Contribution of clinical course to outcome after traumatic brain injury: mining patient trajectories from European intensive care unit data

Existing methods to characterise the evolving condition of traumatic brain injury (TBI) patients in the intensive care unit (ICU) do not capture the context necessary for individualising treatment. We aimed to develop a modelling strategy which integrates all data stored in medical records to produce an interpretable disease course for each TBI patient's ICU stay. From a prospective, European cohort (n=1,550, 65 centres, 19 countries) of TBI patients, we extracted all 1,166 variables collected before or during ICU stay as well as 6-month functional outcome on the Glasgow Outcome Scale-Extended (GOSE). We trained recurrent neural network models to map a token-embedded time series representation of all variables (including missing data) to an ordinal GOSE prognosis every 2 hours. With repeated cross-validation, we evaluated calibration and the explanation of ordinal variance in GOSE with Somers' Dxy. Furthermore, we applied TimeSHAP to calculate the contribution of variables and prior timepoints towards transitions in patient trajectories. Our modelling strategy achieved calibration at 8 hours, and the full range of variables explained up to 52% (95% CI: 50-54%) of the variance in ordinal functional outcome. Up to 91% (90-91%) of this explanation was derived from pre-ICU and admission information. Information collected in the ICU increased explanation (by up to 5% [4-6%]), though not enough to counter poorer performance in longer-stay (>5.75 days) patients. Static variables with the highest contributions were physician prognoses and certain demographic and CT features. Among dynamic variables, markers of intracranial hypertension and neurological function contributed the most. Whilst static information currently accounts for the majority of functional outcome explanation, our data-driven analysis highlights investigative avenues to improve dynamic characterisation of longer-stay patients

From big data to personal narratives: a supervised learning framework for decoding the course of traumatic brain injury in intensive care

The management of traumatic brain injury (TBI) in the intensive care unit (ICU) generates vast clinical data, much of which is never analysed or interpreted. At the same time, the dynamic, complex disease course of TBI is not sufficiently characterised for truly patient-tailored treatment. This thesis capitalises on an opportunity to widen the context of information considered by individualised, dynamic models of functional outcome and therapeutic intensity after TBI. This opportunity is jointly afforded by the large-scale data collection of the Collaborative European NeuroTrauma Effectiveness Research in TBI (CENTER-TBI) study and recent advances in machine learning (ML) for time series modelling. This thesis combines a range of neural network (NN) architectures to propose a methodological framework by which all of the CENTER-TBI data collected before and during a patient's ICU stay can be dynamically mapped to ordinal endpoints. All of the CENTER-TBI variables are tokenised and embedded into lower-dimensional vectors which are then fed into gated recurrent neural networks (RNNs) to identify time-varying, informative patterns from the full dataset. The RNN outputs are then decoded by an ordinal output layer to return probability estimates, calibrated on validation sets, at each threshold of the endpoint. Regularised model weights are trained through supervised learning, and the reliability and information content of the modelling strategy are evaluated with repeated k-fold cross validation. Finally, the contribution of recorded clinical events to trained model outputs is estimated with a temporal extension of the SHapley Additive exPlanations (TimeSHAP) algorithm. The first endpoint of my supervised learning framework is functional outcome at six months according to the Glasgow Outcome Scale – Extended (GOSE). For ordinal GOSE prediction, expanding the predictor set with the tokenisation-embedding encoder (i.e., making models ‘wider’) significantly improves prediction performance whilst adding hidden layers does not (i.e., making models ‘deeper’). Functional outcome prediction is more difficult at higher GOSE thresholds and for patients with longer ICU stays. The full set of CENTER-TBI variables accounts for approximately half of the ordinal variation in GOSE, and static (pre-ICU and admission) variables account for the vast majority of this prognostic information. Variables with the greatest contribution to prognosis include physician-based impressions, imaging features, protein biomarkers, and neurological assessments. Then, I perform a clinimetric validation of the Therapy Intensity Level (TIL) scale and its five-category summary (TIL(Basic)) to measure the overall intensity of intracranial pressure (ICP) management. With TIL(Basic) as the second endpoint of the modelling framework, the full range of CENTER-TBI variables again explain approximately half of the ordinal variation in next-day transitions of ICP management after the second day of ICU stay. A patient's prior treatments, age, brain lesions, ICP, metabolic derangements, serial protein biomarkers, and neurological function are most predictive of future changes in TIL. However, a considerable proportion of these variations remain unaccounted for, suggesting the significant influence of a physician's preferences or unmeasured factors in contemporary ICP management. Supervised ML with NN-based architectures proves useful for improving the detail of model inputs (over time) and outputs but requires thorough assessment of potential overfitting. Insights from this thesis can inform the design of dynamic causal inference models and future data-collection or informatics projects for TBI.Gates Cambridge Scholarship (2020-2024

