Monitoring Trauma Patients in the Prehospitaland Hospital Environments: The Need for Better Monitors and Advanced Automation

Monitoring Trauma Patients in the Prehospitaland Hospital Environments: The Need for Better Monitors and Advanced Automation

José Salinas, PhDjose.salinas4@us.army.mil

210-916-0806

José Salinas, PhDjose.salinas4@us.army.mil

210-916-0806

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1. REPORT DATE 05 APR 2008 2. REPORT TYPE

3. DATES COVERED 00-00-2008 to 00-00-2008

4. TITLE AND SUBTITLE Monitoring Trauma Patients in the Prehospital and HospitalEnvironments: The Need for Better Monitors and Advanced Automation

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7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) U.S. Army Institute of Surgical Research,Fort Sam Houston,TX,78234

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13. SUPPLEMENTARY NOTES Army Telemedicine Partnerships Series 2008: ?Personal Health Monitoring?, Seattle, WA, 5 Apr 2008

14. ABSTRACT

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Report (SAR)

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Standard Form 298 (Rev. 8-98) Prescribed by ANSI Std Z39-18

22

BackgroundBackground

• Diagnosis in the prehospital and/or hospital environments is often inaccurate for trauma patients

• Current prehospital monitors measure vital signs that are not predictive of outcome in many instances– Systolic Blood Pressure– Heart Rate– SpO2

• Diagnosis in the prehospital and/or hospital environments is often inaccurate for trauma patients

• Current prehospital monitors measure vital signs that are not predictive of outcome in many instances– Systolic Blood Pressure– Heart Rate– SpO2

Cooke, et al., Heart Rate Variability in Pre-Hospital Trauma patients. J Trauma, 2006

33

Standard Vital SignsStandard Vital Signs

lived (n = 15) lived (n = 15)

died (n = 15) died (n = 15)

systolicsystolic

diastolicdiastolicmeanmean

2020

4040

6060

8080

100100

120120ar

teria

l pre

ssur

e,

mm

Hg

arte

rial p

ress

ure,

m

mH

g

livedlived dieddied00

2020

4040

6060

8080

100100

Art

eria

l O2

Sat

ura

tio

n, %

Art

eria

l O2

Sat

ura

tio

n, %

00

2020

4040

6060

8080

100100

120120

Pu

lse

Rat

e, b

pm

Pu

lse

Rat

e, b

pm

livedlived dieddied

44

Grouped Vital Signs Do Not Predict Need for Life Saving Interventions

Grouped Vital Signs Do Not Predict Need for Life Saving Interventions

• Considered 3 data sets based on instrument requirements– Manual (MG)– Semi-Automated (SG)– Automated (AG)

• Considered 3 data sets based on instrument requirements– Manual (MG)– Semi-Automated (SG)– Automated (AG)

Manual GroupManual GroupSemi-AutomatedGroup

Semi-AutomatedGroup

Automated GroupAutomated Group

• Demographics (Age, Sex)• Pulse Character (Radial, Femoral, Carotid)• GCS Motor, GCS Verbal• Capillary Refill

• Demographics (Age, Sex)• Pulse Character (Radial, Femoral, Carotid)• GCS Motor, GCS Verbal• Capillary Refill

GCS Eye SpO2

GCS Eye SpO2

• Electronic monitor• Automated NIBP

(Systolic, Diastolic)

• Heart Rate• EtCO2• Automated

Respiration Rate

• Electronic monitor• Automated NIBP

(Systolic, Diastolic)

• Heart Rate• EtCO2• Automated

Respiration Rate

Holcomb, et al. Manual Vital Signs Reliably Predict the Need for LSI’s. Oct 2005, J Trauma, 2005.

55

ProblemProblem

==

Manual Vital SignsPhysical Exam

Manual Vital SignsPhysical Exam Electronic Vital SignsElectronic Vital Signs

No difference in prediction of outcomeNo difference in prediction of outcome

66

Triage ProblemTriage Problem

• Civilian:– Consistent overtriage and undertriage problem in the

prehospital trauma environment– Not unusual to have a 50% overtriage within a large urban

area

• Military: – Inability to effectively predict evacuation requirements

balanced with tactical considerations

• Civilian:– Consistent overtriage and undertriage problem in the

prehospital trauma environment– Not unusual to have a 50% overtriage within a large urban

area

• Military: – Inability to effectively predict evacuation requirements

balanced with tactical considerations

Cause:Inability to accurately determine

patient status using current methodologies/technologies.

Cause:Inability to accurately determine

patient status using current methodologies/technologies.

77

Environment: Medic InjuredTrying to Save Fallen Comrade

Environment: Medic InjuredTrying to Save Fallen Comrade

11 22

33 44

88

Bottom LineBottom Line

Current monitors present data that is not predictive of eventual physiologic decompensation and are thus inadequate!

Current monitors present data that is not predictive of eventual physiologic decompensation and are thus inadequate!

