How the blood goes round is a system question, not a variable question.
That claim sits at the centre of this project, because it exposes a larger problem in clinical cardiovascular physiology. We often take variables from equations, curves and bedside monitors, then treat them as if they are independent drivers of the circulation. Usually they are better understood as descriptors of the state into which the system has settled.
A term in an equation is not necessarily a controller. A pressure in a model is not necessarily a back pressure in the real circulation. A gradient associated with flow is not necessarily an autonomous force driving flow. A point on a venous return curve is not necessarily an independent input. A measured value at the bedside may be important without being causal in the way we assume.
This publication is called The Dependent Variable because cardiovascular physiology becomes clearer when we ask a simple question:
Are we looking at a cause, a constraint, or the resolved state of a coupled system?
Cardiac output is not an isolated property of the heart. It is the flow that emerges from the interaction between the heart and the circulation. Blood pressure is not perfusion. It is one expression of the mechanical and energetic state of the system. Central venous pressure is not simply preload. It is the pressure at the interface between venous return and cardiac acceptance. Fluid responsiveness is not hypovolaemia. It is a property of where the circulation sits on a functional curve.
None of these variables is useless. They are clues. But they mislead when we treat them as independent mechanisms.
I have often described my work as haemodynamics. That is true, but it is too narrow. Lots of people write about haemodynamics. The more interesting question is how we should think about the cardiovascular system in the first place.
The circulation is not a set of independent numbers. It is a coupled physical system.
Change one part, and another usually changes with it. Increase vascular tone and you may alter arterial pressure, venous return, stressed volume, right atrial pressure, cardiac output, ventricular loading and regional flow. Give fluid and you may increase stressed volume, venous pressure, cardiac filling, cardiac output, capillary pressure and oedema formation. Improve cardiac function and you may lower right atrial pressure while increasing venous return and cardiac output. Raise downstream pressure and you may preserve global flow while impairing regional perfusion.
This is why simple driver language often fails.
The circulation is not controlled by one variable pushing on another. It is constrained by the properties of the system: cardiac power, vascular tone, stressed volume, compliance, capacitance, resistance, impedance, venous return, ventricular interaction, collapsible vessels, downstream pressures and regional vascular beds.
That is why some familiar ideas need to be handled carefully.
Pressure gradients are required for flow in resistive pathways, but they are not free-standing drivers detached from the system that generated them. Guyton’s venous return equation is a powerful abstraction, but it can become misleading if mean systemic pressure is treated as a literal upstream pressure pushing blood back to the heart during ongoing flow. Venous return curves can clarify the interaction between heart and vessels, but they can also mislead if we forget that several of their terms are dependent on the state of the coupled system. Starling’s law describes how the heart responds to filling, but it does not mean the heart alone determines flow.
These are not semantic objections. They affect bedside reasoning.
If we call CVP “preload”, we may treat a downstream pressure as if it directly measures cardiac filling. If we call fluid responsiveness “volume depletion”, we may give fluid because of curve position rather than deficit. If we call blood pressure “perfusion”, we may mistake a pressure state for tissue flow. If we say the heart “generates cardiac output”, we may forget that cardiac output is only possible because the vasculature returns blood to the heart at the same rate. But the opposite error is just as important. The venous system does not supply energy for flow as though it were a second pump. Mean systemic pressure is not a hidden motor sitting upstream of the right atrium. It is an abstract description of the elastic state of the circulation under defined conditions. The heart supplies energy; the vasculature stores, distributes and dissipates it; the observed flow is the result of their interaction.
The heart and vessels do not act in sequence.
They interact.
The numbers we measure are outputs of that interaction. They are not meaningless, but they are not self-explanatory.
The same problem appears in fluid physiology. Sepsis is often described as if vasodilatation and capillary leak automatically produce hypovolaemia. Oedema is described as “fluid in the wrong place”, as though the interstitial compartment can expand while the circulation remains meaningfully empty at steady state. Albumin is sometimes discussed as if it pulls fluid back into the circulation by a simple oncotic trick, rather than acting within a dynamic system of capillary pressure, endothelial permeability, glycocalyx function, lymphatic return and venous pressure. These are not separate topics. They are the same problem: dependent variables, simplified language and system behaviour being mistaken for direct mechanisms.
That is the recurring theme of The Dependent Variable: not replacing one slogan with another, but making the machinery underneath the slogan visible.
If there is a single promise behind The Dependent Variable, it is this:
We will take the familiar language of cardiovascular physiology and ask what it actually means.
Not what we casually use it to mean.
Not what it implies in a teaching diagram.
Not what it seems to mean when we are rushing at the bedside.
What it actually means.
Because in cardiovascular physiology, the most important question is often not whether a variable is high or low.
It is whether we have mistaken the dependent variable for the cause.