Diabetic cardiomyopathy (DCM) is a cardiac dysfunction which affects approximately 12% of diabetic patients, leading to overt heart failure and death. However, there is not an efficient and specific methodology for DCM diagnosis, possibly because molecular mechanisms are not fully elucidated, and it remains asymptomatic for many years. Also, DCM frequently coexists with other comorbidities such as hypertension, obesity, dyslipidemia, and vasculopathies. Thus, human DCM is not specifically identified after heart failure is established. In this sense, echocardiography has been traditionally considered the gold standard imaging test to evaluate the presence of cardiac dysfunction, although other techniques may cover earlier DCM detection by quantification of altered myocardial metabolism and strain. In this sense, Phase-Magnetic Resonance Imaging and 2D/3D-Speckle Tracking Echocardiography may potentially diagnose and stratify diabetic patients. Additionally, this information could be completed with a quantification of specific plasma biomarkers related to related to initial stages of the disease. Cardiotrophin-1, activin A, insulin-like growth factor binding protein-7 (IGFBP-7) and Heart fatty-acid binding protein have demonstrated a stable positive correlation with cardiac hypertrophy, contractibility and steatosis responses. Thus, we suggest a combination of minimally-invasive diagnosis tools for human DCM recognition based on imaging techniques and measurements of related plasma biomarkers.

Aims Monitoring capabilities of cardiac implantable electronic devices have revealed that a large proportion of patients present silent atrial fibrillation (AF) detected as atrial high rate episodes (AHREs). Atrial high rate episodes >5 min have been linked to increased risk of clinical stroke, but a high proportion of ischaemic brain lesions (IBLs) could be subclinical. Methods and results We prospectively analysed the incidence of AHRE > 5 min in 109 patients (56% men, aged 74 ± 9 years) and the presence of silent IBL on computed tomography (CT) scan. Mean CHADS2 and CHA2DS2VASc scores were 2.3 ± 1.3 and 3.9 ± 1.6, respectively. Seventy-five patients (69%) had no history of AF or stroke/transient ischaemic attack (TIA). After 12 months, 28 patients (25.7%) showed at least one AHRE. Patients with AHREs were more likely to have history of AF. Computed tomography scan showed silent IBL in 28 (25.7%). The presence of IBL was significantly related to older patients, prior history of AF or stroke/TIA, higher CHADS2 or CHA2DS2VASc scores, and the presence of AHRE. Multivariable analysis demonstrated that AHRE was an independent predictor for silent IBL in overall population [hazard ratio (HR) 3.05 (1.06–8.81; P < 0.05)] but also in patients without prior history of AF or stroke/TIA [HR 9.76 (1.76–54.07; P < 0.05)]. Conclusion Cardiac implantable electronic devices can accurately detect AF as AHRE. Atrial high rate episodes were associated to a higher incidence of silent IBL on CT scan. Atrial high rate episodes represent a kind of silent AF where management recommendations are lacking despite the fact that a higher embolic risk is present.

Aims AutoCapture (St Jude Medical) is a technological development that confirms ventricular capture analysing the evoked response after a pacing impulse and adjusts the energy output to changes in the stimulation threshold. Although this algorithm is aimed to assure capture minimizing energy consumption, some patients might not benefit from it. The objective of this study is to identify them. Methods and results Long-term AutoCapture efficiency was assessed using the data recorded in the programmer reports of patients undergoing scheduled pacemaker check-ups during 2012 in our institution. We have evaluated 160 consecutive patients (58% men) aged 78 ± 9 years. Pacemaker stimulation mode was DDD in 116 patients (72.5%) and VVI in 44 patients (27.5%). During the scheduled visits for pacemaker check-up, 73 patients (45.6%) showed abnormalities in the long-term AutoCapture function report (high variability in the AutoCapture stimulation threshold and/or out-of-range values). After multivariate analysis, abnormal AutoCapture pattern was associated to the presence of atrial fibrillation [odds ratio (OR) 3.96 (1.59–9.82; P < 0.05)]; and a ventricular pacing ≤25% of the time [OR 4.80 (2.09–11.05; P < 0.05)]. AutoCapture abnormalities were also described in three (1.8%) patients with very low stimulation threshold. Conclusion Although AutoCapture algorithm has shown both efficacy and safety, our findings suggest that some patients with atrial fibrillation or those requiring ventricular pacing ≤25% of the time may not benefit from it. Activation of the algorithm should be individualized according to the patient's characteristics and long-term AutoCapture pattern checked in the routine follow-up.