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. Database Inputs Ventilator (P aw, Flow,…) Gas analysis (O 2, CO 2 ) Clinical monitor (ECG, HR,…) Inputs Ventilator (P aw, Flow,…) Gas analysis (O 2, CO 2 ) Clinical monitor (ECG, HR,…) Interpretation Metabolic (VO 2, VCO 2,…) Lung (Shunt, V/Q,…) Blood (Base excess, DPG,…) Interpretation Metabolic (VO 2, VCO 2,…) Lung (Shunt, V/Q,…) Blood (Base excess, DPG,…) Decision support Monitoring Ventilator control Glucose regulation Decision support Monitoring Ventilator control Glucose 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. Database ECG, S a O 2,… CO 2, V t,... CO, MAP,… ALL data input is written to the database. V t, P aw, …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. Database Body surface area Weight Height BSA Cardiac Index BSA CO CI Oxygen Consumption F et O 2, V t,… VO 2 ALPE VO 2, CI,… Shunt,  PO 2 AutomaticRequires 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. Database Plot history Relevant information only INVENT PEEP, V t,… Shunt,  PO 2 Hyperglycaemia Optimal insulin infusion Heart failure, ARDS, COPD,… History of: Shunt, Deadspace, Insulin,… Optimal: PEEP, FiO 2, V t, …

Implementation CO, MAP, ITBV, … ECG, SaO2,… CO 2, V t, P aw, … O 2, CO 2 O 2, CO 2 CO 2, V t, P aw, … Decision Support 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, VO 2 ). 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).