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) Truly blessed by collaboration with Geoff Manley, Prof of Neurology and Neurosurgery at UCSF and Chief of Neurotrauma, SFGH I’m an AI researcher and also chair of CS; excuse biomedical ignorance

This is what an ICU looks like: ventilator, fluids, monitors; ~200 medical procedures per day, many potentially fatal

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)

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) DBN technology =~ stochastic differential equations x discrete Markov chains (factored)

Human physiology v0.1

Brain Neurotransmitters Heart Vasculature Blood flow 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 Vasculature Blood flow Medullary cardiovascular center Medullary cardiovascular center Cardiac parasympathetic output Cardiac sympathetic output Cardiac parasympathetic output Cardiac sympathetic output Card. M2 Card. β1 Card. β2 Vasc. α1 Vasc. α2 Vasc. β2 Card. M2 Card. β1 Card. β2 Vasc. α1 Vasc. α2 Vasc. β2 Heart rate Cardiac contrac-tility Venous tone Arterio-lar tone Heart rate Cardiac contrac-tility Venous tone Arterio-lar tone Cardiac preload Capillary pressure Cardiac preload Capillary pressure Cardiac stroke volume Cardiac stroke volume Example doesn’t show: patient-specific parameter variables (eg blood volume), Cardiac output Vascular resistance Cardiac output Vascular resistance Mean arterial blood pressure Mean arterial blood pressure Barorecep-tor discharge Barorecep-tor discharge

Medullary cardiovascular center Medullary cardiovascular center Setpoint inputs from ANS, CNS, intracranial, blood Setpoint inputs from ANS, CNS, intracranial, blood Medullary cardiovascular center Medullary cardiovascular center Cardiac parasympathetic output Cardiac sympathetic output Cardiac parasympathetic output Cardiac sympathetic output Card. M2 Card. β1 Card. β2 Vasc. α1 Vasc. α2 Vasc. β2 Card. M2 Card. β1 Card. β2 Vasc. α1 Vasc. α2 Vasc. β2 Heart rate Cardiac contrac-tility Venous tone Blood [volume] Pulm. [intra-thoracic press.] Arterio-lar tone Heart rate Cardiac contrac-tility Venous tone Blood [volume] Pulm. [intra-thoracic press.] Arterio-lar tone Cardiac preload Capillary pressure Cardiac preload Capillary pressure Cardiac stroke volume Cardiac stroke volume Blood [transu-dation] Blood [transu-dation] Cardiac output Vascular resistance Cardiac output Vascular resistance Mean arterial blood pressure Mean arterial blood pressure Intracranial physiology Tissues-NOS [perfusion] GI/Liver [perfusion] Barorecep-tor discharge Intracranial physiology Tissues-NOS [perfusion] GI/Liver [perfusion] Barorecep-tor discharge

Medullary cardiovascular center Medullary cardiovascular center Setpoint inputs from ANS, CNS, intracranial, blood Setpoint inputs from ANS, CNS, intracranial, blood Medullary cardiovascular center Medullary cardiovascular center Cardiac parasympathetic output Cardiac sympathetic output Cardiac parasympathetic output Cardiac sympathetic output Card. M2 Card. β1 Card. β2 Vasc. α1 Vasc. α2 Vasc. β2 Card. M2 Card. β1 Card. β2 Vasc. α1 Vasc. α2 Vasc. β2 Heart rate Cardiac contrac-tility Venous tone Blood [volume] Pulm. [intra-thoracic press.] Arterio-lar tone Heart rate Cardiac contrac-tility Venous tone Blood [volume] Pulm. [intra-thoracic press.] Arterio-lar tone Cardiac preload Capillary pressure Cardiac preload Capillary pressure Heart rate sensor model Heart rate sensor model Cardiac stroke volume Cardiac stroke volume Central venous pressure sensor model Blood [transu-dation] Central venous pressure sensor model Blood [transu-dation] Cardiac output Vascular resistance Cardiac output Vascular resistance Mean arterial blood pressure Mean arterial blood pressure MAP sensor model MAP sensor model Intracranial physiology Tissues-NOS [perfusion] GI/Liver [perfusion] Barorecep-tor discharge Intracranial physiology Tissues-NOS [perfusion] GI/Liver [perfusion] Barorecep-tor discharge

Medullary cardiovascular center Medullary cardiovascular center Setpoint inputs from ANS, CNS, intracranial, blood Setpoint inputs from ANS, CNS, intracranial, blood Medullary cardiovascular center Medullary cardiovascular center PK [conc. of phenyl-ephrine] PK [conc. of phenyl-ephrine] Cardiac parasympathetic output Cardiac sympathetic output Cardiac parasympathetic output Cardiac sympathetic output Card. M2 Card. β1 Card. β2 Vasc. α1 Vasc. α2 Vasc. β2 Card. M2 Card. β1 Card. β2 Vasc. α1 Vasc. α2 Vasc. β2 Heart rate Cardiac contrac-tility Venous tone Blood [volume] Pulm. [intra-thoracic press.] Arterio-lar tone Heart rate Cardiac contrac-tility Venous tone Blood [volume] Pulm. [intra-thoracic press.] Arterio-lar tone Cardiac preload Capillary pressure Cardiac preload Capillary pressure Heart rate sensor model Heart rate sensor model Cardiac stroke volume Cardiac stroke volume Central venous pressure sensor model Blood [transu-dation] Central venous pressure sensor model Blood [transu-dation] Cardiac output Vascular resistance Cardiac output Vascular resistance Mean arterial blood pressure Mean arterial blood pressure MAP sensor model MAP sensor model Intracranial physiology Tissues-NOS [perfusion] GI/Liver [perfusion] Barorecep-tor discharge Intracranial physiology Tissues-NOS [perfusion] GI/Liver [perfusion] Barorecep-tor discharge

Real data are messy

A DBN that reflects that model, along with probabilistic occurrence and blood-pressure transition models 12

Maybe another from Diane? 13

ALARM 2 days, 19 false alarms, 7 true alarms, 1 false+true; all correct 14

Zoomed in, filtered. When done, segue: when does this do poorly Zoomed in, filtered. When done, segue: when does this do poorly? When blood pressure changes quickly – because the blood pressure transition doesn’t allow for it (and if I make it broader, artifacts aren’t detected). System needs more knowledge of blood pressure physiology (and some redundant data, say from heart rate, would be useful too). How to do this? 15

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