Prediction of Pregnancy from Adhesion of CD56 bright Cells Azim Bhamani 1, Julie Horrocks 1,...
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Prediction of Pregnancy from Adhesion of CD56 bright Cells Azim Bhamani 1 , Julie Horrocks 1 , Marianne van den Heuvel 2 , Francis Tekpetey 3 , B. Anne Croy 4 Funding provided by: CHRP 1 Mathematics & Statistics, University of Guelph, 2 Biomedical Sciences, University of Guelph, 3 Obstetrics and Gynecology, University of Western Ontario, 4 Anatomy & Cell Biology, Queen’s University
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Transcript of Prediction of Pregnancy from Adhesion of CD56 bright Cells Azim Bhamani 1, Julie Horrocks 1,...
Prediction of Pregnancy from Adhesion of CD56bright Cells
Azim Bhamani1, Julie Horrocks1, Marianne van
den Heuvel2, Francis Tekpetey3, B. Anne Croy4
Funding provided by: CHRP
1Mathematics & Statistics, University of Guelph, 2Biomedical Sciences, University of Guelph, 3Obstetrics and Gynecology, University of Western
Ontario, 4Anatomy & Cell Biology, Queen’s University
Introduction
• Diagnosis and treatment of infertility is expensive, hence, methods for prediction of successful treatment are needed.
• Van den Heuvel et al. have shown that a particular type of human blood cell, a CD56bright lymphocyte, exhibits enhanced in vitro capacity to adhere to decidual vascular endothelium from pregnant mice during the peri-ovulatory period of the menstrual cycle.
Introduction cont’d.
• A subsequent study by Van den Heuvel et al. examines data from 18 infertile women undergoing hormonal stimulation for in vitro fertilization treatment.
• The data came from the study performed by physicians in the Reproductive Endocrinology and Infertility Program at the London Health Sciences Centre.
• 7 of the 18 women became pregnant as a result of the treatment.
Materials and Methods
• 8.5 ml of blood was drawn from the participants every other day.
• Blood lymphocyte cells were applied to 12 micron sections of mouse uterine tissue.
• The number of adherent CD56bright cells were counted in 25 high power fields per slide by two independent and blinded researchers.
Graphs of Adhesion Data
Graphs of Adhesion Data
Method #1: Mixed Linear Model
• The mixed linear model for the log adhesion data is:
log(adhesionij) = β1 + β2*dayj + u1i + u2i*dayj + εij,
εij ~ N(0, σ2) u1i ~ N(0, σ12) u2i ~ N(0, σ2
2)
where i indexes individual (i=1,…,18), and j indexes measurement day (j=-6,…,1).
• The following SAS code uses the mixed model to predict the means for each measurement day:
proc mixed data=adlongIVF(where=(mday>=-6 & mday<=1))
method=ml;class outcome id;model ladhes=mday|outcome mday0P /s chisq htype=3 outpm=residsIVF;repeated /type=cs subject=id ;title "Final Model IVF";run;
Method #1: Mixed Linear Model
Results
Type 3 Tests of Fixed Effects
Num Den
Effect DF DF Chi-Square F Value Pr > ChiSq Pr > F
mday 1 85 3.30 3.30 0.0694 0.0729
outcome 1 16 11.89 11.89 0.0006 0.0033
mday*outcome 1 85 3.53 3.53 0.0604 0.0639
mday0P 1 85 16.14 16.14 <.0001 0.0001
Predicted Adhesion
out come2 Pr eg xNot
Pr edi ct ed Mean
0. 8
0. 9
1. 0
1. 1
1. 2
1. 3
1. 4
1. 5
1. 6
1. 7
mday
- 6 - 5 - 4 - 3 - 2 - 1 0 1
Method #2: Discriminant Analysis
• It is used to classify observations into two or more groups.
• In our case, the two groups are pregnant and non-pregnant.
Method #2: Discriminant Analysis
• The following SAS code uses discriminant analysis to classify observations into the two groups:
proc discrim data=imputeddata list crossvalidate outstat=output outcross=outpcrss;class outcome;id id;var avget_6 avget_5 avget_4 avget_3 avget_2 avget_1 avget avgtest1;run;
Method #2: Discriminant Analysis
• The results are based on Fisher’s linear rule.
• The coefficients for the linear rule area1 = S-1ū1 and a2 = S-1ū2
andc1 = -½ūT
1 a1 and c2 = -½ūT2 a2
• An observation is classified into the pregnant group if
(a2 – a1)Tu ≤ (c1 – c2)
Results
• Here we focus on the cross-validation results.
• This method removes each observation, produces a discriminant rule without it, then classifies the observation that was removed.
• As a result, we obtain an unbiased estimate of the errors of misclassification
• The mixed linear model (shown earlier) is used to impute any missing values in the predictors.
Results
Number of Observations and Percent Classified into outcome
From outcome Preg xNot Total
Preg 5 2 7 71.43 28.57 100.00
xNot 1 10 11 9.09 90.91 100.00
Total 6 12 18 33.33 66.67 100.00
Priors 0.5 0.5
Conclusions
• The mixed linear model provides predictions of log adhesion for a specific measurement day.
• Discriminant analysis is used to classify an observation into the pregnant or the non-pregnant group.
• Future work includes exploring other imputation techniques and comparing the results.
References
1. M Van Den Heuvel, J Horrocks, S Bashar, S Taylor, S Burke, K Hatta, JE Lewis, A Croy. (Submitted). Menstrual Cycle Hormones Induce Changes in Functional Interactions Between Lymphocytes and Endothelial Cells
2. M Van Den Heuvel, J Horrocks, K Hatta, S Burke, S Bashar, ME Junkins, SS Evans, BA Croy, FR Tekpetey. (Submitted). Peri-ovulatory Increases
in Tissue Homing Potential of Circulating CD56bright Cells Are Associated With Fertile Menstrual Cycles
3. SAS OnlineDoc®, Version 8. February 2000. Copyright ©2000, SAS Institute Inc.
4. Carter, E.M. (2004). Methods for analyzing Multivariate Data, University of Guelph Campus Bookstore.
Questions?
