Derivation and Validation of a Screening Model for Hypertrophic Cardiomyopathy Based on Electrocardiogram Features
BackgroundHypertrophic cardiomyopathy (HCM) is a widely distributed, but clinically heterogeneous genetic heart disease, affects approximately 20 million people worldwide. Nowadays, HCM is treatable with the advancement of medical interventions. However, due to occult clinical presentations and a la...
| Main Authors: | , , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-05-01
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| Series: | Frontiers in Cardiovascular Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fcvm.2022.889523/full |
| _version_ | 1818212861681336320 |
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| author | Lanyan Guo Chao Gao Weiping Yang Zhiling Ma Mengyao Zhou Jianzheng Liu Hong Shao Bo Wang Guangyu Hu Hang Zhao Ling Zhang Xiong Guo Chong Huang Zhe Cui Dandan Song Fangfang Sun Liwen Liu Fuyang Zhang Ling Tao |
| author_facet | Lanyan Guo Chao Gao Weiping Yang Zhiling Ma Mengyao Zhou Jianzheng Liu Hong Shao Bo Wang Guangyu Hu Hang Zhao Ling Zhang Xiong Guo Chong Huang Zhe Cui Dandan Song Fangfang Sun Liwen Liu Fuyang Zhang Ling Tao |
| author_sort | Lanyan Guo |
| collection | DOAJ |
| description | BackgroundHypertrophic cardiomyopathy (HCM) is a widely distributed, but clinically heterogeneous genetic heart disease, affects approximately 20 million people worldwide. Nowadays, HCM is treatable with the advancement of medical interventions. However, due to occult clinical presentations and a lack of easy, inexpensive, and widely popularized screening approaches in the general population, 80–90% HCM patients are not clinically identifiable, which brings certain safety hazards could have been prevented. The majority HCM patients showed abnormal and diverse electrocardiogram (ECG) presentations, it is unclear which ECG parameters are the most efficient for HCM screening.ObjectiveWe aimed to develop a pragmatic prediction model based on the most common ECG features to screen for HCM.MethodsBetween April 1st and September 30th, 2020, 423 consecutive subjects from the International Cooperation Center for Hypertrophic Cardiomyopathy of Xijing Hospital [172 HCM patients, 251 participants without left ventricular hypertrophy (non-HCM)] were prospectively included in the training cohort. Between January 4th and February 30th, 2021, 163 participants from the same center were included in the temporal internal validation cohort (62 HCM patients, 101 non-HCM participants). External validation was performed using retrospectively collected ECG data from Xijing Hospital (3,232 HCM ECG samples from January 1st, 2000, to March 31st, 2020; 95,184 non-HCM ECG samples from January 1st to December 31st, 2020). The C-statistic was used to measure the discriminative ability of the model.ResultsAmong 30 ECG features examined, all except abnormal Q wave significantly differed between the HCM patients and non-HCM comparators. After several independent feature selection approaches and model evaluation, we included only two ECG features, T wave inversion (TWI) and the amplitude of S wave in lead V1 (SV1), in the HCM prediction model. The model showed a clearly useful discriminative performance (C-statistic > 0.75) in the training [C-statistic 0.857 (0.818–0.896)], and temporal validation cohorts [C-statistic 0.871 (0.812–0.930)]. In the external validation cohort, the C-statistic of the model was 0.833 [0.825–0.841]. A browser-based calculator was generated accordingly.ConclusionThe pragmatic model established using only TWI and SV1 may be helpful for predicting the probability of HCM and shows promise for use in population-based HCM screening. |
| first_indexed | 2024-12-12T05:55:07Z |
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| id | doaj.art-933c758bc8f44d9baf9eb29a5a2159dd |
| institution | Directory Open Access Journal |
| issn | 2297-055X |
| language | English |
| last_indexed | 2024-12-12T05:55:07Z |
| publishDate | 2022-05-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Cardiovascular Medicine |
| spelling | doaj.art-933c758bc8f44d9baf9eb29a5a2159dd2022-12-22T00:35:34ZengFrontiers Media S.A.Frontiers in Cardiovascular Medicine2297-055X2022-05-01910.3389/fcvm.2022.889523889523Derivation and Validation of a Screening Model for Hypertrophic Cardiomyopathy Based on Electrocardiogram FeaturesLanyan Guo0Chao Gao1Weiping Yang2Zhiling Ma3Mengyao Zhou4Jianzheng Liu5Hong Shao6Bo Wang7Guangyu Hu8Hang Zhao9Ling Zhang10Xiong Guo11Chong Huang12Zhe Cui13Dandan Song14Fangfang Sun15Liwen Liu16Fuyang Zhang17Ling Tao18Department of Cardiology, Xijing Hospital, The Fourth Military Medical University, Xi’an, ChinaDepartment of Cardiology, Xijing Hospital, The Fourth Military Medical University, Xi’an, ChinaDepartment of Cardiology, Xijing Hospital, The Fourth Military Medical University, Xi’an, ChinaDepartment of Cardiology, Xijing Hospital, The Fourth Military Medical University, Xi’an, ChinaDepartment of Ultrasound, Xijing Hospital, The Fourth Military Medical University, Xi’an, ChinaDepartment of Cardiology, Xijing Hospital, The Fourth Military Medical University, Xi’an, ChinaDepartment of Cardiology, Xijing Hospital, The Fourth Military Medical University, Xi’an, ChinaDepartment