APACHE II Calculator
The formula
What the apache ii tells you
Sums the 12-variable acute physiology score with age and chronic-health points into the ICU severity index that anchored three decades of critical-care benchmarking.
Calculation: APACHE II = APS + (15−GCS) + age + chronic; mortality = exp(−0.7363+0.0346×R)/1+exp(...). Enter your inputs and every substitution is shown below the result.
Reading the result
Use it for population benchmarking and research enrollment, not bedside individual decisions — the logistic curve is a cohort average.
Rules of thumb: Median academic ICU admission ≈ 16–18; ≥25 marks high-mortality territory
Limitations and best practice
The original coefficients predate sepsis-bundle care; scores computed from worst-first-24h data read higher than early-goal data — state the timing.
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
What data window does APACHE II use?
Is APACHE II still used?
Source & methodology
Knaus et al. Crit Care Med 1985;07.
APACHE II: what this tool answers
This page was written for one job: turning your own inputs into a defensible figure, with the working shown. There are plenty of pages about APACHE II; almost none of them show the working. This one does: the formula, every input, a fully computed example, and a sensitivity analysis calculated live from this engine’s own defaults.
It helps to separate the mechanics from the interpretation. The mechanics are fixed: APACHE II = APS + (15−GCS) + age + chronic; mortality = exp(−0.7363+0.0346×R)/1+exp(...). Anyone, anywhere, entering the same inputs will get the same 17. The interpretation is where experience counts: whether 17 is good, normal or alarming depends on comparisons covered below, and that is the part most articles about APACHE II never explain.
The number itself is the easy part. What takes judgement is knowing which inputs are facts in your situation and which are guesses wearing a decimal point — a distinction this page makes explicit for every field above.
Nothing below is stock text pasted around a number: the worked example computes APACHE II score = 17 from this tool’s own defaults, and the sensitivity section ranks the inputs that move the result — Acute Physiology Score first, at ≈7.1 % of the headline for a ten-percent error either way in that input — which is the thread the rest of this guide pulls on.
The APACHE II formula, explained
One formula, published in full: APACHE II = APS + (15−GCS) + age + chronic; mortality = exp(−0.7363+0.0346×R)/1+exp(...). 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.
Acute Physiology Score (0–60) (example value 12) is a measured number in the unit named by the label; the formula does not convert for you. Replace the example with the figure from your own records — the tool recomputes instantly and the printed steps follow your numbers.
Glasgow deficit from 15 (example value 2) is a quantity rather than a category — precision here is cheap and worth having. Replace the example with the figure from your own records — the tool recomputes instantly and the printed steps follow your numbers.
Age points (example value 3 55–64) is a choice rather than a quantity — pick the option that matches your situation, because the calculation path branches here. Be precise about the boundary (exact date, start of month); time inputs decide the exponent in most formulas.
Chronic health points (example value 0 None) is a selector, not a measurement: the engine follows a different route for each option. If you are unsure what belongs in this field, look at the printed steps: the substitution shows exactly how the engine uses it.
What each “Age points” choice actually does: 0 ≤44 → 14 · 2 45–54 → 16 · 3 55–64 → 17 · 5 ≥65 → 19 That table is the honest answer to “does this option matter”, re-derived from the formula rather than hard-coded into page copy.
What each “Chronic health points” choice actually does: 0 None → 17 · 2 Non-op/fi → 19 · 5 Emergency post-op → 22 That table is the honest answer to “does this option matter”, re-derived from the formula rather than hard-coded into page copy.
A fully worked APACHE II example
Rather than describe the calculation, here it is end to end — every substitution in order, starting from the inputs above. Starting figures: Acute Physiology Score (0–60), Glasgow deficit from 15, Age points, Chronic health points. The endpoint of that chain is 17 of APACHE II score, sitting in the “A score of 17 predicts roughly 46.3% ICU mortality — APACHE II was built on 1985 data, so modern-device recalibration is expected” band.
- APS 12 + GCS deficit 2 + age 3 + chronic 0 = 17
- Knaus logistic: exp(−0.7363 + 0.0346 × 17)
- Predicted hospital mortality ≈ 46.3%
Predicted mortality — 46.3%. Compare it against the main result of 17 before drawing conclusions.
Score range — 0–71. Read next to the headline, this is the figure that shows the shape of the trade-off.
Component split — APS 12 + GCS 2 + age 3 + health 0. Read next to the headline, this is the figure that shows the shape of the trade-off.
Sensitivity to 10 more APS pts — 8.6 points. 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.
Scenario table: APACHE II at ±10 % and ±20 % of the key input
Single-point answers invite false confidence, so the same engine was run across a band around the input that matters most here. Each row is a full recalculation of APACHE II, not an interpolation.
| Acute Physiology Score setting | APACHE II score |
|---|---|
| 80 % of the example value | 15 |
| 90 % of the example value | 16 |
| example inputs | 17 |
| 110 % of the example value | 18 |
| 120 % of the example value | 19 |
Across 5 settings of Acute Physiology Score, the result spans 15 to 19. 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 Acute Physiology Score alone; the substitution steps will follow your figures exactly.
