Clinical trial NCT06977516: Large Language Model and Atrial Fibrillation Recurrence
Official title: Development and Validation of an Atrial Fibrillation Recurrence Model Based on Large Language Models: A Retrospective, Multicenter Study
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Atrial Fibrillation Recurrence
Keywords: Atrial Fibrillation Recurrence
Large Language Model
Deep Learning
Catheter Ablation
Lead sponsor: The Fourth Affiliated Hospital of Zhejiang University School of Medicine
Sponsor class: OTHER
Collaborators: name: Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University
name: The Affiliated Hospital of Yunnan University
name: Jinhua People's Hospital
name: Beilun District People's Hospital
First posted: 2025-05-18
Enrollment: 3000
Sex: ALL
Minimum age (years): 18
Maximum age (years): 90
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: We plan to extract data from patients diagnosed with atrial fibrillation who underwent their first atrial fibrillation ablation between January 2016 and December 2023 at the Fourth Affiliated Hospital of Zhejiang University School of Medicine, Taizhou Hospital of Zhejiang Province, the Affiliated Hospital of Yunnan University, Jinhua People's Hospital, and Beilun District Hospital. The collected data will include the patient's general information, laboratory tests, preoperative dynamic electrocardiogram text modality, echocardiogram text modality, and surgical procedure. The primary observation endpoint will be atrial fibrillation recurrence (defined as the detection of a persistent atrial arrhythmia lasting more than 30 seconds on ECG or dynamic ECG after ablation), with a follow-up period extending until December 2024.

Brief summary: This study aims to develop a risk prediction model for atrial fibrillation (AF) recurrence by leveraging large language model (LLM) technology to analyze semantic relationships across multimodal textual data, including pre-ablation clinical baseline characteristics, echocardiography reports, ambulatory electrocardiogram reports, and procedural records. The proposed model seeks to provide actionable clinical insights for electrophysiologists managing AF ablation patients.

Detailed description: Atrial fibrillation (AF) is a common cardiac arrhythmia characterized by rapid and disorganized electrical activity in the atria. It significantly increases the risk of mortality, stroke, and heart failure. Radiofrequency ablation (RFA) is a first-line treatment for AF, yet the recurrence rate remains high.

Traditional clinical risk factors, such as left atrial diameter, AF duration, and AF type, have been proven to be closely associated with AF recurrence. Additionally, derived scoring systems like the CAAP-AF and APPLE scores have demonstrated good predictive value for post-ablation outcomes. However, these assessment methods still fail to comprehensively account for all relevant factors in AF patients.

Large Language Models (LLMs) are deep learning models trained on vast amounts of textual data, capable of generating natural language text and understanding its meaning. By training on massive datasets, these models can provide in-depth knowledge on various topics and exhibit strong language generation capabilities. Prominent LLMs like GPT-4 and LLaMA have achieved remarkable success in natural language processing (NLP) and are gradually being applied in the medical field. Nevertheless, research on LLM-based AF recurrence risk prediction models remains unexplored. Therefore, this study aims to develop an AF recurrence risk prediction model using LLMs, providing further diagnostic and therapeutic insights for both AF ablation patients and clinical electrophysiologists.

Study Design

1. Data Cleaning

   * Remove noisy data from the text, such as garbled characters, duplicates, and irrelevant symbols.
   * Standardize the text, including unifying date formats, units, abbreviations, etc.
   * Handle missing values by reasonably filling or flagging them based on context.
2. Data Annotation

   * Perform structured annotation on text data, including but not limited to:

     * Entity recognition (e.g., disease names, symptoms, test results, treatment plans).
     * Text paragraph classification (e.g., pre-operative echocardiography texts, dynamic ECG texts, surgical records).
   * Combine manual and automated annotation to ensure quality.
3. Data Desensitization

   * Desensitize sensitive information (e.g., patient names) to ensure data privacy and security.
   * Use encrypted storage and transmission to safeguard data during preprocessing.
4. Data Augmentation

   * Expand the dataset by generating additional training samples through synonym replacement, sentence restructuring, etc.
   * Apply sampling techniques to imbalanced data to ensure balanced model training.
5. Data Format Conversion

   * Convert text data into formats required for model training (e.g., JSON, CSV).
   * Segment or chunk data for easier model input.
6. Model Selection and Configuration

   * Choose suitable pre-trained model versions (e.g., Qwen, Deepseek) for medical text processing.
   * Configure hyperparameters (e.g., learning rate, batch size, training epochs).
7. Transfer Learning and Fine-Tuning

   * Perform transfer learning on large language models using medical text data provided by various centers.
   * Design multi-task learning (MTL) strategies for intelligent diagnosis tasks, optimizing evaluations such as ablation prognosis and survival outcomes for AF patients.
8. Model Training

   * Conduct training on hardware environments provided by major centers or high-performance computing platforms like OnekeyAI.
   * Monitor the training process to ensure model convergence and prevent overfitting.
9. Model Optimization

   * Adjust model architecture and hyperparameters based on training results to improve performance.
   * Incorporate reinforcement learning (RL) mechanisms to refine model outputs through interactions with medical experts.
10. Test Set Construction

    * Partition test sets from text data provided by centers, ensuring alignment with training set distributions.
    * Create diverse test scenarios.
11. Model Performance Evaluation

    * Evaluate the model's predictive performance for late-stage AF recurrence using the following metrics:

      * Accuracy: Consistency between model predictions and ground truth.
      * Recall: Proportion of correctly identified cases among all true cases.
      * F1 Score: Harmonic mean of precision and recall.
      * ROC-AUC: Performance assessment for binary classification tasks.
    * Conduct manual evaluation of model outputs, with medical experts reviewing diagnostic results.
12. Error Analysis and Improvement - Analyze erroneous cases in the test set to identify root causes (e.g., insufficient data, labeling errors, model bias).

Eligibility criteria: Inclusion Criteria:

We plan to retrospectively collect data from five atrial fibrillation treatment centers, including The Fourth Affiliated Hospital of Zhejiang University School of Medicine, Taizhou Hospital of Zhejiang Province, The Affiliated Hospital of Yunnan University, Jinhua People's Hospital, and Beilun District People's Hospital, for patients diagnosed with atrial fibrillation who underwent their first catheter ablation between January 2016 and December 2023.

Exclusion Criteria:

1. Patients undergoing repeated AF ablation procedures;
2. AF patients with incomplete follow-up data;
3. AF patients lacking preoperative laboratory tests or key textual modality results (echocardiography, ambulatory ECG);
4. Patients with comorbid conditions including acute myocardial infarction, valvular heart disease, malignant tumors, or hyperthyroidism, and those with AF recurrence within 3 months post-ablation.

Primary outcomes:
measure: Late Atrial Fibrillation Recurrence
timeFrame: Recurrence occurring after 3 months are considered late recurrences.
Secondary outcomes:
measure: Early Atrial Fibrillation Recurrence
timeFrame: Recurrences occurring within 3 months postoperatively are classified as early recurrences.
Other outcomes:
Locations:
city: Kunming
country: China
facility: The Affiliated Hospital of Yunnan University
state: Yunnan
city: Jinhua
country: China
facility: Jinhua People's Hospital
state: Zhejiang
city: Ningbo
country: China
facility: Beilun District People's Hospital
state: Zhejiang
city: Taizhou
country: China
facility: Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University
state: Zhejiang
city: Yiwu
country: China
facility: The Fourth Affiliated Hospital of Zhejiang University School of Medicine
state: Zhejiang
Study officials:
References: