Optimizing baseline and post-concussion assessments through identification, confirmation, and equivalence of latent factor structures: findings from the NCAA-DoD CARE Consortium.

Objective: Concussion evaluations use a multidimensional assessment to evaluate unique patient function dimensions (e.g., subjective symptoms differ from balance assessments), but the overarching latent factor structure has not been empirically substantiated. Our objective was to determine the cumulative latent factor structure of pre-injury baseline and acute (<48-h) post-concussion assessment battery outcomes, and determine measurement equivalence among common factors in collegiate student-athletes. Methods: Collegiate student-athletes at baseline (n = 21,865) and post-concussion (n = 1,537) across 25-institutions completed standardized assessments. Individual items were used from the baseline and post-concussion assessments and consisted of: Sport Concussion Assessment Tool, Brief Symptom Inventory-18, Standardized Assessment of Concussion, Balance Error Scoring System, Immediate Post-Concussion Assessment and Cognitive Test, and vestibular-ocular motor screening. Exploratory factor analysis was used on half the baseline data, and confirmatory factor analysis on the remaining baseline data and post-concussion data separately. Measurement equivalence was assessed between sex, sport contact classification, concussion history, and time. Results: A 10-factor exploratory model was established and comprised of: depression, somatic, vestibulo-ocular, headache, postural stability, neurocognition, emotional, fatigue, cognitive, consciousness clouding. The 10-factor model was confirmed at baseline and post-concussion with strong measurement equivalence between timepoints. Strong to strict measurement equivalence was observed for sex, sport contact classification, and concussion history at both timepoints separately. Conclusion: Our findings established a robust 10-factor latent factor model equivalent across timepoints and common factors among healthy and concussed collegiate athletes. Clinicians can use these findings to target specific factors while reducing redundant elements to provide efficient, comprehensive post-concussion assessments.

Estimating the Relationship Between the Symptom-Free Waiting Period and Injury Rates After Return-to-Play from Concussion: A Simulation Analysis Using CARE Consortium Data.

BACKGROUND: A key component of return-to-play (RTP) from sport-related concussion is the symptom-free waiting period (SFWP), i.e., the period during which athletes must remain symptom-free before permitting RTP. Yet, the exact relationship between SFWP and post-RTP injury rates is unclear. OBJECTIVE: We design computational simulations to estimate the relationship between the SFWP and rates of repeat concussion and non-concussion time-loss injury up to 30 days post-RTP for male and female collegiate athletes across 13 sports. METHODS: We leverage N = 735 female and N = 1,094 male post-injury trajectories from the National Collegiate Athletic Association-Department of Defense Concussion Assessment, Research, and Education Consortium. RESULTS: With a 6-day SFWP, the mean [95% CI] rate of repeat concussion per 1,000 simulations was greatest in ice hockey for females (20.31, [20.16, 20.46]) and American football for males (24.16, [24.05, 24.28]). Non-concussion time-loss injury rates were greatest in field hockey for females (153.66, [152.59, 154.74]) and wrestling for males (247.34, [246.20, 248.48]). Increasing to a 13-day SFWP, ice hockey for females (18.88, [18.79, 18.98]) and American football for males (23.16, [23.09, 24.22]) exhibit the greatest decrease in repeat concussion rates across all sports within their respective sexes. Field hockey for females (143.24, [142.53, 143.94]) and wrestling for males (237.73, [236.67, 237.90]) exhibit the greatest decrease in non-concussion time-loss injury rates. Males receive marginally smaller reductions in injury rates for increased SFWP compared to females (OR = 1.003, p ≤ 0.002). CONCLUSION: Longer SFWPs lead to greater reductions in post-RTP injury rates for athletes in higher risk sports. Moreover, SFWPs should be tailored to sport-specific post-RTP injury risks.

Interpretable machine learning to predict adverse perinatal outcomes: examining marginal predictive value of risk factors during pregnancy.

