Biomedical Solutions to Problems in
Intensive Care
Model-Based Therapeutics: Adding Quality but not Cost to Care
Measuring the Un-Measurable to Protocolise and Improve Care
Patient-Specific “One Method Fits All” Care
Decentralizing Patient Care to the Bedside
Today’s Heresy / Vision Presented by: Prof Geoff Chase
Why bother? … Economics 101
•
Health care grows by about 0.24% of GDP per year
– Over the last 20 years that’s ~5% (more) of GDP (NZ$7B and A$70B-ish, more)
– Imagine what a “free” 5% of GDP would be worth to govts these days!
•
Critical care is ~10% of all health care costs, which are in turn (currently)
~10% of GDP
•
Critical care has several difficult problems reducing cost and improving /
protocolising care in several core areas despite obvious potential
improvements in outcome if they could be sussed
– Mechanical Ventilation (MV), CVS diagnosis and treatment, Glycemic control to
name 3 breads + butter
•
The current growth of costs, in part demographic, is not sustainable
•
Expectations are also rising faster than our ability provide the expected care
quality (I blame this on TV Doctor shows)
The inevitable why and what to do ??
• Why? Productivity improvements driven by technology solutions that
have occurred in many other areas haven’t reached medicine
– Or education for that matter! So, I straddle 2 unproductive sectors!
• The Difficulty(ies)?
– Improving productivity is easy, just reduce care and spread resources over
more patients. This already occurs to an extent if you look at patient-nurse
ratios in ICUs in the US and EU
– There is an inevitable increase in demand to “do more” often “with less” that
is not sustainable or really possible without giving something useful up
– Increasing protocolisation helps, but typically provides a “one size fits all”
evidenced based approach that cannot necessarily improve care for
everyone and thus doesn’t meet increasing expectations.
– Patient-specific care could improve things within a “one method fits all”
approach, but we already heard that there aren’t enough resources to spend
the time to customise care for each patient individually.
• Some say we wouldn’t necessarily know how anyway!
• So then… How? … Today’s topic… I think…
A vision of the future?
•
Pay no attention to the man with the computer! … Just the computer…
The lack of technology itself isn't an issue!
Ventilators
A HUGE number of sensors
Computers
Each one is individually computerised (often)
Infusion pumps:
Deliver insulin and other
medications to IV lines
Interestingly, no one really notices it all…
Image removed for copyright reasons
And many engineers tend to only notice all of
the cool gear! And then add more!!!
What’s missing? Technology is not well tied
to clinical use and outcome!
Ventilators
A HUGE number of sensors
Computers
Devices need to work together to get more out of them!
Infusion pumps:
Deliver insulin and other
medications to IV lines
The real problem
•
1: A wealth of numerical data that don’t necessarily have direct clinical
meaning or do not provide a “clear physiological picture”
– The numbers change moment to moment
– They require a “mental model” to sort into a picture of what is happening
– Clinical staff are not trained to think about numbers like the engineers who
designed the equipment and thus much information is essentially lost
– All this creates an aura of confusion/uncertainty that suppresses critical thinking
– Simplification is needed so clinical caregivers can “rule the technology” to
improve outcomes.
•
2: ICU patients are difficult to manage because are highly variable
– in care
– in response to care
•
If all sepsis patients age 55-65 w/ heart failure were the same we could
treat them the same, AND we wouldn’t be having this conversation
Yielding 3: The greater the variability arising from either the patient or the
interpretation of the data…
– the more difficult the patient’s management
– the more variable the care
– the more likely a lesser outcome
What about Protocolised Care?
• Goal: To reduce the iatrogenic component due to variability in care
• BUT applicable to groups with well-known clinical pathways
• “One size fits all approach”
• Reduces variability in how care is given, but …
• Not all patients are the same so it cannot take into account interand intra- patient variability in response to care!
• What is needed is a patient-specific “One method fits all approach”
– That doesn’t add effort, time or cost to care
Less is more: 2 Kinds of Variability
Model-based methods can provide patient-specific care
that is robust to intra- and inter- patient variabilities in response
to care and disease state that much protocolised care cannot
Summary of the Problem or
The end of the beginning!
• Goals:
– Break cycle of low productivity growth
– Increase productivity significantly without simply working harder or doing
less for each patient
• Will require: doing patient-care much differently, but, in the absence of
the “cures all” drug, with the same technology tools to hand
• This is actually a huge ask and requires something more revolutionary
and disruptive than evolutionary
– Yet, in medicine “evolution” is the preferred route of change for many good
historical reasons
• So, how to “evolve in a revolutionary fashion”?
– And for a minute I thought this would be straightforward!
Engineering-based solutions?
• When in doubt, apply manly force". (The 1st Rule of Mechanical
Engineering; 1996; a ”colleague”)
• To heal something that doesn’t work or that makes too much
noise, it is necessary and enough to hit on it with something
that works better or that is noisier". (Shadoks Logic, 1968;
Jacques Rouxel and René Borg)
• Apply finesse to create patient-specific solutions
– Or ‘Age and craft beat youth and speed every time’ (Unknown, a
long long time ago)
• So, what is engineering … ?
• And why is it relevant here?
What can an Engineer do about it?
Computational fluids analysis
Mechanical stress analysis
Engineering
analysis is
used in many
different
applications
Navier-Stokes equations:
Building structural analysis
Rocket and satellite motion
Thermodynamics
Finite-element equations, Newton’s
laws of motion:
What can an Engineer do about it?
Computational fluids analysis
Navier-Stokes equations:
Mechanical stress analysis
...each
application area
is described by a
set of equations
representing the
physical world...
Building structural analysis
Rocket and satellite motion
Thermodynamics
Finite-element equations, Newton’s
laws of motion:
What can an Engineer do about it?
Computational fluids analysis
Navier-Stokes equations:
These systems
of equations are
often analysed
on computer to
help design and
optimisation.
Mechanical stress analysis
Building structural analysis
Rocket and satellite motion
Thermodynamics
Finite-element equations, Newton’s
laws of motion:
What can an Engineer do about it?
Computational fluids analysis
...and results are
used to make
safer and more
efficient cars,
buildings, etc.
Mechanical stress analysis
Navier-Stokes equations:
Building structural analysis
Rocket and satellite motion
Thermodynamics
Finite-element equations, Newton’s
laws of motion:
Model-based Therapeutics (MBT)?
What we do in modelbased therapeutics is
very similar...
Model-based Therapeutics (MBT)?
First, we describe
the physical
systems to analyse
Model-based Therapeutics (MBT)?
Next, we build up a
mathematical
representation of the
system
.
G   pG G (t )  S I G (t )
.
min( d 2 P2 , Pmax )  EGPb  CNS  PN (t )
Q(t )

