Getting the Most from Clinical Data through Physiological Modelling & Medical Decision Support

Bram Smith

Stephen Rees, Toke Christensen,

Dan Karbing, Steen Andreassen

Center for Model-based Medical Decision Support, Aalborg University, Denmark

Introduction

EXISTING TECHNOLOGY

• Clinical databases allowing easy, automated storage and retrieval of patient data.

• Medical equipment allowing data collection on a PC.

• Physiological models and decision support systems.

BUT:

• Doctors are still faced with interpreting large amounts of data to diagnose patients.

PROPOSED SOLUTION:

• An architecture that combines existing database technology with physiological models and decision support algorithms to assist clinicians in diagnosing and treating patients.

Systems architecture

• Clients on the architecture can be divided into 3 types:

– Inputs – User inputs, or data taken automatically from medical equipment.

– Interpretation – Uses equations and physiological models to expand knowledge about the patient.

– Decision support – Uses decision support algorithms to assisting in choosing suitable treatment strategies.

DatabaseDatabase

InputsVentilator (Paw, Flow,…)Gas analysis (O2, CO2)Clinical monitor (ECG, HR,…)

InputsVentilator (Paw, Flow,…)Gas analysis (O2, CO2)Clinical monitor (ECG, HR,…)

InterpretationMetabolic (VO2, VCO2,…)Lung (Shunt, V/Q,…)Blood (Base excess, DPG,…)

InterpretationMetabolic (VO2, VCO2,…)Lung (Shunt, V/Q,…)Blood (Base excess, DPG,…)

Decision supportMonitoringVentilator controlGlucose regulation

Decision supportMonitoringVentilator controlGlucose regulation

Architecture is compartmentalised to allow independent development of each client.

Input clients

• Many monitors allow data logging on a computer for automated data collection.

• The user interface also allows clinicians to add data that can not be logged automatically.

DatabaseDatabase

ECG,SaO2,…

CO2,Vt,...

CO, MAP,…

ALL data input is written to the database.

Vt, Paw, …Values,Events

Interpretation clients

• Physiological models and more basic calculations are carried out on data in the database to determine more abstract measurements or patient condition and extend the knowledge of the patient.

• Some clients can be automatic, carrying out calculations when ever new data is available, while more complex clients may require user control.

DatabaseDatabase

Body surface area

Body surface area

WeightHeight

BSA

Cardiac Index

Cardiac Index

BSACO

CI

Oxygen Consumption

Oxygen Consumption

FetO2,

Vt,…VO2

ALPEALPE

VO2,

CI,…Shunt,PO2

Automatic Requires user control

Decision support clients

• The extended data set can be sorted and displayed in a way that assists clinicians in diagnosis and treatment selection. For example:

– Analysing how a particular measurement has changed with time.

– Displaying only data that is relevant for the patient’s disorder.

– Methods of assisting in optimising treatment selection.

DatabaseDatabase

Plot historyPlot historyRelevant

information only

Relevant information only

INVENTINVENT

PEEP,

Vt,…Shunt,PO2

HyperglycaemiaHyperglycaemia

Optimal insulin infusion

• Heart failure,• ARDS,• COPD,…

History of:• Shunt,• Deadspace,• Insulin,…

Optimal: • PEEP,

• FiO2,

• Vt, …

Implementation

CO, MAP, ITBV, …

ECG,SaO2,…

CO2, Vt, Paw, …

O2, CO2

O2, CO2

CO2, Vt,Paw, …

DecisionSupport e.g. ALPE

Conclusions

• A generic architecture has been implemented for development of new calculation methods, physiological models and decision support systems.

• The compartmental design means that clients can be developed and function independently, yet interact if possible to improve functionality (eg, cardiopulmonary interaction, VO2).

• This architecture presents a method for moving information systems from audit to clinical support tools, through:

– Calculation of abstract representations of patient condition (e.g. cardiac index, shunt).

– Assistance in interpreting patient information and choosing optimal treatment strategies (e.g. optimising ventilator settings).