This case study describes a patient undergoing coronary artery bypass grafting (CABG) surgery who is now in the surgical intensive care unit (SICU) post-operatively on mechanical ventilation. Effective clinical decision support could help clinicians: 1. Monitor the patient's vital signs and other parameters to determine when spontaneous breathing trials should begin to assess viability for extubation. 2. Utilize guidelines and best practices for extubation readiness based on the patient's status and progress to safely discontinue mechanical ventilation as quickly as possible to reduce health risks and costs. 3. Provide alerts and recommendations during the weaning process based on continuous monitoring to prevent complications and optimize outcomes.

1. Part 2: Clinical Decision Support Systems
JOHN R. ZALESKI, PHD, CPHIMS
VICE PRESIDENT OF CLINICAL APPLICATIONS & CTO
JZALESKI@NUVON.COM
C: +1 484 319 7345
O: +1 215 966 6142

2. Clinical Decision Support Systems (CDSS)—
A Definition
• Greenes (quoting Shortliffe, 1987) defines clinical
decision support as follows:
– “any computer program designed to help health
professionals make clinical decisions, deal with
medical data about patients or with the knowledge of
medicine necessary to interpret such data.”
• Augmented:
– “provide stakeholders with actionable knowledge
presented in a timely manner to enhance the quality
of care.”
Source: Clinical Decision Support: The Road Ahead. © 2007, page 143
2
Sunday, May 29, 2011

3. Principal CDS Methodologies
Methodology Major Uses Key Developments
Information retrieval Finding information, answering questions Taxonomies, ontologies, text-based
methods, patient-specific context keys,
automatic invocation
Source: Clinical Decision Support: The Road Ahead. © 2007, page 32
3
Sunday, May 29, 2011

4. Principal CDS Methodologies
Methodology Major Uses Key Developments
Information retrieval Finding information, answering questions Taxonomies, ontologies, text-based
methods, patient-specific context keys,
automatic invocation
Evaluation of logical conditions Alerts, reminders, constraints, Decision tables, event-condition-action
inferencing systems rules, production rules
Source: Clinical Decision Support: The Road Ahead. © 2007, page 32
4
Sunday, May 29, 2011

5. Principal CDS Methodologies
Methodology Major Uses Key Developments
Information retrieval Finding information, answering questions Taxonomies, ontologies, text-based
methods, patient-specific context keys,
automatic invocation
Evaluation of logical conditions Alerts, reminders, constraints, Decision tables, event-condition-action
inferencing systems rules, production rules
Probabilistic and data-driven Diagnosis, technology assessment, Bayes theorem, decision theory, ROC
classification or prediction treatment selection, classification and analysis, data mining, logistic regression,
prediction, prognosis estimation, artificial neural networks, belief
evidence-based medicine networks, meta-analysis
Source: Clinical Decision Support: The Road Ahead. © 2007, page 32
5
Sunday, May 29, 2011

6. Principal CDS Methodologies
Methodology Major Uses Key Developments
Information retrieval Finding information, answering questions Taxonomies, ontologies, text-based
methods, patient-specific context keys,
automatic invocation
Evaluation of logical conditions Alerts, reminders, constraints, Decision tables, event-condition-action
inferencing systems rules, production rules
Probabilistic and data-driven Diagnosis, technology assessment, Bayes theorem, decision theory, ROC
classification or prediction treatment selection, classification and analysis, data mining, logistic regression,
prediction, prognosis estimation, artificial neural networks, belief
evidence-based medicine networks, meta-analysis
Heuristic modeling and expert systems Diagnostic and therapeutic reasoning, Rule-based systems, frame-based
capturing nuances of human expertise reasoning
Source: Clinical Decision Support: The Road Ahead. © 2007, page 32
6
Sunday, May 29, 2011

7. Principal CDS Methodologies
Methodology Major Uses Key Developments
Information retrieval Finding information, answering questions Taxonomies, ontologies, text-based
methods, patient-specific context keys,
automatic invocation
Evaluation of logical conditions Alerts, reminders, constraints, Decision tables, event-condition-action
inferencing systems rules, production rules
Probabilistic and data-driven Diagnosis, technology assessment, Bayes theorem, decision theory, ROC
classification or prediction treatment selection, classification and analysis, data mining, logistic regression,
prediction, prognosis estimation, artificial neural networks, belief
evidence-based medicine networks, meta-analysis
Heuristic modeling and expert systems Diagnostic and therapeutic reasoning, Rule-based systems, frame-based
capturing nuances of human expertise reasoning
Calculations, algorithms, and multistep Execution of computational processes, Process flow and workflow modeling,
processes flow-chart-based guidelines and guideline formalisms and modeling
consultations, interactive dialogue languages
control, biomedical image and signal
processing
Source: Clinical Decision Support: The Road Ahead. © 2007, page 32
7
Sunday, May 29, 2011

