examined to establish the prevalence of ACEIs use, and the use of aspirin at its most common doses of 100mg and 150mg, together with haematinic deficiencies. Multinomial logistic regression and the Student’s t-test were utilised for the analysis of data. Statistical significance was pre-set at p<0.05. Results: Ninety-six patients were eligible for analysis, with 26% having anaemia. The use of ACEIs had a RR of 17.4 for the presence of anaemia. Perindopril was associated with a RR of 20.8, while the use of ramipril was not significantly associated with such a high RR. Haematinic anaemia occurred only at a rate of 3.3%, but borderline deficiencies were found in more than a third of all patients. An aspirin dose of 150mg was associated with a higher risk for anaemia, compared to a dose of 100mg. Conclusions: ACEIs are associated with the presence of anaemia, with perindopril posing more risk than ramipril when used in heart failure patients. The dose of aspirin may also be a factor in the development of anaemia, with lower doses being safer. Despite the lack of high prevalence of haematinic anaemia among this cohort of patients, borderline haematinic deficiencies were common

Self-reported depression symptoms in haemodialysis patients: Bi-factor structures of two common measures and their association with clinical factors

Copyright © 2018 Elsevier Inc. All rights reserved.Objective: To validate the factor structure of two common self-report depression tools in a large sample of haemodialysis (HD) patients and to examine their demographic and clinical correlates, including urine output, history of depression and transplantation. Methods: Factor structures of the Beck Depression Inventory (BDI-II) and Patient Health Questionnaire (PHQ-9) were evaluated using confirmatory factor analysis (CFA). Data was utilised from the screening phase (n = 709) of a placebo-controlled feasibility randomised control trial (RCT) of sertraline in HD patients with mild to moderate Major Depressive Disorder. Alternative factor models including bi-factor models for the BDI-II and PHQ-9 were evaluated. Coefficient omega and omega-hierarchical were calculated. Results: For both measures, bi-factor measurement models had the overall best fit to the data, with dominant general depression factors. Omega-hierarchical for the general BDI-II and PHQ-9 factors was 0.94 and 0.88 respectively. Both general factors had high reliability (coefficient omega = 0.97 and 0.94 respectively) and explained over 85% of the explained common variance within their respective models. BDI-II and PHQ-9 general depression factors were negatively associated with age and urine output and positively with a history of depression, antidepressant use within the last 3 months and a history of failed transplantation. In adjusted regression models, age, urine output and a history of depression remained significant. Conclusions: These data suggest that both the BDI-II and PHQ-9 are sufficiently unidimensional to warrant the use of a total score. Younger age, lower urine output and a history of depression appear consistent correlates of depression severity among HD patients.Peer reviewedFinal Accepted Versio

Impact of different IMRT techniques to improve conformity and normal tissue sparing in upper esophageal cancer

Purpose: Intensity modulated radiotherapy (IMRT) for cervical esophageal cancer is challenging. Although IMRT techniques using inverse planning algorithms are facilitating the treatment planning process, the irradiation dose to the normal tissues can be a critical issue. This study was performed to investigate the effect of beam numbers and their directions and local optimization on: (1) dose conformity and homogeneity to the planning target volume (PTV) and (2) dose to the organ at risks (OARs).Methods: Four upper esophageal cancer cases were randomly selected for this treatment planning study. Eight IMRT plans were generated for each case with the same dose-volume constraints but with different beam numbers and arrangements. Local optimization using regular structures drawn automatically around the PTV with margins from 0.5-1.5 cm was performed. IMRT plans were evaluated with respect to isodose distributions, dose-volume histograms (DVHs) parameters, homogeneity index (HI), and conformity index (CI). The statistical comparison between the types of plans was done using the One Way ANOVA test.Results: The results showed that IMRT using three or five beams was not sufficient to obtain good dose optimization. The seven field plans showed the best coverage for the PTV with tolerable doses for the OARs, and the beam orientation was very critical. Increasing beams (Bs) number from 7 to 13 did not show significant differences in the PTV coverage, while the mean lung dose was increased. The PTV coverage were 95.1, 95.1, 98.1, 97.3, 97.3, 97.3, 97.0, and 97.0% for 3Bs, 5Bs, 7Bs, 9Bs, 13Bs, 7Bs(30), 7Bs(60) (beam angles were changed from 0o to 30o and 60o), and 7Bs(R) (seven IMRT plans with ring), respectively. The mean heart dose did not exceed 0.36 Gy with p &lt; 0.05. For lung doses, the best plan was the one with 9Bs which reduced lung volume doses V20Gy (%) and V30Gy (%), and reduced mean lung dose from 5.4 to 4.5 Gy with p &lt; 0.05 for 7Bs(R) plans. IMRT improved the homogeneity indices (p &gt; 0.05), yet conformity was better with 9Bs and 7Bs(R) IMRT plans with p &lt; 0.05.Conclusion: Seven equispaced coplanar intensity-modulated beams with an addition of a ring structure can produce desirable dose distributions to the PTV. Moreover, dose-volume of exposed normal lung can be reduced with 9Bs and 7Bs(R) IMRT plans

Measuring Fatigue Using the Multidimensional Fatigue Inventory-20: A Questionable Factor Structure in Haemodialysis Patients

BACKGROUND/AIMS: Fatigue is recognised as a common and burdensome symptom among dialysis patients. A growing body of research is devoted to understanding fatigue in advanced kidney disease, yet its measurement is challenging within this context. Our aim was to evaluate the factor structure underlying the multidimensional fatigue inventory (MFI-20) and to examine its associations with clinical factors and mood. METHODS: Data was evaluated for confirmatory factor analysis (CFA) from the screening phase of a multicentre randomised placebo-controlled trial of sertraline in haemodialysis (HD) patients. Four hundred seventy patients completed the MFI-20, which purports to measure 5 components of fatigue (general fatigue, mental fatigue, physical fatigue, reduced motivation and reduced activity). CFA models were evaluated in MPlus 7.3 using the robust maximum likelihood (MLR) estimation. RESULTS: The evaluation of the original 5 factors revealed low internal reliability for the general factor and reduced activity, and high intercorrelations between all sum scores. CFA revealed poor model fit for the original 5-factor MFI-20 model (confirmatory fit index = 0.738; Tucker-Lewis index = 0.689; root mean squared error of approximation = 0.101). Alternative models, including 1, 3 and bi-factor models all demonstrated poor fit to the data. No reliable factor model was confirmed prohibiting the examination of factors associated with fatigue. CONCLUSIONS: We were not able to confirm the factor structure of the MFI-20 in a large sample of HD patients. Certain items may lack suitable face validity in this context

Differential Analysis of Ovarian and Endometrial Cancers Identifies a Methylator Phenotype

Despite improved outcomes in the past 30 years, less than half of all women diagnosed with epithelial ovarian cancer live five years beyond their diagnosis. Although typically treated as a single disease, epithelial ovarian cancer includes several distinct histological subtypes, such as papillary serous and endometrioid carcinomas. To address whether the morphological differences seen in these carcinomas represent distinct characteristics at the molecular level we analyzed DNA methylation patterns in 11 papillary serous tumors, 9 endometrioid ovarian tumors, 4 normal fallopian tube samples and 6 normal endometrial tissues, plus 8 normal fallopian tube and 4 serous samples from TCGA. For comparison within the endometrioid subtype we added 6 primary uterine endometrioid tumors and 5 endometrioid metastases from uterus to ovary. Data was obtained from 27,578 CpG dinucleotides occurring in or near promoter regions of 14,495 genes. We identified 36 locations with significant increases or decreases in methylation in comparisons of serous tumors and normal fallopian tube samples. Moreover, unsupervised clustering techniques applied to all samples showed three major profiles comprising mostly normal samples, serous tumors, and endometrioid tumors including ovarian, uterine and metastatic origins. The clustering analysis identified 60 differentially methylated sites between the serous group and the normal group. An unrelated set of 25 serous tumors validated the reproducibility of the methylation patterns. In contrast, >1,000 genes were differentially methylated between endometrioid tumors and normal samples. This finding is consistent with a generalized regulatory disruption caused by a methylator phenotype. Through DNA methylation analyses we have identified genes with known roles in ovarian carcinoma etiology, whereas pathway analyses provided biological insight to the role of novel genes. Our finding of differences between serous and endometrioid ovarian tumors indicates that intervention strategies could be developed to specifically address subtypes of epithelial ovarian cancer

A national clinical decision support infrastructure to enable the widespread and consistent practice of genomic and personalized medicine

<p>Abstract</p> <p>Background</p> <p>In recent years, the completion of the Human Genome Project and other rapid advances in genomics have led to increasing anticipation of an era of genomic and personalized medicine, in which an individual's health is optimized through the use of all available patient data, including data on the individual's genome and its downstream products. Genomic and personalized medicine could transform healthcare systems and catalyze significant reductions in morbidity, mortality, and overall healthcare costs.</p> <p>Discussion</p> <p>Critical to the achievement of more efficient and effective healthcare enabled by genomics is the establishment of a robust, nationwide clinical decision support infrastructure that assists clinicians in their use of genomic assays to guide disease prevention, diagnosis, and therapy. Requisite components of this infrastructure include the standardized representation of genomic and non-genomic patient data across health information systems; centrally managed repositories of computer-processable medical knowledge; and standardized approaches for applying these knowledge resources against patient data to generate and deliver patient-specific care recommendations. Here, we provide recommendations for establishing a national decision support infrastructure for genomic and personalized medicine that fulfills these needs, leverages existing resources, and is aligned with the <it>Roadmap for National Action on Clinical Decision Support </it>commissioned by the U.S. Office of the National Coordinator for Health Information Technology. Critical to the establishment of this infrastructure will be strong leadership and substantial funding from the federal government.</p> <p>Summary</p> <p>A national clinical decision support infrastructure will be required for reaping the full benefits of genomic and personalized medicine. Essential components of this infrastructure include standards for data representation; centrally managed knowledge repositories; and standardized approaches for leveraging these knowledge repositories to generate patient-specific care recommendations at the point of care.</p