Vasoactive-Inotropic Score as a Worked Example

Introduction: The Data Is There. The Variable Is Not.

Every conversation about clinical AI opens on the same premise: hospitals are sitting on enormous quantities of data. True enough, and almost useless as a starting point. An electronic health record is a transactional system, built for documentation, for billing, and for medico-legal defence — three purposes, none of them research. It was never designed to emit variables. It emits events, and an event is not a measurement.

Take the hardest table in a cardiac surgical ICU, which is the infusion record. Open it for one patient on postoperative day zero. Four or five drugs running concurrently. Norepinephrine in micrograms per kilogram per minute; vasopressin in units per minute, or units per kilogram per minute, depending on who configured the pump library and when. Milrinone at a rate nobody has touched since the patient left theatre. Two rows for the same drug at 03:14, because a pump was swapped. A retrospective correction entered at 07:00 by the night nurse. And somewhere in there, a dose documented in mL/h whose concentration lives in a free-text order comment.

Everything a researcher would want to know about hemodynamic support is in that table. Almost none of it is in a form that a regression, a Cox model or a transformer can consume.

The distance between “we have the data” and “we have a dataset” is where clinical informatics does most of its actual work, and the standard instrument for closing it is the derived clinical variable: a hand-specified function mapping a messy, irregularly sampled, multi-unit set of events onto a single interpretable number. The Vasoactive-Inotropic Score (VIS) and the norepinephrine equivalent dose (NEE) are two such functions. Same raw material, different assumptions, different answers.

I use them here as a case study. The clinical content matters, but what I am really arguing about is the transformation.

The Derived Clinical Variable as a Design Pattern

Anyone who has built an ICU dataset has reinvented this pattern without giving it a name. SOFA, APACHE II, SAPS, the Charlson index, the VIS — structurally the same object. A clinician-specified projection from a high-dimensional, ragged, partially observed event stream onto a low-dimensional vector that some downstream method can actually handle.

Three properties of raw infusion data make it resistant to direct modelling. The derived variable takes them one at a time.

• Unit heterogeneity. Six drugs, four unit systems, and at least two documentation conventions for the same agent. No arithmetic is possible until this is resolved — and resolving it is not a data-cleaning step. It is a clinical decision about what counts as an equivalent dose.
• Irregular, informative sampling. Rates get recorded when someone changes them, or when a nurse charts a shift observation. Never on a fixed grid. The sampling pattern itself carries information about how sick the patient is, which turns out to be a problem in its own right; I come back to it below.
• Dimensionality with treatment-driven correlation. Six drug channels, mostly zero, heavily correlated because they reflect a single escalation policy rather than six independent decisions. Hand a model the raw channels and it will spend much of its capacity relearning that policy.

The alternative to hand-crafting is to feed the raw multivariate series into a sequence model and let it build its own representation. On large datasets that works, and sometimes works better. What it costs is interpretability, portability across institutions, and the ability to state in a methods section what was actually measured. For most clinical research — and for anything that will eventually face a regulator or a reviewer — the hand-crafted feature is still the pragmatic option. My only insistence is that it be treated as a choice, with consequences, argued for rather than inherited from whatever the last paper in the field happened to do.

Case Study One: The VIS as a Compression Function

The VIS descends from the inotrope score of Wernovsky and colleagues, defined in 1995 inside a randomized comparison of low-flow bypass against circulatory arrest for the arterial switch operation [4]:

Inotrope score = dopamine (µg/kg/min) + dobutamine (µg/kg/min) + 100 × epinephrine (µg/kg/min)

Gaies and colleagues extended it in 2010 to the drugs a modern pediatric cardiac ICU actually uses, and validated the result against outcome [5]:

VIS =
Dopamine (µg/kg/min)

• Dobutamine (µg/kg/min)
• 100 × Epinephrine (µg/kg/min)
• 100 × Norepinephrine (µg/kg/min)
• 10 × Milrinone (µg/kg/min)
• 10 000 × Vasopressin (units/kg/min)

Read structurally rather than clinically, that is a fixed linear projection from a six-dimensional dose vector onto a scalar, with weights chosen once and never fitted. It is the framing I would keep in mind throughout, because it makes the design assumptions visible.

Worked example, infant on postoperative day zero:

The weights are conventions. They are not pharmacology. Of everything written about this score, that is the point most consistently misreported. The multipliers were assigned so the arithmetic would stay easy, and so that agents used at wildly different absolute doses would land in a comparable numerical range. Belletti and colleagues say so without hedging: the correction factors were attributed arbitrarily to permit simple calculation and may not reflect real equipotency [6].

