Data mining to understand health status preceding traumatic brain injury.
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ABSTRACT:
SUBMITTER: Mollayeva T
PROVIDER: S-EPMC6447542 | biostudies-literature | 2019 Apr
REPOSITORIES: biostudies-literature
Publications
Data mining to understand health status preceding traumatic brain injury.
Mollayeva Tatyana T Sutton Mitchell M Chan Vincy V Colantonio Angela A Jana Sayantee S Escobar Michael M
Scientific reports 20190403 1
The use of precision medicine is poised to increase in complex injuries such as traumatic brain injury (TBI), whose multifaceted comorbidities and personal circumstances create significant challenges in the domains of surveillance, management, and environmental mapping. Population-wide health administrative data remains a rather unexplored, but accessible data source for identifying clinical associations and environmental patterns that could lead to a better understanding of TBIs. However, the a ...[more]
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Project description:ObjectivesTo understand how health status preceding traumatic brain injury (TBI) affects relative functional gain after inpatient rehabilitation using a data mining approach.DesignPopulation-based, sex-stratified, retrospective cohort study using health administrative data from Ontario, Canada (39% of the Canadian population).SettingInpatient rehabilitation.ParticipantsPatients 14 years or older (N=5802; 63.4% male) admitted to inpatient rehabilitation within 1 year of a TBI-related acute care discharge between April 1, 2008, and March 31, 2015.InterventionsNot applicable.Main outcome measuresRelative functional gain (RFG) in percentage, calculated as ([discharge FIM-admission FIM]/[126-admission FIM]×100). Health status prior to TBI was identified and internally validated using a data mining approach that categorized all International Classification of Diseases, 10th revision, codes for each patient.ResultsThe average RFG was 52.8%±27.6% among male patients and 51.6%±27.1% among female patients. Sex-specific Bonferroni adjusted multivariable linear regressions identified 10 factors of preinjury health status related to neurology, emergency medicine, cardiology, psychiatry, geriatrics, and gastroenterology that were significantly associated with reduced RFG in FIM for male patients. Only 1 preinjury health status category, geriatrics, was significantly associated with RFG in female patients.ConclusionsComorbid health conditions present up to 5 years preceding the TBI event were significantly associated with RFG. These findings should be considered when planning and executing interventions to maximize functional gain and to support an interdisciplinary approach. Best practices guidelines and clinical interventions for older male and female patients with TBI should be developed given the increasingly aging population with TBI.
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Project description:ObjectiveTo understand how pre-injury health status present five-years preceding traumatic brain injury (TBI) affects direct medical cost two years post-injury.MethodsPatients age ≥19 years in the emergency department (ED) or acute care for a TBI between April 1, 2007 and March 31, 2014 in Ontario, Canada (N = 55,669) were identified from population-based health administrative data. Forty-three factors of pre-injury health status (i.e., comorbidities and personal, social, and environmental factors) that were internally validated for the TBI population were assessed in this study. The outcome of interest was direct medical cost within two years of discharge. Sex-specific multivariable linear regressions were conducted to understand the associations between direct medical cost within two years of discharge and pre-injury health status.ResultsPatients who received care in the ED (81.9% of total sample) incurred a median cost of $2,492/male patient (average $12,342/patient) and $3,508/female patient (average $65,285/patient) within two years of injury; 37 pre-injury factors were significantly associated with increased direct medical costs. Patients who first received care for their TBI in acute care (18.1%) incurred a median cost of $25,081/male patient (average $63,060/patient) and $30,277/female patient (average $65,285/patient) within two years of injury; 21 factors were significantly associated with increased direct medical costs. Among more prevalent factors, those associated with increased medical cost by at least 50% included mental health disorders, substance abuse, disorders or medical conditions frequently observed among the elderly, cardiovascular disorders, stroke and emergencies involving the brain, metabolic disorders and abdominal symptoms, conditions and symptoms of abdomen and pelvis, genitourinary disorders and disorders of prostate, and pulmonary abdominal and other emergencies.ConclusionsDirect medical costs two years post-TBI differed significantly between patients with and without adverse pre-existing health status. Interdisciplinary teams to promote early identification of pre-existing health conditions and appropriate management and integration of these conditions in TBI care across the continuum of healthcare may be opportunities to reduce direct medical costs post-injury.
