computerized predictive model of distress/distraction
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TRANSCRIPT
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Computerized Predictive Computerized Predictive Model of Model of
Distress/DistractionDistress/Distraction
Computerized Predictive Computerized Predictive Model of Model of
Distress/DistractionDistress/DistractionAnn Marie McCarthyAnn Marie McCarthy
The University of IowaThe University of Iowa
MNRS, 2009MNRS, 2009Pediatric Nursing PreconferencePediatric Nursing Preconference
NINR NINR 1 R01 NR05269-01; Funding from 2002-20111 R01 NR05269-01; Funding from 2002-2011
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Translation Science – Research into Practice
ClinicalProblem
Research StudyDIG
Clinical Practice
CPMD
Research StudyCPMD
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Research Team•University of Iowa, Iowa City IA
– Ann Marie McCarthy, Primary Investigator– Charmaine Kleiber, Co-PI– Deb Schutte, Investigator– Kirsten Hanrahan, Program Associate– Nancy Weathers, Project Director– Brenda Gordley, Research Assistant– Bridget Zimmerman, Nick Street, Jeff Murray,
Consultants•Blank Children’s Hospital, Des Moines IA
– Susan Allen, Investigator– Julia Krapfl, Research Assistant
•St. Louis University, St. Louis MO– Nina Westhus, Investigator
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Background• Procedural pain is a major source of
distress in children.• Distraction- diverting a child’s attention
away from the medical procedure and focusing on other things- can decrease child distress with procedures.
• We can teach parents to use distraction to decreases child distress.
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Long Term Goal
To develop a clinically useful predictive distress profile that can be used to identify interventions appropriate to individual family needs
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Predicting Children’s Response to Distraction from Pain
orDistraction Intervention Grant (DIG)
NIH/NINR NIH/NINR 1 R01 NR05269; Funding from 2002-20071 R01 NR05269; Funding from 2002-2007
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DIG: Purpose
To identify predictors of child response to a peripheral IV insertion when a parent provides distraction
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DIG: Methods
• Design: Random assignment to control or experimental groups
• Sample: 542 children, 4-10 years old, planned IV insertion
• Intervention: – Educational material & discussion– Video– Distractors
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Participants -All SiteEnrolled W/draw Refusals
UIHC 306 2 *language, child uncooperative
85 (22%)
CG 149 1 *parent not allowed in room
23 (13%)
Blank 87 1 *lack of data 66 (42%)
Totals 542 4 174 (24%)
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Model: Child Responses to Painful Medical Procedures When Distraction is Provided By a Trained Parent
Parent Response
• Distraction Performance• Perception of Success and Satisfaction with Procedure & Intervention
Parent Characteristics• Ethnicity• Gender• Experience• Credibility and Efficacy of Intervention• Parenting Style• Anxiety
Child Characteristics
• Age • Temperament• Gender • Ability to Attend• Diagnosis • Anxiety• Ethnicity • Coping Styles• Experience • Genotype• Severity of Illness • Pain Sensitivity
Child's Perception ofPsychological and
Physical Discomfort
Child's Response
• Behavioral• Biological• Self Report of Pain• Parent Report of Child Distress
Extraneous Procedural Variables
• Difficulty of Procedure• Use of Topical Analgesics• Enviroment Variables• HCP Variables
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Predictors and Outcomes
• Predictors: Child, parent, procedure variables– Genotype
• Child Outcomes:– Behavioral: Observation Scale of Behavioral
Distress-Revised (OSBD-R) (Elliott et al, 1987; Jay et al, 1983; Jay & Elliott, 1984)
– Biological: Salivary cortisol levels (Kirschbaum & Hellhammer, 1994)
– Self Perception of Pain: Oucher Scale (Aradine et al., 1988; Beyer & Aradine, 1988)
– Parent Perception of Child Distress (Kazak et al, 1996)
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DIG Predictors of High DistressBehavioral Physiological Child Report Parent Report
•Young child
•Childs coping style
•Parent expectation of highly distressed child during IV
•Low quality Distraction coaching
•Parent perception of highly distressed child the morning of procedure
•No diagnosis of ADHD
•Diagnosis of anxiety disorder
•Child finds out about IV at time of procedure
•Low level of Distraction
•Young child
•High impulsivity
•Parent expectation of highly distressed child during IV
•Requires special assistance at school
•Young child
•Child’s typical coping style is loud and demonstrative
•Parent expectation of highly distressed child during IV
•Parent perception that child is highly distressed during routine clinic visits
•Child prefers to watch procedure
•Low level of Distraction
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Developing and Testing the Computerized Predictive Model for
