Social Support Is Protective Against the Effects of Discrimination on Parental Mental Health Outcomes.

BACKGROUND: Discrimination, or unfair treatment based on individual characteristics such as gender, race, skin color, and or sexual orientation, is a pervasive social stressor that perpetuates health disparities by limiting social and economic opportunity and is associated with poor mental and physical health outcomes. AIMS: The purpose of the present study is to (1) examine the association between maternal experiences of discrimination and paternal experiences of discrimination; (2) explore how discrimination relates to parental (maternal and paternal) stress and depressive symptoms; and (3) examine whether social support exerts protective effects. METHODS: The sample was 2,510 mothers and 1,249 fathers from the Child Community Health Network study. Linear regression models were conducted to explore associations between maternal and paternal discrimination. In addition, mediation analyses were conducted to explore if social support functioned as a mediator between discrimination on parental stress and depressive symptoms. RESULTS: Most mothers (40.3%) and fathers (50.7%) identified race as the predominant reason for discrimination. Experiencing discrimination was significantly related to stress and depressive symptoms for both parents, and all forms of social support mediated these relationships. Our findings suggest that social support can act as a protective factor against the negative association between discrimination and both stress and depressive symptoms. CONCLUSIONS: These findings highlight the need to integrate social support into existing interventions and include fathers in mental health screenings in primary-care settings. Finally, we briefly describe the role of nurses and other allied health professionals in addressing discrimination in health care and health policy implications.

An efficient machine learning framework to identify important clinical features associated with pulmonary embolism.

A misdiagnosis of pulmonary embolism (PE) can have severe consequences such as disability or death. It's crucial to accurately identify key clinical features of PE in clinical practice to promptly identify potential PE patients who may present asymptomatically, and to prevent misdiagnosing PE as asthma exacerbation in patients with symptoms like dyspnea or chest pain. However, reliably identifying these important features can be challenging due to many factors influencing the likelihood of PE development in complex fashions (e.g., the interactions among these factors). To address this difficulty, we presented an effective framework using the deep neural network (DNN) model and the permutation-based feature importance test (PermFIT) procedure, i.e., PermFIT-DNN. We applied the PermFIT-DNN framework to the analysis of data from a PE study for asthma exacerbation patients. Our analysis results show that the PermFIT-DNN framework can robustly identify key features for classifying PE status. The important features identified can also aid in accurately predicting the PE risk.

The role of social adversity on emotional dysregulation during infancy and early childhood.

PURPOSE: The purpose of this study was to investigate if social adversity is associated with mother reported emotional dysregulation behaviors and trajectories during infancy and early childhood. DESIGN & METHODS: A secondary data analysis from the Durham Child Health and Development study study included 206 child-mother dyads. Three models were used to explore the relationship between social adversity and mother reported emotional dysregulation during infancy (Infant Behavior Questionnaire-Revised) and early childhood (Child Behavior Checklist - Dysregulation Profile). Linear mixed effects models were adopted to investigate if social adversity was associated with mother reported emotional dysregulation longitudinally. Regression analysis was conducted to explore if social adversity was associated with maternal reported emotional dysregulation trajectory slope scores and maternal reported emotional dysregulation trajectory class. Maternal psychological distress and the child's sex assigned at birth were included as covariates in each analysis. RESULTS: Infants with greater social adversity scores had significantly higher maternal reported fear responses across the first year of life. Social adversity was associated with maternal reported distress to limitations trajectory, dysregulated recovery class, and dysregulated distress to limitations class. During early childhood social adversity was significantly associated with maternal reported emotional dysregulation but not trajectories which showed little variability. CONCLUSION & PRACTICAL IMPLICATIONS: Our results indicate that social adversity is associated with maternal reported emotional dysregulation during infancy and early childhood. Nursing and other professionals can participate in early screening to determine risk and provide intervention.

A deep neural network framework to derive interpretable decision rules for accurate traumatic brain injury identification of infants.

