Artificial intelligence for hemodynamic monitoring with a wearable electrocardiogram monitor
Abstract Background The ability to non-invasively measure left atrial pressure would facilitate the identification of patients at risk of pulmonary congestion and guide proactive heart failure care. Wearable cardiac monitors, which record single-lead electrocardiogram data, provide information that...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Communications Medicine |
| Online Access: | https://doi.org/10.1038/s43856-024-00730-5 |
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| author | Daphne E. Schlesinger Ridwan Alam Roey Ringel Eugene Pomerantsev Srikanth Devireddy Pinak Shah Joseph Garasic Collin M. Stultz |
| author_facet | Daphne E. Schlesinger Ridwan Alam Roey Ringel Eugene Pomerantsev Srikanth Devireddy Pinak Shah Joseph Garasic Collin M. Stultz |
| author_sort | Daphne E. Schlesinger |
| collection | DOAJ |
| description | Abstract Background The ability to non-invasively measure left atrial pressure would facilitate the identification of patients at risk of pulmonary congestion and guide proactive heart failure care. Wearable cardiac monitors, which record single-lead electrocardiogram data, provide information that can be leveraged to infer left atrial pressures. Methods We developed a deep neural network using single-lead electrocardiogram data to determine when the left atrial pressure is elevated. The model was developed and internally evaluated using a cohort of 6739 samples from the Massachusetts General Hospital (MGH) and externally validated on a cohort of 4620 samples from a second institution. We then evaluated model on patch-monitor electrocardiographic data on a small prospective cohort. Results The model achieves an area under the receiver operating characteristic curve of 0.80 for detecting elevated left atrial pressures on an internal holdout dataset from MGH and 0.76 on an external validation set from a second institution. A further prospective dataset was obtained using single-lead electrocardiogram data with a patch-monitor from patients who underwent right heart catheterization at MGH. Evaluation of the model on this dataset yielded an area under the receiver operating characteristic curve of 0.875 for identifying elevated left atrial pressures for electrocardiogram signals acquired close to the time of the right heart catheterization procedure. Conclusions These results demonstrate the utility and the potential of ambulatory cardiac hemodynamic monitoring with electrocardiogram patch-monitors. |
| format | Article |
| id | doaj-art-1c95f992c1fd48c39927cc3c717098f9 |
| institution | Kabale University |
| issn | 2730-664X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Medicine |
| spelling | doaj-art-1c95f992c1fd48c39927cc3c717098f92025-01-12T12:37:17ZengNature PortfolioCommunications Medicine2730-664X2025-01-01511810.1038/s43856-024-00730-5Artificial intelligence for hemodynamic monitoring with a wearable electrocardiogram monitorDaphne E. Schlesinger0Ridwan Alam1Roey Ringel2Eugene Pomerantsev3Srikanth Devireddy4Pinak Shah5Joseph Garasic6Collin M. Stultz7Harvard-MIT Division of Health Sciences and TechnologyResearch Laboratory of Electronics, MITDivision of Cardiology, Massachusetts General HospitalDivision of Cardiology, Massachusetts General HospitalHarvard Medical SchoolHarvard Medical SchoolHarvard Medical SchoolHarvard-MIT Division of Health Sciences and TechnologyAbstract Background The ability to non-invasively measure left atrial pressure would facilitate the identification of patients at risk of pulmonary congestion and guide proactive heart failure care. Wearable cardiac monitors, which record single-lead electrocardiogram data, provide information that can be leveraged to infer left atrial pressures. Methods We developed a deep neural network using single-lead electrocardiogram data to determine when the left atrial pressure is elevated. The model was developed and internally evaluated using a cohort of 6739 samples from the Massachusetts General Hospital (MGH) and externally validated on a cohort of 4620 samples from a second institution. We then evaluated model on patch-monitor electrocardiographic data on a small prospective cohort. Results The model achieves an area under the receiver operating characteristic curve of 0.80 for detecting elevated left atrial pressures on an internal holdout dataset from MGH and 0.76 on an external validation set from a second institution. A further prospective dataset was obtained using single-lead electrocardiogram data with a patch-monitor from patients who underwent right heart catheterization at MGH. Evaluation of the model on this dataset yielded an area under the receiver operating characteristic curve of 0.875 for identifying elevated left atrial pressures for electrocardiogram signals acquired close to the time of the right heart catheterization procedure. Conclusions These results demonstrate the utility and the potential of ambulatory cardiac hemodynamic monitoring with electrocardiogram patch-monitors.https://doi.org/10.1038/s43856-024-00730-5 |
| spellingShingle | Daphne E. Schlesinger Ridwan Alam Roey Ringel Eugene Pomerantsev Srikanth Devireddy Pinak Shah Joseph Garasic Collin M. Stultz Artificial intelligence for hemodynamic monitoring with a wearable electrocardiogram monitor Communications Medicine |
| title | Artificial intelligence for hemodynamic monitoring with a wearable electrocardiogram monitor |
| title_full | Artificial intelligence for hemodynamic monitoring with a wearable electrocardiogram monitor |
| title_fullStr | Artificial intelligence for hemodynamic monitoring with a wearable electrocardiogram monitor |
| title_full_unstemmed | Artificial intelligence for hemodynamic monitoring with a wearable electrocardiogram monitor |
| title_short | Artificial intelligence for hemodynamic monitoring with a wearable electrocardiogram monitor |
| title_sort | artificial intelligence for hemodynamic monitoring with a wearable electrocardiogram monitor |
| url | https://doi.org/10.1038/s43856-024-00730-5 |
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