Rapid prediction of secondary neurologic decline after traumatic brain injury: a data analytic approach
Abstract Secondary neurologic decline (ND) after traumatic brain injury (TBI) is independently associated with outcome, but robust predictors of ND are lacking. In this retrospective analysis of consecutive isolated TBI admissions to the R. Adams Cowley Shock Trauma Center between November 2015 and...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-26318-4 |
| _version_ | 1828063779096625152 |
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| author | Jamie Podell Shiming Yang Serenity Miller Ryan Felix Hemantkumar Tripathi Gunjan Parikh Catriona Miller Hegang Chen Yi-Mei Kuo Chien Yu Lin Peter Hu Neeraj Badjatia |
| author_facet | Jamie Podell Shiming Yang Serenity Miller Ryan Felix Hemantkumar Tripathi Gunjan Parikh Catriona Miller Hegang Chen Yi-Mei Kuo Chien Yu Lin Peter Hu Neeraj Badjatia |
| author_sort | Jamie Podell |
| collection | DOAJ |
| description | Abstract Secondary neurologic decline (ND) after traumatic brain injury (TBI) is independently associated with outcome, but robust predictors of ND are lacking. In this retrospective analysis of consecutive isolated TBI admissions to the R. Adams Cowley Shock Trauma Center between November 2015 and June 2018, we aimed to develop a triage decision support tool to quantify risk for early ND. Three machine learning models based on clinical, physiologic, or combined characteristics from the first hour of hospital resuscitation were created. Among 905 TBI cases, 165 (18%) experienced one or more ND events (130 clinical, 51 neurosurgical, and 54 radiographic) within 48 h of presentation. In the prediction of ND, the clinical plus physiologic data model performed similarly to the physiologic only model, with concordance indices of 0.85 (0.824–0.877) and 0.84 (0.812–0.868), respectively. Both outperformed the clinical only model, which had a concordance index of 0.72 (0.688–0.759). This preliminary work suggests that a data-driven approach utilizing physiologic and basic clinical data from the first hour of resuscitation after TBI has the potential to serve as a decision support tool for clinicians seeking to identify patients at high or low risk for ND. |
| first_indexed | 2024-04-10T22:48:30Z |
| format | Article |
| id | doaj.art-b031e0a3eac04b3382d7d56c5bf02934 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-10T22:48:30Z |
| publishDate | 2023-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-b031e0a3eac04b3382d7d56c5bf029342023-01-15T12:10:55ZengNature PortfolioScientific Reports2045-23222023-01-0113111110.1038/s41598-022-26318-4Rapid prediction of secondary neurologic decline after traumatic brain injury: a data analytic approachJamie Podell0Shiming Yang1Serenity Miller2Ryan Felix3Hemantkumar Tripathi4Gunjan Parikh5Catriona Miller6Hegang Chen7Yi-Mei Kuo8Chien Yu Lin9Peter Hu10Neeraj Badjatia11Program in Trauma, Shock Trauma Neurocritical Care, University of Maryland School of MedicineProgram in Trauma, Shock Trauma Neurocritical Care, University of Maryland School of MedicineProgram in Trauma, Shock Trauma Neurocritical Care, University of Maryland School of MedicineProgram in Trauma, Shock Trauma Neurocritical Care, University of Maryland School of MedicineProgram in Trauma, Shock Trauma Neurocritical Care, University of Maryland School of MedicineProgram in Trauma, Shock Trauma Neurocritical Care, University of Maryland School of MedicineProgram in Trauma, Shock Trauma Neurocritical Care, University of Maryland School of MedicineProgram in Trauma, Shock Trauma Neurocritical Care, University of Maryland School of MedicineProgram in Trauma, Shock Trauma Neurocritical Care, University of Maryland School of MedicineProgram in Trauma, Shock Trauma Neurocritical Care, University of Maryland School of MedicineProgram in Trauma, Shock Trauma Neurocritical Care, University of Maryland School of MedicineProgram in Trauma, Shock Trauma Neurocritical Care, University of Maryland School of MedicineAbstract Secondary neurologic decline (ND) after traumatic brain injury (TBI) is independently associated with outcome, but robust predictors of ND are lacking. In this retrospective analysis of consecutive isolated TBI admissions to the R. Adams Cowley Shock Trauma Center between November 2015 and June 2018, we aimed to develop a triage decision support tool to quantify risk for early ND. Three machine learning models based on clinical, physiologic, or combined characteristics from the first hour of hospital resuscitation were created. Among 905 TBI cases, 165 (18%) experienced one or more ND events (130 clinical, 51 neurosurgical, and 54 radiographic) within 48 h of presentation. In the prediction of ND, the clinical plus physiologic data model performed similarly to the physiologic only model, with concordance indices of 0.85 (0.824–0.877) and 0.84 (0.812–0.868), respectively. Both outperformed the clinical only model, which had a concordance index of 0.72 (0.688–0.759). This preliminary work suggests that a data-driven approach utilizing physiologic and basic clinical data from the first hour of resuscitation after TBI has the potential to serve as a decision support tool for clinicians seeking to identify patients at high or low risk for ND.https://doi.org/10.1038/s41598-022-26318-4 |
| spellingShingle | Jamie Podell Shiming Yang Serenity Miller Ryan Felix Hemantkumar Tripathi Gunjan Parikh Catriona Miller Hegang Chen Yi-Mei Kuo Chien Yu Lin Peter Hu Neeraj Badjatia Rapid prediction of secondary neurologic decline after traumatic brain injury: a data analytic approach Scientific Reports |
| title | Rapid prediction of secondary neurologic decline after traumatic brain injury: a data analytic approach |
| title_full | Rapid prediction of secondary neurologic decline after traumatic brain injury: a data analytic approach |
| title_fullStr | Rapid prediction of secondary neurologic decline after traumatic brain injury: a data analytic approach |
| title_full_unstemmed | Rapid prediction of secondary neurologic decline after traumatic brain injury: a data analytic approach |
| title_short | Rapid prediction of secondary neurologic decline after traumatic brain injury: a data analytic approach |
| title_sort | rapid prediction of secondary neurologic decline after traumatic brain injury a data analytic approach |
| url | https://doi.org/10.1038/s41598-022-26318-4 |
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