The association between iron overload indices and pathology of the heart and liver in transfusion-dependent patients with β thalassemia major (TM) has been extensively studied. Nonetheless, data on endocrine disease remains limited. This was a cross-sectional study of 382 TM patients treated with regular transfusions and desferrioxamine at the Thalassemia Center in Dubai, UAE. Retrieved data included demographics, splenectomy status, steady-state serum ferritin levels, and the presence of endocrinopathies (diabetes mellitus, hypothyroidism, hypoparathyroidism, and hypogonadism). Multivariate logistic regression analyses were used to determine which variables were independently associated with the occurrence of each endocrinopathy. The mean age of patients was 15.4 ± 7.6 years, with an equal sex distribution. The mean serum ferritin level was 2597.2 ± 1976.8 μg/l. The frequencies of specific endocrinopathies were diabetes mellitus (10.5%), hypothyroidism (6.3%), hypoparathyroidism (10.5%), and hypogonadism (25.9%). On multivariate logistic regression analysis, patients with a serum ferritin level >2,500 μg/l, but not >1,000-2,500 μg/l, were 3.53 times (95% CI 1.09-11.40) more likely to have diabetes mellitus, 3.25 times (95% CI 1.07-10.90) more likely to have hypothyroidism, 3.27 times (95% CI 1.27-8.39) more likely to have hypoparathyroidism, and 2.75 times (95% CI 1.38-5.49) more likely to have hypogonadism compared to patients with a serum ferritin level ≤1,000 μg/l. However, splenectomized patients with serum ferritin levels ≤2,500 μg/l had comparably high rates of all endocrinopathies as patients with serum ferritin levels >2,500 μg/l. Endocrinopathy is common in TM patients treated with desferrioxamine therapy, especially in patients with serum ferritin levels >2,500 μg/l or those splenectomized.

Compound heterozygosity for Hb D-Punjab [β121(GH4)Glu→Gln, GAA>CAA] /β-thalassemia (β-thal) must be carefully differentiated from homozygous Hb D-Punjab in premarital screening. This is essential when the partner is a carrier of β-thal trait. The case of a baby born affected with β-thal major (β-TM), from a marriage between a mother with β-thal trait and a father with Hb D-Punjab/β-thal, is presented. The father had been misdiagnozed as homozygous Hb D-Punjab during premarital screening, even though the screening program utilized complete blood counts and high performance liquid chromatography (HPLC). The factors that may have contributed to this midsiagnosis are presented and discussed. It is recommended that cases of Hb D-Punjab, or any other hemoglobin (Hb) variant appearing as homozygous, are carefully evaluated if microcytic hypochromic parameters not associated with α-thal are present. In all cases of suspected hemizygosis, molecular analysis should always be performed, and in particular if one partner is a β-thal carrier.

The management cost of iron overload is rising with the introduction of magnetic resonance T2-star (T2*), oral iron chelation and combination therapy. The aim of this study was to develop and validate a simple and cheap clinical risk assessment model that would predict cardiac disease risk among patients with thalassemia major. The data of patients (254, aged > 15 years) with b-thalassemia major, who had been chronically transfused at Dubai Thalassemia Center from 2007 to 2011, were used to develop the model. Gathered data included data related to cardiac disease, T2*, and six other clinical and biochemical parameters known to be associated with an increased risk of cardiac siderosis. The parameters were: age, sex, serum ferritin, diabetes, hypogonadism, and splenectomy. Cardiac disease patients were defined as patients (living or dead) who developed cardiac failure or arrhythmias during the study period. Data was analyzed using SPSS version 20. The predictive model was developed using logistic regression analysis. Thalassemia patients who developed cardiac disease were used as the outcome measure. All parameters were included in the model. A weighted scoring system was obtained by rounding down odds ratios to the nearest integer. Model validity analysis was performed on patients with available T2* data (102). Several standard validation measures were computed including sensitivity, specificity, positive predictive value (PPV), the receiver operating characteristic (ROC) curve and the Youden Index. Several model cutoffs were tested until the model delivered the best prediction outcome. A total of 254 thalassemia major patients were studied. The mean age was 23.3 years ± 5.4 (49% were female and 51% were male). Sixteen (6.3%) patients experienced cardiac disease, 55 (21.7%) had hypogonadism, 51 (21%) had diabetes and 29 (11.4%) had undergone splenectomy. Cardiac disease was found to be significantly associated with age > 22 years (p=0.004), hypogonadism (p=0.000), splenectomy (p=0.002) and diabetes (p=0.000). While the association of gender and serum ferritin with cardiac disease was not significant (p= 0.146, p=0.158). The model delivered the best prediction outcome at a T2*value ≤ 20 and a model score cutoff ≥ 8 with a sensitivity of 80%, a specificity of 45%, a PPV of 57% and an NPV of 71%. Among the 102 patients with available T2* data, all cardiac disease patients were equally identified by a T2*value ≤20 and by the newly developed model. The model defined 38% of the patients as high risk, which is lower but close to the value (48%) determined by the T2*value ≤20. In the present study, we developed and validated a clinical risk prediction model for cardiac siderosis by using patients (aged >15 years) with thalassemia major at Dubai Thalassemia Centre as the study population. The model is easy to use by health care providers and compares well with the predictions determined from T2* data. The fact that this model was developed using data from patients who were mostly of Asian ethnic origin should make it useful in areas where there is a high risk of cardiac siderosis and limited resources. The current study has some limitations, including its retrospective study design, the relatively low event rate and the lack of T2* data for all of the studied population. The impact of this model for predicting clinical outcomes needs to be further assessed by evaluating other variables before it can be applied in clinical setup. Disclosures: No relevant conflicts of interest to declare.