Matthew Mouawad Medical Biophysics Presented April 3 rd, 2012 Western University QUANTITATIVE ANALYSIS OF WHITE MATTER INJURY AND REDUCTION OF DEEP GRAY MATTER VOLUME IN MAJOR PRE-TERM INFANTS WITH MAJOR MORBIDITIES

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) Fail to reject the null hypothesis as p-level is > 0.05 No association between DGM and sepsis DGM and SepsisNegative for SepsisPositive for SepsisSignificance (  Sample Size2017 Means19.2 cm cm 3 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 Fail to reject the null hypothesis No change in WMV means between having sepsis or not WMV and SepsisNegative for SepsisPositive for SepsisSignificance (  Sample Size1916 Mean Values156.1 cm cm 3 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 ComparedExpected Count (having both disease states) Actual CountSignificance (alpha of 0.05) BPD and IVH PDA and IVH BLDINF and IVH ROP and IVH 69 BLDINF and ROP7.111

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 S error and S treatment)

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 disease2Yes disease2 No disease1Actual count: Expected count: Actual Count: Expected count: Yes disease1Actual count: Expected count: Actual count : Expected count: