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Summary: Background: Risk stratification for ventricular arrhythmias currently relies on static measurements that fail to adequately capture dynamic interactions between arrhythmic substrate and triggers over time. We trained and internally validated a dynamic machine learning (ML) model and neural network that extracted features from longitudinally collected electrocardiograms (ECG), and used these to predict the risk of malignant ventricular arrhythmias. Methods: A multicentre study in patients implanted with an implantable cardioverter-defibrillator (ICD) between 2007 and 2021 in two academic hospitals was performed. Variational autoencoders (VAEs), which combine neural networks with variational inference principles, and can learn patterns and structure in data without explicit labelling, were trained to encode the mean ECG waveforms from the limb leads into 16 variables. Supervised dynamic ML models using these latent ECG representations and clinical baseline information were trained to predict malignant ventricular arrhythmias treated by the ICD. Model performance was evaluated on a hold-out set, using time-dependent receiver operating characteristic (ROC) and calibration curves. Findings: 2942 patients (61.7 ± 13.9 years, 25.5% female) were included, with a total of 32,129 ECG recordings during a mean follow-up of 43.9 ± 35.9 months. The mean time-varying area under the ROC curve for the dynamic model was 0.738 ± 0.07, compared to 0.639 ± 0.03 for a static (i.e. baseline-only model). Feature analyses indicated dynamic changes in latent ECG representations, particularly those affecting the T-wave morphology, were of highest importance for model predictions. Interpretation: Dynamic ML models and neural networks effectively leverage routinely collected longitudinal ECG recordings for personalised and updated predictions of malignant ventricular arrhythmias, outperforming static models. Funding: This publication is part of the project DEEP RISK ICD (with project number 452019308) of the research programme Rubicon which is (partly) financed by the Dutch Research Council (NWO). This research is partly funded by the Amsterdam Cardiovascular Sciences (personal grant F.V.Y.T).

Objective: Ovarian cancer is the fifth cause of cancer-related death among women. The benefit of targeted therapy for ovarian cancer patients is limited even if treatment is stratified by molecular signature. There remains a high unmet need for alternative diagnostics that better predict targeted therapy, as current diagnostics are generally inaccurate predictors. Quantitative assessment of functional signal transduction pathway (STP) activity from mRNA measurements of target genes is an alternative approach. Therefore, we aim to identify aberrantly activated STPs in tumour tissue of patients with recurrent ovarian cancer and start phenotype-guided targeted therapy to improve survival without compromising quality of life. Study design: Patients with recurrent ovarian cancer and either 1) have platinum-resistant disease, 2) refrain from standard therapy or 3) are asymptomatic and not yet eligible for standard therapy will be included in this multi-centre prospective cohort study with multiple stepwise executed treatment arms. Targeted therapy will be available for patients with aberrantly high functional activity of the oestrogen receptor, androgen receptor, phosphoinositide 3-kinase or Hedgehog STP. The primary endpoint of this study is the progression-free survival (PFS) ratio (PFS2/PFS1 ratio) according to RECIST 1.1 determined by the PFS on matched targeted therapy (PFS2) compared to PFS on prior therapy (PFS1). Secondary endpoints include among others best overall response, overall survival, side effects, health-related quality of life and cost-effectiveness. Conclusion: The results of this study will show the clinical applicability of STP activity in selecting recurrent ovarian cancer patients for effective therapies.

Background Acute type B aortic dissection is a cardiovascular emergency with considerable mortality and morbidity risk. Male–female differences have been observed in cardiovascular disease; however, literature on type B aortic dissection is scarce. Methods and Results A retrospective cohort study was conducted including all consecutive patients with acute type B aortic dissection between 2007 and 2017 in 4 tertiary hospitals using patient files and questionnaires for late morbidity. In total, 384 patients were included with a follow‐up of 6.1 (range, 0.02–14.8) years, of which 41% (n=156) were female. Women presented at an older age than men (67 [interquartile range (IQR), 57–73] versus 62 [IQR, 52–71]; P=0.015). Prior abdominal aortic aneurysm (6% versus 15%; P=0.009), distally extending dissections (71 versus 85%; P=0.001), and clinical malperfusion (18% versus 32%; P=0.002) were less frequently observed in women. Absolute maximal descending aortic diameters were smaller in women (36 [IQR: 33–40] mm versus 39 [IQR, 36–43] mm; P

Background: Acute type A aortic dissection (ATAAD) is a highly lethal event, associated with aortic dilatation. It is not well known if patient height, weight or sex impact the thoracic aortic diameter (TAA) at ATAAD. The study aim was to identify male–female differences in TAA at ATAAD presentation. Methods: This retrospective cross-sectional study analysed all adult patients who presented with ATAAD between 2007 and 2017 in two tertiary care centres and underwent contrast enhanced computed tomography (CTA) before surgery. Absolute aortic diameters were measured at the sinus of Valsalva (SoV), ascending (AA) and descending thoracic aorta (DA) using double oblique reconstruction, and indexed for body surface area (ASI) and height (AHI). Z-scores were calculated using the Campens formula. Results: In total, 59 % (181/308) of ATAAD patients had CT-scans eligible for measurements, with 82 female and 99 male patients. Females were significantly older than males (65.5 ± 12.4 years versus 60.3 ± 2.3, p = 0.024). Female patients had larger absolute AA diameters than male patients (51.0 mm [47.0–57.0] versus 49.0 mm [45.0–53.0], p = 0.023), and larger ASI and AHI at all three levels. Z-scores for the SoV and AA were significantly higher for female patients (2.99 ± 1.66 versus 1.34 ± 1.77, p

Objective As thoracic aortic disease (TAD) is generally asymptomatic, biomarkers are needed to provide insight into early progression. We aimed to examine the association between circulating blood biomarkers and the maximal thoracic aortic diameter (TADmax).Methods In this cross-sectional study, consecutive adult patients with a thoracic aortic diameter ≥40 mm and/or genetically proven hereditary TAD (HTAD) visiting our specialised outpatient clinic between 2017 and 2020 were prospectively included. Venous blood sampling and CT angiography and/or transthoracic echocardiography of the aorta were performed. Linear regression analyses were performed and estimates were presented as mean difference in TADmax in mm per doubling of standardised biomarker level.Results In total, 158 patients were included (median age 61 (50.3–68.8) years, 37.3% female). HTAD diagnosis was confirmed in 36 of 158 (22.7%) patients. TADmax was 43.9±5.2 mm in men vs 41.9±5.1 in women (p=0.030). In unadjusted analysis, significant associations with TADmax were found for interleukin-6 (1.15 (95% CI 0.33 to 1.96), p=0.006), growth differentiation factor-15 (1.01 (95% CI 0.18 to 1.84), p=0.018), microfibrillar-associated protein 4 (MFAP4) (−0.88 (95% CI −1.71 to 0.05), p=0.039) and triiodothyronine (T3) (−2.00 (95%CI −3.01 to 0.99), p