J. Randall Moorman, M.D.Professor of Medicine, Physiology, and Biomedical Engineering,

University of Virginia

Predictive monitoring for early detection of subacute, potentially catastrophic

illnesses

On behalf of many:J Randall Moorman, MD

Professor of Medicine, Physiology, BMEUniversity of Virginia

CMO, Adult Medical Predictive Devices, Diagnostics, DisplaysShareholder, Medical Predictive Science Corporation

Vice-president, Society for Complex Acute IllnessEditor-in-chief, Physiological Measurement

Predictive monitoring@virginia.edu

Randall Moorman, M.D.*

Doug Lake, Ph.D.*

Karen Fairchild, M.D. John Kattwinkel, M.D.

Forrest Calland, M.D.

Travis Moss, M.D. Kyle Enfield, M.D.

Robert Sinkin, M.D.

George Stukenborg, Ph.D. Chris Moore, M.D.

Matthew Clark, Ph.D.*

Laura Barnes, Ph.D. John Delos, Ph.D.

Christian Rieser, Ph.D.

Milan, Columbia, MGH, Emory, UCSF, MITRE …

Mary Mohr (Ph.D. 2015)

Marta Carrara, M.S.

Luca Carozzi, M.S.

Marco DePasquale (M.S. 2015)

Nate Ivanek, M.D.

Diana Gomez, M.D.

Eric Holland, M.D.

Rich Kronfol, M.D.

Brynne Sullivan, M.D.

Manisha Patel, M.D.

Christina McClure (M.D. 2015)

Abel David (M.D. 2018)

Caroline Ruminski (M.D. 2018)

Blee Moffett, RN

Cathy Horton, RN

*Equity, consulting, employment: MPSC, AMP3D, Cville

Mission

• Save lives

• Multicenter development and validation of predictive algorithms

Precepts

• Apparently sudden critical illness can have sub-clinical prodromes with illness signatures

• Diagnosis and treatment at this early stage can save lives

Is this baby septic?We developed a predictive monitor for early detection

Who is sick in my NICU?

Mortality reduced 10.2% 8.1% with display of predictive monitor

One extra survivor per 48 VLBW, 23 ELBW, and

12 septic infants monitored

Predictive monitoring

Conventional monitoring

Predictive monitoring in adults

• 3 subacute potentially catastrophic illnesses:• Sepsis (>3X ICU stay, >3X mortality)

• Bleeding leading to large transfusion (>3X, >3X)

• Lung failure leading to urgent unplanned intubation (>4X, >4X)

• 2 ICUs: Medical and Surgical/Trauma/Burn

• >7000 patients with >1000 events

• >70TB data

• Method: Big Data, high performance computing

• Result: efficient algorithms deployed by low tech

Sepsis Bleeding Lungfailure

Death

Big Data reveals illness signatures

Sepsis Bleeding Lungfailure

Death

UnitROC AUC# events

Big Data reveals illness signatures

Who’s sick in my Surgery ICU?

BP

T

HRO2

RR

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Relevance to healthcare in the US?

We envision a stable framework for a national multicenter

collaborative effort to develop predictive monitoring and other

clinical analytics tools we can all use to save lives with.