Www.ccm.pitt.edu UPMC Critical Care. Making Sense of Complex Monitoring Signals Michael R. Pinsky,...

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UPMC Critical Care

Making Sense of Complex Monitoring Signals

Michael R. Pinsky, MD, Dr hcDepartment of Critical Care Medicine

University of Pittsburgh

Conflicts of Interest• Michael R. Pinsky, MD is the inventor of a US patents “Use of aortic

pulse pressure and flow in bedside hemodynamic management ,” “Device and system that identifies cardiovascular insufficiency” and

• “A system and method of determining a susceptibility to cardiorespiratory insufficiency” owned by the University of Pittsburgh.

• Co-founder and stockholder of Intelomed • Is or was* a medical advisor for:

– Abbott Corporation* Edwards Lifesciences– Applied Physiology Ltd. Cheetah Medical*– Arrow International* väsamed*– Hutchinson Medical* LiDCO Ltd

• Is or was* receiving research funding from: – Deltex Ltd*– Pulsion Ltd*– Edwards LifeSciences

• Michael R. Pinsky, MD is receiving research funding as Principal Investigator from the NIH– T32 HL07820, R01 HL074316, R01 NR013912 and UM1 HL120877

Monitoring TruthNo monitoring device, no matter how

accurate or insightful its data will improve outcome,

Unless coupled to a treatment, which itself improves outcome

Pinsky & Payen. Functional Hemodynamic Monitoring, pp 1-4, 2004Pinsky & Payen. Crit Care 9:566-72 2005Pinsky. Chest 123:2020-9, 2007

Three Primary Clinical Problems

• How to identify patients who are becoming hemodynamically unstable before they progress too far?

• How to determine the most appropriate therapy to reverse the primary cause for impending circulatory shock?

• How to you implement the most appropriate therapy when individual training of care givers and responses of patients vary?

Three Primary Clinical Problems

• How to identify patients who are becoming hemodynamically unstable before they progress too far?

• How to determine the most appropriate therapy to reverse the primary cause for impending circulatory shock?

• How to you implement the most appropriate therapy with non-physician when individual responses of patients and care givers vary?

Systems Issues in Critical Illness

• Disease phenotype is a mixture of the process and the hosts response to the process

• No two people will present and respond the same even if they have the same diagnosis

• Healthcare systems are inherently complex– Monitoring, alerts, physical plants– Bedside nursing, Physicians, Specialists

• Goals of therapy are often unknown and commonly change

MAP = 80 surface

Why Blood Pressure Does Not DefineCardiovascular Status

Heart failure

Hypovolemia

Pms

EhSVR

Sepsis

Why Cardiac Output Does Not DefineCardiovascular Status

CO = 5 L/min surface

Hypovolemiaor Sepsis

Heart failure

Pms

EhSVR

MAP=80 CO=5

MAP=80 & CO=5 line

for Eh

Heart failure

Pms

EhSVR

Hypovolemia

Combining pressure and flow helps

Separating Pms, Eh and SVR for Hypovolemia, Heart Failure and Sepsis

Eh

0

5

10

15

20

25

30

24

68

1012

1416

1820

0.20.3

0.40.5

0.60.7

0.80.9

Navagator 3-D Display

Septic ShockCardiogenic ShockHemorrhagic Shock

Static Risk Prediction Scoring

• APACHE– Acute physiologic and chronic health evaluation

• MEWS– Medical early warning system

• LODS– Logistic regression score

• SOFA– Sequential organ failure assessment

Static Risk Prediction ScoringLimitations

Retrospective

Usualy require manual data entry

Useful for predicting longer term outcomes

Useful for nurse and related resource allocation

A Modest Proposal

Make the patient the center• Unique personal goals and desires

– Defining start and stopping rules

• Unique expression of disease– Defining threshold values for homeostasis

• Unique response to treatment– Physiologic and regenerative reserve

Efferent Arm:Medical Emergency Teams (MET)

Improve Acute Care• System-wide ICU-based MET activation to evaluate

and treat patients at risk to develop adverse events• Pre-defined MET activation criteria by non-MD staff• Reduces adverse events Relative Risk Reduction:

– Stroke: 78%, Severe Sepsis: 74%, Respiratory failure: 79%

• Saves lives: post-op death decreased 37%• Decreases costs: less ICU transfers, decreased LOS

• Bellomo et al. Crit Care Med 32: 916-21, 2004

But first one must identify these unstable patients

Electronic Automated Vital Sign Advisory Reduces Morbidity in General Hospital Wards