1   G Q(t )
VG
Q  nI ( I (t )  Q(t ))  nc
.
I 
Q(t )
1   G Q(t )
u (t )
u (G )
nL I (t )
 nK I (t )  nI ( I (t )  Q(t ))  ex  (1  xL ) en
1   I I (t )
VI
VI
Model-based Therapeutics (MBT)?
Finally, we use computational
analysis to solve these
equations to help us design
and implement new, safer
therapies.
.
G   pG G (t )  S I G (t )
.
min( d 2 P2 , Pmax )  EGPb  CNS  PN (t )
Q(t )

1   G Q(t )
VG
Q  nI ( I (t )  Q(t ))  nc
.
I 
Q(t )
1   G Q(t )
u (t )
u (G )
nL I (t )
 nK I (t )  nI ( I (t )  Q(t ))  ex  (1  xL ) en
1   I I (t )
VI
VI
Where does this model go?
•
•
•
•
•
•

Insulin

Glucose

Sedation

Steroids and vaso-pressors

Inotropes

And many many more …
Glucose levels
Cardiac output
Blood pressures
SPO2 / FiO2
HR and ECG
And many more…
Doctors clinical
experience
and intuition

Insulin Sensitivity

Sepsis detection

Circulation resistance

A better picture of
the patient-specific
physiology in real-time
at the bedside

Optimise glucose
control

Manage ventilation

Diagnose and treat
CVS disease

And many other
things…
Where does this model go?
•
•
•
•
•
•

Insulin

Glucose

Sedation

Steroids and vaso-pressors

Inotropes

And many many more …
Glucose levels
Cardiac output
Blood pressures
SPO2 / FiO2
HR and ECG
And many more…
Physiological
Models
And
Algorithms

Insulin Sensitivity

Sepsis detection

Circulation resistance

A clear picture of the
patient-specific
physiology in real-time
at the bedside

Optimise glucose
control

Manage ventilation

Diagnose and treat
CVS disease

And many other
things…
What we do with these models in Chch and beyond
Valve
L.tc
R.tc
L.pv R.pv
P.rv
V.rv
Q.tc
P.pa
V.pa
Q.pv
E.pcd
Q.pul
P.ra
Q.sys
R.sys
P.vc
V.vc
E.pa
E.rv
R.pul
E.vc
E.pu
E.lv
E.ao
P.ao
V.ao
Systemic Circulation
R.av
Q.av
L.av
P.lv
V.lv
R.mt
L.mt
P.la
Q.mt
P.pu
V.pu
P.peri
P.th Thoracic Cavity
BG: Metabolism
CVS: Heart and Circulation
MV: Pulmonary Mechanics
Clear Physiological Picture?
•
We can measure from clinical data:
–
–
–
•
What we get:
–
–
–
•
Lung Elastance: is added PEEP stretching the lung or recruiting
more volume?
Lung Volume: is added PEEP recruiting more volume? Enough?
How have these things changed over time?
Patient status
Monitored over time (what’s changing? Getting better?)
Response to therapy
All in a breath to breath (real-time) clear physiological picture of
clinically relevant metrics that can be used to guide therapy
Clear Physiological Picture?
•
•
“Not your father’s 1/compliance!”
A dynamic measure of “system elastance” in response to
pressure and flow patterns (separated from resistance)
–
–
–
–
•
Captures COPD for example as seen by suddenly decreasing
elastance as trapped volume is opened to inflowing gases – which
is effectively an auto-PEEP
A dynamic measure that is patient-specific
It is not a super-syringe or tissue (ex vivo) equivalent!
Can differentiate ARDS and COPD, as well as changes in resistance
(R) due to tube blockage as all are seen dynamically in different
ways in the PV data
Thus, it represents the real situation for that patient’s
recruitment response to pressure and flow (volume) – not
measurable w/o model
Clear Physiological Picture?
•
All at high resolution so we can clearly see changes over
time as conditions change and patient variability rears its
head to change things
•
None can be measured now with the same resolution
•
A direct measurement of something you can titrate to (as
the model makes it visible) since it reflects recruitment vs
resistance vs overstretch directly for that patient.
•
… “Measure the un-measurable” (with any accuracy)
Clear Physiological Picture?
We can measure from clinical data:
–
–
What we get:
–
–
Patient status monitored over time (what’s changing?)
Response to therapy
Valve
E.vc
P.ra
L.tc
R.tc
Q.tc
P.pa
V.pa
Q.pv
E.pcd
E.pu
E.lv
E.ao
P.ao
V.ao
Systemic Circulation
•
L.pv R.pv
P.rv
V.rv
Q.sys
R.sys
P.vc
V.vc
E.pa
E.rv
Q.pul
•
Pulmonary and System resistances that change for sepsis
(Rsys) and pulmonary embolism (Rpu)
Changes in SV (from pressure only measurements, and no
cheap surrogate!) in response to inotropes
R.pul
•
R.av
Q.av
L.av
P.lv
V.lv
R.mt
L.mt
P.la
Q.mt
P.pu
V.pu
P.peri
P.th Thoracic Cavity
More “Un-Measurable” values that can be used to better
diagnose and guide treatment of CVS dysfunction
Clear Physiological Picture?
•
We can measure from clinical data:
–
–
–
•
What we get:
–
–
–
•
Real-time insulin sensitivity (SI) in response to glucose and
insulin administration
SI changes with patient condition (e.g. sepsis) and over time
sometimes quite dramatically (e.g. onset of atrial fibrillation)
Ability to forecast changes in SI so we can dose to account for
future variability and reduce hypoglcyemia.
Patient status monitored over time (what’s changing?)
Response to therapy
Far less hypoglycemia, optimised care and improved outcomes
SI is our un-measurable quantity, and is the dynamics
system balance that guides response to care
–
Most if not all other protocols use BG as a surrogate ignoring
half of the balance
Un-Measurables?
•
Many clinical decisions are partly blind as they can only measure surrogates of
the disease state
– Thus, they rely on clinical staff intuition and experience more than “firm data”
– Outcome is variability and reduced quality of care in a more hectic world
•
Models offer a clear physiological picture that makes diagnosis, treatment and
evaluation of response far clearer, and thus less variable
– Available to everyone from the Sr Specialist to Junior Nurse
– Clear pictures = easy diagnosis and treatment decisions with no 2nd guesses
– Made visible by models and data  patient-specific models (and time specific!)
•
They do this in a patient-specific fashion by linking patient-specific data from all
those technologies with a model and a touch of computational magic!
