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

Author

Bhattacharyay, Shubhayu, Caruso, Pier Francesco, Åkerlund, Cecilia, Wilson, Lindsay, Stevens, Robert D, Menon, David K, Steyerberg, Ewout W, Nelson, David W, Ercole, Ari, and investigators/participants, the CENTER-TBI

Subjects
Computer Science - Machine Learning, Quantitative Biology - Quantitative Methods, Statistics - Applications
Abstract

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. Here, we integrate all heterogenous data stored in medical records (1,166 pre-ICU and ICU variables) to model the individualised contribution of clinical course to six-month functional outcome on the Glasgow Outcome Scale - Extended (GOSE). On a prospective cohort (n=1,550, 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 two hours. 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 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.

Published
2023
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7. The leap to ordinal: detailed functional prognosis after traumatic brain injury with a flexible modelling approach

Author

Bhattacharyay, Shubhayu, Milosevic, Ioan, Wilson, Lindsay, Menon, David K., Stevens, Robert D., Steyerberg, Ewout W., Nelson, David W., Ercole, Ari, and investigators/participants, the CENTER-TBI

Subjects
Computer Science - Machine Learning, J.3, I.2.0, I.5.1
Abstract

When 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 8, 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] or functional independence [GOSE>4]). 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 2 design elements on ordinal model performance: (1) the baseline predictor set, ranging from a concise set of 10 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 8 high-impact predictors (2 demographic variables, 4 protein biomarkers, and 2 severity assessments) 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' D). Our results motivate the search for informative predictors for higher GOSE and the development of ordinal dynamic prediction models., Comment: 68 pages, 4 figures, 4 tables, 1 appendix, 6 supplementary figures, 4 supplementary tables, 3 supplementary methods, 1 supplementary result

Published
2022
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11. Therapies to Restore Consciousness in Patients with Severe Brain Injuries: A Gap Analysis and Future Directions.

Author

Edlow, Brian L, Sanz, Leandro RD, Polizzotto, Len, Pouratian, Nader, Rolston, John D, Snider, Samuel B, Thibaut, Aurore, Stevens, Robert D, Gosseries, Olivia, and Curing Coma Campaign and its contributing members

Subjects
Curing Coma Campaign and its contributing members, Humans, Consciousness Disorders, Coma, Consciousness, Neuroimaging, Brain Injuries, Traumatic, Disorders of consciousness, Gap analysis, Precision medicine, Clinical Research, Rehabilitation, Neurosciences, Clinical Trials and Supportive Activities, Behavioral and Social Science, Injury (total) Accidents/Adverse Effects, Brain Disorders, Neurology & Neurosurgery, Clinical Sciences
Abstract

Background/objectiveFor patients with disorders of consciousness (DoC) and their families, the search for new therapies has been a source of hope and frustration. Almost all clinical trials in patients with DoC have been limited by small sample sizes, lack of placebo groups, and use of heterogeneous outcome measures. As a result, few therapies have strong evidence to support their use; amantadine is the only therapy recommended by current clinical guidelines, specifically for patients with DoC caused by severe traumatic brain injury. To foster and advance development of consciousness-promoting therapies for patients with DoC, the Curing Coma Campaign convened a Coma Science Work Group to perform a gap analysis.MethodsWe consider five classes of therapies: (1) pharmacologic; (2) electromagnetic; (3) mechanical; (4) sensory; and (5) regenerative. For each class of therapy, we summarize the state of the science, identify gaps in knowledge, and suggest future directions for therapy development.ResultsKnowledge gaps in all five therapeutic classes can be attributed to the lack of: (1) a unifying conceptual framework for evaluating therapeutic mechanisms of action; (2) large-scale randomized controlled trials; and (3) pharmacodynamic biomarkers that measure subclinical therapeutic effects in early-phase trials. To address these gaps, we propose a precision medicine approach in which clinical trials selectively enroll patients based upon their physiological receptivity to targeted therapies, and therapeutic effects are measured by complementary behavioral, neuroimaging, and electrophysiologic endpoints.ConclusionsThis personalized approach can be realized through rigorous clinical trial design and international collaboration, both of which will be essential for advancing the development of new therapies and ultimately improving the lives of patients with DoC.

Published
2021

12. Proceedings of the First Curing Coma Campaign NIH Symposium: Challenging the Future of Research for Coma and Disorders of Consciousness.

Author

Claassen, Jan, Akbari, Yama, Alexander, Sheila, Bader, Mary Kay, Bell, Kathleen, Bleck, Thomas P, Boly, Melanie, Brown, Jeremy, Chou, Sherry H-Y, Diringer, Michael N, Edlow, Brian L, Foreman, Brandon, Giacino, Joseph T, Gosseries, Olivia, Green, Theresa, Greer, David M, Hanley, Daniel F, Hartings, Jed A, Helbok, Raimund, Hemphill, J Claude, Hinson, HE, Hirsch, Karen, Human, Theresa, James, Michael L, Ko, Nerissa, Kondziella, Daniel, Livesay, Sarah, Madden, Lori K, Mainali, Shraddha, Mayer, Stephan A, McCredie, Victoria, McNett, Molly M, Meyfroidt, Geert, Monti, Martin M, Muehlschlegel, Susanne, Murthy, Santosh, Nyquist, Paul, Olson, DaiWai M, Provencio, J Javier, Rosenthal, Eric, Sampaio Silva, Gisele, Sarasso, Simone, Schiff, Nicholas D, Sharshar, Tarek, Shutter, Lori, Stevens, Robert D, Vespa, Paul, Videtta, Walter, Wagner, Amy, Ziai, Wendy, Whyte, John, Zink, Elizabeth, Suarez, Jose I, and Curing Coma Campaign

Subjects
Curing Coma Campaign, Biomarkers, Coma, Consciousness, Electrophysiology, Magnetic resonance imaging, Clinical Sciences, Neurosciences, Neurology & Neurosurgery
Abstract

Coma and disorders of consciousness (DoC) are highly prevalent and constitute a burden for patients, families, and society worldwide. As part of the Curing Coma Campaign, the Neurocritical Care Society partnered with the National Institutes of Health to organize a symposium bringing together experts from all over the world to develop research targets for DoC. The conference was structured along six domains: (1) defining endotype/phenotypes, (2) biomarkers, (3) proof-of-concept clinical trials, (4) neuroprognostication, (5) long-term recovery, and (6) large datasets. This proceedings paper presents actionable research targets based on the presentations and discussions that occurred at the conference. We summarize the background, main research gaps, overall goals, the panel discussion of the approach, limitations and challenges, and deliverables that were identified.

Published
2021

13. Mechanisms Underlying Disorders of Consciousness: Bridging Gaps to Move Toward an Integrated Translational Science

Author

Luppi, Andrea I, Cain, Joshua, Spindler, Lennart RB, Górska, Urszula J, Toker, Daniel, Hudson, Andrew E, Brown, Emery N, Diringer, Michael N, Stevens, Robert D, Massimini, Marcello, Monti, Martin M, Stamatakis, Emmanuel A, and Boly, Melanie

Subjects
Basic Behavioral and Social Science, Prevention, Behavioral and Social Science, Neurosciences, Brain Disorders, Neurological, Brain Injuries, Coma, Consciousness, Consciousness Disorders, Humans, Brain injury, Electroencephalography, Magnetic resonance imaging, Neuroimaging, Mechanism, Curing Coma Campaign and Its Contributing Collaborators, Clinical Sciences, Neurology & Neurosurgery
Abstract

AimIn order to successfully detect, classify, prognosticate, and develop targeted therapies for patients with disorders of consciousness (DOC), it is crucial to improve our mechanistic understanding of how severe brain injuries result in these disorders.MethodsTo address this need, the Curing Coma Campaign convened a Mechanisms Sub-Group of the Coma Science Work Group (CSWG), aiming to identify the most pressing knowledge gaps and the most promising approaches to bridge them.ResultsWe identified a key conceptual gap in the need to differentiate the neural mechanisms of consciousness per se, from those underpinning connectedness to the environment and behavioral responsiveness. Further, we characterised three fundamental gaps in DOC research: (1) a lack of mechanistic integration between structural brain damage and abnormal brain function in DOC; (2) a lack of translational bridges between micro- and macro-scale neural phenomena; and (3) an incomplete exploration of possible synergies between data-driven and theory-driven approaches.ConclusionIn this white paper, we discuss research priorities that would enable us to begin to close these knowledge gaps. We propose that a fundamental step towards this goal will be to combine translational, multi-scale, and multimodal data, with new biomarkers, theory-driven approaches, and computational models, to produce an integrated account of neural mechanisms in DOC. Importantly, we envision that reciprocal interaction between domains will establish a "virtuous cycle," leading towards a critical vantage point of integrated knowledge that will enable the advancement of the scientific understanding of DOC and consequently, an improvement of clinical practice.

Published
2021

15. A Physiology-Driven Computational Model for Post-Cardiac Arrest Outcome Prediction

Author

Kim, Han B., Nguyen, Hieu, Jin, Qingchu, Tamby, Sharmila, Romer, Tatiana Gelaf, Sung, Eric, Liu, Ran, Greenstein, Joseph, Suarez, Jose I., Storm, Christian, Winslow, Raimond, and Stevens, Robert D.

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning, J.3, I.2.1, I.6.4, G.3
Abstract

Patients resuscitated from cardiac arrest (CA) face a high risk of neurological disability and death, however pragmatic methods are lacking for accurate and reliable prognostication. The aim of this study was to build computational models to predict post-CA outcome by leveraging high-dimensional patient data available early after admission to the intensive care unit (ICU). We hypothesized that model performance could be enhanced by integrating physiological time series (PTS) data and by training machine learning (ML) classifiers. We compared three models integrating features extracted from the electronic health records (EHR) alone, features derived from PTS collected in the first 24hrs after ICU admission (PTS24), and models integrating PTS24 and EHR. Outcomes of interest were survival and neurological outcome at ICU discharge. Combined EHR-PTS24 models had higher discrimination (area under the receiver operating characteristic curve [AUC]) than models which used either EHR or PTS24 alone, for the prediction of survival (AUC 0.85, 0.80 and 0.68 respectively) and neurological outcome (0.87, 0.83 and 0.78). The best ML classifier achieved higher discrimination than the reference logistic regression model (APACHE III) for survival (AUC 0.85 vs 0.70) and neurological outcome prediction (AUC 0.87 vs 0.75). Feature analysis revealed previously unknown factors to be associated with post-CA recovery. Results attest to the effectiveness of ML models for post-CA predictive modeling and suggest that PTS recorded in very early phase after resuscitation encode short-term outcome probabilities., Comment: 51 pages, 7 figures, 4 supplementary figures

Published
2020
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16. Computational modelling in disorders of consciousness: Closing the gap towards personalised models for restoring consciousness

Author

Luppi, Andrea I., Cabral, Joana, Cofre, Rodrigo, Mediano, Pedro A.M., Rosas, Fernando E., Qureshi, Abid Y., Kuceyeski, Amy, Tagliazucchi, Enzo, Raimondo, Federico, Deco, Gustavo, Shine, James M., Kringelbach, Morten L., Orio, Patricio, Ching, ShiNung, Sanz Perl, Yonatan, Diringer, Michael N., Stevens, Robert D., and Sitt, Jacobo Diego

Published
2023
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18. Extubation in neurocritical care patients: the ENIO international prospective study

Author

Cinotti, Raphaël, Mijangos, Julio Cesar, Pelosi, Paolo, Haenggi, Matthias, Gurjar, Mohan, Schultz, Marcus J., Kaye, Callum, Godoy, Daniel Agustin, Alvarez, Pablo, Ioakeimidou, Aikaterini, Ueno, Yoshitoyo, Badenes, Rafael, Suei Elbuzidi, Abdurrahmaan Ali, Piagnerelli, Michaël, Elhadi, Muhammed, Reza, Syed Tariq, Azab, Mohammed Atef, McCredie, Victoria, Stevens, Robert D., Digitale, Jean Catherine, Fong, Nicholas, and Asehnoune, Karim

Published
2022
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19. The Curing Coma Campaign: Framing Initial Scientific Challenges—Proceedings of the First Curing Coma Campaign Scientific Advisory Council Meeting

Author

Provencio, J Javier, Hemphill, J Claude, Claassen, Jan, Edlow, Brian L, Helbok, Raimund, Vespa, Paul M, Diringer, Michael N, Polizzotto, Len, Shutter, Lori, Suarez, Jose I, Stevens, Robert D, Hanley, Daniel F, Akbari, Yama, Bleck, Thomas P, Boly, Melanie, Foreman, Brandon, Giacino, Joseph T, Hartings, Jed A, Human, Theresa, Kondziella, Daniel, Ling, Geoffrey SF, Mayer, Stephan A, McNett, Molly, Menon, David K, Meyfroidt, Geert, Monti, Martin M, Park, Soojin, Pouratian, Nader, Puybasset, Louis, Rohaut, Benjamin, Rosenthal, Eric S, Schiff, Nicholas D, Sharshar, Tarek, Wagner, Amy, Whyte, John, and Olson, DaiWai M

Subjects
Health Services and Systems, Biomedical and Clinical Sciences, Health Sciences, Brain Disorders, Good Health and Well Being, Advisory Committees, Biomarkers, Clinical Trials as Topic, Coma, Consciousness Disorders, Critical Care, Humans, Implementation Science, Neurological Rehabilitation, Neurology, Proof of Concept Study, Stakeholder Participation, Endotype, Biomarker, Consciousness, Recovery, Neurocritical Care Society Curing Coma Campaign, Clinical Sciences, Neurosciences, Neurology & Neurosurgery, Clinical sciences, Nursing
Abstract

Coma and disordered consciousness are common manifestations of acute neurological conditions and are among the most pervasive and challenging aspects of treatment in neurocritical care. Gaps exist in patient assessment, outcome prognostication, and treatment directed specifically at improving consciousness and cognitive recovery. In 2019, the Neurocritical Care Society (NCS) launched the Curing Coma Campaign in order to address the "grand challenge" of improving the management of patients with coma and decreased consciousness. One of the first steps was to bring together a Scientific Advisory Council including coma scientists, neurointensivists, neurorehabilitationists, and implementation experts in order to address the current scientific landscape and begin to develop a framework on how to move forward. This manuscript describes the proceedings of the first Curing Coma Campaign Scientific Advisory Council meeting which occurred in conjunction with the NCS Annual Meeting in October 2019 in Vancouver. Specifically, three major pillars were identified which should be considered: endotyping of coma and disorders of consciousness, biomarkers, and proof-of-concept clinical trials. Each is summarized with regard to current approach, benefits to the patient, family, and clinicians, and next steps. Integration of these three pillars will be essential to the success of the Curing Coma Campaign as will expanding the "curing coma community" to ensure broad participation of clinicians, scientists, and patient advocates with the goal of identifying and implementing treatments to fundamentally improve the outcome of patients.

Published
2020

21. Proceedings of the Second Curing Coma Campaign NIH Symposium: Challenging the Future of Research for Coma and Disorders of Consciousness

Author

Mainali, Shraddha, Aiyagari, Venkatesh, Alexander, Sheila, Bodien, Yelena, Boerwinkle, Varina, Boly, Melanie, Brown, Emery, Brown, Jeremy, Claassen, Jan, Edlow, Brian L., Fink, Ericka L., Fins, Joseph J., Foreman, Brandon, Frontera, Jennifer, Geocadin, Romergryko G., Giacino, Joseph, Gilmore, Emily J., Gosseries, Olivia, Hammond, Flora, Helbok, Raimund, Claude Hemphill, J., Hirsch, Karen, Kim, Keri, Laureys, Steven, Lewis, Ariane, Ling, Geoffrey, Livesay, Sarah L., McCredie, Victoria, McNett, Molly, Menon, David, Molteni, Erika, Olson, DaiWai, O’Phelan, Kristine, Park, Soojin, Polizzotto, Len, Javier Provencio, Jose, Puybasset, Louis, Venkatasubba Rao, Chethan P., Robertson, Courtney, Rohaut, Benjamin, Rubin, Michael, Sharshar, Tarek, Shutter, Lori, Sampaio Silva, Gisele, Smith, Wade, Stevens, Robert D., Thibaut, Aurore, Vespa, Paul, Wagner, Amy K., Ziai, Wendy C., Zink, Elizabeth, and I Suarez, Jose

Published
2022
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30. Nationwide Clinical Practice Patterns of Anesthesiology Critical Care Physicians: A Survey to Members of the Society of Critical Care Anesthesiologists

Author

Shaefi, Shahzad, Pannu, Ameeka, Mueller, Ariel L., Flynn, Brigid, Evans, Adam, Jabaley, Craig S., Mladinov, Domagoj, Wall, Michael, Siddiqui, Shahla, Douin, David J., Boone, M. Dustin, Monteith, Erika, Abalama, Vivian, Nunnally, Mark E., Cobas, Miguel, Warner, Matthew A., and Stevens, Robert D.

Published
2023
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36. Correction to: Proceedings of the Second Curing Coma Campaign NIH Symposium: Challenging the Future of Research for Coma and Disorders of Consciousness

Author

Mainali, Shraddha, Aiyagari, Venkatesh, Alexander, Sheila, Bodien, Yelena, Boerwinkle, Varina, Boly, Melanie, Brown, Emery, Brown, Jeremy, Claassen, Jan, Edlow, Brian L., Fink, Ericka L., Fins, Joseph J., Foreman, Brandon, Frontera, Jennifer, Geocadin, Romergryko G., Giacino, Joseph, Gilmore, Emily J., Gosseries, Olivia, Hammond, Flora, Helbok, Raimund, Claude Hemphill, J., Hirsch, Karen, Kim, Keri, Laureys, Steven, Lewis, Ariane, Ling, Geoffrey, Livesay, Sarah L., McCredie, Victoria, McNett, Molly, Menon, David, Molteni, Erika, Olson, DaiWai, O’Phelan, Kristine, Park, Soojin, Polizzotto, Len, Javier Provencio, Jose, Puybasset, Louis, Venkatasubba Rao, Chethan P., Robertson, Courtney, Rohaut, Benjamin, Rubin, Michael, Sharshar, Tarek, Shutter, Lori, Sampaio Silva, Gisele, Smith, Wade, Stevens, Robert D., Thibaut, Aurore, Vespa, Paul, Wagner, Amy K., Ziai, Wendy C., Zink, Elizabeth, and Suarez, Jose I.

