No abstract

ObjectiveCurrent 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.MethodsBetween 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.ResultsAmong 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).ConclusionThe 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.

No abstract

Book music is extensively used in street organs. It consists of thick cardboard, containing perforated holes specifying the musical notes. We propose to represent clinical time-dependent data in a tabular form inspired from this principle. The sheet represents a statistical individual, each row represents a binary time-dependent variable, and each hole denotes the “true” value. Data from electronic health records or nationwide medical-administrative databases can then be represented: demographics, patient flow, drugs, laboratory results, diagnoses, and procedures. This data representation is suitable for survival analysis (e.g., Cox model with repeated outcomes and changing covariates) and different types of temporal association rules. Quantitative continuous variables can be discretized, as in clinical studies. The “book music” approach could become an intermediary step in feature extraction from structured data. It would enable to better account for time in analyses, notably for historical cohort analyses based on healthcare data reuse.

No abstract