Adapting lean management to prevent healthcare-associated infections: a low-cost strategy involving Kamishibai cards to sustain bundles' compliance.

Lean healthcare visual management has been successfully integrated into infection control programs, leading to lower healthcare-associated infection (HAI) rates and greater provider compliance with evidence-based prevention practices; however, its implementation during quality improvement (QI) initiatives in limited-resource settings has not yet been well exploited. We aimed to describe a low-cost strategy involving Kamishibai cards to sustain bundles' adherence to prevent HAIs in a middle-income country. This descriptive case study evaluated the implementation of a lean healthcare visual management tool-Kamishibai board (K-board)-during a nationwide QI collaborative preventing three critical HAIs in 189 adult and pediatric/neonatal intensive care units (ICUs) from September 2021 to January 2023.

Machine learning-powered, device-embedded heart sound measurement can optimize AV delay in patients with CRT.

Background: Continuous optimization of atrioventricular (AV) delay for cardiac resynchronization therapy (CRT) is mainly performed by electrical means.

Objective: The purpose of this study was to develop an estimation model of cardiac function that uses a piezoelectric microphone embedded in a pulse generator to guide CRT optimization.

Methods: Electrocardiogram, left ventricular pressure (LVP), and heart sounds were simultaneously collected during CRT device implantation procedures.

Heart sound-derived systolic time intervals for atrioventricular delay optimization in cardiac resynchronization therapy.

Background: Phonocardiography (PCG) can be used to determine systolic time intervals (STIs) from ventricular pacing spike to the first heart sound (VS1) and from the first to the second heart sound (S1S2).

Objective: The purpose of this study was to investigate the relations between STIs and hemodynamics during atrioventricular (AV) delay optimization of biventricular pacing (BiVP) in animals and patients.

Methods: Five pigs with AV block underwent BiVP, while PCG was collected from an epicardial accelerometer.

Hemodynamics-driven mathematical model of third heart sound generation.

The proto-diastolic third heart sound (S3) is observed in various hemodynamic conditions in both normal and diseased hearts. We propose a novel, one-degree of freedom mathematical model of mechanical vibrations of heart and blood that generates the third heart sound, implemented in a real-time model of the cardiovascular system (CircAdapt). To examine model functionality, S3 simulations were performed for conditions mimicking the normal heart as well as heart failure with preserved ejection fraction (HFpEF), atrioventricular valve regurgitation (AVR), atrioventricular valve stenosis (AVS) and septal shunts (SS).

Left Ventricular Pressure Estimation Using Machine Learning-Based Heart Sound Classification.

Objective: A method to estimate absolute left ventricular (LV) pressure and its maximum rate of rise (LV dP/dtmax) from epicardial accelerometer data and machine learning is proposed.

Methods: Five acute experiments were performed on pigs. Custom-made accelerometers were sutured epicardially onto the right ventricle, LV, and right atrium.