Bellomo et al. Crit Care Med 40:2349-61, 2012

Effect of an Electronic Advisory Alert in General Medical Wards

• Alerts for vital signs outside of “safe” range• 9617 patients before, 8688 patients after• MET activation not directly linked to alerts• Improved Survival with Advisory Alerts

Bellomo et al. Crit Care Med 40:2349-61, 2012

Medical Issues• Identify circulatory insufficiency before secondary

tissue injury occurs• Assess disease severity• Accurately predict response to treatment• Gauge adequacy of specific therapies• Estimate improved predictions of disease severity

by additional biological measures (biosensors)• Need to develop metrics to assess these challenges

• Functional Hemodynamic Monitoring• Fused parameters (smart alarms)• Machine learning-based pattern recognition

• Am J Respir Crit Care Med 190: 606-10, 2014

Health and Disease Defined as a Time-Space Continuum

• In a static field of single point-in-time data health and disease can be separated in stochastic fashion using Neuronet approach to create a probabilistic equation

• In a dynamic field of continuously changing but inter-related variables, health and disease can be defined by the differences their Lorenz Attractors (ρ) independent of the actual physiological variables raw values.

BackgroundMET activation is grouped around morning and afternoon

rounds, suggesting instability was missed at other timesDeVita et al. Crit Care Med 34:2463-78, 2006

An electronic integrated monitoring systems (BioSigns) was developed to identify cardio-respiratory instability using Neuronet analysis of existing ICU patient behavior

Tarassenko et al. Br J Anaesth 97:64-8, 2006

Existing bedside monitor

Integrated Monitor (BioSign)

Example of the BioSign™ screen developing a single physiologic index value; alert threshold for Phase 1

was 3.0 based on prior ICU patient data

Added Information

333 patient admissions representing all patients, reflecting 18,692 hours continuous monitoring

All 7 MET activation events of respiratory and/or cardiac cause were detected by BSI in advance of MET activation

Mean advanced detection time prior to MET activation was 6.3h78% of METfull did not result in MET activation and <1/2 were

commented on in nursing notesCardio-respiratory deterioration was generally characterized by

progressive increases in BSI over time rather than step increases

Most patients were stable during their SDU stay: 75%Unstable patients were only unstable at most 20% of the time

Hravnak et al. Arch Intern Med 168:1300-8, 2008

Phase 1 Results

Example of Phase 1 METfull patientwho had MET Activation called at 13:29

BSI

HR

RR

SpO2

BP

06:00 09:00 12:00 13:2911:00

Completely normal values

Smart Monitoring Conclusions

Cardiorespiratory deterioration requiring MET activation was uncommon but was preceded by BioSign Index (VSI) values in danger range prior to MET activation

Neither watching a central monitor nor intermitted direct patient observation will measurably improve identification of unstable non-ICU patients

Hravnak et al. Arch Intern Med 168:1300-8, 2008

Phase 3: Using BSI to Drive MET ActivationClinical Algorithm

Percentage of Patients in Each Phase who Experienced a MET State

0

5

10

15

20

25

30

35

40

METall METmin METfull

% A

ll P

atie

nts

in P

hase

Phase1

Phase 3

Hravnak et al. Crit Care Med 39:65-72, 2011

Total Cumulative Duration Patients in MET State

Phase 1

Phase 3

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000


Tot

al M

inu

tes * * *

* P < 0.05


Duration Patients in MET State(for those who experienced it)

0

5

10

15

20

25

30

35

40

45


Mea

n M

inu

tes

Phase 1

Phase 3


Using Neuronet Modeling to Look into the Future

Using the VSI fusion parameter to quantify instability

• VSImin occurred before METmin 80% of the time with a mean advance time 9.4 ± 9.2 minutes and correlated well (r=0.815, p < 0.001)

• VSIfull occurred 6.4 ± 1.5h before CODE called


HealthNormal Homeostasis

Dis

ease

Sev

erity

Ada

ptiv

e-M

alad

aptiv

e R

espo

nses

Present ClinicalThreshold of Detection

Potential Threshold of Pathologic Stress Detection

Threshold of Stress

Time

Early Detection of Disease Model

Health and Disease Defined as a Time-Space Continuum

• In a static field of single point-in-time data health and disease can be separated in stochastic fashion using Neuronet approach to create a probabilistic equation

• In a dynamic field of continuously changing but inter-related variables, health and disease can be defined by the differences their Lorenz Attractors (ρ) and dynamical analysis independent of the actual physiological variable raw values

Chaos Theory and Biology

• Non-linearity– Chaotic Behavior– Fractal appearance

• Non-linear thinking can result in solutions to otherwise unsolvable problems

• Benoît Mandelbrot & James A. Yorke

Complexity TheorySelf-Organizing Behavior

Complexity TheorySelf-Organizing Behavior

Thermal scan of Petri dishduring E. coli log growth at 37 C With heating to 40 C, the thermal bands

driven to randomness

Tool: Variability Analysis

Comprehensive analysis of degree & character of patterns of variation over intervals in time.