For anyone building a model, this is a specification rather than a defect. It makes the VIS a stable, reproducible convention rather than a physiological measurement, and it should be documented in a methods section the way one documents a normalisation constant — not the way one documents a lab assay. A VIS of 20 driven entirely by norepinephrine and a VIS of 20 driven by milrinone plus low-dose dopamine are the same number describing two rather different patients. Lossy compression, then, but lossy in a specific and knowable direction.

Case Study Two: The NEE, or the Same Problem Solved Differently

General critical care converged on a different function over the same raw data. The norepinephrine equivalent dose asks a narrower question — how much vasopressor is this patient on, expressed in norepinephrine-equivalent terms — and answers it with ratios taken from comparative potency studies rather than assigned by fiat. Goradia and colleagues synthesised 21 such studies into a working formula [7]; Kotani and colleagues later extended it into an updated NEE score covering the agents that have entered practice since [8].

Two functions, one dataset, different design commitments:

What I find instructive is that neither is the true value of anything. They are two competing operationalisations of one latent construct — how much hemodynamic support is this patient receiving — and they will rank the same cohort differently. Once the infusion data are normalised, computing both costs almost nothing, and reporting both turns an arbitrary choice into a sensitivity analysis. I have come to treat that as the default for any study using either, though the literature has not caught up.

The Extraction Pipeline: Five Layers That Each Fail Differently

This is the part that gets compressed into a single sentence in most methods sections — “vasoactive doses were extracted from the EHR and the VIS was calculated” — and that determines, far more than any modelling choice downstream, whether the result means anything at all.

Layer 0. Source semantics: what does a row actually mean?

Before a line of code is written, someone has to establish what the source table records. Ordered? Administered? Documented? A medication administration record row can represent a prescription that was never hung, a rate that was set on the pump, or a nurse’s retrospective observation of what the pump was doing. Three different variables. Most systems mix them, and the mixture is rarely documented anywhere you can find it.

The companion question is what an absent row means. There is no record of norepinephrine between 04:00 and 08:00. Was the infusion off, or running unchanged and therefore not re-charted? The answer is institution-specific and it cannot be recovered from the data alone. It has to be established with the people who do the charting, which in practice means sitting down with the nursing coordinator rather than reading a data dictionary. Get this wrong and you have made the most consequential error in the whole pipeline — one that passes every downstream quality check without a murmur.

Layer 1. Concept normalisation

Local drug codes have to be resolved to a controlled vocabulary before anything is comparable across systems or sites. The mature answer is the OMOP Common Data Model with the OHDSI standardised vocabularies: drugs to RxNorm concepts, measurements to LOINC, conditions to SNOMED CT [1]. The practical payoff is that identical extraction code runs at every site holding data in the model, which is what makes federated multicentre analysis possible at all.

For anyone learning this, MIMIC-IV is still the most useful sandbox — a fully open ICU EHR extract from Beth Israel Deaconess, with published derived-concept code you can read, criticise and fork [2]. Reading someone else’s vasopressor extraction against a dataset you can also download yourself is worth more than any amount of methodological reading. It certainly taught me more.

Layer 2. Dose harmonisation

Every VIS component wants µg/kg/min or U/kg/min. Source systems rarely oblige. This is the layer where clinical judgement travels in disguise, dressed as unit conversion.

Volumetric rates in mL/h need the drug concentration, sometimes structured, sometimes buried in an order comment. Then a weight — and there the question has no neutral answer. Admission weight, daily weight, or dry weight? In an edematous postoperative patient the three can differ by more than ten percent, and whichever you pick propagates into every score in the cohort. Pick one, state it, hold it constant.

Then the vasopressin trap, which deserves spelling out because it keeps recurring in published work. Pediatric cardiac practice doses vasopressin at roughly 0.0003 to 0.002 U/kg/min, so a typical infusion contributes 3 to 20 VIS points. Adults are prescribed a fixed rate in U/min, commonly 0.01 to 0.04, which has to be divided by body weight before the multiplier applies: for a 70 kg patient, 0.03 U/min works out to about 0.00043 U/kg/min, roughly 4 points. Feed an adult-convention value into a pediatric-convention formula and a plausible score of 23 becomes 300. In a cohort of ten thousand stays, nobody reads the outliers. The model does.

Any pipeline touching both populations has to handle the convention explicitly, per source system. Not inferred, and certainly not assumed.

