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Performance of risk models to predict mortality risk for patients with heart failure: evaluation in an integrated health system.

Author information

Affiliations

  • 1. Division of Cardiology, Department of Medicine, Feinberg School of Medicine, Northwestern University, 676 North Saint Clair Street, Suite 600, Chicago, IL, 60611, USA.
      Authors
    • Ahmad FS 1, 2, 5
    • Wilcox JE 1, 5
    • Mutharasan RK 1, 5
    (3 authors)
  • 2. Institute for Augmented Intelligence in Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
      Authors
    • Ahmad FS 1, 2, 5
    • Hu TL 2
    • Nardone B 2
    (3 authors)
  • 3. Division of Cardiology, Department of Medicine, UC San Diego School of Medicine, La Jolla, CA, USA.
      Authors
    • Adler ED 3
    • Greenberg B 3
    (2 authors)
  • 4. Division of Biostatistics, Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
      Authors
    • Petito LC 4
    (1 author)
  • 5. Bluhm Cardiovascular Institute Center for Artificial Intelligence, Northwestern Medicine, Chicago, IL, USA.
      Authors
    • Ahmad FS 1, 2, 5
    • Wehbe RM 5
    • Wilcox JE 1, 5
    • Mutharasan RK 1, 5
    (4 authors)
    • Show all (7)

ORCIDs linked to this article

Clinical Research in Cardiology : Official Journal of the German Cardiac Society, 02 Apr 2024, 113(9):1343-1354
https://doi.org/10.1007/s00392-024-02433-2 PMID: 38565710 

This article is based on a previously available preprint.
Free full text available after 01 Sep 2025

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.

Full text links 

Read article at publisher's site: https://doi.org/10.1007/s00392-024-02433-2

References 

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    Funding 

    Funders who supported this work.

    AHRQ HHS (1)

    Agency for Healthcare Research and Quality (1)

    NCATS NIH HHS (2)

    NHLBI NIH HHS (2)

    National Center for Advancing Translational Sciences (1)

    National Heart, Lung, and Blood Institute (1)