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/
Shock isn’t “give fluids, then pressors, then inotropes.”
That recipe misses the physiology.
Here’s how to manage shock properly: 🧵
#MedX #haemodynamics 2/
First, rule out mechanical causes (tamponade, tension PTX, massive PE, acute valve rupture, etc). These need specific fixes.

1/
In our last thread, we explored how vessels can collapse when MAP falls below a threshold — the Critical Closing Pressure (CCP) — creating a vascular choke point.
But there’s a common misunderstanding we need to clear up 🧵👇
#MedX #Physiology #Haemodynamics

https://x.com/icmteaching/status/1950201324659704249

2/
The concept of the vascular waterfall helps explain this:
When CCP rises above venous pressure, blood flow can cease completely — even if a pressure gradient still exists.

Why “waterfall”? Because flow is no longer influenced by the pressure downstream.

🧵 What is Critical Closing Pressure — and why does it matter for perfusion?

A thread to clear up one of the most misused and misunderstood ideas in circulatory physiology.
👇 1.
We talk a lot about MAP, CVP, and perfusion pressure.
But there’s a hidden threshold that can choke flow completely — even when there’s still a gradient.
It’s called Critical Closing Pressure (CCP).
And it’s time to make sense of it.

🧵 "What really determines tissue perfusion?"

– and why most explanations get it wrong.

Let’s sort out MAP, CVP, CCP, autoregulation, vasopressors, and the flow that actually reaches your organs.
👇 1/
You’ve probably heard:
“Perfusion pressure = MAP − CVP”

Or sometimes:
“Perfusion = MAP − CCP”

But both are context-dependent.
Let’s unpack what truly drives tissue perfusion — and why it’s more dynamic than most realise.

1/
Shock is complex. But our tools are often simplistic.
This paper proposes a new model:
🩸 Four circulatory interfaces that must stay coupled to maintain perfusion.
Uncouple any one — and shock worsens.
#Shock #MedX #FOAMcc
Here’s the framework.
🔗 doi.org/10.3390/jpm150… 2/
🔧 The model outlines 4 key interfaces:
1. LV to systemic arterial
2. Arteriolar to capillary
3. Capillary to venular
4. RV to pulmonary arterial

Each can be assessed. Each can fail. And each demands tailored therapy.

🧵 Starling’s Law: Misunderstood, Misapplied, and Still Misleading
1
🚨 “Starling’s Law explains how the heart increases cardiac output.”

You’ve probably heard this a thousand times.
But it’s wrong.
Or at least - very incomplete.
Let’s fix it.
Because this matters - for heart failure, fluids, vasopressors, inotropes, afterload, and how we think about the whole system. 2
🫀 Textbook Starling curves show:

Preload (RAP or EDV) on the x-axis
Cardiac output on the y-axis
Upward shifts with “more inotropy” or “less afterload”
❌ But that’s not how the system really works.
Because in a real circulation, the system sets flow - not the heart.

Important pushback on the Anderson/Guyton model: is pressure really a driver, or just a consequence of volume and compliance? A quick thread to unpack the physiology (and clinical relevance) 🧵👇

https://twitter.com/DiegoEscarraman/status/1938769536053805099

1/
Thanks Diego @DiegoEscarraman
You're absolutely right: pressure isn't a primary “source” in energetic terms. It emerges from the interaction between volume, compliance, and flow — and real understanding must include that dynamic.

1/
Most people think the heart drives circulation.
But what if that’s backwards?
Anderson’s model flips the whole idea of cardiac output on its head — and it changes how you think about fluid, flow, and failure.
🧵👇
#physiology #FOAMed #MedTwitter #criticalCare #cardiacOutput 2/
🚿The system sets the flow based on metabolic need.
It adjusts vascular tone and volume to change Pms (mean systemic pressure) and venous return.
The heart simply ejects what arrives — unless it fails.

Thanks for some great comments on this thread. Some nuance and further explanation is needed here.
Being ill activates the symp NS. I’m sure you have all noticed being tachy and having a bounding pulse when you have flu or are hungover! CO goes up to meet ⬆️ metabolic demand.

https://twitter.com/icmteaching/status/1788893274591088875

Sepsis of course is the same and can result in a ⬆️or ⬇️ cardiac output. The key factor is the balance between sepsis causing venoplegia (reducing Pms) and the sympathetic response trying to counter this (which increases Pms and HR).

#haemodynamics #hemodynamics myths.
Myth 1.
'Sepsis causes low SVR and high stroke volume/CO' This is a common misconception. Why? Because the moment a sick patient comes anywhere near a health care provider they get fluid boluses.