Population Phase Portraits for the Individual-Based Predator-Prey System

Flake GW. The Computational Beauty of Nature: Computer Explorations of Fractals, Chaos, Complex Systems and Adaptation. MIT Press 1998.

Pla

nt P

opul

atio

nH

erbi

vore

P

opul

atio

nC

arni

vore

Pop

ulat

ion

Population Phase Portraits for the Individual-Based Predator-Prey System

Flake GW. The Computational Beauty of Nature: Computer Explorations of Fractals, Chaos, Complex Systems and Adaptation. MIT Press 1998.

Plant Population

Her

bivo

re

Pop

ulat

ion

Car

nivo

re P

opul

atio

n

Car

nivo

re P

opul

atio

n

Plant Population

Herbivore Population

Solving common Solutions for Three Variables:

Plants, Herbivores, Carnivores

Plotted as animal change

D Herbivore

DCarnivore

Pinsky. Crit Care Med 38:S649-55, 2010

Identifying Hemodynamically Unstable Patients using Machine Learning

• What is the minimal data set needed to predict instability: Monitoring parsimony– Number of independent monitoring variable– Lead time– Sampling frequency

• What additional information will improve specificity

• Monitoring response to therapy and define end-points of resuscitation

Heart Rate Variability of Trauma Patient Survival by analysis of HR Complexity Reduces Data Set Size Requirements

Batchinsky et al. Shock 32:565-71, 2009

Survivor

Non-Survivor

Survivors

Non-Survivors

Survivors

Non-Survivors

Sample entropy: SampEn

RRIntervals

(sec)



Survivor

Non-Survivor

Survivors

Non-Survivors

Survivors

Non-Survivors

Approximate entropy: ApEn



ROC curves with 800, 200 or 100

beat long data sets

Decreased Respiratory Rate Variability during mechanical ventilation in associated with Increased Mortality

Gutierrez et al. Intensive Care Med 10.1007/s00134-013-2937-5

Ratio of first harmonic (H1) to zero frequency (DC) by

Fourier Analysis

Decreased Respiratory Rate Variability during mechanical ventilation in associated with Increased Mortality

Gutierrez et al. Intensive Care Med 10.1007/s00134-013-2937-5

Extracting Features from Data StreamsInput

Bedside monitor: heart rate, respiratory rate, SpO2 and

arterial pressure, CVP, etc.

Electronic health record: medications, co-morbidities, age

Processing

A large number of features is extracted from raw data:

Moving averages (uniform, triangular, exponential), median,

standard deviation, ranges, derivatives, cumulative sums,

hysteresis with various thresholds, quality of signal, MACD

derivatives, calendar events (hours, days, etc.).

Output

Symbolic and scalar time sequences:

~ 7,400 types of secondary observations (features)

per each instance of time and patient

~ 32,300,000 such instances

Building a Model to PredictSubsequent Instability

E19 E18 E17 E16 E15 E14 E13 E12…………E2 Event

ALL NON-EVENT EPOCHS

EARLY NON-EVENT EPOCHS

LATE NON-EVENT EPOCHS

No Event

No Event

Stable Control

Case

Ogundele et al. Am J Respir Crit Care Med 187: A5067, 2013

Building Physiologic Trends

E 14

E 13

E 12

E 11

E 10

E9 E8 E7 E6 E5 E4 E3 E2 E1E 15

E483

E 16

E243

Observation window (up to 8 hours)Trans-epoch variables (5-15-30-60-240-480)

