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Clinical Research Informatics in Pediatric Critical Care Clinical Research Informatics in Pediatric Critical Care

Clinical Research Informatics in Pediatric Critical Care - PowerPoint Presentation

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Clinical Research Informatics in Pediatric Critical Care - PPT Presentation

J Michael Dean MD MBA Katherine Sward PhD RN Context Critically ill and injured children typically receive care in the ED andor the pediatric intensive care unit PICU A spectrum of heterogeneous conditions lead to need for intensive care ID: 718945

care protocol pediatric data protocol care data pediatric ventilation research adult clinical critical ventilator report amp registry mechanical pecarn

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Slide1

Clinical Research Informatics in Pediatric Critical Care

J. Michael Dean, MD, MBA

Katherine Sward, PhD, RNSlide2

Context

Critically ill and injured children typically receive care in the ED

and/or

the pediatric intensive care unit (PICU). A spectrum of heterogeneous conditions lead to need for “intensive care” : traumatic brain injury, other traumas, lung injury, sepsis, postoperative careEpstein, D. & Brill, J.E. (2005). A history of pedicatric critical care medicine. Pediatric Research, 58, 987–996; doi:10.1203/01.PDR.0000182822.16263.3DUnfortunately, much of the technology and many therapies in pediatric critical care have evolved without adequate study or have been adopted uncritically from adult, neonatal, or anesthetic practice… NIH/NICHD has a new branch (Pediatric Trauma and Critical Illness), recognizing the need for research in this environment. Rigorous use of appropriate scientific methodology, deployed across a network structure, achieves the numbers of patients required to provide answers...

http://www.nichd.nih.gov/research/supported/Pages/cpccrn.aspx

http://www.pecarn.org/

http://cpccrn.org/Slide3

Context

Clinical Research Informatics

involves the use of informatics tools and methods in the discovery and management of new knowledge relating to health and disease.

It includes management of information related to clinical trials and … secondary research use of clinical data. http://www.amia.org/applications-informatics/clinical-research-informaticsSlide4

Context

Intensive Care Informatics – a relatively new working group in AMIASlide5

Data Coordinating Center

U of Utah Division of Pediatric Critical Care encompasses (but is not limited to): clinical services, Intermountain Injury Control Research Center, and a

data coordinating center

that supports a large number of research networks and clinical studies.Informatics tools and methods are threaded throughout DCC activities study conception & protocol development LaTex, GitHub data modelsdata collection standard CRFs, TrialDB OpenClinica RedCap CheckBoxcustom software developmentdata quality

QueryManagerdata analysis and publicationnetwork communications and logistics

eRoom, teleconferencingregulatory/compliance monitoring; IT infrastructure (FISMA; HIPAA …)

http://medicine.utah.edu/pediatrics/critical_care/Slide6

Two examples

PECARN: registry project (Dean)

Pediatric emergency care applied research network.

CPCCRN: CDS tools (Sward)Collaborative pediatric critical care research networkSlide7

PECARN

TBI prediction CDS

Neuroimaging decision rule

Decision rule for intra-abdominal injuriesPediatric Emergency Care Quality *http://www.pecarn.org/pecarnNetwork/documents/BrochureFall2013.pdfSlide8

PECARN Registry Protocol Objectives

Develop the registry by

merging EHR data from participating

EDs. Use the registry to collect stakeholder-prioritized performance improvement measuresReport performance improvement measures to individual clinics and to sites and measure subsequent changes in quality performance.Slide9
Slide10

PECARN Registry Protocol

Study Procedures

Database Construction

Deidentification ProceduresNatural Language Processing ProceduresDetermining Benchmarks for Report CardReport Card FeedbackSlide11

PECARN Registry Protocol

Database Construction

Identify potential sources of relevant data elements in the specific EHR at each site.

Finalize the types of data elements that will be extracted.Extract data for one day of data at each clinical site.Transmit one day data to the DCC for de-identification.Establish de-identification procedure at each clinical site.Extract and de-identify one month data from calendar year (CY) 2012 at each site.

Transmit one month de-identified data to DCC from each site.Finalize and test import procedures from one month extracts into Registry.

Analyze frequencies of missing, out of range, or unexpected values for key data elements.Extract, de-identify, and transmit entire CY 2012 from each site to the DCC.Create Registry with entire CY 2012 from all participating sites.Slide12

The process

What it really looks likeSlide13

PECARN Registry ETLSlide14

PECARN Registry ETLSlide15
Slide16
Slide17
Slide18

Report Card Development:Expert Panel

Panel met on 2/24/14 to review data for pre-selected Performance Measures

Develop “ideal” benchmarks

ABC calculated by stats, presented in summary & full documentsNew methodology, difficult to understandWorks well for dichotomous; continuous causes confusionDefinitions of Performance Measures ever changingHuge stats effortsSlide19
Slide20
Slide21
Slide22

Report Card:Designing

Formatting

the Report Card,

SimplifyFinalize Performance Measure definitions (yes. Still changing…)~17 Performance Measures, reported with 4-5 benchmarks, want visuals & simple reportsWhat will we looseComparison with past data, visual, quickRedesign data warehouse to improve performanceSlide23

Examples of Report Card draft todaySlide24
Slide25

Differences between development & production

Automation (eventually)

Only data needed for report cards is kept ‘active’

4 months of data kept in active database for trendlinesOlder data is archivedData is locked & not allowed to update after a cut-offIncludes grouped & manually derived dataData becomes more static – no resubmissions past deadlines!Slide26

Next Steps

Data collection

Real-time” monthly submission of 2014 dataTest the whole production cycleWhat happens when we really do this? How does it look?Report Card deliveryAutomation is critical, several IT methods/approaches – will develop over timeStart small – email a report cardMeasuring provider improvement once they get a Report CardStaggered startsCollect Report Card feedback from providersImplement into the Report Card – improve!Slide27

Future directions?

Conversations about data for “this study” (Report Cards)

vs

“registry as a whole” (future uses, add-on projects)Adding more sites? Roll-up?Adding more data?Using the data for clinical trials?Slide28

CDS toolsSlide29

CDS tools

ICU is information-dense environment (information overload is likely)

Many interventions in PICU were adopted from adult practice, neonatal practice, anesthesiology, or other areas – lack evidence from pediatric environment For many critical care conditions, the “intervention” is not single point in time, but is the cumulative effect of multiple decisions and actions across daysSlide30

Critical Care Medicine, 2008Slide31

Even for a relatively simple

protocol like glucose/insulin

A single “decision” in reality

requires multiple steps thatare conducted in sequence.Challenge – production rulessystems like Drools are designedto run every rule at the same time.Replicating eProtocol-insulinallowed us to validate the Java/Drools approach – could wegenerate the same recommendations

in both versions of the software.A subsequent project (Hypertonic Saline) allowed us to examine issues related to

timing – labs not synchronous wclinical data entry

Each node is a set of rulesNext project : ventilator management for ARDS/ALISlide32

ARDS in children

Estimates of ARDS in children range from 1.4 – 2.8% of all PICU admissions

Estimated: 3-4 ARDS cases per year per 100,000 population < 15 years of age

The one prospective US publication suggested a rate triple this at 9.5 per year per 100,000 admissionsTherefore, likely there are 1800 – 5700 cases per year in US pediatric population < 15 years of age (2011 US Census = 60*106)Hence, ARDS remains a significant Public Health issue as overall there is a significant (~18%) rate of mortality

ALI is less severe form…even more common?Slide33

Adult vs P

ediatric ICU

Developmental differences and differences in clinical practices may contribute to the

selective application of evidence derived in other settings. Ventilator modes such as high-frequency oscillatory ventilation (HFOV) are more common in the pediatric setting than in adults, for example; while invasive arterial monitoring is increasingly less common in the PICU.Khemani, R & Newth, CJL (2010). The design of future pediatric mechanical ventilation trials for acute lung injury. Am J Respir Crit Care Med 182, 1465–1474Slide34

Adult vs Pediatric ICU

Inspired oxygen fraction changes

Size of FiO2 change (0.1

vs. 0.05)SpO2 ranges: <88, 88-93, >93%; PaO2 ranges: <55, 56-68, >68

TorrpH ranges

Adult: >7.45, 7.30 – 7.45, 7.15 – 7.29, <7.15Pediatric: >7.45, 7.34 – 7.45, 7.25 – 7.34, 7.15 – 7.24, <7.15Body weight for calculating tidal volume

Adult: predicted BW (obesity, BW calculated from height & gender)Pediatric: actual BW (obesity & FTT, contractures; now formula for height from ulnar length )

Tidal Volume (VT exhaled)

measurement

Adult: measured at ventilator – use SET volume

Pediatrics: should be measured at ETT

Mode of Ventilation

ARDSNet – volume controlled

Pediatric

– Pressure control - PC or PRVC (volume targeted)

Evolution in thinking re HFOV modeSlide35

Mechanical ventilation

Frequently used

intervention in ICU

Care of patients on mechanical ventilator was a motivating factor in the development of ICUs (Watson, R.S. & Hartman, M.E. (2009). Epidemiology of Critical Illness. In D.S. Wheeler et al. (eds). Science and Practice of Pediatric Critical Care Medicine. London: Springer-Verlag.)Primary treatment for respiratory failure (ARDS/ALI). Also a common intervention for other conditions.20-64% (mean 30%) of PICU children require mechanical ventilation for some portion of their stayA common outcome measure in pediatric trials (vent free days, days on ventilator, etc.)Labor intensive, accounts for disproportionate amount of resource usage and costs12% of overall hospital costs (Wunsch, H., Linde-Zwirble

, W.T., Angus, D.C. et al. (2010). The epidemiology of mechanical ventilation use in the United States. Crit Care Med, 38 (10), 1947-53)

Although life saving, mechanical ventilation has inherent risks (Bezzant & Mortensen, 1994; Newth et al., 2014)Oxygen toxicityBarotrauma, pneumothorax, damage to lung tissue from excessive pressure, volume, and flow

Complications from intubation (tracheal damage)Ventilator associated pneumoniaDangers from drugs, stress; nutritional problemsDiscomfort, pain, distress

High volume, High cost, High riskSlide36

Need for MV protocols

Heterogeneous patient characteristics

Many possible causes of ALI/ARDS

Best/optimal practices are not well understood Slide37

MV protocols

Reported

benefits of MV protocols

in adult ICUs include decreased duration and costs of mechanical ventilation and improved collaboration between health care team membersSlide38

Variable results in peds

Schultz et al (2001) showed reduced

time to

extubationRandolph et al (2002) showed no decrease in weaning time. two complex paper-based protocol arms with poor compliance in each armBoth of these studies were limited to the weaning phase alone. Slide39

Mechanical Ventilation Course

End time

Extubation

MV

course

Acute phase

Weaning

Stable

Intubation

Intubation

criteria

Stabilization

criteria

Weaning

Readiness

test

Extubation

Readiness

test

Weaning

extubation

failure

Extubation

criteria

End

NIV

criteria

NIV

criteria

Definitions

Weaning

Stabilization

“Routine management”Slide40

Variable results in peds

Variability

occurs throughout the entire length of ventilator

management, not just the weaning phase.Restepro et al (2004) found reduced time to spontaneous breathing but no difference in overall ventilator duration. That study used a paper protocol to manage the overall course of ventilation, but the authors noted as a limitation their inability to determine compliance with the protocol. Slide41

MV research in peds

Willson

et

al. used a paper protocol outlining a broadly defined lung protective strategy. Curley et al. used the adult ARDSNet ventilation paper protocol. Neither manuscript addressed protocol compliance.Neither protocol was explicit Jouvet et al.17 used a closed loop protocol for mechanical ventilation, but provided no details regarding its derivation from an adult protocol . Slide42

Intensive Care Medicine, 2009

Providers say they

adopted

ARDSnet

lung

protective ventilation – but we saw high variability in practices (single center)Slide43

MV in CPCCRN

Early

CPCCRN ideas about trials of different modes of

ventilation… Led to discussions of outcomes, paper on weaning and extubation, recognition that reducing practice variation can improve signal-to-noise (both for studies of MV; and for studies in which MV is a surrogate outcome.)This led to thinking about how MV decisions are made in the PICU … which led to our R21 grantSlide44

MV Protocol - Adult

ARDSnet

studies – most sites used a paper based protocol

Intermountain: Tom East, Alan Morris, and colleagues developed an explicit, computer-based protocol for mechanical ventilation, for care of adult ICU patients with ALI/ARDSMV purposes (simplified)Increase oxygen levelReduce CO2 (reflected in the pH)

Protocol has rule sets for

each (oxygenation and “ventilation”)Different sets for different MODE and patient stateSlide45

