Silent atrial fibrillation in pacemaker early post-implantation period: an unintentionally provoked situation?

Aims: Atrial high-rate episodes (AHREs) compatible with silent AF detected in pacemakers (PM) are related to an increased risk of stroke and silent ischaemic brain lesions (IBL) on CT scan. AHREs soon after PM implantation could be related with the procedure itself and the prognosis might be different. Methods and results: We analysed the incidence of AHREs >5 min and the presence of silent IBL in 110 patients (56% men, aged 75 ± 9 year-old) with PM and no history of AF, in relation to time from implantation (≤3 months vs. >3 months) and the atrial lead fixation (LF) (active vs. passive). Mean CHADS2 and CHA2DS2VASc scores were 1.9 ± 1.2 and 3.5 ± 1.5, respectively. Time from implantation was ≤3 months in 88 patients (80%). Active LF was used in 55 patients (50%). After 24 ± 9 months, AHREs were present in 40 patients (36.4%). CT-scan showed silent IBL in 26 patients (23.6%). The presence of AHREs at 3 months was more frequent in the patients with recent PM implantation (17% vs. 4.5%, P = 0.09) and significantly related to active LF (OR 5.36, 1.43-20.07; P < 0.05). The presence of silent IBL was related to the detection of AHREs during follow up (OR 3.12, 1.29-7.97; P < 0.05) but not with AHREs at first 3 months (OR 1.58, 0.49-5.05; P = 0.44). Conclusions: AHREs occur frequently during the first 3 months after PM implantation and could be related with procedure itself and the use of active LF. AHREs in this period might not be related to worse outcomes and should be interpreted cautiously.

CorVue algorithm efficacy to predict heart failure in real life: Unnecessary and potentially misleading information?

BACKGROUND: Heart failure (HF) hospitalizations have a negative impact on quality of life and imply important costs. Intrathoracic impedance (ITI) variations detected by cardiac devices have been hypothesized to predict HF hospitalizations. Although Optivol™ algorithm (Medtronic, Minneapolis, MN, USA) has been widely studied, CorVue™ algorithm's (St. Jude Medical, St. Paul, MN, USA) long-term efficacy has not been systematically evaluated in a "real-life" cohort. METHODS: CorVue™ was activated in implantable cardioverter defibrillator (ICD)/cardiac resynchronization therapy defibrillator (CRT-D) patients to store information about ITI measures. Clinical events (new episodes of HF requiring treatment and hospitalizations) and CorVue™ data were recorded every 3 months. Appropriate CorVue™ detection for HF was considered if it occurred in the 4 prior weeks to the clinical event. RESULTS: Fifty-three ICD/CRT-D (26 ICD and 27 CRT-D) patients (67 ± 1 years old, 79% male) were included. Device position was subcutaneous in 28 patients. At inclusion, mean left ventricular ejection fraction was 25 ± 7% and 27 patients (51%) were in New York Heart Association class I, 18 (34%) in class II, and eight (15%) in class III. After a mean follow-up of 17 ± 9 months, 105 ITI drops alarms were detected in 32 patients (60%). Only six alarms were appropriate (true positive) and required hospitalization. Eighteen patients (34%) presented 25 clinical episodes (12 hospitalizations and 13 emergency room/ambulatory treatment modifications). Nineteen of these clinical episodes (76%) remained undetected by the CorVue™ (false negative). Sensitivity of CorVue™ resulted in 24%, specificity was 70%, positive predictive value of 6%, and negative predictive value of 93%. CONCLUSIONS: CorVue™ showed a low sensitivity to predict HF events. Therefore, routinely activation of this algorithm could generate misleading information.

Inappropriate automatic mode switching episodes: What's the mechanism?

We present a case series of five patients reporting abnormal automatic mode switching (AMS) episodes during routinary cardiac defibrillator (ICD) and pacemaker (PM) follow-up. This non-previously described phenomenon was reported to St. Jude Medical (Abbott) Technical Support that confirmed the inappropriate automatic mode switching.

Silent ischaemic brain lesions related to atrial high rate episodes in patients with cardiac implantable electronic devices.

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.

Limitations of the AutoCapture™ Pacing System in patients with cardiac stimulation devices.

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.

Standardizing nursing degree curriculum structure in Spain: A mixed-methods study.

