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National: Early Warning score (NEWS) is expected as one of the solutions, but it is not: validated in he Japanese population. This study aimed to validate NEWS among: Japanese population.: Methods: This was retrospective observational study. Adult patients registered in the: In-Hospital Emergency Registry in Japan between January 2014 and March 2018: were eligible for this study. The primary outcome was the mortality rate at after 30: days of RRS activation. First, accuracy of NEWS was analyzed with the correlation: coefficient and area under the receiver operating characteristics curve (AUC). Second,: NEWS parameters were analyzed for its weight for prediction, using multiple logistic: regression and CART (Classification and regression trees, a machine learning: method).: Results: There were 2,255 cases from 33 facilities included for this study. Correlation: coefficient of NEWS for 30 days mortality was 0.95 (95%CI, 0.88-0.98) and AUC: 3: was 0.668 (95%CI, 0.642-0.693). Sensitivity and specificity with seven as cut-off: score were 89.8% and 45.1%, respectively. In terms of the prediction value of the: parameters, oxygen saturation showed highest odds ratio of 1.36 (95%CI, 1.25-1.48),: followed by altered mental status (AMS) 1.23 (95%CI, 1.14-1.32), heart rate 1.21: (95%CI, 1.09-1.34), systolic blood pressure 1.12 (95%CI, 1.04-1.22), respiratory rate: 1.03 (95%CI, 1.05-1.26). Body temperature and oxygen supplement were not: significantly associated. CART showed that oxygen saturation was the most weighted: parameter, followed by AMS, and respiratory rate.: Conclusions: NEWS could stratify the risk of 30 days mortality after RRS activation in Japanese: population. Oxygen saturation was most weighted parameter for predicting cardiac: arrest. This work was supported by JSPS KAKENHI (#JP18K16548).: Keywords: in-hospital cardiac arrest, medical emergency team, rapid response team,: rapid response system, national early warning score, statistical evaluation, machine: learning", "subitem_description_type": "Abstract"}]}, "item_10006_dissertation_number_12": {"attribute_name": "学位授与番号", "attribute_value_mlt": [{"subitem_dissertationnumber": "32633公修専第051"}]}, "item_creator": {"attribute_name": "著者", "attribute_type": "creator", "attribute_value_mlt": [{"creatorNames": [{"creatorName": "内藤, 貴基"}, {"creatorName": "ナイトウ, タカキ", "creatorNameLang": "ja-Kana"}], "nameIdentifiers": [{"nameIdentifier": "4970", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Naito, Takaki", "creatorNameLang": "en"}], "nameIdentifiers": [{"nameIdentifier": "6623", "nameIdentifierScheme": "WEKO"}]}]}, "item_files": {"attribute_name": "ファイル情報", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_date", "date": [{"dateType": "Available", "dateValue": "2021-09-15"}], "displaytype": "detail", "download_preview_message": "", "file_order": 0, "filename": "MP[051]_abst.pdf", "filesize": [{"value": "79.3 kB"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_11", "mimetype": "application/pdf", "size": 79300.0, "url": {"label": "論文要旨", "url": "https://luke.repo.nii.ac.jp/record/2135/files/MP[051]_abst.pdf"}, "version_id": "5052600b-af5a-4dc6-a25d-9ce5ffddd304"}]}, "item_keyword": {"attribute_name": "キーワード", "attribute_value_mlt": [{"subitem_subject": "in-hospital cardiac arrest", "subitem_subject_scheme": "Other"}, {"subitem_subject": "madical emergency team", "subitem_subject_scheme": "Other"}, {"subitem_subject": "rapid response team", "subitem_subject_scheme": "Other"}, {"subitem_subject": "rapid response system", "subitem_subject_scheme": "Other"}, {"subitem_subject": "national early warning score", "subitem_subject_scheme": "Other"}, {"subitem_subject": "statistical evaluation", "subitem_subject_scheme": "Other"}, {"subitem_subject": "machine learning", "subitem_subject_scheme": "Other"}]}, "item_language": {"attribute_name": "言語", "attribute_value_mlt": [{"subitem_language": "eng"}]}, "item_resource_type": {"attribute_name": "資源タイプ", "attribute_value_mlt": [{"resourcetype": "thesis", "resourceuri": "http://purl.org/coar/resource_type/c_46ec"}]}, "item_title": "Validation of National Early Warning Score for Predicting 30 Days Mortality after Rapid Response System Activation: Report from Multicenter Rapid Response System Online Registry in Japan", "item_titles": {"attribute_name": "タイトル", "attribute_value_mlt": [{"subitem_title": "Validation