Where APACHE II is fragile — and where it is not
This list was computed for this specific engine just now: each numeric input was nudged ±10 % while every other field held its example value, and the APACHE II score was recalculated from scratch. Read it as a map of where APACHE II is fragile — drawn for the example inputs, which is the map you want when deciding what to measure carefully.
- Acute Physiology Score +10 % → APACHE II = 18 · −10 % → 16 (the APACHE II score shifts ≈7.1 %)
- Glasgow deficit from 15: ten percent up gives 17, ten percent down gives 17 — ≈1.2 % of the APACHE II score either way
Acute Physiology Score is the dominant lever here: ten percent either way moves the result by about 7.1 %, from 16 to 18. Spend your verification effort there first, and treat Glasgow deficit from 15 as the second priority (≈1.2 % per ten percent). Everything below that can stay approximate, which is a licence to stop chasing data that would not change the decision.
Is your APACHE II good — reading the result bands
Nobody acts on a bare figure — they act on a figure with a comparison attached. The default case lands in the “A score of 17 predicts roughly 46.3% ICU mortality — APACHE II was built on 1985 data, so modern-device recalibration is expected” band, and bands are deliberately wide because real situations overlap. Before treating the band as fact, note its width: ten percent either way on Acute Physiology Score already spans 16 to 18. The practical reading rules: ask what would have to change to move you one band. If the answer is “a small, plausible input difference”, the band itself is not the finding — the uncertainty is.
Related measures answer related questions, and the distinctions matter: APACHE II is the quantity, APACHE II score is this tool’s reading of it, and the decision that follows belongs to neither of them separately.
Common mistakes with APACHE II (and the fix)
The original coefficients predate sepsis-bundle care; scores computed from worst-first-24h data read higher than early-goal data — state the timing. 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 Glasgow deficit from 15 while Acute Physiology Score (0–60) 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 Acute Physiology Score dominating — a careful figure there beats false precision everywhere else.
A final trap is presentation: reporting APACHE II to more decimal places than the inputs support. The tool prints what the arithmetic gives; you should quote only what your least precise field justifies.
Before you trust your APACHE II: a short checklist
- Verify each input against its original source document, not memory.
- Recompute once with the dominant input deliberately set low, and see whether the decision changes.
- Check Acute Physiology Score before anything else: it is the measured dominant input, ≈7.1 % of APACHE II per ten percent.
- State, in one sentence, which convention each rate, unit or period in the form is using.
- Confirm the example values are plausible for your own situation before replacing them.
- Confirm the answer against one independent method or published benchmark for APACHE II.
Step by step: getting a trustworthy APACHE II out of this tool
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 — start from evidence. The inputs behind APACHE II are acute physiology score (0–60), glasgow deficit from 15, age points, chronic health points. Write them down with their source next to each one; if you cannot name where a figure came from, that is the input most likely to be wrong. Precision is a promise you make about your data; make sure your inputs can keep it.
Step 2 — enter the conservative case first. Resist the flattering scenario. Type the inputs you would defend in front of a sceptical reader, read the APACHE II score (17 for the example values), and note the verdict band. The fastest sanity test is to guess the answer first and see how far the calculation disagrees.
Step 3 — isolate your variables. Change one field, observe, revert. The live sensitivity list does the ranking for the default inputs; verify it against your own, because dominance shifts when the starting point shifts. If the answer changes your behaviour, it deserves a second run with different assumptions.
Step 4 — compute a corridor, not a point. Three runs — worst, likely, best — turn APACHE II from a snapshot into a range. Plan against the pessimistic end and treat the optimistic end as the upside story. Write the figure down before moving on; memory flatters whichever number you saw last.
Step 5 — record the context, not just the number. Note today’s date and the inputs alongside the 17 output. Six months from now the context is what turns this figure into a trend. Half of a good calculation is knowing which input you would hate to be wrong about.
The real-world conversations APACHE II settles
Numbers earn their keep in conversations, and APACHE II 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. A figure with a date beside it becomes evidence; without one it stays an anecdote.
Professionals in Clinical Calculators use this style of calculation as a screening filter before spending real money or effort: a quick APACHE II check eliminates weak options in minutes, and the survivors earn the deeper, slower analysis. Rounding early is the quietest way to introduce a permanent error.
Re-running this page costs nothing and catches the slow drifts that never announce themselves. In Health & Medical the expensive mistakes are rarely sudden; they are small changes in Acute Physiology Score (0–60) that nobody recalculated for a year. A figure with a date beside it becomes evidence; without one it stays an anecdote.
APACHE II — frequently asked questions
What affects APACHE II the most?