OBJECTIVE: We aimed to develop a robust framework to model the complex association between clinical features and traumatic brain injury (TBI) risk in children under age two, and identify significant features to derive clinical decision rules for triage decisions. METHODS: In this retrospective study, four frequently used machine learning models, i.e., support vector machine (SVM), random forest (RF), deep neural network (DNN), and XGBoost (XGB), were compared to identify significant clinical features from 24 input features associated with the TBI risk in children under age two under the permutation feature importance test (PermFIT) framework by using the publicly available data set from the Pediatric Emergency Care Applied Research Network (PECARN) study. The prediction accuracy was determined by comparing the predicted TBI status with the computed tomography (CT) scan results since CT scan is the gold standard for diagnosing TBI. RESULTS: At a significance level of [Formula: see text], DNN, RF, XGB, and SVM identified 9, 1, 2,  and 4 significant features, respectively. In a comparison of accuracy (Accuracy), the area under the curve (AUC), and the precision-recall area under the curve (PR-AUC), the permutation feature importance test for DNN model was the most powerful framework for identifying significant features and outperformed other methods, i.e., RF, XGB, and SVM, with Accuracy, AUC, and PR-AUC as 0.915, 0.794, and 0.974, respectively. CONCLUSION: These results indicate that the PermFIT-DNN framework robustly identifies significant clinical features associated with TBI status and improves prediction performance. The findings could be used to inform the development of clinical decision tools designed to inform triage decisions.

A mixed-effects two-part model for twin-data and an application on identifying important factors associated with extremely preterm children's health disorders.

Our recent studies identifying factors significantly associated with the positive child health index (PCHI) in a mixed cohort of preterm-born singletons, twins, and triplets posed some analytic and modeling challenges. The PCHI transforms the total number of health disorders experienced (of the eleven ascertained) to a scale from 0 to 100%. While some of the children had none of the eleven health disorders (i.e., PCHI = 1), others experienced a subset or all (i.e., 0 ≤PCHI< 1). This indicates the existence of two distinct data processes-one for the healthy children, and another for those with at least one health disorder, necessitating a two-part model to accommodate both. Further, the scores for twins and triplets are potentially correlated since these children share similar genetics and early environments. The existing approach for analyzing PCHI data dichotomizes the data (i.e., number of health disorders) and uses a mixed-effects logistic or multiple logistic regression to model the binary feature of the PCHI (1 vs. < 1). To provide an alternate analytic framework, in this study we jointly model the two data processes under a mixed-effects two-part model framework that accounts for the sample correlations between and within the two data processes. The proposed method increases power to detect factors associated with disorders. Extensive numerical studies demonstrate that the proposed joint-test procedure consistently outperforms the existing method when the type I error is controlled at the same level. Our numerical studies also show that the proposed method is robust to model misspecifications and it is applicable to a set of correlated semi-continuous data.

Visual Outcomes and Prognostic Factors for Pediatric Open Globe Injuries.

OBJECTIVES: Open globe injuries (OGIs) in children can be visually devastating traumas and are a common cause of unilateral blindness in children. Three commonly used ocular trauma scores (Ocular Trauma Score [OTS], Pediatric Penetrating Ocular Trauma Score [POTS], and Toddler/Infant Ocular Trauma Score [TOTS]) can be used to help predict visual outcomes in ocular injuries. Each has strengths and weaknesses, but these scores have not been studied extensively in the pediatric population. METHODS: The medical records of all pediatric patients presenting at a single institution with OGIs from 2011 to 2016 were retrospectively reviewed. Initial clinical presentation and subsequent examinations were reviewed. The 3 trauma scoring systems were applied to patient data to determine the effectiveness at predicting final visual acuity (VA). RESULTS: A total of 15 patients met inclusion criteria. The mean age at presentation was 6.97 years. Seven of 15 (47%) of the patients had a final VA of 20/40 or better. The OTS was applied to 8 of 15 patients, as the OTS requires initial VA, which could not be obtained in 7 patients. The POTS and TOTS were applied to all 15 patients. The POTS, TOTS, and OTS were all significantly correlated to final VA. Incidence of relative afferent pupillary defect and more posterior zone of injury were correlated with poorer visual outcomes. CONCLUSIONS: All 3 available ocular trauma scores were effective at predicting final VA in our cohort of pediatric patients with OGIs even outside of the age ranges for which they have been created. Of all the factors included in the scores, relative afferent pupillary defect and zone of injury were most closely correlated with poor final VA.

