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AI-Assisted Simple Scoring Algorithm Was Helpful in the Risk Assessment of Cardiac Involvement in Patients with
Malgorzata Dybowska1, Witold Z Tomkowski1, Katarzyna B Lewandowska1
11st Department of Lung Diseases, National Tuberculosis and Lung Diseases Research Institute, Plocka 26, 01-138 Warsaw, Poland.
Insights
An AI scoring system accurately predicts cardiac sarcoidosis in patients with pulmonary sarcoidosis. Holter ECG abnormalities and liver/spleen involvement are key predictors, improving early detection and management of this serious complication.
Area of Science:
- Cardiology
- Pulmonology
- Medical Imaging
Background:
- Cardiac sarcoidosis (CS) is a severe complication of sarcoidosis, often presenting with subtle symptoms.
- Early and accurate diagnosis of CS is crucial for timely intervention and improved patient outcomes.
Purpose of the Study:
- To evaluate clinical predictors for the development of cardiac sarcoidosis in patients with pulmonary sarcoidosis.
- To develop and validate an AI-assisted scoring system for predicting cardiac involvement.
Main Methods:
- Retrospective analysis of 393 pulmonary sarcoidosis patients undergoing cardiac magnetic resonance (CMR).
- Application of original Lake Louise criteria for active myocarditis identification.
- Development of an AI scoring system using logistic regression, incorporating ECG, Holter, liver/spleen involvement, gender, and disease stage.
Main Results:
- Cardiac sarcoidosis confirmed in 52% (48/92) of patients who underwent CMR.
- Holter ECG abnormalities and liver/spleen sarcoidosis were significantly associated with CS.
- The AI scoring system achieved 76% sensitivity and 74% specificity for CS prediction.
Conclusions:
- An AI-assisted scoring algorithm effectively predicts cardiac involvement in pulmonary sarcoidosis patients.
- Holter ECG abnormalities and liver/spleen involvement are significant predictors of CS.
- Prospective validation is required to confirm the clinical utility of the AI scoring system.
Abstract:
Background: Cardiac involvement, one of the most life-threatening complications of sarcoidosis, remains under-recognized due to its oligo-symptomatic presentation in some patients. This retrospective study aimed to evaluate the utility of various clinical predictors of cardiac sarcoidosis (CS) development. Methods: The study included patients with pulmonary sarcoidosis diagnosed according to the recent ATS guidelines between January 2020 and July 2024 who underwent cardiac magnetic resonance (CMR) due to clinical suspicion of CS. The original Lake Louise criteria were used to identify active myocarditis. Results: Out of 393 patients diagnosed with pulmonary sarcoidosis, CMR was performed in 92 patients. Cardiac sarcoidosis was confirmed in 48 patients (52%, CS+), and excluded in 44 patients (48%, CS-). CS(+) patients demonstrated significantly more frequent Holter ECG abnormalities and liver/spleen sarcoidosis compared to CS(-) patients. Stage IV pulmonary disease, ECG abnormalities, and hypercalcemia were more common in CS(+) than in CS(-) patients; however, these differences did not reach statistical significance. Multivariate analysis identified Holter ECG abnormalities and liver/spleen involvement as significant predictive factors for CS, increasing the risk of cardiac involvement by approximately 4- and 6-fold, respectively. An AI-assisted simple scoring system based on five parameters: ECG abnormalities, Holter ECG abnormalities, liver/spleen involvement, gender, and stage of sarcoidosis predicted CS with a sensitivity of 76% and specificity of 74%, using an optimal cut-off value of ≥7.6 points. Conclusions: In patients with pulmonary sarcoidosis, an AI-assisted scoring algorithm derived from L1-regularized logistic regression results accurately predicted cardiac involvement on CMR with high specificity and sensitivity. Prospective validation of this algorithm is necessary to confirm its clinical utility in predicting cardiac sarcoidosis.
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