BACKGROUND: The timely identification of nulliparas at high risk of adverse fetal and neonatal outcomes during pregnancy is crucial for initiating clinical interventions to prevent perinatal complications. Although machine learning methods have been applied to predict preterm birth and other pregnancy complications, many models do not provide explanations of their predictions, limiting the clinical use of the model. OBJECTIVE: This study aimed to develop interpretable prediction models for a composite adverse perinatal outcome (stillbirth, neonatal death, estimated Combined Apgar score of <10, or preterm birth) at different points in time during the pregnancy and to evaluate the marginal predictive value of individual predictors in the context of a machine learning model. STUDY DESIGN: This was a secondary analysis of the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-be data, a prospective cohort study in which 10,038 nulliparous pregnant individuals with singleton pregnancies were enrolled. Here, interpretable prediction models were developed using L1-regularized logistic regression for adverse perinatal outcomes using data available at 3 study visits during the pregnancy (visit 1: 6 0/7 to 13 6/7 weeks of gestation; visit 2: 16 0/7 to 21 6/7 weeks of gestation; visit 3: 22 0/7 to 29 6/7 weeks of gestation). We identified the important predictors for each model using SHapley Additive exPlanations, a model-agnostic method of computing explanations of model predictions, and evaluated the marginal predictive value of each predictor using the DeLong test. RESULTS: Our interpretable machine learning model had an area under the receiver operating characteristic curves of 0.617 (95% confidence interval, 0.595-0.639; all predictor variables at visit 1), 0.652 (95% confidence interval, 0.631-0.673; all predictor variables at visit 2), and 0.673 (95% confidence interval, 0.651-0.694; all predictor variables at visit 3). For all visits, the placental biomarker inhibin A was a valuable predictor, as including inhibin A resulted in better performance in predicting adverse perinatal outcomes (P<.001, all visits). At visit 1, endoglin was also a valuable predictor (P<.001). At visit 2, free beta human chorionic gonadotropin (P=.001) and uterine artery pulsatility index (P=.023) were also valuable predictors. At visit 3, cervical length was also a valuable predictor (P<.001). CONCLUSION: Despite various advances in predictive modeling in obstetrics, the accurate prediction of adverse perinatal outcomes remains difficult. Interpretable machine learning can help clinicians understand how predictions are made, but barriers exist to the widespread clinical adoption of machine learning models for adverse perinatal outcomes. A better understanding of the evolution of risk factors for adverse perinatal outcomes throughout pregnancy is necessary for the development of effective interventions.

Primary and Secondary Risk Factors Associated With Concussion Symptom Clusters in Collegiate Athletes: Results From the NCAA-DoD Grand Alliance CARE Consortium.

Background: There is a broad and diverse range of symptoms after a concussion, from irritability to nausea. This heterogeneity of symptoms is a challenge for clinicians managing the different presentations among injuries. Prior research has investigated the structure of postconcussive symptoms to determine if they can be grouped into clusters of related symptoms. Purpose/Hypothesis: The purpose of this study was to identify symptom clusters during the acute phase after a sports-related concussion using exploratory factor analysis and to understand the relationship between risk factors for postconcussion symptoms (ie, demographics, injury characteristics, mental health, and sleep qualities) and different symptom clusters. We hypothesized that certain factors would be predictive of specific symptom clusters. Study Design: Cross-sectional study; Level of evidence, 3. Methods: Collegiate athletes (N = 1104) from the Concussion, Assessment, Research, and Education (CARE) Consortium completed the Sport Concussion Assessment Tool-Third Edition symptom assessment tool 24 to 48 hours after concussion. Exploratory factor analysis was conducted on the symptom evaluation to determine symptom clusters 24 to 48 hours after concussion. Regression analysis was used to examine the effects of pre- and postinjury characteristics. Results: Exploratory factor analysis revealed a 4-cluster structure for acute postconcussive symptoms that explained 62% of the variance in symptom reporting: vestibular-cognitive, migrainous, cognitive fatigue, and affective. Delayed reporting, less sleep before assessment, female sex, and being hurt outside of competition (during practice/training) was correlated with increased symptoms for 4 symptom clusters. Depression predicted higher vestibular-cognitive and affective symptoms. Amnesia was correlated with higher vestibular-cognitive and migrainous symptoms, whereas migraine history was associated with more migrainous and affective symptoms. Conclusion: Symptoms can be grouped into 1 of 4 distinct clusters. Certain variables were associated with increased symptoms across multiple clusters and may be indicative of greater injury severity. Other factors (ie, migraine history, depression, amnesia) were associated with a more specific symptom presentation and may be mechanistically related to concussion outcomes and biological markers.

Temporal Network Analysis of Neurocognitive Functioning and Psychological Symptoms in Collegiate Athletes After Concussion.

