Computational Physiology for Critical Care Monitoring

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January 18, 2008 Computational Physiology for Critical Care Monitoring Stuart Russell, UC Berkeley Stuart Russell, UC Berkeley Joint work with Joint work with Geoff Manley Geoff Manley , Mitch Cohen, Kristan , Mitch Cohen, Kristan Staudenmayer, Diane Morabito (UCSF), Norm Aleks, Nimar Staudenmayer, Diane Morabito (UCSF), Norm Aleks, Nimar Arora, Shaunak Chatterjee (UCB) Arora, Shaunak Chatterjee (UCB)

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Computational Physiology for Critical Care Monitoring. Stuart Russell, UC Berkeley Joint work with Geoff Manley , Mitch Cohen, Kristan Staudenmayer, Diane Morabito (UCSF), Norm Aleks, Nimar Arora, Shaunak Chatterjee (UCB). $300B/yr in US, high morbidity/mortality - PowerPoint PPT Presentation

Transcript of Computational Physiology for Critical Care Monitoring

January 18, 2008

Computational Physiology for Critical Care Monitoring

Stuart Russell, UC BerkeleyStuart Russell, UC BerkeleyJoint work with Joint work with Geoff ManleyGeoff Manley, Mitch Cohen, Kristan Staudenmayer, Diane , Mitch Cohen, Kristan Staudenmayer, Diane Morabito (UCSF), Norm Aleks, Nimar Arora, Shaunak Chatterjee (UCB)Morabito (UCSF), Norm Aleks, Nimar Arora, Shaunak Chatterjee (UCB)

January 18, 2008

January 18, 2008

Critical care $300B/yr in US, high morbidity/mortality

Goal: improve outcomes, reduce length of stay, do science

Approach: Large-scale data repository for worldwide research use

Currently 60GB, 16 ICU beds monitored 24/7, soon multi-institutional First release any day now ….

Data mining for outcome prediction, early warning, etc. Real-time model-based estimation of patient state (And systems physiology model-building)

January 18, 2008

Critical care state estimation Given

~140 initial presentation fields ~40 real-time sensor streams ~1500 asynchronous measures (blood, drugs, etc.)

Compute posterior probability distribution for ~100 (patho)physiological state variables

Method Patient-adaptive dynamic Bayesian network (DBN): stochastic models of physiology and sensor dynamics (c.f. Guyton et al., 1972, 354-variable nonlinear ODE)

Flexible across time scales, models, sensors (images, text, etc.) Can incorporate genetic factors (observed or unobserved)

January 18, 2008

Human physiology v0.1

January 18, 2008

Medullary cardiovascular center

Cardiac parasympathetic output

Cardiac sympathetic output

Card. M2 Card. β1 Card. β2 Vasc. α1 Vasc. α2 Vasc. β2

Heart rate

Cardiac contrac-

tility

Venous tone

Arterio-lar tone

Cardiac preload

Capillary pressure

Cardiac stroke

volume

Cardiac output

Vascular resistance

Mean arterial blood

pressure

Barorecep-tor

discharge

Brain

Neurotransmitters

Heart

Blood flow

Vasculature

January 18, 2008

Medullary cardiovascular center

Cardiac parasympathetic output

Cardiac sympathetic output

Card. M2 Card. β1 Card. β2 Vasc. α1 Vasc. α2 Vasc. β2

Heart rate

Cardiac contrac-

tility

Venous tone

Arterio-lar tone

Cardiac preload

Capillary pressure

Cardiac stroke

volume

Cardiac output

Vascular resistance

Mean arterial blood

pressure

Barorecep-tor

discharge

Medullary cardiovascular center

Cardiac parasympathetic output

Cardiac sympathetic output

Card. M2 Card. β1 Card. β2 Vasc. α1 Vasc. α2 Vasc. β2

Heart rate

Cardiac contrac-

tility

Venous tone

Arterio-lar tone

Cardiac preload

Capillary pressure

Cardiac stroke

volume

Cardiac output

Vascular resistance

Mean arterial blood

pressure

Barorecep-tor

discharge

Brain

Neurotransmitters

Heart

Blood flow

Vasculature

January 18, 2008

Medullary cardiovascular center

Cardiac parasympathetic output

Cardiac sympathetic output

Card. M2 Card. β1 Card. β2 Vasc. α1 Vasc. α2 Vasc. β2

Heart rate

Cardiac contrac-

tility

Venous tone

Blood [volume]

Arterio-lar tone

Cardiac preload

Capillary pressure

Cardiac stroke

volume

Cardiac output

Vascular resistance

Mean arterial blood

pressure

Barorecep-tor

discharge

Intracranial physiology

Tissues-NOS [perfusion]

GI/Liver [perfusion]

Blood [transu-dation]

Setpoint inputs from ANS, CNS,

intracranial, blood

Pulm. [intra-

thoracic press.]

