Biomedical Solutions to Problems in Intensive Care Model-Based Therapeutics: Adding Quality but not Cost to Care Measuring the Un-Measurable to Protocolise

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 Todays 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 thats ~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 havent 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 doesnt meet increasing expectations. Patient-specific care could improve things within a one method fits all approach, but we already heard that there arent enough resources to spend the time to customise care for each patient individually. Some say we wouldnt necessarily know how anyway! So then How? Todays 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 Infusion pumps: Deliver insulin and other medications to IV lines Computers Each one is individually computerised (often)

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!!!

Whats missing? Technology is not well tied to clinical use and outcome! A HUGE number of sensors Infusion pumps: Deliver insulin and other medications to IV lines Computers Ventilators Devices need to work together to get more out of them!

The real problem 1: A wealth of numerical data that dont 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 Yielding 3: The greater the variability arising from either the patient or the interpretation of the data the more difficult the patients management the more variable the care the more likely a lesser outcome If all sepsis patients age 55-65 w/ heart failure were the same we could treat them the same, AND we wouldnt be having this conversation

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 inter- and intra- patient variability in response to care! What is needed is a patient-specific One method fits all approach That doesnt 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 doesnt 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 Ren 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? Navier-Stokes equations: Computational fluids analysis Mechanical stress analysis Building structural analysis Finite-element equations, Newtons laws of motion: Engineering analysis is used in many different applications Rocket and satellite motion Thermodynamics

...each application area is described by a set of equations representing the physical world... What can an Engineer do about it? Navier-Stokes equations: Computational fluids analysis Mechanical stress analysis Building structural analysis Finite-element equations, Newtons laws of motion: Rocket and satellite motion Thermodynamics

What can an Engineer do about it? Navier-Stokes equations: Computational fluids analysis Mechanical stress analysis Building structural analysis Finite-element equations, Newtons laws of motion: Rocket and satellite motion Thermodynamics These systems of equations are often analysed on computer to help design and optimisation.

What can an Engineer do about it? Navier-Stokes equations: Computational fluids analysis Mechanical stress analysis Building structural analysis Finite-element equations, Newtons laws of motion: Rocket and satellite motion Thermodynamics...and results are used to make safer and more efficient cars, buildings, etc.

Model-based Therapeutics (MBT)? What we do in model- based 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

Model-based Therapeutics (MBT)? Finally, we use computational analysis to solve these equations to help us design and implement new, safer therapies.

Doctors clinical experience and intuition 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 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 BG: Metabolism CVS: Heart and Circulation MV: Pulmonary Mechanics

Clear Physiological Picture? We can measure from clinical data: 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? What we get: Patient status Monitored over time (whats 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 fathers 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 patients 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)

We can measure from clinical data: 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 What we get: Patient status monitored over time (whats changing?) Response to therapy 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: 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. What we get: Patient status monitored over time (whats 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 2 nd 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 patient- specific 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

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 Example Variable PEEP with Average Respiratory System Elastance 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 8: (Aspiration) Minimal Elastance PEEP = 25cmH2O Inflection PEEP = 12~18cmH2O Pt 6: (Intra-abdominal sepsis, CHF) Minimal Elastance PEEP = 15cmH2O Inflection PEEP = 7.5~10cmH2O Pt 10: (Legionnaires, COPD) Minimal Elastance PEEP = 20cmH2O Inflection PEEP = 12~15cmH2O

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 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 Example Variable PEEP with Dynamic Respiratory System Elastance Edrs drops = recruiting Edrs rises = stretching not recruiting Best PEEP thus between 5-10 cmH2O 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 8: (Aspiration) Pt 6: (Intra- abdominal sepsis, CHF) Pt 10: Legionnaires, COPD

Some other answers Clear ability to monitor patient outcome and response to therapy Consider patient specifics and Changing PEEP

Some other answers volume response to PEEP Clear ability to monitor patient outcome and response to therapy 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

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?

Standard infuser equipment adjusted by nurses Patient management Measured data Feedback control Nurse-in-the-loop system. Standard ICU equipment and/or low-cost commodity hardware. Decision Support System Identify and utilise immeasurable patient parameters For insulin sensitivity (SI)

ICU bed setup Nutrition pumps: Feed patient through nasogastric tube, IV routes or meals Glucometers: Measure blood sugar levels Infusion pumps: Deliver insulin and other medications to IV lines. Sub-cut insulins may also be used. INPUTOUTPUT

Blood Glucose levels Variability, not physiology or medicine Controller Fixed dosing systems Typical care Fixed dosing systems Typical care Adaptive control Engineering approach Adaptive control Engineering approach Variability flows through to BG control Variability stopped at controller Models offer the opportunity to identify, diagnose and manage variability directly, to guaranteed risk levels. Fixed protocol treats everyone much the same Controller identifies and manages patient-specific variability Patient response to insulin