Sport-related concussion (SRC) is associated with several post-injury consequences, including neurocognitive decrements and psychological distress. Yet, how these clinical markers interact with each other, the magnitude of their interrelationships, and how they may vary over time following SRC are not well understood. Network analysis has been proposed as a statistical and psychometric method to conceptualize and map the complex interplay of interactions between observed variables (e.g., neurocognitive functioning and psychological symptoms). For each collegiate athlete with SRC (n = 565), we constructed a temporal network as a weighted graph, with nodes, edges, and the set of weights associated with each edge at three time-points (baseline, 24-48 h post-injury, and asymptomatic), that graphically depicts the interrelated nature of neurocognitive functioning and symptoms of psychological distress throughout the recovery process. This graph shows that the inter-group relationships between neurocognitive functioning and symptoms of psychological distress were stronger at the 24-48 h time-point than at baseline or at the asymptomatic time-point. Further, all symptoms of psychological distress and neurocognitive functioning significantly improved from the 24-48 h time-point to asymptomatic status. The effect sizes of these changes ranged from 0.126 (small) to 0.616 (medium). This research suggests that significant improvements in symptoms of psychological distress appear necessary to drive related improvements in neurocognitive functioning and vice versa. Therefore, clinical interventions should consider the importance of managing psychological distress during the acute care of individuals with SRC to help ameliorate negative outcomes.

Test-Retest Reliability and Efficacy of Individual Symptoms in Concussion Management.

OBJECTIVE: (1) To determine test-retest reliability of individual Sport Concussion Assessment Tool-Third Edition (SCAT-3) symptom scores and symptom severity scores, (2) to examine the specificity/sensitivity of individual SCAT-3 symptom severity scores acutely (24-48 hours) postconcussion, and (3) to develop a model of symptoms best able to differentiate concussed from nonconcussed student athletes and cadets. DESIGN: Prospective, longitudinal, and cross-sectional. SETTING: Twenty-six civilian schools and 3 US service academies. PARTICIPANTS: Collegiate student athletes (n = 5519) and cadets (n = 5359) from the National Collegiate Athletic Association-Department of Defense Grand Alliance: Concussion Assessment, Research and Education Consortium, including 290 student athletes and 205 cadets, assessed 24 to 48 hours postconcussion. INDEPENDENT VARIABLES: Concussed and nonconcussed student athlete and cadet groups. MAIN OUTCOME MEASURES: Sport Concussion Assessment Tool-Third Edition individual symptom severity scores, total symptom scores, and symptom severity scores. RESULTS: Results indicated poor test-retest reliability across all symptom scores (intraclass correlation coefficient = 0.029-0.331), but several individual symptoms had excellent predictive capability in discriminating concussed from nonconcussed participants (eg, headache, pressure in the head, and don't feel right had area under the curve >0.8, sensitivity >70%, and specificity >85%) regardless of baseline testing. These symptoms were consistent with Chi-square Automatic Interaction Detector classification trees with the highest mean probability. CONCLUSIONS: Findings support the excellent diagnostic accuracy of honest symptom reporting, notwithstanding the known limitations in symptom underreporting, and suggest that there may be added value in examining individual symptoms rather than total symptom scores and symptom severity scores alone. Finally, findings suggest that baseline testing is not necessary for interpreting postconcussion symptom scores.

Sensitivity and Specificity of the ImPACT Neurocognitive Test in Collegiate Athletes and US Military Service Academy Cadets with ADHD and/or LD: Findings from the NCAA-DoD CARE Consortium.

BACKGROUND AND OBJECTIVE: Computer-based neurocognitive tests are widely used in sport-related concussion management, but the performance of these tests is not well understood in the participant population with attention-deficit/hyperactivity disorder (ADHD) and/or learning disorder (LD). This research estimates the sensitivity and specificity performance of the Immediate Post-Concussion Assessment and Cognitive Testing (ImPACT) computer-based neurocognitive test in identifying concussion in this population. METHODS: Study participants consisted of collegiate university athletes and military service academy cadets from the National Collegiate Athletic Association-Department of Defense CARE Consortium who completed the ImPACT test between 2014 and 2021. Participants who self-identified as belonging to one of the subgroups of interest (ADHD with or without LD [ADHD:LD+/-], LD with or without ADHD [LD:ADHD+/-], ADHD and/or LD [ADHD a/o LD]) and completed a baseline (1874 ADHD:LD+/-, 779 LD:ADHD+/-, 2338 ADHD a/o LD) or 24-48 h post-concussion (175 ADHD:LD+/-, 77 LD:ADHD+/-, 216 ADHD a/o LD) ImPACT assessment were included. Sensitivity and specificity were calculated using a normative data method and three machine learning classification methods: logistic regression, classification and regression tree, and random forest. RESULTS: Using the four methods, participants with ADHD:LD+/- had sensitivities that ranged from 0.474 to 0.697, and specificities that ranged from 0.538 to 0.686. Participants with LD:ADHD+/- had sensitivities that ranged from 0.455 to 0.688, and specificities that ranged from 0.456 to 0.588. For participants with ADHD a/o LD, sensitivities ranged from 0.542 to 0.755, and specificities ranged from 0.451 to 0.724. CONCLUSIONS: For all subgroups and analytical methods, the results illustrate sensitivity and specificity values below typically accepted levels indicative of clinical utility. These findings support that using ImPACT alone may be insufficient to inform concussion diagnoses and encourages the use of a multi-dimensional concussion assessment.

Identifying and Characterizing Medical Advice-Seekers on a Social Media Forum for Buprenorphine Use.

Background: Online communities such as Reddit can provide social support for those recovering from opioid use disorder. However, it is unclear whether and how advice-seekers differ from other users. Our research addresses this gap by identifying key characteristics of r/suboxone users that predict advice-seeking behavior. Objective: The objective of this analysis is to identify and describe advice-seekers on Reddit for buprenorphine-naloxone use using text annotation, social network analysis, and statistical modeling techniques. Methods: We collected 5258 posts and their comments from Reddit between 2014 and 2019. Among 202 posts which met our inclusion criteria, we annotated each post to determine which were advice-seeking (n = 137) or not advice-seeking (n = 65). We also annotated each posting user's buprenorphine-naloxone use status (current versus formerly taking and, if currently taking, whether inducting or tapering versus other stages) and quantified their connectedness using social network analysis. To analyze the relationship between Reddit users' advice-seeking and their social connectivity and medication use status, we constructed four models which varied in their inclusion of explanatory variables for social connectedness and buprenorphine use status. Results: The stepwise model containing "total degree" (p = 0.002), "using: inducting/tapering" (p < 0.001), and "using: other" (p = 0.01) outperformed all other models. Reddit users with fewer connections and who are currently using buprenorphine-naloxone are more likely to seek advice than those who are well-connected and no longer using the medication, respectively. Importantly, advice-seeking behavior is most accurately predicted using a combination of network characteristics and medication use status, rather than either factor alone. Conclusions: Our findings provide insights for the clinical care of people recovering from opioid use disorder and the nature of online medical advice-seeking overall. Clinicians should be especially attentive (e.g., through frequent follow-up) to patients who are inducting or tapering buprenorphine-naloxone or signal limited social support.

Opioid overdose decedent characteristics during COVID-19.