99

SolutionSolution

• We need a new type of monitor!

• Requirements– Accurate diagnosis of patient status– Small/Lightweight– Remote/wireless operation– Intelligent Decision assist

technology– Autonomous/closed loop technology

for long term care

• We need a new type of monitor!

• Requirements– Accurate diagnosis of patient status– Small/Lightweight– Remote/wireless operation– Intelligent Decision assist

technology– Autonomous/closed loop technology

for long term care

1010

Accurate Diagnosis of Trauma Patients

Accurate Diagnosis of Trauma Patients

• Need to use new/advanced vital signs– Pulse Pressure– Shock Index

• Explore combinations of vital signs to enhance probability of correct diagnosis

• Make use of vital sign trends

• Use characteristics of high frequency waveforms– Heart Rate Variability– Heart Rate Complexity– Non Linear ECG Dynamics

• Need to use new/advanced vital signs– Pulse Pressure– Shock Index

• Explore combinations of vital signs to enhance probability of correct diagnosis

• Make use of vital sign trends

• Use characteristics of high frequency waveforms– Heart Rate Variability– Heart Rate Complexity– Non Linear ECG Dynamics

1111

Pulse PressurePulse Pressure

• Statistically significant for tracking stroke volume

• Statistically significant for tracking stroke volume

1212

Combination Vital SignsCombination Vital SignsCombination of Shock Index, Shock Index Trend,

Respiratory Rate

No Physical ExamPhysical Exam

1313

Waveform HR variabilityWaveform HR variability

frequency, Hz0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40

RR

I PSD

, ms2 /H

z

0

2000

4000

6000

8000

10000

12000patients who diedpatients who lived

Measures Sympathetic/Parasympathetic activity based on the frequency distribution of the RRI values.

1414

Waveform ComplexityWaveform Complexity

1 .00 .80 .60 .40 .20 .01 - S p ec ific ity

1 .0

0 .8

0 .6

0 .4

0 .2

0 .0

Sens

itivi

ty

1 .00 .80 .60 .40 .20 .01 - S p ec ific ity

1 .0

0 .8

0 .6

0 .4

0 .2

0 .0

Sens

itivi

ty

PhysicalExam

HRV

Physical Exam, HRV,Complexity

Quantifies the regularity of the HR signal through entropy calculations with in the RRI values.

Pred

ictio

n of

LSI

1515

Addition of more parametersAddition of more parameters

• Parameter Survival ROC• Single Vital Signs 0.75• Physiology and Trends 0.80• Physiological Variability 0.85• Physiology, Coagulation, 0.96

and Immunology

• The more significant data we get, the more predictive we can be!

• Parameter Survival ROC• Single Vital Signs 0.75• Physiology and Trends 0.80• Physiological Variability 0.85• Physiology, Coagulation, 0.96

and Immunology

• The more significant data we get, the more predictive we can be!

1616

ProblemProblem

• Monitoring can be enhanced to further improve patient care…However:

“We keep monitoring patients to death”-- Anonymous

• Monitoring can be enhanced to further improve patient care…However:

“We keep monitoring patients to death”-- Anonymous

1717

Now What?Now What?

• Use enhanced prediction algorithms and inputs into intelligent medical systems to better assist medical providers– Decision Support Systems– Advanced Triage Systems

• Move validated medical systems into full automation for closed loop care of patients

• Use enhanced prediction algorithms and inputs into intelligent medical systems to better assist medical providers– Decision Support Systems– Advanced Triage Systems

• Move validated medical systems into full automation for closed loop care of patients

1818

Decision Assist (support) Technology

Decision Assist (support) Technology

• Decision support systems are a class of computer-based information systems that support decision making activities– Type of intervention

• What intervention to use based on the expertise of the user

– When do we apply intervention?– Where should a patient be taken based on

readings?– Where should a patient be transported to?

• Decision support systems are a class of computer-based information systems that support decision making activities– Type of intervention

• What intervention to use based on the expertise of the user

– When do we apply intervention?– Where should a patient be taken based on

readings?– Where should a patient be transported to?

1919

BenefitsBenefits

• Helps to remind experts on proper patient care during critical procedures and/or mass casualty situations

• “Pushes” the expertise built into the software to non-expert providers– i.e. burn standard of care procedures for field

use

• Maintains the open loop concept – Not a replacement for good clinical judgment

• Helps to remind experts on proper patient care during critical procedures and/or mass casualty situations

