Outcomes of pediatric patients with therapy-related myeloid neoplasms

Cytogenetic abnormalities predict survival after allogeneic hematopoietic stem cell transplantation for pediatric acute myeloid leukemia: a PDWP/EBMT study

Poor-risk (PR) cytogenetic/molecular abnormalities generally direct pediatric patients with acute myeloid leukemia (AML) to allogeneic hematopoietic stem cell transplant (HSCT). We assessed the predictive value of cytogenetic risk classification at diagnosis with respect to post-HSCT outcomes in pediatric patients. Patients younger than 18 years at the time of their first allogeneic HSCT for AML in CR1 between 2005 and 2022 who were reported to the European Society for Blood and Marrow Transplantation registry were subgrouped into four categories. Of the 845 pediatric patients included in this study, 36% had an 11q23 abnormality, 24% had monosomy 7/del7q or monosomy 5/del5q, 24% had a complex or monosomal karyotype, and 16% had other PR cytogenetic abnormalities. In a multivariable model, 11q23 (hazard ratio [HR] = 0.66, P = 0.03) and other PR cytogenetic abnormalities (HR = 0.55, P = 0.02) were associated with significantly better overall survival when compared with monosomy 7/del7q or monosomy 5/del5q. Patients with other PR cytogenetic abnormalities had a lower risk of disease relapse after HSCT (HR = 0.49, P = 0.01) and, hence, better leukemia-free survival (HR = 0.55, P = 0.01). Therefore, we conclude that PR cytogenetic abnormalities at diagnosis predict overall survival after HSCT for AML in pediatric patients.

Handling missing covariate data in clinical studies in haematology

Missing data are frequently encountered across studies in clinical haematology. Failure to handle these missing values in an appropriate manner can complicate the interpretation of a study's findings, as estimates presented may be biased and/or imprecise. In the present work, we first provide an overview of current methods for handling missing covariate data, along with their advantages and disadvantages. Furthermore, a systematic review is presented, exploring both contemporary reporting of missing values in major haematological journals, and the methods used for handling them. A principal finding was that the method of handling missing data was explicitly specified in a minority of articles (in 76 out of 195 articles reporting missing values, 39%). Among these, complete case analysis and the missing indicator method were the most common approaches to dealing with missing values, with more complex methods such as multiple imputation being extremely rare (in 7 out of 195 articles). An example analysis (with associated code) is also provided using hematopoietic stem cell transplantation data, illustrating the different approaches to handling missing values. We conclude with various recommendations regarding the reporting and handling of missing values for future studies in clinical haematology.