Clinical descriptors of disease trajectories in patients with traumatic brain injury in the intensive care unit (CENTER-TBI):a multicentre observational cohort study

BACKGROUND: Patients with traumatic brain injury are a heterogeneous population, and the most severely injured individuals are often treated in an intensive care unit (ICU). The primary injury at impact, and the harmful secondary events that can occur during the first week of the ICU stay, will affect outcome in this vulnerable group of patients. We aimed to identify clinical variables that might distinguish disease trajectories among patients with traumatic brain injury admitted to the ICU.METHODS: We used data from the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) prospective observational cohort study. We included patients aged 18 years or older with traumatic brain injury who were admitted to the ICU at one of the 65 CENTER-TBI participating centres, which range from large academic hospitals to small rural hospitals. For every patient, we obtained pre-injury data and injury features, clinical characteristics on admission, demographics, physiological parameters, laboratory features, brain biomarkers (ubiquitin carboxy-terminal hydrolase L1 [UCH-L1], S100 calcium-binding protein B [S100B], tau, neurofilament light [NFL], glial fibrillary acidic protein [GFAP], and neuron-specific enolase [NSE]), and information about intracranial pressure lowering treatments during the first 7 days of ICU stay. To identify clinical variables that might distinguish disease trajectories, we applied a novel clustering method to these data, which was based on a mixture of probabilistic graph models with a Markov chain extension. The relation of clusters to the extended Glasgow Outcome Scale (GOS-E) was investigated.FINDINGS: Between Dec 19, 2014, and Dec 17, 2017, 4509 patients with traumatic brain injury were recruited into the CENTER-TBI core dataset, of whom 1728 were eligible for this analysis. Glucose variation (defined as the difference between daily maximum and minimum glucose concentrations) and brain biomarkers (S100B, NSE, NFL, tau, UCH-L1, and GFAP) were consistently found to be the main clinical descriptors of disease trajectories (ie, the leading variables contributing to the distinguishing clusters) in patients with traumatic brain injury in the ICU. The disease trajectory cluster to which a patient was assigned in a model was analysed as a predictor together with variables from the IMPACT model, and prediction of both mortality and unfavourable outcome (dichotomised GOS-E ≤4) was improved.INTERPRETATION: First-day ICU admission data are not the only clinical descriptors of disease trajectories in patients with traumatic brain injury. By analysing temporal variables in our study, variation of glucose was identified as the most important clinical descriptor that might distinguish disease trajectories in the ICU, which should direct further research. Biomarkers of brain injury (S100B, NSE, NFL, tau, UCH-L1, and GFAP) were also top clinical descriptors over time, suggesting they might be important in future clinical practice.FUNDING: European Union 7th Framework program, Hannelore Kohl Stiftung, OneMind, Integra LifeSciences Corporation, and NeuroTrauma Sciences.</p

Clinical descriptors of disease trajectories in patients with traumatic brain injury in the intensive care unit (CENTER-TBI):a multicentre observational cohort study

BACKGROUND: Patients with traumatic brain injury are a heterogeneous population, and the most severely injured individuals are often treated in an intensive care unit (ICU). The primary injury at impact, and the harmful secondary events that can occur during the first week of the ICU stay, will affect outcome in this vulnerable group of patients. We aimed to identify clinical variables that might distinguish disease trajectories among patients with traumatic brain injury admitted to the ICU.METHODS: We used data from the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) prospective observational cohort study. We included patients aged 18 years or older with traumatic brain injury who were admitted to the ICU at one of the 65 CENTER-TBI participating centres, which range from large academic hospitals to small rural hospitals. For every patient, we obtained pre-injury data and injury features, clinical characteristics on admission, demographics, physiological parameters, laboratory features, brain biomarkers (ubiquitin carboxy-terminal hydrolase L1 [UCH-L1], S100 calcium-binding protein B [S100B], tau, neurofilament light [NFL], glial fibrillary acidic protein [GFAP], and neuron-specific enolase [NSE]), and information about intracranial pressure lowering treatments during the first 7 days of ICU stay. To identify clinical variables that might distinguish disease trajectories, we applied a novel clustering method to these data, which was based on a mixture of probabilistic graph models with a Markov chain extension. The relation of clusters to the extended Glasgow Outcome Scale (GOS-E) was investigated.FINDINGS: Between Dec 19, 2014, and Dec 17, 2017, 4509 patients with traumatic brain injury were recruited into the CENTER-TBI core dataset, of whom 1728 were eligible for this analysis. Glucose variation (defined as the difference between daily maximum and minimum glucose concentrations) and brain biomarkers (S100B, NSE, NFL, tau, UCH-L1, and GFAP) were consistently found to be the main clinical descriptors of disease trajectories (ie, the leading variables contributing to the distinguishing clusters) in patients with traumatic brain injury in the ICU. The disease trajectory cluster to which a patient was assigned in a model was analysed as a predictor together with variables from the IMPACT model, and prediction of both mortality and unfavourable outcome (dichotomised GOS-E ≤4) was improved.INTERPRETATION: First-day ICU admission data are not the only clinical descriptors of disease trajectories in patients with traumatic brain injury. By analysing temporal variables in our study, variation of glucose was identified as the most important clinical descriptor that might distinguish disease trajectories in the ICU, which should direct further research. Biomarkers of brain injury (S100B, NSE, NFL, tau, UCH-L1, and GFAP) were also top clinical descriptors over time, suggesting they might be important in future clinical practice.FUNDING: European Union 7th Framework program, Hannelore Kohl Stiftung, OneMind, Integra LifeSciences Corporation, and NeuroTrauma Sciences.</p

Clinical descriptors of disease trajectories in patients with traumatic brain injury in the intensive care unit (CENTER-TBI): a multicentre observational cohort study

Background Patients with traumatic brain injury (TBI) are a heterogeneous population, and the most severely injured individuals are often treated in an intensive care unit (ICU). The primary injury at impact, and the secondary insults that occur during the first week of the ICU stay, will affect outcome in this vulnerable group of patients. We aimed to identify clinical descriptors of disease trajectories among patients with traumatic brain injury admitted to the ICU. Methods We included patients with TBI who were admitted to the ICU at centres participating in the European multinational prospective observational Collaborative NeuroTrauma Effectiveness Research in TBI (CENTER-TBI) study, ranging from large academic to smaller rural hospitals. For every patient, we obtained pre-injury data and injury features, clinical characteristics on admission, demographics, physiological parameters, laboratory features, brain biomarkers (S100B, NSE, NFL, tau, UCH-L1 and GFAP), and ICP lowering treatments during the first 7 days of ICU stay. To identify temporal clinical disease trajectories, we applied a novel clustering method to these data, which was based on a mixture of probabilistic graph models with a Markov chain extension. The relation of clusters to the Glasgow Outcome Scale (GOS-E) was investigated. Findings We included 1728 patients with TBI. We found that glucose variation (defined as the difference between daily maximum and minimum glucose concentrations) and brain biomarkers (S100B, NSE, NFL, tau, UCH-L1 and GFAP) were consistently the main clinical descriptors of disease trajectories (defined as the leading variables contributing to and distinguishing clusters) in TBI patients in the ICU. Although information related to patient’s outcome was not included in the clustering analysis, different disease trajectories had different outcome profiles. Trajectory membership (to which cluster a patient is assigned in a model) was analysed as a predictor together with the variables of the IMPACT model and improved prediction of both mortality, and unfavourable outcome (dichotomized GOS-E levels < 5), increasing Nagelkerke’s R2 with 0·09 (from 0·44 to 0·53 and 0·36 to 0·45, respectively). Interpretation First day ICU admission variables are not the only clinical descriptors of disease trajectories in patients with TBI. With the addition of temporal variables in our study, variation of glucose was identified as the most important descriptor of disease trajectories in the ICU, which should motivate further research. Biomarkers of brain injury were consistently found to be top descriptors over time, suggesting they may be important in future clinical practice

The Therapy Intensity Level scale for traumatic brain injury: clinimetric assessment on neuro-monitored patients across 52 European intensive care units.

Intracranial pressure (ICP) data from traumatic brain injury (TBI) patients in the intensive care unit (ICU) cannot be interpreted appropriately without accounting for the effect of administered therapy intensity level (TIL) on ICP. A 15-point scale was originally proposed in 1987 to quantify the hourly intensity of ICP-targeted treatment. This scale was subsequently modified &ndash; through expert consensus &ndash; during the development of TBI Common Data Elements to address statistical limitations and improve usability. The latest, 38-point scale (hereafter referred to as TIL) permits integrated scoring for a 24-hour period and has a five-category, condensed version (TIL(Basic)) based on qualitative assessment. Here, we perform a total- and component-score analysis of TIL and TIL(Basic) to: (1) validate the scales across the wide variation in contemporary ICP management, (2) compare their performance against that of predecessors, and (3) derive guidelines for proper scale use. From the observational Collaborative European NeuroTrauma Effectiveness Research in TBI (CENTER-TBI) study, we extract clinical data from a prospective cohort of ICP-monitored TBI patients (n=873) from 52 ICUs across 19 countries. We calculate daily TIL and TIL(Basic) scores (TIL24 and TIL(Basic)24, respectively) from each patient&rsquo;s first week of ICU stay. We also calculate summary TIL and TIL(Basic) scores by taking the first-week maximum (TILmax and TIL(Basic)max) and first-week median (TILmedian and TIL(Basic)median) of TIL24 and TIL(Basic)24 scores for each patient. We find that, across all measures of construct and criterion validity, the latest TIL scale performs significantly greater than or similarly to all alternative scales (including TIL(Basic)) and integrates the widest range of modern ICP treatments. TILmedian outperforms both TILmax and summarised ICP values in detecting refractory intracranial hypertension (RICH) during ICU stay. The RICH detection thresholds which maximise the sum of sensitivity and specificity are TILmedian&ge;7.5 and TILmax&ge;14. The TIL24 threshold which maximises the sum of sensitivity and specificity in the detection of surgical ICP control is TIL24&ge;9. The median scores of each TIL component therapy over increasing TIL24 reflect a credible staircase approach to treatment intensity escalation, from head positioning to surgical ICP control, as well as considerable variability in the use of cerebrospinal fluid drainage and decompressive craniectomy. Since TIL(Basic)max suffers from a strong statistical ceiling effect and only covers 17% (95% CI: 16&ndash;18%) of the information in TILmax, TIL(Basic) should not be used instead of TIL for rating maximum treatment intensity. TIL(Basic)24 and TIL(Basic)median can be suitable replacements for TIL24 and TILmedian, respectively (with up to 33% [95% CI: 31&ndash;35%] information coverage) when TIL assessment is infeasible. Accordingly, we derive numerical ranges for categorising TIL24 scores into TIL(Basic)24 scores. In conclusion, our results validate TIL across a spectrum of ICP management and monitoring approaches. TIL is a more sensitive surrogate for pathophysiology than ICP..

Therapy intensity level scale for traumatic brain injury : clinimetric assessment on neuro-monitored patients across 52 European intensive care units

