Cardiac Magnetic Resonance Identifies Responders to Cardiac Resynchronization Therapy with an Assessment of Septal Scar and Left Ventricular Dyssynchrony.

: The response to cardiac resynchronization therapy (CRT) depends on septal viability and correction of abnormal septal motion. This study investigates if cardiac magnetic resonance (CMR) as a single modality can identify CRT responders with combined imaging of pathological septal motion (septal flash) and septal scar. : In a prospective, multicenter, observational study of 136 CRT recipients, septal scar was assessed using late gadolinium enhancement (LGE) ( = 127) and septal flash visually from cine CMR sequences.

Scar imaging in the dyssynchronous left ventricle: Accuracy of myocardial metabolism by positron emission tomography and function by echocardiographic strain.

Purpose: Response to cardiac resynchronization therapy (CRT) is reduced in patients with high left ventricular (LV) scar burden, in particular when scar is located in the LV lateral wall or septum. Late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) can identity scar, but is not feasible in all patients. This study investigates if myocardial metabolism by F-fluorodeoxyglucose positron emission tomography (FDG-PET) and contractile function by echocardiographic strain are alternatives to LGE-CMR.

Determinants of LV dP/dt and QRS duration with different fusion strategies in cardiac resynchronisation therapy.

Background: We designed this study to assess the acute effects of different fusion strategies and left ventricular (LV) pre-excitation/post-excitation on LV dP/dt and QRS duration (QRSd).

Methods: We measured LV dP/dt and QRSd in 19 patients having cardiac resynchronisation therapy (CRT). Two groups of biventricular pacing were compared: pacing the left ventricle (LV) with FUSION with intrinsic right ventricle (RV) activation (FUSION), and pacing the LV and RV with NO FUSION with intrinsic RV activation.

Early detection of sepsis utilizing deep learning on electronic health record event sequences.

Background: The timeliness of detection of a sepsis incidence in progress is a crucial factor in the outcome for the patient. Machine learning models built from data in electronic health records can be used as an effective tool for improving this timeliness, but so far, the potential for clinical implementations has been largely limited to studies in intensive care units. This study will employ a richer data set that will expand the applicability of these models beyond intensive care units.