Objective Patients with cancer are at increased bleeding risk, and anticoagulants increase this risk even more. Yet, validated bleeding risk models for prediction of bleeding risk in patients with... Show moreObjective Patients with cancer are at increased bleeding risk, and anticoagulants increase this risk even more. Yet, validated bleeding risk models for prediction of bleeding risk in patients with cancer are lacking. The aim of this study is to predict bleeding risk in anticoagulated patients with cancer.Methods We performed a study using the routine healthcare database of the Julius General Practitioners’ Network. Five bleeding risk models were selected for external validation. Patients with a new cancer episode during anticoagulant treatment or those initiating anticoagulation during active cancer were included. The outcome was the composite of major bleeding and clinically relevant non-major (CRNM) bleeding. Next, we internally validated an updated bleeding risk model accounting for the competing risk of death.Results The validation cohort consisted of 1304 patients with cancer, mean age 74.0±10.9 years, 52.2% males. In total 215 (16.5%) patients developed a first major or CRNM bleeding during a mean follow-up of 1.5 years (incidence rate; 11.0 per 100 person-years (95% CI 9.6 to 12.5)). The c-statistics of all selected bleeding risk models were low, around 0.56. Internal validation of an updated model accounting for death as competing risk showed a slightly improved c-statistic of 0.61 (95% CI 0.54 to 0.70). On updating, only age and a history of bleeding appeared to contribute to the prediction of bleeding risk.Conclusions Existing bleeding risk models cannot accurately differentiate bleeding risk between patients. Future studies may use our updated model as a starting point for further development of bleeding risk models in patients with cancer. Show less
The aim of this study was to examine cognitive emotion regulation strategies (CERS) of help-seeking adolescents diagnosed with personality disorders. At pre-treatment, patients (N = 116) were found... Show moreThe aim of this study was to examine cognitive emotion regulation strategies (CERS) of help-seeking adolescents diagnosed with personality disorders. At pre-treatment, patients (N = 116) were found to use some maladaptive but also some adaptive CERS more often than adolescents from the general population. Less than 4% of these pre-treatment CERS predicted treatment outcome. In patients whose treatment outcome according to the Symptom Checklist-90 (SCL-90) showed significant improvement (N = 75), a reduction of maladaptive CERS and an increase of adaptive CERS occurred. Patients that were unchanged or deteriorated (N = 41) showed no significant changes in CERS. In conclusion, pre-treatment CERS are not predictive for treatment outcome in this sample of adolescents diagnosed with personality disorders. Even though patients who use more adaptive and less maladaptive CERS have fewer symptoms, the relationship between these CERS and symptoms in this group of severe patients remains unclear. Show less
Identification of flow patterns within the heart has long been recognized as a potential contribution to the understanding of physiological and pathophysiological processes of cardiovascular... Show moreIdentification of flow patterns within the heart has long been recognized as a potential contribution to the understanding of physiological and pathophysiological processes of cardiovascular diseases. Although the pulsatile flow itself is multi-dimensional and multi-directional, current available non-invasive imaging modalities in clinical practice provide calculation of flow in only 1-direction and lack 3-dimensional volumetric velocity information. Four-dimensional flow cardiovascular magnetic resonance imaging (4D flow CMR) has emerged as a novel tool that enables comprehensive and critical assessment of flow through encoding velocity in all 3 directions in a volume of interest resolved over time. Following technical developments, 4D flow CMR is not only capable of visualization and quantification of conventional flow parameters such as mean/peak velocity and stroke volume but also provides new hemodynamic parameters such as kinetic energy. As a result, 4D flow CMR is being extensively exploited in clinical research aiming to improve understanding of the impact of cardiovascular disease on flow and vice versa. Of note, the analysis of 4D flow data is still complex and accurate analysis tools that deliver comparable quantification of 4D flow values are a necessity for a more widespread adoption in clinic. In this article, the acquisition and analysis processes are summarized and clinical applications of 4D flow CMR on the heart including conventional and novel hemodynamic parameters are discussed. Finally, clinical potential of other emerging intra-cardiac 4D flow imaging modalities is explored and a near-future perspective on 4D flow CMR is provided. Show less
Berger, F.A.; Sijs, H. van der; Becker, M.L.; Gelder, T. van; Bemt, P.M.L.A. van den 2020
Background The exact risk of developing QTc-prolongation when using a combination of QTc-prolonging drugs is still unknown, making it difficult to interpret these QT drug-drug interactions (QT-DDIs... Show moreBackground The exact risk of developing QTc-prolongation when using a combination of QTc-prolonging drugs is still unknown, making it difficult to interpret these QT drug-drug interactions (QT-DDIs). A tool to identify high-risk patients is needed to support healthcare providers in handling automatically generated alerts in clinical practice. The main aim of this study was to develop and validate a tool to assess the risk of QT-DDIs in clinical practice. Methods A model was developed based on risk factors associated with QTc-prolongation determined in a prospective study on QT-DDIs in a university medical center inthe Netherlands. The main outcome measure was QTc-prolongation defined as a QTc interval > 450 ms for males and > 470 ms for females. Risk points were assigned to risk factors based on their odds ratios. Additional risk factors were added based on a literature review. The ability of the model to predict QTc-prolongation was validated in an independent dataset obtained from a general teaching hospital against QTc-prolongation as measured by an ECG as the gold standard. Sensitivities, specificities, false omission rates, accuracy and Youden's index were calculated. Results The model included age, gender, cardiac comorbidities, hypertension, diabetes mellitus, renal function, potassium levels, loop diuretics, and QTc-prolonging drugs as risk factors. Application of the model to the independent dataset resulted in an area under the ROC-curve of 0.54 (95% CI 0.51-0.56) when QTc-prolongation was defined as > 450/470 ms, and 0.59 (0.54-0.63) when QTc-prolongation was defined as > 500 ms. A cut-off value of 6 led to a sensitivity of 76.6 and 83.9% and a specificity of 28.5 and 27.5% respectively. Conclusions A clinical decision support tool with fair performance characteristics was developed. Optimization of this tool may aid in assessing the risk associated with QT-DDIs. Show less
The multifactorial likelihood analysis method has demonstrated utility for quantitative assessment of variant pathogenicity for multiple cancer syndrome genes. Independent data types currently... Show moreThe multifactorial likelihood analysis method has demonstrated utility for quantitative assessment of variant pathogenicity for multiple cancer syndrome genes. Independent data types currently incorporated in the model for assessing BRCA1 and BRCA2 variants include clinically calibrated prior probability of pathogenicity based on variant location and bioinformatic prediction of variant effect, co-segregation, family cancer history profile, co-occurrence with a pathogenic variant in the same gene, breast tumor pathology, and case-control information. Research and clinical data for multifactorial likelihood analysis were collated for 1,395 BRCA1/2 predominantly intronic and missense variants, enabling classification based on posterior probability of pathogenicity for 734 variants: 447 variants were classified as (likely) benign, and 94 as (likely) pathogenic; and 248 classifications were new or considerably altered relative to ClinVar submissions. Classifications were compared with information not yet included in the likelihood model, and evidence strengths aligned to those recommended for ACMG/AMP classification codes. Altered mRNA splicing or function relative to known nonpathogenic variant controls were moderately to strongly predictive of variant pathogenicity. Variant absence in population datasets provided supporting evidence for variant pathogenicity. These findings have direct relevance for BRCA1 and BRCA2 variant evaluation, and justify the need for gene-specific calibration of evidence types used for variant classification. Show less
