Development of a Thalassemia International Prognostic Scoring System (TIPSS)

A prognostic scoring system that can differentiate beta-thalassemia patients based on mortality risk is lacking. We analysed data from 3145 beta-thalassemia patients followed through a retrospective cohort design for the outcome of death. An a priori list of prognostic variables was collected. beta Coefficients from a multivariate cox regression model were used from a development dataset (n = 2516) to construct a formula for a Thalassemia International Prognostic Scoring System (TIPSS) which was subsequently applied to a validation dataset (n = 629). The median duration of observation was 10.0 years. The TIPSS score formula was constructed as exp (1.4 x heart disease + 0.9 x liver disease + 0.9 x diabetes + 0.9 x sepsis + 0.6 x alanine aminotransferase >= 42 IU/L + 0.6 x he-moglobin = 1850 ng/mL). TIPSS score thresholds of greatest differentiation were assigned as = 5.0 (high-risk). The TIPSS score was a good predictor for the outcome of death in the validation dataset (AUC: 0.722, 95%CI: 0.641-0.804) and survival was significantly different between patients in the three risk categories (P < 0.001). Compared to low-risk patients, the hazard ratio for death was 2.778 (95%CI: 1.335-5.780) in patients with intermediate-risk and 6.431 (95%CI: 3.151-13.128) in patients with high-risk. This study provides a novel tool to support mortality risk categorization for patients with beta-thalassemia that could help management and research decisions

Random Forest Clustering Identifies Three Subgroups of β-Thalassemia with Distinct Clinical Severity

In this work, we aimed to establish subgroups of clinical severity in a global cohort of β-thalassemia through unsupervised random forest (RF) clustering. We used a large global dataset of 7910 β-thalassemia patients and evaluated 19 indicators of phenotype severity (IPhS) to determine their contribution and relatedness in grouping β-thalassemia patients into clusters using RF analysis. RF clustering suggested that three clusters with minimal overlapping exist (classification error rate: 4.3%), and six important IPhS were identified: the current age of the patient, the mean serum ferritin level, the age at diagnosis, the age at first transfusion, the age at first iron chelation, and the number of complications. Cluster 3 represented patients with early initiation of transfusion and iron chelation, considerable iron overload, and early mortality from heart failure. Patients in Cluster 2 had lower serum ferritin levels, although they had a higher number of complications manifesting overtime. Patients in Cluster 1 represented a subgroup with delayed or absent transfusion and iron chelation, but with a high morbidity rate. Hepatic disease and cancer were dominant causes of death in patients in Cluster 1 and 2. Our findings established that patients with β-thalassemia can be clustered into three groups based on six parameters of phenotype severity