Layer 3. Temporal reconstruction

Layers 0 to 2 leave you with a set of point events. What a model needs is a function of time. Three decisions bridge the two:

• Interval reconstruction. Start times, stop times and rate changes become contiguous intervals of constant rate, per drug and per line.
• Overlap resolution. Two concurrent entries for the same agent nearly always mean a pump swap or a double-documented change, not two genuine infusions. Sum them and you have silently doubled the dose. Deduplication rules belong in code, with the counts reported.
• Projection onto a grid. Hourly maximum and hourly time-weighted mean are different operators, and they produce measurably different values of VIS_max. Neither is wrong. Choosing without declaring which is.

That last point travels well beyond this score. An aggregation operator is an inductive bias. A maximum encodes the belief that peak support is what matters; a time-weighted mean, that cumulative exposure matters; a slope, that trajectory matters. Clinical hypotheses, all three, wearing the costume of a data-processing step.

Layer 4. Feature derivation

Only now is the score computed, and it should be computed against a versioned coefficient table rather than a hard-coded expression. Coefficients drift — Belletti’s extended version, the various phenylephrine conventions, the NEE updates — and a study that cannot state which version it used cannot be replicated.

-- vis_coefficient(drug, coefficient, expected_unit, version)
-- infusion_normalised: output of layers 0-2, rates in ug/kg/min or U/kg/min

WITH gridded AS (
    SELECT
        patient_id,
        date_trunc('hour', charttime) AS hr,
        drug,
        MAX(rate_per_kg_min) AS rate        -- layer 3 decision: hourly maximum
    FROM infusion_normalised
    WHERE charttime >= :window_start
      AND charttime <  :window_end
    GROUP BY patient_id, date_trunc('hour', charttime), drug
)
SELECT
    g.patient_id,
    g.hr,
    SUM(g.rate * c.coefficient)                       AS vis,
    COUNT(*) FILTER (WHERE c.drug IS NULL)            AS unmapped_agents
FROM gridded g
LEFT JOIN vis_coefficient c
       ON c.drug    = g.drug
      AND c.version = 'gaies_2010'
GROUP BY g.patient_id, g.hr;

-- vis_coefficient(drug, coefficient, expected_unit, version)
-- infusion_normalised: output of layers 0-2, rates in ug/kg/min or U/kg/min

WITH gridded AS (
    SELECT
        patient_id,
        date_trunc('hour', charttime) AS hr,
        drug,
        MAX(rate_per_kg_min) AS rate        -- layer 3 decision: hourly maximum
    FROM infusion_normalised
    WHERE charttime >= :window_start
      AND charttime <  :window_end
    GROUP BY patient_id, date_trunc('hour', charttime), drug
)
SELECT
    g.patient_id,
    g.hr,
    SUM(g.rate * c.coefficient)                       AS vis,
    COUNT(*) FILTER (WHERE c.drug IS NULL)            AS unmapped_agents
FROM gridded g
LEFT JOIN vis_coefficient c
       ON c.drug    = g.drug
      AND c.version = 'gaies_2010'
GROUP BY g.patient_id, g.hr;

Two deliberate choices in that query. The join is a LEFT JOIN with an explicit count of unmapped agents, because a cohort in which fifteen percent of vasoactive exposure falls outside the coefficient table is a cohort where the VIS is quietly measuring something incomplete — and that number belongs in the paper, not in a code comment. Second, drugs outside the classic six are never folded in implicitly: where phenylephrine or levosimendan matter, generate parallel variables (vis_classic, vis_phe100, vis_phe10) so the coefficient choice surfaces as a reported sensitivity analysis rather than an assumption buried in a CASE statement.

The scalar is rarely the end point. Out of the same hourly series come the features that actually enter models: maximum over a window, time-weighted mean, slope or weaning rate, cumulative time above a threshold. VIS_max is the best validated of these and tells you least about trajectory.

Layer 5. Provenance

The extraction code is part of the method, not an implementation detail. Version the coefficient table, version the deduplication rules, record the grid resolution and the aggregation operator, publish the code alongside the paper. The alternative is what we have now, where two groups reporting “maximum VIS in the first 24 hours” may be computing quantities that differ by ten or twenty percent for reasons neither group can reconstruct.

What These Choices Do to Downstream Models

Here the extraction stops being plumbing and becomes epistemology. Four effects, each with a literature behind it.