• “Pushes” the expertise built into the software to non-expert providers– i.e. burn standard of care procedures for field

use

• Maintains the open loop concept – Not a replacement for good clinical judgment

2020

Types of DSSTypes of DSS

• Model Driven– Used for statistical data manipulation– Example: Expected response of a company’s stock to a

selloff• Communication Driven

– Used for coordination of tasks between users• Data Driven

– Used for manipulation of time series data• Document Driven

– Manipulation of unstructured information from documents• Knowledge Driven

– Most appropriate for medical systems– Emphasizes problem solving skills

• Model Driven– Used for statistical data manipulation– Example: Expected response of a company’s stock to a

selloff• Communication Driven

– Used for coordination of tasks between users• Data Driven

– Used for manipulation of time series data• Document Driven

– Manipulation of unstructured information from documents• Knowledge Driven

– Most appropriate for medical systems– Emphasizes problem solving skills

2121

USAISR DSS ExamplesUSAISR DSS Examples

• ICU DSS Framework– Decision support framework for management of

burn patients admitted to the USAISR Burn Center ICU

• Resuscitation – Implemented• Tight glucose control - Implemented• Hypotension management – Working• Albumin use - Working• Abdominal compartment syndrome diagnosis and

management - Working

• Mobile Burn Resuscitation– Decision support for burn resuscitation in a

mobile/handheld system for field resuscitation

• ICU DSS Framework– Decision support framework for management of

burn patients admitted to the USAISR Burn Center ICU

• Resuscitation – Implemented• Tight glucose control - Implemented• Hypotension management – Working• Albumin use - Working• Abdominal compartment syndrome diagnosis and

management - Working

• Mobile Burn Resuscitation– Decision support for burn resuscitation in a

mobile/handheld system for field resuscitation

2222

Automation and Closed LoopAutomation and Closed Loop

• Use accurate inputs to control therapy for patient care– Examples:

• Resuscitation• Ventilation• Pain Control• Hemorrhage

• Feedback system allows for more accurate delivery of therapy

• Use accurate inputs to control therapy for patient care– Examples:

• Resuscitation• Ventilation• Pain Control• Hemorrhage

• Feedback system allows for more accurate delivery of therapy

2323

Action Shots of DSS (ICU Version)Action Shots of DSS (ICU Version)

2424

Action shots of DSS (Mobile)Action shots of DSS (Mobile)

2525

The FutureThe Future

• Full Automation– Closed loop

• Computer control of sensors and actuators

– Automated patient management

• Full Automation– Closed loop

• Computer control of sensors and actuators

– Automated patient management

2626

Modules• Aspirator• I.V. pumps• Multipatient monitor (SpO2, ECG,

NIBP)• Patient controlled analgesia • Spirometer• O2 concentrator• Patient warming• Stress test• Anesthesia Module• Ultrasound imaging• Visualization

– Oto/opthalmoscope– macrolens camera

Integral• Ventilator• 12-lead ECG• Pulse-Ox• NIBP• Data I/O• Electronic med.

record

• CO2• AED• Temperature• Invasive pressure• Remote C2

• Smart help• Closed-loop control

Lightweight Trauma Module (LTM), Impact Instrumentations

Lightweight Trauma Module (LTM), Impact Instrumentations

• Respiratory mechanics• Pulse Pressure, Shock Index, HRV

2727

Wireless Vital Signs Monitor (WVSM), Athena GTX

Wireless Vital Signs Monitor (WVSM), Athena GTX

An Automated Wireless, Battery Powered Medical Tool to Assist the Medical Personnel in the Field and

Hospitals in Tracking and Monitoring Patients’ Vital Signs

Continuously….in a small and affordable package using WLAN and

MS Windows O/S

An Automated Wireless, Battery Powered Medical Tool to Assist the Medical Personnel in the Field and

Hospitals in Tracking and Monitoring Patients’ Vital Signs

Continuously….in a small and affordable package using WLAN and

MS Windows O/S

2828

ClosedClosed--Loop Resuscitation of Burn InjuryLoop Resuscitation of Burn Injury

PC controller

Bard® Urine Monitor

PC-Visual Basic controller

IV Pump

Sheep +IV access & bladder catheter

* G Kramer, University of Texas Medical Branch

2929

17338%

p = 0.651.10 (CI 0.74,

1.65)

7316%

p = 0.020.58 (CI 0.38,

0.89)

21447%

p = 0.231.31 (CI 0.85, 2.0)

CLR controlled

10 sheep, 475 measurement

s

17535%

12225%

19840%

Tech Control 11 sheep, 508 measurement

s

over-targetunder-target

on-targetGroup

target range defined as 1.0 – 2.0 mL/hr target

Hourly Urinary Outputs: on-target, under-target and over-target

* G Kramer, University of Texas Medical Branch

3030

ConclusionConclusion

• Need better monitoring technologies– New vital signs– Combinations of standard, new, and trended

vital signs– Advanced complexity vital signs

• Need to develop decision support systems– Additional tools to expert users– Better care for non-expert care providers– Open loop concept

• Future: Closed loop and automation– Computer control of sensors and actuators

• Need better monitoring technologies– New vital signs– Combinations of standard, new, and trended

vital signs– Advanced complexity vital signs

• Need to develop decision support systems– Additional tools to expert users– Better care for non-expert care providers– Open loop concept

• Future: Closed loop and automation– Computer control of sensors and actuators

3131

QUESTIONS?QUESTIONS?QUESTIONS?