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  1. Article ; Online: An upset to the standard model.

    Campagnari, Claudio / Mulders, Martijn

    Science (New York, N.Y.)

    2022  Volume 376, Issue 6589, Page(s) 136

    Abstract: Latest measurement of ... ...

    Abstract Latest measurement of the
    Language English
    Publishing date 2022-04-07
    Publishing country United States
    Document type Journal Article ; Comment
    ZDB-ID 128410-1
    ISSN 1095-9203 ; 0036-8075
    ISSN (online) 1095-9203
    ISSN 0036-8075
    DOI 10.1126/science.abm0101
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  2. Article ; Online: A machine learning risk score predicts mortality across the spectrum of left ventricular ejection fraction.

    Greenberg, Barry / Adler, Eric / Campagnari, Claudio / Yagil, Avi

    European journal of heart failure

    2021  Volume 23, Issue 6, Page(s) 995–999

    Abstract: Aims: Heart failure (HF) guideline recommendations categorize patients according to left ventricular ejection (LVEF). Mortality risk, however, varies considerably within each category and the likelihood of death in an individual patient is often ... ...

    Abstract Aims: Heart failure (HF) guideline recommendations categorize patients according to left ventricular ejection (LVEF). Mortality risk, however, varies considerably within each category and the likelihood of death in an individual patient is often uncertain. Accurate assessment of mortality risk is an important component in the decision-making process for many therapies. In this report, we assess the accuracy of MARKER-HF, a recently described machine learning-based risk score, in predicting mortality of patients in the three guideline-defined HF categories and its ability to distinguish risk of death for patients within each category.
    Methods and results: MARKER-HF was used to calculate mortality risk in a hospital-based cohort of 4064 patients categorized into groups with reduced, mid-range, or preserved LVEF. MARKER-HF was substantially more accurate than LVEF in predicting mortality and was highly accurate in all three HF categories, with c-statistics ranging between 0.83 to 0.89. Moreover, MARKER-HF accurately discriminated between patients at high, intermediate and low levels of mortality risk within each of the three categories of HF used by guidelines.
    Conclusions: MARKER-HF accurately predicts mortality in patients within the three categories of HF used in guidelines for management recommendations and it discriminates between magnitude of risk of patients in each category. MARKER-HF mortality risk prediction should be helpful to providers in making recommendations regarding the advisability of therapies designed to mitigate this risk, particularly when they are costly or associated with adverse events, and for patients and their families in making future plans.
    MeSH term(s) Heart Failure ; Humans ; Machine Learning ; Prognosis ; Risk Factors ; Stroke Volume ; Ventricular Function, Left
    Language English
    Publishing date 2021-04-06
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Extramural
    ZDB-ID 1483672-5
    ISSN 1879-0844 ; 1388-9842
    ISSN (online) 1879-0844
    ISSN 1388-9842
    DOI 10.1002/ejhf.2155
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  3. Article ; Online: A machine learning-derived risk score predicts mortality in East Asian patients with acute heart failure.

    Park, Jin Joo / Jang, Se Yong / Adler, Eric / Ahmad, Faraz / Campagnari, Claudio / Yagil, Avi / Greenberg, Barry

    European journal of heart failure

    2023  Volume 25, Issue 12, Page(s) 2331–2333

    MeSH term(s) Humans ; Heart Failure ; East Asian People ; Risk Factors ; Machine Learning ; Risk Assessment
    Language English
    Publishing date 2023-10-31
    Publishing country England
    Document type Letter
    ZDB-ID 1483672-5
    ISSN 1879-0844 ; 1388-9842
    ISSN (online) 1879-0844
    ISSN 1388-9842
    DOI 10.1002/ejhf.3059
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  4. Article ; Online: Performance of risk models to predict mortality risk for patients with heart failure: evaluation in an integrated health system.

    Ahmad, Faraz S / Hu, Ted Ling / Adler, Eric D / Petito, Lucia C / Wehbe, Ramsey M / Wilcox, Jane E / Mutharasan, R Kannan / Nardone, Beatrice / Tadel, Matevz / Greenberg, Barry / Yagil, Avi / Campagnari, Claudio

    Clinical research in cardiology : official journal of the German Cardiac Society

    2024  

    Abstract: Background: Referral of patients with heart failure (HF) who are at high mortality risk for specialist evaluation is recommended. Yet, most tools for identifying such patients are difficult to implement in electronic health record (EHR) systems.: ... ...

    Abstract Background: Referral of patients with heart failure (HF) who are at high mortality risk for specialist evaluation is recommended. Yet, most tools for identifying such patients are difficult to implement in electronic health record (EHR) systems.
    Objective: To assess the performance and ease of implementation of Machine learning Assessment of RisK and EaRly mortality in Heart Failure (MARKER-HF), a machine-learning model that uses structured data that is readily available in the EHR, and compare it with two commonly used risk scores: the Seattle Heart Failure Model (SHFM) and Meta-Analysis Global Group in Chronic (MAGGIC) Heart Failure Risk Score.
    Design: Retrospective, cohort study.
    Participants: Data from 6764 adults with HF were abstracted from EHRs at a large integrated health system from 1/1/10 to 12/31/19.
    Main measures: One-year survival from time of first cardiology or primary care visit was estimated using MARKER-HF, SHFM, and MAGGIC. Discrimination was measured by the area under the receiver operating curve (AUC). Calibration was assessed graphically.
    Key results: Compared to MARKER-HF, both SHFM and MAGGIC required a considerably larger amount of data engineering and imputation to generate risk score estimates. MARKER-HF, SHFM, and MAGGIC exhibited similar discriminations with AUCs of 0.70 (0.69-0.73), 0.71 (0.69-0.72), and 0.71 (95% CI 0.70-0.73), respectively. All three scores showed good calibration across the full risk spectrum.
    Conclusions: These findings suggest that MARKER-HF, which uses readily available clinical and lab measurements in the EHR and required less imputation and data engineering than SHFM and MAGGIC, is an easier tool to identify high-risk patients in ambulatory clinics who could benefit from referral to a HF specialist.
    Language English
    Publishing date 2024-04-02
    Publishing country Germany
    Document type Journal Article
    ZDB-ID 2213295-8
    ISSN 1861-0692 ; 1861-0684
    ISSN (online) 1861-0692
    ISSN 1861-0684
    DOI 10.1007/s00392-024-02433-2
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  5. Article ; Online: Improving clinical trial efficiency using a machine learning-based risk score to enrich study populations.

    Jering, Karola S / Campagnari, Claudio / Claggett, Brian / Adler, Eric / Klein, Liviu / Ahmad, Faraz S / Voors, Adriaan A / Solomon, Scott / Yagil, Avi / Greenberg, Barry

    European journal of heart failure

    2022  Volume 24, Issue 8, Page(s) 1418–1426

    Abstract: Aims: Prognostic enrichment strategies can make trials more efficient, although potentially at the cost of diminishing external validity. Whether using a risk score to identify a population at increased mortality risk could improve trial efficiency is ... ...

    Abstract Aims: Prognostic enrichment strategies can make trials more efficient, although potentially at the cost of diminishing external validity. Whether using a risk score to identify a population at increased mortality risk could improve trial efficiency is uncertain. We aimed to assess whether Machine learning Assessment of RisK and EaRly mortality in Heart Failure (MARKER-HF), a previously validated risk score, could improve clinical trial efficiency.
    Methods and results: Mortality rates and association of MARKER-HF with all-cause death by 1 year were evaluated in four community-based heart failure (HF) and five HF clinical trial cohorts. Sample size required to assess effects of an investigational therapy on mortality was calculated assuming varying underlying MARKER-HF risk and proposed treatment effect profiles. Patients from community-based HF cohorts (n = 11 297) had higher observed mortality and MARKER-HF scores than did clinical trial patients (n = 13 165) with HF with either reduced ejection fraction (HFrEF) or preserved ejection fraction (HFpEF). MARKER-HF score was strongly associated with risk of 1-year mortality both in the community (hazard ratio [HR] 1.48, 95% confidence interval [CI] 1.44-1.52) and clinical trial cohorts with HFrEF (HR 1.41, 95% CI 1.30-1.54), and HFpEF (HR 1.74, 95% CI 1.53-1.98), per 0.1 increase in MARKER-HF. Using MARKER-HF to identify patients for a hypothetical clinical trial assessing mortality reduction with an intervention, enabled a reduction in sample size required to show benefit.
    Conclusion: Using a reliable predictor of mortality such as MARKER-HF to enrich clinical trial populations provides a potential strategy to improve efficiency by requiring a smaller sample size to demonstrate a clinical benefit.
    MeSH term(s) Clinical Trials as Topic ; Heart Failure/therapy ; Humans ; Machine Learning ; Prognosis ; Risk Factors ; Stroke Volume ; Ventricular Function, Left
    Language English
    Publishing date 2022-05-22
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Extramural
    ZDB-ID 1483672-5
    ISSN 1879-0844 ; 1388-9842
    ISSN (online) 1879-0844
    ISSN 1388-9842
    DOI 10.1002/ejhf.2528
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  6. Article ; Online: Improving risk prediction in heart failure using machine learning.