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Project description:For centuries, the study of traumatic brain injury (TBI) has been centred on historical observation and analyses of personal, social, and environmental processes, which have been examined separately. Today, computation implementation and vast patient data repositories can enable a concurrent analysis of personal, social, and environmental processes, providing insight into changes in health status transitions over time. We applied computational and data visualization techniques to categorize decade-long health records of 235,003 patients with TBI in Canada, from preceding injury to the injury event itself. Our results highlighted that health status transition patterns in TBI emerged along with the projection of comorbidity where many disorders, social and environmental adversities preceding injury are reflected in external causes of injury and injury severity. The strongest associations between health status preceding TBI and health status at the injury event were between multiple body system pathology and advanced age-related brain pathology networks. The interwoven aspects of health status on a time continuum can influence post-injury trajectories and should be considered in TBI risk analysis to improve prevention, diagnosis, and care.
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Project description:Traumatic brain injury (TBI) is a serious condition in which trauma to the head causes damage to the brain, leading to a disruption in brain function. This is a significant health issue worldwide, with around 69 million people suffering from TBI each year. Immediately following the trauma, damage occurs in the acute phase of injury that leads to the primary outcomes of the TBI. In the hours-to-days that follow, secondary damage can also occur, leading to chronic outcomes. TBIs can range in severity from mild to severe, and can be complicated by the fact that some individuals sustain multiple TBIs, a risk factor for worse long-term outcomes. Although our knowledge about the pathophysiology of TBI has increased in recent years, unfortunately this has not been translated into effective clinical therapies. The U.S. Food and Drug Administration has yet to approve any drugs for the treatment of TBI; current clinical treatment guidelines merely offer supportive care. Outcomes between individuals greatly vary, which makes the treatment for TBI so challenging. A blow of similar force can have only mild, primary outcomes in one individual and yet cause severe, chronic outcomes in another. One of the reasons that have been proposed for this differential response to TBI is the underlying genetic differences across the population. Due to this, many researchers have begun to investigate the possibility of using precision medicine techniques to address TBI treatment. In this review, we will discuss the research detailing the identification of genetic risk factors for worse outcomes after TBI, and the work investigating personalized treatments for these higher-risk individuals. We highlight the need for further research into the identification of higher-risk individuals and the development of personalized therapies for TBI.
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Project description:Background: The initial injury burden from incident TBI is significantly amplified by recurrent TBI (rTBI). Unfortunately, research assessing the accuracy to conduct rTBI surveillance is not available. Accurate surveillance information on recurrent injuries is needed to justify the allocation of resources to rTBI prevention and to conduct high quality epidemiological research on interventions that mitigate this injury burden. This study evaluates the accuracy of administrative health data (AHD) surveillance case definitions for rTBI and estimates the 1-year rTBI incidence adjusted for measurement error. Methods: A 25% random sample of AHD for Montreal residents from 2000 to 2014 was used in this study. Four widely used TBI surveillance case definitions, based on the International Classification of Disease and on radiological exams of the head, were applied to ascertain suspected rTBI cases. Bayesian latent class models were used to estimate the accuracy of each case definition and the 1-year rTBI measurement-error-adjusted incidence without relying on a gold standard rTBI definition that does not exist, across children (<18 years), adults (18-64 years), and elderly (> =65 years). Results: The adjusted 1-year rTBI incidence was 4.48 (95% CrI 3.42, 6.20) per 100 person-years across all age groups, as opposed to a crude estimate of 8.03 (95% CrI 7.86, 8.21) per 100 person-years. Patients with higher severity index TBI had a significantly higher incidence of rTBI compared to patients with lower severity index TBI. The case definition that identified patients undergoing a radiological examination of the head in the context of any traumatic injury was the most sensitive across children [0.46 (95% CrI 0.33, 0.61)], adults [0.79 (95% CrI 0.64, 0.94)], and elderly [0.87 (95% CrI 0.78, 0.95)]. The most specific case definition was the discharge abstract database in children [0.99 (95% CrI 0.99, 1.00)], and emergency room visits claims in adults/elderly [0.99 (95% CrI 0.99, 0.99)]. Median time to rTBI was the shortest in adults (75 days) and the longest in children (120 days). Conclusion: Conducting accurate surveillance and valid epidemiological research for rTBI using AHD is feasible when measurement error is accounted for.
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