Browsing by Subject "Disorders of consciousness"

Now showing 1 - 4 of 4
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    Comparison of Common Outcome Measures for Assessing Independence in Patients Diagnosed with Disorders of Consciousness: A Traumatic Brain Injury Model Systems Study
    (Mary Ann Liebert, 2022) Snider, Samuel B.; Kowalski, Robert G.; Hammond, Flora M.; Izzy, Saef; Shih, Shirley L.; Rovito, Craig; Edlow, Brian L.; Zafonte, Ross D.; Giacino, Joseph T.; Bodien, Yelena G.; Physical Medicine and Rehabilitation, School of Medicine
    Patients with disorders of consciousness (DoC) after traumatic brain injury (TBI) recover to varying degrees of functional dependency. Dependency is difficult to measure but critical for interpreting clinical trial outcomes and prognostic counseling. In participants with DoC (i.e., not following commands) enrolled in the TBI Model Systems National Database (TBIMS NDB), we used the Functional Independence Measure (FIM®) as the reference to evaluate how accurately the Glasgow Outcome Scale-Extended (GOSE) and Disability Rating Scale (DRS) assess dependency. Using the established FIM-dependency cut-point of <80, we measured the classification performance of literature-derived GOSE and DRS cut-points at 1-year post-injury. We compared the area under the receiver operating characteristic curve (AUROC) between the DRSDepend, a DRS-derived marker of dependency, and the data-derived optimal GOSE and DRS cut-points. Of 18,486 TBIMS participants, 1483 met inclusion criteria (mean [standard deviation (SD)] age = 38 [18] years; 76% male). The sensitivity of GOSE cut-points of ≤3 and ≤4 (Lower Severe and Upper Severe Disability, respectively) for identifying FIM-dependency were 97% and 98%, but specificities were 73% and 51%, respectively. The sensitivity of the DRS cut-point of ≥12 (Severe Disability) for identifying FIM-dependency was 60%, but specificity was 100%. The DRSDepend had a sensitivity of 83% and a specificity of 94% for classifying FIM-dependency, with a greater AUROC than the data-derived optimal GOSE (≤3, p = 0.01) and DRS (≥10, p = 0.008) cut-points. Commonly used GOSE and DRS cut-points have limited specificity or sensitivity for identifying functional dependency. The DRSDepend identifies FIM-dependency more accurately than the GOSE and DRS cut-points, but requires further validation.
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    Predicting Functional Dependency in Patients with Disorders of Consciousness: A TBI-Model Systems and TRACK-TBI Study
    (Wiley, 2023) Snider, Samuel B.; Temkin, Nancy R.; Barber, Jason; Edlow, Brian L.; Giacino, Joseph T.; Hammond, Flora M.; Izzy, Saef; Kowalski, Robert G.; Markowitz, Amy J.; Rovito, Craig A.; Shih, Shirley L.; Zafonte, Ross D.; Manley, Geoffrey T.; Bodien, Yelena G.; TRACK-TBI investigators; Physical Medicine and Rehabilitation, School of Medicine
    Objective: It is not currently possible to predict long-term functional dependency in patients with disorders of consciousness (DoC) after traumatic brain injury (TBI). Our objective was to fit and externally validate a prediction model for 1-year dependency in patients with DoC ≥ 2 weeks after TBI. Methods: We included adults with TBI enrolled in TBI Model Systems (TBI-MS) or Transforming Research and Clinical Knowledge in TBI (TRACK-TBI) studies who were not following commands at rehabilitation admission or 2 weeks post-injury, respectively. We fit a logistic regression model in TBI-MS and validated it in TRACK-TBI. The primary outcome was death or dependency at 1 year post-injury, defined using the Disability Rating Scale. Results: In the TBI-MS Discovery Sample, 1,960 participants (mean age 40 [18] years, 76% male, 68% white) met inclusion criteria, and 406 (27%) were dependent 1 year post-injury. In a TBI-MS held out cohort, the dependency prediction model's area under the receiver operating characteristic curve was 0.79 (95% CI 0.74-0.85), positive predictive value was 53% and negative predictive value was 86%. In the TRACK-TBI external validation (n = 124, age 40 [16] years, 77% male, 81% white), the area under the receiver operating characteristic curve was 0.66 (0.53, 0.79), equivalent to the standard IMPACTcore + CT score (p = 0.8). Interpretation: We developed a 1-year dependency prediction model using the largest existing cohort of patients with DoC after TBI. The sensitivity and negative predictive values were greater than specificity and positive predictive values. Accuracy was diminished in an external sample, but equivalent to the IMPACT model. Further research is needed to improve dependency prediction in patients with DoC after TBI.
