QTc Fridericia Calculator
The formula
What the fridericia qtc tells you
The cube-root rate correction that stays honest at fast and slow heart rates, favored in anesthesia and drug-safety work.
Calculation: QTc = QT ÷ (RR)^(1/3). Enter your inputs and every substitution is shown below the result.
Reading the result
If Bazett flags a patient whose rate is 100+, Fridericia is the tiebreaker before you change therapy.
Rules of thumb: Same thresholds as Bazett; difference >15 ms from Bazett signals rate bias
Limitations and best practice
Fridericia can still over-correct at very fast rates; no formula is perfect — consistent serial comparison on the same method is what counts.
This calculator gives an estimate based on standard formulas; it is not professional financial, medical, legal or engineering advice. Verify anything you plan to act on with a qualified professional and your own records.
Frequently asked questions
Is Fridericia validated for risk?
Which leads for QT?
Source & methodology
Fridericia 1920; FDA QT guidance.
QTc Fridericia: what this tool answers
This page was written for one job: turning your own inputs into a defensible figure, with the working shown. QTc Fridericia is the kind of number people argue about from memory. This page ends that argument with arithmetic you can watch happen: enter your figures, get QTc (Fridericia) in real time, and see every substitution printed below.
People rarely disagree about arithmetic; they disagree about assumptions that were never written down. Printing the substitution and the sensitivity ranking removes that category of disagreement for QTc Fridericia.
Trends beat snapshots. QTc Fridericia measured once tells you where you stand today; measured monthly against Measured QT (ms) tells you which way the needle is moving — and direction is the actionable information in Clinical Calculators.
Nothing below is stock text pasted around a number: the worked example computes qTc (Fridericia) = 485 ms from this tool’s own defaults, and the sensitivity section ranks the inputs that move the result — Measured QT first, at ≈10.0 % of the headline for a ten-percent error either way in that input — which is the thread the rest of this guide pulls on.
Every input in QTc Fridericia, and what it does to the answer
One formula, published in full: QTc = QT ÷ (RR)^(1/3). Anything you can read, you can reproduce in a spreadsheet, and that reproducibility is the whole point of printing it rather than hiding it behind a button.
Measured QT (ms) (example value 419) is a plain measured quantity — the unit shown in the label is the unit the formula expects. If you are unsure what belongs in this field, look at the printed steps: the substitution shows exactly how the engine uses it.
Heart rate (bpm) (example value 93) is a plain measured quantity — the unit shown in the label is the unit the formula expects. Check the convention first: if the source says “6.5 % per year”, type 6.5 and let the formula do the rest.
QTc Fridericia on the example inputs: the complete substitution
Rather than describe the calculation, here it is end to end — every substitution in order, starting from the inputs above. Starting figures: Measured QT (ms), Heart rate (bpm). The endpoint of that chain is 485 ms of qTc (Fridericia), sitting in the “Fridericia ‘s cube-root correction gives 485 ms at 93 bpm where Bazett would claim 522 — the rate-bias difference is the whole reason this formula exists” band.
- RR = 0.645 s
- QTc = 419 ÷ 0.864 = 485 ms
- At this rate Bazett would read 522 ms
Bazett comparison — 522 ms. Read next to the headline, this is the figure that shows the shape of the trade-off.
Difference vs Bazett — -37 ms. If your inputs change, this is usually the first companion figure to move.
Risk band — prolonged. If your inputs change, this is usually the first companion figure to move.
Preferred use — rate extremes, drug trials. Read next to the headline, this is the figure that shows the shape of the trade-off.
Re-running with your own figures is instant; re-running by hand once is the best way to know the tool is telling you the truth.
How far QTc Fridericia can reasonably move
Read this table as the practical error bar on the headline figure. It answers the question people actually have after seeing a single number: how wrong could this be if the main input is off by ten or twenty percent?
| Measured QT setting | QTc (Fridericia) |
|---|---|
| 80 % of the example value | 388 ms |
| 90 % of the example value | 436 ms |
| example inputs | 485 ms |
| 110 % of the example value | 533 ms |
| 120 % of the example value | 582 ms |
Across 5 settings of Measured QT, the result spans 388 ms to 582 ms. A decision that only works at one row of this table is a decision waiting for an input to move. Reproduce it in the form above by editing Measured QT alone; the substitution steps will follow your figures exactly.
The levers behind QTc Fridericia, ranked by effect
Nobody should have to guess which input matters. The engine was re-run once per field below — ten percent up, ten percent down, everything else frozen — and the results ranked by how far they moved the qTc (Fridericia). Read it as a map of where QTc Fridericia is fragile — drawn for the example inputs, which is the map you want when deciding what to measure carefully.