5m15m30m60m

4h

8h

Target Window(Event) Intra-epoch features


Epoch Specific Models for Cases

E…48

E14

E13

E12

E11

E10

E9

E8

E7

E6

E5

E4

E3

E2

E1

E…49…

E14

E13

E12

E11

E10

E9

E8

E7

E6

E5

E4

E3

E2

E1

E…50

E14

E13

E12

E11

E10

E9

E8

E7

E6

E5

E4

E3

E2

E1

E…51

E14

E13

E12

E11

E10

E9

E8

E7

E6

E5

E4

E3

E2

E1

E…48

E14

E13

E12

E11

E10

E9

E8

E7

E6

E5

E4

E3

E2

E1

E…48

E14

E13

E12

E11

E10

E9

E8

E7

E6

E5

E4

E3

E2

E1


Example of Model Predictors across epochs

Epoch HR sdHR hipHR RR regRR acRR SaO2 regSaO2 acSaO2

2 hr sdhr hiphr regrr acrr sao2 regsao2 acsao2

3 hr sdhr hiphr regrr acrr sao2 regsao2 acsao2

4 hr sdhr hiphr regrr acrr regsao2 acsao2

5 hr sdhr hiphr regsao2

6 sdhr hiphr regrr acrr sao2 regsao2 acsao2


8 sdhr hiphr regrr acrr sao2





Heart Rate Variability Index

0244872961201.5

2

2.5

3

3.5

4

4.5

Unstable HRVI

Stable HRVI

Hours from Unstable Event

Var

iabi

lity

Ind

ex


Choosing Appropriate ModelsArea under the ROC Curves

Lasso Regression Ridge Regression

Are

a

Are

a

Prediction Algorithm Before Event Prediction Algorithm Before Event

event event


02448729612012

13

14

15

16

17

18

19

20

21

Stable RRM

Unstable RRM

Hours from Unstable Event

Mea

n R

espi

rato

ry R

ate

Mean Respiratory Rate Historic Trend


Improving Diagnosis Using Machine Learning Modeling Approaches

• Machine learning tools to– Define the relation between physiological variables as

either healthy or not– Identify the “value” of adding additional measures

(monitoring) or extending lead time• Artur Dubrawski, Carnegie Mellon University• Gilles Clermont, University of Pittsburgh

Patterns of Cardiovascular, Humoral and Immunologic Response to Hemorrhagic Shock

30 mmHg

40 mmHg

n = 9

n = 3

Zenker et al. J Trauma 63:573-80, 2007Namas et al. Plus One Medicine 4:e8406, 2009Gomez et al. J Surg Trauma 187:358-69, 2012

Various methods to detect instability

• Anomaly detection• Trigger alerts upon significant departure from the

envelope of expected variability

• Classification• Classify current state of a patient as stable or

unstable, perhaps identify specific type of instability

• Regression• Estimate the magnitude of instability as a function

of the extent of departure from stable behavior

Example of Flow of Machine Learning Analysis

Experimental setup• 40 pigs bleed at 20 ml/min• Random Forest classifier and regressor• 20-fold cross validation• Various groups of vitals, grouped by invasiveness and frequency

of observation:• Intermittent, observed at 5 minute intervals (SysBP, DiaBP, MAP, HR,

PPV, SPV, SVO2)

• Beat-to-Beat (ditto)• Waveform (central venous line allowed)

• Features include:• Common statistical operators (mean, std. dev., trends, etc.)• CVP + Art trackers• FFT projections• Normalization using personal baseline reference

Variability of PCA in Response to Hemorrhage

Video of 20 pig two derived feature PCA changes q1 min

Identify Onset of Bleed (20 ml/min)

Predicted bleeding time (min). The bleeding begins at time = 0.Mathieu Guillame-Bert & Andre Holder

Train a multivariate regressive model (Random Forest)47 pigs Leave one out

Some pigs are more predictable than others. Predicted bleeding of the top 5 most predictable pigs with the lowest mean absolute error.

20 ml/min Bleed All pigs-Leave one out

Predictable behavior(No measurable VS changes

until 5-6 min)

Pre-bleed blood

sampling

Predicted bleeding of the top 5 pigs with the highest mean absolute predicting error.

20 ml/min Bleed All pigs-Leave one out

Less Predictable behavior(No measurable VS changes

until 3-4 min)

20s moving average of pig #57

Anesthesia increased

Evaluation method

Given a threshold

Number of false positives Time until detection

Beginning of bleeding

Evaluation method

Given a threshold



Time until detection

Nu

mb

er o

f fa

lse

pos

itiv

es

Evaluation method

new threshold




Num

ber

of f

alse

pos

itiv

es

last threshold

Evaluation method

new threshold



last threshold


Num

ber

of f

alse

pos

itiv

es

Activity Monitoring Operating

Characteristic (AMOC)

