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

1. 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

2. 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.

3. 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. Inputs Ventilator (Paw, Flow,…) Gas analysis (O2, CO2) Clinical monitor (ECG, HR,…) Interpretation Metabolic (VO2, VCO2,…) Lung (Shunt, V/Q,…) Blood (Base excess, DPG,…) Decision support Monitoring Ventilator control Glucose regulation Database Architecture is compartmentalised to allow independent development of each client.

4. 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. CO, MAP,… Values, Events ECG, SaO2,… Vt, Paw, … CO2, Vt,... Database ALL data input is written to the database.

5. 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. Automatic Requires user control Body surface area Cardiac Index Oxygen Consumption ALPE Weight Height Shunt, DPO2 BSA CO FetO2, Vt,… VO2, CI,… BSA CI VO2 Database

6. 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. • Optimal: • PEEP, • FiO2, • Vt, … • History of: • Shunt, • Deadspace, • Insulin,… • Heart failure, • ARDS, • COPD,… Optimal insulin infusion INVENT Relevant information only Hyperglycaemia Plot history Shunt, DPO2 PEEP, Vt,… Database

7. Implementation CO, MAP, ITBV, … ECG, SaO2,… CO2, Vt, Paw, … O2, CO2 O2, CO2 CO2, Vt, Paw, … Decision Support e.g. ALPE

8. 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).