Cook Children's Health Care System

Delineating the epileptogenic zone (EZ) is essential for achieving seizure freedom in drug-resistant epilepsy (DRE). Conventionally, seizure onset derived from ictal intracranial EEG (iEEG) approximates the EZ, but acquiring ictal data can be challenging. Interictal iEEG abnormalities offer abundant, non-seizure-dependent markers of the epileptogenic tissue; however, these biomarkers offer limited specificity. Here, we propose a machine learning framework that integrates interictal spike and ripple features to predict the epileptogenic contacts targeted for surgical removal and the patient’s surgical outcome. We retrospectively analyzed iEEG data from 62 children with DRE [34 with good outcome (Engel I)], automatically detected spikes and ripples, and computed temporal, spectral, and spatial features for each channel. Using combinations of these features and the resected contacts as targets, we trained Random Forest classifiers using only good outcome patients to estimate epileptogenic contacts. Spike-based and combined spike and ripple features outperformed individual biomarkers in predicting the epileptogenic contacts with an area under the receiver operating characteristic curve of 0.89 and 74% spatial overlap with resection. Although most individual features and classifiers predicted outcome, the combined feature model performed best (i.e., sensitivity 88%, specificity 68%, and accuracy 79%). Our findings demonstrate that integrating multimodal interictal features improves the identification of epileptogenic contacts providing valuable prognostic insights for epilepsy surgery.

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Background: Tracheobronchial disease, including tracheomalacia (TM) and tracheobronchomalacia (TBM), is a spectrum of congenital and acquired airway disorders characterized by the collapse of the tracheal or mainstem bronchial walls during expiration, particularly when there are increased intrathoracic pressures. Traditional surgical approaches to treat severe medically refractory TM include anterior approaches, such as aortopexy or anterior tracheopexy. Recently, posterior tracheopexy has emerged to address the widened and mobile posterior tracheal membrane which can cause transient airway obstruction. Method: The National Institute of Health, National Library of Medicine, PubMed, and MEDLINE databases were queried for manuscripts related to posterior tracheopexy in the pediatric population. Preoperative diagnostics, anesthetic considerations, operative technique, clinical outcomes, and operative complications were analyzed in each manuscript. Results: Patients with severe medically refractory cases of TM who are being considered for posterior tracheopexy should undergo thorough preoperative workup by a multidisciplinary team. Cross-sectional, dynamic thoracic imaging and a "quadruple endoscopy", incorporating laryngoscopy, dynamic bronchoscopy, distal bronchoscopy, and esophagogastroduodenoscopy (EGD) should be obtained as part of a standardized preoperative assessment. Posterior tracheopexy for pre-existing TM significantly improves respiratory symptoms, respiratory infection rates, brief resolved unexplained events, and ventilatory dependence. Recently, posterior tracheopexy during TEF/EA repair has been described and aims to reduce the risk of patients developing TM, the risk of TEF recurrence, and respiratory morbidity following TEF/EA repair. An ongoing randomized controlled trial may help to elucidate the efficacy of primary posterior tracheopexy in select neonates with TEF/EA. Conclusions: Posterior tracheopexy is a valuable surgical technique for the treatment of TM or the reduction in respiratory morbidity following TEF/EA repair in select neonates.

Epileptic seizures involve a cortical excitation–inhibition imbalance driven by dysfunctional interneurons that contribute to gamma oscillation generation. While impaired gamma oscillations are commonly reported in epilepsy, the dynamics of broadband and narrowband gamma as well as beta oscillations remain underexplored. These oscillations may serve as noninvasive electrophysiological markers associated with altered cortical dynamics, potentially reflecting underlying excitation–inhibition imbalance in epilepsy. Here, we recorded high-density electroencephalography and magnetoencephalography data to investigate visual stimuli-elicited cortical oscillations in 48 neurotypical (20 females) and 49 children (26 females) with epilepsy. We found that epilepsy is characterized by reduced amplitude and prolonged latency of evoked cortical response compared with controls after visual stimulation, with alterations in N1 peaks and M100, M150, and M250 components (p < 0.05). Additionally, source imaging revealed disrupted oscillatory features in epilepsy patients, including suppressed power, reduced amplitude, and increased latency in evoked and induced beta and gamma oscillations from the visual cortex (p < 0.05). These alterations were consistent across diverse epilepsy subtypes, including focal, generalized, and nonlesional epilepsy cases. Utilizing these differences, we developed a novel classification model that differentiates individuals with epilepsy from controls with high accuracy, offering potential clinical utility in epilepsy diagnosis. Our results suggest disrupted beta and gamma oscillations may be associated with impaired inhibitory mechanisms and altered cortical dynamics, potentially indicative of an excitation–inhibition imbalance. Our findings highlight the potential of noninvasive electrophysiological biomarkers to capture cortical dynamics possibly influenced by excitation–inhibition imbalance in epilepsy, supporting their use in early diagnosis and disease monitoring.

