Michael R. Pinsky, M.D., C.M., Dr.h.c., FCCP, MCCMProfessor of Critical Care Medicine, Bioengineering, Anesthesiology, Cardiovascular Diseases, and Clinical & Translational Sciences, Vice

Chair for Academic Affairs, University of Pittsburgh

Using Machine-Learning

Principals to Predict the

Future in Acute Care

Medicine

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

University of Pittsburgh

Systems Issues in Critical Illness

• Disease phenotype is a mixture of the process

and the hosts response to the process

• No two people are alike

• Healthcare systems are inherently complex

and inefficient

• Patient safety alerts often artifacts leading to

alarm fatigue and failure to rescue

• Goals of therapy are often unknown and

commonly change

A Modest Proposal

Make the patient the center

Personalized Medicine

• 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

How do we identify patterns of

health and disease?

Music Name that tune

Which instruments to listen to

(number of independent sensors)

How long to listen

(lead time)

Sampling frequency

(once a second, one a year)

Sometimes it is easy to predict

the future

Dynamic Changes

Box & Jenkins, 1976, p. 531

Situational Awareness

Variability is often a sign of health

It allows for adaptation

Loss of variability occurs with stress

Heart Rate Variability Indicies Predict Instability

1.5

2

2.5

3

3.5

4

4.5

024487296120

Heart

Rate

Va

riab

ilit

y I

nd

ex

Hours from Unstable Event

Unstable HRVI

Stable HRVI

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

12

13

14

15

16

17

18

19

20

21

22

024487296120

Mean

Res

pir

ato

ry R

ate

Hours from Unstable Event

Stable RRM

Unstable RRM

Mean Respiratory Rate Does Not

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

Increasing Dimensionality

Improves Recognition

Faces

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 Artificial Neural Networkapproach to create a probabilistic equation:

Fused parameter VSI

• In a dynamic field of continuously changing but inter-related variables, Machine Learningdata-driven classification techniques:Principal Component Analysis, Support Vector Machines, K Nearest Neighbors, Random Forests, Naïve Bayesian Classifier

VSI

HR

RR

SpO2

BP

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

Completely normal values

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

Vital Sign Trends Over 8 Hours in a SDU Patient

Mean time from 1st alert to Code = 6.3h

Of 40 patients who met CODE criteria

only 7 had codes called

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

ati

en

ts i

n P

ha

se

Phase1

Phase 3

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

Cut total time alarms sounding >50%

Duration Patients in MET State

(for those who experienced it)

0

5

10

15

20

25

30

35

40

45

METall METmin METfull

Mea

n M

inu

tes

Phase 1

Phase 3

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

Alarm Fatigue: Is the Alert Real, Real

Important or Artifacts

• ECRI Institute’s Top Patient Safety Concern:

Health Data Integrity Failures

Using Machine Learning we could discriminate real

from artifact SpO2, RR & HR alerts >90% of the time

Hravnak et al. Crit Care Med 42:42, 2015

Health

Normal Homeostasis

Dis

ease

Sev

erit

y

Ad

apti

ve-

Mal

adap

tive

Res

pon

ses

Present Clinical

Threshold of Detection

Potential Threshold of

Pathologic Stress Detection

Threshold of Stress

Time

Early Detection of Disease Model

Identifying Hemodynamically

Unstable Patients

• What is the minimal data set needed to

predict instability: Monitoring parsimony

– Number of independent monitoring variables

– Lead time

– Sampling frequency

• What additional information will improve

specificity

• Monitoring response to therapy and define

end-points of resuscitation

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

Pinsky & Dubrawski. AJRCCM 190: 606-10, 2014

Identify Onset of Bleeding Earlier Train a multivariate regressive

model (Random Forest)

Leave one out

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

N=47

Slide 19 Copyright © 2015 CMU Auton Lab

Balancing False Alert Rates with early Bleeding detectionComplete multivariate model vs. univariate detectors

This way towards the ideal performance

Individual vital signs

Holder et al. J Crit Care doi.org/10.1016/j.jcrc2013.07.028, 2013

Bleeding rate20 ml/min

Detect 3’ 40”False alerts 6 hr

Detect 3’ 40”False alerts 5 min

Slide 20 Copyright © 2015 CMU Auton Lab

Density of Data Affects Bleeding DetectionMultivariate models for various groups of measurements

[Sampled every 5 minutes]

[Sampled every 20 seconds]

[CVP + Arterial Pressure, FFT features, CVP, normalized using personalized baselines]

Holder et al. J Crit Care doi.org/10.1016/j.jcrc2013.07.028, 2013

Slide 21 Copyright © 2015 CMU Auton Lab

Knowing Baseline Important if Data SparsePersonalized baselines

Holder et al. J Crit Care doi.org/10.1016/j.jcrc2013.07.028, 2013

PITT Index

Probability of Event

Time to event<5 15

Type of Event

Cardiac

Volume

Vasomotor tone

VSI Index

3090

60

Predicting

Instability

Time and

Treatment

Severity Index

Future of Monitoring

• Monitor the monitors

• Treat the patients

• Robust artifact detection increases value of alerts

and reduces alarm fatigue

• Using fused parameters to define stability

• Scaling of monitoring devices and sampling

frequency will vary as patient conditions change

Thank YouCo-Investigators:

Artur Dubrawski, PhD

Auton Lab

Gilles Clermont, MD MS

Marilyn Hravnak, BSN, PhD