Un-measurables and Endpoints
•
Importantly, chosen well, these metrics are direct markers of health
and response related to core ICU therapies, and can thus be used to
protocolise using patient-specific values to create and guide patientspecific care
– i.e. One method fits all (since patient-specific implies different “sizes”)
• These are patient-specific treatment metrics that allow more
complete insight into patient state than directly measured
endpoints
– E.g. Insulin sensitivity is to glucose what GFR is to urine output
Short Case Examples in MBT
1. Mechanical Ventilation (emerging)
2. Glycemic Control (existing)
Lung Mechanics and MV
A wish list
• If I add PEEP will I stretch the lung more or recruit more lung units?
• What extra volume can I recruit with a change in PEEP?
• Did my recruitment maneuver work? How well, exactly?
• Is patient condition changing?
• Does PEEP need to be changed?
• Broadly, the answers are obvious, yet patient-specific variability over
time and different interpretations or mental models to evaluate that
data means that significant uncertainty creeps into each decision.
– Uncertainty often leads to less decisions or lesser changes
Example – Variable PEEP with Average
Respiratory System Elastance
•
•
Elastance = 1/Compliance
Falling elastance as pressure
rises implies you recruit
volume faster than pressure
rises == good!
•
Minimal Elastance (Maximum
Compliance) was observed at
PEEP 15cmH2O
The inflection line is identified
as +5~10 % above minimal
Elastance.
•
•
Measured by model and PV
data from the vent, it is far
more accurate than any
estimate or inflection point
approximation
Diminishing returns and thus best PEEP here
Examples – Variable PEEP with Average Respiratory
System Elastance (all were at PEEP = 10 cmH2O)
Pt 2:
(Trauma)
Minimal Elastance PEEP
= 15cmH2O
Inflection PEEP
= 6~9cmH2O
Pt 6:
(Intra-abdominal sepsis,
CHF)
Minimal Elastance PEEP
= 15cmH2O
Inflection PEEP
= 7.5~10cmH2O
Pt 8:
(Aspiration)
Pt 10:
(Legionnaires, COPD)
Minimal Elastance PEEP
= 25cmH2O
Minimal Elastance PEEP
= 20cmH2O
Inflection PEEP
= 12~18cmH2O
Inflection PEEP
= 12~15cmH2O
Example – Variable PEEP with Dynamic
Respiratory System Elastance
•
Dynamically over a breath at every
pressure point = Edrs = dynamic
elastance
•
Identifies change of Respiratory
Elastance within a breathing cycle
•
Falling Edrs indicates volume rises
faster than pressure = Recruitment
Rising Edrs indicates Overstretch
more than recruitment
•
Best PEEP thus between 5-10 cmH2O
Edrs drops = recruiting
•
Flat Edrs (at minimum) would
thus be theoretically ideal
•
Can be monitored every breath
•
Edrs potentially provides higher
resolution in monitoring and more
detailed information where a
constant value cannot
Edrs rises = stretching not recruiting
Change flow pattern to get a better Edrs shape w/o initial rise?
Examples – Variable PEEP with Dynamic Respiratory
System Elastance (all were at PEEP = 10 cmH2O)
Pt 2: (Trauma)
Pt 6: (Intraabdominal
sepsis, CHF)
Pt 8: (Aspiration)
Pt 10:
Legionnaires,
COPD
Elastance
increase
Consider patient specifics and
Changing PEEP
PEEP (cmH2O)
Elastance (cmH2O/L)
Some other answers …
• Clear ability to monitor
patient outcome and
response to therapy
Some other answers … volume response to PEEP
dFRC volume rises 150mL over 0.9 hours
dFRC volume constant over 0.8-0.9 hours
dFRC declines more than 200mL over 10 hours
• Clear ability to monitor patient outcome and response to therapy
Potential Clinical Use and Outcome?
•
A clear physiological picture can help guide therapy by adding more
and better information that is not normally available
•
Can we guide PEEP and MV based on Edrs or Elung profiles/values to
get beter clinical outcomes (LoMV or number of desaturation events)?
•
In testing at Christchurch Hospital now!
BG: Glycemic Control
A wish list
• What will happen if I add more insulin?
• What is the hypoglycemia risk for this insulin dose?
– Over time?
– When should I measure next to be sure?
• How good is my control? Does it need to be better?
• Should I change nutrition? What happens if someone else has
changed it? How should I then change my insulin dose?
– Many if not all protocols are “carbohydrate blind” and thus BG is a
very poor surrogate of response to insulin
• Is patient condition changing? What happens if it changes between
measurements?
Feedback control
Decision Support System
Measured data
.
G   pG G (t )  S I G (t )
.
min( d 2 P2 , Pmax )  EGPb  CNS  PN (t )
Q(t )

1   G Q(t )
VG
Q  nI ( I (t )  Q(t ))  nc
.
I 
Q(t )
1   G Q(t )
u (t )
u (G )
nL I (t )
 nK I (t )  nI ( I (t )  Q(t ))  ex  (1  xL ) en
1   I I (t )
VI
VI
Identify and utilise
“immeasurable”
patient parameters
For insulin sensitivity
(SI)
Patient management
Standard infuser equipment
adjusted by nurses
“Nurse-in-the-loop” system. Standard ICU equipment and/or low-cost commodity
hardware.
ICU bed setup
INPUT
OUTPUT
OUTPUT
Glucometers:
Measure blood
sugar levels
Nutrition pumps:
Feed patient through
nasogastric tube, IV
routes or meals
Infusion pumps:
Deliver insulin and other
medications to IV lines.
Sub-cut insulins may
also be used.
Variability, not physiology or medicine…
Fixed dosing systems
Typical care
Adaptive control
Engineering approach
Patient
response to
insulin
Controller identifies and
manages patient-specific
variability
Fixed protocol treats
everyone much the same
Controller
Variability flows through to
BG control
Variability stopped at
controller
Blood Glucose
levels
Models offer the opportunity to identify, diagnose and
manage variability directly, to guaranteed risk levels.
Models, Variability and Risk
5th, 25th, 50th (median), 75th, 95th
percentile bounds for SI(t) variation
based on current value
Stochastic model
predicts SI
SI percentile bounds
+
known insulin
+
system model
tnow+(1-3)hr= ...
tnow
tnow
tnow+(1-3)hr
Insulin sensitivity
Stochastic model shows the
bounds (5th – 95th percentile)
for insulin sensitivity variation
over next 1-3 hours from the
initially identified level
Insulin sensitivity
95th
75th
50th
25th
75th
50th
25th
Iterative
process targets this BG
5th
forecast to the range we want:
BG
[mg/dL]
Blood glucose
25th
50th
75th
95th
Patient response forecast
can be recalculated for
For a given feed+insulin
different treatments
intevention an output BG
distribution can be forecast
using the model
bounds (5th
for insulin s
over next 1initially ide
A predicted patient response!
5th
5th
m
Forecast BG percentileStochastic
bounds:
95th
Blood glucose
= optimal treatment found!
5th
6.5
25th
50th
4.4
75th
95th
Time
For a given
intevention
distribution
using the m
Maximum 5% Risk of BG < 4.4 mmol/L
5th, 25th, 50th (median), 75th, 95th
percentile bounds for SI(t) variation
based on current value
Stochastic model
predicts SI
SI percentile bounds
+
known insulin
+
system model
tnow+(1-3)hr= ...
tnow
tnow
tnow+(1-3)hr
Insulin sensitivity
Stochastic model shows the
bounds (5th – 95th percentile)
for insulin sensitivity variation
over next 1-3 hours from the
initially identified level
Insulin sensitivity
95th
75th
50th
25th
75th
50th
25th
Iterative
5th process targets this BG
forecast to the range we want:
BG
[mg/dL]
Blood glucose
25th
50th
75th
95th
Patient response forecast
can be recalculated for
For a given feed+insulin
different treatments
intevention an output BG
distribution can be forecast
using the model
bounds (5th
for insulin s
over next 1initially ide
A predicted patient response!
5th
5th
m
Forecast BG percentileStochastic
bounds:
95th
Blood glucose
= optimal treatment found!
5th
6.5
25th
50th
4.4
75th
95th
Time
For a given
intevention
distribution
using the m
Why this approach?
• Model lets us guarantee and fix risk of hypo- and hyper- glycemia
