Usefulness of the American Heart Association's Ideal Cardiovascular Health Measure to Predict Long-term Major Adverse Cardiovascular Events (From the Heart SCORE Study)

Nguyen, Anthony; Saeed, Anum; Bambs, Claudia; Swanson, Justin; Emechebe, Nnadozie; Mansuri, Fahad; Talreja, Karan; Reís, Steven E.; Kip, Kevin E.

doi:10.1016/j.amjcard.2020.10.019

Cited by 21 publications

(19 citation statements)

References 22 publications

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“…The essential task, not just in orthopaedics, is to identify the best-suited therapy for an individual patient, as well as to minimise the harm associated with a particular intervention, because even well-established therapeutic interventions have been questioned in the last few decades [14,32]. Several other examples in the literature identify risk factors for various diseases, such as diabetes [33,34], cardiovascular disease [35,36], breast [37,38] and lung [39,40] cancer and many others that are straightforwardly applicable to HT management, as shown in Table 2.…”

Section: Discussionmentioning

confidence: 99%

“…The advantage of this and other data-driven approaches is that if, for example, the impact of different factors on health varies in different regions, then HT management [35,36] Being overweight or obese, physical inactivity, unhealthy eating, alcohol consumption, smoking, high blood pressure, diabetes, sodium intake Breast cancer [37,38] Long-term use of combination hormone replacement therapy (oestrogen-progestin), obesity, alcohol consumption, late pregnancy or never being pregnant, night-shift work, physical inactivity…”

Section: Discussionmentioning

confidence: 99%

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A theoretical model of health management using data-driven decision-making: the future of precision medicine and health

et al. 2021

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Background The burden of chronic and societal diseases is affected by many risk factors that can change over time. The minimalisation of disease-associated risk factors may contribute to long-term health. Therefore, new data-driven health management should be used in clinical decision-making in order to minimise future individual risks of disease and adverse health effects. Methods We aimed to develop a health trajectories (HT) management methodology based on electronic health records (EHR) and analysing overlapping groups of patients who share a similar risk of developing a particular disease or experiencing specific adverse health effects. Formal concept analysis (FCA) was applied to identify and visualise overlapping patient groups, as well as for decision-making. To demonstrate its capabilities, the theoretical model presented uses genuine data from a local total knee arthroplasty (TKA) register (a total of 1885 patients) and shows the influence of step by step changes in five lifestyle factors (BMI, smoking, activity, sports and long-distance walking) on the risk of early reoperation after TKA. Results The theoretical model of HT management demonstrates the potential of using EHR data to make data-driven recommendations to support both patients’ and physicians’ decision-making. The model example developed from the TKA register acts as a clinical decision-making tool, built to show surgeons and patients the likelihood of early reoperation after TKA and how the likelihood changes when factors are modified. The presented data-driven tool suits an individualised approach to health management because it quantifies the impact of various combinations of factors on the early reoperation rate after TKA and shows alternative combinations of factors that may change the reoperation risk. Conclusion This theoretical model introduces future HT management as an understandable way of conceiving patients’ futures with a view to positively (or negatively) changing their behaviour. The model’s ability to influence beneficial health care decision-making to improve patient outcomes should be proved using various real-world data from EHR datasets.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

A theoretical model of health management using data-driven decision-making: the future of precision medicine and health

et al. 2021

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“…A second finding in our study was that incorporating digital health device measured data instead of self-reported data affected the Healthy Weight and Activity scores for women in both phenogroups as well as men. This also has implications for assessing ideal cardiovascular health using the Life’s Simple 7 Health Score as a recent longitudinal study found that when Life’s Simple 7 was calculated on a 0–14 point scale, each 1 unit increase in the Health Score was associated with an estimated 12% lower risk of major adverse cardiovascular events 34 . In our study, participants under- and overreported their weight and overreported minutes of moderate activity while underreporting minutes of vigorous activity.…”

Section: Discussionmentioning

confidence: 99%

A precision medicine approach to sex-based differences in ideal cardiovascular health

