Motivation, acceptability and attitudes toward lung cancer screening among persons who attend a tobacco cessation program: A multicenter survey in Italy.

This study aimed to evaluate smoking cessation (SC) motivation and the acceptability of a lung cancer screening (LCS) program with low-dose computed tomography (LDCT) among people who attend SC programs. A multicenter survey was conducted in the period January-December 2021 involving 197 people who attended group or individual SC courses in Reggio Emilia and Tuscany. Questionnaires, information sheets, and decision aids about the potential benefits and harms of LCS with LDCT were distributed at different time points during the course.

Classification of cardiac rhythm using heart rate dynamical measures: validation in MIT-BIH databases.

Background: Identification of atrial fibrillation (AF) is a clinical imperative. Heartbeat interval time series are increasingly available from personal monitors, allowing new opportunity for AF diagnosis.

Goal: Previously, we devised numerical algorithms for identification of normal sinus rhythm (NSR), AF, and SR with frequent ectopy using dynamical measures of heart rate.

Heart rate dynamics distinguish among atrial fibrillation, normal sinus rhythm and sinus rhythm with frequent ectopy.

Atrial fibrillation (AF) is usually detected by inspection of the electrocardiogram waveform, a task made difficult when the signal is distorted by noise. The RR interval time series is more frequently available and accurate, yet linear and nonlinear time series analyses that detect highly varying and irregular AF are vulnerable to the common finding of frequent ectopy. We hypothesized that different nonlinear measures might capture characteristic features of AF, normal sinus rhythm (NSR), and sinus rhythm (SR) with frequent ectopy in ways that linear measures might not.

Application of dynamical analyses of heart rate to rhythm classification and prognosis.

We have developed numerical approaches to dynamical analysis of heart rates, measured as interbeat or RR, intervals, based on entropy and fluctuation analyses in a large data base of consecutive Holter monitor recordings. In Part I, we present a RR interval-based classifier that distinguishes normal sinus rhythm (NSR), atrial fibrillation (AF) and sinus rhythm with ectopy with an accuracy of 99%, 81% and 77%respectively, using 10-minute segments. In Part II, we present 2-year mortality estimation based on the entropy calculations.