An assessment of validity and responsiveness of generic measures of health-related quality of life in hearing impairment

This article is made available through the Brunel Open Access Publishing Fund. This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.Purpose: This review examines psychometric performance of three widely used generic preference-based measures, that is, EuroQol 5 dimensions (EQ-5D), Health Utility Index 3 (HUI3) and Short-form 6 dimensions (SF-6D) in patients with hearing impairments. Methods: A systematic search was undertaken to identify studies of patients with hearing impairments where health state utility values were measured and reported. Data were extracted and analysed to assess the reliability, validity (known group differences and convergent validity) and responsiveness of the measures across hearing impairments. Results: Fourteen studies (18 papers) were included in the review. HUI3 was the most commonly used utility measures in hearing impairment. In all six studies, the HUI3 detected difference between groups defined by the severity of impairment, and four out of five studies detected statistically significant changes as a result of intervention. The only study available suggested that EQ-5D only had weak ability to discriminate difference between severity groups, and in four out of five studies, EQ-5D failed to detected changes. Only one study involved the SF-6D; thus, the information is too limited to conclude on its performance. Also evidence for the reliability of these measures was not found. Conclusion: Overall, the validity and responsiveness of the HUI3 in hearing impairment was good. The responsiveness of EQ-5D was relatively poor and weak validity was suggested by limited evidence. The evidence on SF-6D was too limited to make any judgment. More head-to-head comparisons of these and other preference measures of health are required.Medical Research Counci

Clinical prediction models for mortality in patients with covid-19: external validation and individual participant data meta-analysis

OBJECTIVE: To externally validate various prognostic models and scoring rules for predicting short term mortality in patients admitted to hospital for covid-19. DESIGN: Two stage individual participant data meta-analysis. SETTING: Secondary and tertiary care. PARTICIPANTS: 46 914 patients across 18 countries, admitted to a hospital with polymerase chain reaction confirmed covid-19 from November 2019 to April 2021. DATA SOURCES: Multiple (clustered) cohorts in Brazil, Belgium, China, Czech Republic, Egypt, France, Iran, Israel, Italy, Mexico, Netherlands, Portugal, Russia, Saudi Arabia, Spain, Sweden, United Kingdom, and United States previously identified by a living systematic review of covid-19 prediction models published in The BMJ, and through PROSPERO, reference checking, and expert knowledge. MODEL SELECTION AND ELIGIBILITY CRITERIA: Prognostic models identified by the living systematic review and through contacting experts. A priori models were excluded that had a high risk of bias in the participant domain of PROBAST (prediction model study risk of bias assessment tool) or for which the applicability was deemed poor. METHODS: Eight prognostic models with diverse predictors were identified and validated. A two stage individual participant data meta-analysis was performed of the estimated model concordance (C) statistic, calibration slope, calibration-in-the-large, and observed to expected ratio (O:E) across the included clusters. MAIN OUTCOME MEASURES: 30 day mortality or in-hospital mortality. RESULTS: Datasets included 27 clusters from 18 different countries and contained data on 46 914patients. The pooled estimates ranged from 0.67 to 0.80 (C statistic), 0.22 to 1.22 (calibration slope), and 0.18 to 2.59 (O:E ratio) and were prone to substantial between study heterogeneity. The 4C Mortality Score by Knight et al (pooled C statistic 0.80, 95% confidence interval 0.75 to 0.84, 95% prediction interval 0.72 to 0.86) and clinical model by Wang et al (0.77, 0.73 to 0.80, 0.63 to 0.87) had the highest discriminative ability. On average, 29% fewer deaths were observed than predicted by the 4C Mortality Score (pooled O:E 0.71, 95% confidence interval 0.45 to 1.11, 95% prediction interval 0.21 to 2.39), 35% fewer than predicted by the Wang clinical model (0.65, 0.52 to 0.82, 0.23 to 1.89), and 4% fewer than predicted by Xie et al's model (0.96, 0.59 to 1.55, 0.21 to 4.28). CONCLUSION: The prognostic value of the included models varied greatly between the data sources. Although the Knight 4C Mortality Score and Wang clinical model appeared most promising, recalibration (intercept and slope updates) is needed before implementation in routine care

Comparative analysis of four methods to extract DNA from paraffin-embedded tissues: effect on downstream molecular applications