To examine associations between special education, chronic health conditions (CHCs), and college graduation in survivors of childhood cancer and their siblings. Childhood Cancer Survivor Study participants included 23,082 5-year survivors (53.7% male; median [IQR] age at diagnosis, 6 [3-13] years; age at evaluation, 31.0 [24-39] years; treated between 1970 and 1999) and 5,037 siblings (47.7% male; 36.0 [28-44] years at evaluation). Special education use, reasons for special education, CHCs, and college graduation were self-reported. Primary cancer diagnosis and treatment exposures were abstracted from medical records. Comparisons between survivors and siblings were made using chi-square statistics; demographic and treatment factors associated with outcomes were examined using modified Poisson regression models. More survivors reported special education use than siblings (26.5% v 8.6%; relative risk [RR], 2.55 [95% CI, 2.32 to 2.80]). Of those survivors and siblings who had special education services, use was highest between kindergarten and fifth grade (64.4% of survivors and 71.9% of siblings in kindergarten-fifth grade, 14.4% of survivors and 12.5% of siblings in sixth-eighth grade, and 9.2% of survivors and 9.0% of siblings in ninth-12th grade), and primarily attributable to learning and concentration problems. Despite receiving special education, survivors were less likely to graduate college compared with siblings requiring special education (RR, 0.76 [95% CI, 0.66 to 0.88]). Risk for not graduating college included history of CNS tumor (RR, 1.47 [95% CI, 1.40 to 1.55]), cranial irradiation (20-29 Gy, RR, 1.16 [95% CI, 1.09 to 1.25]; 30-49 Gy, RR, 1.37 [95% CI, 1.26 to 1.49]; ≥50 Gy, RR, 1.35 [95% CI, 1.28 to 1.42]), or the presence of a severe, disabling or life-threatening CHC (Common Terminology Criteria for Adverse Events grade 3-4, RR, 1.15 [95% CI, 1.07 to 1.24]). Cognitive problems and CHCs increase risk for not graduating college; these problems are not alleviated by special education.

Abstract Background Perceived household support during childhood may have long-term effects on mental and physical health across the life course. However, the specific associations between early supportive environments and adult health outcomes remain underexplored. Methods We conducted a cross-sectional analysis using data from the Behavioral Risk Factor Surveillance System (BRFSS) collected between 2016 and 2023. The study included 31,233 U.S. adults aged 18 years and older who provided complete responses regarding perceived childhood household support, depression diagnosis, and the number of poor mental and physical health days. The primary exposure was self-reported childhood support, categorized as: “Never,” “A Little of the Time,” “Some of the Time,” “Most of the Time,” or “All of the Time.” Outcomes included lifetime diagnosis of depression, average monthly poor mental health days, and poor physical health days. Analyses were adjusted using inverse probability weighting and controlled for sociodemographic factors, survey weights, and state, year, and month fixed effects. Results Among respondents (mean age 52.2 years; 63.4% female; 76.0% White), individuals who reported “Never” being supported during childhood were 19.4 percentage points more likely to report a depression diagnosis (95% CI: 11.6–27.2), experienced 5.33 more poor mental health days (95% CI: 3.64–7.03), and 2.77 more poor physical health days per month (95% CI: 1.23– 4.32), compared to those who reported being “Always” supported. A clear dose-response relationship was observed across all categories of household support. Conclusions Lower levels of perceived childhood household support are significantly associated with increased risk of adult depression and greater burden of poor mental and physical health. Interventions targeting early supportive environments may improve population health outcomes across the life span. Key Points Question Is perceived childhood household support associated with depression and self-reported mental and physical health outcomes in adulthood? Findings In this cross-sectional study of 31,233 U.S. adults, individuals reporting they were never supported during childhood had significantly higher depression risk and reported poorer mental and physical health days compared to those always supported, with results showing a consistent gradient across varying support levels. Meaning These findings suggest policies promoting consistent childhood household support may enhance lifelong mental and physical health outcomes.

Background: Loneliness is a significant public health concern and a well-established social determinant of health, affecting both mental and physical well-being. It has been linked to an increased risk of depression, anxiety, cardiovascular disease, and premature mortality. Despite growing awareness, loneliness remains an underrecognized and undertreated factor influencing population health. Objective: This study examines the impact of loneliness on the likelihood of being diagnosed with depression, as well as its association with self-reported poor mental and physical health days. Methods: Data were analyzed from the Behavioral Risk Factor Surveillance System (BRFSS) (2016–2023). The primary exposure was self-reported loneliness, captured through the question, "How often do you feel lonely?" with responses ranging from "Always" to "Never." Main outcomes included depression diagnosis, poor mental health days, and poor physical health days. Covariates included age, race, gender, marital status, education, employment, state, year, metropolitan status, and language spoken at home. Inverse Probability Weighting (IPW) was used to estimate the Average Treatment Effect (ATE), accounting for confounders and state and year fixed effects. Sampling weights ensured national representativeness, and robust standard errors accounted for clustering by state. Results: Among 47,026 participants, 82.4% reported experiencing some degree of loneliness, with 6.2% feeling "Always" lonely, 8.3% feeling "Usually" lonely, 37.9% feeling "Sometimes" lonely, and 29.9% feeling "Rarely" lonely. In contrast, 17.7% of participants reported "Never" feeling lonely. For further analysis, 2,609 individuals who reported feeling lonely were matched with 2,609 individuals who reported "Never" feeling lonely", forming a balanced comparison group. The "Always Lonely" population was predominantly White (64.5%) and female (55.0%), with the majority aged 45–64 years. Loneliness was significantly associated with an increased likelihood of depression diagnosis, with a 39.3% percentage-point increase for those reporting Always lonely (ATE = 0.39, 95% CI: 0.34–0.44, p &lt; 0.001). Loneliness was also associated with a 10.9-day increase in poor mental health days (ATE = 10.9, 95% CI: 9.8–11.9, p &lt; 0.001) and a 5.0 day increase in poor physical health days (ATE = 5.0, 95% CI: 3.8–6.1, p &lt; 0.001). Conclusions: Loneliness is a strong predictor of depression and poor mental and physical health. Interventions addressing social isolation could mitigate the negative health impacts associated with loneliness, improving population health outcomes.