1. Missingness is informative, and it leaks. Agniel, Kohane and Weber went through 272 laboratory tests across 669 452 patients and found something that ought to unsettle anyone building ICU models: for many tests, the timing of the order — hour of day, day of week, ordering frequency — predicted three-year survival better than the result did [3]. The healthcare process writes itself into the data. Applied here, the frequency with which an infusion rate gets re-charted is itself a marker of instability. Imputing “no record” as “no infusion” therefore does not merely lose information. It converts a strong predictor into a silent bias, which is worse.

2. The VIS is a treatment variable, and models learn treatment policy. The deepest issue on this list, and not specific to the VIS at all. The canonical illustration remains Caruana’s pneumonia model, which learned that asthma lowered mortality risk, because asthmatic patients were triaged straight to intensive care and treated aggressively [13]. The model had faithfully learned the treatment policy. Deployed, it would have killed people.

A VIS is a record of a decision. It measures what clinicians chose to prescribe in response to a patient they were watching, using information that is largely absent from the structured record. Include it as a predictor of mortality and the model is partly learning the local escalation protocol, partly learning the clinicians’ unrecorded gestalt. Sometimes that is exactly what you want — in a severity-adjustment model, for instance. It is precisely what you do not want in a causal analysis of a hemodynamic intervention, where the same variable is a time-varying confounder affected by prior treatment, and where standard adjustment will bias the estimate rather than repair it.

3. Prescribing culture becomes dataset shift. Two centres treating identical patients, one norepinephrine-first and one dopamine-first, will produce VIS distributions differing by an order of magnitude, with no difference whatever in physiology. Finlayson and colleagues catalogue precisely this class of failure: a model trained where the feature encodes local practice degrades wherever practice differs [14]. The external validation of the Epic Sepsis Model at Michigan Medicine — an AUC of 0.63 against a vendor-claimed 0.76 to 0.83, with substantial alert burden — remains the most widely cited demonstration that the concern is not theoretical [15].

The mitigation is unglamorous. Report the drug-mix distribution alongside the score, stratify or recalibrate by site, and treat any multicentre VIS-based model as requiring external validation before it means anything.

4. Convention beats optimality for portability. A weighting fitted on one cohort will outperform the 2010 coefficients on that cohort, and will not transfer. The arbitrary weights have one large compensating virtue: they are the same everywhere. For cross-study comparability, a stable convention is worth more than a locally optimal one — which is an argument, I think, for computing the classic VIS even when you also compute something better.

Does the Compression Retain Signal?

Having established that the VIS is lossy, convention-based and treatment-derived, the fair question is whether it survives contact with outcome data. It does. Moderate discrimination, which is about what a single hand-crafted scalar ought to buy.

• Pediatric cardiac surgery. In the original Michigan cohort of 174 infants, a high maximum VIS over the first 48 postoperative hours carried an adjusted odds ratio of 8.1 (95% CI 3.4 to 19.2) for a composite poor outcome [5]. Multicentre confirmation across the PC4 and VPS registries, 391 infants, gave odds ratios of 6.5 (95% CI 2.9 to 14.6) for poor outcome and 13.2 (95% CI 3.7 to 47.6) for mortality [9]. Wide intervals, small cohorts — but the direction is not in doubt.
• Adult cardiac surgery. Among 3213 patients, maximal VIS over 24 hours predicted a composite outcome with an AUC of 0.72 (95% CI 0.69 to 0.75), and 30-day mortality with an AUC of 0.76 (95% CI 0.69 to 0.83) [10]. Yamazaki’s group measured at the end of surgery instead and reached comparable conclusions [11].
• Pooled across surgical populations. A 2024 systematic review of 58 studies and 29 920 patients confirmed associations with prolonged ventilation, AKI, ICU length of stay and mortality, and reported optimal cutoffs ranging from 10 to 30 depending on population, window and outcome [12]. Outside cardiac surgery, the score has also been validated as a surrogate outcome in pediatric sepsis [16].

That cutoff range is the thing to take away. There is no universal VIS threshold, and any number quoted without its population, its time window and its outcome definition should be read as decoration. “VIS above 20 predicts mortality” is a serviceable rule of thumb for infants after bypass. In a mixed adult ICU it is close to meaningless.

An AUC in the low 0.7s from one scalar is a reasonable return. It is also a ceiling, and one worth remembering when somebody proposes the score as the backbone of a decision-support tool.

When to Hand-Craft and When to Learn

The honest answer is that it depends on what the feature is for. The tradeoff, at least, is fairly clean.

Hand-crafted derived variables — VIS, NEE, SOFA — win on interpretability, on portability across institutions and coding systems, on sample efficiency in the small cohorts typical of surgical subspecialties, and on regulatory and editorial acceptability. They lose information by construction, and whoever picked the coefficients also chose which information gets lost.

Learned representations over the raw multivariate infusion series win on retained information and, given enough data, on discrimination. They give up the ability to state what was measured. They overfit to local practice more readily rather than less, and they make the treatment-policy problem harder to see rather than easier — which is the part that worries me most.

In practice the useful configuration is both: the derived variable as an interpretable, reportable, comparable summary, with the raw normalised series retained so that a learned model can be trained and, more to the point, so that the two can be compared against each other. The layers 0 to 3 work is identical either way. That, rather than anything about the score itself, is the real argument for investing in the pipeline.

Limitations Worth Stating Out Loud

1. The coefficients are conventions [6]. Any comparison across drug regimens is weaker than a single number implies.
2. The score reflects prescribing culture as much as patient state, which makes cross-centre modelling hazardous without recalibration [14].
3. It ignores response. Identical infusions in a patient with a MAP of 45 and one with a MAP of 75 give identical scores. Composite constructs such as the vasoactive-ventilation-renal score try to address this; none has become standard, and I am not convinced any of them will.
4. Mechanical circulatory support is invisible to it. Cannulate a patient onto VA-ECMO and the VIS falls. Any analysis spanning MCS has to handle this explicitly, or it will record improvement where there was escalation.
5. No consensus time window. End of surgery, 1 h, 24 h, 48 h and 72 h all appear in the literature, and they are not interchangeable [12].
6. Extraction variance is unquantified. As far as I know, nobody has published a study in which several groups extract the VIS from the same source data and compare what they get. That study would be more useful than most of the validation literature we already have.

Conclusion

The recurring claim that hospitals are rich in data and poor in insight puts the problem in the wrong place. The data really are there. What is missing is the transformation, and the transformation is neither automatic nor neutral: it encodes clinical judgement at every layer, from what an absent row means, through which body weight to use, to whether a maximum or a time-weighted mean better represents hemodynamic burden.

The VIS is a good object to think with precisely because it is so simple. Six drugs, six constants, one addition. And yet getting to a defensible number out of a real EHR means resolving source semantics, mapping to a controlled vocabulary, harmonising two incompatible dosing conventions, reconstructing a time series from event records, choosing an aggregation operator, and versioning the lot so that someone else can reproduce it. The score is the easy part. The pipeline is the contribution.

Which suggests where the effort should go. Not into a seventh variant of the coefficients, but into making the extraction layers explicit, shared and testable, so that a VIS computed in Ancona and a VIS computed in Michigan are the same variable. Whether that happens through OMOP, through published derived-concept libraries on MIMIC, or not at all, I would not care to predict. What does seem clear is that the current arrangement, in which every group rebuilds the pipeline privately and reports only the number, will not hold if any of these models are meant to leave the institution where they were trained.

Frequently Asked Questions

What is a derived clinical variable?
A clinician-specified function that maps raw, irregularly sampled EHR events onto a single interpretable value. SOFA, APACHE II, the Charlson index, the Vasoactive-Inotropic Score and the norepinephrine equivalent dose are all examples. They perform dimensionality reduction on a clinically meaningful basis, at the cost of discarding information by design.

Why can’t a machine learning model just use the raw EHR data?
It can. But the raw infusion record is multi-unit, irregularly sampled, mostly missing and strongly shaped by local treatment policy, so a model trained directly on it spends capacity relearning the prescribing protocol and transfers poorly to other institutions. Derived variables trade information for interpretability and portability.

How is the vasoactive-inotropic score calculated?
VIS = dopamine (µg/kg/min) + dobutamine (µg/kg/min) + 100 × epinephrine (µg/kg/min) + 100 × norepinephrine (µg/kg/min) + 10 × milrinone (µg/kg/min) + 10 000 × vasopressin (U/kg/min). All doses weight-indexed and per minute.

Are the VIS coefficients based on drug potency?
No. They were assigned arbitrarily so that agents used at very different absolute doses would fall on a comparable numerical scale. A stable convention, not a pharmacological equivalence.

VIS or norepinephrine equivalent dose?
VIS includes inotropes and measures total cardiovascular pharmacological support, which fits postcardiotomy physiology. NEE is vasopressor-only, with partly evidence-based potency ratios, and fits vasoplegic and septic shock. Once the infusion data are normalised, computing both is nearly free and turns the choice into a sensitivity analysis.

What is the most common error in automated VIS extraction?
The unit convention for vasopressin — U/kg/min in pediatrics against U/min in adults. Second place goes to treating an absent infusion record as a documented zero.

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