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

Research Needs for Prognostic Modeling and Trajectory Analysis in Patients with Disorders of Consciousness

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.

The Traumatic Brain Injury Model Systems National Database: A Review of Published Research

The Traumatic Brain Injury Model Systems (TBIMS) is the largest longitudinal TBI data set in the world. Our study reviews the works using TBIMS data for analysis in the last 5 years. A search (2015–2020) was conducted across PubMed, EMBASE, and Google Scholar for studies that used the National Institute on Disability, Independent Living and Rehabilitation Research NIDILRR/VA-TBIMS data. Search terms were as follows: [“TBIMS” national database] within PubMed and Google Scholar, and [“TBIMS” AND national AND database] on EMBASE. Data sources, study foci (in terms of data processing and outcomes), study outcomes, and follow-up information usage were collected to categorize the studies included in this review. Variable usage in terms of TBIMS' form-based variable groups and limitations from each study were also noted. Assessment was made on how TBIMS' objectives were met by the studies. Of the 74 articles reviewed, 23 used TBIMS along with other data sets. Fifty-four studies focused on specific outcome measures only, 6 assessed data aspects as a major focus, and 13 explored both. Sample sizes of the included studies ranged from 11 to 15,835. Forty-two of the 60 longitudinal studies assessed follow-up from 1 to 5 years, and 15 studies used 10 to 25 years of the same. Prominent variable groups as outcome measures were “Employment,” “FIM,” “DRS,” “PART-O,” “Satisfaction with Life,” “PHQ-9,” and “GOS-E.” Limited numbers of studies were published regarding tobacco consumption, the Brief Test of Adult Cognition by Telephone (BTACT), the Supervision Rating Scale (SRS), general health, and comorbidities as variables of interest. Generalizability was the most significant limitation mentioned by the studies. The TBIMS is a rich resource for large-sample longitudinal analyses of various TBI outcomes. Future efforts should focus on under-utilized variables and improving generalizability by validation of results across large-scale TBI data sets to better understand the heterogeneity of TBI.

Linking Individual Data From the Spinal Cord Injury Model Systems Center and Local Trauma Registry: Development and Validation of Probabilistic Matching Algorithm

BACKGROUND

Linking records from the National Spinal Cord Injury Model Systems (SCIMS) database to the National Trauma Data Bank (NTDB) provides a unique opportunity to study early variables in predicting long-term outcomes after traumatic spinal cord injury (SCI). The public use data sets of SCIMS and NTDB are stripped of protected health information, including dates and zip code.

OBJECTIVES

To develop and validate a probabilistic algorithm linking data from an SCIMS center and its affiliated trauma registry.

METHOD

Data on SCI admissions 2011-2018 were retrieved from an SCIMS center (n = 302) and trauma registry (n = 723), of which 202 records had the same medical record number. The SCIMS records were divided equally into two data sets for algorithm development and validation, respectively. We used a two-step approach: blocking and weight generation for linking variables (race, insurance, height, and weight).

RESULTS

In the development set, 257 SCIMS-trauma pairs shared the same sex, age, and injury year across 129 clusters, of which 91 records were true-match. The probabilistic algorithm identified 65 of the 91 true-match records (sensitivity, 71.4%) with a positive predictive value (PPV) of 80.2%. The algorithm was validated over 282 SCIMS-trauma pairs across 127 clusters and had a sensitivity of 73.7% and PPV of 81.1%. Post hoc analysis shows the addition of injury date and zip code improved the specificity from 57.9% to 94.7%.

CONCLUSION

We demonstrate the feasibility of probabilistic linkage between SCIMS and trauma records, which needs further refinement and validation. Gaining access to injury date and zip code would improve record linkage significantly.

Evaluating the Cross-Sectional and Longitudinal Relationships Predicting Suicidal Ideation Following Traumatic Brain Injury

OBJECTIVE

Characterize relationships among substance misuse, depression, employment, and suicidal ideation (SI) following moderate to severe traumatic brain injury (TBI).

DESIGN

Prospective cohort study.

SETTING

Inpatient rehabilitation centers with telephone follow-up; level I/II trauma centers in the United States.

PARTICIPANTS

Individuals with moderate to severe TBI with data in both the National Trauma Data Bank and the Traumatic Brain Injury Model Systems National Database, aged 18 to 59 years, with SI data at year 1 or year 2 postinjury (N = 1377).

MAIN OUTCOME MEASURE

Primary outcome of SI, with secondary employment, substance misuse, and depression outcomes at years 1 and 2 postinjury.

