Intracranial pressure wave morphological classification: automated analysis and clinical validation.

BACKGROUND: Recently, different software has been developed to automatically analyze multiple intracranial pressure (ICP) parameters, but the suggested methods are frequently complex and have no clinical correlation. The objective of this study was to assess the clinical value of a new morphological classification of the cerebrospinal fluid pulse pressure waveform (CSFPPW), comparing it to the elastance index (EI) and CSF-outflow resistance (Rout), and to test the efficacy of an automatic ICP analysis. METHODS: An artificial neural network (ANN) was trained to classify 60 CSFPPWs in four different classes, according to their morphology, and its efficacy was compared to an expert examiner's classification. The morphology of CSFPPW, recorded in 60 patients at baseline, was compared to EI and Rout calculated at the end of an intraventricular infusion test to validate the utility of the proposed classification in patients' clinical evaluation. RESULTS: The overall concordance in CSFPPW classification between the expert examiner and the ANN was 88.3 %. An elevation of EI was statistically related to morphological class' progression. All patients showing pathological baseline CSFPPW (class IV) revealed an alteration of CSF hydrodynamics at the end of their infusion test. CONCLUSIONS: The proposed morphological classification estimates the global ICP wave and its ability to reflect or predict an alteration in CSF hydrodynamics. An ANN can be trained to efficiently recognize four different CSF wave morphologies. This classification seems helpful and accurate for diagnostic use.

The MOnitoring Resynchronization dEvices and CARdiac patiEnts (MORE-CARE) randomized controlled trial: phase 1 results on dynamics of early intervention with remote monitoring.

BACKGROUND: Remote monitoring (RM) in patients with advanced heart failure and cardiac resynchronization therapy defibrillators (CRT-D) may reduce delays in clinical decisions by transmitting automatic alerts. However, this strategy has never been tested specifically in this patient population, with alerts for lung fluid overload, and in a European setting. OBJECTIVE: The main objective of Phase 1 (presented here) is to evaluate if RM strategy is able to reduce time from device-detected events to clinical decisions. METHODS: In this multicenter randomized controlled trial, patients with moderate to severe heart failure implanted with CRT-D devices were randomized to a Remote group (with remote follow-up and wireless automatic alerts) or to a Control group (with standard follow-up without alerts). The primary endpoint of Phase 1 was the delay between an alert event and clinical decisions related to the event in the first 154 enrolled patients followed for 1 year. RESULTS: The median delay from device-detected events to clinical decisions was considerably shorter in the Remote group compared to the Control group: 2 (25(th)-75(th) percentile, 1-4) days vs 29 (25(th)-75(th) percentile, 3-51) days respectively, P=.004. In-hospital visits were reduced in the Remote group (2.0 visits/patient/year vs 3.2 visits/patient/year in the Control group, 37.5% relative reduction, P<.001). Automatic alerts were successfully transmitted in 93% of events occurring outside the hospital in the Remote group. The annual rate of all-cause hospitalizations per patient did not differ between the two groups (P=.65). CONCLUSIONS: RM in CRT-D patients with advanced heart failure allows physicians to promptly react to clinically relevant automatic alerts and significantly reduces the burden of in-hospital visits. TRIAL REGISTRATION: Clinicaltrials.gov NCT00885677; http://clinicaltrials.gov/show/NCT00885677 (Archived by WebCite at http://www.webcitation.org/6IkcCJ7NF).

Efficacy of a tool combining guide-wire and stylet for the left ventricular lead positioning.

AIMS: The left ventricular (LV) lead for cardiac resynchronization therapy (CRT) is usually positioned in the coronary sinus via a stylet-guided or an 'over-the-wire' approach. Recently, a new tool has been developed, the Medtronic Attain Hybrid, that combines guide-wire and stylet features. We assessed its safety and efficacy in comparison with standard tools currently used in clinical practice. METHODS AND RESULTS: Patients undergoing standard CRT device implantation were enrolled in seven Italian centres. In the preliminary phase of the study (Phase I), data were collected during implantation procedures performed with standard tools (three patients per centre). Subsequently, the Attain Hybrid was made available in the centres and data were collected for all consecutive patients undergoing implantation during the following year. A learning phase was considered (Phase II), and the last three patients per centre (Phase III) were used for comparison with Phase I. One hundred and seventeen patients were enrolled: 21 patients in Phase I, 75 in Phase II, and 21 in Phase III. Rates of successful implantation were similar in Phases I and III (95 vs. 100%, P=1.000). The pre-defined target vein was reached in 15 (71%) patients in Phase I and in 21 (100%) patients in Phase III (P=0.021). In 10 (48%) procedures during Phase I, LV lead positioning necessitated switching from guide-wire to stylet; this proportion decreased during Phase III (14%, P=0.043). Mean LV positioning time was 16±7 min in Phase I and 11±6 min in Phase III (P=0.040). No adverse events or lead-related complications were detected on implantation or during a follow-up of 6±4 months. CONCLUSION: The Attain Hybrid is safe and effective. It significantly improves target vein accessibility and reduces procedural time in comparison with conventional tools.

A convergent hybrid decomposition algorithm model for SVM training.

Training of support vector machines (SVMs) requires to solve a linearly constrained convex quadratic problem. In real applications, the number of training data may be very huge and the Hessian matrix cannot be stored. In order to take into account this issue, a common strategy consists in using decomposition algorithms which at each iteration operate only on a small subset of variables, usually referred to as the working set. Training time can be significantly reduced by using a caching technique that allocates some memory space to store the columns of the Hessian matrix corresponding to the variables recently updated. The convergence properties of a decomposition method can be guaranteed by means of a suitable selection of the working set and this can limit the possibility of exploiting the information stored in the cache. We propose a general hybrid algorithm model which combines the capability of producing a globally convergent sequence of points with a flexible use of the information in the cache. As an example of a specific realization of the general hybrid model, we describe an algorithm based on a particular strategy for exploiting the information deriving from a caching technique. We report the results of computational experiments performed by simple implementations of this algorithm. The numerical results point out the potentiality of the approach.