MARKER-HF demonstrated similar discrimination for 1-year mortality (AUC 0.70) compared to the Seattle Heart Failure Model (AUC 0.71) and MAGGIC (AUC 0.71), while requiring less data engineering.
Cohort (n=6,764)
Yes
Does the MARKER-HF risk model perform comparably to SHFM and MAGGIC for predicting 1-year mortality in patients with heart failure while requiring less data engineering?
MARKER-HF provides comparable 1-year mortality risk prediction to SHFM and MAGGIC in heart failure patients but is significantly easier to implement in electronic health records using readily available structured data.
Absolute Event Rate: 0.7% vs 0.71%
p-value: p=0.64
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 6,764 adults with HF were abstracted from EHRs at a large integrated health system from 1/1/10-12/31/19. 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.
Ahmad et al. (Sun,) conducted a cohort in Heart failure (n=6,764). MARKER-HF risk model vs. Seattle Heart Failure Model (SHFM) and MAGGIC Heart Failure Risk Score was evaluated on Discrimination for 1-year all-cause mortality (AUC) (95% CI 0.69-0.72, p=0.64). MARKER-HF demonstrated similar discrimination for 1-year mortality (AUC 0.70) compared to the Seattle Heart Failure Model (AUC 0.71) and MAGGIC (AUC 0.71), while requiring less data engineering.