• Giving insulin (and nutrition) is a lot easier if you know the range of
what is likely to happen.
• We know this and dose appropriately
• Allows clinicians to select a target band of desired BG and guarantee
risk of BG above or below
• We tend to fix a 5% risk of BG < 4.4 mmol/L which translates to less
than 1/10,000 (interventions) risk of BG < 2.2 mmol/L (should be about
2% by patient)
– Fyi, this is how airplanes are designed and how Christchurch's high rises
should have been designed!
Some Results to Date
STAR Chch
STAR Gyula
SPRINT Chch SPRINT Gyula
# BG measurements:
1,486
622
26,646
1088
Measures/day:
13.5
12.8
16.1
16.4
6.1
[5.7 – 6.8]
89.4
6.0
[5.4 – 6.8]
84.1
5.6
[5.0 – 6.4]
86.0
6.30
[5.5 – 7.5]
76.4
2.48
7.7
2.0
2.8
% BG < 4.0 mmol/L
1.54
4.5
2.89
1.90
% BG < 2.2 mmol/L
0.0
0.16
1 (started
hypo)
0.04
0
8 (4%)
0
Workload
Control performance
BG median [IQR] (mmol/L):
% BG in target range
% BG > 10 mmol/L
•
Very tight
•
Very safe
•
Works over several
countries and clinical
practice styles
•
Also been used in
Belgium
•
Measuring SI is very
handy whether you
do it with a model
(STAR) or estimated
by response
(SPRINT)
Safety
# patients < 2.2 mmol/L
0
Clinical interventions
Median insulin (U/hr):
3
2.5
3.0
3.0
Median glucose (g/hr):
4.9
4.4
4.1
7.4
So, because we know the risk …
•
We get tight control safely
•
We do it by identifying insulin sensitivity (SI) every intervention
– Measuring SI is a direct surrogate of patient response to all aspects of metabolism,
and is not available without a (good) model
– Using just BG level is a very poor surrogate because it lacks insulin/nutrition context.
Like trying to estimate kidney function from just urine output – it lacks context
•
We can minimise interventions, measurements and clinical effort with confidence
and exact knowledge of the risk
•
We know what to do when nutrition changes, and can change it directly if we
require!
•
So, what’s the target you ask.. (not yet answered for MV case)
– All we know is that level is bad and so is variability with about 1M opinions as to what
and how much….
– We, of course, have an answer… we think…
cTIB = cumulative time in band
A measure of exposure / badness over time
•
Measures both level and variability
•
We examined 3 “intermediate ranges” that most would think are not at all different!
•
And 4 thresholds (50, 60, 70 and 80%) versus outcome (odds ratio)
4.0 – 7.0
5.0 – 8.0
4.0 – 8.0
cTIB
cTIB > 50%
Survival Odds Ratio
cTIB > 60%
cTIB > 70%
•
1700 patients from SPRINT and before
SPRINT, and both arms (high and low)
of Glucontrol trial in 7 EU countries
•
Is there a difference between 7 and 8
mmol/L or 3-4 mmol/L of variability???
•
Yes, significantly so from day 2-3
onward
•
Difference is more stark if you eliminate
patients who have at least 1 hypo (BG
< 2.2)
•
We think the answer is clear and
know how to safely achieve those
goals
•
Because you can calculate it in real
time you can use it as an endpoint
for a RCT
cTIB > 80%
Day (1-14)
A brief pause for reflection …
Engineering + Medicine = Patient-Specific Care
• The main goal of models and engineering in critical care might readily be
summarised as:
– Turning a wealth of data and technology into a coordinated, predictive
and, most importantly, patient-specific picture of the clinical situation
