Machine Learning to Predict Long-Term Cardiac-Relative Prognosis in Patients With Extra-Cardiac Vascular Disease
Aim: Patients with ischemic stroke (IS), transient ischemic attack (TIA), and/or peripheral artery disease (PAD) represent a population with an increased risk of coronary artery disease. Prognostic risk assessment to identify those with the highest risk that may benefit from more intensified treatme...
Guardado en:
| Autores principales: | , , , , , , , , , , , , |
|---|---|
| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Frontiers Media S.A.
2021
|
| Materias: | |
| Acceso en línea: | https://doaj.org/article/5eb29879f486411493acc44a78946e55 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| id |
oai:doaj.org-article:5eb29879f486411493acc44a78946e55 |
|---|---|
| record_format |
dspace |
| spelling |
oai:doaj.org-article:5eb29879f486411493acc44a78946e552021-12-01T01:02:59ZMachine Learning to Predict Long-Term Cardiac-Relative Prognosis in Patients With Extra-Cardiac Vascular Disease2297-055X10.3389/fcvm.2021.771504https://doaj.org/article/5eb29879f486411493acc44a78946e552021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fcvm.2021.771504/fullhttps://doaj.org/toc/2297-055XAim: Patients with ischemic stroke (IS), transient ischemic attack (TIA), and/or peripheral artery disease (PAD) represent a population with an increased risk of coronary artery disease. Prognostic risk assessment to identify those with the highest risk that may benefit from more intensified treatment remains challenging. To explore the feasibility and capability of machine learning (ML) to predict long-term adverse cardiac-related prognosis in patients with IS, TIA, and/or PAD.Methods: We analyzed 636 consecutive patients with a history of IS, TIA, and/or PAD. All patients underwent a coronary CT angiography (CCTA) scan. Thirty-five clinical data and 34 CCTA metrics underwent automated feature selection for ML model boosting. The clinical outcome included all-cause mortality (ACM) and major adverse cardiac events (MACE) (ACM, unstable angina requiring hospitalization, non-fatal myocardial infarction (MI), and revascularization 90 days after the index CCTA).Results: During the follow-up of 3.9 ± 1.6 years, 21 patients had unstable angina requiring hospitalization, eight had a MI, 23 had revascularization and 13 deaths. ML demonstrated a significant higher area-under-curve compared with the modified Duke index (MDI), segment stenosis score (SSS), segment involvement score (SIS), and Framingham risk score (FRS) for the prediction of ACM (ML:0.92 vs. MDI:0.66, SSS:0.68, SIS:0.67, FRS:0.51, all P < 0.001) and MACE (ML:0.84 vs. MDI:0.82, SSS:0.76, SIS:0.73, FRS:0.53, all P < 0.05).Conclusion: Among the patients with IS, TIA, and/or PAD, ML demonstrated a better capability of predicting ACM and MCAE than clinical scores and CCTA metrics.Guisen LinGuisen LinQile LiuYuchen ChenXiaodan ZongYue XiTingyu LiYuelong YangAn ZengMinglei ChenChen LiuYanting LiangXiaowei XuMeiping HuangFrontiers Media S.A.articlecoronary artery diseasecoronary computed tomography angiographymachine learningprognosisextravascular diseaseDiseases of the circulatory (Cardiovascular) systemRC666-701ENFrontiers in Cardiovascular Medicine, Vol 8 (2021) |
| institution |
DOAJ |
| collection |
DOAJ |
| language |
EN |
| topic |
coronary artery disease coronary computed tomography angiography machine learning prognosis extravascular disease Diseases of the circulatory (Cardiovascular) system RC666-701 |
| spellingShingle |
coronary artery disease coronary computed tomography angiography machine learning prognosis extravascular disease Diseases of the circulatory (Cardiovascular) system RC666-701 Guisen Lin Guisen Lin Qile Liu Yuchen Chen Xiaodan Zong Yue Xi Tingyu Li Yuelong Yang An Zeng Minglei Chen Chen Liu Yanting Liang Xiaowei Xu Meiping Huang Machine Learning to Predict Long-Term Cardiac-Relative Prognosis in Patients With Extra-Cardiac Vascular Disease |
| description |
Aim: Patients with ischemic stroke (IS), transient ischemic attack (TIA), and/or peripheral artery disease (PAD) represent a population with an increased risk of coronary artery disease. Prognostic risk assessment to identify those with the highest risk that may benefit from more intensified treatment remains challenging. To explore the feasibility and capability of machine learning (ML) to predict long-term adverse cardiac-related prognosis in patients with IS, TIA, and/or PAD.Methods: We analyzed 636 consecutive patients with a history of IS, TIA, and/or PAD. All patients underwent a coronary CT angiography (CCTA) scan. Thirty-five clinical data and 34 CCTA metrics underwent automated feature selection for ML model boosting. The clinical outcome included all-cause mortality (ACM) and major adverse cardiac events (MACE) (ACM, unstable angina requiring hospitalization, non-fatal myocardial infarction (MI), and revascularization 90 days after the index CCTA).Results: During the follow-up of 3.9 ± 1.6 years, 21 patients had unstable angina requiring hospitalization, eight had a MI, 23 had revascularization and 13 deaths. ML demonstrated a significant higher area-under-curve compared with the modified Duke index (MDI), segment stenosis score (SSS), segment involvement score (SIS), and Framingham risk score (FRS) for the prediction of ACM (ML:0.92 vs. MDI:0.66, SSS:0.68, SIS:0.67, FRS:0.51, all P < 0.001) and MACE (ML:0.84 vs. MDI:0.82, SSS:0.76, SIS:0.73, FRS:0.53, all P < 0.05).Conclusion: Among the patients with IS, TIA, and/or PAD, ML demonstrated a better capability of predicting ACM and MCAE than clinical scores and CCTA metrics. |
| format |
article |
| author |
Guisen Lin Guisen Lin Qile Liu Yuchen Chen Xiaodan Zong Yue Xi Tingyu Li Yuelong Yang An Zeng Minglei Chen Chen Liu Yanting Liang Xiaowei Xu Meiping Huang |
| author_facet |
Guisen Lin Guisen Lin Qile Liu Yuchen Chen Xiaodan Zong Yue Xi Tingyu Li Yuelong Yang An Zeng Minglei Chen Chen Liu Yanting Liang Xiaowei Xu Meiping Huang |
| author_sort |
Guisen Lin |
| title |
Machine Learning to Predict Long-Term Cardiac-Relative Prognosis in Patients With Extra-Cardiac Vascular Disease |
| title_short |
Machine Learning to Predict Long-Term Cardiac-Relative Prognosis in Patients With Extra-Cardiac Vascular Disease |
| title_full |
Machine Learning to Predict Long-Term Cardiac-Relative Prognosis in Patients With Extra-Cardiac Vascular Disease |
| title_fullStr |
Machine Learning to Predict Long-Term Cardiac-Relative Prognosis in Patients With Extra-Cardiac Vascular Disease |
| title_full_unstemmed |
Machine Learning to Predict Long-Term Cardiac-Relative Prognosis in Patients With Extra-Cardiac Vascular Disease |
| title_sort |
machine learning to predict long-term cardiac-relative prognosis in patients with extra-cardiac vascular disease |
| publisher |
Frontiers Media S.A. |
| publishDate |
2021 |
| url |
https://doaj.org/article/5eb29879f486411493acc44a78946e55 |
| work_keys_str_mv |
AT guisenlin machinelearningtopredictlongtermcardiacrelativeprognosisinpatientswithextracardiacvasculardisease AT guisenlin machinelearningtopredictlongtermcardiacrelativeprognosisinpatientswithextracardiacvasculardisease AT qileliu machinelearningtopredictlongtermcardiacrelativeprognosisinpatientswithextracardiacvasculardisease AT yuchenchen machinelearningtopredictlongtermcardiacrelativeprognosisinpatientswithextracardiacvasculardisease AT xiaodanzong machinelearningtopredictlongtermcardiacrelativeprognosisinpatientswithextracardiacvasculardisease AT yuexi machinelearningtopredictlongtermcardiacrelativeprognosisinpatientswithextracardiacvasculardisease AT tingyuli machinelearningtopredictlongtermcardiacrelativeprognosisinpatientswithextracardiacvasculardisease AT yuelongyang machinelearningtopredictlongtermcardiacrelativeprognosisinpatientswithextracardiacvasculardisease AT anzeng machinelearningtopredictlongtermcardiacrelativeprognosisinpatientswithextracardiacvasculardisease AT mingleichen machinelearningtopredictlongtermcardiacrelativeprognosisinpatientswithextracardiacvasculardisease AT chenliu machinelearningtopredictlongtermcardiacrelativeprognosisinpatientswithextracardiacvasculardisease AT yantingliang machinelearningtopredictlongtermcardiacrelativeprognosisinpatientswithextracardiacvasculardisease AT xiaoweixu machinelearningtopredictlongtermcardiacrelativeprognosisinpatientswithextracardiacvasculardisease AT meipinghuang machinelearningtopredictlongtermcardiacrelativeprognosisinpatientswithextracardiacvasculardisease |
| _version_ |
1718406023417430016 |