TILTomorrow today: dynamic factors predicting changes in intracranial pressure treatment intensity after traumatic brain injury
Abstract Practices for controlling intracranial pressure (ICP) in traumatic brain injury (TBI) patients admitted to the intensive care unit (ICU) vary considerably between centres. To help understand the rational basis for such variance in care, this study aims to identify the patient-level predicto...
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Nature Portfolio
2025-01-01
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| Online Access: | https://doi.org/10.1038/s41598-024-83862-x |
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| author | Shubhayu Bhattacharyay Florian D. van Leeuwen Erta Beqiri Cecilia A. I. Åkerlund Lindsay Wilson Ewout W. Steyerberg David W. Nelson Andrew I. R. Maas David K. Menon Ari Ercole the CENTER-TBI investigators and participants |
| author_facet | Shubhayu Bhattacharyay Florian D. van Leeuwen Erta Beqiri Cecilia A. I. Åkerlund Lindsay Wilson Ewout W. Steyerberg David W. Nelson Andrew I. R. Maas David K. Menon Ari Ercole the CENTER-TBI investigators and participants |
| author_sort | Shubhayu Bhattacharyay |
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| description | Abstract Practices for controlling intracranial pressure (ICP) in traumatic brain injury (TBI) patients admitted to the intensive care unit (ICU) vary considerably between centres. To help understand the rational basis for such variance in care, this study aims to identify the patient-level predictors of changes in ICP management. We extracted all heterogeneous data (2008 pre-ICU and ICU variables) collected from a prospective cohort (n = 844, 51 ICUs) of ICP-monitored TBI patients in the Collaborative European NeuroTrauma Effectiveness Research in TBI study. We developed the TILTomorrow modelling strategy, which leverages recurrent neural networks to map a token-embedded time series representation of all variables (including missing values) to an ordinal, dynamic prediction of the following day’s five-category therapy intensity level (TIL(Basic)) score. With 20 repeats of fivefold cross-validation, we trained TILTomorrow on different variable sets and applied the TimeSHAP (temporal extension of SHapley Additive exPlanations) algorithm to estimate variable contributions towards predictions of next-day changes in TIL(Basic). Based on Somers’ D xy , the full range of variables explained 68% (95% CI 65–72%) of the ordinal variation in next-day changes in TIL(Basic) on day one and up to 51% (95% CI 45–56%) thereafter, when changes in TIL(Basic) became less frequent. Up to 81% (95% CI 78–85%) of this explanation could be derived from non-treatment variables (i.e., markers of pathophysiology and injury severity), but the prior trajectory of ICU management significantly improved prediction of future de-escalations in ICP-targeted treatment. Whilst there was no significant difference in the predictive discriminability (i.e., area under receiver operating characteristic curve) between next-day escalations (0.80 [95% CI 0.77–0.84]) and de-escalations (0.79 [95% CI 0.76–0.82]) in TIL(Basic) after day two, we found specific predictor effects to be more robust with de-escalations. The most important predictors of day-to-day changes in ICP management included preceding treatments, age, space-occupying lesions, ICP, metabolic derangements, and neurological function. Serial protein biomarkers were also important and may serve a useful role in the clinical armamentarium for assessing therapeutic needs. Approximately half of the ordinal variation in day-to-day changes in TIL(Basic) after day two remained unexplained, underscoring the significant contribution of unmeasured factors or clinicians’ personal preferences in ICP treatment. At the same time, specific dynamic markers of pathophysiology associated strongly with changes in treatment intensity and, upon mechanistic investigation, may improve the timing and personalised targeting of future care. |
| format | Article |
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| institution | Kabale University |
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| spelling | doaj-art-124c7b20a8544c3d9656d3c409c1e7cc2025-01-05T12:18:36ZengNature PortfolioScientific Reports2045-23222025-01-0115111810.1038/s41598-024-83862-xTILTomorrow today: dynamic factors predicting changes in intracranial pressure treatment intensity after traumatic brain injuryShubhayu Bhattacharyay0Florian D. van Leeuwen1Erta Beqiri2Cecilia A. I. Åkerlund3Lindsay Wilson4Ewout W. Steyerberg5David W. Nelson6Andrew I. R. Maas7David K. Menon8Ari Ercole9the CENTER-TBI investigators and participantsDivision of Anaesthesia, University of CambridgeDepartment of Biomedical Data Sciences, Leiden University Medical CenterBrain Physics Laboratory, Division of Neurosurgery, University of CambridgeDepartment of Physiology and Pharmacology, Section for Perioperative