INTRODUCTION: Alongside the emergence of COVID-19 in the United States, several reports highlighted increasing rates of opioid overdose from preliminary data. Yet, little is known about how state-level opioid overdose death trends and decedent characteristics have evolved using official death records. METHODS: We requested vital statistics data from 2018-2020 from all 50 states and the District of Columbia, receiving data from 14 states. Accounting for COVID-19, we excluded states without data past March 2020, leaving 11 states for analysis. We defined state-specific analysis periods from March 13 until the latest reliable date in each state's data, then conducted retrospective year-over-year analyses comparing opioid-related overdose death rates, the presence of specific opioids and other psychoactive substances, and decedents' sex, race, and age from 2020 to 2019 and 2019 to 2018 within each state's analysis period. We assessed whether significant changes in 2020 vs. 2019 in opioid overdose deaths were new or continuing trends using joinpoint regression. RESULTS: We found significant increases in opioid-related overdose death rates in Alaska (55.3%), Colorado (80.2%), Indiana (40.1%), Nevada (50.0%), North Carolina (30.5%), Rhode Island (29.6%), and Virginia (66.4%) - all continuing previous trends. Increases in synthetic opioid-involved overdose deaths were new in Alaska (136.5%), Indiana (27.6%), and Virginia (16.5%), whilst continuing in Colorado (44.4%), Connecticut (3.6%), Nevada (75.0%), and North Carolina (14.6%). We found new increases in male decedents in Indiana (12.0%), and continuing increases in Colorado (15.2%). We also found continuing increases in Black non-Hispanic decedents in Massachusetts (43.9%) and Virginia (33.7%). CONCLUSION: This research analyzes vital statistics data from 11 states, highlighting new trends in opioid overdose deaths and decedent characteristics across 10 of these states. These findings can inform state-specific public health interventions and highlight the need for timely and comprehensive fatal opioid overdose data, especially amidst concurrent crises such as COVID-19. Key messages:Our results highlight shifts in opioid overdose trends during the COVID-19 pandemic that cannot otherwise be extracted from aggregated or provisional opioid overdose death data such as those published by the Centres for Disease Control and Prevention.Fentanyl and other synthetic opioids continue to drive increases in fatal overdoses, making it difficult to separate these trends from any possible COVID-19-related factors.Black non-Hispanic people are making up an increasing proportion of opioid overdose deaths in some states.State-specific limitations and variations in data-reporting for vital statistics make it challenging to acquire and analyse up-to-date data on opioid-related overdose deaths. More timely and comprehensive data are needed to generate broader insights on the nature of the intersecting opioid and COVID-19 crises.

Developing Insights for Possible and Probable Acute Concussions Using Cluster Analysis.

Few studies have analyzed the Sport Concussion Assessment Tool's (SCAT) utility among athletes whose concussion assessment is challenging. Using a previously published algorithm, we identified possible and probable concussions at <6 h (n = 393 males, n = 265 females) and 24-48 h (n = 323 males, n = 236 females) post-injury within collegiate student-athletes and cadets from the Concussion Assessment, Research, and Education (CARE) Consortium. We applied cluster analysis to characterize performance on the Standard Assessment of Concussion (SAC), Balance Error Scoring System (BESS), and the SCAT symptom checklist for these athletes. Among the cluster sets that best separated acute concussions and normal performances, total symptom number raw score and change and post-traumatic migraine raw score and change score were the most frequent clustering variables across males and females at <6 h and 24-48 h. Similarly, total symptom number raw score and change score and post-traumatic migraine raw score and change score were most significantly different between clusters for males and females at <6 h and 24-48 h. Our results suggest that clinicians should focus on total symptom number, post-traumatic migraine symptoms, and cognitive-fatigue symptoms when assessing possible and probable concussions, followed by the SAC and BESS scores.

Changes in characteristics of drug overdose death trends during the COVID-19 pandemic.

BACKGROUND: Reports analyzing drug overdose (OD) mortality data during the COVID-19 pandemic are limited. Outcomes across states are heterogenous, necessitating assessments of associations between COVID-19 and OD deaths on a state-by-state level. This report aims to analyze trends in OD deaths in Massachusetts during COVID-19. METHODS: Analyzing 3,924 death records, we characterize opioid-, cocaine-, and amphetamine-involved OD mortality and substance co-presence trends from March 24-November 8 in 2020 as compared to 2018 and 2019. RESULTS: OD deaths involving amphetamines increased by 85% from 2019 to 2020 (61 vs. 113; P<0.001) but were steady from 2018 to 2019. Heroin's presence continued to decrease (341 in 2018, 247 in 2019, 157 in 2020; P<0.001); however, fentanyl was present in more than 85% of all OD deaths across all periods. Among OD deaths, alcohol involvement consistently increased, present in 250 deaths in 2018, 299 in 2019 (P=0.02), and 350 in 2020 (P=0.04). In 2019, 78% of OD decedents were White and 7% were Black, versus 73% and 10% in 2020 (P=0.02). CONCLUSION: Increased deaths involving stimulants, alcohol, and fentanyl reflect concerning trends in the era of COVID-19. Rising OD death rates among Black residents underscore that interventions focused on racial equity are necessary.

Sensitivity and Specificity of Computer-Based Neurocognitive Tests in Sport-Related Concussion: Findings from the NCAA-DoD CARE Consortium.

