Prediction of new-onset atrial fibrillation in patients with non-small cell lung cancer treated with curative-intent conventional radiotherapy.

Atrial fibrillation Lung cancer Machine learning Prediction model Radiotherapy

Journal

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
ISSN: 1879-0887
Titre abrégé: Radiother Oncol
Pays: Ireland
ID NLM: 8407192

Informations de publication

Date de publication:
26 Sep 2024
Historique:
received: 03 04 2024
revised: 03 09 2024
accepted: 20 09 2024
medline: 29 9 2024
pubmed: 29 9 2024
entrez: 28 9 2024
Statut: aheadofprint

Résumé

Atrial fibrillation (AF) is an important side effect of thoracic Radiotherapy (RT), which may impair quality of life and survival. This study aimed to develop a prediction model for new-onset AF in patients with Non-Small Cell Lung Cancer (NSCLC) receiving RT alone or as a part of their multi-modal treatment. Patients with stage I-IV NSCLC treated with curative-intent conventional photon RT were included. The baseline electrocardiogram (ECG) was compared with follow-up ECGs to identify the occurrence of new-onset AF. A wide range of potential clinical predictors and dose-volume measures on the whole heart and six automatically contoured cardiac substructures, including chambers and conduction nodes, were considered for statistical modeling. Internal validation with optimism-correction was performed. A nomogram was made. 374 patients (mean age 69 ± 10 years, 57 % male) were included. At baseline, 9.1 % of patients had AF, and 42 (11.2 %) patients developed new-onset AF. The following parameters were predictive: older age (OR=1.04, 95 % CI: 1.013-1.068), being overweight or obese (OR=1.791, 95 % CI: 1.139-2.816), alcohol use (OR=4.052, 95 % CI: 2.445-6.715), history of cardiac procedures (OR=2.329, 95 % CI: 1.287-4.215), tumor located in the upper lobe (OR=2.571, 95 % CI: 1.518-4.355), higher forced expiratory volume in 1 s (OR=0.989, 95 % CI: 0.979-0.999), higher creatinine (OR=1.008, 95 % CI: 1.002-1.014), concurrent chemotherapy (OR=3.266, 95 % CI: 1.757 to 6.07) and left atrium D This prediction model employs readily available predictors to identify patients at high risk of new-onset AF who could potentially benefit from active screening and timely management of post-RT AF.

Sections du résumé

BACKGROUND BACKGROUND
Atrial fibrillation (AF) is an important side effect of thoracic Radiotherapy (RT), which may impair quality of life and survival. This study aimed to develop a prediction model for new-onset AF in patients with Non-Small Cell Lung Cancer (NSCLC) receiving RT alone or as a part of their multi-modal treatment.
PATIENTS AND METHODS METHODS
Patients with stage I-IV NSCLC treated with curative-intent conventional photon RT were included. The baseline electrocardiogram (ECG) was compared with follow-up ECGs to identify the occurrence of new-onset AF. A wide range of potential clinical predictors and dose-volume measures on the whole heart and six automatically contoured cardiac substructures, including chambers and conduction nodes, were considered for statistical modeling. Internal validation with optimism-correction was performed. A nomogram was made.
RESULTS RESULTS
374 patients (mean age 69 ± 10 years, 57 % male) were included. At baseline, 9.1 % of patients had AF, and 42 (11.2 %) patients developed new-onset AF. The following parameters were predictive: older age (OR=1.04, 95 % CI: 1.013-1.068), being overweight or obese (OR=1.791, 95 % CI: 1.139-2.816), alcohol use (OR=4.052, 95 % CI: 2.445-6.715), history of cardiac procedures (OR=2.329, 95 % CI: 1.287-4.215), tumor located in the upper lobe (OR=2.571, 95 % CI: 1.518-4.355), higher forced expiratory volume in 1 s (OR=0.989, 95 % CI: 0.979-0.999), higher creatinine (OR=1.008, 95 % CI: 1.002-1.014), concurrent chemotherapy (OR=3.266, 95 % CI: 1.757 to 6.07) and left atrium D
CONCLUSION CONCLUSIONS
This prediction model employs readily available predictors to identify patients at high risk of new-onset AF who could potentially benefit from active screening and timely management of post-RT AF.

Identifiants

pubmed: 39341504
pii: S0167-8140(24)03522-9
doi: 10.1016/j.radonc.2024.110544
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

110544

Informations de copyright

Copyright © 2024. Published by Elsevier B.V.

Déclaration de conflit d'intérêts

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Fariba Tohidinezhad (F)

Department of Radiation Oncology (Maastro Clinic), School for Oncology and Reproduction (GROW), Maastricht University Medical Center, Maastricht, the Netherlands.

Leonard Nürnberg (L)

Department of Radiation Oncology (Maastro Clinic), School for Oncology and Reproduction (GROW), Maastricht University Medical Center, Maastricht, the Netherlands; Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA; Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, the Netherlands.

Femke Vaassen (F)

Department of Radiation Oncology (Maastro Clinic), School for Oncology and Reproduction (GROW), Maastricht University Medical Center, Maastricht, the Netherlands.

Rachel Ma Ter Bekke (R)

Department of Cardiology, Cardiovascular Research Institute Maastricht, Maastricht University Medical Center, Maastricht, the Netherlands.

Hugo Jwl Aerts (H)

Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA; Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, the Netherlands; Departments of Radiation Oncology and Radiology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.

Lizza El Hendriks (L)

Department of Pulmonary Diseases, School for Oncology and Reproduction (GROW), Maastricht University Medical Center, Maastricht, the Netherlands.

Andre Dekker (A)

Department of Radiation Oncology (Maastro Clinic), School for Oncology and Reproduction (GROW), Maastricht University Medical Center, Maastricht, the Netherlands.

Dirk De Ruysscher (D)

Department of Radiation Oncology (Maastro Clinic), School for Oncology and Reproduction (GROW), Maastricht University Medical Center, Maastricht, the Netherlands.

Alberto Traverso (A)

Department of Radiation Oncology (Maastro Clinic), School for Oncology and Reproduction (GROW), Maastricht University Medical Center, Maastricht, the Netherlands; School of Medicine, Libera Università Vita-Salute San Raffaele, Milan, Italy. Electronic address: traverso.alberto@hsr.it.

Classifications MeSH