Latent Class Analysis to Classify Injury Severity in Pediatric Traumatic Brain Injury

Linked head injury and conduct problem symptom pathways from early childhood to adolescence and their associated risks: Evidence from the millennium cohort study

Conduct problems and head injuries increase the risk of delinquency and share a bidirectional association. However, how they link across development is unknown. The present study aimed to identify their linked developmental pathways and associated risk factors. Latent class analysis was modeled from Millennium Cohort Study data (n = 8,600) to identify linked pathways of conduct problem symptoms and head injuries. Head injuries were parent-reported from ages 3 to 14 and conduct problems from ages 3 to 17 using the Strengths and Difficulties Questionnaire (SDQ). Multinomial logistic regression then identified various risk factors associated with pathway membership. Four distinct pathways were identified. Most participants displayed low-level conduct problem symptoms and head injuries (n = 6,422; 74.7%). Three groups were characterized by clinically relevant levels of conduct problem symptoms and high-risk head injuries in childhood (n = 1,422; 16.5%), adolescence (n = 567; 6.6%), or persistent across development (n = 189; 2.2%). These clinically relevant pathways were associated with negative maternal parenting styles. These findings demonstrate how pathways of conduct problem symptoms are uniquely linked with distinct head injury pathways. Suggestions for general preventative intervention targets include early maternal negative parenting styles. Pathway-specific interventions are also required targeting cumulative risk at different ecological levels.

Cognitive, social, and health functioning of children with TBI engaged in a formal support program

BACKGROUND

Students with traumatic brain injury (TBI) often demonstrate difficulties that impact their successful return to school (RTS).

OBJECTIVE

To explore injury severity, age at injury, and time since injury as predictors for performance on measures of cognitive, social and health functioning for students' participating in a formal RTS cohort at the time of their enrollment in the School Transition After Traumatic Brain Injury (STATBI) research project.

METHODS

Outcome measures across cognitive, social, and health domains were analyzed for association with the explanatory variables of interest using quantile regressions and ordinary least squares regression, as appropriate.

RESULTS

Students (N = 91) injured after age 13 showed significantly lower cognitive outcomes than students whose injury occurred earlier. Additionally, students more than one-year post-injury demonstrated poorer social outcome on one measure compared to students whose injury occurred more recently. Health outcomes showed no significant association to any predictors.

CONCLUSION

The results of this analysis provide a baseline for a group of students with TBI as they enter a RTS research study. This data can now be paired with longitudinal measures and qualitative data collected simultaneously to gain a deeper understanding of how students with TBI present for RTS.

A study on differences about the influencing factors of depressive symptoms between medical staff and residents during 2022 city-wide temporary static management period to fighting against COVID-19 pandemic in Shanghai

Objectives

Our study aimed to identify the latent class of depressive symptoms in the Shanghai population during the city-wide temporary static management period and compare differences in the factors influencing depressive symptoms between medical staff and residents.

Methods

An online cross-sectional survey was conducted with 840 participants using questionnaires, including Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder-7 (GAD-7), Pittsburgh Sleep Quality Index (PSQI), and self-compiled questionnaire (demographic characteristics and internet usage time). Latent class analysis (LCA) was performed based on participants' depressive symptoms. The latent class subgroups were compared using the chi-square test and t-test. Logistic regression was used in our study to analyze the factors influencing depressive symptoms within the medical staff group and residents group and then compare their differences.

Results

Two distinct subgroups were identified based on the LCA: the group with low-depressive symptoms and the group with high-depressive symptoms. There were significant differences between the two groups (P < 0.05) on age, education level, marital status, internet usage time, identity characteristics (medical staff or residents), family income level, living style, overall quality of sleep, and anxiety levels. Furthermore, logistic regression analysis results showed that compared with the residents group, the participants in the group of medical staff with "increasing internet usage time" and the "daytime dysfunction" would have nearly two times the possibility of getting serious depressive symptoms.

Conclusions

There are differences in the factors influencing depression symptoms between medical staff and residents during the 2022 city-wide temporary static management period to fighting against the COVID-19 pandemic in Shanghai. We should pay special attention to those with increasing internet usage time and daytime dysfunction in medical staff working in a special environment such as the COVID-19 pandemic.

Characterizing Profiles of TBI Severity: Predictors of Functional Outcomes and Well-Being

OBJECTIVE

To identify profiles of acute traumatic brain injury (TBI) severity and relate profiles to functional and well-being outcomes.

SETTING

Acute inpatient rehabilitation and general community settings.

PARTICIPANTS

Three hundred and seventy-nine individuals with moderate-severe TBI participating in the Traumatic Brain Injury Model Systems.

DESIGN

Longitudinal observational study.

MAIN MEASURES

At discharge-length of stay, Functional Independence Measure (FIM), and Disability Rating Scale (DRS). One-year post-injury-Glasgow Outcome Scale-Extended (GOS-E), FIM, and Satisfaction with Life Scale (SWLS).

RESULTS

Latent profile analysis (LPA) was used to identify subgroups with similar patterns across 12 indicators of acute injury severity, including duration of posttraumatic amnesia, Glasgow Coma Scale, time to follow commands, and head CT variables. LPA identified 4 latent classes, least to most severe TBI (Class 1: n = 75, 20.3%; Class 2: n = 124, 33.5%; Class 3: n = 144, 38.9%; Class 4: n = 27, 7.3%); younger age, lower education, rural residence, injury in motor vehicle accidents, and earlier injury years were associated with worse acute severity. Latent classes were related to outcomes. Compared with Class 1, hospital stays were longer, FIM scores lower, and DRS scores larger at discharge among individuals in Class 3 and Class 4 (all P s < .01). One-year post-injury, GOS-E and FIM scores were significantly lower among individuals in Class 3 and Class 4 than those in Class 1 ( P s < .01). SWLS scores were lower only among individuals in Class 3 ( P = .036) compared with Class 1; other comparisons relative to Class 1 were not significant.

CONCLUSIONS

Meaningful profiles of TBI severity can be identified from acute injury characteristics and may suggest etiologies, like injury in motor vehicle accidents, and premorbid characteristics, including younger age, rural residence, and lower education, that heighten risk for worse injuries. Improving classification may help focus on those at elevated risk for severe injury and inform clinical management and prognosis.

The Impact of Preinjury and Secondary Attention-Deficit/Hyperactivity Disorder on Outcomes After Pediatric Traumatic Brain Injury

OBJECTIVE

The objective of this study was to examine the impact of preinjury attention-deficit/hyperactivity disorder (PADHD) and secondary ADHD (SADHD) on outcomes after pediatric traumatic brain injury (TBI).

METHODS

Two hundred eighty-four individuals aged 11 to 18 years hospitalized overnight for a moderate-to-severe TBI were included in this study. Parents completed measures of child behavior and functioning and their own functioning. Linear models examined the effect of ADHD status (PADHD vs SADHD vs no ADHD) on the child's executive functioning (EF), social competence, and functional impairment, and parental depression and distress.

RESULTS

ADHD status had a significant effect on EF [F(2,269] = 9.19, p = 0.0001), social competence (F[2,263] = 32.28, p < 0.0001), functional impairment (F[2,269] = 16.82, p < 0.0001), parental depression (F[2,263] = 5.53, p = 0.005), and parental distress (F[2,259] = 3.57, p = 0.03). PADHD and SADHD groups had greater EF deficits, poorer social competence, and greater functional impairment than the no ADHD group. The SADHD group had greater levels of parental depression than the no ADHD and PADHD groups, and the SADHD group had higher parental distress than the no ADHD group.

CONCLUSION

The results highlight the importance of early identification and management of ADHD symptoms after injury to mitigate downstream functional problems. Supporting parents managing new-onset ADHD symptoms may also be important.

Sleep and Executive Functioning in Pediatric Traumatic Brain Injury Survivors after Critical Care

Over 50,000 children are hospitalized annually for traumatic brain injury (TBI) and face long-term cognitive morbidity. Over 50% develop sleep/wake disturbances (SWDs) that can affect brain development and healing. We hypothesized SWDs would portend worse executive function outcomes in children aged 3−18 years with TBI 1−3 months after hospital discharge. SWDs were defined using the Sleep Disturbances Scale for Children (t-scores ≥ 60). Outcomes included the Global Executive Composite (GEC, t-score) from the Behavior Rating Inventory of Executive Function, Second and Preschool Editions, and multiple objective executive function assessments combined through Principal Components Analysis into a Neurocognitive Index (NCI, z-score). Multiple linear regression evaluated associations between SWDs and executive function outcomes, controlling for covariates. Among 131 children, 68% had clinically significant SWDs, which were associated with significantly worse median scores on the GEC (56 vs. 45) and NCI (−0.02 vs. 0.42; both p < 0.05). When controlling for baseline characteristics and injury severity in multivariable analyses, SWDs were associated with worse GEC (β-coefficient = 7.8; 95% Confidence Interval = 2.5, 13.1), and worse NCI (β-coefficient = −0.4; 95% Confidence Interval = −0.8, −0.04). SWDs in children with TBI are associated with worse executive function outcomes after hospital discharge, and may serve as modifiable targets to improve outcomes.

All body region injuries are not equal: Differences in pediatric discharge functional status based on Abbreviated Injury Scale (AIS) body regions and severity scores

PURPOSE

Functional outcomes have been proposed for assessing quality of pediatric trauma care. Outcomes assessments often rely on Abbreviated Injury Scale (AIS) severity scores to adjust for injury characteristics, but the relationship between AIS severity and functional impairment is unknown. This study's primary aim was to quantify functional impairment associated with increasing AIS severity scores within body regions. The secondary aim was to assess differences in impairment between body regions based on AIS severity.

METHODS

Children with serious (AIS≥ 3) isolated body region injuries enrolled in a multicenter prospective study were analyzed. The primary outcome was functional status at discharge measured using the Functional Status Scale (FSS). Discharge FSS was compared (1) within each body region across increasing AIS severity scores, and (2) between body regions for injuries with matching AIS scores.

RESULTS

The study included 266 children, with 16% having abnormal FSS at discharge. Worse FSS was associated with increasing AIS severity only for spine injuries. Abnormal FSS was observed in a greater proportion of head injury patients with a severely impaired initial Glasgow Coma Scale (GCS) (GCS< 9) compared to those with a higher GCS score (43% versus 9%; p < 0.01). Patients with AIS 3 extremity and severe head injuries had a higher proportion of abnormal FSS at discharge than AIS 3 abdomen or non-severe head injuries.

CONCLUSIONS

AIS severity does not account for variability in discharge functional impairment within or between body regions. Benchmarking based on functional status assessment requires clinical factors in addition to AIS severity for appropriate risk adjustment.

LEVEL OF EVIDENCE

1 (Prognostic and Epidemiological).

Individualized Prognostic Prediction of the Long-Term Functional Trajectory in Pediatric Acquired Brain Injury

In pediatric acquired brain injury, heterogeneity of functional response to specific rehabilitation treatments is a key confound to medical decisions and outcome prediction. We aimed to identify patient subgroups sharing comparable trajectories, and to implement a method for the early prediction of the long-term recovery course from clinical condition at first discharge. 600 consecutive patients with acquired brain injury (7.4 years ± 5.2; 367 males; median GCS = 6) entered a standardized rehabilitation program. Functional Independent Measure scores were measured yearly, until year 7. We classified the functional trajectories in clusters, through a latent class model. We performed single-subject prediction of trajectory membership in cases unseen during model fitting. Four trajectory types were identified (post.prob. > 0.95): high-start fast (N = 92), low-start fast (N = 168), slow (N = 130) and non-responders (N = 210). Fast responders were older (chigh = 1.8; clow = 1.1) than non-responders and suffered shorter coma (chigh = -14.7; clow = -4.3). High-start fast-responders had shorter length of stay (c = -1.6), and slow responders had lower incidence of epilepsy (c = -1.4), than non-responders (p < 0.001). Single-subject trajectory could be predicted with high accuracy at first discharge (accuracy = 0.80). In conclusion, we stratified patients based on the evolution of their response to a specific treatment program. Data at first discharge predicted the response over 7 years. This method enables early detection of the slow responders, who show poor post-acute functional gains, but achieve recovery comparable to fast responders by year 7. Further external validation in other rehabilitation programs is warranted.

Novel Claims-Based Outcome Phenotypes in Survivors of Pediatric Traumatic Brain Injury

OBJECTIVE

For children hospitalized with acute traumatic brain injury (TBI), to use postdischarge insurance claims to identify: (1) healthcare utilization patterns representative of functional outcome phenotypes and (2) patient and hospitalization characteristics that predict outcome phenotype.

SETTING

Two pediatric trauma centers and a state-level insurance claim aggregator.

PATIENTS

A total of 289 children, who survived a hospitalization after TBI between 2009 and 2014, were in the hospital trauma registry, and had postdischarge insurance eligibility.

DESIGN

Retrospective cohort study.

MAIN MEASURES

Unsupervised machine learning to identify phenotypes based on postdischarge insurance claims. Regression analyses to identify predictors of phenotype.

RESULTS

Median age 5 years (interquartile range 2-12), 29% (84/289) female. TBI severity: 30% severe, 14% moderate, and 60% mild. We identified 4 functional outcome phenotypes. Phenotypes 3 and 4 were the highest utilizers of resources. Morbidity burden was highest during the first 4 postdischarge months and subsequently decreased in all domains except respiratory. Severity and mechanism of injury, intracranial pressure monitor placement, seizures, and hospital and intensive care unit lengths of stay were phenotype predictors.

CONCLUSIONS

Unsupervised machine learning identified postdischarge phenotypes at high risk for morbidities. Most phenotype predictors are available early in the hospitalization and can be used for prognostic enrichment of clinical trials targeting mitigation or treatment of domain-specific morbidities.