Factors and reasons associated with low COVID-19 vaccine uptake among highly hesitant communities in the US.

BACKGROUND: The inability to achieve high COVID-19 vaccination rates can continue to have serious harm to our communities. Vaccine hesitancy is a major barrier towards high vaccination rates. We evaluated the relationship between COVID-19 vaccine uptake and vaccine hesitancy, and then examined whether community factors were associated with COVID-19 vaccine uptake and hesitancy. METHODS: We constructed and evaluated a cross-sectional, county-level dataset that included the levels of vaccination uptake and vaccine hesitancy, and population characteristics based on those included in the CDC's Social Vulnerability Index. RESULTS: Across 3142 US counties, vaccine hesitancy was significantly and negatively correlated with vaccine uptake rates (r = -0.06, P value <.01). The 2 predictors associated with a low vaccination level within highly hesitant communities were: no high school education (OR:0.70, P value <.001), and concern on vaccine availability and distribution (CVAC) (OR:0.00, P value <.001). The most common reason driving vaccine hesitancy was lack of trust in COVID-19 vaccines (55%), followed by concerns around side effects of the vaccine (48%), and lack of trust in government (46%). CONCLUSIONS: COVID-19 vaccine hesitancy is a public health threat. Our findings suggest that low education levels are a major contributor to vaccine hesitancy and ultimately vaccination levels. Since education levels are not easily modifiable, our results suggest that policymakers would be best served by closing knowledge gaps to overcome negative perceptions of the vaccine through tailored interventions.

A Systematic Review on Outcomes of Preterm Small for Gestational Infants Born to Women With Hypertensive Disorders in Pregnancy.

There is a lack of knowledge on the intersection between prematurity, small for gestational age, and hypertensive disorders of pregnancy (HDP). Therefore, the aim of this systematic review was to examine the outcomes of preterm infants who were small for gestational age born to women with HDP. Searches were conducted with no date restriction through the final search date of May 13, 2020, in the following databases: PubMed, Web of Science Core Collection, Cumulative Index of Nursing and Allied Health Literature Plus with Full Text (EBSCOhost), and Embase (Elsevier). A total of 6 studies were eligible for this review. The adjusted odds of mortality and necrotizing enterocolitis were significantly lower in the pregnancy-induced hypertension (PIH)/HDP group than in the non-PIH/HDP group. There was no significant difference in the odds of respiratory distress syndrome, bronchopulmonary dysplasia, and intraventricular hemorrhage between PIH/HDP and non-PIH/HDP groups. There was no significant difference between PIH/HDP and non-PIH/HDP groups in cystic periventricular leukomalacia, retinopathy of prematurity, late-onset sepsis, patent ductus arteriosus, length of hospital stays, duration of supplemental oxygen use, duration of mechanical ventilation, and continuous airway pressure. The studies included in this systematic review demonstrated that PIH/HDP is associated with lower infant mortality and necrotizing enterocolitis.

Effects of Patient and Surgery Characteristics on Persistent Postoperative Pain: A Mediation Analysis.

OBJECTIVE: Acute postoperative pain intensity is associated with persistent postsurgical pain (PPP) risk. However, it remains unclear whether acute postoperative pain intensity mediates the relationship between clinical factors and persistent pain. MATERIALS AND METHODS: Participants from a mixed surgical population completed the Brief Pain Inventory and Pain Catastrophizing Scale before surgery, and the Brief Pain Inventory daily after surgery for 7 days and at 30 and 90 days after surgery. We considered mediation models using the mean of the worst pain intensities collected daily on each of postoperative days (PODs) 1 to 7 against outcomes of worst pain intensity at the surgical site endpoints reflecting PPP (POD 90) and subacute pain (POD 30). RESULTS: The analyzed cohort included 284 participants for the POD 90 outcome. For every unit increase of maximum acute postoperative pain intensity through PODs 1 to 7, there was a statistically significant increase of mean POD 90 pain intensity by 0.287 after controlling for confounding effects. The effects of female versus male sex (m=0.212, P=0.034), pancreatic/biliary versus colorectal surgery (m=0.459, P=0.012), thoracic cardiovascular versus colorectal surgery (m=0.31, P=0.038), every minute increase of anesthesia time (m=0.001, P=0.038), every unit increase of preoperative average pain score (m=0.012, P=0.015), and every unit increase of catastrophizing (m=0.044, P=0.042) on POD 90 pain intensity were mediated through acute PODs 1 to 7 postoperative pain intensity. DISCUSSION: Our results suggest the mediating relationship of acute postoperative pain on PPP may be predicated on select patient and surgical factors.

