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John K. Yue, Ethan A. Winkler, John F. Burke, Andrew K. Chan, Sanjay S. Dhall, Mitchel S. Berger, Geoffrey T. Manley, and Phiroz E. Tarapore

OBJECTIVE

Traumatic brain injury (TBI) in children is a significant public health concern estimated to result in over 500,000 emergency department (ED) visits and more than 60,000 hospitalizations in the United States annually. Sports activities are one important mechanism leading to pediatric TBI. In this study, the authors characterize the demographics of sports-related TBI in the pediatric population and identify predictors of prolonged hospitalization and of increased morbidity and mortality rates.

METHODS

Utilizing the National Sample Program of the National Trauma Data Bank (NTDB), the authors retrospectively analyzed sports-related TBI data from children (age 0–17 years) across 5 sports categories: fall or interpersonal contact (FIC), roller sports, skiing/snowboarding, equestrian sports, and aquatic sports. Multivariable regression analysis was used to identify predictors of prolonged length of stay (LOS) in the hospital or intensive care unit (ICU), medical complications, inpatient mortality rates, and hospital discharge disposition. Statistical significance was assessed at α < 0.05, and the Bonferroni correction (set at significance threshold p = 0.01) for multiple comparisons was applied in each outcome analysis.

RESULTS

From 2003 to 2012, in total 3046 pediatric sports-related TBIs were recorded in the NTDB, and these injuries represented 11,614 incidents nationally after sample weighting. Fall or interpersonal contact events were the greatest contributors to sports-related TBI (47.4%). Mild TBI represented 87.1% of the injuries overall. Mean (± SEM) LOSs in the hospital and ICU were 2.68 ± 0.07 days and 2.73 ± 0.12 days, respectively. The overall mortality rate was 0.8%, and the prevalence of medical complications was 2.1% across all patients. Severities of head and extracranial injuries were significant predictors of prolonged hospital and ICU LOSs, medical complications, failure to discharge to home, and death. Hypotension on admission to the ED was a significant predictor of failure to discharge to home (OR 0.05, 95% CI 0.03–0.07, p < 0.001). Traumatic brain injury incurred during roller sports was independently associated with prolonged hospital LOS compared with FIC events (mean increase 0.54 ± 0.15 days, p < 0.001).

CONCLUSIONS

In pediatric sports-related TBI, the severities of head and extracranial traumas are important predictors of patients developing acute medical complications, prolonged hospital and ICU LOSs, in-hospital mortality rates, and failure to discharge to home. Acute hypotension after a TBI event decreases the probability of successful discharge to home. Increasing TBI awareness and use of head-protective gear, particularly in high-velocity sports in older age groups, is necessary to prevent pediatric sports-related TBI or to improve outcomes after a TBI.

Free access

Ethan A. Winkler, John K. Yue, John F. Burke, Andrew K. Chan, Sanjay S. Dhall, Mitchel S. Berger, Geoffrey T. Manley, and Phiroz E. Tarapore

OBJECTIVE

Sports-related traumatic brain injury (TBI) is an important public health concern estimated to affect 300,000 to 3.8 million people annually in the United States. Although injuries to professional athletes dominate the media, this group represents only a small proportion of the overall population. Here, the authors characterize the demographics of sports-related TBI in adults from a community-based trauma population and identify predictors of prolonged hospitalization and increased morbidity and mortality rates.

METHODS

Utilizing the National Sample Program of the National Trauma Data Bank (NTDB), the authors retrospectively analyzed sports-related TBI data from adults (age ≥ 18 years) across 5 sporting categories—fall or interpersonal contact (FIC), roller sports, skiing/snowboarding, equestrian sports, and aquatic sports. Multivariable regression analysis was used to identify predictors of prolonged hospital length of stay (LOS), medical complications, inpatient mortality rates, and hospital discharge disposition. Statistical significance was assessed at α < 0.05, and the Bonferroni correction for multiple comparisons was applied for each outcome analysis.

RESULTS

From 2003 to 2012, in total, 4788 adult sports-related TBIs were documented in the NTDB, which represented 18,310 incidents nationally. Equestrian sports were the greatest contributors to sports-related TBI (45.2%). Mild TBI represented nearly 86% of injuries overall. Mean (± SEM) LOSs in the hospital or intensive care unit (ICU) were 4.25 ± 0.09 days and 1.60 ± 0.06 days, respectively. The mortality rate was 3.0% across all patients, but was statistically higher in TBI from roller sports (4.1%) and aquatic sports (7.7%). Age, hypotension on admission to the emergency department (ED), and the severity of head and extracranial injuries were statistically significant predictors of prolonged hospital and ICU LOSs, medical complications, failure to discharge to home, and death. Traumatic brain injury during aquatic sports was similarly associated with prolonged ICU and hospital LOSs, medical complications, and failure to be discharged to home.

CONCLUSIONS

Age, hypotension on ED admission, severity of head and extracranial injuries, and sports mechanism of injury are important prognostic variables in adult sports-related TBI. Increasing TBI awareness and helmet use—particularly in equestrian and roller sports—are critical elements for decreasing sports-related TBI events in adults.

Free access

Andrew K. Chan, Michele Santacatterina, Brenton Pennicooke, Shane Shahrestani, Alexander M. Ballatori, Katie O. Orrico, John F. Burke, Geoffrey T. Manley, Phiroz E. Tarapore, Michael C. Huang, Sanjay S. Dhall, Dean Chou, Praveen V. Mummaneni, and Anthony M. DiGiorgio

OBJECTIVE

Spine surgery is especially susceptible to malpractice claims. Critics of the US medical liability system argue that it drives up costs, whereas proponents argue it deters negligence. Here, the authors study the relationship between malpractice claim density and outcomes.

METHODS

The following methods were used: 1) the National Practitioner Data Bank was used to determine the number of malpractice claims per 100 physicians, by state, between 2005 and 2010; 2) the Nationwide Inpatient Sample was queried for spinal fusion patients; and 3) the Area Resource File was queried to determine the density of physicians, by state. States were categorized into 4 quartiles regarding the frequency of malpractice claims per 100 physicians. To evaluate the association between malpractice claims and death, discharge disposition, length of stay (LOS), and total costs, an inverse-probability-weighted regression-adjustment estimator was used. The authors controlled for patient and hospital characteristics. Covariates were used to train machine learning models to predict death, discharge disposition not to home, LOS, and total costs.

RESULTS

Overall, 549,775 discharges following spinal fusions were identified, with 495,640 yielding state-level information about medical malpractice claim frequency per 100 physicians. Of these, 124,425 (25.1%), 132,613 (26.8%), 130,929 (26.4%), and 107,673 (21.7%) were from the lowest, second-lowest, second-highest, and highest quartile states, respectively, for malpractice claims per 100 physicians. Compared to the states with the fewest claims (lowest quartile), surgeries in states with the most claims (highest quartile) showed a statistically significantly higher odds of a nonhome discharge (OR 1.169, 95% CI 1.139–1.200), longer LOS (mean difference 0.304, 95% CI 0.256–0.352), and higher total charges (mean difference [log scale] 0.288, 95% CI 0.281–0.295) with no significant associations for mortality. For the machine learning models—which included medical malpractice claim density as a covariate—the areas under the curve for death and discharge disposition were 0.94 and 0.87, and the R2 values for LOS and total charge were 0.55 and 0.60, respectively.

CONCLUSIONS

Spinal fusion procedures from states with a higher frequency of malpractice claims were associated with an increased odds of nonhome discharge, longer LOS, and higher total charges. This suggests that medicolegal climate may potentially alter practice patterns for a given spine surgeon and may have important implications for medical liability reform. Machine learning models that included medical malpractice claim density as a feature were satisfactory in prediction and may be helpful for patients, surgeons, hospitals, and payers.