8. Principal CDS Methodologies
Methodology Major Uses Key Developments
Information retrieval Finding information, answering questions Taxonomies, ontologies, text-based
methods, patient-specific context keys,
automatic invocation
Evaluation of logical conditions Alerts, reminders, constraints, Decision tables, event-condition-action
inferencing systems rules, production rules
Probabilistic and data-driven Diagnosis, technology assessment, Bayes theorem, decision theory, ROC
classification or prediction treatment selection, classification and analysis, data mining, logistic regression,
prediction, prognosis estimation, artificial neural networks, belief
evidence-based medicine networks, meta-analysis
Heuristic modeling and expert systems Diagnostic and therapeutic reasoning, Rule-based systems, frame-based
capturing nuances of human expertise reasoning
Calculations, algorithms, and multistep Execution of computational processes, Process flow and workflow modeling,
processes flow-chart-based guidelines and guideline formalisms and modeling
consultations, interactive dialogue languages
control, biomedical image and signal
processing
Associative groupings of elements Structured data entry, structured reports, Report generators and document
order sets, other specialized construction tools, document
presentations and data views architectures, templates, markup
languages, ontology tools, ontology
languages
Source: Clinical Decision Support: The Road Ahead. © 2007, page 32
8
Sunday, May 29, 2011

9. Principal CDS Methodologies
Methodology Major Uses Key Developments
Effective and accurate Clinical Decision Support
Information retrieval Finding information, answering questions Taxonomies, ontologies, text-based
methods, patient-specific context keys,
Systems require holistic knowledge of patient state, automatic invocation
Evaluation of logical conditions Alerts, reminders, constraints, Decision tables, event-condition-action
environment, and population inferencing systems rules, production rules
Probabilistic and data-driven Diagnosis, technology assessment, Bayes theorem, decision theory, ROC
classification or prediction treatment selection, classification and analysis, data mining, logistic regression,
prediction, prognosis estimation, artificial neural networks, belief
evidence-based medicine networks, meta-analysis
Heuristic modeling and expert systems Diagnostic and therapeutic reasoning, Rule-based systems, frame-based
capturing nuances of human expertise reasoning
Calculations, algorithms, and multistep Execution of computational processes, Process flow and workflow modeling,
processes flow-chart-based guidelines and guideline formalisms and modeling
consultations, interactive dialogue languages
control, biomedical image and signal
processing
Associative groupings of elements Structured data entry, structured reports, Report generators and document
order sets, other specialized construction tools, document
presentations and data views architectures, templates, markup
languages, ontology tools, ontology
languages
Source: Clinical Decision Support: The Road Ahead. © 2007, page 32
9
Sunday, May 29, 2011

10. NEEDS IN THE CLINICAL WORKSPACES
10
Sunday, May 29, 2011

11. Key Workspaces with Unmet Needs
• OR, ICU, Med-Surg
– Staffing & Resource shortages top list of unmet needs
associated with high-acuity environments
– Others:
• Faster/More accurate diagnoses
• Faster/unimpeded access to patient information
• Improved care protocols
• Better alerting and notification of patient status
• Treatment maps and pathways
• Risk-scoring and acuity prioritization support
Clinical Decision Support (CDS):
(1) Enables early prediction and identification of ICU patients at risk,.
(2) Allows ICU clinicians to focus their attention on critical cases, preventing
complications, reducing length of stay, and improving outcomes.
11
Sunday, May 29, 2011

12. State of Acute Care
 American College of Physicians estimates 500,000 deaths
annually in ICUs (U.S.)
 Key Drivers
 Patient safety
 Longitudinal EMR deployment
 Increase efficiency
 Staffing shortages
 Increasing numbers of CC beds
 Larger amounts of hemodynamic, respiratory, I&O
information will be automated
 Motivates enterprise integration
 Reduces charting workload
 Improves completeness, accuracy
12
Sunday, May 29, 2011

13. Surgical Intensive Care
Anesthesia
Intra-
Aortic
Balloon
Monitors
Mechanical Pumps
Ventilation
Highly Technologically-Dependent Patients
Bed
Infusion
13
Sunday, May 29, 2011

14. Types of Data Most Used in ICU
Clinical Decision Making
Data Type Value
Monitors and monitoring 13%
Observations 21%
Laboratory 33%
Drugs, I&O, IV 22%
Blood gas 9%
Other 2%
14
Sunday, May 29, 2011 Source: E.H. Shortliffe and J.J. Cimino, Biomedical Informatics Computer Applications in Health Care and Biomedicine, page 605.

15. Types of Data Most Used in ICU
Clinical Decision Making
Data Type Value
Monitors and monitoring 13%
Observations 21%
Laboratory 33%
Drugs, I&O, IV 22%
Blood gas 9%
Other 2%
15
Sunday, May 29, 2011 Source: E.H. Shortliffe and J.J. Cimino, Biomedical Informatics Computer Applications in Health Care and Biomedicine, page 605.

16. Types of Data Most Used in ICU
Clinical Decision Making
Data Type Value
Monitors and monitoring 13%
Observations 21%
Laboratory 33%
Drugs, I&O, IV 22%
Blood gas 9%
Other 2%
16
Sunday, May 29, 2011 Source: E.H. Shortliffe and J.J. Cimino, Biomedical Informatics Computer Applications in Health Care and Biomedicine, page 605.

17. Types of Data Most Used in ICU
Clinical Decision Making
Data Type Value
Monitors and monitoring 13%
Observations 21%
Laboratory 33%
Drugs, I&O, IV 22%
Blood gas 9%
Other 2%
17
Sunday, May 29, 2011 Source: E.H. Shortliffe and J.J. Cimino, Biomedical Informatics Computer Applications in Health Care and Biomedicine, page 605.

18. Types of Data Most Used in ICU
Clinical Decision Making
Data Type Value
Monitors and monitoring 13%
Observations 21%
Laboratory 33%
Drugs, I&O, IV 22%
Blood gas 9%
Other 2%
18
Sunday, May 29, 2011 Source: E.H. Shortliffe and J.J. Cimino, Biomedical Informatics Computer Applications in Health Care and Biomedicine, page 605.

19. CASE STUDY 1
Needs in the Clinical Workspaces
Case Study 2
Mobile Device Connectivity Benefits
Supporting Technologies
Summary
19
Sunday, May 29, 2011

20. CDSS Sample Case:
When to discontinue post-operative mechanical ventilation
• Discontinuation from mechanical ventilation a key activity in
surgical intensive care unit (SICU), yet, no guarantees as to
outcomes:
– When to begin spontaneous breathing trials?
– When is patient viable to be extubated?
• Discontinue as quickly as possible
– Longer time on ventilator higher likelihood of adverse events
• Ventilator acquired pneumonia
• Respiratory distress
– Can exacerbate co-morbidities
– Cost
• Candidate patients: Coronary artery bypass grafting (CABG)
– Fairly common procedure
– Technologically-dependent patients
20
Sunday, May 29, 2011

21. Source: J. Zaleski
Case Study: CABG Patient
Restart Determine
Patient
On Heart / Transfer Monitoring & Viability
Arrives in Induction Extubate
Bypass Off to SICU Management for
OR
Bypass Weaning
21
Sunday, May 29, 2011

22. Source: J. Zaleski
Case Study: CABG Patient
Restart Determine
Patient
On Heart / Transfer Monitoring & Viability
Arrives in Induction Extubate
Bypass Off to SICU Management for
OR
Bypass Weaning
Time In: 7:15 Induction: Isoflurane Pt Ht: 157 cm
CABG x 3 40 CCs fentanyl (15 g/kg) BSA: 1.7 m^2
15 mg
Pancuronium
pancuronium
Time HR (bpm) ABP (s/d) O2Sat CO (L/m) T Core T blad ETCO2 RR Vt fentanyl g lopressor Notes
mg
7:15 76 121/64 98 7 0.5
7:30
7:40
83
57
117/66
93/52
99
100
4.3
Meds & Drips
7:45 66 100/55 100 300 7
8:00 61 95/57 100 Swan in place
8:05 62 101/60 100 34.3
8:10
8:25
64
86
Continuous
97/58
132/78
100
100
34.4
34.3
34.9
34.7 29
8:30 116 Monitoring
116/76 99 34.3 35.2 27
8:35 98 116/75 99 34.2 35 29
8:40 92 112/74 100 34.1 34.9 29
8:45 100 113/70 99 34.1 34.8 29
8:50 96 112/71 99 34 34.7 29
9:00 91 97/62 99 34 34.7 31
9:05 97 109/70 100 33.9 34.5 30
9:20 93 114/68 100 33.8 34.4 31
22
Sunday, May 29, 2011
9:30 103 95/61 100 33.7 34.2 32

23. Source: J. Zaleski
Case Study: CABG Patient
Restart Determine
Patient
On Heart / Transfer Monitoring & Viability
Arrives in Induction Extubate
Bypass Off to SICU Management for
OR
Bypass Weaning
pancuronium
Time HR (bpm) ABP (s/d) O2Sat CO (L/m) T Core T blad ETCO2 RR Vt fentanyl g lopressor Notes
mg
Canula placed-
9:35 94 93/60 100 33.6 34.2 30 rt. atria;
bypassing heart
9:40 94 103/65 100 33.6 34.1 36
Core temperature reduction
9:45 94 112/67 100 33.6 34.1 36 3 mg (up)
9:50 94 113/68 100 33.6 34 33
9:55 95 103/69 100 33.6 33.9 29
Fibrillation.
10:00 99 101/68 100 33.6 33.9 28 12 0.48
Cross-Clamp
K injection
10:07
Heart stoppage 20.8
commenced
10:08 16
10:09 12
K injection
10:11 10
complete
10:15 33 32.5
10:20 32.8 32.7
Myocard temp:
10:30 32.9 33
14
10:35 33.1 33
10:45 33 33
23
Sunday, May 29, 2011
10:50 33.3 33.4 Begin re-warm

24. Source: J. Zaleski
Case Study: CABG Patient
Restart Determine
Patient
On Heart / Transfer Monitoring & Viability
Arrives in Induction Extubate
Bypass Off to SICU Management for
OR
Bypass Weaning
pancuronium
Time HR (bpm) ABP (s/d) O2Sat CO (L/m) T Core T blad ETCO2 RR Vt fentanyl g lopressor Notes
mg
10:55 33.8 33.7 5 mg (up)
250 mics
11:00 34.9 34.9 2 mg (up)
(up)
11:05 35.4 35.3
11:10 35.9 35.4
11:15 Heart restart 36 35.5 Restart / Defib
11:16 36.2 35.9
11:17 90 79/65 36.3 36
11:20 77 107/58 100 4.18 36.2 36 24 Off Bypass
11:25 79 103/56 100 35.9 35.9 25
11:30 88 103/52 100 35.5 35.9
11:35 89 106/55 100 35.4 35.7 26
11:40 96 108/61 100 35.2 35.6 24
11:45 93 115/64 100 35.1 35.5 25
11:50 93 96/53 100 34.9 35.3 23
11:55 96 112/65 100 34.8 35.1 25
12:00 108 104/62 100 34.7 35 24
12:05 105 107/66 100 34.7 34.8 24
12:10 88 103/63 100 34.6 34.4 23 2.5 mg
12:15 87 99/60 100 34.6 34.9
12:20 88 121/73 100 34.8 24 Move to SICU
Sunday, May 29, 2011 24

25. Source: J. Zaleski
Case Study: CABG Patient
Restart Determine
Patient
On Heart / Transfer Monitoring & Viability
Arrives in Induction Extubate
Bypass Off to SICU Management for
OR
Bypass Weaning
Time HR (bpm) ABP (s/d) O2Sat CO (L/m) T Core CVP PAP
12:40 100 99/62 99 5.4 34.8 6 23/10
13:15 99 99/59 100 4.48 35.3 7 24/14
13:45 104 115/63 100 5.18 35.7 10 26/15
14:15 101 102/54 98 5.18 36.3 9 25/12
14:40 98 108/53 100 5.2 36.6 18 31/14
14:50 105 128/62 99 5.2 36.6 20 39/16
14:55 104 128/62 100 5.2 36.7 19 35/18
15:00 101 128/63 100 5.2 36.7 16 35/17
15:25 102 110/58 100 5.2 36.7 18 28/13
15:50 103 107/57 100 5.2 37 32 28/15
16:45 100 107/59 100 5.2 37.1 13 30/15
17:00 104 98/56 98 5.2 37.2 13 40/21
17:20 103 97/56 97 5.2 37.3 13 32/18
17:40 100 98/57 98 5.2 37.3 12 29/16
17:45 102 94/54 98 5.2 37.3 12 31/18
19:05 104 97/58 97 5.2 37.3 13 30/18
20:15 106 99/59 97 5.2 37.5 11 31/16
21:15 101 101/60 98 4.8 37.6 15 33/19
22:35 Extubated: Vc 1.2 liters
NIF -25 cmH2O 25
Sunday, May 29, 2011

