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 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
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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.
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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.
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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.
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Graphs of Adhesion Data
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Graphs of Adhesion Data
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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).
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• 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
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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
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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
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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.
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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;
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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)
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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.
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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
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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.
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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.
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Questions?