BACKGROUND: To optimally care for concussed individuals, a multi-dimensional approach is critical and a key component of this assessment in the athletic environment is computer-based neurocognitive testing. However, there continues to be concerns about the reliability and validity of these testing tools. The purpose of this study was to determine the sensitivity and specificity of three common computer-based neurocognitive tests (Immediate Post-Concussion Assessment and Cognitive Testing [ImPACT], CNS Vital Signs, and CogState Computerized Assessment Tool [CCAT]), to provide guidance on their clinical utility. METHODS: This study analyzed assessments from a cohort of collegiate athletes and non-varsity cadets from the NCAA-DoD CARE Consortium. The data were collected from 2014-2018. Study participants were divided into two testing groups [concussed, n = 1414 (baseline/24-48 h) and healthy, n = 8305 (baseline/baseline)]. For each test type, change scores were calculated for the components of interest. Then, the Normative Change method, which used normative data published in a similar cohort, and the Reliable Change Index (RCI) method were used to determine if the change scores were significant. RESULTS: Using the Normative Change method, ImPACT performed best with an 87.5%-confidence interval and 1 number of components failed (NCF; sensitivity = 0.583, specificity = 0.625, F1 = 0.308). CNS Vital Signs performed best with a 90%-confidence interval and 1 NCF (sensitivity = 0.587, specificity = 0.532, F1 = 0.314). CCAT performed best when using a 75%-confidence interval and 2 NCF (sensitivity = 0.513, specificity = 0.715, F1 = 0.290). When using the RCI method, ImPACT performed best with an 87.5%-confidence interval and 1 NCF (sensitivity = 0.626, specificity = 0.559, F1 = 0.297). CONCLUSION: When considering all three computer-based neurocognitive tests, the overall low sensitivity and specificity results provide additional evidence for the use of a multi-dimensional assessment for concussion diagnosis, including symptom evaluation, postural control assessment, neuropsychological status, and other functional assessments.

The Relationship between Sport-Related Concussion and Sensation-Seeking.

Sensation-seeking, or the need for novel and exciting experiences, is thought to play a role in sport-related concussion (SRC), yet much remains unknown regarding these relationships and, more importantly, how sensation-seeking influences SRC risk. The current study assessed sensation-seeking, sport contact level, and SRC history and incidence in a large sample of NCAA collegiate athletes. Data included a full study sample of 22,374 baseline evaluations and a sub-sample of 2037 incident SRC. Independent samples t-test, analysis of covariance, and hierarchical logistic regression were constructed to address study hypotheses. Results showed that (1) among participants without SRC, sensation-seeking scores were higher in athletes playing contact sports compared to those playing limited- or non-contact sports (p < 0.001, R2 = 0.007, η2p = 0.003); (2) in the full study sample, a one-point increase in sensation-seeking scores resulted in a 21% greater risk of prior SRC (OR = 1.212; 95% CI: 1.154-1.272), and in the incident SRC sub-sample, a 28% greater risk of prior SRC (OR = 1.278; 95% CI: 1.104-1.480); (3) a one-point increase in sensation-seeking scores resulted in a 12% greater risk of incident SRC among the full study sample; and (4) sensation-seeking did not vary as a function of incident SRC (p = 0.281, η2p = 0.000). Our findings demonstrate the potential usefulness of considering sensation-seeking in SRC management.

Optimizing Components of the Sport Concussion Assessment Tool for Acute Concussion Assessment.

BACKGROUND: The Sport Concussion Assessment Tool (SCAT) could be improved by identifying critical subsets that maximize diagnostic accuracy and eliminate low information elements. OBJECTIVE: To identify optimal SCAT subsets for acute concussion assessment. METHODS: Using Concussion Assessment, Research, and Education (CARE) Consortium data, we compared student-athletes' and cadets' preinjury baselines (n = 2178) with postinjury assessments within 6 h (n = 1456) and 24 to 48 h (n = 2394) by considering demographics, symptoms, Standard Assessment of Concussion (SAC), and Balance Error Scoring System (BESS) scores. We divided data into training/testing (60%/40%) sets. Using training data, we integrated logistic regression with an engineering methodology-mixed integer programming-to optimize models with ≤4, 8, 12, and 16 variables (Opt-k). We also created models including only raw scores (Opt-RS-k) and symptom, SAC, and BESS composite scores (summary scores). We evaluated models using testing data. RESULTS: At <6 h and 24 to 48 h, most Opt-k and Opt-RS-k models included the following symptoms: do not feel right, headache, dizziness, sensitivity to noise, and whether physical or mental activity worsens symptoms. Opt-k models included SAC concentration and delayed recall change scores. Opt-k models had lower Brier scores (BS) and greater area under the curve (AUC) (<6 h: BS = 0.072-0.089, AUC = 0.95-0.96; 24-48 h: BS = 0.085-0.093, AUC = 0.94-0.95) than Opt-RS-k (<6 h: BS = 0.082-0.087, AUC = 0.93-0.95; 24-48 h: BS = 0.095-0.099, AUC = 0.92-0.93) and summary score models (<6 h: BS = 0.14, AUC = 0.89; 24-48 h: BS = 0.15, AUC = 0.87). CONCLUSION: We identified SCAT subsets that accurately assess acute concussion and improve administration time over the complete battery, highlighting the importance of eliminating "noisy" elements. These findings can direct clinicians to the SCAT components that are most sensitive to acute concussion.

