Real-time machine learning model to predict in-hospital cardiac arrest using heart rate variability in ICU
Abstract Predicting in-hospital cardiac arrest in patients admitted to an intensive care unit (ICU) allows prompt interventions to improve patient outcomes. We developed and validated a machine learning-based real-time model for in-hospital cardiac arrest predictions using electrocardiogram (ECG)-ba...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-11-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-023-00960-2 |
| _version_ | 1797451336020656128 |
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| author | Hyeonhoon Lee Hyun-Lim Yang Ho Geol Ryu Chul-Woo Jung Youn Joung Cho Soo Bin Yoon Hyun-Kyu Yoon Hyung-Chul Lee |
| author_facet | Hyeonhoon Lee Hyun-Lim Yang Ho Geol Ryu Chul-Woo Jung Youn Joung Cho Soo Bin Yoon Hyun-Kyu Yoon Hyung-Chul Lee |
| author_sort | Hyeonhoon Lee |
| collection | DOAJ |
| description | Abstract Predicting in-hospital cardiac arrest in patients admitted to an intensive care unit (ICU) allows prompt interventions to improve patient outcomes. We developed and validated a machine learning-based real-time model for in-hospital cardiac arrest predictions using electrocardiogram (ECG)-based heart rate variability (HRV) measures. The HRV measures, including time/frequency domains and nonlinear measures, were calculated from 5 min epochs of ECG signals from ICU patients. A light gradient boosting machine (LGBM) algorithm was used to develop the proposed model for predicting in-hospital cardiac arrest within 0.5–24 h. The LGBM model using 33 HRV measures achieved an area under the receiver operating characteristic curve of 0.881 (95% CI: 0.875–0.887) and an area under the precision-recall curve of 0.104 (95% CI: 0.093–0.116). The most important feature was the baseline width of the triangular interpolation of the RR interval histogram. As our model uses only ECG data, it can be easily applied in clinical practice. |
| first_indexed | 2024-03-09T14:53:13Z |
| format | Article |
| id | doaj.art-5fefbcff5c4e439892c05e23da4dfc15 |
| institution | Directory Open Access Journal |
| issn | 2398-6352 |
| language | English |
| last_indexed | 2024-03-09T14:53:13Z |
| publishDate | 2023-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj.art-5fefbcff5c4e439892c05e23da4dfc152023-11-26T14:19:48ZengNature Portfolionpj Digital Medicine2398-63522023-11-016111010.1038/s41746-023-00960-2Real-time machine learning model to predict in-hospital cardiac arrest using heart rate variability in ICUHyeonhoon Lee0Hyun-Lim Yang1Ho Geol Ryu2Chul-Woo Jung3Youn Joung Cho4Soo Bin Yoon5Hyun-Kyu Yoon6Hyung-Chul Lee7Department of Anesthesiology and Pain Medicine, Seoul National University HospitalDepartment of Anesthesiology and Pain Medicine, Seoul National University HospitalDepartment of Anesthesiology and Pain Medicine, Seoul National University College of Medicine, Seoul National University HospitalDepartment of Anesthesiology and Pain Medicine, Seoul National University College of Medicine, Seoul National University HospitalDepartment of Anesthesiology and Pain Medicine, Seoul National University College of Medicine, Seoul National University HospitalDepartment of Anesthesiology and Pain Medicine, Seoul National University College of Medicine, Seoul National University HospitalDepartment of Anesthesiology and Pain Medicine, Seoul National University College of Medicine, Seoul National University HospitalDepartment of Data Science Research, Innovative Medical Technology Research Institute, Seoul National University HospitalAbstract Predicting in-hospital cardiac arrest in patients admitted to an intensive care unit (ICU) allows prompt interventions to improve patient outcomes. We developed and validated a machine learning-based real-time model for in-hospital cardiac arrest predictions using electrocardiogram (ECG)-based heart rate variability (HRV) measures. The HRV measures, including time/frequency domains and nonlinear measures, were calculated from 5 min epochs of ECG signals from ICU patients. A light gradient boosting machine (LGBM) algorithm was used to develop the proposed model for predicting in-hospital cardiac arrest within 0.5–24 h. The LGBM model using 33 HRV measures achieved an area under the receiver operating characteristic curve of 0.881 (95% CI: 0.875–0.887) and an area under the precision-recall curve of 0.104 (95% CI: 0.093–0.116). The most important feature was the baseline width of the triangular interpolation of the RR interval histogram. As our model uses only ECG data, it can be easily applied in clinical practice.https://doi.org/10.1038/s41746-023-00960-2 |
| spellingShingle | Hyeonhoon Lee Hyun-Lim Yang Ho Geol Ryu Chul-Woo Jung Youn Joung Cho Soo Bin Yoon Hyun-Kyu Yoon Hyung-Chul Lee Real-time machine learning model to predict in-hospital cardiac arrest using heart rate variability in ICU npj Digital Medicine |
| title | Real-time machine learning model to predict in-hospital cardiac arrest using heart rate variability in ICU |
| title_full | Real-time machine learning model to predict in-hospital cardiac arrest using heart rate variability in ICU |
| title_fullStr | Real-time machine learning model to predict in-hospital cardiac arrest using heart rate variability in ICU |
| title_full_unstemmed | Real-time machine learning model to predict in-hospital cardiac arrest using heart rate variability in ICU |
| title_short | Real-time machine learning model to predict in-hospital cardiac arrest using heart rate variability in ICU |
| title_sort | real time machine learning model to predict in hospital cardiac arrest using heart rate variability in icu |
| url | https://doi.org/10.1038/s41746-023-00960-2 |
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