Re-Engineering Critical Care:Precision physiology

Peter C. Laussen MB.BS., FANZCA, FCICM

Department Critical Care Medicine

Hospital for Sick Children

Toronto

Disclosures• Tracking, Trajectory and Trigger tool (T3)

– Co-Developer

– Data visualization platform

– Owned by Boston Children’s Hospital: Royalty stream

• Etiometry LLC:

– License for T3

– Scientific Advisory Committee: no remuneration

Engineering critical care

• Critical care is at the intersection of

technology and human behaviors

• Engineers embrace uncertainty

• New era of data utilization & knowledge

acquisition

Assisted Augmented Automated

Perhaps TrustedExplainable

Stages of Artificial Intelligence:Engineering and human interaction

Department Critical Care Medicine

• 2 divisions: PICU / CCCU + intra & extramural RRT

• 42 beds (36 funded) + 5 satellite (NICU)

• 2200 admissions / 14,500 patient days

• 17.3 FTE staff / 22 fellows including 4 CaMRS residents

• Education focus; Immersive Reality

• Research focus: 5 labs, 26 support staff, >$5m funding

Critical care landscape

Resource intense & costly:

~ 14% of hospital costs (USA)

~ 4 % of national health expenditures (~$80 billion)

- Increasing ICU bed numbers & occupancy

Challenges in Pediatric Critical Care

1. Define modifiable risk in critical care

–Our contribution to outcome

2. Manage uncertainty

–Failure to “predict”

Aim: Safe and efficient patient journey

Admission Discharge

Guidelines / Protocols

Early Warning Systems

Quality Metrics

Outcomes

Benchmarks

Mortality

Morbidity

Quality of Life

Risk Adjustment

Disease

Procedure

Acuity Index

Environment

Teams

Work flow

Practice variability

Vo

lum

e

Outcome

Low uncertainty / complexity

Standard Practices

Little adaption

Shift clinical behavior

Higher uncertainty / complexity

Predictive Practices

Adaption

Understand deviation

Aim: Safe and efficient patient journey

Admission Discharge

Guidelines / Protocols

Early Warning Systems

Quality Metrics

Outcomes

Benchmarks

Mortality

Morbidity

Quality of Life

Risk Adjustment

Disease

Procedure

Acuity Index

Environment

Teams

Work flow

Practice variability Physiologic variability

Population-based & Personalized physiology

Utilizing continuous physiologic data to describe a physiologic state and predict events within that state

sharing ideas

Lessons learned from high risk industries

• Johannesburg March 8-9, 2018

• London (UK) June 7-8, 2018

• Toronto November 5-6, 2018

www.risky-business.com

Monitoring & display

Periodic

Transfer

Philips Network Gateway

Server

Hospital Network: “Source of

Truth”

Usual data flow

Data

Data

Data

Data

Data

All patientsAll data

Permanently

Clinical useResearch

Training / labelling

InteractiveUsable

Platform

Augment decision making

SickKids unique differences about data science and utilizing continuous physiologic signals at

• Patient own the data:

–Permanent storage

• Understand clinical time-series data:

–Translatable

• Point-of-care:–Usability and functionality

Laboratory Information

System

T3 Production Software

Test T3 Production Software

T3 User Interface

Web-Browser

Data FlowCCU

April 2013

Test T3 User Interface Web-

Browser5 second

Data

CCCU / PICU(42 Beds)

Waveform Data

Ventilator / other data

Via EMR in HL7

Admit Discharge Database

Via Oracle Database

T3 Production

Server

T3 Analytics

Server

HSC EMRServers

T3 Risk Analytics Software

Serial Connection to Philips Monitors

Ethernet Connection

Philips Gateway

Server

5 Second Data HL7

GatewayServers

Server Role

T3 Production • Short-Term Data Storage• Web Interface Hosting

T3 Analytics • Long-Term Data Storage• Analytics Engine

T3 Staging • Testing / Evaluation of New T3 Software

T3 Staging Server

Server Role

Philips Gateway Server

• Hosts HL7 5s device metric data feed

HSC EMR Servers • Provides HL7 feed of patient lab information

• Provides ADT database access

5 second intervals / digitalVisualization & hosting platform

Laboratory Information

System

T3 Production Software

Test T3 Production Software

T3 User Interface Web-

Browser

AtriumDBPhysiological

DatabaseViNES

Data FlowTest T3 User

Interface Web-Browser

Infusion / other data

feeds

5 second Data

CCCU / PICU(42 Beds)