of Ultrasound, Xijing Hospital, The Fourth Military Medical University, Xi’an, ChinaDepartment of Cardiology, Xijing Hospital, The Fourth Military Medical University, Xi’an, ChinaDepartment of Cardiology, Xijing Hospital, The Fourth Military Medical University, Xi’an, ChinaDepartment of Cardiology, Xijing Hospital, The Fourth Military Medical University, Xi’an, ChinaDepartment of Cardiology, Xijing Hospital, The Fourth Military Medical University, Xi’an, ChinaDepartment of Cardiology, Xijing Hospital, The Fourth Military Medical University, Xi’an, ChinaDepartment of Cardiology, Xijing Hospital, The Fourth Military Medical University, Xi’an, ChinaDepartment of Cardiology, Xijing Hospital, The Fourth Military Medical University, Xi’an, ChinaDepartment of Cardiology, Xijing Hospital, The Fourth Military Medical University, Xi’an, ChinaDepartment of Ultrasound, Xijing Hospital, The Fourth Military Medical University, Xi’an, ChinaDepartment of Cardiology, Xijing Hospital, The Fourth Military Medical University, Xi’an, ChinaDepartment of Cardiology, Xijing Hospital, The Fourth Military Medical University, Xi’an, ChinaBackgroundHypertrophic cardiomyopathy (HCM) is a widely distributed, but clinically heterogeneous genetic heart disease, affects approximately 20 million people worldwide. Nowadays, HCM is treatable with the advancement of medical interventions. However, due to occult clinical presentations and a lack of easy, inexpensive, and widely popularized screening approaches in the general population, 80–90% HCM patients are not clinically identifiable, which brings certain safety hazards could have been prevented. The majority HCM patients showed abnormal and diverse electrocardiogram (ECG) presentations, it is unclear which ECG parameters are the most efficient for HCM screening.ObjectiveWe aimed to develop a pragmatic prediction model based on the most common ECG features to screen for HCM.MethodsBetween April 1st and September 30th, 2020, 423 consecutive subjects from the International Cooperation Center for Hypertrophic Cardiomyopathy of Xijing Hospital [172 HCM patients, 251 participants without left ventricular hypertrophy (non-HCM)] were prospectively included in the training cohort. Between January 4th and February 30th, 2021, 163 participants from the same center were included in the temporal internal validation cohort (62 HCM patients, 101 non-HCM participants). External validation was performed using retrospectively collected ECG data from Xijing Hospital (3,232 HCM ECG samples from January 1st, 2000, to March 31st, 2020; 95,184 non-HCM ECG samples from January 1st to December 31st, 2020). The C-statistic was used to measure the discriminative ability of the model.ResultsAmong 30 ECG features examined, all except abnormal Q wave significantly differed between the HCM patients and non-HCM comparators. After several independent feature selection approaches and model evaluation, we included only two ECG features, T wave inversion (TWI) and the amplitude of S wave in lead V1 (SV1), in the HCM prediction model. The model showed a clearly useful discriminative performance (C-statistic > 0.75) in the training [C-statistic 0.857 (0.818–0.896)], and temporal validation cohorts [C-statistic 0.871 (0.812–0.930)]. In the external validation cohort, the C-statistic of the model was 0.833 [0.825–0.841]. A browser-based calculator was generated accordingly.ConclusionThe pragmatic model established using only TWI and SV1 may be helpful for predicting the probability of HCM and shows promise for use in population-based HCM screening.https://www.frontiersin.org/articles/10.3389/fcvm.2022.889523/fullelectrocardiogram (ECG)screening modelhypertrophic cardiomyopathyleft ventricular hypertrophyC-statistic |
| spellingShingle | Lanyan Guo Chao Gao Weiping Yang Zhiling Ma Mengyao Zhou Jianzheng Liu Hong Shao Bo Wang Guangyu Hu Hang Zhao Ling Zhang Xiong Guo Chong Huang Zhe Cui Dandan Song Fangfang Sun Liwen Liu Fuyang Zhang Ling Tao Derivation and Validation of a Screening Model for Hypertrophic Cardiomyopathy Based on Electrocardiogram Features Frontiers in Cardiovascular Medicine electrocardiogram (ECG) screening model hypertrophic cardiomyopathy left ventricular hypertrophy C-statistic |
| title | Derivation and Validation of a Screening Model for Hypertrophic Cardiomyopathy Based on Electrocardiogram Features |
| title_full | Derivation and Validation of a Screening Model for Hypertrophic Cardiomyopathy Based on Electrocardiogram Features |
| title_fullStr | Derivation and Validation of a Screening Model for Hypertrophic Cardiomyopathy Based on Electrocardiogram Features |
| title_full_unstemmed | Derivation and Validation of a Screening Model for Hypertrophic Cardiomyopathy Based on Electrocardiogram Features |
| title_short | Derivation and Validation of a Screening Model for Hypertrophic Cardiomyopathy Based on Electrocardiogram Features |
| title_sort | derivation and validation of a screening model for hypertrophic cardiomyopathy based on electrocardiogram features |
| topic | electrocardiogram (ECG) screening model hypertrophic cardiomyopathy left ventricular hypertrophy C-statistic |
| url | https://www.frontiersin.org/articles/10.3389/fcvm.2022.889523/full |
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