by making key patient-specific parameters “visible” to enhance
monitoring and diagnosis, and guide/optimise care
• The technology is there what is missing is the “finesse” and elegant
solutions, but, we feel those are coming
– I.e. it’s not about the technology but how it’s used.
• MBT can provide patient-specific “one method fits all” care that
improves care, decentralises care to the bedside, and, in doing, reduces
cost and increases productivity
– PS: we didn’t say, but we implement these with cheap tablet computers
which over 1000 patients means the added cost is about $0.50!
And the salient sign that it’s “right”
• The nurses have not thrown it out the window yet…
• And, in fact, appear to like these solutions …
• It’s all about better tools to do a better job for patients with less
time, stress, effort, uncertainty or worry…
• In a world where demand outstrips supply this the most important
goal, and thus I am back to the beginning of my talk!
Acknowledgements
Glycemia PG Researchers
Jess Lin
Aaron LeCompte
Thomas Lotz
Carmen Doran
Kate Moorhead
Stephan Schaller
Sam Sah Pri
Sheng-Hui Wang
Sophie Penning
Brian
Juliussen
Jason Wong et al
Uli
Simone
Goltenbott
Scheurle
Harry Chen
Ulrike Pielmeier
Leesa
Pfeifer
Klaus
Mayntzhusen
Hans
Gschwendtner
Ummu
Jamaludin
Matt Signal
Lusann
Amy Blakemore &
Yang
Piers Lawrence
Jackie
Normy Razak
Fatanah
Suhaimi
Azlan Othman
Chris Pretty
Darren Hewett
Liam Fisk
Parente
James Revie
Jenn Dickson
Acknowledgements
Glycemia - 1
Dunedin
Dr Kirsten
McAuley
Prof Jim Mann
The Danes
Prof Steen
Andreassen
Dr Thomas Desaive
Math, Stats and Engineering Gurus
Dr Dom Lee
Dr Bob
Broughton
Prof
Graeme Wake
Hungarians
The Belgians
Dr Jean-Charles
Preiser
Dr Balazs Benyo
Some guy named Geoff
Geoff Shaw and Geoff Chase
Dr
Paul Docherty
Belgium: Dr. Fabio Taccone, Dr JL Vincent, Dr P Massion, Dr R Radermecker
Hungary: Dr B Fulesdi, Dr Z Benyo, Dr P Soos, Dr I Attila, and 12 others ...
... And all the clinical staff at over 12 different ICUs
Don’t let this happen to you!
Acknowledgements
(Neonatal) Glycemia - 2
Auckland and Waikato
Prof Jane Harding
Ms Deb Harris RN
Dr Phil Weston
And Dr Adrienne Lynn and all the clinical staff at Christchurch Women's Hospital, and
all the clinical staff Waikato Hospital
Acknowledgements
Cardiovascular Systems
The Belgians
Engineers, Math and Docs
Prof Geoff Chase
Dr. Chris Hann
Dr Geoff Shaw
Dr Thomas Desaive
Dr. Bernard
Lambermont
Dr Philippe Kolh
The Kiwi’s French and Germans
Sabine
Paeme
David Stevenson
Claire Froissart
Honorary
The Danes
Danes
Dr. Christina
Starfinger
James Revie
Stefan Heldmann
Prof Steen
Andreassen
DrDr
Bram
Smith
Bram
Smith
Acknowledgements
ARDS and Lung Mechanics
Acknowledgements
Agitation / Sedation
Dr. Christina
Starfinger
ZhuHui Lam
Dr. Andrew Rudge
Dr. Geoff Shaw
Dr. Franck
Agogue’
2nd Lt S. Hunt
Carmen Doran
Dr. Dominic
Lee
eTIME (Eng Tech and Innovation in Medicine) Consortia
4 countries, 7 universities, 12+ hospitals and ICUs and 35+ people
Last but hardly least!
Intensive Care Nursing Staff, Christchurch Hospital
Thank you for your time and attention!
CVS Monitoring
Valve
L.tc
R.tc
L.pv R.pv
P.rv
V.rv
Q.tc
P.pa
V.pa
Q.pv
E.pcd
Q.pul
P.ra
Q.sys
R.sys
P.vc
V.vc
E.pa
E.rv
R.pul
