Testing Asbru Guidelines and Protocols

for Neonatal Intensive Care

Christian Fuchsberger,

Jim Hunter and Paul McCue

Acknowledgements

• Clinical collaborators: Christian Popow, Neil McIntosh and Yvonne Freer

• Silvia Miksch

• UK Research Councils – ESRC and EPSRC

• Austrian Research Council

Intensive Care

• Intensive!– Patients with multiple problems– High rate of intervention– Increasing sophistication of available treatments– Increased levels of monitoring

• Errors do happen– Majority are unimportant– Some are significant

• Missed symptoms and signs• Attentional overload

Intensive Care is Expensive

In 2002 • expenditure on health care as % of GDP:

– from: 10.7% (Germany) to 6.7% (Ireland) – say 8% overall

• expenditure on intensive care as % of health care costs:– from 2.6% (Netherlands) to 1% in UK – say 1.5% overall

• European GDP ~ €10,000,000 M• so ~ €12,000 M spent on intensive care• every 1% saved ~ €120 M

Solutions?

• Display the data:

• Not demonstrated to help junior nurses and doctors.

• Decision support …

Clinical Guidelines and Protocols

• Clear statements of the optimal management for a specific group of patients which, when properly applied, will improve the quality of the care they receive.

• Guideline:– often formulated nationally or internationally– often evidence-based– widely disseminated

• Protocol:– more detailed– local (one clinician or group of clinicians)– often mandatory

Computerised Guidelines

• Formal representation of a guideline

• Languages: – Guide, Prodigy, GLIF, SAGE, EON, ProForma, Asbru

• Automatic application to electronic data (EPR)

• Often envisaged as operation in ‘encounters’ with patient– 10’s or 100’s of data items– daily or weekly– possibility of clinician data input

• Data volume– continuous physiological data (heart rate, oxygen, carbon dioxide,

blood pressures) as often as every second – 100,000’s of data items

– sporadic data – lab results, blood gases, …– ‘paperless’ ICU – data input from nurses and doctors

• Complex abstractions– bridge the gap between raw data and guideline– some data not available electronically (sight, touch, …)

• Automatic application– medical staff have no time to answer questions

• Continuous advice provision– system often has access to actions taken by staff

Computerised Guidelinesin Intensive Care

What do we need for development?

• Formal language: Asbru (Shahar, Miksch and Johnson,1998)

• Guideline (protocol)• Translation of guideline• Visualisation of guideline• Data Abstraction• Execution Engine: AsbruRTM• Test Data• Infrastructure• Evaluation

NB: All testing is off-ward

Architecture

Data Abstraction

Execution Engine

Test Data

Visualisation

Guideline

Recommendations

Guideline

Data Abstraction

Execution Engine

Test Data

Visualisation

Guideline

Recommendations

Guideline

• Maintain suitable oxygen (O2) level in the blood (as measured) …

• … by adjusting the fraction of inspired oxygen (FiO2) on the ventilator

IF O2 > O2-High THEN

Rec_FiO2 = FiO2 - 5

IF O2-High> O2 > O2-Low THEN

Rec_FiO2 = FiO2

IF O2-Low > O2 THEN

Rec_FiO2 = FiO2 + 10

O2-High

O2-Low

O2

FiO2 -

FiO2 +

8 kPa

6 kPa

Guideline<!-- ################# PtcO2 too <!-- ################# PtcO2 too high--><if-then-else> <simple-condition> <comparison type="greater-than"> <left-hand-side> <parameter-ref name="PtcO2"/> </left-hand-side> <right-hand-side> <numerical-constant value="8"/> </right-hand-side> </comparison> </simple-condition> <then-branch> <variable-assignment variable="REC_SETTING:VENTILATOR:Rec_FiO2"> <operation operator="subtract"> <parameter-ref name="VENTILATOR:FiO2"/> <numerical-constant value="5"/> </operation> </variable-assignment> </then-branch></if-then-else>

Coded by hand

IF O2 > O2-High THEN Rec_FiO2 = FiO2 - 5

Guideline

• Also simple guideline to maintain suitable oxygen

(CO2) level in the blood (as measured) …

• … by adjusting respiration rate on the ventilator

Test Data

Data Abstraction

Execution Engine

Test Data

Visualisation

Guideline

Recommendations

Test Data

• Taken from the ‘Neonate’ database(Hunter, Ferguson, Freer, Ewing, Logie, McCue and McIntosh, 2003)

• Continuous monitoring of physiological variables– 1 second; heart rate, blood pressure, O2, CO2 …

• Discontinuous numerical data– Ventilator settings, blood gas, laboratory results

• Discontinuous symbolic data– Observations of physical state– Actions taken by the staff

‘Neonate’ Database

• 407 hours• 31 individual babies• Background information

– sex, gestation, weight at birth, …• Anonymised• Microsoft Access• Available

Discontinuous Data

Individual records

Actions 19,610

Observations 1,831

Settings 4,512

Laboratory results 1,343

Blood gas 2,187

Medication 148

Comments 2,443

Observer present 403

TOTAL 32,477

Subset Used

• measured O2 (OX) sampled 1/second

• measured CO2 (CO) sampled 1/second

• FiO2 setting when changed

• respiration rate when changed

• actions taken

Data Abstraction

Data Abstraction

Execution Engine

Test Data

Visualisation

Guideline

Recommendations

Data Abstraction

• Compression – median value every 60 seconds• Artefact removal (Cao et al., 1999)

– limit-based detector flags as artefact values outside extreme centiles

– deviation-based detector flags as artefact values which cause the standard deviation to exceed a limit

– correlation-based detector: uses lower standard deviation limits when a ‘correlated’ channel is flagged

Execution Engine

Data Abstraction

Execution Engine

Test Data

Visualisation

Guideline

Recommendations

Execution Engine

• Based on AsbruRTM (Fuchsberger and Miksch, 2003)

Execution Engine

CORBA

Simplified and data abstraction done externally

Detailed Architecture

Rec_Resp_Rate

MD[OX]+CMD[OX]OX

CO

Median

Median

ArtiDetector AsbruRTM

MD[CO]

Rec_FiO2

FiO2

Resp_Rate

Guideline

MD[CO]+C

Infrastructure

Data Abstraction

Execution Engine

Test Data

Visualisation

Guideline

Recommendations

Time Series Workbench (Delphi)

Asbru RTM (Java)

CORBA

Results

Where now?

• Can we derive abstractions for more complex guidelines which may refer to data which is not available electronically?

• How do we deliver advice in a ‘continuous’ environment when the guideline can “see” what the clinical staff are doing?

• How do we integrate the work of different groups?

Distributed Infrastructure

Clients

Test Data

Filters(Data Abstraction)

Guidelines

Servers

Plots

Execution Engines

Guideline Visualisation

Data and Recommendation

VisualisationTest

Management

Internet