The Impact of Quality of Life on Cardiac Arrhythmias: A Clinical, Demographic, and AI-Assisted Statistical Investigation

<b>Background/Objectives</b>: Cardiac arrhythmias impact quality of life (QoL) and are often linked to psychological distress. This study examines the relationship between QoL, depression, and arrhythmias using AI-assisted analysis to enhance patient management. <b>Methods</b>...

Full description

Saved in:
Bibliographic Details
Main Authors: Luiza Camelia Nechita, Ancuta Elena Tupu, Aurel Nechita, Daniel Voipan, Andreea Elena Voipan, Dana Tutunaru, Carmina Liana Musat
Format: Article
Language:English
Published: MDPI AG 2025-03-01
Series:Diagnostics
Subjects:
Online Access:https://www.mdpi.com/2075-4418/15/7/856
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1849730770457853952
author Luiza Camelia Nechita
Ancuta Elena Tupu
Aurel Nechita
Daniel Voipan
Andreea Elena Voipan
Dana Tutunaru
Carmina Liana Musat
author_facet Luiza Camelia Nechita
Ancuta Elena Tupu
Aurel Nechita
Daniel Voipan
Andreea Elena Voipan
Dana Tutunaru
Carmina Liana Musat
author_sort Luiza Camelia Nechita
collection DOAJ
description <b>Background/Objectives</b>: Cardiac arrhythmias impact quality of life (QoL) and are often linked to psychological distress. This study examines the relationship between QoL, depression, and arrhythmias using AI-assisted analysis to enhance patient management. <b>Methods</b>: A total of 145 patients with arrhythmias were assessed using an SF-36 health survey (QoL) and a PHQ-9 questionnaire (depression). Statistical analyses included regression, clustering, and AI-based models such as K-means and logistic regression to identify risk factors and patient subgroups. <b>Results</b>: Patients with comorbidities had lower QoL and higher depression scores. PHQ-9 scores negatively correlated with SF-36 mental health components. AI-assisted clustering identified distinct patient subgroups, with older individuals and those with longer disease duration exhibiting the lowest QoL. Logistic regression predicted depression with 93% accuracy, and XGBoost achieved an AUC of 0.97. <b>Conclusions</b>: QoL plays a key role in arrhythmia management, with depression significantly influencing outcomes. AI-driven predictive models offer personalized interventions, improving early detection and treatment. Future research should integrate wearable technology and AI-based monitoring to optimize patient care.
format Article
id doaj-art-28261ca26f0b4fd996445b431b58d665
institution DOAJ
issn 2075-4418
language English
publishDate 2025-03-01
publisher MDPI AG
record_format Article
series Diagnostics
spelling doaj-art-28261ca26f0b4fd996445b431b58d6652025-08-20T03:08:46ZengMDPI AGDiagnostics2075-44182025-03-0115785610.3390/diagnostics15070856The Impact of Quality of Life on Cardiac Arrhythmias: A Clinical, Demographic, and AI-Assisted Statistical InvestigationLuiza Camelia Nechita0Ancuta Elena Tupu1Aurel Nechita2Daniel Voipan3Andreea Elena Voipan4Dana Tutunaru5Carmina Liana Musat6Faculty of Medicine and Pharmacy, ‘Dunarea de Jos’ University of Galati, 800008 Galati, RomaniaFaculty of Medicine and Pharmacy, ‘Dunarea de Jos’ University of Galati, 800008 Galati, RomaniaFaculty of Medicine and Pharmacy, ‘Dunarea de Jos’ University of Galati, 800008 Galati, RomaniaFaculty of Automation, Computers, Electrical Engineering and Electronics, ‘Dunarea de Jos’ University of Galati, 800008 Galati, RomaniaFaculty of Automation, Computers, Electrical Engineering and Electronics, ‘Dunarea de Jos’ University of Galati, 800008 Galati, RomaniaFaculty of Medicine and Pharmacy, ‘Dunarea de Jos’ University of Galati, 800008 Galati, RomaniaFaculty of Medicine and Pharmacy, ‘Dunarea de Jos’ University of Galati, 800008 Galati, Romania<b>Background/Objectives</b>: Cardiac arrhythmias impact quality of life (QoL) and are often linked to psychological distress. This study examines the relationship between QoL, depression, and arrhythmias using AI-assisted analysis to enhance patient management. <b>Methods</b>: A total of 145 patients with arrhythmias were assessed using an SF-36 health survey (QoL) and a PHQ-9 questionnaire (depression). Statistical analyses included regression, clustering, and AI-based models such as K-means and logistic regression to identify risk factors and patient subgroups. <b>Results</b>: Patients with comorbidities had lower QoL and higher depression scores. PHQ-9 scores negatively correlated with SF-36 mental health components. AI-assisted clustering identified distinct patient subgroups, with older individuals and those with longer disease duration exhibiting the lowest QoL. Logistic regression predicted depression with 93% accuracy, and XGBoost achieved an AUC of 0.97. <b>Conclusions</b>: QoL plays a key role in arrhythmia management, with depression significantly influencing outcomes. AI-driven predictive models offer personalized interventions, improving early detection and treatment. Future research should integrate wearable technology and AI-based monitoring to optimize patient care.https://www.mdpi.com/2075-4418/15/7/856quality of lifecardiac arrhythmiasdepressionartificial intelligencestatistical methodsPHQ-9
spellingShingle Luiza Camelia Nechita
Ancuta Elena Tupu
Aurel Nechita
Daniel Voipan
Andreea Elena Voipan
Dana Tutunaru
Carmina Liana Musat
The Impact of Quality of Life on Cardiac Arrhythmias: A Clinical, Demographic, and AI-Assisted Statistical Investigation
Diagnostics
quality of life
cardiac arrhythmias
depression
artificial intelligence
statistical methods
PHQ-9
title The Impact of Quality of Life on Cardiac Arrhythmias: A Clinical, Demographic, and AI-Assisted Statistical Investigation
title_full The Impact of Quality of Life on Cardiac Arrhythmias: A Clinical, Demographic, and AI-Assisted Statistical Investigation
title_fullStr The Impact of Quality of Life on Cardiac Arrhythmias: A Clinical, Demographic, and AI-Assisted Statistical Investigation
title_full_unstemmed The Impact of Quality of Life on Cardiac Arrhythmias: A Clinical, Demographic, and AI-Assisted Statistical Investigation
title_short The Impact of Quality of Life on Cardiac Arrhythmias: A Clinical, Demographic, and AI-Assisted Statistical Investigation
title_sort impact of quality of life on cardiac arrhythmias a clinical demographic and ai assisted statistical investigation
topic quality of life
cardiac arrhythmias
depression
artificial intelligence
statistical methods
PHQ-9
url https://www.mdpi.com/2075-4418/15/7/856
work_keys_str_mv AT luizacamelianechita theimpactofqualityoflifeoncardiacarrhythmiasaclinicaldemographicandaiassistedstatisticalinvestigation
AT ancutaelenatupu theimpactofqualityoflifeoncardiacarrhythmiasaclinicaldemographicandaiassistedstatisticalinvestigation
AT aurelnechita theimpactofqualityoflifeoncardiacarrhythmiasaclinicaldemographicandaiassistedstatisticalinvestigation
AT danielvoipan theimpactofqualityoflifeoncardiacarrhythmiasaclinicaldemographicandaiassistedstatisticalinvestigation
AT andreeaelenavoipan theimpactofqualityoflifeoncardiacarrhythmiasaclinicaldemographicandaiassistedstatisticalinvestigation
AT danatutunaru theimpactofqualityoflifeoncardiacarrhythmiasaclinicaldemographicandaiassistedstatisticalinvestigation
AT carminalianamusat theimpactofqualityoflifeoncardiacarrhythmiasaclinicaldemographicandaiassistedstatisticalinvestigation
AT luizacamelianechita impactofqualityoflifeoncardiacarrhythmiasaclinicaldemographicandaiassistedstatisticalinvestigation
AT ancutaelenatupu impactofqualityoflifeoncardiacarrhythmiasaclinicaldemographicandaiassistedstatisticalinvestigation
AT aurelnechita impactofqualityoflifeoncardiacarrhythmiasaclinicaldemographicandaiassistedstatisticalinvestigation
AT danielvoipan impactofqualityoflifeoncardiacarrhythmiasaclinicaldemographicandaiassistedstatisticalinvestigation
AT andreeaelenavoipan impactofqualityoflifeoncardiacarrhythmiasaclinicaldemographicandaiassistedstatisticalinvestigation
AT danatutunaru impactofqualityoflifeoncardiacarrhythmiasaclinicaldemographicandaiassistedstatisticalinvestigation
AT carminalianamusat impactofqualityoflifeoncardiacarrhythmiasaclinicaldemographicandaiassistedstatisticalinvestigation