Permutation-based identification of important biomarkers for complex diseases via machine learning models.

Study of human disease remains challenging due to convoluted disease etiologies and complex molecular mechanisms at genetic, genomic, and proteomic levels. Many machine learning-based methods have been developed and widely used to alleviate some analytic challenges in complex human disease studies. While enjoying the modeling flexibility and robustness, these model frameworks suffer from non-transparency and difficulty in interpreting each individual feature due to their sophisticated algorithms. However, identifying important biomarkers is a critical pursuit towards assisting researchers to establish novel hypotheses regarding prevention, diagnosis and treatment of complex human diseases. Herein, we propose a Permutation-based Feature Importance Test (PermFIT) for estimating and testing the feature importance, and for assisting interpretation of individual feature in complex frameworks, including deep neural networks, random forests, and support vector machines. PermFIT (available at https://github.com/SkadiEye/deepTL ) is implemented in a computationally efficient manner, without model refitting. We conduct extensive numerical studies under various scenarios, and show that PermFIT not only yields valid statistical inference, but also improves the prediction accuracy of machine learning models. With the application to the Cancer Genome Atlas kidney tumor data and the HITChip atlas data, PermFIT demonstrates its practical usage in identifying important biomarkers and boosting model prediction performance.

Effects of unsupervised behavioral and pelvic floor muscle training programs on nocturia, urinary urgency, and urinary frequency in postmenopausal women: Secondary analysis of a randomized, two-arm, parallel design, superiority trial (TULIP study).

OBJECTIVE: To determine and compare the effects of an unsupervised behavioral and pelvic floor muscle training (B-PFMT) program delivered in two formats on nocturia, urinary urgency, and urinary frequency in postmenopausal women. STUDY DESIGN: A secondary analysis used data collected from women enrolled in the TULIP study. Women aged 55 years or more with no urinary incontinence were provided the B-PFMT program. Each woman was randomly assigned to a face-to-face class that took about 2 h (2-hrClass) or to a DVD showing essentially the same information as a 20-minute video (20-minVideo). All women were instructed to independently continue the program following their education session. Three urinary outcomes were assessed at baseline, 3, 12, and 24 months. MAIN OUTCOME MEASURES: Nocturia and urinary urgency were examined with one item each from the questionnaire-based voiding diary, and urinary frequency was assessed with patients' self-documenting 3-day bladder diary. RESULTS: Women in the 2-hrClass group experienced significantly fewer nocturia episodes and longer average inter-void interval at each follow-up and fewer urinary urgency episodes at 12 months. Women in the 20-minVideo group experienced significantly fewer episodes of nocturia and urinary urgency and longer average inter-void interval at each follow-up time point. No significant between-group differences were found for any outcome, except for nocturia at 24 months, when effectiveness favored women in the 20-minVideo group. CONCLUSIONS: Unsupervised B-PFMT programs are effective for improving postmenopausal women's urinary outcomes regardless of the format. The optimal format to deliver B-PFMT programs in terms of effectiveness should be explored in future studies.

On kernel machine learning for propensity score estimation under complex confounding structures.

Post marketing data offer rich information and cost-effective resources for physicians and policy-makers to address some critical scientific questions in clinical practice. However, the complex confounding structures (e.g., nonlinear and nonadditive interactions) embedded in these observational data often pose major analytical challenges for proper analysis to draw valid conclusions. Furthermore, often made available as electronic health records (EHRs), these data are usually massive with hundreds of thousands observational records, which introduce additional computational challenges. In this paper, for comparative effectiveness analysis, we propose a statistically robust yet computationally efficient propensity score (PS) approach to adjust for the complex confounding structures. Specifically, we propose a kernel-based machine learning method for flexibly and robustly PS modeling to obtain valid PS estimation from observational data with complex confounding structures. The estimated propensity score is then used in the second stage analysis to obtain the consistent average treatment effect estimate. An empirical variance estimator based on the bootstrap is adopted. A split-and-merge algorithm is further developed to reduce the computational workload of the proposed method for big data, and to obtain a valid variance estimator of the average treatment effect estimate as a by-product. As shown by extensive numerical studies and an application to postoperative pain EHR data comparative effectiveness analysis, the proposed approach consistently outperforms other competing methods, demonstrating its practical utility.

