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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
LE
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