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The thalassaemia syndromes (TS) show different phenotype severity. Developing a reliable, practical and global tool to determine disease severity and tailor treatment would be of great value. Overall, 7910 patients were analysed with the aim of constructing a complication risk score (CoRS) to evaluate the probability of developing one or more complications. Nine independent variables were included in the investigation as predictors. Logistic regression models were used for Group A [transfusion-dependent thalassaemia (TDT)], Group B [transfused non-TDT (NTDT)] and Group C (non-transfused NTDT). Statistically significant predictors included age (years), haemoglobin levels, hepatic transaminases [alanine aminotransferase (ALT) and aspartate aminotransferase] and left-ventricular ejection fraction (LVEF) for Group A; age (years), age at first chelation (months), ALT and LVEF for Group B; and age (years), mean serum ferritin (SF) levels and LVEF for Group C. The area under the receiver operating characteristic curve was 84·5%, 82·1% and 80·0% for Groups A, Group B and Group C respectively, suggesting the models had good discrimination. Finally, the CoRS for each group was categorised into four risk classes (low, intermediate, high, and very high) using the centiles of its distribution. In conclusion, we have developed a CoRS for TS that can assist physicians in prospectively tailoring patients’ treatment.

This study analysed the impact of liver steatosis (LS) on the parameters of iron overload in 110 patients with non-transfusion dependent thalassaemia (NTDT). LS was diagnosed by ultrasound. Liver iron concentration (LIC) measurements were available for 64 patients who underwent a magnetic resonance imaging (MRI) scan. LS was frequent (35·5%) and was significantly more prevalent in males than in females (49·0% vs. 24·6%, P = 0·008). Patients with LS had significant higher levels of alanine aminotransferase (ALT), aspartate aminotransferase (AST), ALT/AST ratio and ferritin than those without, but LIC values were comparable. An ALT/AST ratio0·89 predicted the presence of LS with a sensitivity of 0·872 and a specificity of 0·901 (P 0·0001). Ferritin levels correlated with LIC values (R = 0·558, P 0·0001) but the correlation was stronger in patients without LS (R = 0·656, P 0·0001) than in patients with LS (R = 0·426, P = 0·05). LS is a frequent issue in NTDT patients and should be suspected in the presence of an ALT/AST ratio0·89. Recently, serum ferritin thresholds that predict clinically relevant LIC for guiding iron chelation therapy when MRI is unavailable have been determined. Our data show that LS may cause increase in ferritin levels and may be responsible for anticipating/exceeding chelation treatment in NTDT patients in the absence of LIC evaluation.

We prospectively assessed the efficacy of deferasirox versus deferiprone or desferrioxamine as monotherapy in thalassaemia major (TM) patients by magnetic resonance imaging (MRI). We selected the patients enrolled in the Myocardial Iron Overload in Thalassaemia network who received only one chelator between two MRIs (deferasirox = 235, deferiprone = 142, desferrioxamine = 162). Iron overload was measured by T2* technique and biventricular function by cine images. Among the patients with baseline myocardial iron, in all three groups there was a significant improvement in global heart T2* values. The deferiprone and desferrioxamine groups showed a significant improvement in left ventricular ejection fraction (LVEF). Only the deferiprone group showed a significant improvement in right ventricular ejection fraction (RVEF). The improvement in global heart T2* was significantly lower in the deferasirox versus the deferiprone group. The improvement in the LVEF was significantly higher in the deferiprone and desferrioxamine groups than in the deferasirox group and the improvement in the RVEF was significantly higher in the deferiprone than in deferasirox group. Among the patients with baseline hepatic iron, the changes in hepatic iron were comparable in deferasirox versus the other groups. Deferasirox monotherapy was less effective than deferiprone in improving myocardial siderosis and biventricular function and less effective than desferrioxamine in improving the LVEF.

Summary The relationship between diabetes mellitus (DM) and cardiac complications has never been systematically studied in thalassaemia major (TM). We evaluated a large retrospective historical cohort of TM to determine whether DM is associated with a higher risk of heart complications. We compared 86 TM patients affected by DM with 709 TM patients without DM consecutively included in the Myocardial Iron Overload in Thalassaemia database where clinical/instrumental data are recorded from birth to the first cardiovascular magnetic resonance (CMR) exam. All of the cardiac events considered were developed after the DM diagnosis. In DM patients versus non-DM patients we found a significantly higher frequency of cardiac complications (46·5% vs. 16·9%, P

Precise, reproducible measurements of liver iron concentration (LIC) are critical for the early diagnosis, treatment and follow-up of patients with primary or secondary iron overload. Magnetic resonance imaging (MRI), which exploits the paramagnetic properties of iron, has gained acceptance as a noninvasive and accurate tool for LIC assessment. Two different techniques have been described: methods measuring signal intensity ratio between liver and muscles (Gandon, et al 2004), and relaxometry methods measuring absolute R2 (St Pierre, et al 2005) or R2* (Wood, et al 2005) values, which increase proportionally to iron concentration. The gradient-echo R2* technique is most widely used in clinical practice (Anderson, et al 2001, Hankins, et al 2009, Meloni, et al 2011, Wood, et al 2005), because it is faster and easier than R2 acquisition. R2* values can be converted to underlying tissue iron concentration, using appropriate calibration curves. The first calibration curve was proposed by Anderson et al (2001) and later updated by Garbowski et al (2009). This methodology has been implemented in a popular analysis software named “ThalassaemiaTools”, a CMRtools (Cardiovascular Imaging Solutions Ltd, London, UK) plug-in. A second calibration curve was proposed (Wood et al 2005) and later confirmed (Hankins et al 2009), but it demonstrated a 15% lower scaling coefficient between R2* and LIC. The two R2* analysis methods differ in the size of analysed region of interest (ROI) and the model used to fit the signal decay at different echo times. A systematic comparison of R2* and LIC values obtained with the two methodologies has never been reported. This study aimed to detect the potential differences in R2* values obtained with different post-processing approaches and to explore whether the detected differences would be corrected when converted into LIC values. Single- and multi-centre patient cohorts were used. The single-centre cohort included 45 patients (25 males, 16.4±10.2 years) scanned at the Children’s Hospital of Los Angeles (CHLA). The multi-centre cohort (N=47; 19 females, 28.1±8.9 years) was baseline data from a phase II clinical trial of the iron chelator FBS0701 and was included to obtain higher generalizability The study was approved by the CHLA Committee for the Protection of Human Subjects and the institutional review boards of all participating hospitals. With the ThalassaemiaTools, a ROI was defined in an area of homogeneous liver tissue. All pixels were averaged together and fit to a single-exponential model. Later echo times were manually excluded from the fit in images where iron-mediated signal loss was high (Figures 1a–1b). R2* values (=1000/T2*) were converted into LIC by (Garbowski, et al 2009): LICPennell=0.03·R2*Pennell+0.7 [Equation 1] Figure 1 a) and b) Screenshot of CMRtools software (region of interest definition in the left and signal decay fitting in the right) for a patient with moderate iron overload and for a patient with severe iron overload, respectively. For the patient with severe ... In our laboratory R2* measurements were performed using a custom-written software. The ROI included the entire liver profile in the slice, excluding the major hilar vessels. The signal in each pixel was fit to an exponential-plus-constant model, producing a R2* map (Figure 1c). The mean was calculated (R2*Wood). LIC values (LICWood) were calculated as: LICWood=0.0254·R2*Wood+0.2 [Equation 2] R2* assessment, by either method, is limited to LIC