26. 10
15
20
25
30
35
40
45
0
5
OR
12:44:18
Patient
Arrives in
12:57:33
13:35:52
13:47:42
13:59:32
Sunday, May 29, 2011
14:11:23
RRsp
(/min)
14:23:13
Induction
14:35:03
RRm (/min)
14:46:53
14:58:43
15:10:34
15:22:24
15:34:15
On
15:46:05
Bypass
15:57:55
16:09:45
• pH = 7.44
16:21:35
16:33:25
16:45:16 • Time: 12:45
16:57:07
Off
17:08:57
Bypass
Heart /
Restart
• PO2 = 100 mmHg
• PCO2 = 31 mmHg
17:20:47
17:32:37
17:44:27
17:56:17
18:08:07
18:19:57
18:31:48
to SICU
18:43:38
Transfer
18:55:28
19:07:18
19:19:08
19:30:59
19:42:49
19:54:39
20:06:29
• Initial blood gas obtained upon patient arrival
20:18:20
20:30:11
Monitoring &
Management
20:42:01
20:53:51
21:05:41
21:17:31
21:29:22
21:41:12
for
21:53:02
Viability
Weaning
Determine
Case Study: CABG Patient
Extubate
26
Source: J. Zaleski

27. Source: J. Zaleski
Case Study: CABG Patient
Restart Determine
Patient
On Heart / Transfer Monitoring & Viability
Arrives in Induction Extubate
Bypass Off to SICU Management for
OR
Bypass Weaning
• Patient initially supported by
45 mechanical ventilator on synchronous
40
RRm (/min)
intermittent mandatory ventilation
35 RRsp
(/min)
(SIMV) mode of 12 breaths per
30 minute, tidal volume of 0.85
25 liters, PEEP of 5 cmH2O
20
• Patient spontaneous breathing is absent upon
15
arrival due to the anesthesia and paralytic drugs
10 administered during surgery
5
0
12:44:18
12:57:33
13:35:52
13:47:42
13:59:32
14:11:23
14:23:13
14:35:03
14:46:53
14:58:43
15:10:34
15:22:24
15:34:15
15:46:05
15:57:55
16:09:45
16:21:35
16:33:25
16:45:16
16:57:07
17:08:57
17:20:47
17:32:37
17:44:27
17:56:17
18:08:07
18:19:57
18:31:48
18:43:38
18:55:28
19:07:18
19:19:08
19:30:59
19:42:49
19:54:39
20:06:29
20:18:20
20:30:11
20:42:01
20:53:51
21:05:41
21:17:31
21:29:22
21:41:12
21:53:02
27
Sunday, May 29, 2011

28. Source: J. Zaleski
Case Study: CABG Patient
Restart Determine
Patient
On Heart / Transfer Monitoring & Viability
Arrives in Induction Extubate
Bypass Off to SICU Management for
OR
Bypass Weaning
45
RRm (/min)
• Second blood gas obtained
40
RRsp
• Time: 14:00
35
(/min) • pH = 7.41
30
• PCO2 = 29 mmHg
25 • PO2 = 202 mmHg
20
15
• Decision made to reduce ventilatory support
10
5
0
12:44:18
12:57:33
13:35:52
13:47:42
13:59:32
14:11:23
14:23:13
14:35:03
14:46:53
14:58:43
15:10:34
15:22:24
15:34:15
15:46:05
15:57:55
16:09:45
16:21:35
16:33:25
16:45:16
16:57:07
17:08:57
17:20:47
17:32:37
17:44:27
17:56:17
18:08:07
18:19:57
18:31:48
18:43:38
18:55:28
19:07:18
19:19:08
19:30:59
19:42:49
19:54:39
20:06:29
20:18:20
20:30:11
20:42:01
20:53:51
21:05:41
21:17:31
21:29:22
21:41:12
21:53:02
28
Sunday, May 29, 2011

