Logo

OpiCalc

FavoritesSpecialtiesDrugsGuidelinesMost Used

Quick Access

Favorites
Most Used

All Specialties

OpiCalc Logo
Clinical CalculatorsDrugsGuidelines
SpecsDrugsGuides
ABC-AF Bleeding ScoreABC-AF Stroke ScoreABCD2 ScoreADD-RSAortic Valve Calcium ScoreAPPLE ScoreASCVD (Pooled Cohort)AVA (Continuity Equation)BAG-AHF ScoreBiplane Simpson EFBlood Pressure PercentilesBrugada Criteria (VT vs SVT)Cardiac Output IndexCHA2DS2-VAScCHADS2Cornell Voltage CriteriaCRUSADE Bleeding ScoreDAPT ScoreDASIDuke Treadmill ScoreE/A RatioEDACS ScoreEHMRGEHRA ScoreEmbolic Risk ScoreEROA (PISA Method)FFR (Fractional Flow Reserve)Fick Cardiac OutputFramingham 10-Year RiskFriedewald LDL EquationGorlin EquationGRACE ScoreGupta MICA (NSQIP)GWTG-HF ScoreH2FPEF ScoreHakki FormulaHAS-BLEDHEART PathwayHEART ScoreHEMORR2HAGEShs-Troponin 0h/1h ESC AlgorithmiFRINTERCHEST ScoreKillip ClassificationLee's RCRILV Mass IndexLV Stroke Work IndexMAGGIC Risk ScoreMAP CalculatorMartin/Hopkins LDLModified Duke CriteriaModified Sgarbossa CriteriaMVA (Pressure Half-Time)Non-HDL CholesterolNT-proBNP Age-Adjusted ThresholdsORBIT ScoreOttawa Heart Failure RiskPREVENT EquationsPulse PressurePVR CalculatorPVR IndexQRISK3QTc (Bazett)QTc (Fridericia)REVEAL 2.0 ScoreREVEAL Lite 2Reynolds Risk ScoreROSIRVSP CalculatorSchwartz Score (LQTS)SCORE2Seattle Heart Failure Model (SHFM)Sgarbossa CriteriaShock IndexSokolow-Lyon VoltageStroke Volume IndexSVR CalculatorSYNTAX ScoreSYNTAX Score IITAPSETeichholz FormulaTIMI (STEMI)TIMI (UA/NSTEMI)Troponin Delta CalculatorValvular GradientsVancouver Chest Pain RuleVereckei AlgorithmWATCHDM ScoreWilkins ScoreWood Units Calculator
OpiCalc Logo

OpiCalc

Easy, fast, and private medical tools for clinicians. Always free.

No Login Required
Ready for the Bedside

Resources

About UsEditorial PolicyMedical DisclaimerPrivacy PolicyTerms of UseCookie Policy

Support

Contact Us

Clinical Notice:OpiCalc is not a substitute for professional clinical judgment. Always verify dosages and guidelines.

OpiCalc © 2026

•

All Rights Reserved

Seattle Heart Failure Model (SHFM)

Seattle Heart Failure Model: Multivariable tool to predict survival in heart failure. Incorporates labs, meds, and devices.

Demographics & NYHA

Vital Labs

Therapies

Guidelines & Evidence

Verified

Last Review: 2026-07-17

When to Use

When to Use

Prognostic assessment of patients with chronic heart failure (both HFrEF and HFpEF).
To guide clinical decision-making regarding advanced therapies, including referral for heart transplantation or Left Ventricular Assist Device (LVAD) evaluation.
To demonstrate the potential survival benefit of guideline-directed medical therapy (GDMT) to patients.

How it Works

Model Overview

The SHFM is a multivariable Cox proportional hazards model derived from several prospective randomized trials. It incorporates 24 variables to estimate the hazard ratio and absolute survival probability over 1, 2, and 5 years.

Key Predictors

01
Demographics: Age and Sex.
02
Functional Status: NYHA Class and Ischemic vs. Non-ischemic etiology.
03
Physiology: LVEF and Systolic BP.
04
Labs: Sodium, Hemoglobin, Percent Lymphocytes, and Uric Acid.
05
Meds: Doses of diuretics, and presence of ACEi/ARB, Beta-blockers, and Statins.
06
Devices: Implantation of ICD or CRT.

Clinical Pearls

The Vicious Cycle of Diuretics

The model notably high-weights the dose of loop diuretics. While diuretics are necessary for symptom management, high doses are often markers of advanced refractory disease and neurohormonal activation, contributing strongly to the predicted hazard.

Shared Decision Making

The SHFM is uniquely suited for patient counseling. By toggling "protective" entries like ACEi or BB, a clinician can show a patient the projected "years of life gained" by adherence to GDMT.

The Evidence

Original Derivation

The Seattle Heart Failure Model: prediction of survival in heart failure.

Levy WC et al. • Circulation.. 2006;113(11):1424-33. Derived from 1,125 patients and validated in 5 additional cohorts totalling 9,942 patients.

Validation of the Seattle Heart Failure Model in a modern heart failure cohort.

Kochi AN et al. • ESC Heart Fail.. 2021;8(2):1201-1208. Confirmed continued accuracy in the era of modern GDMT.

Last Comprehensive Review: 2026-07-17

Guidelines & Evidence

Verified

Last Review: 2026-07-17

When to Use

When to Use

Prognostic assessment of patients with chronic heart failure (both HFrEF and HFpEF).
To guide clinical decision-making regarding advanced therapies, including referral for heart transplantation or Left Ventricular Assist Device (LVAD) evaluation.
To demonstrate the potential survival benefit of guideline-directed medical therapy (GDMT) to patients.

How it Works

Model Overview

The SHFM is a multivariable Cox proportional hazards model derived from several prospective randomized trials. It incorporates 24 variables to estimate the hazard ratio and absolute survival probability over 1, 2, and 5 years.

Key Predictors

01
Demographics: Age and Sex.
02
Functional Status: NYHA Class and Ischemic vs. Non-ischemic etiology.
03
Physiology: LVEF and Systolic BP.
04
Labs: Sodium, Hemoglobin, Percent Lymphocytes, and Uric Acid.
05
Meds: Doses of diuretics, and presence of ACEi/ARB, Beta-blockers, and Statins.
06
Devices: Implantation of ICD or CRT.

Clinical Pearls

The Vicious Cycle of Diuretics

The model notably high-weights the dose of loop diuretics. While diuretics are necessary for symptom management, high doses are often markers of advanced refractory disease and neurohormonal activation, contributing strongly to the predicted hazard.

Shared Decision Making

The SHFM is uniquely suited for patient counseling. By toggling "protective" entries like ACEi or BB, a clinician can show a patient the projected "years of life gained" by adherence to GDMT.

The Evidence

Original Derivation

The Seattle Heart Failure Model: prediction of survival in heart failure.

Levy WC et al. • Circulation.. 2006;113(11):1424-33. Derived from 1,125 patients and validated in 5 additional cohorts totalling 9,942 patients.

Validation of the Seattle Heart Failure Model in a modern heart failure cohort.

Kochi AN et al. • ESC Heart Fail.. 2021;8(2):1201-1208. Confirmed continued accuracy in the era of modern GDMT.

Last Comprehensive Review: 2026-07-17

In Recent Clinical News

Scanning Medical Journals

No new significant updates or guidelines matching this topic were found today. We will check again soon.