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Using Text Content From Coronary Catheterization Reports to Predict 5-Year Mortality Among Patients Undergoing
Yu-Hsuan Li1,2, I-Te Lee2,3,4, Yu-Wei Chen5
1Department of Computer Science & Information Engineering, National Taiwan University, Taipei, Taiwan.
Insights
A new deep learning model uses coronary catheterization report text to predict 5-year mortality after angiography. This approach outperforms existing scores, offering simpler clinical application for patient prognosis.
Area of Science:
- Cardiovascular Medicine
- Artificial Intelligence in Healthcare
- Biomedical Informatics
Background:
- Current predictive models for coronary angiography patients are complex and limited in clinical use.
- Coronary catheterization reports contain valuable data on coronary artery disease severity and revascularization completeness.
- No prior predictive models have utilized the textual content of these reports.
Purpose of the Study:
- To develop a deep learning model using coronary catheterization report text to predict 5-year all-cause and cardiovascular mortality.
- To compare the novel model's performance against established clinical scores.
Main Methods:
- A retrospective cohort study of 11,576 patients undergoing coronary angiography (2006-2015).
- Utilized BioBERT, a biomedical domain-specific BERT model, for text analysis.
- Assessed model performance using the area under the receiver operating characteristic curve (AUC) and compared it to the residual SYNTAX score.
Main Results:
- The model achieved an AUC of 0.822 for 5-year all-cause mortality and 0.858 for 5-year cardiovascular mortality.
- Outperformed the residual SYNTAX score in predicting both 5-year all-cause (AUC 0.867 vs. 0.590) and cardiovascular mortality (AUC 0.880 vs. 0.649) in a subset of patients post-PCI.
- 12.2% of patients experienced all-cause mortality and 5.8% cardiovascular mortality within 5 years.
Conclusions:
- A predictive model was successfully developed using the text from coronary catheterization reports.
- The model accurately predicts 5-year mortality in patients undergoing coronary angiography.
- The model's reliance on routinely generated reports facilitates easy clinical implementation.
Background:
Current predictive models for patients undergoing coronary angiography have complex parameters which limit their clinical application. Coronary catheterization reports that describe coronary lesions and the corresponding interventions provide information of the severity of the coronary artery disease and the completeness of the revascularization. This information is relevant for predicting patient prognosis. However, no predictive model has been constructed using the text content from coronary catheterization reports before.
Objective:
To develop a deep learning model using text content from coronary catheterization reports to predict 5-year all-cause mortality and 5-year cardiovascular mortality for patients undergoing coronary angiography and to compare the performance of the model to the established clinical scores.
Method:
This retrospective cohort study was conducted between January 1, 2006, and December 31, 2015. Patients admitted for coronary angiography were enrolled and followed up until August 2019. The main outcomes were 5-year all-cause mortality and 5-year cardiovascular mortality. In total, 11,576 coronary catheterization reports were collected. BioBERT (bidirectional encoder representations from transformers for biomedical text mining), which is a BERT-based model in the biomedical domain, was utilized to construct the model. The area under the receiver operating characteristic curve (AUC) was used to assess model performance. We also compared our results to the residual SYNTAX (SYNergy between PCI with TAXUS and Cardiac Surgery) score.
Results:
The dataset was divided into the training (60%), validation (20%), and test (20%) sets. The mean age of the patients in each dataset was 65.5 ± 12.1, 65.4 ± 11.2, and 65.6 ± 11.2 years, respectively. A total of 1,411 (12.2%) patients died, and 664 (5.8%) patients died of cardiovascular causes within 5 years after coronary angiography. The best of our models had an AUC of 0.822 (95% CI, 0.790-0.855) for 5-year all-cause mortality, and an AUC of 0.858 (95% CI, 0.816-0.900) for 5-year cardiovascular mortality. We randomly selected 300 patients who underwent percutaneous coronary intervention (PCI), and our model outperformed the residual SYNTAX score in predicting 5-year all-cause mortality (AUC, 0.867 [95% CI, 0.813-0.921] vs. 0.590 [95% CI, 0.503-0.684]) and 5-year cardiovascular mortality (AUC, 0.880 [95% CI, 0.873-0.925] vs. 0.649 [95% CI, 0.535-0.764]), respectively, after PCI among these patients.
Conclusions:
We developed a predictive model using text content from coronary catheterization reports to predict the 5-year mortality in patients undergoing coronary angiography. Since interventional cardiologists routinely write reports after procedures, our model can be easily implemented into the clinical setting.
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