29. Source: J. Zaleski
Case Study: CABG Patient
Restart Determine
Patient
On Heart / Transfer Monitoring & Viability
Arrives in Induction Extubate
Bypass Off to SICU Management for
OR
Bypass Weaning
45
• Support reduced to 8 br/min
RRm (/min)
40
35 RRsp • Some spontaneous breathing.
(/min)
30
Clinicians choose to evaluate and
25
await re-warming and third blood gas
20
before attempting spontaneous
15
breathing trial
10
5
0
12:44:18
12:57:33
13:35:52
13:47:42
13:59:32
14:11:23
14:23:13
14:35:03
14:46:53
14:58:43
15:10:34
15:22:24
15:34:15
15:46:05
15:57:55
16:09:45
16:21:35
16:33:25
16:45:16
16:57:07
17:08:57
17:20:47
17:32:37
17:44:27
17:56:17
18:08:07
18:19:57
18:31:48
18:43:38
18:55:28
19:07:18
19:19:08
19:30:59
19:42:49
19:54:39
20:06:29
20:18:20
20:30:11
20:42:01
20:53:51
21:05:41
21:17:31
21:29:22
21:41:12
21:53:02
29
Sunday, May 29, 2011

30. Source: J. Zaleski
Case Study: CABG Patient
Restart Determine
Patient
On Heart / Transfer Monitoring & Viability
Arrives in Induction Extubate
Bypass Off to SICU Management for
OR
Bypass Weaning
45 • Third blood gas obtained
40
RRm (/min)
• Time: 16:35
35 RRsp • pH = 7.40
(/min)
30 • PCO2 = 37 mmHg
25
• PO2 = 183 mmHg
20
• Re-warming complete
15
• Decision made to reduce to CPAP in
10
preparation for spontaneous breathing
5
trials
0
12:44:18
12:57:33
13:35:52
13:47:42
13:59:32
14:11:23
14:23:13
14:35:03
14:46:53
14:58:43
15:10:34
15:22:24
15:34:15
15:46:05
15:57:55
16:09:45
16:21:35
16:33:25
16:45:16
16:57:07
17:08:57
17:20:47
17:32:37
17:44:27
17:56:17
18:08:07
18:19:57
18:31:48
18:43:38
18:55:28
19:07:18
19:19:08
19:30:59
19:42:49
19:54:39
20:06:29
20:18:20
20:30:11
20:42:01
20:53:51
21:05:41
21:17:31
21:29:22
21:41:12
21:53:02
30
Sunday, May 29, 2011

31. 10
15
20
25
30
35
40
45
0
5
OR
12:44:18
Patient
Arrives in
12:57:33
13:35:52
13:47:42
13:59:32
Sunday, May 29, 2011
14:11:23
RRsp
(/min)
14:23:13
Induction
14:35:03
RRm (/min)
14:46:53
14:58:43
15:10:34
15:22:24
15:34:15
On
15:46:05
Bypass
15:57:55
16:09:45
16:21:35
16:33:25
16:45:16
• Respirations, RSBI normal
16:57:07
Off
17:08:57
Bypass
Heart /
Restart
17:20:47
17:32:37
17:44:27
17:56:17
18:08:07
18:19:57
18:31:48
to SICU
18:43:38
Transfer
18:55:28
19:07:18
19:19:08
19:30:59
19:42:49
19:54:39
20:06:29
20:18:20
20:30:11
Monitoring &
Management
20:42:01
20:53:51
21:05:41
21:17:31
21:29:22
21:41:12
for
21:53:02
Viability
Weaning
Determine
Case Study: CABG Patient
Extubate
31
Source: J. Zaleski

32. Key Parameters Used to Determine
Viability for Extubation
Parameter Threshold Value/Range Our Patient
Vital Capacity, Vc > 10mL/kg
Positive End-Expiratory 5 cm H2O
Pressure, PEEP
Negative Inspiratory Force, NIF -20 cm H2O
Inspired Oxygen Fraction, FiO2 < 0.6
Spontaneous Tidal Volume, Vt > 5 mL/kg
Parameters,
Spontaneous Respirations Value Rresp < 30
8 < Thresholds, Patient Values,
P i Vpth Vpti
Blood Alkalinity/Acidity 7.32 < pH <i 7.48
Partial Pressure of Oxygen, PO2 > 80 mmHg
Partial Pressure of Carbon Dioxide, 30 mmHg < PCO2 < 50 mmHg
PCO2
Normal Body Temperature, Tcore ~37 C
Ventilation Mode CPAP
32
Sunday, May 29, 2011

33. Key Parameters Used to Determine
Viability for Extubation
Parameter Threshold Value/Range Our Patient
Vital Capacity, Vc > 10mL/kg
Positive End-Expiratory 5 cm H2O
Pressure, PEEP
Negative Inspiratory Force, NIF -20 cm H2O
Inspired Oxygen Fraction, FiO2 < 0.6
Spontaneous Tidal Volume, Vt > 5 mL/kg
Spontaneous Respirations 8 < Rresp < 30
Blood Alkalinity/Acidity 7.32 < pH < 7.48
Partial Pressure of Oxygen, PO2 > 80 mmHg
Partial Pressure of Carbon Dioxide, 30 mmHg < PCO2 < 50 mmHg
PCO2
Normal Body Temperature, Tcore ~37 C
Ventilation Mode CPAP
33
Sunday, May 29, 2011

34. Key Parameters Used to Determine
Viability for Extubation
Parameter Threshold Value/Range Our Patient
Vital Capacity, Vc > 10mL/kg
Positive End-Expiratory 5 cm H2O
Pressure, PEEP
Negative P1
Inspiratory Force, NIF -20 cm H2O
Inspired Oxygen Fraction, FiO2 < 0.6
P2
Spontaneous Tidal Volume, Vt Parameters Used to Determine
Key > 5 mL/kg Extubation Viability
P3
Spontaneous Respirations 8 < Rresp < 30
Blood…
Alkalinity/Acidity 7.32 < pH < 7.48 CDSS
Partial Pressure of Oxygen, PO2 > 80 mmHg
Partial Pressure of Carbon Dioxide, <
Vpt1 30 mmHg < PCO2 < 50 mmHg
Vpt2 < Vpti <
PCO2 … Action
Vpth1 Vpth2 Vpthi
Normal Body Temperature, Tcore ~37 C
Ventilation Mode CPAP 34
Sunday, May 29, 2011

35. 10
15
20
25
30
35
40
45
0
5
OR
12:44:18
Patient
Arrives in
12:57:33
13:35:52
13:47:42
13:59:32
Sunday, May 29, 2011
14:11:23
RRsp
(/min)
14:23:13
Induction
14:35:03
RRm (/min)
14:46:53
14:58:43
15:10:34
15:22:24
15:34:15
On
15:46:05
Bypass
15:57:55
16:09:45
16:21:35
16:33:25
16:45:16
• Respirations, RSBI normal
16:57:07
Off
17:08:57
Bypass
Heart /
Restart
17:20:47
17:32:37
17:44:27
17:56:17
18:08:07
18:19:57
18:31:48
to SICU
18:43:38
Transfer
18:55:28
19:07:18
19:19:08
19:30:59
19:42:49
19:54:39
20:06:29
20:18:20
20:30:11
Monitoring &
Management
20:42:01
20:53:51
21:05:41
• Vc = 1.2 liters
21:17:31
21:29:22
and in normal range
21:41:12
• NIF = -24 cmH2O
for
21:53:02
Viability
Weaning
Determine
Case Study: CABG Patient
• Vital capacity & NIF test performed
Extubate
35
Source: J. Zaleski

36. 10
15
20
25
30
35
40
45
0
5
OR
12:44:18
Patient
Arrives in
12:57:33
13:35:52
13:47:42
13:59:32
Sunday, May 29, 2011
14:11:23
RRsp
(/min)
14:23:13
Induction
14:35:03
RRm (/min)
14:46:53
14:58:43
15:10:34
15:22:24
15:34:15
On
15:46:05
Bypass
15:57:55
16:09:45
16:21:35
16:33:25
16:45:16
16:57:07
Off
17:08:57
Bypass
Heart /
Restart
17:20:47
17:32:37
17:44:27
17:56:17
18:08:07
18:19:57
18:31:48
to SICU
18:43:38
Transfer
18:55:28
19:07:18
19:19:08
19:30:59
19:42:49
19:54:39
20:06:29
20:18:20
20:30:11
Monitoring &
Management
20:42:01
20:53:51
21:05:41
21:17:31
could have led to earlier extubation
21:29:22
21:41:12
for
21:53:02
Updated real-time knowledge of patient data
Viability
Weaning
Determine
Case Study: CABG Patient
Extubate
36
Source: J. Zaleski

