Detection and Prognostic Stratification of Left Ventricular Systolic Dysfunction in Left Bundle Branch Block Using an Artificial Intelligence-enabled ECG
Abstract Body (Do not enter title and authors here): Background Left bundle branch block (LBBB) significantly increases the risk of left ventricular systolic dysfunction (LVSD) due to cardiac dyssynchrony. While recent advances in artificial intelligence (AI) have enabled ECG-based models to accurately detect LVSD, their performance in LBBB-specific population remains insufficiently validated.
Hypothesis We hypothesized that AiTiALVSD, a clinically validated AI-ECG model for detecting LVSD, can accurately detect LVSD and predict future risk in LBBB patients.
Methods This retrospective multicenter study analyzed 5,689 expert-curated LBBB ECGs of 2,813 patients from two hospitals (2016–2024) using AiTiALVSD V2.00.00 to detect LVSD. Patients with paired ECG and echocardiography within 14 days were included. LBBB was identified through automated screening and expert validation. LVSD was defined as EF ≤40%. Diagnostic performance was evaluated using area under the receiver operating characteristic curve (AUROC), area under precision-recall curve (AUPRC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and 95% confidence intervals (CI). Patients were stratified into high- and low-risk groups by AiTiALVSD score, using a predefined threshold to achieve 90% sensitivity. Kaplan–Meier curves were used to assess differences in clinical outcomes between risk groups.
Results Among 2,813 LBBB patients (mean age 70.7, male 43.7%), hypertension and heart failure were common, and the mean QRS duration was 153.4 ms. The AiTiALVSD model showed strong diagnostic performance for identifying LVSD (AUROC 0.930 [95% CI, 0.924–0.937]; AUPRC 0.913 [95% CI, 0.902–0.923]; sensitivity 0.979 [95% CI, 0.974-0.985]; specificity 0.473 [95%CI, 0.455-0.490]; PPV 0.594 [95% CI, 0.579-0.609]; NPV 0.967 [95%CI, 0.58-0.976]). Mean follow-up duration was 4.1 years. High-risk patients had significantly higher hazards for all-cause mortality (HR 2.29, 95% CI 1.89–2.77), implantable cardioverter defibrillator (ICD)/cardiac resynchronization therapy (CRT) implantation (HR 2.29, 95% CI 1.89–2.77), and cardiovascular hospitalization (HR 1.40, 95% CI 1.22–1.60)(all p values < 0.001).
Conclusion This multicenter study demonstrates that AiTiALVSD accurately detects LVSD in LBBB patients and effectively stratifies long-term risk for adverse cardiovascular outcomes. These findings support its integration into clinical workflows to enhance early detection and guide proactive management strategies in this high-risk population.
Yoo, Ah-hyun
( Medical AI Co., Ltd.
, Seoul
, Korea (the Republic of)
)
Kim, Kyung-hee
( Incheon Sejong Hospital
, Incheon
, Korea (the Republic of)
)
Lee, Soo Youn
( Incheon Sejong Hospital
, Incheon
, Korea (the Republic of)
)
Lee, Hak Seung
( Medical AI Co., Ltd.
, Seoul
, Korea (the Republic of)
)
Kang, Sora
( Medical AI Co., Ltd.
, Seoul
, Korea (the Republic of)
)
Lee, Min Sung
( Medical AI Co., Ltd.
, Seoul
, Korea (the Republic of)
)
Han, Ga In
( Medical AI Co., Ltd.
, Seoul
, Korea (the Republic of)
)
Son, Jeong Min
( Medical AI Co., Ltd.
, Seoul
, Korea (the Republic of)
)
Jang, Jong-hwan
( Medical AI Co., Ltd.
, Seoul
, Korea (the Republic of)
)
Jo, Yong-yeon
( Medical AI Co., Ltd.
, Seoul
, Korea (the Republic of)
)
Kwon, Joon-myoung
( Medical AI Co., Ltd.
, Seoul
, Korea (the Republic of)
)
Author Disclosures:
Ah-Hyun Yoo:DO NOT have relevant financial relationships
| Kyung-Hee Kim:No Answer
| Soo Youn Lee:No Answer
| Hak Seung Lee:DO have relevant financial relationships
;
Employee:Medical AI:Active (exists now)
| Sora Kang:DO NOT have relevant financial relationships
| Min Sung Lee:DO have relevant financial relationships
;
Employee:Medical AI Co., Ltd.:Active (exists now)
| Ga In Han:DO NOT have relevant financial relationships
| Jeong Min Son:DO have relevant financial relationships
;
Employee:Medical AI, Co., Ltd.:Active (exists now)
| Jong-Hwan Jang:DO NOT have relevant financial relationships
| Yong-Yeon Jo:No Answer
| Joon-Myoung Kwon:No Answer
Lee Hak Seung, Kwon Joon-myoung, Kim Kyung-hee, Lee Soo Youn, Kang Sora, Han Ga In, Yoo Ah-hyun, Jang Jong-hwan, Jo Yong-yeon, Son Jeong Min, Lee Min Sung
Lee Hak Seung, Kwon Joon-myoung, Lee Heesun, Rhee Tae-min, Kang Sora, Lee Min Sung, Han Ga In, Yoo Ah-hyun, Jang Jong-hwan, Jo Yong-yeon, Son Jeong Min
4365169_File000000.jpg
4365169_File000003.jpg
You have to be authorized to contact abstract author. Please, Login