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  1. Article ; Online: Cardiovascular Disease Risk Prediction in Young Adults-The Next Frontier.

    Khan, Sadiya Sana / Pencina, Michael J

    JAMA cardiology

    2023  Volume 8, Issue 2, Page(s) 137–138

    MeSH term(s) Humans ; Young Adult ; Cardiovascular Diseases/epidemiology
    Language English
    Publishing date 2023-01-26
    Publishing country United States
    Document type Editorial ; Comment
    ISSN 2380-6591
    ISSN (online) 2380-6591
    DOI 10.1001/jamacardio.2022.4887
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Clinical Trials in the 21st Century - Promising Avenues for Better Studies.

    Pencina, Michael J / Thompson, B Taylor

    NEJM evidence

    2022  Volume 1, Issue 6, Page(s) EVIDctw2200060

    Abstract: ... in randomized controlled trials but there is a pressing need for innovative designs. Pencina and Thompson introduce a new series ...

    Abstract Innovation in Clinical Trials in the 21st CenturyMedical evidence is rooted in randomized controlled trials but there is a pressing need for innovative designs. Pencina and Thompson introduce a new series that reviews the most promising innovations in trial design and interpretation.
    Language English
    Publishing date 2022-05-24
    Publishing country United States
    Document type Journal Article ; Review
    ISSN 2766-5526
    ISSN (online) 2766-5526
    DOI 10.1056/EVIDctw2200060
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Testing Clinical Prediction Models.

    Goldstein, Benjamin A / Pencina, Michael J

    JAMA

    2020  Volume 324, Issue 19, Page(s) 1998–1999

    Language English
    Publishing date 2020-11-16
    Publishing country United States
    Document type Letter ; Comment
    ZDB-ID 2958-0
    ISSN 1538-3598 ; 0254-9077 ; 0002-9955 ; 0098-7484
    ISSN (online) 1538-3598
    ISSN 0254-9077 ; 0002-9955 ; 0098-7484
    DOI 10.1001/jama.2020.19392
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: An adverse lipoprotein phenotype-hypertriglyceridaemic hyperapolipoprotein B-and the long-term risk of type 2 diabetes: a prospective, longitudinal, observational cohort study.

    Pencina, Karol M / Pencina, Michael J / Dufresne, Line / Holmes, Michael / Thanassoulis, George / Sniderman, Allan D

    The Lancet. Healthy longevity

    2022  Volume 3, Issue 5, Page(s) e339–e346

    Abstract: Background: This study examines the risk of new-onset diabetes in patients with hypertriglyceridaemic hyperapolipoprotein B (high triglycerides, high apolipoprotein B [apoB], low LDL cholesterol to apoB ratio, and low HDL cholesterol). The aim was to ... ...

