Preventing Adverse Events: Just try harder?

Transcription

1 OpenPediatrics: Pediatric Critical Care Coming of Age Preventing Adverse Events: Just try harder? Predicting and Preventing - Proof of Concept Peter C. Laussen, MBBS, FRANZCA, FCICM Chief, Department of Critical Care, Hospital for Sick Children Professor of Anaesthesia, University of Toronto

2 Disclosures Tracking, Trajectory and Trigger tool (T 3 ) Co-Developer Data visualization platform Owned by Boston Children s Hospital: Royalty stream Etiometry LLC: License for T 3 Chair, Scientific Advisory Committee: no remuneration

3 Aim: Safe and efficient patient journey Admission Risk Adjustment Disease Procedure Acuity Index Environment Teams Work flow Practice variability Discharge Outcomes Benchmarks Mortality Morbidity Quality of Life Guidelines / Protocols Early Warning Systems Quality Metrics

4 For many clinical domains, high quality evidence is lacking or non-existent Often rely on lower quality evidence, consensus or expert opinion Static documents, often designed to force standardized work; no active learning

5 Decision Support Systems Dynamic Risk Profiles

6 Aim: Safe and efficient patient journey Admission Risk Adjustment Disease Procedure Acuity Index Environment Teams Work flow Physiologic Practice variability Discharge Outcomes Benchmarks Mortality Morbidity Quality of Life Guidelines / Protocols Early Warning Systems Quality Metrics

7 Can Artificial Intelligence in Critical Care help us to be Precise and Predictive? Detection and recognition: Diagnosis Training & validation sets, labelled or unlabelled Pattern and features, Machine / deep learning

8 AI & Critical Care Detection and recognition: Diagnosis Prediction and prevention: precision, personalized Physiologic signals and response to treatment Context, Iterative display Labelling of data / events / capturing decisions: Machine / Deep learning Non-measurable signals: e.g., perfusion Natural language processing Visualization, behaviour

9 Personalized physiology Utilizing continuous physiologic data to describe a physiologic state and predict events within that state Get the data Manage the data See the data Model the data

10 All patients All data Permanently Clinical use Research Sharing Data Data Data Data Data Interactive Usable Platform

11 Server Philips Gateway Server Data Flow CCCU / PICU (42 Beds) 5 second Data Waveform Data Ventilator / other data Role Ethernet Connection Serial Connection to Philips Monitors Hosts HL7 5s device metric data feed HSC EMR Servers Provides HL7 feed of patient lab information Provides ADT database access Server Role T3 Production Short-Term Data Storage Web Interface Hosting T3 Analytics Long-Term Data Storage Analytics Engine Philips Gateway Server Gateway Servers T3 Staging Testing / Evaluation of New T3 Software TSC Server Permanent storage of device metric and waveform storage Analytics Engine Programming Interface Web User Interface Hosting HSC EMR Laboratory Servers Informatio n System 5 Second Data HL7 Via EMR in HL7 Admit Discharge Database Via Oracle Databas e ViN ES Serv er Infusion / other data feeds ViNES T3 Analytic s Server ADT via Oracle Databas e High frequency data Low frequency data (5 second) T3 User Interface Web-Browser T3 Production Software T3 Risk Analytics Software T3 Productio n Server TSC Physiologica l Database TSC Analytics Engine TSC API 5 Secon d Data and Labs Test T3 User Interface Web- Browser Test T3 Productio n Software TSC Web User Interface TSC Server T3 Staging Server ViNES Server Device bridge and data aggregator for waveform and 1 second metric data

12 Courtesy Professor Carolyn McGregor, University of Ontario Institute of Technology

13 Research Clinical

14 Data in motion: Time Series Data Architecture Quality Continuity Research Clinical

15 Our QUANTS of Physiologic data New members embedded in the CV team Mjaye Mazwi MBChB Lead, Computational Physiology and Translational Engineering Danny Eytan MD, Ph.D. Senior Research Associate Neural networks & machine learning Andrew Goodwin B.Eng. Senior data analyst Environmental engineer, Data architect Robert Greer B.Eng. MSc Data Analyst Systems & architecture Sebastian Goodfellow B. Eng, PhD Senior Research Associate Signal processing & machine learning Anirudh Thommandram B.Eng., MSc. Data Analyst, Computer engineer Azadeh Assadi HBSc, BScN, RN(EC), MN-NP(Pediatrics) Computer programing

16 Courtesy Professor Carolyn McGregor, University of Ontario Institute of Technology

17 Data Analysis: Proofs of concept Phenotype: targets and alerts Physiologic state Risk for an event Decision support

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19 Danny Eytan, Andrew J. Goodwin, Robert Greer, Anne-Marie Guerguerian, Peter C. Laussen

20 Proofs of concept: Phenotype Physiologic state Risk for an event Decision support

21 Physiologic state: Probability of Inadequate Oxygen delivery

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23 Proofs of concept: Phenotype Physiologic state Risk for an event Decision support

24 Complex physiologic systems Healthy biologic systems characterized by innate and complex patterns of variability Illness associated with altered variability and reduced complexity, reduced adaptability HRV (R-R interval), RRV: ETCO2, respiratory impedance monitoring

25 CIMVA Universal TM - Continuous Individual Multi-organ Variability Analysis - Frequency-variability analysis (particularly HRV and RRV) & relationship to clinical events (infection, extubation failure) - Waveform analysis, loss of variability

26 WAVE index: Weaning and Variability Evaluation

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28 COMPUTING IN CARDIOLOGY September 24-27, 2017 Rennes, France Classification of Atrial Fibrillation Using Multidisciplinary Features and Gradient Boosting. Andrew Goodwin, Sebastian Goodfellow*, Robert Greer, Mjaye Mazwi, Peter Laussen, Danny Eytan.

29 Proofs of concept: Phenotype Physiologic state Risk for an event Decision support

30 Challenges in Cardiac Critical Care 1. Define modifiable risk in critical care Our contribution to outcome Targets for improvement 2. Performance metrics Failure to predict

31 Preventing Adverse Events: Just try harder? No, think different Fundamentally these are engineering problems Data science & Human interactions Proofs of concept: Describe plausibility & understand uncertainty AI & data science will augment our decision making