    Adler, Eric D / Voors, Adriaan A / Klein, Liviu / Macheret, Fima / Braun, Oscar O / Urey, Marcus A / Zhu, Wenhong / Sama, Iziah / Tadel, Matevz / Campagnari, Claudio / Greenberg, Barry / Yagil, Avi

    European journal of heart failure

    2019  Volume 22, Issue 1, Page(s) 139–147

    Abstract: Background: Predicting mortality is important in patients with heart failure (HF). However, current strategies for predicting risk are only modestly successful, likely because they are derived from statistical analysis methods that fail to capture ... ...

    Abstract Background: Predicting mortality is important in patients with heart failure (HF). However, current strategies for predicting risk are only modestly successful, likely because they are derived from statistical analysis methods that fail to capture prognostic information in large data sets containing multi-dimensional interactions.
    Methods and results: We used a machine learning algorithm to capture correlations between patient characteristics and mortality. A model was built by training a boosted decision tree algorithm to relate a subset of the patient data with a very high or very low mortality risk in a cohort of 5822 hospitalized and ambulatory patients with HF. From this model we derived a risk score that accurately discriminated between low and high-risk of death by identifying eight variables (diastolic blood pressure, creatinine, blood urea nitrogen, haemoglobin, white blood cell count, platelets, albumin, and red blood cell distribution width). This risk score had an area under the curve (AUC) of 0.88 and was predictive across the full spectrum of risk. External validation in two separate HF populations gave AUCs of 0.84 and 0.81, which were superior to those obtained with two available risk scores in these same populations.
    Conclusions: Using machine learning and readily available variables, we generated and validated a mortality risk score in patients with HF that was more accurate than other risk scores to which it was compared. These results support the use of this machine learning approach for the evaluation of patients with HF and in other settings where predicting risk has been challenging.
    MeSH term(s) Cohort Studies ; Heart Failure/diagnosis ; Humans ; Machine Learning ; Risk Assessment ; Risk Factors
    Language English
    Publishing date 2019-11-12
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 1483672-5
    ISSN 1879-0844 ; 1388-9842
    ISSN (online) 1879-0844
    ISSN 1388-9842
    DOI 10.1002/ejhf.1628
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  7. Article ; Online: Study of the decay K+--> pi +e+e-

    Alliegro / Campagnari / Chaloupka / Cooper / Egger / Gordon / Hadley / Herold / Kaspar / Lee / Lazarus / Lubatti / Rehak / Zeller / Zhao

    Physical review letters

    1992  Volume 68, Issue 3, Page(s) 278–281

    Language English
    Publishing date 1992-01-20
    Publishing country United States
    Document type Journal Article
    ZDB-ID 208853-8
    ISSN 1079-7114 ; 0031-9007
    ISSN (online) 1079-7114
    ISSN 0031-9007
    DOI 10.1103/PhysRevLett.68.278
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  8. Article ; Online: Improved limit on the branching ratio of K+--> pi + micro+e-

    Lee / Alliegro / Campagnari / Chaloupka / Cooper / Egger / Gordon / Hadley / Herold / Jagel / Kaspar / Lazarus / Lubatti / Rehak / Zeller / Zhao

    Physical review letters

    1990  Volume 64, Issue 2, Page(s) 165–168

    Language English
    Publishing date 1990-01-08
    Publishing country United States
    Document type Journal Article
    ZDB-ID 208853-8
    ISSN 1079-7114 ; 0031-9007
    ISSN (online) 1079-7114
    ISSN 0031-9007
    DOI 10.1103/PhysRevLett.64.165
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  9. Article ; Online: Search for the decay K+--> pi + micro+e-

    Campagnari / Alliegro / Chaloupka / Cooper / Egger / Gordon / Hadley / Herold / Jagel / Kaspar / Lazarus / Lee / Lubatti / Rehak / Zeller

    Physical review letters

    1988  Volume 61, Issue 18, Page(s) 2062–2065

    Language English
    Publishing date 1988-10-31
    Publishing country United States
    Document type Journal Article
    ZDB-ID 208853-8
    ISSN 1079-7114 ; 0031-9007
    ISSN (online) 1079-7114
    ISSN 0031-9007
    DOI 10.1103/PhysRevLett.61.2062
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  10. Article ; Online: Search for short-lived neutral particles emitted in K+ decay.

    Baker / Gordon / Lazarus / Polychronakos / Rehak / Tannenbaum / Egger / Herold / Kaspar / Chaloupka / Jagel / Lubatti / Alliegro / Campagnari / Cooper / Hadley / Lee / Zeller

    Physical review letters

    1987  Volume 59, Issue 25, Page(s) 2832–2835

    Language English
    Publishing date 1987-12-21
    Publishing country United States
    Document type Journal Article
    ZDB-ID 208853-8
    ISSN 1079-7114 ; 0031-9007
    ISSN (online) 1079-7114
    ISSN 0031-9007
    DOI 10.1103/PhysRevLett.59.2832
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