Predict Severe Traumatic Brain Injury

Overview

Severe traumatic brain injury (TBI) is associated with a 20-30% mortality rate and significant disability among most survivors. The Centers for Disease Control and Prevention (CDC) estimate that 2% of the U.S. population lives with disabilities directly attributable to TBI, with annual costs exceeding $76.5 billion. Current treatments are largely ineffective because they are instituted after irreversible damage has already occurred. By the time intracranial pressure (ICP) increases or brain tissue oxygen tension (PbtO2) decreases to harmful levels, it is often too late to reverse or repair the damage. A computerized method has been developed that can predict these injurious events ahead of time, allowing clinicians to intervene before further damage occurs. The goal of this proposal is to test these predictions in real time. The first phase of the project (Year 1) involves setting up the informatics infrastructure, with no patient interaction. In the second phase (Year 2), subjects, through surrogate decision-makers, will be enrolled in an observational study where data on intracranial pressure and brain tissue oxygen tension will be collected, and the prediction algorithm will be tested for accuracy. Clinical management will follow standard care protocols, and no additional interventions will be performed. Approximately 120 individuals will participate in this study at the University of Chicago and Ben Taub General Hospital in Houston. Data collected will include both the electronic medical record and data from bedside intensive care unit monitors. The electronic medical record includes demographic information, injury characteristics, laboratory values, and imaging data, while the intensive care unit monitor provides real-time vital signs such as intracranial pressure, brain tissue oxygen tension, and mean arterial pressure. These data will be securely stored in a research computer database. Efforts will be made to contact subjects or their caretakers at 6 months to follow up on recovery. This research aims to improve patient outcomes by providing predictions of further brain injury, with the potential for future interventions to prevent permanent brain damage.

Recently, the investigators achieved prediction of episodes of intracranial hypertension (ICH) and brain tissue hypoxia (BTH) 30 minutes to several hours in advance of overt deterioration using intuitive and simple models whose inputs include recent intracranial pressure (ICP) or partial brain tissue oxygen tension (PbtO2) values, the time since the last crisis, and the fraction of time spent with high/low ICP or PbtO2, respectively. A retrospective study was conducted based on prospectively collected physiologic data, and machine-learning-based algorithms were developed to recognize physiologic patterns in severe traumatic brain injury (TBI) patients that occur in advance of ICP and PbtO2 crises. These events were defined as ICP ≥ 20 mmHg lasting at least 15 minutes and PbtO2 values \< 10 mmHg for at least 10 minutes, respectively. The physiologic data preceding each crisis event were used to identify precursors associated with crisis onset. Further investigation was conducted to assess how model performance changed as the prediction time increased, providing an estimate of the clinical timescale of crisis precursors. Multivariate classification models were applied to recorded data in 30-minute epochs of time to predict crises between 15 and 360 minutes in the future. The cohort consisted of 817 TBI patients admitted to the neurocritical care unit at Ben Taub Hospital (Houston, Texas). The algorithm predicted the onset of ICP crises with 30 minutes advance warning with an area under the curve (AUC) of 0.86, in independent data, using only ICP measurements and time since last crisis. An analogous algorithm predicted the onset of PbtO2 crises with 30 minutes advance warning with an AUC of 0.91. This phase seeks to study prospectively the performance of algorithms and the alerting system for the prediction of intracranial hypertension and brain hypoxia crises. The virtual monitor (VM) will be applied to all consecutive severe traumatic brain injury (TBI) patients. Alerts will be generated by the algorithms and sent to study team members. Detailed annotation of clinical interventions and events will be collected; attending physicians and all clinical providers will be blinded to the presence of the monitor and to the alerts. This will allow for the comparison of prospectively collected crisis alerts with actual events of intracranial hypertension and brain tissue hypoxia that follow them (or not). The goal is to establish the sensitivity of the prediction algorithms as defined by the rate of true positive (TP) predictions (TP predictions are the ones accompanied by actual crises of ICP and/or PbtO2 within 30 minutes of the generated alert). Instances of actual crisis episodes that have not been preceded by an alert will also be collected and analyzed. This phase of the study will be run in 4 cycles, each consisting of 2 months of data collection, followed by 1 month of system improvements. Technical improvements to the system (such as adjusting the alerting threshold, user interface features, etc.) will be made at the end of each cycle, based on feedback provided by the study team from the previous 2 months of data collection. This phase involves clinical management per local neurocritical care unit standards and as per Brain Trauma Foundation (BTF) guidelines (see below baseline standard assessments, and Table 1 for tiered approaches to violations of ICP and PbtO2 thresholds). Alerts will only be available to study personnel (study physician members not directly involved in patient care, and study coordinators) for the purposes of evaluating real-time applied accuracy of the algorithm and alerting system. Each year in the United States, about 2 million people sustain traumatic brain injury (TBI), and of these, 500,000 require hospital care. Severe traumatic brain injury (TBI) is associated with 20-30% mortality and significant disability among most survivors. The Centers for Disease Control and Prevention (CDC) estimate that 2% of the U.S. population lives with disabilities directly attributable to TBI, with annual costs exceeding $76.5 billion. Largely, current treatments are ineffective due to being instituted at a time when irreversible damage has already occurred. By the time it is recognized that the traumatized brain is being further injured due to high pressure or lack of oxygen, it is too late to reverse or repair the insult. A computerized method is proposed to recognize further brain injury before it occurs. The ability to predict these events ahead of time will allow clinicians to prepare and prevent these episodes before they cause permanent brain damage. The investigators have already developed a method that can provide accurate prediction of these injurious events. This proposal aims to test predictions in real time. The patients to benefit from this project include individuals who suffer all kinds of severe trauma to the brain. This research promises to improve the outcome of these patients by providing predictions of further brain injury and, in the future, a way to act upon them to prevent permanent brain damage. This proposal is divided into two yearly phases. The first year focuses on establishing the capability of extracting and analyzing data from bedside monitors in the intensive care unit (ICU). The second year will test in real time the accuracy of the predictions. The first part of the project (Year 1) does not involve any patient or subject interaction. This year is solely for informatics infrastructure setup. In the second part of the project (Year 2), subjects (via their surrogate decision-makers) will be asked to be enrolled in an observational study where data on ICP and PbtO2 are collected, and the prediction algorithm is tested for accuracy and performance. During this phase, clinical management will be per standard of care, and no additional interventions will be performed. This phase will be conducted under this protocol and a dedicated consent form (consent for Phase 2; attached at the end). Efforts will be made to contact the subject or their caretakers at 6 months for follow-up on recovery. Approximately 120 individuals will take part in this study at the University of Chicago and Ben Taub General Hospital in Houston. This project utilizes a computerized method to predict ongoing and future brain injury. The data to be collected includes two sources: the first is the electronic medical record (EMR), which includes demographic information, characteristics of the injury, laboratory values, and data from imaging. The second source is directly from the bedside ICU monitor, including vital signs such as intracranial pressure (ICP), brain tissue oxygen (PbtO2), and mean arterial pressure (MAP). All data will be securely stored in a research computer database. Expanded Acronyms/Abbreviations: * Intracranial Hypertension (ICH) * Brain Tissue Hypoxia (BTH) * Intracranial Pressure (ICP) * Partial Brain Tissue Oxygen Tension (PbtO2) * Traumatic Brain Injury (TBI) * Area Under the Curve (AUC) * True Positive (TP) * Virtual Monitor (VM) * Brain Trauma Foundation (BTF) * Centers for Disease Control and Prevention (CDC) * Intensive Care Unit (ICU) * Electronic Medical Record (EMR) * Mean Arterial Pressure (MAP)