Leopold

Antman

2021

Sci Rep

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Cardiovascular disease risk factor profiles and health behaviors are known to differ between women and men. Sex-based differences in ideal cardiovascular health were examined in the My Research Legacy study, which collected cardiovascular health and lifestyle data via Life’s Simple 7 survey and digital health devices. As the study overenrolled women (n = 1251) compared to men (n = 310), we hypothesized that heterogeneity among women would affect comparisons of ideal cardiovascular health. We identified 2 phenogroups of women in our study cohort by cluster analysis. The phenogroups differed significantly across all 7 cardiovascular health and behavior domains (all p < 0.01) with women in phenogroup 1 having a lower Life’s Simple 7 Health Score than those in phenogroup 2 (5.9 ± 1.3 vs. 7.6 ± 1.3, p < 0.01). Compared to men, women in phenogroup 1 had a higher burden of cardiovascular disease risk factors, exercised less, and had lower ideal cardiovascular health scores (p < 0.01). In contrast, women in phenogroup 2 had fewer cardiovascular risk factors but similar exercise habits and higher ideal cardiovascular health scores than men (p < 0.01). These findings suggest that heterogeneity among study participants should be examined when evaluating sex-based differences in ideal cardiovascular health.

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“…Life’s Simple 7 defines ideal cardiovascular health based on 7 modifiable health factors (maintaining a healthy weight, normal blood pressure, blood glucose and cholesterol levels) and health behaviors (abstinence from smoking, subscribing to a heart healthy diet, and exercise) [ 5 , 7 ]. The Life’s Simple 7 Health Score, a measure of ideal cardiovascular health, has been associated with both incident cardiovascular disease and adverse cardiovascular events [ 8 , 9 , 10 ]. Similarly, individuals with fewer than 5 of 7 cardiovascular health factors or health behaviors in the ideal category are also at increased risk for incident cardiovascular disease [ 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, individuals with fewer than 5 of 7 cardiovascular health factors or health behaviors in the ideal category are also at increased risk for incident cardiovascular disease [ 11 , 12 ]. The importance of the Life’s Simple 7 Health Score is demonstrated by the inverse relationship between the Health Score and risk of cardiovascular events: the risk of events increases by 12% for each 1 unit decrease in the Health Score [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Ideal Cardiovascular Health in Former Smokers

Leopold

Antman

2021

JCM

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Former smokers remain at increased risk for cardiovascular diseases compared to never smokers, but have lower risk than current smokers. We therefore hypothesized that former smokers would have an ideal cardiovascular health phenotype that was intermediate between current and never smokers. Differences in ideal cardiovascular health between never (n = 1025), former (n = 428), and current (n = 108) smokers were evaluated in the My Research Legacy study, which collected cardiovascular health data from the Life’s Simple 7 survey and digital health devices. Former smokers had a higher burden of prevalent cardiovascular disease, hypertension, diabetes mellitus, and hypercholesterolemia compared to current and never smokers (all p < 0.01). Former smokers’ Life’s Simple 7 Health Scores, a measure of ideal cardiovascular health, were intermediate between current and never smokers (4.9 ± 1.3 vs. 6.3 ± 1.5 vs. 7.0 ± 1.4, p < 0.01). As former smokers shared similarities with both current and never smokers, we performed a cluster analysis, which identified two phenogroups of former smokers. The phenogroups differed significantly across all 7 cardiovascular health and behavior categories (all p < 0.01). These findings suggest that former smokers are a heterogeneous group and increased attention to cardiovascular health factors and behaviors is warranted to achieve ideal cardiovascular health.

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Usefulness of the American Heart Association's Ideal Cardiovascular Health Measure to Predict Long-term Major Adverse Cardiovascular Events (From the Heart SCORE Study)

Cited by 21 publications

References 22 publications

A theoretical model of health management using data-driven decision-making: the future of precision medicine and health

A theoretical model of health management using data-driven decision-making: the future of precision medicine and health

A precision medicine approach to sex-based differences in ideal cardiovascular health

Ideal Cardiovascular Health in Former Smokers

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