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    Proceedings of the Second Curing Coma Campaign NIH Symposium: Challenging the Future of Research for Coma and Disorders of Consciousness
    (Springer, 2022) 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; Hemphill, J. Claude; 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; Provencio, Jose Javier; Puybasset, Louis; Venkatasubba Rao, Chethan P.; Robertson, Courtney; Rohaut, Benjamin; Rubin, Michael; Sharshar, Tarek; Shutter, Lori; Silva, Gisele Sampaio; Smith, Wade; Steven, Robert D.; Thibaut, Aurore; Vespa, Paul; Wagner, Amy K.; Ziai, Wendy C.; Zink, Elizabeth; Suarez, Jose I.; Physical Medicine and Rehabilitation, School of Medicine
    This proceedings article presents actionable research targets on the basis of the presentations and discussions at the 2nd Curing Coma National Institutes of Health (NIH) symposium held from May 3 to May 5, 2021. Here, we summarize the background, research priorities, panel discussions, and deliverables discussed during the symposium across six major domains related to disorders of consciousness. The six domains include (1) Biology of Coma, (2) Coma Database, (3) Neuroprognostication, (4) Care of Comatose Patients, (5) Early Clinical Trials, and (6) Long-term Recovery. Following the 1st Curing Coma NIH virtual symposium held on September 9 to September 10, 2020, six workgroups, each consisting of field experts in respective domains, were formed and tasked with identifying gaps and developing key priorities and deliverables to advance the mission of the Curing Coma Campaign. The highly interactive and inspiring presentations and panel discussions during the 3-day virtual NIH symposium identified several action items for the Curing Coma Campaign mission, which we summarize in this article.
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    Research Needs for Prognostic Modeling and Trajectory Analysis in Patients with Disorders of Consciousness
    (Springer, 2021) Hammond, Flora M.; Katta-Charles, Sheryl; Russell, Mary Beth; Zafonte, Ross D.; Claassen, Jan; Wagner, Amy K.; Puybasset, Louis; Egawa, Satoshi; Laureys, Steven; Diringer, Michael; Stevens, Robert D.; Curing Coma Campaign and its Contributing Members; Physical Medicine and Rehabilitation, School of Medicine
    Background: The current state of the science regarding the care and prognosis of patients with disorders of consciousness is limited. Scientific advances are needed to improve the accuracy, relevance, and approach to prognostication, thereby providing the foundation to develop meaningful and effective interventions. Methods: To address this need, an interdisciplinary expert panel was created as part of the Coma Science Working Group of the Neurocritical Care Society Curing Coma Campaign. Results: The panel performed a gap analysis which identified seven research needs for prognostic modeling and trajectory analysis ("recovery science") in patients with disorders of consciousness: (1) to define the variables that predict outcomes; (2) to define meaningful intermediate outcomes at specific time points for different endotypes; (3) to describe recovery trajectories in the absence of limitations to care; (4) to harness big data and develop analytic methods to prognosticate more accurately; (5) to identify key elements and processes for communicating prognostic uncertainty over time; (6) to identify health care delivery models that facilitate recovery and recovery science; and (7) to advocate for changes in the health care delivery system needed to advance recovery science and implement already-known best practices. Conclusion: This report summarizes the current research available to inform the proposed research needs, articulates key elements within each area, and discusses the goals and advances in recovery science and care anticipated by successfully addressing these needs.