Slow Dynamics of Acute Postoperative Pain Intensity Time Series Determined via Wavelet Analysis Are Associated With the Risk of Severe Postoperative Day 30 Pain.

BACKGROUND: Evidence suggests that increased early postoperative pain (POP) intensities are associated with increased pain in the weeks following surgery. However, it remains unclear which temporal aspects of this early POP relate to later pain experience. In this prospective cohort study, we used wavelet analysis of clinically captured POP intensity data on postoperative days 1 and 2 to characterize slow/fast dynamics of POP intensities and predict pain outcomes on postoperative day 30. METHODS: The study used clinical POP time series from the first 48 hours following surgery from 218 patients to predict their mean POP on postoperative day 30. We first used wavelet analysis to approximate the POP series and to represent the series at different time scales to characterize the early temporal profile of acute POP in the first 2 postoperative days. We then used the wavelet coefficients alongside demographic parameters as inputs to a neural network to predict the risk of severe pain 30 days after surgery. RESULTS: Slow dynamic approximation components, but not fast dynamic detailed components, were linked to pain intensity on postoperative day 30. Despite imbalanced outcome rates, using wavelet decomposition along with a neural network for classification, the model achieved an F score of 0.79 and area under the receiver operating characteristic curve of 0.74 on test-set data for classifying pain intensities on postoperative day 30. The wavelet-based approach outperformed logistic regression (F score of 0.31) and neural network (F score of 0.22) classifiers that were restricted to sociodemographic variables and linear trajectories of pain intensities. CONCLUSIONS: These findings identify latent mechanistic information within the temporal domain of clinically documented acute POP intensity ratings, which are accessible via wavelet analysis, and demonstrate that such temporal patterns inform pain outcomes at postoperative day 30.

Sex Differences in Co-occurring Conditions Among Autistic Children and Youth in Florida: A Retrospective Cohort Study (2012-2019).

Autistic children have a high prevalence of co-occurring mental health, developmental/behavioral, and medical conditions, but research on sex/gender differences has been mixed. We used Florida healthcare claims data to characterize sex differences (female/male) in co-occurring conditions among autistic children ages 1-21 (N = 83,500). After adjusting for age, race, ethnicity, urbanicity, and insurance, autistic girls had significantly higher odds of anxiety disorders, mood disorders, intellectual disability, developmental disorders, epilepsy, metabolic disorders, gastrointestinal disorders, and sleep disorders compared to autistic boys. Autistic girls had significantly lower odds of ADHD. The findings contribute to the growing body of research on the unique healthcare needs of autistic girls.

Sucrase-isomaltase Gene Variants in Patients With Abnormal Sucrase Activity and Functional Gastrointestinal Disorders.

OBJECTIVES: The aim of the study was to determine prevalence and characterize sucrase-isomaltase (SI) gene variants of congenital sucrase-isomaltase deficiency in non-Hispanic white pediatric and young adult patients with functional gastrointestinal disorders (FGIDs), and abnormal sucrase activity on histologically normal duodenal biopsy. METHODS: Clinical symptoms and disaccharidase activities data were collected for an abnormal (low) sucrase (≤25.8 U, n = 125) activity group, and 2 normal sucrase activity groups with moderate (≥25.8-≤55 U, n = 250) and high (>55 U, n = 250) sucrase activities. SI gene variants were detected by next-generation sequencing of DNA from formalin-fixed paraffin-embedded tissues of these patients. FGIDs symptoms based on Rome IV criteria and subsequent clinical management of abnormal sucrase activity cases with pathogenic SI gene variants were analyzed. RESULTS: Thirteen SI gene variants were found to be significantly higher in abnormal sucrase cases with FGIDs symptoms (36/125, 29%; 71% did not have a pathogenic variant) compared to moderate normal (16/250, 6.4%, P < 0.001) or high normal (5/250, 2.0%, P < 0.001) sucrase groups. Clinical management data were available in 26 of abnormal sucrase cases, and only 10 (38%) were correctly diagnosed and managed by the clinicians. Concomitant lactase deficiency (24%; 23/97) and pan-disaccharidase deficiency (25%; 13/51) were found in the abnormal sucrase group. CONCLUSIONS: Heterozygous and compound heterozygous mutations in the SI gene were more prevalent in cases with abnormal sucrase activity presenting with FGIDs, and normal histopathology. This suggests heterozygous pathogenic variants of congenital sucrase-isomaltase deficiency may present as FGIDs. Concomitant lactase or pan-disaccharidase deficiencies were common in abnormal sucrase cases with SI gene variants.

