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OPTIMIZE-HF Discharge Readmission Model (OPTIMIZE-HF Readmission)

  • Age (years)
  • Admission serum creatinine (mg/dL)
  • Systolic blood pressure at admission (mmHg)
  • Admission hemoglobin (g/dL)
  • Serum sodium at admission (mmol/L)
  • Admission weight (kg)
  • Known pulmonary disease (COPD/asthma) ?
    Yes No
  • Depression documented ?
    Yes No
  • Liver disease documented ?
    Yes No
  • Discharge ACE inhibitor or ARB prescribed ?
    Yes No
  • Discharge beta-blocker prescribed ?
    Yes No
  • OPTIMIZE-HF Discharge Readmission Model: Explanation and Clinical Context
    OPTIMIZE-HF (the Organized Program to Initiate Lifesaving Treatment in Hospitalized Patients with Heart Failure) was a large hospital registry and quality-improvement programme that prospectively collected 60–90 day post-discharge outcomes in a sample of enrolled patients. OPTIMIZE-HF analyses identified several clinical variables strongly associated with early postdischarge mortality and the composite outcome of death or rehospitalization (notably admission serum creatinine, lower systolic blood pressure, lower admission hemoglobin, presence of pulmonary disease, and whether guideline-directed therapy such as ACE inhibitor/ARB was prescribed at discharge).

    This page implements an evidence-informed, approximate point score that translates these important predictors into a simple points total and maps that total to an approximate 60–90 day combined risk category (low/moderate/high/very high). The original OPTIMIZE-HF publications reported model discrimination (C-index ~0.72–0.74 for related short-term outcomes) and selected an 8-factor point system for 60-day mortality; however the full published regression coefficients for an exact discharge readmission logistic formula are not freely available in the public full text sources accessible here, so this implementation intentionally uses transparent, clinically plausible cutoffs and point weights and should be treated as an unvalidated estimate rather than a validated probability calculator.
    Clinicians should interpret the result together with bedside assessment and local care pathways; high estimated risk patients may merit closer transitional care, medication reconciliation, early follow-up and consideration for disease management resources. Local validation, calibration, and prospective testing are required before clinical deployment.

    Reference:
    O'Connor CM, Abraham WT, Albert NM, et al. Predictors of mortality after discharge in patients hospitalized with heart failure: an analysis from the Organized Program to Initiate Lifesaving Treatment in Hospitalized Patients with Heart Failure (OPTIMIZE-HF). Am Heart J. 2008 Oct;156(4):662–673. DOI:10.1016/j.ahj.2008.04.030.
    (For context on alternative 30-day readmission prediction models, see: Huynh QL et al. Predictive Score for 30-Day Readmission or Death in Heart Failure. JAMA Cardiol. 2016.)

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