A real-time LSTM-MAML prediction model for atrial fibrillation in the ICU achieved an AUC of 0.92 internally and 0.89 in external validation, though precision was low.
Observational
Yes
Does an LSTM-MAML machine learning model accurately predict the onset of atrial fibrillation in ICU patients?
A real-time machine learning model using ICU data showed high discrimination (AUC 0.89-0.92) for predicting incident atrial fibrillation, though precision was low.
Effect estimate: AUC 0.92
Abstract Objective Atrial fibrillation (AF) is a common arrhythmia affecting a large fraction of patients in intensive care units (ICUs). Predicting patients at risk of AF in the ICU is a challenging task and is not commonly practiced but could have clinical implications considering that AF is a proxy for poorer outcomes. Our goal is to develop a real-time AF prediction model using ICU numerical data to improve alarm quality, helping clinicians to identify at-risk patients and intervene before AF onset, thereby potentially improving clinical outcomes. Methods We employed AmsterdamUMCdb, Europe’s first openly accessible ICU dataset. The dataset includes static features, including demographics, as well as dynamic bedside monitoring data, like blood pressure and respiratory rate, along with detailed records of medication and fluid administration. To enhance generalizability across diverse ICU patients, we trained a long short-term memory (LSTM) model combined with a Model-Agnostic Meta-Learning (MAML) approach. Model performance was tested on an imbalanced test set, reflective of the real-world ratio of AF to non-AF patients. Direct external validation was performed using the MIMIC-IV database to test generalizability across different clinical settings. Results The LSTM-MAML model achieved an AUC of 0.92, accuracy of 0.89, and precision of 0.29 on the internal validation set. In external validation with MIMIC-IV, it showed near-equivalent performance with an AUC of 0.89, accuracy of 0.85, and precision of 0.18. Conclusions The real-time prediction model demonstrated predictive value for AF and has potential for future clinical benefits. However, for clinical implementation, further refinement is necessary to improve its performance in identifying patients at risk for AF.
Moazeni et al. (Tue,) conducted a observational in Atrial fibrillation in intensive care unit patients. LSTM-MAML prediction model was evaluated on Model performance for predicting atrial fibrillation (AUC 0.92). A real-time LSTM-MAML prediction model for atrial fibrillation in the ICU achieved an AUC of 0.92 internally and 0.89 in external validation, though precision was low.