Published
2022
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40. Mechanical ventilation in patients with acute brain injury: recommendations of the European Society of Intensive Care Medicine consensus

Author

Robba, Chiara, Poole, Daniele, McNett, Molly, Asehnoune, Karim, Bösel, Julian, Bruder, Nicolas, Chieregato, Arturo, Cinotti, Raphael, Duranteau, Jacques, Einav, Sharon, Ercole, Ari, Ferguson, Niall, Guerin, Claude, Siempos, Ilias I., Kurtz, Pedro, Juffermans, Nicole P., Mancebo, Jordi, Mascia, Luciana, McCredie, Victoria, Nin, Nicolas, Oddo, Mauro, Pelosi, Paolo, Rabinstein, Alejandro A., Neto, Ary Serpa, Seder, David B., Skrifvars, Markus B., Suarez, Jose I., Taccone, Fabio Silvio, van der Jagt, Mathieu, Citerio, Giuseppe, and Stevens, Robert D.

Published
2020
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41. Physiologic Insults and Individualized Treatments in Traumatic Brain Injury.

Author

Azad, Tej D. and Stevens, Robert D.

Subjects
*BRAIN injuries, *COVID-19, *CEREBRAL vasospasm
Abstract

The article explores the impact of physiological factors on patient outcomes in traumatic brain injury (TBI). It discusses the association between intracranial pressure, cerebral perfusion pressure, and pressure reactivity index with unfavorable outcomes in TBI patients. The study introduces the concept of a "physiologic insult dose" and suggests the potential use of multiparameter visualizations in assessing autoregulation impairment. However, the study is limited by its single-center cohort and the need for further research to determine the practical application of these visualizations in clinical settings. [Extracted from the article]

Published
2024
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50. Incidence and Outcomes of Acute Respiratory Distress Syndrome in Brain-Injured Patients Receiving Invasive Ventilation: A Secondary Analysis of the ENIO Study.

Author

Taran, Shaurya, Stevens, Robert D., Perrot, Bastien, McCredie, Victoria A., Cinotti, Raphael, Asehnoune, Karim, Pelosi, Paolo, and Robba, Chiara

Subjects
*ADULT respiratory distress syndrome, *BRAIN injuries, *SUBARACHNOID hemorrhage, *INTRACRANIAL hemorrhage, *CONFIDENCE intervals
Abstract

Background: Acute respiratory distress syndrome (ARDS) is an important pulmonary complication in brain-injured patients receiving invasive mechanical ventilation (IMV). We aimed to evaluate the incidence and association between ARDS and clinical outcomes in patients with different forms of acute brain injury requiring IMV in the intensive care unit (ICU). Methods: This was a preplanned secondary analysis of a prospective, multicenter, international cohort study (NCT 03400904). We included brain-injured patients receiving IMV for ≥ 24 h. ARDS was the main exposure of interest and was identified during index ICU admission using the Berlin definition. We examined the incidence and adjusted association of ARDS with ICU mortality, ICU length of stay, duration of IMV, and extubation failure. Outcomes were evaluated using mixed-effect logistic regression and cause-specific Cox proportional hazards models. Results: 1492 patients from 67 hospitals and 16 countries were included in the analysis, of whom 137 individuals developed ARDS (9.2% of overall cohort). Across countries, the median ARDS incidence was 5.1% (interquartile range [IQR] 0-10; range 0-27.3). ARDS was associated with increased ICU mortality (adjusted odds ratio (OR) 2.66; 95% confidence interval [CI], 1.29-5.48), longer ICU length of stay (adjusted hazard ratio [HR] 0.59; 95% CI, 0.48-0.73), and longer duration of IMV (adjusted HR 0.54; 95% CI, 0.44-0.67). The association between ARDS and extubation failure approached statistical significance (adjusted HR 1.48; 95% CI 0.99-2.21). Higher ARDS severity was associated with incrementally longer ICU length of stay and longer cumulative duration of IMV. Findings remained robust in a sensitivity analysis evaluating the magnitude of unmeasured confounding. Conclusions: In this cohort of acutely brain-injured patients, the incidence of ARDS was similar to that reported in other mixed cohorts of critically ill patients. Development of ARDS was associated with worse outcomes. [ABSTRACT FROM AUTHOR]

Published
2024
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