Summary A considerable proportion of patients with chronic myeloid leukaemia (CML) may present at diagnosis with high platelet counts. This may result in thrombosis or bleeding complications due to binding of von Willebrand factor (VWF) multimers to platelets. Paediatric CML is very rare and no systematic investigation on clinical complications of elevated platelets has been reported. Data on platelet count and associated haemostaseological complications were retrospectively analysed in a cohort of 156 children with CML. Fifty‐one percent (81/156) patients presented with thrombocytosis (platelet count> 500 × 109/l), and were extreme (>1 000 × 109/l) in 23/156 (16%). There were no cases of thrombosis but mild bleeding signs were present in 12% (n = 9) children with thrombocytosis. Bleeding occurred without correlation to elevated platelet counts and was associated with reduced large VWF multimers, indicating a diagnosis of acquired von Willebrand syndrome (AVWS), which resolved after initiation of CML treatment. Patients with paediatric CML frequently exhibit high platelet counts not resulting in thrombosis. In patients with thrombocytosis mild bleeding signs due to a low percentage of large VWF multimers can be demonstrated. AVWS may be underdiagnosed in paediatric CML (Clinical‐Trials.gov NCT00445822, 9 March 2007).

Acute lymphoblastic leukaemia (ALL) is the most common form of paediatric cancer and epigenetic aberrations are determinants of leukaemogenesis. The aim of this study was to investigate the methylation degree of a distinct phospholipase A2 receptor 1 (PLA2R1) promoter region in paediatric ALL patients and to evaluate its relevance as new biomarker for monitoring treatment response and burden of residual disease. The impact of PLA2R1 re-expression on proliferative parameters was assessed in vitro in Jurkat cells with PLA2R1 naturally silenced by DNA methylation. Genomic DNA was isolated from bone marrow (BM) and peripheral blood (PB) of 44 paediatric ALL patients. PLA2R1 methylation was analysed using digital PCR and compared to 20 healthy controls. Transfected Jurkat cells were investigated using cell growth curve analysis and flow cytometry. PLA2R1 was found hypermethylated in BM and PB from pre-B and common ALL patients, and in patients with the disease relapse. PLA2R1 methylation decreased along with leukaemic blast cell reduction during ALL induction treatment. In vitro analysis revealed an anti-proliferative phenotype associated with PLA2R1 re-expression, suggesting a tumour-suppressive function of PLA2R1. Collected data indicates that PLA2R1 promoter methylation quantitation can be used as biomarker for ALL induction treatment control, risk stratification, and early detection of ALL relapse.

All individual heterogeneously-methylated epialleles were quantifiable by a set of fluorescence-labeled probes with complementary sequences to epialleles in a closed-tube and high-throughput manner. The new method named epiallele-sensitive droplet digital PCR (EAST-ddPCR) may give new insights in the generation and regulation of epialleles and may help in finding new biomarkers for the diagnosis of benign und malignant diseases.

Background and purpose: The detection of paroxysmal atrial fibrillation (pAF) in patients presenting with ischaemic stroke shifts secondary stroke prevention to oral anticoagulation. In order to deal with the time-and resourceconsuming manual analysis of prolonged electrocardiogram (ECG)-monitoring data, we investigated the effectiveness of pAF detection with an automated algorithm (AA) in comparison to a manual analysis with software support within the IDEAS study [study analysis (SA)]. Methods: We used the dataset of the prospective IDEAS cohort of patients with acute ischaemic stroke/transient ischaemic attack presenting in sinus rhythm undergoing prolonged 72-h Holter ECG with central adjudication of atrial fibrillation (AF). This adjudicated diagnosis of AF was compared with a commercially available AA. Discordant results with respect to the diagnosis of pAF were resolved by an additional cardiological reference confirmation. Results: Paroxysmal AF was finally diagnosed in 62 patients (5.9%) in the cohort (n = 1043). AA more often diagnosed pAF (n = 60, 5.8%) as compared with SA (n = 47, 4.5%). Due to a high sensitivity (96.8%) and negative predictive value (99.8%), AA was able to identify patients without pAF, whereas abnormal findings in AA required manual review (specificity 96%; positive predictive value 60.6%). SA exhibited a lower sensitivity (75.8%) and negative predictive value (98.5%), and showed a specificity and positive predictive value of 100%. Agreement between the two methods classified by kappa coefficient was moderate (0.591). Conclusion: Automated determination of 'absence of pAF' could be used to reduce the manual review workload associated with review of prolonged Holter ECG recordings.

Objective: Identification of ischemic stroke patients at high risk for paroxysmal atrial fibrillation (pAF) during 72 hours Holter ECG might be useful to individualize the allocation of prolonged ECG monitoring times, currently not routinely applied in clinical practice. Methods: In a prospective multicenter study, the first analysable hour of raw ECG data from prolonged 72 hours Holter ECG monitoring in 1031 patients with acute ischemic stroke/TIA presenting in sinus rhythm was classified by an automated software (AA) into "no risk of AF" or "risk of AF" and compared to clinical variables to predict AF during 72 hours Holter-ECG. Results: pAF was diagnosed in 54 patients (5.2%; mean age: 78 years; female 56%) and was more frequently detected after 72 hours in patients classified by AA as "risk of AF" (n = 21, 17.8%) compared to "no risk of AF" (n = 33, 3.6%). AA-based risk stratification as "risk of AF" remained in the prediction model for pAF detection during 72 hours Holter ECG (OR3.814, 95% CI 2.024-7.816, P < 0.001), in addition to age (OR1.052, 95% CI 1.021-1.084, P = 0.001), NIHSS (OR 1.087, 95% CI 1.023-1.154, P = 0.007) and prior treatment with thrombolysis (OR2.639, 95% CI 1.313-5.306, P = 0.006). Similarly, risk stratification by AA significantly increased the area under the receiver operating characteristic curve (AUC) for prediction of pAF detection compared to a purely clinical risk score (AS5F alone: AUC 0.751; 95% CI 0.724-0.778; AUC for the combination: 0.789, 95% CI 0.763-0.814; difference between the AUC P = 0.022). Interpretation: Automated software-based ECG risk stratification selects patients with high risk of AF during 72 hours Holter ECG and adds predictive value to common clinical risk factors for AF prediction.