Prediction of Skin Lesions in Patients Undergoing Allogeneic Hematopoietic Stem Cell Transplantation Using Generalized Additive Models

In this study, we established a predictive regression model using generalized additive models (GAM) to predict the incidence of skin lesions (SLs) in patients undergoing allogeneic hematopoietic stem cell transplantation (SCT). Among 81 patients who underwent SCT in the SCT unit of the Hyogo College of Medicine between April 2012 and March 2013, 28 developed SL (SL group), and the remaining 53 did not (control group). We defined the following events as states in our multistate model: Diarrhea, need for oxygen supply, hemorrhagic cystitis, skin graft-versus-host disease, encephalitis, and disease relapse. Of these events, diarrhea, need for oxygen supply, and hemorrhagic cystitis occurred more frequently in the SL group than in the control group. A comparison of the function independence measure score revealed more severe muscle weakness in the SL group. A logistic regression analysis using GAMs verified that SL development could be predicted based on serum albumin, blood sugar levels, daily activity scores, and post-transplant day. Of these four predictive covariates, only the post-transplant day exhibited a non-linear curve, with a susceptible peak at approximately 30 days after SCT. GAMs may be a powerful tool for prediction analyses involving time-dependent and non-linear covariates. Patient characteristics may also affect the SL development.