ArticleJournal of translational medicine2025
Coronary health index based on immunoglobulin light chains to assess coronary heart disease risk with machine learning: a diagnostic trial.
Article in Journal of translational medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- A 3-tier information fusioned framework featuring explainable deep active optimized CRNet for accurate heart disease prediction.Journal of translational medicine · 2026Article
- Relationship between amino acid metabolism and inflammation in coronary heart disease (Review).International journal of molecular medicine · 2025Review
- An online explainable ensemble machine learning model for predicting epidermal growth factor receptor mutation status in lung adenocarcinoma.Translational lung cancer research · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
backgroundRecent studies suggest a connection between immunoglobulin light chains (IgLCs) and coronary heart disease (CHD). However, current diagnostic methods using peripheral blood IgLCs levels or subtype ratios show limited accuracy for CHD, lacking comprehensive assessment and posing challenges in early detection and precise disease severity evaluation. We aim to develop and validate a Coronary Health Index (CHI) incorporating total IgLCs levels and their distribution. Additionally, we aim to evaluate its effectiveness by integrating patient data and using machine learning models through diagnostic trial.
methodsThe CHI was developed and combined with other clinical data. Nine machine learning models were screened to identify optimal diagnostic performance, with the XGBoost model emerging as the top performer. Performance was assessed based on accuracy, sensitivity, and its ability to identify severe CHD cases characterized by complex lesions (SYNTAX score > 33).
resultsThe XGBoost model demonstrated high accuracy and sensitivity in diagnosing CHD, with an area under the curve (AUC) of 0.927. It also accurately identified patients with severe CHD, achieving an AUC of 0.991. An online web tool was introduced for broader external validation, confirming the model's effectiveness.
conclusionsCombining the CHI with the XGBoost model offers significant advantages in diagnosing CHD and assessing disease severity. This approach can guide clinical interventions and improve large-scale CHD screening.
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Registered trials
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