Measured on this engine, Acute Physiology Score is the dominant lever: moving it ±10 % shifts the APACHE II score across 16 to 18 — about 7.1 % of the headline, with Glasgow deficit from 15 next at ≈1.2 %. The ranked list in the sensitivity section is computed live, not asserted from a template.
Which matters more: glasgow deficit from 15 or acute physiology score?
For APACHE II, Acute Physiology Score wins the head-to-head (≈7.1 % against ≈1.2 % per ten-percent change). Knowing which input dominates is the difference between careful work and merely slow work.
How much does the “Age points” choice change the answer?
Across every option this tool offers, the APACHE II score ranges from 14 (0 ≤44) to 19 (5 ≥65) with all other inputs held at their example values. The per-option table is printed in the inputs section — this is a decision that genuinely moves the number, not a cosmetic dropdown.
What is a typical APACHE II?
On the worked example above APACHE II comes out at 17. 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 APACHE II by hand?
Three steps: write the formula (APACHE II = APS + (15−GCS) + age + chronic; mortality = exp(−0.7363+0.0346×R)/1+exp(...)), insert your values, and simplify. The worked example section shows exactly that chain for the default inputs; replicating it once on paper teaches you more about APACHE II than any number of calculator runs.
What happens to APACHE II if acute physiology score changes by 10 %?
Try it in the form above and the printed substitution will follow you; the pre-computed answer for the defaults is 16 to 18 across the ten-percent probe, i.e. ≈7.1 % sensitivity on the APACHE II score.
Is the APACHE II result of 17 good or bad?
The tool answers “what”, not “whether”: 17 carries the verdict “A score of 17 predicts roughly 46.3% ICU mortality — APACHE II was built on 1985 data, so modern-device recalibration is expected” for the default case, which is a starting interpretation rather than a verdict on your situation. Re-run with your own figures and compare the bands.
Is APACHE II accurate?
The arithmetic is exact: APACHE II = APS + (15−GCS) + age + chronic; mortality = exp(−0.7363+0.0346×R)/1+exp(...) is evaluated with full floating-point precision and every substitution is printed in the worked example. What limits accuracy is input quality and the model’s assumptions, so treat the 17 result as the correct answer to the question you typed — and check the assumptions section before acting on it.
What affects APACHE II the most?
Sensitivity depends on where you start, but the inputs Acute Physiology Score (0–60) and Glasgow deficit from 15 typically move APACHE II further than the rest because they enter the formula multiplicatively. If you can only measure two things well, measure those and hold the others at conservative defaults.
What data window does APACHE II use?
Worst values in the first 24 hours of ICU stay, which is why day-3 rescoring is a different instrument.
Is APACHE II still used?
For benchmarking and triage trials yes; SAPS 3 and MPM are the newer competitors.
Do I need an account to use this APACHE II calculator?
No, and no data leaves the tab. The engine runs in the page itself, which is why the worked example, the bands and the sensitivity ranking all respond instantly to your keystrokes.
Key terms on this page
- Predicted mortality
- 46.3% for the APACHE II worked case; it sits next to the main result because it carries context the headline leaves out.
- Score range
- 0–71 in the default APACHE II calculation — one of the secondary figures an experienced reader checks before trusting the primary number.
- Component split
- APS 12 + GCS 2 + age 3 + health 0 on the example inputs — a companion figure printed beside the headline of APACHE II so that no result is read in isolation.
- APACHE II score
- The primary output of this tool: 17 for the default inputs, computed exactly from APACHE II = APS + (15−GCS) + age + chronic; mortality = exp(−0.7363+0.0346×R)/1+exp(...).
- Worked example
- The calculation written out in full so that nothing on this page has to be taken on trust.
- Verdict band
- A named zone on the range of plausible results; the example inputs land in “A score of 17 predicts roughly 46.3% ICU mortality — APACHE II was built on 1985 data, so modern-device recalibration is expected”, and your inputs may not.
- Sensitivity (±10 %)
- How far the APACHE II moves when a single input is nudged ten percent either way; the ranked list on this page is computed live from this engine.
Where the APACHE II model comes from
There is no black box on this page — the engine evaluates APACHE II = APS + (15−GCS) + age + chronic; mortality = exp(−0.7363+0.0346×R)/1+exp(...). Source of record: Knaus et al. Crit Care Med 1985;07. Where a professional version of this calculation differs, it differs in inputs and jurisdiction-specific coefficients, not in the algebra — which is why re-checking your inputs beats hunting for a second calculator.
This page is an estimation aid, not professional advice. The design rule is simple: two people entering identical figures must get identical answers. The design constraint was set by usefulness rather than by comfort: an estimate you can interrogate beats an answer you have to trust.
What to do with your APACHE II next
Save the number together with its inputs and today’s date; an APACHE II figure without context is unusable the moment an input changes.
This page was written for people who need one trustworthy number before their next decision, and the promise is that nothing above required trust. Bookmark the tool, re-run it whenever an input moves by more than a few percent, and keep the date with the number.