Reproducibility of lymphovascular space invasion (LVSI) assessment in endometrial cancer

Aims Lymphovascular space invasion (LVSI) in endometrial cancer (EC) is an important prognostic variable impacting on a patient's individual recurrence risk and adjuvant treatment recommendations. Recent work has shown that grading the extent of LVSI further improves its prognostic strength in patients with stage I endometrioid EC. Despite this, there is little information on the reproducibility of LVSI assessment in EC. Therefore, we designed a study to evaluate interobserver agreement in discriminating true LVSI from LVSI mimics (Phase I) and reproducibility of grading extent of LVSI (Phase II). Methods and results Scanned haematoxylin and eosin (H&E) slides of endometrioid EC (EEC) with a predefined possible LVSI focus were hosted on a website and assessed by a panel of six European gynaecological pathologists. In Phase I, 48 H&E slides were included for LVSI assessment and in Phase II, 42 H&E slides for LVSI grading. Each observer was instructed to apply the criteria for LVSI used in daily practice. The degree of agreement was measured using the two-way absolute agreement average-measures intraclass correlation coefficient (ICC). Reproducibility of LVSI assessment (ICC = 0.64, P < 0.001) and LVSI grading (ICC = 0.62, P < 0.001) in EEC was substantial among the observers. Conclusions Given the good reproducibility of LVSI, this study further supports the important role of LVSI in decision algorithms for adjuvant treatment

Multivariate paired data analysis: multilevel PLSDA versus OPLSDA

Metabolomics data obtained from (human) nutritional intervention studies can have a rather complex structure that depends on the underlying experimental design. In this paper we discuss the complex structure in data caused by a cross-over designed experiment. In such a design, each subject in the study population acts as his or her own control and makes the data paired. For a single univariate response a paired t-test or repeated measures ANOVA can be used to test the differences between the paired observations. The same principle holds for multivariate data. In the current paper we compare a method that exploits the paired data structure in cross-over multivariate data (multilevel PLSDA) with a method that is often used by default but that ignores the paired structure (OPLSDA). The results from both methods have been evaluated in a small simulated example as well as in a genuine data set from a cross-over designed nutritional metabolomics study. It is shown that exploiting the paired data structure underlying the cross-over design considerably improves the power and the interpretability of the multivariate solution. Furthermore, the multilevel approach provides complementary information about (I) the diversity and abundance of the treatment effects within the different (subsets of) subjects across the study population, and (II) the intrinsic differences between these study subjects

Individual differences in metabolomics: individualised responses and between-metabolite relationships

Many metabolomics studies aim to find ‘biomarkers’: sets of molecules that are consistently elevated or decreased upon experimental manipulation. Biological effects, however, often manifest themselves along a continuum of individual differences between the biological replicates in the experiment. Such differences are overlooked or even diminished by methods in standard use for metabolomics, although they may contain a wealth of information on the experiment. Properly understanding individual differences is crucial for generating knowledge in fields like personalised medicine, evolution and ecology. We propose to use simultaneous component analysis with individual differences constraints (SCA-IND), a data analysis method from psychology that focuses on these differences. This method constructs axes along the natural biochemical differences between biological replicates, comparable to principal components. The model may shed light on changes in the individual differences between experimental groups, but also on whether these differences correspond to, e.g., responders and non-responders or to distinct chemotypes. Moreover, SCA-IND reveals the individuals that respond most to a manipulation and are best suited for further experimentation. The method is illustrated by the analysis of individual differences in the metabolic response of cabbage plants to herbivory. The model reveals individual differences in the response to shoot herbivory, where two ‘response chemotypes’ may be identified. In the response to root herbivory the model shows that individual plants differ strongly in response dynamics. Thereby SCA-IND provides a hitherto unavailable view on the chemical diversity of the induced plant response, that greatly increases understanding of the system

Frailty is associated with in-hospital mortality in older hospitalised COVID-19 patients in the Netherlands:the COVID-OLD study

BACKGROUND: During the first wave of the coronavirus disease 2019 (COVID-19) pandemic, older patients had an increased risk of hospitalisation and death. Reports on the association of frailty with poor outcome have been conflicting. OBJECTIVE: The aim of the present study was to investigate the independent association between frailty and in-hospital mortality in older hospitalised COVID-19 patients in the Netherlands. METHODS: This was a multicentre retrospective cohort study in 15 hospitals in the Netherlands, including all patients aged ≥70 years, who were hospitalised with clinically confirmed COVID-19 between February and May 2020. Data were collected on demographics, co-morbidity, disease severity and Clinical Frailty Scale (CFS). Primary outcome was in-hospital mortality. RESULTS: A total of 1,376 patients were included (median age 78 years (interquartile range 74-84), 60% male). In total, 499 (38%) patients died during hospital admission. Parameters indicating presence of frailty (CFS 6-9) were associated with more co-morbidities, shorter symptom duration upon presentation (median 4 versus 7 days), lower oxygen demand and lower levels of C-reactive protein. In multivariable analyses, the CFS was independently associated with in-hospital mortality: compared with patients with CFS 1-3, patients with CFS 4-5 had a two times higher risk (odds ratio (OR) 2.0 (95% confidence interval (CI) 1.3-3.0)) and patients with CFS 6-9 had a three times higher risk of in-hospital mortality (OR 2.8 (95% CI 1.8-4.3)). CONCLUSIONS: The in-hospital mortality of older hospitalised COVID-19 patients in the Netherlands was 38%. Frailty was independently associated with higher in-hospital mortality, even though COVID-19 patients with frailty presented earlier to the hospital with less severe symptoms

UvA-DARE (Digital Academic Repository) A classification model for the Leiden proteomics competition A Classification Model for the Leiden Proteomics Competition A Classification Model for the Leiden Proteomics Competition

Abstract A strategy is presented to build a discrimination model in proteomics studies. The model is built using cross-validation. This cross-validation step can simply be combined with a variable selection method, called rank products. The strategy is especially suitable for the low-samplesto-variables-ratio (undersampling) case, as is often encountered in proteomics and metabolomics studies. As a classification method, Principal Component Discriminant Analysis is used; however, the methodology can be used with any classifier. A data set containing serum samples from breast cancer patients and healthy controls is analysed. Double cross-validation shows that the sensitivity of the model is 82% and the specificity 86%. Potential putative biomarkers are identified using the variable selection method. In each cross-validation loop a classification model is built. The final classification uses a majority voting scheme from the ensemble classifier