Development of a Risk Prediction Model for New Episodes of Atrial Fibrillation in Medical-Surgical Critically Ill Patients Using the AmsterdamUMCdb.

AmsterdamUMCdb ICU atrial fibrillation critically ill patients risk prediction

Journal

Frontiers in cardiovascular medicine
ISSN: 2297-055X
Titre abrégé: Front Cardiovasc Med
Pays: Switzerland
ID NLM: 101653388

Informations de publication

Date de publication:
2022
Historique:
received: 16 03 2022
accepted: 26 04 2022
entrez: 1 6 2022
pubmed: 2 6 2022
medline: 2 6 2022
Statut: epublish

Résumé

The occurrence of atrial fibrillation (AF) represents clinical deterioration in acutely unwell patients and leads to increased morbidity and mortality. Prediction of the development of AF allows early intervention. Using the AmsterdamUMCdb, clinically relevant variables from patients admitted in sinus rhythm were extracted over the full duration of the ICU stay or until the first recorded AF episode occurred. Multiple logistic regression was performed to identify risk factors for AF. Input variables were automatically selected by a sequential forward search algorithm using cross-validation. We developed three different models: For the overall cohort, for ventilated patients and non-ventilated patients. 16,144 out of 23,106 admissions met the inclusion criteria. 2,374 (12.8%) patients had at least one AF episode during their ICU stay. Univariate analysis revealed that a higher percentage of AF patients were older than 70 years (60% versus 32%) and died in ICU (23.1% versus 7.1%) compared to non-AF patients. Multivariate analysis revealed age to be the dominant risk factor for developing AF with doubling of age leading to a 10-fold increased risk. Our logistic regression models showed excellent performance with AUC.ROC > 0.82 and > 0.91 in ventilated and non-ventilated cohorts, respectively. Increasing age was the dominant risk factor for the development of AF in both ventilated and non-ventilated critically ill patients. In non-ventilated patients, risk for development of AF was significantly higher than in ventilated patients. Further research is warranted to identify the role of ventilatory settings on risk for AF in critical illness and to optimise predictive models.

Identifiants

pubmed: 35647039
doi: 10.3389/fcvm.2022.897709
pmc: PMC9135978
doi:

Types de publication

Journal Article

Langues

eng

Pagination

897709

Informations de copyright

Copyright © 2022 Ortega-Martorell, Pieroni, Johnston, Olier and Welters.

Déclaration de conflit d'intérêts

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Auteurs

Sandra Ortega-Martorell (S)

School of Computer Science and Mathematics, Liverpool John Moores University, Liverpool, United Kingdom.
Liverpool Centre for Cardiovascular Science, Liverpool, United Kingdom.

Mark Pieroni (M)

School of Computer Science and Mathematics, Liverpool John Moores University, Liverpool, United Kingdom.
Liverpool Centre for Cardiovascular Science, Liverpool, United Kingdom.

Brian W Johnston (BW)

Liverpool Centre for Cardiovascular Science, Liverpool, United Kingdom.
Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, United Kingdom.
Liverpool University Hospitals NHS Foundation Trust, Liverpool, United Kingdom.

Ivan Olier (I)

School of Computer Science and Mathematics, Liverpool John Moores University, Liverpool, United Kingdom.
Liverpool Centre for Cardiovascular Science, Liverpool, United Kingdom.

Ingeborg D Welters (ID)

Liverpool Centre for Cardiovascular Science, Liverpool, United Kingdom.
Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, United Kingdom.
Liverpool University Hospitals NHS Foundation Trust, Liverpool, United Kingdom.

Classifications MeSH