Development of clinical decision support for patients older than 65 years with fall-related TBI using artificial intelligence modeling.
<h4>Background</h4>Older persons comprise most traumatic brain injury (TBI)-related hospitalizations and deaths and are particularly susceptible to fall-induced TBIs. The combination of increased frailty and susceptibility to clinical decline creates a significant ongoing challenge in th...
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Public Library of Science (PLoS)
2025-01-01
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| Online Access: | https://doi.org/10.1371/journal.pone.0316462 |
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| author | Biche Osong Eric Sribnick Jonathan Groner Rachel Stanley Lauren Schulz Bo Lu Lawrence Cook Henry Xiang |
| author_facet | Biche Osong Eric Sribnick Jonathan Groner Rachel Stanley Lauren Schulz Bo Lu Lawrence Cook Henry Xiang |
| author_sort | Biche Osong |
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| description | <h4>Background</h4>Older persons comprise most traumatic brain injury (TBI)-related hospitalizations and deaths and are particularly susceptible to fall-induced TBIs. The combination of increased frailty and susceptibility to clinical decline creates a significant ongoing challenge in the management of geriatric TBI. As the population ages and co-existing medical conditions complexify, so does the need to improve the quality of care for this population. Utilizing early hospital admission variables, this study will create and validate a multinomial decision tree that predicts the discharge disposition of older patients with fall-related TBI.<h4>Methods</h4>From the National Trauma Data Bank, we retrospectively analyzed 11,977 older patients with a fall-related TBI (2017-2021). Clinical variables included Glasgow Coma Scale (GCS) score, intracranial pressure monitor use, venous thromboembolism (VTE) prophylaxis, and initial vital signs. Outcomes included hospital discharge disposition re-categorized into home, care facility, or deceased. Data were split into two sets, where 80% developed a decision tree, and 20% tested predictive performance. We employed a conditional inference tree algorithm with bootstrap (B = 100) and grid search options to grow the decision tree and measure discrimination ability using the area under the curve (AUC) and calibration plots.<h4>Results</h4>Our decision tree used seven admission variables to predict the discharge disposition of older TBI patients. Significant non-modifiable variables included total GCS and injury severity scores, while VTE prophylaxis type was the most important interventional variable. Patients who did not receive VTE prophylaxis treatment had a higher probability of death. The predictive performance of the tree in terms of AUC value (95% confidence intervals) in the training cohort for death, care, and home were 0.66 (0.65-0.67), 0.75 (0.73-0.76), and 0.77 (0.76-0.79), respectively. In the test cohort, the values were 0.64 (0.62-0.67), 0.75 (0.72-0.77), and 0.77 (0.73-0.79).<h4>Conclusions</h4>We have developed and internally validated a multinomial decision tree to predict the discharge destination of older patients with TBI. This tree could serve as a decision support tool for caregivers to manage older patients better and inform decision-making. However, the tree must be externally validated using prospective data to ascertain its predictive and clinical importance. |
| format | Article |
| id | doaj-art-300ada32eef142f7a9ce7f240d9b5720 |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
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| series | PLoS ONE |
| spelling | doaj-art-300ada32eef142f7a9ce7f240d9b57202025-02-09T05:30:39ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01202e031646210.1371/journal.pone.0316462Development of clinical decision support for patients older than 65 years with fall-related TBI using artificial intelligence modeling.Biche OsongEric SribnickJonathan GronerRachel StanleyLauren SchulzBo LuLawrence CookHenry Xiang<h4>Background</h4>Older persons comprise most traumatic brain injury (TBI)-related hospitalizations and deaths and are particularly susceptible to fall-induced TBIs. The combination of increased frailty and susceptibility to clinical decline creates a significant ongoing challenge in the management of geriatric TBI. As the population ages and co-existing medical conditions complexify, so does the need to improve the quality of care for this population. Utilizing early hospital admission variables, this study will create and validate a multinomial decision tree that predicts the discharge disposition of older patients with fall-related TBI.<h4>Methods</h4>From the National Trauma Data Bank, we retrospectively analyzed 11,977 older patients with a fall-related TBI (2017-2021). Clinical variables included Glasgow Coma Scale (GCS) score, intracranial pressure monitor use, venous thromboembolism (VTE) prophylaxis, and initial vital signs. Outcomes included hospital discharge disposition re-categorized into home, care facility, or deceased. Data were split into two sets, where 80% developed a decision tree, and 20% tested predictive performance. We employed a conditional inference tree algorithm with bootstrap (B = 100) and grid search options to grow the decision tree and measure discrimination ability using the area under the curve (AUC) and calibration plots.<h4>Results</h4>Our decision tree used seven admission variables to predict the discharge disposition of older TBI patients. Significant non-modifiable variables included total GCS and injury severity scores, while VTE prophylaxis type was the most important interventional variable. Patients who did not receive VTE prophylaxis treatment had a higher probability of death. The predictive performance of the tree in terms of AUC value (95% confidence intervals) in the training cohort for death, care, and home were 0.66 (0.65-0.67), 0.75 (0.73-0.76), and 0.77 (0.76-0.79), respectively. In the test cohort, the values were 0.64 (0.62-0.67), 0.75 (0.72-0.77), and 0.77 (0.73-0.79).<h4>Conclusions</h4>We have developed and internally validated a multinomial decision tree to predict the discharge destination of older patients with TBI. This tree could serve as a decision support tool for caregivers to manage older patients better and inform decision-making. However, the tree must be externally validated using prospective data to ascertain its predictive and clinical importance.https://doi.org/10.1371/journal.pone.0316462 |
| spellingShingle | Biche Osong Eric Sribnick Jonathan Groner Rachel Stanley Lauren Schulz Bo Lu Lawrence Cook Henry Xiang Development of clinical decision support for patients older than 65 years with fall-related TBI using artificial intelligence modeling. PLoS ONE |
| title | Development of clinical decision support for patients older than 65 years with fall-related TBI using artificial intelligence modeling. |
| title_full | Development of clinical decision support for patients older than 65 years with fall-related TBI using artificial intelligence modeling. |
| title_fullStr | Development of clinical decision support for patients older than 65 years with fall-related TBI using artificial intelligence modeling. |
| title_full_unstemmed | Development of clinical decision support for patients older than 65 years with fall-related TBI using artificial intelligence modeling. |
| title_short | Development of clinical decision support for patients older than 65 years with fall-related TBI using artificial intelligence modeling. |
| title_sort | development of clinical decision support for patients older than 65 years with fall related tbi using artificial intelligence modeling |
| url | https://doi.org/10.1371/journal.pone.0316462 |
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