Transcript Quantitative Analysis of White Matter Injury and Reduction

QUANTITATIVE ANALYSIS OF WHITE MATTER
INJURY AND REDUCTION OF DEEP GRAY MATTER
VOLUME IN MAJOR PRE-TERM INFANTS WITH
MAJOR MORBIDITIES
Matthew Mouawad
Medical Biophysics
Presented April 3 rd, 2012
Western University
INTRODUCTION
• Association between major neonatal complications and
adverse neurodevelopmental outcomes
• Sepsis, Intraventricular Hemorrhage, Bronchopulmonary
Dysplasia, Patent Ductus Arteriosus, Retinopathy of
Prematurity
• Quantification of this association hasn’t been established
• It has been suggested that white matter (WM) injury or
deep gray matter (DGM) volume could be used to test for
outcome
OBJECTIVES
• To gain an understanding of inferential statistics
• Chi square test
• Multivariate test
• Covariates
• Determine if there is an association between white matter
volume, deep gray matter volume and sepsis in major pre-term
infants
• Secondary – to determine association between different
morbidities
• Other Questions – not addressed here
APPROACH
• Statistical tests
• SPSS
• Determine relationships
• DGM and sepsis
• WM and sepsis
• Other morbidities with each other
HYPOTHESES
• Primary
• There is a mean difference between WM volumes,
depending on whether you have sepsis not
• There is a mean difference between DGM volumes,
depending on whether you have sepsis or not
• Secondary
• There is an association between having a certain
disease state and having a different one
METHODS - CONTEXT
• Quantitative MRI
• Enables direct measurement of tissue properties
associated with white matter and deep gray matter
T2*, Apparent diffusion coefficient…
 White matter volume and deep gray matter volume
• Database set up containing all variables
METHODS
• 48 pre-term infants
• Selection criteria:
• Gestational age ≤ 30 weeks
• Survival until discharge
• Clinically stable for MRI
• MRI’s preformed at term equivalent age
RESULTS – PRIMARY HYPOTHESIS
• Strong association between DGM and head volume
• Must correct for head volume as a confounder (ANCOVA)
DGM and Sepsis
Negative for Sepsis
Positive for Sepsis
Sample Size
20
17
Means
19.2 cm3
18.1 cm3
• Fail to reject the null hypothesis as p-level is > 0.05
• No association between DGM and sepsis
Significance
(a = 0.05)
ns
RESULTS – PRIMARY HYPOTHESIS 2
• Testing for difference between having sepsis or not and effect on white
matter volume
• No confounder of head volume – wasn’t significant enough
WMV and Sepsis
Negative for Sepsis
Positive for Sepsis
Sample Size
19
16
Mean Values
156.1 cm3
144.3 cm3
• Fail to reject the null hypothesis
• No change in WMV means between having sepsis or not
Significance
(a = 0.05)
ns
RESULTS – SECONDARY HYPOTHESIS
•
Determine if there is an association between different disease states
• ASSOCIATION not causation
• Pearson Chi Square test
• 5 different disease states
• Bronchopulmonary Dysplasia(BPD - lung problem)
• Patent Ductus Arteriosus (PDA - heart complications)
• Intraventricular Hemorrhage (IVH - brain bleed)
• Retinopathy of Prematurity (ROP – eye problems)
• Sepsis (Blood infection)
RESULTS – SECONDARY HYPOTHESIS
•
Association were found:
Diseases Compared Expected Count
(having both
disease states)
Actual Count
Significance (alpha
of 0.05)
BPD and IVH
12.4
15
Sig < 0.05
PDA and IVH
12.4
16
Sig < 0.01
BLDINF and IVH
7.6
11
Sig < 0.05
ROP and IVH
6
9
Sig < 0.05
BLDINF and ROP
7.1
11
Sig < 0.01
DISCUSSION – SAMPLE SIZE
• Biggest problem of this experiment is the
severely limited sample size.
• Affects every single test
• Chi Square has many issues with counts less
than 5
• There were a few with less than 5
• Validity – representative of population
DISCUSSION – IVH
• Results of chi square test
• All disease states were correlated with having IVH
• Possible explanation
• IVH and Blood infection
• More invasive procedures must be done when having
IVH
• More likely to get a blood infection
• More research has to be done
CONCLUSION
• No association between DGM volume and sepsis
morbidity
• No association between WM volume and sepsis
morbidity
• Association between IVH and all other morbidities
• BDP, PDA, Sepsis, ROP
• Sample errors (validity)
QUESTIONS
ANOVA
• Generalized t-test
• Test multiple means
• Multiple T-tests leads to increased chance of type 1
error
• Test of variance
• Sum of squares (partitioned into Serror and Streatment)
CONFOUNDER
• Wiki:
• “a confounding variable is an extraneous variable in
a statistical model that correlates (positively or
negatively) with both the dependent variable and the
independent variable.”
• Threatens validity
• If you are looking for cause or association, may
create a causal relationship that shouldn’t be there
ANCOVA
• Simply, removes covariates
• Can remove confounders (in a sense)
• Remove the variance explained by the covariate from
both the dependant and independent variables
CHI SQUARED TEST
•
Assumes null true, predicts outcome of contingency table
• Based on the assumption that it comes from chi distribution
No disease2
Yes disease2
No disease1
Actual count:
Expected count:
Actual Count:
Expected count:
Yes disease1
Actual count:
Expected count:
Actual count :
Expected count: