Afterload 🧵

The physics of circulation part 5

Afterload is tricky to understand.

It is not blood pressure.
It is not SVR.
It is not “how tight the arteries are.”

To understand it, we need to separate three things that are routinely blurred. 2/
First: Arterial impedance – the load.

This is a property of the arterial system.

It is determined by:
• resistance (energy dissipation)
• compliance (energy storage)
• inertia (acceleration cost)

It exists whether the ventricle is strong or weak.

This is what the heart ejects into.

Resistance vs Impedance 🧵

The physics of circulation part 4

We’re taught that resistance controls flow.

Sometimes that intuition works.
Often, in the circulation, it fails.

Here’s why. 👇 2/
Resistance is a property of a system that tells you how much energy is dissipated for a given flow.

That’s all.

It doesn’t act.
It doesn’t push.
It doesn’t “control” anything.

It describes cost per unit flow.

Resistance only matters once flow is occurring because dissipation only exists when blood is moving.

Flow 🧵

The physics of circulation part 3

We talk a lot about flow in cardiovascular physiology.
But our understanding is often weaker.

Flow isn’t a substance.
It doesn’t get pushed. 2/ Flow is a rate.

Specifically:
flow = volume per unit time

More fundamentally:
flow describes how volume moves through a system while energy is supplied and lost.

Pressure 🧵

The physics of circulation part 2

We use the word pressure constantly in medicine (blood pressure, filling pressure, perfusion pressure).
And we usually treat pressure as an agent.
It isn’t.👇 2/
Physically, pressure is a state variable.
It describes how much energy is stored per unit volume in a constrained fluid
(i.e. an energy density).

It tells you the state of the system, not what is driving it.

🧵 Thread: Why cardiovascular physiology feels confusing (even to experts)

Part 1 of my 'The physics of circulation' series

Cardiovascular physiology isn’t confusing because it’s complex.
It’s confusing because we routinely mix up description with causation. And our language subtly reinforces the error. 2️⃣
This isn’t academic nit-picking.
If you confuse what a variable describes with what actually causes change, you may end up choosing the wrong intervention.

🧵 What drives blood flow – the heart or the vessels?

Eminent physiologists have argued this for decades.

The disagreement survives because of imprecise causality.

Here’s the resolution 👇 2/ To resolve the heart vs circulation debate, we first need to be precise about one word:

“Drive.”

Do we mean:
– supplying energy?
– setting conditions?
– constraining what’s possible?

Most confusion comes from mixing these up.

🧵What makes the blood go round?

And why does such a simple sounding question cause such heated arguments between physiologists?

Everyone agrees on the observations.
The disagreement is about causality. 👇

#FOAMed #Physiology #MedX 2/ What everyone agrees on

At steady state:
• Venous return = cardiac output
• Flow is always associated with pressure differences
• Sustained flow requires a pump

So why the disagreement?
Because the same observations can be given very different causal interpretations.

🧵 Albumin in Critical Care: 70 Years, 700 Papers… Zero Benefit

1/
Albumin is the most studied fluid in critical care.
Decades of trials. Endless meta-analyses.
And yet – not a single clinically meaningful benefit.

Here’s why the entire theory collapses once you understand Extended Starling. 👇 2/ The old idea

We were all taught:
• albumin “pulls” fluid into vessels
• albumin “stays intravascular”
• albumin “treats oedema”

All based on the obsolete 19th-century Starling model.

🧵 Why you cannot be oedematous and hypovolaemic at steady state

“Puffy but intravascularly dry”?

⚠️The most persistent myth in IV fluid therapy

#FOAMed #physiology #MedX 2/
Capillaries filter fluid continuously.
Lymphatics return that filtered fluid continuously.

Daily:
• Filtration ≈ several litres
• Lymph return ≈ several litres
• Net plasma loss ≈ zero

No oedema. No hypovolaemia.

1️⃣
We can remove fluid at rates up to 12 mL/kg/h and blood pressure often holds.
That limit isn’t arbitrary – it comes from dialysis data showing steep rises in hypotension and mortality above it.
It marks the upper boundary of how fast plasma can be refilled from the interstitium and lymph 👇 2️⃣ The background

In most tissues, fluid filters out of capillaries and returns via the lymphatics.
At steady state, filtration ≈ lymph flow (~10 L/day ≈ 400 mL/h), so plasma and interstitial volumes stay constant.
This is the equilibrium that ultrafiltration later exploits.

1️⃣We talk endlessly about “capillary leak” – but most of what we say about it is wrong.

Here’s what actually drives fluid movement across the microcirculation – and why Starling’s model needed an upgrade. A 🧵👇 2️⃣ The old picture
Starling (1896) imagined that filtration dominates early in the capillary and re-absorption later, where pressure is lower.
In most tissues, direct re-absorption almost never happens.
Capillary pressure (Pc) slightly exceeds oncotic pressure along the whole capillary, so filtration (Jv) predominates at both ends.
All that filtrate returns to the circulation through the lymphatics.

