1. Multi-parameter data collection example
J. Zaleski
October 4th, 2009

2. Discontinuation from mechanical ventilation
Parameter
Threshold Value/Range
Vital Capacity, Vc
> 10mL/kg
Positive End-Expiratory Pressure, PEEP
5 cm H2O
Negative Inspiratory Force, NIF
-20 cm H2O
Inspired Oxygen Fraction, FiO2
< 0.6
Spontaneous Tidal Volume, Vt
> 5 mL/kg
Spontaneous Respirations, fR
8 < Resp Rate < 30
Blood Alkalinity/Acidity
7.32 < pH < 7.48
Partial Pressure of Oxygen, SPO2
> 80 mmHg
Partial Pressure of Carbon Dioxide, PCO2
30 mmHg < PCO2 < 50 mmHg
Normal Body Temperature, Tcore
~37 C
Ventilation Mode
CPAP
Clinical decision making is a multi-parameter action
Behavior, trending of several parameters coincidentally influences the recommended ordering of clinicians

3. Discontinuation from mechanical ventilation
Parameter
Threshold Value/Range
Vital Capacity, Vc
> 10mL/kg
Positive End-Expiratory Pressure, PEEP
5 cm H2O
Negative Inspiratory Force, NIF
-20 cm H2O
Inspired Oxygen Fraction, FiO2
< 0.6
Spontaneous Tidal Volume, Vt
> 5 mL/kg
Spontaneous Respirations, fR
8 < Resp Rate < 30
Blood Alkalinity/Acidity
7.32 < pH < 7.48
Partial Pressure of Oxygen, SPO2
> 80 mmHg
Partial Pressure of Carbon Dioxide, PCO2
30 mmHg < PCO2 < 50 mmHg
Normal Body Temperature, Tcore
~37 C
Ventilation Mode
CPAP
Clinical decision making is a multi-parameter action
Behavior, trending of several parameters coincidentally influences the recommended ordering of clinicians
Parameters, Pi
Value Thresholds,
Vpthi

4. Clinical Decision Making is Based Upon Multiple Data Sources
LIS
RIS
CIS
Devices
EHR …
Data from multiple sources (enterprise and departmental health systems, devices, etc.) P1 P2 P3 …
Key Parameters Used to Determine Action
Clinical Decision Support Systems
Vpt1 < Vpth1
Vpt2 < Vpth2 …
Vpti < Vpthi
Action
(Note: NOT simply a linear sum of elements, but a system of mutually-satisified constraints)

5. Examples of Multi-Parameter Medical Device Data
Spontaneous
Mandatory 12
fR (br/min)
VT (l/br)
0.5
CO (l/min) 3
Tc(C) 37
HR (/min) 88
Nitroprusside
(u/kg/min)
0.5 1 2 3 4 …
Time, in hours

6. Synchronous and Asynchronous Data Collection
Spontaneous
Mandatory x 12 x x x x x x x x x x x x x x x x x x x x x x x x x x x
fR (br/min) x x x x x x x x x x x x x
VT (l/br) x x x x x x x x
0.5 x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x
CO (l/min) x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x 3
Tc(C) x 37 x x x x x x
HR (/min) x x x x x x x x x x x x x x x x x 88 x x x x x x x x
Nitroprusside
(u/kg/min) x x x
0.5 x x x x x x x 1 2 3 4 …
Time, in hours

7. Synchronous and Asynchronous Data Collection
Spontaneous
Mandatory 12 x x x x x x x x x x x x x x x x x x x x x x
fR (br/min) x x x x x x x x x x x x x x x x x x x
VT (l/br) x x x x x x x x x x x x x x x x x x
0.5 x x x x x x x x x x x x x x x x x x x x x x x x x
CO (l/min) x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x 3
Tc(C) x 37 x x x x x x
HR (/min) x x x x x x x x x x x x x x 88 x x x x x x x x x x x
Nitroprusside
(u/kg/min)
0.5 x x x x x x x x x x
Time, in hours
Data availability and staleness in relation to decision making can influence decisions

8. Synchronous and Asynchronous Data Collection
Spontaneous
Mandatory 12 x x x x x x x x x x x x x x x x x x x x x x
fR (br/min) x x x x x x x x x x x x x x x x x x x
VT (l/br) x x x x x x x x x x x x x x x x x x
0.5 x x x x x x x x x x x x x x x x x x x x x x x x x
CO (l/min) x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x 3
Tc(C) x 37 x x x x x x
HR (/min) x x x x x x x x x x x x x x 88 x x x x x x x x x x x
Nitroprusside
(u/kg/min)
0.5 x x x x x x x x x x
Data availability and staleness in relation to decision making can influence decisions
Decision support systems may require higher-fidelity data or more frequent collection

9. Synchronous and Asynchronous Data Collection
Spontaneous
Mandatory x 12 x x x x x x x x x x x x x x x x x x x x x x x x x x x
fR (br/min) x x x x x x x x x x x x x
VT (l/br) x x x x x x x x
0.5 x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x
CO (l/min) x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x 3
Tc(C) x 37 x x x x x x x
HR (/min) x x x x x x x x x x x x x 88 x x x x x x x x x x x x x
Nitroprusside
(u/kg/min) x x x x
0.5 x x x x x x x
Trigger queries for data to fill missing gaps (that is RECEIVING system requests missing data) OR
To update parameters requested of rules algorithms based on gaps
Data availability and staleness in relation to decision making can influence decisions

10. Objective
Aim is not on communication (transport), but on method for allowing triggering of new results based on functional needs of algorithms
Can operate using existing or new messages, according to current standards
Independent of real-time messaging protocols

11. Initiate query for missing data to appropriate target device
Example
Hub
Synchronous Data Communication
CIS / Departmental /
CDSS / EMR
Gateway
Interface Engine
CDSS evaluates multiple parameters, determines relevance and importance of each with respect to specific condition being tested (example: spontaneous breathing trials)
Gateway
Hub
Support query for parameter that does not meet certain criteria on currency, or for which data are lacking.
CIS / Departmental /
CDSS / EMR
Gateway
Interface Engine
Initiate query for missing data to appropriate target device
Gateway

12. Motivating Needs
Cardiac Output changes with respect to nitroprusside levels
Early reduction in vasodilator levels can lead to cardiac output reduction and distress;
Associating changes in infusion of specific drips with cardiovascular performance requires timely knowledge of both infusion and CO, heart rate (changes in CO can occur in just a few seconds)
Decision support algorithms require latest information on both to recommend action
Spontaneous breathing rates and rapid shallow breathing with respect to changes in mandatory support levels
Sudden increases in RSBI after changes in support levels are indicators to notify staff of need for intervention
Need for suctioning and events surrounding intubation can occur suddenly and decision support algorithms require synchronous information on spontaneous parameters and work of breathing together with temperature
Decision to reduce support levels driven by multiple parameters
Change in pH and end-tidal CO2 levels influence respiratory support levels. Determination of changes in support require latest information on blood gases and (from Swan) invasive cardiac and real-time blood gas parameters
Need for updated blood gas data (pH, PCO2, B.E., HCT, etc.) can be triggered by the need for a CDSS to operate on new data (i.e., recommend spontaneous breathing trials subject to result of latest blood gas)
Changes in systemic vascular resistance (SVR) leading to variation in cardiac output can occur due to sympathetic and parasympathetic tone changes (and through infused drug) in less than 1 second.
Patient responses cannot be interpolated from past data—discontinuities and sudden changes from moment to moment can occur