Novel score for predicting early emergency endovascular therapy in trauma care using logistic LASSO regression
Aim To support decision‐making for early interventional radiology, this study aimed to derive and validate a novel and simple scoring system for predicting the necessity of interventional radiology therapies in trauma patients. Methods This retrospective study used data derived from the medical reco...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Acute Medicine & Surgery |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/ams2.774 |
| _version_ | 1797976161523859456 |
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| author | Taro Yokoyama Shinji Nakahara Hiroshi Kondo Yasufumi Miyake Tetsuya Sakamoto |
| author_facet | Taro Yokoyama Shinji Nakahara Hiroshi Kondo Yasufumi Miyake Tetsuya Sakamoto |
| author_sort | Taro Yokoyama |
| collection | DOAJ |
| description | Aim To support decision‐making for early interventional radiology, this study aimed to derive and validate a novel and simple scoring system for predicting the necessity of interventional radiology therapies in trauma patients. Methods This retrospective study used data derived from the medical records of patients with severe traumatic injuries treated at a tertiary‐level emergency institution. The score was derived from 168 patients treated between April 2015 and October 2016 and validated using data from 68 patients treated between November 2016 and July 2017. Logistic “least absolute shrinkage and selection operator (LASSO)” regression was used to select predictors. In order to compose the score, odds ratios derived from the logistic model were simplified to integer score coefficients. The score was evaluated using the area under the receiver operating characteristic curve. The best cut‐off point for the score was determined using Youden’s index, and sensitivity and specificity were calculated. Results The derived score comprised three predictors (systolic blood pressure, positive findings in abdominal ultrasound assessment, and pelvic fracture) and ranged from 0 to 30. On validation, the area under the receiver operating characteristic curve for the score was 0.86 (95% confidence interval, 0.64–1.00). The sensitivity and specificity were 80% and 89%, respectively, with a cut‐off point of 3. Conclusion This simple score, requiring variables obtainable immediately after hospital arrival, could aid in facilitating early interventional radiology team activation. |
| first_indexed | 2024-04-11T04:46:58Z |
| format | Article |
| id | doaj.art-b233c26a05264326bb96818da75be241 |
| institution | Directory Open Access Journal |
| issn | 2052-8817 |
| language | English |
| last_indexed | 2024-04-11T04:46:58Z |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Acute Medicine & Surgery |
| spelling | doaj.art-b233c26a05264326bb96818da75be2412022-12-27T12:22:50ZengWileyAcute Medicine & Surgery2052-88172022-01-0191n/an/a10.1002/ams2.774Novel score for predicting early emergency endovascular therapy in trauma care using logistic LASSO regressionTaro Yokoyama0Shinji Nakahara1Hiroshi Kondo2Yasufumi Miyake3Tetsuya Sakamoto4Department of Emergency Medicine Teikyo University School of Medicine Tokyo JapanDepartment of Emergency Medicine Teikyo University School of Medicine Tokyo JapanDepartment of Radiology Teikyo University School of Medicine Tokyo JapanDepartment of Emergency Medicine Teikyo University School of Medicine Tokyo JapanDepartment of Emergency Medicine Teikyo University School of Medicine Tokyo JapanAim To support decision‐making for early interventional radiology, this study aimed to derive and validate a novel and simple scoring system for predicting the necessity of interventional radiology therapies in trauma patients. Methods This retrospective study used data derived from the medical records of patients with severe traumatic injuries treated at a tertiary‐level emergency institution. The score was derived from 168 patients treated between April 2015 and October 2016 and validated using data from 68 patients treated between November 2016 and July 2017. Logistic “least absolute shrinkage and selection operator (LASSO)” regression was used to select predictors. In order to compose the score, odds ratios derived from the logistic model were simplified to integer score coefficients. The score was evaluated using the area under the receiver operating characteristic curve. The best cut‐off point for the score was determined using Youden’s index, and sensitivity and specificity were calculated. Results The derived score comprised three predictors (systolic blood pressure, positive findings in abdominal ultrasound assessment, and pelvic fracture) and ranged from 0 to 30. On validation, the area under the receiver operating characteristic curve for the score was 0.86 (95% confidence interval, 0.64–1.00). The sensitivity and specificity were 80% and 89%, respectively, with a cut‐off point of 3. Conclusion This simple score, requiring variables obtainable immediately after hospital arrival, could aid in facilitating early interventional radiology team activation.https://doi.org/10.1002/ams2.774Decision‐makinginterventional radiologyLASSO regressionnonoperative managementtrauma |
| spellingShingle | Taro Yokoyama Shinji Nakahara Hiroshi Kondo Yasufumi Miyake Tetsuya Sakamoto Novel score for predicting early emergency endovascular therapy in trauma care using logistic LASSO regression Acute Medicine & Surgery Decision‐making interventional radiology LASSO regression nonoperative management trauma |
| title | Novel score for predicting early emergency endovascular therapy in trauma care using logistic LASSO regression |
| title_full | Novel score for predicting early emergency endovascular therapy in trauma care using logistic LASSO regression |
| title_fullStr | Novel score for predicting early emergency endovascular therapy in trauma care using logistic LASSO regression |
| title_full_unstemmed | Novel score for predicting early emergency endovascular therapy in trauma care using logistic LASSO regression |
| title_short | Novel score for predicting early emergency endovascular therapy in trauma care using logistic LASSO regression |
| title_sort | novel score for predicting early emergency endovascular therapy in trauma care using logistic lasso regression |
| topic | Decision‐making interventional radiology LASSO regression nonoperative management trauma |
| url | https://doi.org/10.1002/ams2.774 |
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