- Nudge Measured QT by ±10 % and QTc Fridericia lands between 436 ms and 533 ms (sensitivity ≈10.0 %)
- Heart rate +10 % → QTc Fridericia = 501 ms · −10 % → 468 ms (the qTc (Fridericia) shifts ≈3.5 %)
Measured QT is the dominant lever here: ten percent either way moves the result by about 10.0 %, from 436 ms to 533 ms. Measure that input from a document rather than from memory, and treat Heart rate as the second priority (≈3.5 % per ten percent). The rest of the inputs earn only a rounding-error of attention, and that asymmetry is the practical payoff of measuring rather than asserting.
Is your QTc Fridericia good — reading the result bands
The value above only becomes useful the moment you know what it is being measured against. The verdict attached to the default calculation — “Fridericia ‘s cube-root correction gives 485 ms at 93 bpm where Bazett would claim 522 — the rate-bias difference is the whole reason this formula exists” — is one band on a ladder your own figure may sit somewhere else on. The sensitivity list shows how far a modest input error moves you along that ladder: for Measured QT, ±10 % covers 436 ms to 533 ms. The practical reading rules: read the band as a starting hypothesis, then test it: change the two dominant inputs by ten percent and see whether the verdict survives. Verdicts that survive stress are the ones worth acting on.
Related measures answer related questions, and the distinctions matter: QTc Fridericia is the quantity, QTc (Fridericia) is this tool’s reading of it, and the decision that follows belongs to neither of them separately.
Why QTc Fridericia results go wrong — and how to prevent it
Fridericia can still over-correct at very fast rates; no formula is perfect — consistent serial comparison on the same method is what counts. This calculator gives an estimate based on standard formulas; it is not professional financial, medical, legal or engineering advice. Verify anything you plan to act on with a qualified professional and your own records.
Chasing precision in Heart rate (bpm) while Measured QT (ms) is still a guess. The sensitivity ranking above shows exactly where the accuracy budget should go.
One mistake is specific to this engine: assuming every input matters equally. The computation above shows Measured QT dominating — a careful figure there beats false precision everywhere else.
The subtlest error is answering the wrong question precisely. Confirm that qTc (Fridericia) is actually the quantity your decision needs — the arithmetic is flawless either way, which is what makes the mistake invisible.
QTc Fridericia verification checklist
- Compare the output against one published benchmark or a colleague’s independent estimate.
- Check that every field the formula needs actually arrived from a document, not from recollection.
- Put Measured QT under a magnifier; the sensitivity table above is the reason why.
- Record the date and the inputs alongside the number so future-you knows what was assumed.
- Verify each input against its original source document, not memory.
- Confirm the answer against one independent method or published benchmark for QTc Fridericia.
Five steps from raw inputs to a figure you can defend
The tool does the arithmetic; the routine below is what makes the arithmetic trustworthy. Five steps, a minute total — and afterwards you will know not just what the number is, but which assumption produced it.
Step 1 — one document, one pass. Open the statement, label or record that holds measured qt (ms), heart rate (bpm) and transcribe straight into the form. Transcription from a single source removes the mismatch errors that appear when figures are assembled from three different places. When in doubt, prefer the boring input: the interesting one is usually the one you misread.
Step 2 — compute the version you would not be embarrassed by. If a stranger audited your inputs, which set would you hand over? Enter that set, read the result, and treat everything else as upside. The point of transparency is that a reader can disagree with a specific line rather than with you.
Step 3 — play with the inputs on purpose. Ten percent up, ten percent down, one field at a time. Within a few minutes you will know this calculation better than most people who quote its results. If a result feels obviously right, that is the moment to check the inputs hardest.
Step 4 — run the scenario trio. Expected, pessimistic, optimistic. The gap between pessimistic and expected is the number to plan against; the optimistic result is useful only as a ceiling. Write the assumptions in the same file as the result, and future arguments take minutes instead of meetings.
Step 5 — save the story with the figure. A number without its inputs and its date is unusable within a quarter. Record 485 ms, the assumptions behind it, and today’s date; your future self will be auditing a stranger’s arithmetic. A figure nobody can reproduce is a figure nobody will act on twice.
From calculation to decision: where QTc Fridericia earns its keep
Numbers earn their keep in conversations, and QTc Fridericia is no exception. In Health & Medical discussions around Clinical Calculators, this figure decides whether an option stays on the table: it converts an opinionated debate into an arithmetic one, and arithmetic is the only referee both sides accept. Half of a good calculation is knowing which input you would hate to be wrong about.
The companion outputs — Bazett comparison and Difference vs Bazett — exist for the second stage of that screen: the headline sizes the prize, while the companions describe the trade-off you are accepting to get it. Half of a good calculation is knowing which input you would hate to be wrong about.
Nothing here is stored or transmitted, so the habit of checking is free: open the page, re-enter current figures, compare with the number you saved last time. One decimal place more than your data supports is one decimal place of fiction.
The questions that come up most about QTc Fridericia
What affects QTc Fridericia the most?
QTc Fridericia is driven above all by Measured QT. Perturbing only that field by ten percent either way produced 533 ms against the 485 ms baseline — a ≈10.0 % response, while Heart rate manages ≈3.5 %.
Which matters more: heart rate or measured qt?
By the numbers, Measured QT: a ten-percent change in it moves the result ≈10.0 %, versus ≈3.5 % for Heart rate. Spend your measuring effort in that order — precision on the weaker lever buys almost nothing.
What is a typical QTc Fridericia?
On the worked example above QTc Fridericia comes out at 485 ms. Treat that as one point on a distribution rather than a norm: the bands, the companion outputs and the sensitivity ranking tell you how wide the plausible range really is.
How do I calculate QTc Fridericia by hand?
Use the stated model — QTc = QT ÷ (RR)^(1/3) — and substitute in the order the tool prints (it starts from RR = 0.645 s). On the example inputs the hand route lands on 485 ms, exactly what the calculator shows. A spreadsheet reproduces it in two or three cells; doing it once by hand is what makes the automated answer trustworthy.
What happens to QTc Fridericia if measured qt changes by 10 %?
On these inputs a ±10 % change in measured qt takes QTc Fridericia to 533 ms against the 485 ms example — around 10.0 % of the headline. The same test on every other field is in the sensitivity section.
Is the QTc Fridericia result of 485 ms good or bad?
Neither, on its own — the label “Fridericia ‘s cube-root correction gives 485 ms at 93 bpm where Bazett would claim 522 — the rate-bias difference is the whole reason this formula exists” is the band this figure falls in for the example inputs, and your own inputs will place you elsewhere on the same ladder. Judge the number against your history and your alternatives, and remember that a ten-percent error in Measured QT alone moves it by ≈10.0 %.
Does QTc Fridericia change with age?
Yes — reference bands shift deliberately across the lifespan, which is why this tool prints an interpretation rather than a bare number. Re-read the bands each time inputs like Measured QT (ms) change materially.
How often should I check QTc Fridericia?
Whenever an input changes by more than a few percent, and otherwise on the rhythm of your decisions — before a purchase, a review, a renewal. QTc Fridericia is a snapshot, not a subscription; stale snapshots are the most common silent error in Health & Medical planning.
Is Fridericia validated for risk?
Cohort data link Fridericia-prolonged QT to events similarly to Bazett, with less rate confounding.
Which leads for QT?
Lead II or V5 — whichever shows the T-wave end most clearly; automated global QTc often disagrees with manual measurement.
Can this tool see the numbers I type?
It cannot. There is no submission step: the calculation happens in your browser and the result disappears when you close the page. Treat the fields above as a scratchpad rather than a form.
Glossary for QTc Fridericia
- Bazett comparison
- 522 ms on the example inputs — a companion figure printed beside the headline of QTc Fridericia so that no result is read in isolation.
- Difference vs Bazett
- -37 ms for the QTc Fridericia worked case; it sits next to the main result because it carries context the headline leaves out.
- Risk band
- prolonged for the QTc Fridericia worked case; it sits next to the main result because it carries context the headline leaves out.
- QTc (Fridericia)
- The primary output of this tool: 485 ms for the default inputs, computed exactly from QTc = QT ÷ (RR)^(1/3).
- Worked example
- Every substitution in order, from the example inputs to 485 ms — the audit trail for the headline figure.
- Verdict band
- A named zone on the range of plausible results; the example inputs land in “Fridericia ‘s cube-root correction gives 485 ms at 93 bpm where Bazett would claim 522 — the rate-bias difference is the whole reason this formula exists”, and your inputs may not.
- Sensitivity (±10 %)
- How far the QTc Fridericia moves when a single input is nudged ten percent either way; the ranked list on this page is computed live from this engine.
Method, sources and what this tool cannot tell you
The model is published arithmetic implemented exactly as written: QTc = QT ÷ (RR)^(1/3). Source of record: Fridericia 1920; FDA QT guidance. Specialist software layers extra coefficients on top of the same algebra; none are hidden here, and that transparency is what makes a disagreement traceable to a specific input instead of a black box.
Treat the output as a precise answer to the question you typed: exact arithmetic, transparent formula, published source, no hidden rounding. The design constraint was set by usefulness rather than by comfort: an estimate you can interrogate beats an answer you have to trust.
After the number: what to do with it
Re-run the calculation with your own figures, stress the two most sensitive inputs by ±10 %, and only then act on the result.
The audience here is people who need one trustworthy number before their next decision. Everything you need to defend the result — formula, substitution, sensitivity, source — is printed above, so the only remaining variable is the honesty of your inputs.