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Probabilistic Matching Approach to Link Deidentified Data from a Trauma Registry and a Traumatic Brain Injury Model System Center.

ABSTRACT: OBJECTIVEThere is no civilian traumatic brain injury database that captures patients in all settings of the care continuum. The linkage of such databases would yield valuable insight into possible care interventions. Thus, the objective of this article is to describe the creation of an algorithm used to link the Traumatic Brain Injury Model System (TBIMS) to trauma data in state and national trauma databases.DESIGNThe TBIMS data from a single center was randomly divided into two sets. One subset was used to generate a probabilistic linking algorithm to link the TBIMS data to the center's trauma registry. The other subset was used to validate the algorithm. Medical record numbers were obtained and used as unique identifiers to measure the quality of the linkage. Novel methods were used to maximize the positive predictive value.RESULTSThe algorithm generation subset had 121 patients. It had a sensitivity of 88% and a positive predictive value of 99%. The validation subset consisted of 120 patients and had a sensitivity of 83% and a positive predictive value of 99%.CONCLUSIONSThe probabilistic linkage algorithm can accurately link TBIMS data across systems of trauma care. Future studies can use this database to answer meaningful research questions regarding the long-term impact of the acute trauma complex on health care utilization and recovery across the care continuum in traumatic brain injury populations.

SUBMITTER: Kesinger MR

PROVIDER: S-EPMC5065730 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature

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Probabilistic Matching Approach to Link Deidentified Data from a Trauma Registry and a Traumatic Brain Injury Model System Center.

Kesinger Matthew Ryan MR Kumar Raj Gopalan RG Ritter Anne Connelly AC Sperry Jason Lee JL Wagner Amy Kathleen AK

American journal of physical medicine & rehabilitation 20170101 1

<h4>Objective</h4>There is no civilian traumatic brain injury database that captures patients in all settings of the care continuum. The linkage of such databases would yield valuable insight into possible care interventions. Thus, the objective of this article is to describe the creation of an algorithm used to link the Traumatic Brain Injury Model System (TBIMS) to trauma data in state and national trauma databases.<h4>Design</h4>The TBIMS data from a single center was randomly divided into tw ...[more]

PMID: 27088479

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Project description:The aim of this study was to investigate and compare the injury characteristics, severity, and outcome between underweight and normal-weight patients hospitalized for the treatment of all kinds of trauma injury.This study was based on a level I trauma center Taiwan.The detailed data of 640 underweight adult trauma patients with a body mass index (BMI) of <18.5 kg/m and 6497 normal-weight adult patients (25 > BMI ≥ 18.5 kg/m) were retrieved from the Trauma Registry System between January 1, 2009, and December 31, 2014. Pearson's chi-square test, Fisher's exact test, and independent Student's t-test were performed to compare the differences. Propensity score matching with logistic regression was used to evaluate the effect of underweight on mortality.Underweight patients presented a different bodily injury pattern and a significantly higher rate of admittance to the intensive care unit (ICU) than did normal-weight patients; however, no significant differences in the Glasgow Coma Scale (GCS) score, injury severity score (ISS), in-hospital mortality, and hospital length of stay were found between the two groups. However, further analysis of the patients stratified by two major injury mechanisms (motorcycle accident and fall injury) revealed that underweight patients had significantly lower GCS scores (13.8 ± 3.0 vs 14.5 ± 2.0, P = 0.020), but higher ISS (10.1 ± 6.9 vs 8.4 ± 5.9, P = 0.005), in-hospital mortality (odds ratio, 4.4; 95% confidence interval, 1.69-11.35; P = 0.006), and ICU admittance rate (24.1% vs 14.3%, P = 0.007) than normal-weight patients in the fall accident group, but not in the motorcycle accident group. However, after propensity score matching, logistic regression analysis of well-matched pairs of patients with either all trauma, motorcycle accident, or fall injury did not show a significant influence of underweight on mortality.Exploratory data analysis revealed that underweight patients presented a different bodily injury pattern from that of normal-weight patients, specifically a higher incidence of pneumothorax in those with penetrating injuries and of femoral fracture in those with struck on/against injuries; however, the injury severity and outcome of underweight patients varied depending on the injury mechanism.

Project description:BackgroundIntracranial pressure monitor (ICPm) procedure rates are a quality metric for American College of Surgeons trauma center verification. However, ICPm procedure rates may not accurately reflect the quality of care in TBI. We hypothesized that ICPm and craniotomy/craniectomy procedure rates for severe TBI vary across the United States by geography and institution.MethodsWe identified all patients with a severe traumatic brain injury (head Abbreviated Injury Scale, ≥3) from the 2016 Trauma Quality Improvement Program data set. Patients who received surgical decompression or ICPm were identified via International Classification of Diseases codes. Hospital factors included neurosurgeon group size, geographic region, teaching status, and trauma center level. Two multiple logistic regression models were performed identifying factors associated with (1) craniotomy with or without ICPm or (2) ICPm alone. Data are presented as medians (interquartile range) and odds ratios (ORs) (95% confidence interval).ResultsWe identified 75,690 patients (66.4% male; age, 59 [36-77] years) with a median Injury Severity Score of 17 (11-25). Overall, 6.1% had surgical decompression, and 4.8% had ICPm placement. Logistic regression analysis showed that region of the country was significantly associated with procedure type: hospitals in the West were more likely to use ICPm (OR, 1.34 [1.20-1.50]), while Northeastern (OR, 0.80 [0.72-0.89]), Southern (OR, 0.84 [0.78-0.92]), and Western (OR, 0.88 [0.80-0.96]) hospitals were less likely to perform surgical decompression. Hospitals with small neurosurgeon groups (<3) were more likely to perform surgical intervention. Community hospitals are associated with higher odds of surgical decompression but lower odds of ICPm placement.ConclusionBoth geographic differences and hospital characteristics are independent predictors for surgical intervention in severe traumatic brain injury. This suggests that nonpatient factors drive procedural decisions, indicating that ICPm rate is not an ideal quality metric for American College of Surgeons trauma center verification.Level of evidenceEpidemiological, level III; Care management/Therapeutic level III.

