Diabetes mellitus and cardiovascular mortality across the spectrum of aortic stenosis.

OBJECTIVE: Current data regarding the impact of diabetes mellitus (DM) on cardiovascular mortality in patients with aortic stenosis (AS) are restricted to severe AS or aortic valve replacement (AVR) trials. We aimed to investigate cardiovascular mortality according to DM across the entire spectrum of outpatients with AS. METHODS: Between May 2016 and December 2017, patients with mild (peak aortic velocity=2.5-2.9 m/s), moderate (3-3.9 m/s) and severe (≥4 m/s) AS graded by echocardiography were included during outpatient cardiology visits in the Nord-Pas-de-Calais region in France and followed-up for modes of death between May 2018 and August 2020. RESULTS: Among 2703 patients, 820 (30.3%) had DM, mean age was 76±10.8 years with 46.6% of women and a relatively high prevalence of underlying cardiovascular diseases. There were 200 cardiovascular deaths prior to AVR during the 2.1 years (IQR 1.4-2.7) follow-up period. In adjusted analyses, DM was significantly associated with cardiovascular mortality (HR=1.40, 95% CI 1.04 to 1.89; p=0.029). In mild or moderate AS, the cardiovascular mortality of patients with diabetes was similar to that of patients without diabetes. In severe AS, DM was associated with higher cardiovascular mortality (HR=2.65, 95% CI 1.50 to 4.68; p=0.001). This was almost exclusively related to a higher risk of death from heart failure (HR=2.61, 95% CI 1.15 to 5.92; p=0.022) and sudden death (HR=3.33, 95% CI 1.28 to 8.67; p=0.014). CONCLUSION: The effect of DM on cardiovascular mortality varied across AS severity. Despite no association between DM and outcomes in patients with mild/moderate AS, DM was strongly associated with death from heart failure and sudden death in patients with severe AS.

Visualization of medical concepts represented using word embeddings: a scoping review.

BACKGROUND: Analyzing the unstructured textual data contained in electronic health records (EHRs) has always been a challenging task. Word embedding methods have become an essential foundation for neural network-based approaches in natural language processing (NLP), to learn dense and low-dimensional word representations from large unlabeled corpora that capture the implicit semantics of words. Models like Word2Vec, GloVe or FastText have been broadly applied and reviewed in the bioinformatics and healthcare fields, most often to embed clinical notes or activity and diagnostic codes. Visualization of the learned embeddings has been used in a subset of these works, whether for exploratory or evaluation purposes. However, visualization practices tend to be heterogeneous, and lack overall guidelines. OBJECTIVE: This scoping review aims to describe the methods and strategies used to visualize medical concepts represented using word embedding methods. We aim to understand the objectives of the visualizations and their limits. METHODS: This scoping review summarizes different methods used to visualize word embeddings in healthcare. We followed the methodology proposed by Arksey and O'Malley (Int J Soc Res Methodol 8:19-32, 2005) and by Levac et al. (Implement Sci 5:69, 2010) to better analyze the data and provide a synthesis of the literature on the matter. RESULTS: We first obtained 471 unique articles from a search conducted in PubMed, MedRxiv and arXiv databases. 30 of these were effectively reviewed, based on our inclusion and exclusion criteria. 23 articles were excluded in the full review stage, resulting in the analysis of 7 papers that fully correspond to our inclusion criteria. Included papers pursued a variety of objectives and used distinct methods to evaluate their embeddings and to visualize them. Visualization also served heterogeneous purposes, being alternatively used as a way to explore the embeddings, to evaluate them or to merely illustrate properties otherwise formally assessed. CONCLUSIONS: Visualization helps to explore embedding results (further dimensionality reduction, synthetic representation). However, it does not exhaust the information conveyed by the embeddings nor constitute a self-sustaining evaluation method of their pertinence.

Specifications for the Routine Implementation of Federated Learning in Hospitals Networks.

We collected user needs to define a process for setting up Federated Learning in a network of hospitals. We identified seven steps: consortium definition, architecture implementation, clinical study definition, data collection, initialization, model training and results sharing. This process adapts certain steps from the classical centralized multicenter framework and brings new opportunities for interaction thanks to the architecture of the Federated Learning algorithms. It is open for completion to cover a variety of scenarios.

A Decentralized Framework for Biostatistics and Privacy Concerns.

Biostatistics and machine learning have been the cornerstone of a variety of recent developments in medicine. In order to gather large enough datasets, it is often necessary to set up multi-centric studies; yet, centralization of measurements can be difficult, either for practical, legal or ethical reasons. As an alternative, federated learning enables leveraging multiple centers' data without actually collating them. While existing works generally require a center to act as a leader and coordinate computations, we propose a fully decentralized framework where each center plays the same role. In this paper, we apply this framework to logistic regression, including confidence intervals computation. We test our algorithm on two distinct clinical datasets split among different centers, and show that it matches results from the centralized framework. In addition, we discuss possible privacy leaks and potential protection mechanisms, paving the way towards further research.