37. Key Parameters Used to Determine
Viability for Extubation
Data suggest attempts at Threshold Value/Range trials could begin much
Parameter spontaneous breathing Our Patient
Vital Capacity, Vc sooner than 10mL/kg occurred
> actually 1.2L (70 kg)
Positive End-Expiratory 5 cm H2O 5 cm H2O
Pressure, PEEP
Negative Inspiratory Force, NIF -20 cm H2O -24 cm H2O
Inspired Oxygen Fraction, FiO2 < 0.6 0.35
Spontaneous Tidal Volume, Vt > 5 mL/kg 0.55L (70 kg)
Spontaneous Respirations 8 < Rresp < 30 ~20
Blood Alkalinity/Acidity 7.32 < pH < 7.48 7.4
Partial Pressure of Oxygen, PO2 > 80 mmHg 183 mmHg
Partial Pressure of Carbon Dioxide, 30 mmHg < PCO2 < 50 mmHg 37 mmHg
PCO2
Normal Body Temperature, Tcore ~37 C ~37 C
Ventilation Mode CPAP CPAP
37
Sunday, May 29, 2011

38. Workflow Considerations
• Data show patient meets extubation criteria many hours
before actual extubation
– Indicates clear benefit of utilizing these data for patient care
– Simple reminders to staff can achieve great benefits for patient
• Notification of readiness to wean important for clinical
workflow, patient care management
– Is patient viable or is it too early?
– Any co-morbidities that can influence the outcome?
– All necessary staff so informed and aligned on plans?
• Notification as to life-threatening events requires up-to-
date and accurate information
– Hemodynamic instabilities/Shock
– Respiratory distress
38
Sunday, May 29, 2011

39. CASE STUDY 2
Case Study 1
Mobile Device Connectivity Benefits
Supporting Technologies
Summary
39
Sunday, May 29, 2011

40. HEART RATE VARIABILITY MONITORING
& SEPSIS ONSET
• SEPSIS W/ ACUTE ORGAN
DYSFUNCTION
– IS LEADING CAUSE OF DEATH IN
NON-CORONARY ICU;
– ACCOUNTS FOR MORE THAN
750,000 DIAGNOSED CASES IN
US ANNUALLY1,2,3
• CLINICAL STUDIES: CHANGES IN
HRV HERALD ONSET OF SEPSIS http://biology.about.com/library/organs/heart/blsinoatrialnode.htm
BLOOD BORNE INFECTIONS IN
ADULTS4
1 MedScape Today; 2http://www.procalcitonin.com/default.aspx?tree=_2_0&key=intro1 ;
3http://www.survivingsepsis.org/Pages/default.aspx
4Saif
Ahmad et al., “Continuous Multi-Parameter Heart Rate Variability Analysis Heralds
Onset of Sepsis in Adults.” PLoS ONE, August 2009 | Volume 4 | Issue 8
40
Sunday, May 29, 2011

41. SIGNIFICANCE: ONSET OF SEPSIS CORRELATED TO HRV
IN CONTINUOUSLY MONITORED ADULTS (AHMAD ET AL)
• 24 HOUR HOLTER MONITOR OF PATIENTS UNDERGOING BONE MARROW
TRANSPLANTS (BMT):
– HIGH-RISK GROUP OF PATIENTS, OWING TO HIGH RISK OF INFECTION (80%) &
MORTALITY (5%)
– START MONITORING 1 DAY PRIOR TO BMT, CONTINUING THROUGH RECOVERY OR
WITHDRAWAL (HOLTER MONITORING: ZYMED DIGITRACK-PLUS)
• MONITORED RR INTERVALS OF NORMAL SINUS RHYTHM (NSR) BEATS:
RR Interval
Saif Ahmad et al., “Continuous Multi-Parameter Heart Rate Variability Analysis Heralds
Onset of Sepsis in Adults.” PLoS ONE, August 2009 | Volume 4 | Issue 8
41
Sunday, May 29, 2011

42. KEY STUDY FINDINGS
• ONSET OF SEPSIS DETERMINATION:
– SYSTEMIC INFLAMMATORY RESPONSE SYNDROME (SIRS) W/
CLINICALLY SUSPECTED INFECTION REQUIRING TREATMENT
• 17 PATIENTS OF 21 COMPLETED STUDY:
– 14 PATIENTS DEVELOPED SEPSIS, REQUIRING ANTIBIOTIC THERAPY
– 12 OF 14 INFECTED PATIENTS (86%) SHOWED 25% DROP IN HRV 35
HOURS (AVE) PRIOR TO SEPSIS ONSET
– NO SIGNIFICANT DROP REPRESENTED IN NON-INFECTED POPULATION
• PROMISING: ONSET CORRELATION DETERMINED USING SIMPLE
MEASUREMENTS TYPICALLY AVAILABLE W/O EXPENSIVE LAB TESTS
42
Sunday, May 29, 2011