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Calcimator

BNP Interpreter Calculator

Interpret BNP or NT-proBNP levels for heart failure diagnosis with age-stratified cutoffs and renal/obesity adjustments.

Inputs

pg/mL
pg/mL
years
mL/min/1.73m²
kg/m²

Results

HF Likelihood

1

Marker Value Used500 pg/mL
Rule-Out Threshold300 pg/mL
Rule-In Threshold900 pg/mL
Renal-Adjusted Value500 pg/mL
Obesity-Adjusted Value500 pg/mL
Negative Predictive Value75%
How to Use This Calculator
  1. Enter BNP (pg/mL) or NT-proBNP (pg/mL) — enter 0 for the marker you are not using.
  2. Set patient age, eGFR (mL/min/1.73m²), and BMI.
  3. Review HF Likelihood, Rule-Out Threshold, Rule-In Threshold, and Renal/Obesity-adjusted values.
  4. Values below the rule-out threshold have a >95% negative predictive value for heart failure.

What each input means

BNP level
B-type natriuretic peptide level. Enter 0 if using NT-proBNP instead.
NT-proBNP level
N-terminal pro-BNP level. Enter 0 if using BNP instead.
Age
Patient age. NT-proBNP cutoffs are age-stratified.
eGFR
Estimated glomerular filtration rate. Low eGFR elevates natriuretic peptides.
BMI
Body mass index. Obesity lowers natriuretic peptide levels.

What each result means

HF Likelihood
0 = HF unlikely, 1 = Possible (gray zone), 2 = HF likely.
Marker Value Used
The BNP or NT-proBNP value used for interpretation.
Rule-Out Threshold
Below this threshold, HF is unlikely (NPV >95%).
Rule-In Threshold
Above this threshold, HF is likely. Age-adjusted for NT-proBNP.
Renal-Adjusted Value
Estimated value corrected for renal impairment.
Obesity-Adjusted Value
Estimated value corrected for obesity (BMI ≥30 may falsely lower BNP).
Negative Predictive Value
Confidence that HF is absent when below rule-out threshold.

How this is calculated

Worked example, using the default values

  1. Identify Input Parameters
    4 parameters
    BNP level = 0, NT-proBNP level = 500, Age = 65, eGFR = 60 = 5 input(s) provided
  2. Calculate HF Likelihood
    1 = 1
  3. Calculate Marker Value Used
    500 = 500
  4. Calculate Rule-Out Threshold
    300 = 300

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