Posts Tagged patient prognostication
TBI is the leading cause of death and disability in Americans under age 35 and the leading cause of premature death and disability worldwide (1). In 2009 alone, 2.4 million patients presented to an emergency department (ED) with a TBI and an additional 52,695 died from their injury (2). Currently, at least 5.3 million U.S. residents live with long-term disabilities related to a TBI (3). The economic toll of TBI has been reported as $75 billion for a single year (4). Between 2002 and 2010, the rate of TBI in the U.S. population increased by over 50 percent, (1) excluding the military and those that do not seek medical care (5).
Despite the colossal nature of the problem, little progress has been made in developing new therapeutics for TBI. A PubMed search reveals 30 failed clinical trials for TBI since 1993, 25 of which have been in the last 15 years and 13 of which have been in the last five years (6-35). These 30 trials failed to find a significant effect for treatments that were supported by extensive preclinical studies and Phase I and II trials. They included hypothermia and temperature control (11 studies), hypertonic saline (three studies), progesterone (two studies), prostacyclin (two studies), surgical intervention (one study), intracranial pressure monitoring (one study) and a number of other pharmacological interventions (10 studies). The estimated total cost of these trials is $1.1 billion (36).
All 30 of these trials were prospective, randomized, controlled studies based on well-executed preliminary studies. Ninety-three percent indicated blinded assessment, and 70 percent were done at multiple centers. Patient retention was unprecedented. For example, the SYNAPSE trial tested the effect of progesterone in a double-blind, randomized manner with more than 1,000 participants and 96 percent, six-month retention. Thus, rather than blaming standard methodological limitations for these failed trials, one must look deeper into how TBI trials recruit and assess the outcome of the patients.
|Table 1: Major Inclusion Criteria|
|Inclusion Criteria||Number of Studies|
|GCS + CT||8|
|GCS + Pupil Reactivity||2|
|GCS + ICP||1|
The Glasgow Coma Scale (GCS) was used as a major inclusion criterion in 29 of the 30 studies (97 percent) and was the only major inclusion criteria in 18 studies (Table 1). This measure, which has been in use for 40 years, conveys the clinically-relevant acute exam of the verbal, motor and eye movement response of a patient. Using GCS to select patients for clinical trials may be suboptimal because it does not account for diverse pathophysiology at any level of brain injury severity. For example, a patient with severe TBI (GCS 3-8) can be minimally or non-responsive due to a wide diversity of underlying pathophysiology with different outcomes such as a non-significant impact while intoxicated, a treatable subdural or epidural hematoma or a diffuse axonal or anoxic injury, the latter of which may be associated with very poor outcomes. Recognition of the limitations of the mild/moderate/severe paradigm was the impetus for the 3,000-patient, 13-center TRACK-TBI study (37) and the European CENTER TBI project (38).
Imaging was only used as a major inclusion or exclusion criteria in eight of the 30 failed trials; however, conventional acute imaging performed for trauma may not fully differentiate complex pathophysiology, such as diffuse axonal injury (DAI) or anoxic injury.
|Table 2: Outcome Measures Utilized|
|Outcome Measure||Number of Studies|
|GOS(-E) as Primary Outcome||15|
|GOS(-E) as Secondary Outcome||4|
|Length of Stay||1|
The 5-point Glasgow Outcome Scale (GOS), or its extended 8-point version (GOS-E) was used as the primary outcome measure in 21 of the 30 studies (70 percent), and was the only outcome measure used in six of those studies (Table 2). Four of the remaining studies used the GOS as a secondary outcome measure, and two used an outcome assessment that is structured similarly to the GOS in the pediatric populations (Pediatric Cerebral Performance Category). Three studies used other means of measuring outcome. While GOS or GOS-E accurately captures global phenomena, it may fail to assess subtle differences in outcomes over a wide range of functioning. We speculate that clinical trials for brain injury will have a better probability of success when there are means of detecting and classifying brain injury appropriately, according to its pathophysiology as patients enter a trial and more sensitive outcome measures to assess recovery as patients leave a trial are utilized.
Multimodal algorithmic assessment to obtain an accurate pathophysiologic diagnosis is routine with virtually every other body system. For example, when a patient presents to the emergency room with chest pain, the workup includes imaging of the heart and lungs (echocardiogram, angiogram, chest x-ray and/or CT scan), an assessment of the electrical activity of the heart (electrocardiogram) and blood tests to analyze the concentration of various proteins that indicate cardiac or other pathology, such as troponin or D-dimers. No one would ever conceive of conducting a clinical trial for “chest pain” based on only a physical examination and a single imaging study to assess the impact of a single universal intervention with an 8-point outcome measure. On the contrary, initiatives for precision medicine seek to define even baseline patient characteristics prior to accurate diagnosis. The complexity of the central nervous system even before an injury befuddles simple functional assessment. TBI should warrant a similar multimodal assessment that enables a more clear understanding of the underlying pathophysiology of the type of brain injury before enrollment in a trial.
Among the many possible assessment tools being investigated for better classification of brain injury are serum biomarkers, eye tracking and magnetic resonance imaging (MRI). Our laboratory is among many engaged in research to enable better classification of brain injury. We will prospectively enroll more than 1,000 trauma patients at Hennepin County Medical Center (HCMC) in Minneapolis and will follow these individuals for a year after their injury. This study aims to develop a multi-modal classification scheme for brain injury that will be able to accurately diagnose acute pathophysiology in brain-injured subjects with eye tracking, the analysis of protein markers in patient blood samples and radiographic imaging.
Abnormal eye movements are found in up to 90 percent of patients with so-called “mild” TBI or concussion (39-44). The eye tracking employed in this study will be non-spatially calibrated and used to detect subtle abnormalities in motility and sustained vergence; the ability of the eyes to focus on a single point in space over time (45). Eye tracking fundamentally detects palsies in cranial nerves III and VI (46), and eye tracking metrics correlate with the severity of concussion symptoms and the improvement of those symptoms over time in both an adult emergency department population (47) and in a pediatric concussion center population (48). Disruption of eye tracking metrics clinically correlates with convergence dysfunction and abnormal near point of convergence in children (48). Eye tracking detects concussion as defined by its symptoms with a high sensitivity and specificity (49).
Protein biomarkers in the serum of TBI patients have been extensively researched in the last 20 years and represent potentially promising indicators of the nature of the brain injury, including the exact type of cellular injury that has occurred in the brain (neuronal, glial or axonal).
Preliminary models have already been proposed providing evidence that can aid in determining acute treatment plans, but these models are limited by statistical power and their nature as uni-variate models of blood-based biomarkers (50-53). Combining eye-tracking data with data from biomarkers in blood samples and other clinical data (i.e. brain imaging and the physical exam) and correlating it with detailed outcome measures at a high level of statistical power into a multivariate model has great potential for improving classification of brain injuries.
A model for classification and treatment of brain injury will be beneficial not only for patient prognostication and limiting the economic toll of TBI but also in gaining a more clear understanding of the underlying mechanisms of various brain injuries. With such understanding, we will increase the probability of successful clinical trials for the treatment of TBI.
Uzma Samadani, MD, PhD, has submitted several patents describing the eye tracking technology mentioned in this article. These patents are owned by NYU, HCMC and the VA and licensed to Oculogica Inc., a company co-founded by Dr. Samadani and in which she has an equity interest. The U.S. also has a grant from Abbott Diagnostic Laboratories to investigate serum biomarkers for brain injury.
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