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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!