Introduction: The European Organisation for Research and Treatment of Cancer Quality of Life Core Questionnaire (EORTC QLQ-C30) measures 15 health-related quality of life (HRQoL) scales relevant to... Show moreIntroduction: The European Organisation for Research and Treatment of Cancer Quality of Life Core Questionnaire (EORTC QLQ-C30) measures 15 health-related quality of life (HRQoL) scales relevant to the disease and treatment of patients with cancer. A study by Martinelli (2011) demonstrated that these scales could be grouped into three main clusters: physical, psychological and gastrointestinal. This study aims to validate Martinelli's findings in an independent dataset and evaluate whether these clusters are consistent across cancer types and patient characteristics. Methods: Pre-defined criteria for successful validation were three main clusters should emerge with a minimum R-squared value of 0.51 using pooled baseline-data. A cluster analysis was performed on the 15 QLQ-C30 HRQoL-scales in the overall dataset, as well as by cancer type and selected patient characteristics to examine the robustness of the results. Results: The dataset consisted of 20,066 patients pooled across 17 cancer types. Overall, three main clusters were identified (R-2 = 0.61); physical-cluster included role-functioning, physical functioning, social-functioning, fatigue, pain, and global-health status; psychological-cluster included emotional-functioning, cognitive-functioning, and insomnia; gastro-intestinal-cluster included nausea/vomiting and appetite loss. The results were consistent across different levels of disease severity, socio-demographic and clinical characteristics with minor variations by cancer type. Global-health status was found to be strongly linked to the scales included in the physical-functioning-related cluster. Conclusion: This study successfully validated prior findings by Martinelli (2011): the QLQC30 scales are interrelated and can be grouped into three main clusters. Knowing how these multidimensional HRQoL scales are related to each other can help clinicians and patients with cancer in managing symptom burden, guide policymakers in defining social-support plans and inform selection of HRQoL scales in future clinical trials. (C)2022 Elsevier Ltd. All rights reserved. Show less
Conditionals, or if-then sentences, form a crucial ingredient of everyday reasoning and argumentation, as they enable us to express our thoughts about possible states of the world. They are used in... Show moreConditionals, or if-then sentences, form a crucial ingredient of everyday reasoning and argumentation, as they enable us to express our thoughts about possible states of the world. They are used in very different ways, and the main aim of this dissertation is to investigate to what extent these different uses of conditionals are connected to one another and to their grammatical features.The first part of this dissertation presents an analysis of conditionals in terms of implicatures of 'unassertiveness' and 'connectedness'. Insights from semantics, pragmatics, cognitive linguistics, and neighbouring fields are combined. In the second part, the analysis is tested on a corpus of spoken and written Dutch discourse. To investigate the relation between the meaning and grammar of conditionals, several cluster analyses are conducted. The results show that grammatical features such as verb tense and modal marking do not, or only weakly, license generalised implicatures of unassertiveness and connectedness. This outcome sheds light on difficulties in applying general categories of conditionals to language use data, and it suggests that the fundamentals of categorising conditional constructions need revision.The dissertation shows the benefits of combining semantic and pragmatic analyses of conditionals. It provides an extensive discussion of classifications of conditionals, an overview of the grammatical features of Dutch conditionals, and it presents cluster analyses using state-of-the-art machine-learning techniques. The study should therefore be of interest to anyone concerned with the syntax, semantics, and pragmatics of conditionals, and to anyone working on Dutch grammar, corpus linguistics, theories of argumentation, and the interface between semantics and pragmatics. Show less
Fosse, N.A. du; Lashley, E.E.L.O.; Beelen, E. van; Meuleman, T.; Cessie, S. le; Lith, J.M.M. van; ... ; Hoorn, M.L.P. van der 2021
Background: Seminal plasma contains a wide range of cytokines, chemokines and growth factors. Part of these signalling molecules assist in inducing a state of active maternal immune tolerance... Show moreBackground: Seminal plasma contains a wide range of cytokines, chemokines and growth factors. Part of these signalling molecules assist in inducing a state of active maternal immune tolerance towards the fetus. Disbalances in seminal plasma content may contribute to pregnancy loss. This study investigated cytokine expression profiles in seminal plasma of male partners of couples with unexplained recurrent pregnancy loss (RPL) and the association with clinical and lifestyle characteristics, including smoking, alcohol consumption and body mass index (BMI). Methods: In the seminal plasma of 52 men who visited a specialised RPL clinic the levels of 25 pre-selected cytokines, chemokines and growth factors were measured by Bio-Plex assay or ELISA. Two-way hierarchical cluster analysis was performed. Identified patient clusters were compared on clinical and lifestyle characteristics. Results: Two distinct cytokine expression profiles in the seminal plasma were revealed by cluster analysis. Patient cluster I showed relatively higher levels of pro-inflammatory cytokines, including IL-1 alpha, IL-1 beta, IL-6, IL-8, IL-12, IL-18 and TNF-alpha, compared to Patient cluster II. Men belonging to Patient cluster I were significantly older and had significantly more lifestyle risk factors compared to men in Patient cluster II. Conclusion: Cluster analysis suggested the existence of a less favourable pro-inflammatory cytokine expression profile, being present in part of men affected by RPL and associated with advanced male age and lifestyle risk factors. These findings may serve as a starting point for further research into underlying mechanisms and ultimately lead to novel diagnostic and therapeutic approaches for couples with RPL. Show less
Hebbrecht, K.; Stuivenga, M.; Birkenhager, T.; Morrens, M.; Fried, E.I.; Sabbe, B.; Giltay, E.J. 2020
BackgroundMajor depressive disorder (MDD) shows large heterogeneity of symptoms between patients, but within patients, particular symptom clusters may show similar trajectories. While symptom... Show moreBackgroundMajor depressive disorder (MDD) shows large heterogeneity of symptoms between patients, but within patients, particular symptom clusters may show similar trajectories. While symptom clusters and networks have mostly been studied using cross-sectional designs, temporal dynamics of symptoms within patients may yield information that facilitates personalized medicine. Here, we aim to cluster depressive symptom dynamics through dynamic time warping (DTW) analysis.MethodsThe 17-item Hamilton Rating Scale for Depression (HRSD-17) was administered every 2weeks for a median of 11weeks in 255 depressed inpatients. The DTW analysis modeled the temporal dynamics of each pair of individual HRSD-17 items within each patient (i.e., 69,360 calculated "DTW distances"). Subsequently, hierarchical clustering and network models were estimated based on similarities in symptom dynamics both within each patient and at the group level.ResultsThe sample had a mean age of 51 (SD 15.4), and 64.7% were female. Clusters and networks based on symptom dynamics markedly differed across patients. At the group level, five dynamic symptom clusters emerged, which differed from a previously published cross-sectional network. Patients who showed treatment response or remission had the shortest average DTW distance, indicating denser networks with more synchronous symptom trajectories.ConclusionsSymptom dynamics over time can be clustered and visualized using DTW. DTW represents a promising new approach for studying symptom dynamics with the potential to facilitate personalized psychiatric care. Show less
Aims To assess medication use in adult congenital heart disease (ACHD) patients compared to the age- and sex-matched general population, identify patterns of pharmacotherapy, and analyse... Show moreAims To assess medication use in adult congenital heart disease (ACHD) patients compared to the age- and sex-matched general population, identify patterns of pharmacotherapy, and analyse associations between pharmacotherapy and adverse outcomes in ACHD.Methods and results Data of 14 138 ACHD patients from the CONCOR registry [35 (24-48) years, 49% male] and age- and sex-matched referents (1:10 ratio) were extracted from the Dutch Dispensed Drug Register for the years 2006-14. Adult congenital heart disease patients had more cardiovascular and non-cardiovascular drugs than referents (median 3 vs. 1, P<0.001). Polypharmacy, defined as >= 5 dispensed drug types yearly, was present in 30% of ACHD and 15% of referents {odds ratio [OR]=2.47 [95% confidence interval (CI) 2.39-2.54]}. Polypharmacy was independently associated with female sex [OR=1.92 (95% CI 1.88-1.96)], older age [for men: OR=2.3/10years (95% CI 2.2-2.4) and for women: OR=1.6/10years (95% CI 1.5-1.6); P-interaction<0.001], and ACHD severity [mild: OR=2.51 (95% CI 2.40-2.61), moderate: OR=3.22 (95% CI 3.06-3.40), severe: OR=4.87 (95% CI 4.41-5.38)]. Cluster analysis identified three subgroups with distinct medication patterns; a low medication use group (8-year cumulative survival: 98%), and a cardiovascular and comorbidity group with lower survival (92% and 95%, respectively). Cox regression revealed a strong association between polypharmacy and mortality [hazard ratio (HR)=3.94 (95% CI 3.22-4.81)], corrected for age, sex, and defect severity. Polypharmacy also increased the risk of hospitalization for adverse drug events [HR=4.58 (95% CI 2.04-10.29)].Conclusion Both cardiovascular and non-cardiovascular medication use is high in ACHD with twice as much polypharmacy compared with the matched general population. Patients with polypharmacy had a four-fold increased risk of mortality and adverse drug events. Recognition of distinct medication patterns can help identify patients at highest risk. Drug regimens need repeating evaluation to assess the appropriateness of all prescriptions. More high-quality studies are needed to improve ACHD care with more evidence-based pharmacotherapy. Show less
In neuroscience, clustering subjects based on brain dysfunctions is a promising avenue to subtype mental disorders as it may enhance the development of a brain-based categorization system for... Show moreIn neuroscience, clustering subjects based on brain dysfunctions is a promising avenue to subtype mental disorders as it may enhance the development of a brain-based categorization system for mental disorders that transcends and is biologically more valid than current symptom-based categorization systems. As changes in functional connectivity (FC) patterns have been demonstrated to be associated with various mental disorders, one appealing approach in this regard is to cluster patients based on similarities and differences in FC patterns. To this end, researchers collect three-way fMRI data measuring neural activation over time for different patients at several brain locations and apply Independent Component Analysis (ICA) to extract FC patterns from the data. However, due to the three-way nature and huge size of fMRI data, classical (two-way) clustering methods are inadequate to cluster patients based on these FC patterns. Therefore, a two-step procedure is proposed where, first, ICA is applied to each patient’s fMRI data and, next, a clustering algorithm is used to cluster the patients into homogeneous groups in terms of FC patterns. As some clustering methods used operate on similarity data, the modified RV-coefficient is adopted to compute the similarity between patient specific FC patterns. An extensive simulation study demonstrated that performing ICA before clustering enhances the cluster recovery and that hierarchical clustering using Ward’s method outperforms complete linkage hierarchical clustering, Affinity Propagation and Partitioning Around Medoids. Moreover, the proposed two-step procedure appears to recover the underlying clustering better than (1) a two-step procedure that combines PCA with clustering and (2) Clusterwise SCA-ECP, which performs PCA and clustering in a simultaneous fashion. Additionally, the good performance of the proposed two-step procedure using ICA and Ward’s hierarchical clustering is illustrated in an empirical fMRI data set regarding dementia patients. Show less
Schat, A.; Noorden, M.S. van; Noom, M.J.; Giltay, E.J.; Wee, N.J.A. van der; Griaf, R. de; ... ; Zitman, F.G. 2016
The clinical heterogeneity of Parkinson__s disease (PD) patients may reflect the existence of subtypes of the disease. PD subtypes have often been defined by a classification according to... Show moreThe clinical heterogeneity of Parkinson__s disease (PD) patients may reflect the existence of subtypes of the disease. PD subtypes have often been defined by a classification according to researcher-specified criteria, such as age-at-onset or predominant clinical motor features. The general objective of this thesis was to identify and characterize clinical subtypes in PD by a data-driven approach, based on a comprehensive assessment of all relevant PD domains. In order to obtain insight in the associations and coherence of impairments that are involved in the disease, we evaluated the contributions of impairment and disability domains to health-related quality of life in patients with PD. Subsequently, the data of the PROPARK cohort was used to study coherency patterns within the motor domain and in the spectrum of motor and nonmotor domains. In our study on subtypes, first, we systematically evaluated the result s of earlier studies that performed cluster analysis to identify subtypes in PD, after which we applied cluster analysis on data of the PROPARK cohort in order to identify subtypes of the disease. Show less