of National Early Warning Score for Predicting 30 Days Mortality after Rapid Response System Activation: Report from Multicenter Rapid Response System Online Registry in Japan"}]}, "item_type_id": "10006", "owner": "4", "path": ["1", "17", "30", "217"], "permalink_uri": "http://hdl.handle.net/10285/13583", "pubdate": {"attribute_name": "公開日", "attribute_value": "2020-06-05"}, "publish_date": "2020-06-05", "publish_status": "0", "recid": "2135", "relation": {}, "relation_version_is_last": true, "title": ["Validation of National Early Warning Score for Predicting 30 Days Mortality after Rapid Response System Activation: Report from Multicenter Rapid Response System Online Registry in Japan"], "weko_shared_id": -1}
Validation of National Early Warning Score for Predicting 30 Days Mortality after Rapid Response System Activation: Report from Multicenter Rapid Response System Online Registry in Japan
http://hdl.handle.net/10285/13583
http://hdl.handle.net/10285/135837c904cc3-be99-44f4-b39d-b155d926396f
名前 / ファイル | ライセンス | アクション |
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論文要旨 (79.3 kB)
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Item type | 学位論文 / Thesis or Dissertation(1) | |||||
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公開日 | 2020-06-05 | |||||
タイトル | ||||||
タイトル | Validation of National Early Warning Score for Predicting 30 Days Mortality after Rapid Response System Activation: Report from Multicenter Rapid Response System Online Registry in Japan | |||||
言語 | ||||||
言語 | eng | |||||
キーワード | ||||||
主題 | in-hospital cardiac arrest | |||||
キーワード | ||||||
主題 | madical emergency team | |||||
キーワード | ||||||
主題 | rapid response team | |||||
キーワード | ||||||
主題 | rapid response system | |||||
キーワード | ||||||
主題 | national early warning score | |||||
キーワード | ||||||
主題 | statistical evaluation | |||||
キーワード | ||||||
主題 | machine learning | |||||
資源タイプ | ||||||
資源タイプ | thesis | |||||
著者 |
内藤, 貴基
× 内藤, 貴基× Naito, Takaki |
|||||
抄録 | ||||||
内容記述タイプ | Abstract | |||||
内容記述 | Although the rapid response system (RRS) is a standard system for preventing: adverse event, low activation rate and high mortality were reported in Japan. National: Early Warning score (NEWS) is expected as one of the solutions, but it is not: validated in he Japanese population. This study aimed to validate NEWS among: Japanese population.: Methods: This was retrospective observational study. Adult patients registered in the: In-Hospital Emergency Registry in Japan between January 2014 and March 2018: were eligible for this study. The primary outcome was the mortality rate at after 30: days of RRS activation. First, accuracy of NEWS was analyzed with the correlation: coefficient and area under the receiver operating characteristics curve (AUC). Second,: NEWS parameters were analyzed for its weight for prediction, using multiple logistic: regression and CART (Classification and regression trees, a machine learning: method).: Results: There were 2,255 cases from 33 facilities included for this study. Correlation: coefficient of NEWS for 30 days mortality was 0.95 (95%CI, 0.88-0.98) and AUC: 3: was 0.668 (95%CI, 0.642-0.693). Sensitivity and specificity with seven as cut-off: score were 89.8% and 45.1%, respectively. In terms of the prediction value of the: parameters, oxygen saturation showed highest odds ratio of 1.36 (95%CI, 1.25-1.48),: followed by altered mental status (AMS) 1.23 (95%CI, 1.14-1.32), heart rate 1.21: (95%CI, 1.09-1.34), systolic blood pressure 1.12 (95%CI, 1.04-1.22), respiratory rate: 1.03 (95%CI, 1.05-1.26). Body temperature and oxygen supplement were not: significantly associated. CART showed that oxygen saturation was the most weighted: parameter, followed by AMS, and respiratory rate.: Conclusions: NEWS could stratify the risk of 30 days mortality after RRS activation in Japanese: population. Oxygen saturation was most weighted parameter for predicting cardiac: arrest. This work was supported by JSPS KAKENHI (#JP18K16548).: Keywords: in-hospital cardiac arrest, medical emergency team, rapid response team,: rapid response system, national early warning score, statistical evaluation, machine: learning | |||||
学位名 | ||||||
学位名 | 修士(公衆衛生学) | |||||
学位授与機関 | ||||||
学位授与機関名 | 聖路加国際大学 | |||||
学位授与年度 | ||||||
内容記述タイプ | Other | |||||
内容記述 | 2019 | |||||
学位授与年月日 | ||||||
学位授与年月日 | 2020-03-10 | |||||
学位授与番号 | ||||||
学位授与番号 | 32633公修専第051 |