The Distribution of PROMIS Domains among Patients

prior toUndergoing Spine Surgery

Amy M. Cizik, PhD, MPH

September 27, 2017

Disclosures

• This work is supported by the National Institute of Arthritis and Musculoskeletal and Skin Diseases of the National Institutes of Health under Award Number R21AR068009

Motivation

Spine SCOAP and CERTAIN

• Surgeon-led collaborative using performance surveillance and benchmarking to deliver more appropriate, safer and higher quality surgical spine care across Washington State

• Engaging spine surgeons in the process of selecting evidence-based QI metrics for monitoring

CERTAIN Research Community

• N=157 spine fusion patients from academic (25%) and private practice (75%)

• Age = 62 years (23 – 87 years, Median 64)

• 58% female

• 45% retired

• 14% receive disability or worker’s compensation

• 78% reported having leg pain (radiculopathy)

PRO Measurement in Spine

• Oswestry Disability Index– 10 items– 6 responses (0 – 5)– Pain, Personal Care, Sexual

Function, Driving, Sleeping

• PROMIS Domains Recommended– Physical Function (PF)– Depression (DEP)– Sleep Disturbance (SLEEP)– Pain Interference (PAININ)

• PROMIS Domains Measured– Depression (DEP)

– Anxiety (EDANX)

– Sleep Disturbance (SLEEP)

– Pain Interference (PAININ)

– Emotional Support (FSE)

– Instrumental Support (CCC and SS)

Data Collection Modalities

141

13

00

20

40

60

80

100

120

140

160

Paper Online Phone

Spine Fusion BL Survey Completion Type

94

31

00

20

40

60

80

100

120

140

160

Paper Online Phone

30-90 Day Survey Completion Type

Specific Aim

• Determine the extent to which domains are correlated and understand the patterns of correlation among patients undergoing lumbar fusion

Data Analysis

• Descriptive statistics

• Pearson’s correlation coefficients (r) was used to assess the strength of correlation between domains.

• Principal-component analysis (PCA) was conducted to identify dimensions and to describe relationships between variables within and across PROMIS domains – Oblique rotation was used to allow for correlation

between components

– The number of components was selected by the minimum number of components required to cumulatively explain 75% of the variation observed

Anxiety and Depression

Emotional and Instrumental Support

Pain Interference and Sleep Disturbance

t-score Distributions

PROMIS DOMAINSAMPLE

MEAN

SAMPLE

SDIQR

Anxiety 55.5 9.1 [48.5, 63.1]

Depression 53.0 9.5 [41.0, 60.5]

Sleep Disturbance 52.0 3.6 [49.7, 54.5]

Instrumental Support 55.0 8.8 [49.5, 63.3]

Emotional Support 54.6 8.2 [48.9, 62.0]

Pain Interference 67.1 5.5 [63.9, 71.3]

Pain NRS Leg 5.9 2.5 [4.0, 8.0]

Pain NRS Low Back 6.5 2.1 [5.0, 8.0]

Correlation Heat Map

0.69

0.42

0.41 0.37

-0.34

-0.30

Principal Component Analysis

PC1 PC2 PC3 PC4

Anxiety 0.89 0.07 0.02 0.05

Depression 0.90 -0.02 -0.07 -0.08

Sleep Disturbance 0.00 0.01 0.01 0.98

Instrumental Support 0.11 -0.10 0.90 0.02

Emotional Support -0.29 0.12 0.76 -0.02

Pain Interference 0.16 0.58 0.22 -0.25

Pain Intensity (Leg) -0.12 0.88 -0.13 -0.01

Pain Intensity (Back) 0.26 0.71 0.09 0.07

Proportion of Variance 0.23 0.21 0.19 0.13

Cumulative Proportion 0.23 0.44 0.63 0.76

PC=principal component

Summary

• Barriers to web-based/mobile technology in private practice clinics

• Anxiety and Depression domains explain almost 25% of the variation in baseline PROMIS domain characteristics in patients about to undergo lumbar spine fusion surgery

• Pain domains explains an additional 20% of the variation of all the baseline domains measured

Next Steps• Responder Analysis• Latent class analysis to identify clusters of domains grouped

together within patient subtypes• Association between clusters and non-response using logistic

regression models accounting for repeated testing• Sensitivity analyses

– Repeat the latent class analysis on the raw percentage change of the primary outcome measure (ODI) to examine the robustness of variables that define latent classes

– Examine different degrees of change in the primary outcome beyond the 15% threshold, for example PASS

• Missing data analysis to describe and characterize enrolled participants who do not provide further outcome measures due to attrition using multiple imputation

Acknowledgements• Katie Odem-Davis, PhD

• Jesse Fann, MD, MPH

• Jeffrey G. Jarvik, MD, MPH

• Richard A. Deyo, MD, MPH

• David R. Flum, MD, MPH

• Danielle Lavallee, PhD, PharmD

• Ellie Brewer, MPH

Contact Information

amorgan2@uw.edu