Fluid leaves the circulation continuously – and the lymphatics bring it back.

🧩 Part 3 – Why you usually can’t move one curve without the other
1️⃣
So far, we’ve treated the cardiac and venous return curves as two lines that meet.
In theory, you can move one without the other – and sometimes that’s true.
But in physiology, they almost always move together – because they share the same inlet. 2️⃣
Both curves hinge on the same gateway: the inlet to the heart.
That’s where Ivr – inlet impedance – lives.
Any change in relaxation, stiffness, or pericardial pressure alters Ivr, so both curves tend to shift together.

🧵 Part 2 — The Venous Return Curve

1️⃣
Last time, we fixed the cardiac function curve.
Now let’s look at the other half of the story — venous return — and how the circulation really feeds the heart.

#FOAMed #MedX #physiology 2️⃣
The venous return (VR) curve describes how blood flows into the heart for any steady state of the venous system.
It’s not about forcing RAP up or down — it shows the equilibrium between flow and pressure for a given system tone and volume.

🧵 The Cardiac Function Curve — why it misleads (Part 1)
1/
The cardiac function curve is one of the most recognisable images in physiology.
Unfortunately, it’s also one of the most misdrawn, mislabelled, and misunderstood.
Let’s redraw it — and see what it really tells us about the circulation.

#MedX #FOAMed #physiology 2/
Textbooks teach: ↑ filling pressure → ↑ output.
But that’s backwards.
The heart doesn’t decide flow — it matches whatever venous return delivers.
It’s a servo, not a suction pump.

1️⃣
Some patients with severe venous congestion have almost no oedema — and that’s confusing at first.
It only starts to make sense once you unpack the physiology. 👇

https://twitter.com/NephroP/status/1974492082916962714

Venous congestion ↑RAP → ↑venous pressure (Pv) → potentially ↑capillary pressure (Pc).
But the rise in Pc — and thus filtration — depends on arteriolar tone (Ra)
Pc = (Rv / (Ra + Rv)) * Pa + (Ra / (Ra + Rv)) * Pv

1/
SVR looks precise: (MAP – RAP)/CO.

But this neat number hides traps. It’s not “afterload,” it’s not pure “tone,” and sometimes it’s not even valid.

A thread on why systemic vascular resistance misleads — and when it still helps. 🧵 #MedX 2/
SVR isn’t measured.
It’s calculated from MAP, right atrial pressure, and CO.
That makes it a derived ratio — not a direct property of the circulation.

🧵 Part 3 — Why ICU RCTs fail (beyond colliders)

1. The puzzle

Decades of critical care RCTs.
Huge effort. Tens of thousands of patients.
Very few reproducible breakthroughs.
This is Part 3 of my series on why ICU trials fail — and why physiology must guide us. 2. Heterogeneity (noise, even in “real” diseases)

Even when the trial is valid, patients vary hugely:
– Baseline physiology, comorbidities, genetics
– Different illness phases (early vs late, compensated vs exhausted)
– Co-interventions (ventilation, sedation, antibiotics)

That means a treatment can help some, harm others.
The “needs bigger N” argument reflects the multi-causality of critical illness.

🧵 Part 2. Heterogeneity vs Colliders in Critical Care RCTs

1. The puzzle

Critical care RCTs keep failing.
The usual explanation?
“Patients are too heterogeneous.”
That’s partly true — but there’s a deeper problem.

Part 2 of a 3-thread series on why ICU trials fail and why physiology must guide us. 2. Heterogeneity (the usual alibi)

Heterogeneity = patients in the same trial differ in ways that matter:
– Age, comorbidities, severity
– Disease heterogeneity (e.g. pneumonia due to strep vs haemophilus)
– Different physiology

This makes treatment effect harder to see. Solution? Bigger trials, subgroups, precision medicine.
👉 RCT model still valid — just noisy.

🧵“Thresholds, consensus & physiology”

Decades of critical care research have produced few reproducible breakthroughs.
Maybe the problem isn’t our interventions — it’s that we reduce patients to syndromes, instead of treating them as individuals with distinct physiology. 2. The RCT problem

Critical care RCTs keep failing.
Sepsis and ARDS trials are the classic examples: huge effort, massive cost… yet most results are negative, inconsistent, or impossible to replicate.

1/21
Acid–base interpretation often feels like a maze.
But there’s a simple way to make sense of it at the bedside.
It starts with pH, strong ions, and base excess. 2/21
First: what is pH?
pH = the concentration of hydrogen ions, which come from water splitting into H⁺ and OH⁻.
Anything that changes how much water dissociates changes pH.

1/
Why is CVP in the MAP equation?
MAP = (CO × SVR) + CVP
Because the arterial system must sit above venous pressure to drive flow.

https://twitter.com/icmteaching/status/1956751838666952971

2/
At steady state:
• The (CO × SVR) term is the pressure head needed to push flow through resistance.
• The CVP term is the baseline the system sits on top of.
So whatever CVP is, it contributes that amount to MAP.