E.vc
E.pu
E.lv
E.ao
P.ao
V.ao
Systemic Circulation
R.av
Q.av
L.av
P.lv
V.lv
R.mt
L.mt
P.la
Q.mt
P.peri
P.th Thoracic Cavity
P.pu
V.pu
A wish list
• How is the patient responding?
• I added inotropes and the PiCCO shows no real change in CO but
what I really want to know is what is the stroke volume (SV)?
– Did the inotropes increase SV or just HR?
• What is systemic or pulmonary resistance (i.e. is there an emerging
acute dysfunction?)?
• Is patient condition changing?
• Patient-specific elastance?
Case Study: Post-Mitral Valve Surgery
End diastolic volume
(ml)
Patient 1
Patient 2
Patient 3
Patient 4
Average
150
100
50
Left ventricle
Right ventricle
Systemic
Pulmonary
1.5
1
0.5
End systolic elastance
(mmHg/ml)
Vascular resistance
(mmHgs/ml)
0
2
0
4
Left ventricle
Right ventricle
3
2
1
Pulmonary vein
pressure(mmHg)
0
20
15
10
5
0
0
2
4
6
Time
(hours)
8
10
12 0
2
4
6
Time
(hours)
8
10
12 0
2
4
6
Time
(hours)
8
10
12 0
2
4
6
Time
(hours)
8
10
12 0
2
4
6
Time
(hours)
8
10
12
Patient 4
s)
End diastolic volume
(ml)
150
Average
•
Measured SV and Pao (aortic pressure) from typical sensors
50
•
Decreased
left and right ventricle contractility and increased
Left ventricle
Left ventricle
Right ventricle
Right ventricle
systemic resistance noticed
Vascular resistance
(mmHgs/ml)
Systemic
Pulmonary
1.5
1
Contributed to a decrease in measured stroke volume and
increase in measured aortic pressure.
•
Right ventricle
Right ventricle
The
combination of these factors caused left ventricle dilation
and is symptomatic of patients with decompensated hearts,
where an increase in left ventricle afterload after valve
replacement leads to a decline in ejection fraction.
End systolic elastance
(mmHg/ml)
0.5
Pulmonary vein
pressure(mmHg)
Systemic
Pulmonary
•
0
4
3
2
1
0
20
10
Patient 4
100
0
2
8
Patient 3
Patient 2
Average
Patient 41
Patient
3
15
Left ventricle
•
10
5
0
12 0
Left ventricle
2
44
6
Time
(hours)
88
10
10
12 0
12
2
Overall, a very clear picture emerges of a failure to respond
to the surgery and the weakened contractile state of the left
ventricle does not appear to be able to compensate for this
10
8
6
4
2
12 0
10
8
6
4
2
12 0
10
8
6
4
2
12 0
10
44
6
88
10
12
reduced
pulmonary
Time
Time
Time in afterload and
Timeapparent increase
(hours)
(hours)
(hours)
(hours)
pressure as the left ventricle dilates
12
Patient 1
Patient
Patient
1 3
End diastolic volume
(ml)
2
Patient
Patient
2 4
•
150
Patient
Average
3
100
50
Patient 4
Average
Measured SV and Pao (aortic pressure) from
typical sensors
•
Left ventricle
In contrast
Patient 1 responds well
Right ventricle
Left ventricle
Right ventricle
•
1.5
Systemic
Pulmonary
Systemic
Pulmonary
Left ventricle
Right ventricle
Left ventricle
Right ventricle
Clear differentiation in patient-specific
response
1
0.5
End systolic elastance
(mmHg/ml)
Vascular resistance
(mmHgs/ml)
0
2
0
4
3
2
1
Pulmonary vein
pressure(mmHg)
0
20
8
10
15
10
5
0
12
0 0 2 2
4 4
6 6
8 8 10 10 12 12
0 0 2 2
Time
Time
(hours)
(hours)
4 4
6 6
8 8 10 10 12 12
0 0
Time
Time
(hours)
(hours)
2 2
4 4
6 6
8 8 10 10 12 12
0
Time
Time
(hours)
(hours)
2
4
6
Time
(hours)
8
10
12 0
2
4
6
Time
(hours)
8
10
12
Another factor at play is “culture”
The people who make medical equipment often don’t realise how it’s used
72