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Heart failure (HF) affects at least 26 million people worldwide, so predicting adverse events in HF patients represents a major target of clinical data science. However, achieving large sample sizes sometimes represents a challenge due to difficulties in patient recruiting and long follow-up times, increasing the problem of missing data. To overcome the issue of a narrow dataset cardinality (in a clinical dataset, the cardinality is the number of patients in that dataset), population-enhancing algorithms are therefore crucial. The aim of this study was to design a random shuffle method to enhance the cardinality of an HF dataset while it is statistically legitimate, without the need of specific hypotheses and regression models. The cardinality enhancement was validated against an established random repeated-measures method with regard to the correctness in predicting clinical conditions and endpoints. In particular, machine learning and regression models were employed to highlight the benefits of the enhanced datasets. The proposed random shuffle method was able to enhance the HF dataset cardinality (711 patients before dataset preprocessing) circa 10 times and circa 21 times when followed by a random repeated-measures approach. We believe that the random shuffle method could be used in the cardiovascular field and in other data science problems when missing data and the narrow dataset cardinality represent an issue.

ImportanceThere is growing awareness of sex-related differences in cardiovascular risk profiles, but less is known about whether these extend to pre-menopausal females experiencing an early-onset myocardial infarction (MI), who may benefit from the protective effects of estrogen exposure.MethodsA nationwide study involving 125 Italian Coronary Care Units recruited 2,000 patients between 1998 and 2002 hospitalized for a type I myocardial infarction before the age of 45 years (male, n = 1,778 (88.9%). Patients were followed up for a median of 19.9 years (IQR 18.1–22.6). The primary composite endpoint was the occurrence of cardiovascular death, non-fatal myocardial re-infarction or non-fatal stroke, and the secondary endpoint of hospitalization for revascularisation by means of a percutaneous coronary intervention (PCI) or coronary artery bypass surgery (CABG).ResultsST-elevation MI was the most frequent presentation among both men and women (85.1 vs. 87.4%, p = ns), but the men had a greater baseline coronary atherosclerotic burden (median Duke Coronary Artery Disease Index: 48 vs. 23; median Syntax score 9 vs. 7; both p < 0.001). The primary composite endpoint occurred less frequently among women (25.7% vs. 37.0%; adjusted hazard ratio: 0.69, 95% CI 0.52–0.91; p = 0.01) despite being less likely to receive treatment with most secondary prevention medications during follow up.ConclusionsThere are significant sex-related differences in baseline risk factors and outcomes among patients with early-onset MI: women present with a lower atherosclerotic disease burden and, although they are less frequently prescribed secondary prevention measures, experience better long-term outcomes.Trial Registration4272/98 Ospedale Niguarda, Ca' Granda 03/09/1998.