<p>Abstract</p> <p>Background</p> <p>A large portion of tissues stored worldwide for diagnostic purposes is formalin-fixed and paraffin-embedded (FFPE). These FFPE-archived tissues are an extremely valuable source for retrospective (genetic) studies. These include mutation screening in cancer-critical genes as well as pathogen detection. In this study we evaluated the impact of several widely used DNA extraction methods on the quality of molecular diagnostics on FFPE tissues.</p> <p>Findings</p> <p>We compared 4 DNA extraction methods from 4 identically processed FFPE mammary-, prostate-, colon- and lung tissues with regard to PCR inhibition, real time SNP detection and amplifiable fragment size. The extraction methods, with and without proteinase K pre-treatment, tested were: 1) heat-treatment, 2) QIAamp DNA-blood-mini-kit, 3) EasyMAG NucliSens and 4) Gentra Capture-Column-kit.</p> <p>Amplifiable DNA fragment size was assessed by multiplexed 200-400-600 bp PCR and appeared highly influenced by the extraction method used. Proteinase K pre-treatment was a prerequisite for proper purification of DNA from FFPE. Extractions with QIAamp, EasyMAG and heat-treatment were found suitable for amplification of fragments up to 400 bp from all tissues, 600 bp amplification was marginally successful (best was QIAamp). QIAamp and EasyMAG extracts were found suitable for downstream real time SNP detection. Gentra extraction was unsuitable. Hands-on time was lowest for heat-treatment, followed by EasyMAG.</p> <p>Conclusions</p> <p>We conclude that the extraction method plays an important role with regard to performance in downstream molecular applications.</p

Pregnancy-related pelvic girdle pain: an update

A large number of scientists from a wide range of medical and surgical disciplines have reported on the existence and characteristics of the clinical syndrome of pelvic girdle pain during or after pregnancy. This syndrome refers to a musculoskeletal type of persistent pain localised at the anterior and/or posterior aspect of the pelvic ring. The pain may radiate across the hip joint and the thigh bones. The symptoms may begin either during the first trimester of pregnancy, at labour or even during the postpartum period. The physiological processes characterising this clinical entity remain obscure. In this review, the definition and epidemiology, as well as a proposed diagnostic algorithm and treatment options, are presented. Ongoing research is desirable to establish clear management strategies that are based on the pathophysiologic mechanisms responsible for the escalation of the syndrome's symptoms to a fraction of the population of pregnant women

Gender differences in the use of cardiovascular interventions in HIV-positive persons; the D:A:D Study

Peer reviewe

Effect of culture conditions on endothelial cell growth and responsiveness

The in vitro culture of endothelial cells (EC) is dependent on the presence of a coated surface and the availability of growth factors in the medium. The aim of the present research is to investigate whether in vitro EC culture conditions, such as serum source and surface coating, determine the growth characteristics of EC. The phenotype of EC was studied at the level of adhesion molecule expression and down-regulation by angiogenic factors. We found that human umbilical vein EC adhere well to and stretch well with plastic coated with fibronectin, collagen, gelatin and hyaluronan in contrast to non-coated plastic. While low in hyaluronan-coated wells, the spontaneous proliferation of EC was enhanced in fibronectin-collagen and gelatin-coated wells as compared to non-coated wells. Basic fibroblast growth factor bFGF-induced proliferation, however, was best on hyaluronan-coated plastic. A markedly up-regulated proliferation was measured on fibronectin and collagen while EC on gelatin-coated plastic only showed moderate bFGF-induced proliferation. On non-coated plastic EC were not inducible with bFGF. The induction of apoptosis by serum deprivation on these different matrices was most efficient when no coat was available or when wells were coated with hyaluronan, and bFGF inhibited apoptosis induction under all conditions. The use of different culture media demonstrated that human and bovine serum both can be used for human EC assays. The synthetic medium Utroser G prevented both spontaneous and growth factor-induced proliferation. We found that apart from some magnitude differences, the down-regulation of intercellular adhesion molecule-1 (ICAM-1) by angiogenic factors such as bFGF is not dependent on specific culture conditions

Reporting of prognostic clinical prediction models based on machine learning methods in oncology needs to be improved

Objective Evaluate the completeness of reporting of prognostic prediction models developed using machine learning methods in the field of oncology. Study design and setting We conducted a systematic review, searching the MEDLINE and Embase databases between 01/01/2019 and 05/09/2019, for non-imaging studies developing a prognostic clinical prediction model using machine learning methods (as defined by primary study authors) in oncology. We used the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement to assess the reporting quality of included publications. We described overall reporting adherence of included publications and by each section of TRIPOD. Results Sixty-two publications met the inclusion criteria. 48 were development studies and 14 were development with validation studies. 152 models were developed across all publications. Median adherence to TRIPOD reporting items was 41% [range: 10%-67%] and at least 50% adherence was found in 19% (n=12/62) of publications. Adherence was lower in development only studies (median: 38% [range: 10%-67%]); and higher in development with validation studies (median: 49% [range: 33%-59%]). Conclusion Reporting of clinical prediction models using machine learning in oncology is poor and needs urgent improvement, so readers and stakeholders can appraise the study methods, understand study findings, and reduce research waste