RESULTS

Cross-lagged structural equation modeling analysis showed that year 1 unemployment and substance misuse were associated with a higher prevalence of year 1 depression. Depression was associated with concurrent SI at years 1 and 2. Older adults and women had a greater likelihood of year 1 depression. More severe overall injury (injury severity score) was associated with a greater likelihood of year 1 SI, and year 1 SI was associated with a greater likelihood of year 2 SI.

CONCLUSIONS

Substance misuse, unemployment, depression, and greater extracranial injury burden independently contributed to year 1 SI; in turn, year 1 SI and year 2 depression contributed to year 2 SI. Older age and female sex were associated with year 1 depression. Understanding and mitigating these risk factors are crucial for effectively managing post-TBI SI to prevent postinjury suicide.

Effects of hospital-acquired pneumonia on long-term recovery and hospital resource utilization following moderate to severe traumatic brain injury

BACKGROUND

Individuals with traumatic brain injury (TBI) have extended inpatient hospital stays that include prolonged mechanical ventilation, increasing risk for infections, including pneumonia. Studies show the negative short-term effects of hospital-acquired pneumonia (HAP) on hospital-based outcomes; however, little is known of its long-term effects.

METHODS

A prospective cohort study was conducted. National Trauma Databank and Traumatic Brain Injury Model Systems were merged to derive a cohort of 3,717 adults with moderate-to-severe TBI. Exposure data were gathered from the National Trauma Databank, and outcomes were gathered from the Traumatic Brain Injury Model Systems. The primary outcome was the Glasgow Outcome Scale-Extended (GOS-E), which was collected at 1, 2, and 5 years postinjury. The GOS-E was categorized as favorable (>5) or unfavorable (≤5) outcomes. A generalized estimating equation model was fitted estimating the effects of HAP on GOS-E over the first 5 years post-TBI, adjusting for age, race, ventilation status, brain injury severity, injury severity score, thoracic Abbreviated Injury Scale score of 3 or greater, mechanism of injury, intraventricular hemorrhage, and subarachnoid hemorrhage.

RESULTS

Individuals with HAP had a 34% (odds ratio, 1.34; 95% confidence interval, 1.15-1.56) increased odds for unfavorable GOS-E over the first 5 years post-TBI compared with individuals without HAP, after adjustment for covariates. There was a significant interaction between HAP and follow-up, such that the effect of HAP on GOS-E declined over time. Sensitivity analyses that weighted for nonresponse bias and adjusted for differences across trauma facilities did not appreciably change the results. Individuals with HAP spent 10.1 days longer in acute care and 4.8 days longer in inpatient rehabilitation and had less efficient functional improvement during inpatient rehabilitation.

CONCLUSION

Individuals with HAP during acute hospitalization have worse long-term prognosis and greater hospital resource utilization. Preventing HAP may be cost-effective and improve long-term recovery for individuals with TBI. Future studies should compare the effectiveness of different prophylaxis methods to prevent HAP.

LEVEL OF EVIDENCE

Prospective cohort study, level III.

Deriving Evidence from Secondary Data in Hand Surgery: Strengths, Limitations, and Future Directions

Health services research using secondary data is a powerful tool for guiding quality/performance measure development, payment reform, and health policy. Patient preferences, physical examination findings, use of postoperative care, and other factors specific to hand surgery research are critical pieces of information required to study quality of care and improve patient outcomes. These data often are missing from data sets, causing limitations and challenges when performing secondary data analyses in hand surgery. As the role of secondary data in surgical research expands, hand surgeons must apply novel strategies and become involved in collaborative initiatives to overcome the limitations of existing resources.

Interrelationships Between Post-TBI Employment and Substance Abuse: A Cross-lagged Structural Equation Modeling Analysis

OBJECTIVE

To describe the interrelationship of postinjury employment and substance abuse (SA) among individuals with traumatic brain injury.

DESIGN

Structural equation model (SEM) and logistic regression analytic approach using a merged database of the National Trauma Data Bank (NTDB) and Traumatic Brain Injury Model Systems (TBIMS) National Database, with acute care and rehabilitation hospitalization data and 1, 2, and 5 year follow-up data.

SETTING

United States Level I/II trauma centers and inpatient rehabilitation centers with telephone follow-up.

PARTICIPANTS

Individuals in the TBIMS National Database successfully matched to their NTDB data, aged 18-59 years, with trauma severity, age, sex, employment, and SA data at 1, 2, and/or 5 years postinjury (N=2890).

INTERVENTIONS

Not applicable.