Waveform / 1 Second Data

Ventilator / other data

5 Second Data and Labs

AtriumDBAnalytics Engine

AtriumDBWeb User Interface

AtriumDBAPI

Via EMR in HL7

Admit Discharge Database

Via Oracle Database

T3 Production

Server

T3 Analytics

Server

ViNES Server

HSC EMRServers

T3 Risk Analytics Software

ADT via Oracle

Database

Serial Connection to Philips Monitors

Ethernet Connection

Philips Gateway

Server

5 Second Data HL7

GatewayServers

Server Role

T3 Production • Short-Term Data Storage

• Web Interface Hosting

T3 Analytics • Long-Term Data Storage

• Analytics Engine

T3 Staging • Testing / Evaluation of New

T3 Software

ViNES Server • Device bridge and data

aggregator for waveform and

1 second metric data

T3 Staging Server

Server Role

Philips

Gateway

Server

• Hosts HL7 5s device metric

data feed

HSC EMR

Servers

• Provides HL7 feed of patient

lab information

• Provides ADT database

access

Lower frequency data (5 second)

High frequency data

HPC4Health

Sick Kids

Research Institute

High Speed Private Physical Connection

Server Role

AtriumDB • Permanent storage of device

metric and waveform storage

• Analytics Engine

• Programming Interface

• Web User Interface Hosting

HPC4Heatlh • Large Scale Compute Capability

• Secure Access to Data for

External Collaborators

Physiologic engineering:

Utilizing continuously streaming data

Problem

• Data in motion: Time-series

• Messy data

• Physiologic states are variable & inter-dependent (coupling)

• Uncertainty of signals (the V’s)

• Acquisition, structuring of data poses barriers: I/O bound

Advantage

• Understanding the physiologic state underpins our management in critical care

• New insights & knowledge: matching phenotype with genomics, pharmacogenomics (+)

• Prediction: Risk-based (events) & State-spaced monitoring

Clinical

Research

Quality Continuity

Clinical

Research

Data in motion: Time Series Data

Connected & accurate

QualityArchitecture Continuity

Clinical

Research

Data in motion: Time Series Data

Connected & accurate

AtriumDB: Data Management System

Signal generation & processing

Signal Quality Index

Measured coefficient of variance

Application interface(s)

Time Series Compression

Adaptive compression and file index

Efficient storage, no pre-suppositionsAccessible: fast and structured

Analysis ready

Time-series data: data in motionIn / Out bound: bottlenecks

Outbound:

Analysis ready

Old state: days-months.

New state: minutes / seconds

=> Point of care

Structure the haystack

Inbound:

QualityArchitecture Continuity

Clinical

Research

Representation

State-based monitoring

State-based monitoring

Clinical utility:Guide immediate clinical managementUnderstand and review critical events

Hypotension and bradycardia

Cardiac arrest

Epinephrine administered

ROSC

Measurement of more subtle phenomena

• “Hidden variables”- things not easily measured directly at bedside (variability measures, SVR, oxygenation parameters, autoregulation)

Hering-Traube Mayer waves

Physiologic state: Probability of Inadequate Oxygen delivery

Model Based Risk Assessment: understand the evolving state to inform management

Stable Low DO2

Inadequate DO2

LV Dysfunction

Low Cardiac OutputGrey Box Model

“mechanistic”

Prediction

Data

Utilizing a dynamic Bayesian network to establish estimators and probability of an evolving physiologic state

Confidential and Proprietary 37

Grounded | Refinable | Scalable

Confidential and Proprietary 38

Normal or stable, compensated state

Anemia

Low cardiac output

Death

MorbiditiesDisease process

Resource utilization

Hemodynamics

Respiration

Low minute ventilation

Anatomic/physiologic shunt

Dead space

Respiratory failure• Hypoxemia• Hypercapnia

Disease process

Organ system failure• Cardiac arrest• Respiratory arrest• etc.