    Abstract Background: This study examines the risk of new-onset diabetes in patients with hypertriglyceridaemic hyperapolipoprotein B (high triglycerides, high apolipoprotein B [apoB], low LDL cholesterol to apoB ratio, and low HDL cholesterol). The aim was to establish whether this lipoprotein phenotype identified a substantial group at high risk of developing diabetes over the next 20 years.
    Methods: In this prospective, longitudinal, observational cohort study, we used data from the Framingham Offspring cohort (recruited in Framingham, MA, USA). Participants were aged 40-69 years and free of diabetes and cardiovascular disease at a baseline examination done between April, 1987, and November, 1991, and were followed up until March, 2014. Cox proportional hazards regression with hierarchical adjustment for age and sex, waist circumference, and fasting blood glucose were used to model the relationship between each lipid marker and incident diabetes, as well as the relationship between hypertriglyceridaemic hyperapoB (defined as values greater than sample medians of triglycerides and apoB, and less than medians of HDL cholesterol and LDL cholesterol to apoB ratio) and incident diabetes.
    Findings: Of 3446 individuals aged 40-69 years who completed baseline examination, 2515 participants were eligible and included in all analyses. During median 21·1 years (IQR 11·1-23·1) of follow-up, 402 (16·0%) individuals developed diabetes. Age (p=0·032), waist circumference (p<0·0001), fasting blood glucose (p<0·0001), and natural logarithm-transformed triglycerides (p<0·0001) were associated with new-onset diabetes, as were apoB (p=0·0016), LDL cholesterol to apoB ratio (p=0·0018), and HDL cholesterol (p=0·0016) when added to this model. The age and sex-adjusted incidence of diabetes in the hypertriglyceridaemic hyperapoB group was 32·4% (95% CI 27·8-37·7) versus 5·5% (3·5-8·6) in the optimal lipid phenotype group and 15·5% (13·5-17·7) in the mixed lipid phenotype group. The fully adjusted hazard ratio, including glucose and waist circumference, for individuals with hypertriglyceridaemic hyperapoB was 3·30 (95% CI 2·06-5·30; p=0·0008) and for mixed lipid phenotype was 2·17 (1·38-3·40; p<0·0001) compared with those with the optimal lipid phenotype.
    Interpretation: Our findings suggest that individuals with hypertriglyceridaemic hyperapoB are at high risk of new-onset diabetes and might benefit from intensive measures to prevent diabetes. The association between this phenotype and incident diabetes is consistent with a pro-diabetic effect due to increased clearance of apoB particles from plasma, which could injure pancreatic islet cells. This mechanism might explain the increased risk of diabetes with statin therapy.
    Funding: Doggone Foundation.
    MeSH term(s) Apolipoproteins B ; Blood Glucose ; Cholesterol, HDL ; Cholesterol, LDL ; Cohort Studies ; Diabetes Mellitus, Type 2/epidemiology ; Humans ; Lipoproteins ; Phenotype ; Prospective Studies ; Triglycerides
    Chemical Substances Apolipoproteins B ; Blood Glucose ; Cholesterol, HDL ; Cholesterol, LDL ; Lipoproteins ; Triglycerides
    Language English
    Publishing date 2022-05-04
    Publishing country England
    Document type Journal Article ; Observational Study
    ISSN 2666-7568
    ISSN (online) 2666-7568
    DOI 10.1016/S2666-7568(22)00079-4
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Time to Revisit Using 10-Year Risk to Guide Statin Therapy.

    Navar, Ann Marie / Fonarow, Gregg C / Pencina, Michael J

    JAMA cardiology

    2022  Volume 7, Issue 8, Page(s) 785–786

    MeSH term(s) Cardiovascular Diseases/drug therapy ; Cardiovascular Diseases/prevention & control ; Humans ; Hydroxymethylglutaryl-CoA Reductase Inhibitors/therapeutic use ; Primary Prevention
    Chemical Substances Hydroxymethylglutaryl-CoA Reductase Inhibitors
    Language English
    Publishing date 2022-07-06
    Publishing country United States
    Document type Journal Article
    ISSN 2380-6591
    ISSN (online) 2380-6591
    DOI 10.1001/jamacardio.2022.1883
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: Incremental Benefits of Machine Learning-When Do We Need a Better Mousetrap?

    Engelhard, Matthew M / Navar, Ann Marie / Pencina, Michael J

    JAMA cardiology

    2021  Volume 6, Issue 6, Page(s) 621–623

    MeSH term(s) Algorithms ; Humans ; Machine Learning
    Language English
    Publishing date 2021-03-09
    Publishing country United States
    Document type Editorial ; Comment
    ISSN 2380-6591
    ISSN (online) 2380-6591
    DOI 10.1001/jamacardio.2021.0139
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: Extrapolating Survival From Randomized Clinical Trial Data-Possibilities and Caution.

    Thomas, Laine Elliott / Navar, Ann Marie / Pencina, Michael J

    JAMA cardiology

    2021  Volume 6, Issue 11, Page(s) 1305–1307

    Language English
    Publishing date 2021-07-01
    Publishing country United States
    Document type Journal Article ; Comment
    ISSN 2380-6591
    ISSN (online) 2380-6591
    DOI 10.1001/jamacardio.2021.2629
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article ; Online: Competing Risks, Treatment Switching, and Informative Censoring.