Project description:Traumatic brain injury (TBI) is a huge public health challenge worldwide. Epidemiological monitoring is important to inform healthcare policy. We aimed at determining the prevalence, outcome, and causes of TBI in Cameroon by conducting a 5-year retrospective study in three referral trauma centers. Data on demographics, causes, injury mechanisms, clinical aspects, and discharge status were recorded. Comparisons between two categorical variables were done using Pearson's chi-square test or Fisher's exact test. A total of 6248 cases of TBI were identified of 18,151 trauma cases, yielding a prevalence of 34%. The number of TBI cases increased across the years (915 in 2016, 1406 in 2020). Demographic data and causes of TBI were available for 6248 subjects and detailed data on clinical characteristics on 2178 subjects. Median age was 30.0 (24.0, 41.0) years. Males were more affected (80%). Road traffic incidents (RTIs; 75%) was the main cause of TBI, with professional bike riders being more affected (17%). Computed tomography (CT) imaging was performed in 67.7% of cases. Of the 597 (27.4%) cases who did not undergo neuroimaging, 311 (52.1%) did not have neuroimaging performed because of financial constraints, among which 7% were severe TBI cases. A total of 341 (19.6%) patients were discharged against medical advice, of which 83% had financial limitations. Mortality was 10.3% (225 of 2178) in the overall population, but disproportionately high in patients with severe TBI (55%) compared to those in high-income settings (27%). TBI occurrence is high in Cameroon, and RTIs are the main causes. Disparities in care provision were identified as attributable to financial constraints regarding CT scanning and continuation of care. The data presented can inform preventive interventions to improve care provision and transport policies. Implementation of a universal health insurance may be expected to improve hospital care and reduce the adverse effects of TBI among Cameroonians.

Project description:BackgroundHypotension is associated with worse outcome in patients with traumatic brain injury (TBI) and maintaining a systolic blood pressure (SBP) ≥110 mmHg is recommended. The aim of this study was to assess the incidence of TBI in patients suffering multiple trauma in mountain areas; to describe associated factors, treatment and outcome compared to non-hypotensive patients with TBI and patients without TBI; and to evaluate pre-hospital variables to predict admission hypotension.MethodsData from the prospective International Alpine Trauma Registry including mountain multiple trauma patients (ISS ≥ 16) collected between 2010 and 2019 were analysed. Patients were divided into three groups: 1) TBI with hypotension, 2) TBI without hypotension and 3) no TBI. TBI was defined as Abbreviated Injury Scale (AIS) of the head/neck ≥3 and hypotension as SBP < 110 mmHg on hospital arrival.ResultsA total of 287 patients were included. Fifty (17%) had TBI and hypotension, 92 (32%) suffered TBI without hypotension and 145 (51%) patients did not have TBI. Patients in group 1 were more severely injured (mean ISS 43.1 ± 17.4 vs 33.3 ± 15.3 vs 26.2 ± 18.1 for group 1 vs 2 vs 3, respectively, p < 0.001). Mean SBP on hospital arrival was 83.1 ± 12.9 vs 132.5 ± 19.4 vs 119.4 ± 25.8 mmHg (p < 0.001) despite patients in group 1 received more fluids. Patients in group 1 had higher INR, lower haemoglobin and lower base excess (p < 0.001). More than one third of patients in group 1 and 2 were hypothermic (body temperature < 35 °C) on hospital arrival while the rate of admission hypothermia was low in patients without TBI (41% vs 35% vs 21%, for group 1 vs 2 vs 3, p = 0.029). The rate of hypothermia on hospital arrival was different between the groups (p = 0.029). Patients in group 1 had the highest mortality (24% vs 10% vs 1%, p < 0.001).ConclusionMultiple trauma in the mountains goes along with severe TBI in almost 50%. One third of patients with TBI is hypotensive on hospital arrival and this is associated with a worse outcome. No single variable or set of variables easily obtainable at scene was able to predict admission hypotension in TBI patients.

Dataset Information

Probabilistic Matching Approach to Link Deidentified Data from a Trauma Registry and a Traumatic Brain Injury Model System Center.

Publications

Probabilistic Matching Approach to Link Deidentified Data from a Trauma Registry and a Traumatic Brain Injury Model System Center.

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