MAIN OUTCOME MEASURE

Employment status (employed/unemployed) and SA (present/absent) at year 1, year 2, and year 5 postinjury.

RESULTS

SEM analysis showed older age at injury predicted lower likelihood of employment at all time points postinjury (βYR1=-0.016; βYR2=-0.006; βYR5=-0.016; all P<.001), while higher injury severity score (ISS) predicted lower likelihood of employment (β=-0.008; P=.027) and SA (β=-0.007; P=.050) at year 1. Male sex predicted higher likelihood of SA at each follow-up (βYR1=0.227; βYR2=0.184; βYR5=0.161; all P<.100). Despite associations of preinjury unemployment with higher preinjury SA, postinjury employment at year 1 predicted SA at year 2 (β=0.118; P=.028). Employment and SA during the previous follow-up period predicted subsequent employment and SA, respectively.

CONCLUSIONS

Employment and SA have unique longitudinal interrelationships and are additionally influenced by age, sex, and ISS. The present work suggests the need for more research on causal, confounding, and mediating factors and appropriate screening and intervention tools that minimize SA and facilitate successful employment-related outcomes.

Thirty Years of National Institute on Disability, Independent Living, and Rehabilitation Research Traumatic Brain Injury Model Systems Center Research-An Update

The Traumatic Brain Injury Model Systems Center (TBIMSC) program was established by the National Institute on Disability, Independent Living, and Rehabilitation Research in 1987, with the goal of conducting research to improve the care and outcomes for individuals with moderate-to-severe traumatic brain injury (TBI). This article provides an update on TBIMSC research program activities since 2010 when a similar article was published. It includes (1) discussion of TBIMSC program management and infrastructure; (2) detail on the management, data quality, access, use, and knowledge translation of the TBIMSC National Database, with more than 16 000 participants with follow-up out to 25 years postinjury to date; (3) an overview of the TBIMSC site-specific studies and collaborative module research; (4) highlights of several collaborative initiatives between the TBIMSCs and other federal, advocacy, and research stakeholders; (5) an overview of the vast knowledge translation occurring through the TBIMSC program; and (6) discussion of issues that impact on the data collection methods for and contents of the TBIMSC National Database. On the occasion of the 30th anniversary of the TBIMSC program, this article highlights many of the accomplishments of this well-established, multicenter TBI research consortium.

Deterministic vs. Probabilistic: Best Practices for Patient Matching Based on a Comparison of Two Implementations

In order to successfully share patient data across multiple systems, a reliable method of linking patient records across disparate organizations is required. In Canada, within the province of Ontario, there are four centralized diagnostic imaging repositories (DIRs) that allow multiple hospitals and independent health facilities (IHF) to send diagnostic images and reports for the purpose of sharing patient data across the region (Nagels et al. J Digit Imaging 28: 188, 2015). In 2017, the opportunity to consolidate the two regional DIRs that share the south-central and southeast area of the province was reviewed. The two DIRs use two different methods for patient matching. One uses a deterministic match based on one specific value, while the other uses a probabilistic scorecard that weighs a variety of patient demographics to assess if the patients are a match. An analysis was conducted to measure how a patient identity domain that uses a deterministic approach would compare to the accepted "standard." The intention is to review the analysis as a means of identifying interesting insights in both approaches. For the purpose of this paper, the two DIRs will be referred to as DIR1 and DIR2.

Probabilistic Matching of Deidentified Data From a Trauma Registry and a Traumatic Brain Injury Model System Center: A Follow-up Validation Study

In a previous study, individuals from a single Traumatic Brain Injury Model Systems and trauma center were matched using a novel probabilistic matching algorithm. The Traumatic Brain Injury Model Systems is a multicenter prospective cohort study containing more than 14,000 participants with traumatic brain injury, following them from inpatient rehabilitation to the community over the remainder of their lifetime. The National Trauma Databank is the largest aggregation of trauma data in the United States, including more than 6 million records. Linking these two databases offers a broad range of opportunities to explore research questions not otherwise possible. Our objective was to refine and validate the previous protocol at another independent center. An algorithm generation and validation data set were created, and potential matches were blocked by age, sex, and year of injury; total probabilistic weight was calculated based on of 12 common data fields. Validity metrics were calculated using a minimum probabilistic weight of 3. The positive predictive value was 98.2% and 97.4% and sensitivity was 74.1% and 76.3%, in the algorithm generation and validation set, respectively. These metrics were similar to the previous study. Future work will apply the refined probabilistic matching algorithm to the Traumatic Brain Injury Model Systems and the National Trauma Databank to generate a merged data set for clinical traumatic brain injury research use.