HomeCardiovascular Scores, Indexes, And Algorithms › CHAMPION HF Risk Algorithm (Prototype)

CHAMPION HF Risk Algorithm (Prototype)

  • Age (years)
  • NYHA Class
  • Prior heart failure hospitalization within 12 months?
  • Mean pulmonary artery pressure (mPAP) — mmHg
  • Left ventricular ejection fraction (%)
  • Serum creatinine (mg/dL)
  • NT-proBNP (pg/mL) — optional
  • CHAMPION HF Risk Algorithm (Prototype) — explanation and clinical context
    This page provides a prototype, point-based risk stratification tool informed by variables central to pulmonary artery pressure-guided heart failure management (age, NYHA class, prior HF hospitalization, mean pulmonary artery pressure, LVEF, renal function, and NT-proBNP). The CHAMPION randomized trial evaluated the CardioMEMS pulmonary artery pressure monitoring system and demonstrated reductions in HF hospitalizations when clinicians had access to daily pulmonary artery pressure data. Contemporary multisensor/device algorithms (e.g., multisensor/HeartLogic approaches) and registry/post-approval analyses support the utility of hemodynamic and device-derived signals to detect impending decompensation earlier than symptoms alone. This prototype does not reproduce any published CHAMPION 'risk equation' (no public, peer-reviewed set of regression coefficients labelled "CHAMPION HF Risk Algorithm" was identified). Instead, the tool presents a transparent heuristic mapping of common risk markers to a tiered short-term risk estimate for educational/demo use only.

    Reference:
    Adamson PB, Abraham WT, et al. CHAMPION trial publications and follow-up: Adamson PB et al., (trial rationale/design) and Abraham WT et al., Lancet 2016 (complete follow-up) describing the CardioMEMS/CHAMPION trial methodology and outcomes.
    Boehmer JP, et al. A Multisensor Algorithm Predicts Heart Failure Events (MultiSENSE / HeartLogic) — device multisensor prediction work illustrating how device data can predict HF events.
    Recent reviews and post-approval studies summarizing CardioMEMS real-world performance and implementation considerations.

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