Prediction for Perioperative Stroke Using Intraoperative Parameters
Background Perioperative stroke is a severe complication following surgery. To identify patients at risk for perioperative stroke, several prediction models based on the preoperative factors were suggested. Prediction models often focus on preoperative patient characteristics to assess stroke risk....
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-08-01
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| Series: | Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease |
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| Online Access: | https://www.ahajournals.org/doi/10.1161/JAHA.123.032216 |
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| author | Mi‐Young Oh Young Mi Jung Won‐Pyo Kim Hyung‐Chul Lee Tae Kyong Kim Sang‐Bae Ko Jaehyun Lim Seung Mi Lee |
| author_facet | Mi‐Young Oh Young Mi Jung Won‐Pyo Kim Hyung‐Chul Lee Tae Kyong Kim Sang‐Bae Ko Jaehyun Lim Seung Mi Lee |
| author_sort | Mi‐Young Oh |
| collection | DOAJ |
| description | Background Perioperative stroke is a severe complication following surgery. To identify patients at risk for perioperative stroke, several prediction models based on the preoperative factors were suggested. Prediction models often focus on preoperative patient characteristics to assess stroke risk. However, most existing models primarily base their predictions on the patient's baseline characteristics before surgery. We aimed to develop a machine‐learning model incorporating both pre‐ and intraoperative variables to predict perioperative stroke. Methods and Results This study included patients who underwent noncardiac surgery at 2 hospitals with the data of 15 752 patients from Seoul National University Hospital used for development and temporal internal validation, and the data of 449 patients from Boramae Medical Center used for external validation. Perioperative stroke was defined as a newly developed ischemic lesion on diffusion‐weighted imaging within 30 days of surgery. We developed a prediction model composed of pre‐ and intraoperative factors (integrated model) and compared it with a model consisting of preoperative features alone (preoperative model). Perioperative stroke developed in 109 (0.69%) patients in the Seoul National University Hospital group and 11 patients (2.45%) in the Boramae Medical Center group. The integrated model demonstrated superior predictive performance with area under the curve values of 0.824 (95% CI, 0.762–0.880) versus 0.584 (95% CI, 0.499–0.667; P<0.001) in the internal validation; and 0.716 (95% CI, 0.560–0.859) versus 0.505 (95% CI, 0.343–0.654; P=0.018) in the external validation, compared to the preoperative model. Conclusions We suggest that incorporating intraoperative factors into perioperative stroke prediction models can improve their accuracy. |
| format | Article |
| id | doaj-art-9ce67a61826748178af7fc0e47ca6196 |
| institution | Kabale University |
| issn | 2047-9980 |
| language | English |
| publishDate | 2024-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease |
| spelling | doaj-art-9ce67a61826748178af7fc0e47ca61962024-11-28T09:27:28ZengWileyJournal of the American Heart Association: Cardiovascular and Cerebrovascular Disease2047-99802024-08-01131610.1161/JAHA.123.032216Prediction for Perioperative Stroke Using Intraoperative ParametersMi‐Young Oh0Young Mi Jung1Won‐Pyo Kim2Hyung‐Chul Lee3Tae Kyong Kim4Sang‐Bae Ko5Jaehyun Lim6Seung Mi Lee7Department of Neurology Bucheon Sejong Hospital Bucheon‐si Gyeonggi‐do South KoreaDepartment of Obstetrics and Gynecology Seoul National University College of Medicine Seoul South KoreaR&D Center Lumanlab Inc. Seoul South KoreaDepartment of Anesthesiology and Pain Medicine Seoul National University College of Medicine Seoul South KoreaDepartment of Anesthesiology and Pain Medicine Seoul National University College of Medicine Seoul South KoreaDepartment of Neurology Seoul National University Hospital Seoul South KoreaR&D Center Lumanlab Inc. Seoul South KoreaDepartment of Obstetrics and Gynecology Seoul National University College of Medicine Seoul South KoreaBackground Perioperative stroke is a severe complication following surgery. To identify patients at risk for perioperative stroke, several prediction models based on the preoperative factors were suggested. Prediction models often focus on preoperative patient characteristics to assess stroke risk. However, most existing models primarily base their predictions on the patient's baseline characteristics before surgery. We aimed to develop a machine‐learning model incorporating both pre‐ and intraoperative variables to predict perioperative stroke. Methods and Results This study included patients who underwent noncardiac surgery at 2 hospitals with the data of 15 752 patients from Seoul National University Hospital used for development and temporal internal validation, and the data of 449 patients from Boramae Medical Center used for external validation. Perioperative stroke was defined as a newly developed ischemic lesion on diffusion‐weighted imaging within 30 days of surgery. We developed a prediction model composed of pre‐ and intraoperative factors (integrated model) and compared it with a model consisting of preoperative features alone (preoperative model). Perioperative stroke developed in 109 (0.69%) patients in the Seoul National University Hospital group and 11 patients (2.45%) in the Boramae Medical Center group. The integrated model demonstrated superior predictive performance with area under the curve values of 0.824 (95% CI, 0.762–0.880) versus 0.584 (95% CI, 0.499–0.667; P<0.001) in the internal validation; and 0.716 (95% CI, 0.560–0.859) versus 0.505 (95% CI, 0.343–0.654; P=0.018) in the external validation, compared to the preoperative model. Conclusions We suggest that incorporating intraoperative factors into perioperative stroke prediction models can improve their accuracy.https://www.ahajournals.org/doi/10.1161/JAHA.123.032216intraoperative physiological parametermachine learningperioperative stroke |
| spellingShingle | Mi‐Young Oh Young Mi Jung Won‐Pyo Kim Hyung‐Chul Lee Tae Kyong Kim Sang‐Bae Ko Jaehyun Lim Seung Mi Lee Prediction for Perioperative Stroke Using Intraoperative Parameters Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease intraoperative physiological parameter machine learning perioperative stroke |
| title | Prediction for Perioperative Stroke Using Intraoperative Parameters |
| title_full | Prediction for Perioperative Stroke Using Intraoperative Parameters |
| title_fullStr | Prediction for Perioperative Stroke Using Intraoperative Parameters |
| title_full_unstemmed | Prediction for Perioperative Stroke Using Intraoperative Parameters |
| title_short | Prediction for Perioperative Stroke Using Intraoperative Parameters |
| title_sort | prediction for perioperative stroke using intraoperative parameters |
| topic | intraoperative physiological parameter machine learning perioperative stroke |
| url | https://www.ahajournals.org/doi/10.1161/JAHA.123.032216 |
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