Translation for Pediatrics

CPCCRN investigators

recommended

changes to adult protocol that they believed were necessary for practitioners at their site to accept the protocol recommendations.Most of the changes were a matter of granularity (size)* We also planned to update infrastructure from VB to Java/DroolsSlide46

eProtocol MV – Peds

KE challenges

Complexity

multiple modes (VC, PRVC, PC, HFOV, extubated)rules evaluating timing of ABG Non-invasive (O2 sat) vs invasive (pO2) measuresLarge rule set2824 “main” rules, plus othersDROOLS file 56,410 linesLittle evidence of “best” or optimal practices

e.g., increasing PEEP vs increasing FiO2 for low oxygenationSlide47

R21

1. Examine usual

care

ventilator management practicescompared to what the protocol would have recommendedPremise – when usual care and protocol are similar, the rule is probably going to be seen by clinicians as acceptableWhen different – either rule needs to be examined, OR this is an opportunity to improve careProspective observational study – 8 hospitals2. Examine issues of granularity – larger versus smaller changes; is this affected by large versus small child? And issues of potential acceptability of computer protocolSurvey with 50 fabricated scenarios. ICU attending MD and fellows.Included attitude questions from UTAUTChose survey approach to focus on content (rules/recommendations), rather than delivery method (CDS)Slide48

Δ

N

2449

%

No

Δ

1564

63.9%

363

14.8%

561

23%

↑↓

39

1.6%

Actual Changes Made

to PIP or VR (ventilation)

Stratified actual changes

by protocol bins…compared

what was done

to what protocol would have

recommended.Slide49

Single Institution

PC mode MV

N = 1484 ALI/ARDS

Heat map: correspondence between usual care and protocol rulesSlide50

We found WIDE variability in usual care practices within sitesSlide51

PEEP/FIO2 Data – 8 CPCCRN PICUs

ALI

2012

120

Patients

3894

ventilator

changes

We found WIDE variability in usual care practices across sitesSlide52

Set and forget?

Little change in mode

across course of care

Within a mode – themost frequent decisionwas to NOT CHANGEany setting.Slide53

Aim 2

What are providers WILLING to do, at least in the context of a research study?

Even though they might not make changes to settings in usual care – are they willing to make changes if asked to do so?

Are they willing to have the research protocol communicated by means of a computer protocol (or are they resistant to the idea of using computer protocol)?Slide54

UTAUT constructs

Performance

expectancy

Effort expectancyAttitudes toward technologySocial influenceFacilitating conditionsAnxietySelf-efficacy63.7% agree/strongly agree “using a computer protocol for ventilator management is a good idea”and 68% agree that they have the knowledge necessary to use acomputer protocolBut want help files or a person who can assist with learning to use the protocolSlide55

Granularity, thresholds, modes

About evenly split (36.4%, 38.8%) on whether evaluation for ventilator change should occur every 2 or every 4

hrs

48% ventilate to the weight on admission to the PICU, 35.8% use predicted body weightWide range of responses as to what OI would trigger change from conventional to HFOV modeAim 1 showed VC/AC mode was rare in our sites but 71.9% say they use this mode in their PICUSlide56

Acceptability

Overall ~ 80% accepted

Higher

acceptance for FiO2 instructions (> 95%)Acceptance rate varied by mode (highest acceptance for HFOV recommendations)Recommendations “like protocol” were accepted at a higher rate than recommendations “not like” protocol – except lower acceptance for PEEP instructionsPrefer to adjust FiO2 rather than PEEP to support oxygenation?Clinicians seem uncomfortable with how high the protocol might push pressures (PEEP and PIP)Clinicians WILL decline “poor” recommendations (they stay vigilant) Slide57

eVentilator

– Pediatric Version, May 2013Slide58

New CDS tool: Java/DroolsSlide59

Architecture

Core

Domain specific extension to core

GUI, IE, knowledge base, pt data etc. are separatedSlide60

FDA IDE

FDA considers this type of software a

“device”:

software that…accepts clinical findings … and generates recommendations for treatment…FDA may or may not choose to require an IDE If so this requires extensivedocumentation and several types of validationSlide61

FDA-IDE

Working with colleagues in a U of Utah

basic science lab for technical

and safety evaluation of the CDS tool (e.g., run the software over extended time and watch for “crashes”), initial evaluations of usability, initial estimates of impact on workflow…(naïve users; willing to enter data hourly 24x7)Slide62

Future Directions

Infrastructure updates

model driven architecture

JFX – GUI authoringOther delivery platforms for multi-site researchWeb based?Mobile?Map the terminology; represent knowledge according to standards – Health eDecisions format?Slide63

Alvin Feinstein, MD, 1977 (Yale)