Medullary cardiovascular center

Cardiac parasympathetic output

Cardiac sympathetic output

Card. M2 Card. β1 Card. β2 Vasc. α1 Vasc. α2 Vasc. β2

Heart rate

Cardiac contrac-

tility

Venous tone

Blood [volume]

Arterio-lar tone

Cardiac preload

Capillary pressure

Cardiac stroke

volume

Cardiac output

Vascular resistance

Mean arterial blood

pressure

Barorecep-tor

discharge

Intracranial physiology

Tissues-NOS [perfusion]

GI/Liver [perfusion]

Blood [transu-dation]

Setpoint inputs from ANS, CNS,

intracranial, blood

Pulm. [intra-

thoracic press.]

January 18, 2008

Medullary cardiovascular center

Cardiac parasympathetic output

Cardiac sympathetic output

Card. M2 Card. β1 Card. β2 Vasc. α1 Vasc. α2 Vasc. β2

Heart rate

Cardiac contrac-

tility

Venous tone

Blood [volume]

Arterio-lar tone

Cardiac preload

Capillary pressure

Cardiac stroke

volume

Cardiac output

Vascular resistance

Mean arterial blood

pressure

Barorecep-tor

discharge

Intracranial physiology

Tissues-NOS [perfusion]

MAP sensor model

GI/Liver [perfusion]

Heart rate sensor model

Central venous

pressure sensor model

Blood [transu-dation]

Setpoint inputs from ANS, CNS,

intracranial, blood

Pulm. [intra-

thoracic press.]

Medullary cardiovascular center

Cardiac parasympathetic output

Cardiac sympathetic output

Card. M2 Card. β1 Card. β2 Vasc. α1 Vasc. α2 Vasc. β2

Heart rate

Cardiac contrac-

tility

Venous tone

Blood [volume]

Arterio-lar tone

Cardiac preload

Capillary pressure

Cardiac stroke

volume

Cardiac output

Vascular resistance

Mean arterial blood

pressure

Barorecep-tor

discharge

Intracranial physiology

Tissues-NOS [perfusion]

MAP sensor model

GI/Liver [perfusion]

Heart rate sensor model

Central venous

pressure sensor model

Blood [transu-dation]

Setpoint inputs from ANS, CNS,

intracranial, blood

Pulm. [intra-

thoracic press.]

January 18, 2008

Medullary cardiovascular center

Cardiac parasympathetic output

Cardiac sympathetic output

Card. M2 Card. β1 Card. β2 Vasc. α1 Vasc. α2 Vasc. β2

Heart rate

Cardiac contrac-

tility

Venous tone

Blood [volume]

Arterio-lar tone

Cardiac preload

Capillary pressure

Cardiac stroke

volume

Cardiac output

Vascular resistance

Mean arterial blood

pressure

Barorecep-tor

discharge

Intracranial physiology

Tissues-NOS [perfusion]

MAP sensor model

GI/Liver [perfusion]

Heart rate sensor model

Central venous

pressure sensor model

PK [conc. of phenyl-

ephrine]

Blood [transu-dation]

Setpoint inputs from ANS, CNS,

intracranial, blood

Pulm. [intra-

thoracic press.]

Medullary cardiovascular center

Cardiac parasympathetic output

Cardiac sympathetic output

Card. M2 Card. β1 Card. β2 Vasc. α1 Vasc. α2 Vasc. β2

Heart rate

Cardiac contrac-

tility

Venous tone

Blood [volume]

Arterio-lar tone

Cardiac preload

Capillary pressure

Cardiac stroke

volume

Cardiac output

Vascular resistance

Mean arterial blood

pressure

Barorecep-tor

discharge

Intracranial physiology

Tissues-NOS [perfusion]

MAP sensor model

GI/Liver [perfusion]

Heart rate sensor model

Central venous

pressure sensor model

PK [conc. of phenyl-

ephrine]

Blood [transu-dation]

Setpoint inputs from ANS, CNS,

intracranial, blood

Pulm. [intra-

thoracic press.]

January 18, 2008

Real data are messy

January 18, 2008

January 18, 2008

January 18, 2008

ALARM

January 18, 2008

January 18, 2008

Next Steps

Reduce ICU false alarms from >90% to <5%

Demonstrate clinically relevant inferences, e.g., Vascular stiffness Erroneous drug administration Pulmonary artery pressure (w/o catheter!)

Extend physiology model to all major systems

Multiscale: connect physiology to molecules