    Thomas, Laine Elliott / Turakhia, Mintu P / Pencina, Michael J

    JAMA cardiology

    2021  Volume 6, Issue 8, Page(s) 871–873

    MeSH term(s) Humans ; Models, Statistical ; Risk Assessment ; Treatment Switching
    Language English
    Publishing date 2021-05-19
    Publishing country United States
    Document type Journal Article ; Comment
    ISSN 2380-6591
    ISSN (online) 2380-6591
    DOI 10.1001/jamacardio.2021.1239
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article ; Online: Observability and its impact on differential bias for clinical prediction models.

    Yan, Mengying / Pencina, Michael J / Boulware, L Ebony / Goldstein, Benjamin A

    Journal of the American Medical Informatics Association : JAMIA

    2022  Volume 29, Issue 5, Page(s) 937–943

    Abstract: Objective: Electronic health records have incomplete capture of patient outcomes. We consider the case when observability is differential across a predictor. Including such a predictor (sensitive variable) can lead to algorithmic bias, potentially ... ...

    Abstract Objective: Electronic health records have incomplete capture of patient outcomes. We consider the case when observability is differential across a predictor. Including such a predictor (sensitive variable) can lead to algorithmic bias, potentially exacerbating health inequities.
    Materials and methods: We define bias for a clinical prediction model (CPM) as the difference between the true and estimated risk, and differential bias as bias that differs across a sensitive variable. We illustrate the genesis of differential bias via a 2-stage process, where conditional on having the outcome of interest, the outcome is differentially observed. We use simulations and a real-data example to demonstrate the possible impact of including a sensitive variable in a CPM.
    Results: If there is differential observability based on a sensitive variable, including it in a CPM can induce differential bias. However, if the sensitive variable impacts the outcome but not observability, it is better to include it. When a sensitive variable impacts both observability and the outcome no simple recommendation can be provided. We show that one cannot use observed data to detect differential bias.
    Discussion: Our study furthers the literature on observability, showing that differential observability can lead to algorithmic bias. This highlights the importance of considering whether to include sensitive variables in CPMs.
    Conclusion: Including a sensitive variable in a CPM depends on whether it truly affects the outcome or just the observability of the outcome. Since this cannot be distinguished with observed data, observability is an implicit assumption of CPMs.
    MeSH term(s) Bias ; Humans ; Models, Statistical ; Prognosis
    Language English
    Publishing date 2022-02-24
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Extramural
    ZDB-ID 1205156-1
    ISSN 1527-974X ; 1067-5027
    ISSN (online) 1527-974X
    ISSN 1067-5027
    DOI 10.1093/jamia/ocac019
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article ; Online: A proposal for developing a platform that evaluates algorithmic equity and accuracy.

    Cerrato, Paul / Halamka, John / Pencina, Michael

    BMJ health & care informatics

    2022  Volume 29, Issue 1

    Abstract: We are at a pivotal moment in the development of healthcare artificial intelligence (AI), a point at which enthusiasm for machine learning has not caught up with the scientific evidence to support the equity and accuracy of diagnostic and therapeutic ... ...

    Abstract We are at a pivotal moment in the development of healthcare artificial intelligence (AI), a point at which enthusiasm for machine learning has not caught up with the scientific evidence to support the equity and accuracy of diagnostic and therapeutic algorithms. This proposal examines algorithmic biases, including those related to race, gender and socioeconomic status, and accuracy, including the paucity of prospective studies and lack of multisite validation. We then suggest solutions to these problems. We describe the Mayo Clinic, Duke University, Change Healthcare project that is evaluating 35.1 billion healthcare records for bias. And we propose 'Ingredients' style labels and an AI evaluation/testing system to help clinicians judge the merits of products and services that include algorithms. Said testing would include input data sources and types, dataset population composition, algorithm validation techniques, bias assessment evaluation and performance metrics.
    MeSH term(s) Algorithms ; Artificial Intelligence ; Delivery of Health Care ; Humans ; Machine Learning ; Prospective Studies
    Language English
    Publishing date 2022-04-11
    Publishing country England
    Document type Journal Article ; Review
    ISSN 2632-1009
    ISSN (online) 2632-1009
    DOI 10.1136/bmjhci-2021-100423
    Database MEDical Literature Analysis and Retrieval System OnLINE

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