Models, Variability and Risk BG [mg/dL] Time 4.4 6.5 5 th, 25 th, 50 th (median), 75 th, 95 th percentile bounds for S I (t) variation based on current value Stochastic model predicts SI Forecast BG percentile bounds: A predicted patient response! Forecast BG percentile bounds: A predicted patient response! SI percentile bounds + known insulin + system model =... SI percentile bounds + known insulin + system model =... Iterative process targets this BG forecast to the range we want: = optimal treatment found! Patient response forecast can be recalculated for different treatments

Maximum 5% Risk of BG < 4.4 mmol/L BG [mg/dL] Time 4.4 6.5 5 th, 25 th, 50 th (median), 75 th, 95 th percentile bounds for S I (t) variation based on current value Stochastic model predicts SI Forecast BG percentile bounds: A predicted patient response! SI percentile bounds + known insulin + system model =... Iterative process targets this BG forecast to the range we want: = optimal treatment found! Iterative process targets this BG forecast to the range we want: = optimal treatment found! Patient response forecast can be recalculated for different treatments

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 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) STAR ChchSTAR GyulaSPRINT ChchSPRINT Gyula Workload # BG measurements: 1,48662226,6461088 Measures/day: 13.512.816.116.4 Control performance BG median [IQR] (mmol/L): 6.1 [5.7 6.8] 6.0 [5.4 6.8] 5.6 [5.0 6.4] 6.30 [5.5 7.5] % BG in target range 89.484.186.076.4 % BG > 10 mmol/L 2.487.72.02.8 Safety % BG < 4.0 mmol/L 1.544.52.891.90 % BG < 2.2 mmol/L 0.00.160.040 # patients < 2.2 mmol/L 0 1 (started hypo) 8 (4%)0 Clinical interventions Median insulin (U/hr): 32.53.0 Median glucose (g/hr): 4.94.44.17.4

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, whats 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)

cTIB 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 Day (1-14) Survival Odds Ratio 4.0 7.0 5.0 8.0 4.0 8.0 cTIB > 50% cTIB > 60% cTIB > 70% cTIB > 80%

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. its not about the technology but how its 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 didnt 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 its right The nurses have not thrown it out the window yet And, in fact, appear to like these solutions Its 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 Thomas Lotz Jess Lin Aaron LeCompte Jason Wong et al Hans Gschwendtner LusannYang Amy Blakemore & Piers Lawrence Carmen Doran Kate Moorhead Sheng-Hui Wang SimoneScheurle UliGoltenbott Normy Razak Chris Pretty JackieParente Darren Hewett James Revie FatanahSuhaimi UmmuJamaludin LeesaPfeifer Harry Chen Sophie Penning Stephan Schaller Sam Sah Pri BrianJuliussen Ulrike Pielmeier Klaus Mayntzhusen Matt Signal Azlan Othman Liam Fisk Jenn Dickson

Math, Stats and Engineering Gurus Dr Dom Lee Dr Bob Broughton Dr Paul Docherty Prof Graeme Wake The Danes Prof Steen Andreassen Dunedin Dr Kirsten McAuley Prof Jim Mann Acknowledgements Glycemia - 1 Geoff Shaw and Geoff Chase Dont let this happen to you! Some guy named Geoff The Belgians Dr Thomas Desaive Dr Jean-Charles Preiser Hungarians Dr Balazs Benyo 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

Acknowledgements (Neonatal) Glycemia - 2 And Dr Adrienne Lynn and all the clinical staff at Christchurch Women's Hospital, and all the clinical staff Waikato Hospital Prof Jane Harding Ms Deb Harris RN Dr Phil Weston Auckland and Waikato

Acknowledgements Cardiovascular Systems Dr. Christina Starfinger Engineers, Math and Docs Prof Geoff Chase Dr Geoff Shaw Dr. Chris Hann The Belgians Dr Thomas Desaive Dr. Bernard Lambermont Dr Philippe Kolh David Stevenson Claire Froissart James Revie Stefan Heldmann The Kiwis French and Germans The Danes Prof Steen Andreassen Dr Bram Smith Honorary Danes SabinePaeme

Acknowledgements ARDS and Lung Mechanics

Acknowledgements Agitation / Sedation Dr. Geoff Shaw Dr. Andrew Rudge Carmen Doran 2 nd Lt S. Hunt Dr. Franck Agogue Dr. Dominic Lee Dr. Christina Starfinger ZhuHui Lam

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

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

Patient 4 Measured SV and Pao (aortic pressure) from typical sensors Decreased left and right ventricle contractility and increased systemic resistance noticed Contributed to a decrease in measured stroke volume and increase in measured aortic pressure. 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. 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 apparent increase in afterload and reduced pulmonary pressure as the left ventricle dilates

Patient 1 Measured SV and Pao (aortic pressure) from typical sensors In contrast Patient 1 responds well Clear differentiation in patient-specific response

Another factor at play is culture 72 The people who make medical equipment often dont realise how its used

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Biomedical Solutions to Problems in Intensive Care Model-Based Therapeutics: Adding Quality but not Cost to Care Measuring the Un-Measurable to Protocolise