Bleeding detection - AMOC

Bleeding time (min)0 5 10 15 20 25

Tolerance for false alerts – Lockout threshold

Increasing lock out for alerts >5 min does not improve

performance

HR

HRV

MAP

PPV

SVI

SW

Time

Low

Fre

quen

cy

[1/h

eart

beat

]Primary Hemodynamic Data and PCA Reconstruction Error as a useful detection signal for “Not Normal” during hemorrhage & resuscitation

Lik

elih

ood

of

Err

or

Baseline (stable period) Bleed

Volume

Vasopressoradded

PCA Step III: ProjectionDetection of instability induced by bleeding

HR

HRV

MAP

PPV

SVI

SW

• A PCA model is trained from stable period data (green)• It is tested during periods of slow bleeding (blue) and resuscitation

(orange)

Time

Low

Fre

qu

en

cy

[1/h

eart

beat]

affects latency of detection and false alert probability

Changing sensitivity of the obtained detector

HR

Time

Low

Fre

quen

cy

[1/h

eart

beat

]Primary Hemodynamic Data and PCA Reconstruction Error as a useful detection signal for “Not Normal” during hemorrhage & resuscitation

Lik

elih

ood

of

Err

or

Baseline (stable period)

Bleed

Volume

Vasopressoradded

CO

MAP

SvO2

Volume

AMOC: Complete model versus Univariate Measures

• Complete model allows on average 3’40” of latency of detection at the mean time between false alerts of 6 hours

• At the same speed of detection, the best single-vital model triggers false alerts every 5 minutes

Guillame-Bert et al. Intensive Care Med 40:S287, 2014

AMOC of Random Forest prediction of Not-NormalIncreasing sampling frequency and quality improves identification

Group 1: 5 min

Group 2: Beat-to-beat

Group 3:waveform

Reduced informativeness of raw data reduces detection power


How much could we gain by personalizing the models?

Raw CVP

CVP normalized using the individual

pre-bleed data

Impact of personalized baselines

Not using personalized baselines reduces performance

• Especially of models using less frequent observations

Estimating the amount of blood lostideal response for 20

ml/minaverage

prediction for 20 ml/min

averageprediction

for 5 ml/min

averageprediction

for 0 ml/min


Lessons learned: Summary1. Multivariate models can be more

powerful

4. Various types of instability can be identified and characterized

2. Tradeoffs between invasiveness and value of information can be quantified

Group 1: 5 min

Group 2: Beat-to-beat

Group 3:waveform

3. Personalization is promising

Raw data

Normalized data

Can We Predict Who Will Crash If Stressed Beforehand?• Aim:

Use pre-bleed period data to predict whether the pig would eventually crash during bleeding

• Data: • 24 non crash pigs, 4 crash pigs, Beat-to-beat data• 11 vitals (Sys BP, Dia BP, MAP, PP, PPV, SV, SVV,

SPV, PPV, HR, HRV), each mapped onto four simple derived variables (mean, stand deviation, slope, range) 44 features aggregated over disjoint consecutive 20s intervals, every 20s

• This data featurization approach is coarse and it leaves room for potential further improvement

Dubrawski et al. Intensive Care Med 40:S288, 2014

Experiment setup (continued)

• Leave-One-Pair out cross validation:• 24 non-crashes x 4 crashes = 96 (crash)—(non-crash) pairs• We iteratively leave out one such pair as test set, and train ML

model on the rest of the data• Assemble test results separately for crash and non-crash pigs• Compute the average “crash” scores and their confidence limits• Draw temporal plots of those stats over the pre-bleeding period

(truncating a few initial and a few ending minutes because of substantial noise observed in raw data)


Summary of the resultsCrash Prediction Prior to Stress


Log scale ROCs for classifier based on raw score at selected timestamps (5, 10, and 20 minutes)


PITT Index

Probability of Event

Time to event<5 15

Type of Event

CardiacVolume

Vasomotor tone

309060

Predicting

Instability

Time and

Treatment

Severity Index

Future of Monitoring

• Monitor the monitors• Scaling of monitoring devices and sampling

frequency as patient specifics define• Using fused parameters to define stability• Looking at monitors or intermittent direct

patient observation unlikely to improve instability detection but very likely to increase its undetection

Identifying Hemodynamically Unstable Patients

• Phase One currently available– Modified application of VSI input to identify

better who is sick now or about to be sick

• Phase Two coming soon to a health information system near you– Complexity Modeling of Health and Disease– Identify the dynamical expression of

hemodynamic stability, instability and recovery– Define the Architecture of instability– Clarify the true Picture of health

Thank You

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