Medicine and Intensive Care, Karolinska InstitutetDivision of Psychology, University of StirlingDepartment of Biomedical Data Sciences, Leiden University Medical CenterDepartment of Physiology and Pharmacology, Section for Perioperative Medicine and Intensive Care, Karolinska InstitutetDepartment of Neurosurgery, Antwerp University HospitalDivision of Anaesthesia, University of CambridgeDivision of Anaesthesia, University of CambridgeAbstract Practices for controlling intracranial pressure (ICP) in traumatic brain injury (TBI) patients admitted to the intensive care unit (ICU) vary considerably between centres. To help understand the rational basis for such variance in care, this study aims to identify the patient-level predictors of changes in ICP management. We extracted all heterogeneous data (2008 pre-ICU and ICU variables) collected from a prospective cohort (n = 844, 51 ICUs) of ICP-monitored TBI patients in the Collaborative European NeuroTrauma Effectiveness Research in TBI study. We developed the TILTomorrow modelling strategy, which leverages recurrent neural networks to map a token-embedded time series representation of all variables (including missing values) to an ordinal, dynamic prediction of the following day’s five-category therapy intensity level (TIL(Basic)) score. With 20 repeats of fivefold cross-validation, we trained TILTomorrow on different variable sets and applied the TimeSHAP (temporal extension of SHapley Additive exPlanations) algorithm to estimate variable contributions towards predictions of next-day changes in TIL(Basic). Based on Somers’ D xy , the full range of variables explained 68% (95% CI 65–72%) of the ordinal variation in next-day changes in TIL(Basic) on day one and up to 51% (95% CI 45–56%) thereafter, when changes in TIL(Basic) became less frequent. Up to 81% (95% CI 78–85%) of this explanation could be derived from non-treatment variables (i.e., markers of pathophysiology and injury severity), but the prior trajectory of ICU management significantly improved prediction of future de-escalations in ICP-targeted treatment. Whilst there was no significant difference in the predictive discriminability (i.e., area under receiver operating characteristic curve) between next-day escalations (0.80 [95% CI 0.77–0.84]) and de-escalations (0.79 [95% CI 0.76–0.82]) in TIL(Basic) after day two, we found specific predictor effects to be more robust with de-escalations. The most important predictors of day-to-day changes in ICP management included preceding treatments, age, space-occupying lesions, ICP, metabolic derangements, and neurological function. Serial protein biomarkers were also important and may serve a useful role in the clinical armamentarium for assessing therapeutic needs. Approximately half of the ordinal variation in day-to-day changes in TIL(Basic) after day two remained unexplained, underscoring the significant contribution of unmeasured factors or clinicians’ personal preferences in ICP treatment. At the same time, specific dynamic markers of pathophysiology associated strongly with changes in treatment intensity and, upon mechanistic investigation, may improve the timing and personalised targeting of future care.https://doi.org/10.1038/s41598-024-83862-xTraumatic brain injuryTherapy intensity levelIntracranial pressureIntensive care unitData miningMachine learning |
| spellingShingle | Shubhayu Bhattacharyay Florian D. van Leeuwen Erta Beqiri Cecilia A. I. Åkerlund Lindsay Wilson Ewout W. Steyerberg David W. Nelson Andrew I. R. Maas David K. Menon Ari Ercole the CENTER-TBI investigators and participants TILTomorrow today: dynamic factors predicting changes in intracranial pressure treatment intensity after traumatic brain injury Scientific Reports Traumatic brain injury Therapy intensity level Intracranial pressure Intensive care unit Data mining Machine learning |
| title | TILTomorrow today: dynamic factors predicting changes in intracranial pressure treatment intensity after traumatic brain injury |
| title_full | TILTomorrow today: dynamic factors predicting changes in intracranial pressure treatment intensity after traumatic brain injury |
| title_fullStr | TILTomorrow today: dynamic factors predicting changes in intracranial pressure treatment intensity after traumatic brain injury |
| title_full_unstemmed | TILTomorrow today: dynamic factors predicting changes in intracranial pressure treatment intensity after traumatic brain injury |
| title_short | TILTomorrow today: dynamic factors predicting changes in intracranial pressure treatment intensity after traumatic brain injury |
| title_sort | tiltomorrow today dynamic factors predicting changes in intracranial pressure treatment intensity after traumatic brain injury |
| topic | Traumatic brain injury Therapy intensity level Intracranial pressure Intensive care unit Data mining Machine learning |
| url | https://doi.org/10.1038/s41598-024-83862-x |
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