Accuracy of Kalman Filtering in Forecasting Visual Field and Intraocular Pressure Trajectory in Patients With Ocular Hypertension.

Importance: Techniques that properly identify patients in whom ocular hypertension (OHTN) is likely to progress to open-angle glaucoma can assist clinicians with deciding on the frequency of monitoring and the potential benefit of early treatment. Objective: To test whether Kalman filtering (KF), a machine learning technique, can accurately forecast mean deviation (MD), pattern standard deviation, and intraocular pressure values 5 years into the future for patients with OHTN. Design, Setting, and Participants: This cohort study was a secondary analysis of data from patients with OHTN from the Ocular Hypertension Treatment Study, performed between February 1994 and March 2009. Patients underwent tonometry and perimetry every 6 months for up to 15 years. A KF (KF-OHTN) model was trained, validated, and tested to assess how well it could forecast MD, pattern standard deviation, and intraocular pressure at up to 5 years, and the forecasts were compared with results from the actual trial. Kalman filtering for OHTN was compared with a previously developed KF for patients with high-tension glaucoma (KF-HTG) and 3 traditional forecasting algorithms. Statistical analysis for the present study was performed between May 2018 and May 2019. Main Outcomes and Measures: Prediction error and root-mean-square error at 12, 24, 36, 48, and 60 months for MD, pattern standard deviation, and intraocular pressure. Results: Among 1407 eligible patients (2806 eyes), 809 (57.5%) were female and the mean (SD) age at baseline was 57.5 (9.6) years. For 2124 eyes with sufficient measurements, KF-OHTN forecast MD values 60 months into the future within 0.5 dB of the actual value for 696 eyes (32.8%), 1.0 dB for 1295 eyes (61.0%), and 2.5 dB for 1980 eyes (93.2%). Among the 5 forecasting algorithms tested, KF-OHTN achieved the lowest root-mean-square error (1.72 vs 1.85-4.28) for MD values 60 months into the future. For the subset of eyes that progressed to open-angle glaucoma, KF-OHTN and KF-HTG forecast MD values 60 months into the future within 1 dB of the actual value for 30 eyes (68.2%; 95% CI, 54.4%-82.0%) and achieved the lowest root-mean-square error among all models. Conclusions and Relevance: These findings suggest that machine learning algorithms such as KF can accurately forecast MD, pattern standard deviation, and intraocular pressure 5 years into the future for many patients with OHTN. These algorithms may aid clinicians in managing OHTN in their patients.

Using Kalman Filtering to Forecast Disease Trajectory for Patients With Normal Tension Glaucoma.

PURPOSE: To determine whether a machine learning technique called Kalman filtering (KF) can accurately forecast future values of mean deviation (MD), pattern standard deviation, and intraocular pressure for patients with normal tension glaucoma (NTG). DESIGN: Development and testing of a forecasting model for glaucoma progression. METHODS: We parameterized and validated a KF (KF-NTG) to forecast MD, pattern standard deviation, and intraocular pressure at 24 months into the future using 263 eyes of 263 Japanese patients with NTG. We determined the proportion of patients with MD forecasts within 0.5, 1.0, and 2.5 dBs of the actual values and calculated the root mean squared error (RMSE) for each forecast. We compared KF-NTG with a previously published KF model calibrated using patients with high-tension open-angle glaucoma (KF-HTG) and to 3 conventional forecasting algorithms. RESULTS: The 263 patients with NTG had mean ± standard deviation age of 63.4 ± 10.5 years. KF-NTG forecasted MD values 24 months ahead within 0.5, 1.0, and 2.5 dBs of the actual value for 78 eyes (32.2%), 122 eyes (50.4%), and 211 eyes (87.2%), respectively. The proportion of eyes with MD values forecasted within 2.5 dB of the actual value for the KF-NTG (87.2%) were similar to KF-HTG (86.0%) and the null model (86.4%), and much better than the 2 linear regression-based models (72.7-74.0%; P < .001). When forecasting MD, KF-NTG (RMSE = 2.71) and KF-HTG (RMSE = 2.68) achieved lower RMSE than the other 3 forecasting models (RMSE = 2.81-3.90), indicating better performance. CONCLUSION: As observed previously for patients with HTG, KF can also effectively forecast disease trajectory for many patients with NTG.