Distress/Distraction
NIH/NINR NIH/NINR 1 R01 NR05269; Funding from 2007-200111 R01 NR05269; Funding from 2007-20011
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Next Step
Extraneous Procedural Variables
• Difficulty of Procedure• Use of EMLA
Child's Response
• Behavioral• Biological• Self Report of Pain• Parent Report of Child Distress
• Age • Anxiety• Gender • Coping Styles• Ethnicity • Ability to Attend• Experience • Genotype• Temperament • Illness Severity• Diagnosis • Pain Sensitivity
Parent Response
• Distraction Performance• Perception of Success and Satisfaction with Procedure & Intervention
Parent Characteristics• Gender• Ethnicity• Experience• Anxiety• Parenting Style• Credibility and Efficacy of Intervention
Customized Intervention
Model of Child Responses to Painful Procedures when Distraction is Provided by a Trained Parent(McCarthy & Kleiber, 2006).
Child Perception of Psychologic & Physical Discomfort
Child Characteristics
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CPMD Study Purpose
The purpose is to use the Computerized Predictive Model for Distress/Distraction (CPMD) to predict high, medium, or low risk for distress groups and to test the level of distraction intervention appropriate for each risk group.
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COMPUTERIZED PREDICTIVE MODEL* Predictors of Child Behavioral Response* Predictors of Child Physiological Response* Predictors of Parent Ability to Provide Distraction
Total n = 582
High Risk for Distress
n ~ 117
Medium Risk for Distress
n ~ 378
Low Risk for Distress
n ~ 87
Basic Distraction Intervention
n ~ 213
*Parent coaches child*RA present*Targeted Intervention
Enhanced Distraction Intervention
n ~ 185
*Parent and RA are partnered*Parent "in charge"*RA coaches parent*Tailored Intervention
Intensive Distraction Intervention
n ~ 184
*Research Assistant (RA) "in charge"*RA coaches child*Parent present*Tailored Intervention
Computerized Predictive Model of Distraction
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Data Mining“searching for consistent patterns and relationships among data points based on analysis of a large set of data and turning those discovered relationships into a ‘model.’ The model is then applied to other test cases to determine if the model will ‘hold’ on new cases”
Abbott, P. A., & Zytkowski, M. E. (2002). Supporting clinical decision making. In S.P. Englebardt & R. Nelson (Eds.), Health Care Informatics: An Interdisciplinary Approach. St. Louis, Missouri; Mosby.
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Predictive Modeling Using Data Mining
– All items identified with Data Mining were also identified by traditional regression
– Identified specific predictive items rather than global instruments. For example: – “I feel anxious” item rather than anxiety
subscale of the STAI – “My child never stops moving” item rather than
activity subscale on DOT
– Items from DIG study=244– Items in CPMD=47
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Computerized Predictive Model of Distraction
(CPMD)
Helping Your Child during an I V I nsertion
Helping Your Child Helping Your Child during an I V I nsertionduring an I V I nsertion
Research QuestionsResearch QuestionsParent and Child QuestionsParent and Child Questions
Teaching VideoTeaching Video
NEXT
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Designing the C: Computer Application
• Predicts risk for child distress with medical procedures
• Tailors evidenced based interventions to the parent and child based on their responses to questions
• Randomizes high, medium and low risk dyads to test interventions in next study
• Links to central secured server
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COMPUTERIZED PREDICTIVE MODEL* Predictors of Child Behavioral Response* Predictors of Child Physiological Response* Predictors of Parent Ability to Provide Distraction
Total n = 582
High Risk for Distress
n ~ 117
Medium Risk for Distress
n ~ 378
Low Risk for Distress
n ~ 87
Basic Distraction Intervention
n ~ 213
*Parent coaches child*RA present*Targeted Intervention
Enhanced Distraction Intervention
n ~ 185
*Parent and RA are partnered*Parent "in charge"*RA coaches parent*Tailored Intervention
Intensive Distraction Intervention
n ~ 184
*Research Assistant (RA) "in charge"*RA coaches child*Parent present*Tailored Intervention
Computerized Predictive Model of Distraction
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CPMD Methods
– Design: Randomized Clinical Trial– Subjects: Children ages 4-10
having a scheduled IV (n = 582)– Settings:
• UI Children’s Hospital • Blank Children’s Hospital • Cardinal Glennon SLU
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CPMD Methods
– Instruments• CPMD that includes elements of child,
parent, and procedural variables • Genotyping
– Outcomes• Behavioral (videotaping)• Biological (cortisol)• Oucher (included in CPMD)• Parent report (included in CPMD)
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Login Instructions– Enter the CPMD site: http://beedance.biz.
uiowa.edu/cpmdpro/login.php
– Login ID: demo– Subject ID: enter a 4 digit number– Password: qwe123– Click on menus and follow the prompts.
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Translation Science – Research into Practice
ClinicalProblem
Research StudyDIG
Clinical Practice
CPMD
Research StudyCPMD
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DIG Related Analyses
– Distraction Coaching Index (DCI)– Genetics
• EDNRA • Candidate genes for pain & anxiety
– Cortisol• Norms• ADHD
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Distraction Coaching Index (DCI)
– Treatment Integrity: Is the intervention carried out as expected?
– Development of the Distraction Coaching Index (DCI) (Kleiber et.al., Children’s Health Care, 2007)• Frequency• Quality
– Video taped and scored later
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Genetics Results
• Forty percent of subjects reported high pain (Oucher score 4-10) despite the appropriate application of topical analgesic.
• Pain response did not differ by:• Gender (2 =0.28, .5 < p < .7) • Type of topical anesthetic (2=1.07,
p=0.301) • Ethnicity ( 2 =0.37, p=0.54)
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Logistic Regression for Likeliness of High Pain
Odds Ratio
95% CI
Wald Chi-Square
p- value
Child Age [Unit: 1 year]
1.527 1.288 – 1.811 23.678 <0.0001
Higher General Activity [Unit: 5]
1.386 1.045-1.838
5.129 0.0235
EDNRA TT [TT vs. CT/CC]
2.612 1.391 to 4.906 8.916 0.0028
Kleiber, C., Schutte, D.L., McCarthy, A.M., Floria-Santos, M., Murray, J.C., & Hanrahan, K. (2007). Predictors of topical analgesic effectiveness in children. Journal of Pain, 8(2):168-174.
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Salivary Cortisol Norms for Children
0.120.150.150.16
0.200.22
0.240.30
0.150.11 0.11
0.090.08 0.08 0.08
0.06
0.600.53
0.47 0.42
0.330.28 0.29
0.27
0.03
0.04 0.04
0.08
0.050.050.04
0.04
0.570.540.68
0.880.981.141.18
0.57
0.01
0.10
1.00
10.00
7 8 9 10 11 12 13 14 15 16
Hour
Co
rtis
ol (
mc
g/d
l)
Mean
8am 9am 10am 11am 12pm 1pm 2pm 3pm (n=54) (n=107) (n=98) (n=79) (n=91) (n=71) (n=45) (n=18)
Mean+2SD
Mean-2SD
Mean+1SD
Mean-1SD
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Cortisol Responsivity: ADHD
Non-ADHDn=346
ADHDn=32
Home Clinic Home Clinic
Time 1 0.203+0.011
0.261+0.010
0.329+0.075
0.166+0.028
Time 2 0.185+0.010
0.304+0.014
0.269+0.063
0.161+0.032
Note: Expect cortisol to be higher on the clinic (stressful) day as it was in the Non-ADHD sample.
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Implications for Practice
• We can teach parents to use distraction.• Higher distraction quality is associated with
less behavioral distress.• Children have pain despite topical
anesthetics. There may be a genetic cause related to the EDNRA gene.
• Normal cortisol levels and responsivity follow the pattern of adults. Children with ADHD have atypical cortisol responses.
• Predictors of parent & child responses can be used in practice
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Thank You for Your Interest!