Machine learning algorithm as a prognostic tool for venous thromboembolism in allogeneic transplant patients.

As a serious complication after allogenic hematopoietic stem cell transplantation (allo-HSCT), venous thromboembolism (VTE) is significantly related to increased nonrelapse mortality. Therefore distinguishing patients at high risk of death who should receive specific therapeutic management is key to improving survival. This study aimed to establish a machine learning-based prognostic model for the identification of post-transplantation VTE patients who have a high risk of death.

A prognostic model (BATAP) with external validation for patients with transplant-associated thrombotic microangiopathy.

Transplant-associated thrombotic microangiopathy (TA-TMA) is a potentially life-threatening complication following allogeneic hematopoietic stem cell transplantation (allo-HSCT). Information on markers for early prognostication remains limited, and no predictive tools for TA-TMA are available. We attempted to develop and validate a prognostic model for TA-TMA.

Clinical risk factors and prognostic model for idiopathic inflammatory demyelinating diseases after haploidentical hematopoietic stem cell transplantation in patients with hematological malignancies.

Idiopathic inflammatory demyelinating diseases (IIDDs) of the central nervous system (CNS) are rare but serious neurological complications of haploidentical hematopoietic stem cell transplantation (haplo-HSCT). However, the risk factors and a method to predict the prognosis of post-transplantation CNS IIDDs are not available. This retrospective study first reviewed data from 4532 patients who received haplo-HSCT during 2008-2019 in our center, and 184 patients (4.