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Published byElyse Gulliford Modified about 1 year ago

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Immune Recovery in Children after Bone Marrow Transplant Rollo Hoare Supervisors: Robin Callard & Joseph Standing

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Bone Marrow Transplant (BMT) BMT done post leukemia or other immune system failure Before BMT patients undergo conditioning during which all lymphocytes in the body may be killed We are looking at the recovery of the immune system after BMT In particular the concentration of CD4 in the blood

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The aim of the project 1.Build a model for the CD4 cell count recovery 2.Fit the model to the data (CD4 count time series) 3.Add parameters related to covariates 4.Analyse results of included covariates

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The aim of the project 1.Build a model for the CD4 cell count recovery 2.Fit the model to the data (CD4 count time series) 3.Add parameters related to covariates 4.Analyse results of included covariates

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1. The Model Start with an exponential model: asy int Parameters asy = asymptote int = intercept c = rate of change

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The aim of the project 1.Build a model for the CD4 cell count recovery 2.Fit the model to the data (CD4 count time series) 3.Add parameters related to covariates 4.Analyse results of included covariates

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2. Fit model to data a) Age adjustment CD4 count drops non-linearly with age in healthy children

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2. Fit model to data NLME Modelling

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The aim of the project 1.Build a model for the CD4 cell count recovery 2.Fit the model to the data (CD4 count time series) 3.Add parameters related to covariates 4.Analyse results of included covariates

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3. Add covariates Multivariate analysis: we add covariate parameters into the model for: -BMT Age -Sex -BMT Number -Donor type -Donor cells type -Leukaemia -HCMV -EBV -Alem/anti-CD45/ATG -Total body irradiation -Cyclosporine -Methotrexate -Mycophenolate -Prednisone -Muromonab -No conditioning -Reduced conditioning -Chimerism

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3. Diagnostic Plots

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The aim of the project 1.Build a model for the CD4 cell count recovery 2.Fit the model to the data (CD4 count time series) 3.Add parameters related to covariates 4.Analyse results of included covariates

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4. Results After SCM, we included the following covariates: Int: -Alemtuzumab/Anti-CD45/Anti-thymocyte Globulin -Leukaemia -Donor type Asy: -HCMV -No conditioning c: -Mycophenolate

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4. Results Int: Alem/anti-CD45/ATG

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4. Results Int: Leukaemia

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4. Results Int: Donor type

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4. Results Asy: HCMV

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4. Results Asy: Conditioning

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4. Results C: Mycophenolate

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Next Steps Include age adjustment in the model rather than pre- adjustment -Linear -Free exponential -Fixed exponential -Fixed exponential with ratio Or use another form of age adjustment -Square root ratio -Fourth root ratio Add in further covariates for conditioning drugs and diagnoses Look at dosage information

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Next Steps Include age in model: Linear ASY = THETA(1) + THETA(2) * (3650-AGE)

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Next Steps Include age in model: Linear ASY = THETA(1) + THETA(2) * (3650-AGE)

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Next Steps Include age in model: Free exponential ASY = THETA(1) +THETA(2) * EXP(-THETA(3)*AGE)

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Next Steps Include age in model: Free exponential ASY = THETA(1) +THETA(2) * EXP(-THETA(3)*AGE)

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Next Steps Include age in model: Fixed exponential ASY = * EXP( *AGE)

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Next Steps Include age in model: Fixed exponential ASY = * EXP( *AGE)

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Next Steps Include age in model: Fixed exponential with ratio ASY = THETA(1) * ( * EXP( *AGE))

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Next Steps Include age in model: Fixed exponential with ratio ASY = THETA(1) * ( * EXP( *AGE))

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Next Steps Age adjustment: Fourth root ratio

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Next Steps Additional Covariates to be tested -BMT Failure -Alemtuzumab -Anti CD-45 -Anti-thymocyte globulin -Busulphan -Cyclosporine (Conditioning) -Melphalan -Fludarabine -Treosulphan -Diagnosis -Edited EBV -Edited HCMV -Edited donor cells -Edited donor type

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Conclusion We have modelled the CD4 count recovery of children post BMT using the logratio We have found the following covariates affect recovery: - Int: Alem/anti-CD45/ATG, Leukaemia, donor type - Asy: Conditioning, HCMV - c: mycophenolate More work needs to be done to ascertain the reliability of these results.

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Questions??

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