BACKGROUND: Legislation regulating Spanish and European academic curricula prescribes a certain level of knowledge and skills any student must master. Spanish universities freely decide the number of credits assigned to each subject and in which year the subject will be taught. We hypothesize that this flexibility may give way to excessively heterogeneous training across universities in nursing degrees. Such curricula heterogeneity hinders inter-university transfers and weakens educational excellence. OBJECTIVES: 1) To review the existing differences in nursing degrees in Spanish universities; 2) to compare our results against current legislation; 3) to propose changes in the legislation, if necessary. DESIGN: Mixed-methods approach. SETTING: Spain. METHODS: We reviewed nursing degree curricula of all 60 Spanish universities. Inter-university differences were analyzed and checked against current legislation. A focus group proposed legislative changes accordingly. RESULTS: Several differences between public and private universities were statistically significant. During the first cycle, public universities´ course loads include more theoretical teachings, more credits in core subjects during the first year, and more compulsory subjects in second year. Private universities are more likely to offer external internships during the first cycle whereas the public ones are more likely to offer them during the second cycle. Public universities offer more credits under the following curricular blocks than private ones: "Nutrition/Dietetics," "Psychiatry," "Public and Community Health," and "Geriatrics." In turn, private universities offer more credits in the areas of "Theory/Methodology," "Ethics/Legislation," "English," and "Theology." Academic curricula meet most of the criteria established by the Spanish and European legislation. The proposed legislative changes aim at standardizing curricula by associating specific credits and their timeline to the teaching blocks. CONCLUSIONS: Nursing degree curricula among Spanish universities are highly heterogeneous. Legislative changes to homogenize teaching blocks would facilitate credit validations and student mobility across universities, in addition to increasing nursing degrees´ standardization and excellence.

How to recognize silent atrial fibrillation in pacemakers and defibrillators-the value of atrial electrograms.

Today's pacemakers and defibrillators include diagnostic tools for detecting and treating cardiac arrhythmias like silent atrial fibrillation as atrial high rate episodes (AHREs). This diagnostic capability is crucial to prevent the potential embolic complications this AHREs are related to. However, sometimes data retrieved from diagnostic counters may be misleading reflecting limitations of detection algorithms, which must follow mathematical rules to classify events on a beat-to-beat basis. The incorporation of stored electrograms has been an important milestone in improving the diagnostic capabilities of these devices confirming the arrhythmia diagnosis.

A Two-Stage Combining Classifier Model for the Development of Adaptive Dialog Systems.

This paper proposes a statistical framework to develop user-adapted spoken dialog systems. The proposed framework integrates two main models. The first model is used to predict the user's intention during the dialog. The second model uses this prediction and the history of dialog up to the current moment to predict the next system response. This prediction is performed with an ensemble-based classifier trained for each of the tasks considered, so that a better selection of the next system can be attained weighting the outputs of these specialized classifiers. The codification of the information and the definition of data structures to store the data supplied by the user throughout the dialog makes the estimation of the models from the training data and practical domains manageable. We describe our proposal and its application and detailed evaluation in a practical spoken dialog system.

Silent brain infarcts in high blood pressure patients with cardiac implantable electronic devices: unmasking silent atrial fibrillation.

BACKGROUND: Hypertensive patients present a higher risk for developing atrial fibrillation and its complications. Cardiac implantable electronic devices (CIEDs) have shown reliable atrial fibrillation detection as atrial high-rate episodes (AHREs). The presence of AHRE more than 5 min has been related to increased risk of stroke, but a high proportion of ischemic brain lesions (IBLs) could be subclinical and thromboembolic risk underestimated. METHODS: We included hypertensive patients with CIED and we analyzed the incidence of AHRE and the presence of IBL on computed tomography (CT) scan. RESULTS: One hundred and twenty-three patients (57% men) aged 77 ± 8 years were evaluated during a mean follow-up of 15 ± 9 months. AHREs were documented in 46 patients (37%). Cranial CT scan showed silent IBL in 34 patients (27%). Univariate analysis showed that age, CHADS2 and CHADS2VA2Sc scores, history of prior stroke/ transient ischemic attack and the presence of AHRE were significantly related to higher risk for IBL on CT scan (P < 0.05). Multivariate analysis showed that the presence of AHRE more than 5 min [odds ratio 3.05 (1.19-7.81; P < 0.05)] was an independent predictor of IBL. CONCLUSION: Silent atrial fibrillation detected by CIED as AHRE is really prevalent in hypertensive patients. AHREs were independently associated with a higher incidence of silent IBL on CT scan.

Human activity recognition based on Evolving Fuzzy Systems.

Environments equipped with intelligent sensors can be of much help if they can recognize the actions or activities of their users. If this activity recognition is done automatically, it can be very useful for different tasks such as future action prediction, remote health monitoring, or interventions. Although there are several approaches for recognizing activities, most of them do not consider the changes in how a human performs a specific activity. We present an automated approach to recognize daily activities from the sensor readings of an intelligent home environment. However, as the way to perform an activity is usually not fixed but it changes and evolves, we propose an activity recognition method based on Evolving Fuzzy Systems.