Abstract: Intracranial pressure (ICP) data from traumatic brain injury (TBI) patients in the intensive care unit (ICU) cannot be interpreted appropriately without accounting for the effect of administered therapy intensity level (TIL) on ICP. A 15-point scale was originally proposed in 1987 to quantify the hourly intensity of ICP-targeted treatment. This scale was subsequently modified-through expert consensus-during the development of TBI Common Data Elements to address statistical limitations and improve usability. The latest 38-point scale (hereafter referred to as TIL) permits integrated scoring for a 24-h period and has a five-category, condensed version (TIL(Basic)) based on qualitative assessment. Here, we perform a total- and component-score analysis of TIL and TIL(Basic) to: 1) validate the scales across the wide variation in contemporary ICP management; 2) compare their performance against that of predecessors; and 3) derive guidelines for proper scale use. From the observational Collaborative European NeuroTrauma Effectiveness Research in TBI (CENTER-TBI) study, we extract clinical data from a prospective cohort of ICP-monitored TBI patients (n = 873) from 52 ICUs across 19 countries. We calculate daily TIL and TIL(Basic) scores (TIL24 and TIL(Basic)24, respectively) from each patient's first week of ICU stay. We also calculate summary TIL and TIL(Basic) scores by taking the first-week maximum (TILmax and TIL(Basic)max) and first-week median (TILmedian and TIL(Basic)median) of TIL24 and TIL(Basic)24 scores for each patient. We find that, across all measures of construct and criterion validity, the latest TIL scale performs significantly greater than or similarly to all alternative scales (including TIL(Basic)) and integrates the widest range of modern ICP treatments. TILmedian outperforms both TILmax and summarized ICP values in detecting refractory intracranial hypertension (RICH) during ICU stay. The RICH detection thresholds which maximize the sum of sensitivity and specificity are TILmedian >= 7.5 and TILmax >= 14. The TIL24 threshold which maximizes the sum of sensitivity and specificity in the detection of surgical ICP control is TIL24 >= 9. The median scores of each TIL component therapy over increasing TIL24 reflect a credible staircase approach to treatment intensity escalation, from head positioning to surgical ICP control, as well as considerable variability in the use of cerebrospinal fluid drainage and decompressive craniectomy. Since TIL(Basic)max suffers from a strong statistical ceiling effect and only covers 17% (95% confidence interval [CI]: 16-18%) of the information in TILmax, TIL(Basic) should not be used instead of TIL for rating maximum treatment intensity. TIL(Basic)24 and TIL(Basic)median can be suitable replacements for TIL24 and TILmedian, respectively (with up to 33% [95% CI: 31-35%] information coverage) when full TIL assessment is infeasible. Accordingly, we derive numerical ranges for categorising TIL24 scores into TIL(Basic)24 scores. In conclusion, our results validate TIL across a spectrum of ICP management and monitoring approaches. TIL is a more sensitive surrogate for pathophysiology than ICP and thus can be considered an intermediate outcome after TBI

Mining the contribution of intensive care clinical course to outcome after traumatic brain injury.

Funder: EC | EC Seventh Framework Programm | FP7 Health (FP7-HEALTH - Specific Programme "Cooperation": Health); Grant(s): 602150Existing methods to characterise the evolving condition of traumatic brain injury (TBI) patients in the intensive care unit (ICU) do not capture the context necessary for individualising treatment. Here, we integrate all heterogenous data stored in medical records (1166 pre-ICU and ICU variables) to model the individualised contribution of clinical course to 6-month functional outcome on the Glasgow Outcome Scale -Extended (GOSE). On a prospective cohort (n = 1550, 65 centres) of TBI patients, we train recurrent neural network models to map a token-embedded time series representation of all variables (including missing values) to an ordinal GOSE prognosis every 2 h. The full range of variables explains up to 52% (95% CI: 50-54%) of the ordinal variance in functional outcome. Up to 91% (95% CI: 90-91%) of this explanation is derived from pre-ICU and admission information (i.e., static variables). Information collected in the ICU (i.e., dynamic variables) increases explanation (by up to 5% [95% CI: 4-6%]), though not enough to counter poorer overall performance in longer-stay (>5.75 days) patients. Highest-contributing variables include physician-based prognoses, CT features, and markers of neurological function. Whilst static information currently accounts for the majority of functional outcome explanation after TBI, data-driven analysis highlights investigative avenues to improve the dynamic characterisation of longer-stay patients. Moreover, our modelling strategy proves useful for converting large patient records into interpretable time series with missing data integration and minimal processing

Mining the contribution of intensive care clinical course to outcome after traumatic brain injury

Acknowledgements: This research was supported by the National Institute for Health Research (NIHR) Brain Injury MedTech Co-operative. CENTER-TBI was supported by the European Union 7th Framework Programme (EC grant 602150). Additional funding was obtained from the Hannelore Kohl Stiftung (Germany), from OneMind (USA), and from Integra LifeSciences Corporation (USA). CENTER-TBI also acknowledges interactions and support from the International Initiative for TBI Research (InTBIR) investigators. S.B. is funded by a Gates Cambridge fellowship. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We are grateful to the patients and families of our study for making our efforts to improve TBI care possible. S.B. would like to thank Kathleen Mitchell-Fox (Princeton University) and Andrew Maas (Antwerp University Hospital) for offering comments on the manuscript.Funder: Gates Cambridge Trust; doi: https://doi.org/10.13039/501100005370Funder: EC | EC Seventh Framework Programm | FP7 Health (FP7-HEALTH - Specific Programme "Cooperation": Health); doi: https://doi.org/10.13039/100011272; Grant(s): 602150Existing methods to characterise the evolving condition of traumatic brain injury (TBI) patients in the intensive care unit (ICU) do not capture the context necessary for individualising treatment. Here, we integrate all heterogenous data stored in medical records (1166 pre-ICU and ICU variables) to model the individualised contribution of clinical course to 6-month functional outcome on the Glasgow Outcome Scale -Extended (GOSE). On a prospective cohort (n = 1550, 65 centres) of TBI patients, we train recurrent neural network models to map a token-embedded time series representation of all variables (including missing values) to an ordinal GOSE prognosis every 2 h. The full range of variables explains up to 52% (95% CI: 50–54%) of the ordinal variance in functional outcome. Up to 91% (95% CI: 90–91%) of this explanation is derived from pre-ICU and admission information (i.e., static variables). Information collected in the ICU (i.e., dynamic variables) increases explanation (by up to 5% [95% CI: 4–6%]), though not enough to counter poorer overall performance in longer-stay (>5.75 days) patients. Highest-contributing variables include physician-based prognoses, CT features, and markers of neurological function. Whilst static information currently accounts for the majority of functional outcome explanation after TBI, data-driven analysis highlights investigative avenues to improve the dynamic characterisation of longer-stay patients. Moreover, our modelling strategy proves useful for converting large patient records into interpretable time series with missing data integration and minimal processing

The leap to ordinal: Detailed functional prognosis after traumatic brain injury with a flexible modelling approach.

Funder: Integra LifeSciencesFunder: One MindFunder: ZNS - Hannelore Kohl StiftungWhen a patient is admitted to the intensive care unit (ICU) after a traumatic brain injury (TBI), an early prognosis is essential for baseline risk adjustment and shared decision making. TBI outcomes are commonly categorised by the Glasgow Outcome Scale-Extended (GOSE) into eight, ordered levels of functional recovery at 6 months after injury. Existing ICU prognostic models predict binary outcomes at a certain threshold of GOSE (e.g., prediction of survival [GOSE > 1]). We aimed to develop ordinal prediction models that concurrently predict probabilities of each GOSE score. From a prospective cohort (n = 1,550, 65 centres) in the ICU stratum of the Collaborative European NeuroTrauma Effectiveness Research in TBI (CENTER-TBI) patient dataset, we extracted all clinical information within 24 hours of ICU admission (1,151 predictors) and 6-month GOSE scores. We analysed the effect of two design elements on ordinal model performance: (1) the baseline predictor set, ranging from a concise set of ten validated predictors to a token-embedded representation of all possible predictors, and (2) the modelling strategy, from ordinal logistic regression to multinomial deep learning. With repeated k-fold cross-validation, we found that expanding the baseline predictor set significantly improved ordinal prediction performance while increasing analytical complexity did not. Half of these gains could be achieved with the addition of eight high-impact predictors to the concise set. At best, ordinal models achieved 0.76 (95% CI: 0.74-0.77) ordinal discrimination ability (ordinal c-index) and 57% (95% CI: 54%- 60%) explanation of ordinal variation in 6-month GOSE (Somers' Dxy). Model performance and the effect of expanding the predictor set decreased at higher GOSE thresholds, indicating the difficulty of predicting better functional outcomes shortly after ICU admission. Our results motivate the search for informative predictors that improve confidence in prognosis of higher GOSE and the development of ordinal dynamic prediction models