A Data-Driven Approach to Unlikely, Possible, Probable, and Definite Acute Concussion Assessment.

Kutcher and Giza suggested incorporating levels of certainty in concussion diagnosis decisions. These guidelines were based on clinical experience rather than objective data. Therefore, we combined data-driven optimization with predictive modeling to identify which athletes are unlikely to have concussion and to classify remaining athletes as having possible, probable, or definite concussion with diagnostic certainty. We developed and validated our framework using data from the Concussion Assessment, Research, and Education (CARE) Consortium. Acute concussions had assessments at <6 h (n = 1085) and 24-48 h post-injury (n = 1413). Normal performances consisted of assessments at baseline (n = 1635) and the time of unrestricted return to play (n = 1345). We evaluated the distribution of acute concussions and normal performances across risk categories and identified inter-class and intra-class differences in demographics, time-of-injury characteristics, the Standard Assessment of Concussion (SAC), Sport Concussion Assessment Tool (SCAT) symptom assessments, and Balance Error Scoring System (BESS). Our algorithm accurately classified concussions as probable or definite (sensitivity = 91.07-97.40%). Definite and probable concussions had higher SCAT symptom scores than unlikely and possible concussions (p < 0.05). Definite concussions had lower SAC and higher BESS scores (p < 0.05). Baseline to post-injury change scores for the SAC, SCAT symptoms, and BESS were significantly different between acute possible and probable concussions and normal performances (p < 0.05). There were no consistent patterns in demographics across risk categories, although a greater proportion of concussions classified as unlikely were reported immediately compared with definite concussions (p < 0.05). Although clinical interpretation is still needed, our data-driven approach to concussion risk stratification provides a promising step toward evidence-based concussion assessment.

Quantifying the Value of Multidimensional Assessment Models for Acute Concussion: An Analysis of Data from the NCAA-DoD Care Consortium.

BACKGROUND: Many concussion assessment methods exist, but few studies quantify the performance of these methods to determine which can best assess acute concussion alone or in combination. OBJECTIVES: The objectives of this study were to evaluate: (1) selected concussion assessments for acute concussion assessment; (2) the utility of change scores for acute concussion assessment; and (3) concussion assessment capabilities when constrained to limited clinical data or objective clinical measures. METHODS: The 'acute concussion' group contained assessments from < 6 h post-injury (n = 560) and 24-48 h post-injury (n = 733). The 'normal performance' group contained assessments from baseline testing (n = 842) and unrestricted return to play (n = 707) timepoints. Univariate and multivariate logistic regression models were created separately for < 6- and 24- to 48-h timepoints. Models were evaluated on sensitivity, specificity, and area under the receiver operating characteristic curve. RESULTS: Within the univariate analysis, Sport Concussion Assessment Tool symptom assessments had the highest combination of sensitivity, specificity, and area under the receiver operating characteristic curve, with values up to 0.93, 0.97, and 0.98, respectively. Full models had a sensitivity, specificity, and area under the receiver operating characteristic curve up to 0.94, 0.97, and 0.99, respectively, and outperformed all univariate models, raw score models, and objective models. Objective models were outperformed by all multivariate models and the univariate models containing only Sport Concussion Assessment Tool symptom assessments. CONCLUSION: Results support the use of multidimensional assessment batteries over single instruments and suggest the importance of self-reported symptoms in acute concussion assessment. Balance assessments, however, may not provide additional benefit when symptom and neurocognitive assessments are available. Additionally, change scores provide some clinical utility over raw scores, but the difference may not be clinically meaningful.