Tracked etiologies/condition markers OutcomesHarms

Low cardiac output

Hypovolemia

LV dysfunction

RV dysfunctioniNO ?

Inotropes?

Fluid ?

Inadequate DO2• Global/regional• Tissue level

RBC ?

Development/Test Set (Neonates and Infants)

780 patients (242 neonates and 538 infants who

underwent a surgery involving Cardio-pulmonary bypass).

Boston Children’s Hospital Cardiac Intensive Care Unit.

The data sources :

a. Physiologic data streams acquired by recording

HL7 feeds from bedside monitors

b. Electronic Medical Record,

c. STS Congenital Heart Surgery Database

ExclusionMissing EMR or Physiologic data.Premature & < 2 kgCPR, ECMO, Cardiac Arrest or death

Validation Set (Neonates, Infants, and Children)

1502 patients (131 neonates, 557 infants, 814 children).

Boston Children’s Hospital, Toronto Sick Kids Hospital

Children’s National Medical Center Hospital

IDO2 (Inadequate oxygen delivery index): likelihood that the patient is experiencing inadequate oxygen delivery, defined as mixed venous oxygen

saturation (SvO2) less than 40%. Range: 0 and 100.

Full Dataset - includes the full set of streamed

physiologic measures utilized by the IDO2

software, as well as all available labs utilized by

the software including venous blood gases.

Medium Dataset – includes all data in the Full

dataset except for venous blood gases.

Minimum Dataset – includes only the minimum measurements required to calculate the IDO2 index: HR every 60 seconds, and SpO2 and BP every 10 minutes.

An approach to personalizing physiologyIdentifying normative ranges for abnormal states stratifying by admission diagnosis

Eytan et al. PCCM 2017

Classification of Atrial Fibrillation Using Multidisciplinary Features and Gradient Boosting.

Sebastian Goodfellow, Andrew Goodwin, Robert Greer, Mjaye Mazwi, Peter Laussen, Danny Eytan.

COMPUTING IN

CARDIOLOGY September 24-27, 2017 Rennes, France

Select Current Projects:Clinical Data Science

• T3Bi – assessing ability of high resolution data to characterize compliance and predict patient outcome

• Cardiac arrest prediction• CLABSI prediction• Prediction of unplanned

extubation• Quantitative and qualitative

assessment of cardiopulmonary interactions

• Defining myocardial viability thresholds

• Automated heart rhythm labelling

• Neuro cardiac coupling - Seizure prediction from EKG

Translational Clinical Engineering

• Standardized patient monitoring bundles

• Dynamic Unit acuity mapping• Assessing bias and imprecision

associated with the concept of “time” in critical care.

• Developing signal quality indexes• Data error quantification, imputation

and extrapolation• Signal Drift - Assessing synchronicity in

waveform time series data• My Heart Pass – personalized,

physiology guided resuscitation

Infrastructure

• Development of AtriumDB

Internal :Biomedical Engineering and IT / EMR

Center for Computational Medicine (RI):Anna Goldenberg Lab

Computational Biomed program (Rogers)Digital Health platformUHN, HSCCritical Care: Fall 2018

Cardiovascular Data Management Center:Cedric ManlhoitAnalytics & complex AI

Internal and External linkages

External:Technion (Israel): AITennessee: AIUOITIndustry

Player decides and makes the move

Deep Blue, Chess & ICU

“Don’t fear intelligent machines….work with them”

Kasparov 2017

Experience & Knowledge

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