Predicting long-term postsurgical pain by examining the evolution of acute pain.

BACKGROUND: Increased acute postoperative pain intensity has been associated with the development of persistent postsurgical pain (PPP) in mechanistic and clinical investigations, but it remains unclear which aspects of acute pain explain this linkage. METHODS: We analysed clinical postoperative pain intensity assessments using symbolic aggregate approximations (SAX), a graphical way of representing changes between pain states from one patient evaluation to the next, to visualize and understand how pain intensity changes across sequential assessments are associated with the intensity of postoperative pain at 1 (M1) and 6 (M6) months after surgery. SAX-based acute pain transition patterns were compared using cosine similarity, which indicates the degree to which patterns mirror each other. RESULTS: This single-centre prospective cohort study included 364 subjects. Patterns of acute postoperative pain sequential transitions differed between the 'None' and 'Severe' outcomes at M1 (cosine similarity 0.44) and M6 (cosine similarity 0.49). Stratifications of M6 outcomes by preoperative pain intensity, sex, age group, surgery type and catastrophising showed significant heterogeneity of pain transition patterns within and across strata. Severe-to-severe acute pain transitions were common, but not exclusive, in patients with moderate or severe pain intensity at M6. CONCLUSIONS: Clinically, these results suggest that individual pain-state transitions, even within patient or procedural strata associated with PPP, may not alone offer good predictive information regarding PPP. Longitudinal observation in the immediate postoperative period and consideration of patient- and surgery-specific factors may help indicate which patients are at increased risk of PPP. SIGNIFICANCE: Symbolic aggregate approximations of clinically obtained, acute postoperative pain intraday time series identify different motifs in patients suffering moderate to severe pain 6 months after surgery.

Pulmonary Embolism in Acute Asthma Exacerbation: Clinical Characteristics, Prediction Model and Hospital Outcomes.

PURPOSE: Little is known about the characteristics and impact of acute pulmonary embolism (PE) during episodes of asthma exacerbation. We aimed to characterize patients diagnosed with acute PE in the setting of asthma exacerbation, develop a prediction model to help identify future patients and assess the impact of acute PE on hospital outcomes. METHODS: We included 758 patients who were treated for asthma exacerbation and underwent a computed tomographic pulmonary angiography (CTA) during the same encounter at a university-based hospital between June 2011 and October 2018. We compared clinical characteristics of patients with and without acute PE and developed a machine learning prediction model to classify the PE status based on the clinical variables. We used multivariable regression analysis to evaluate the impact of acute PE on hospital outcomes. RESULTS: Twenty percent of the asthma exacerbation patients who underwent CTA had an acute PE. Factors associated with acute PE included previous history of PE, high CHA2DS2-VASc score, hyperlipidemia, history of deep vein thrombosis, malignancy, chronic systemic corticosteroids use, high body mass index and atrial fibrillation. Using these factors, we developed a random forest machine learning prediction model which had an 88% accuracy in classifying the acute PE status of the patients (area under the receiver operating characteristic curve = 0.899; 95% confidence interval: 0.885-0.913). Acute PE in asthma exacerbation was associated with longer hospital stay and intensive care unit stay. CONCLUSION: It is important to consider acute PE, a potentially life-threatening event, in the setting of asthma exacerbation especially when other risk factors are present.

Safety and efficacy of endoscopic submucosal dissection for rectal neoplasia: a multicenter North American experience.

Background and aims Rectal lesions traditionally represent the first lesions approached during endoscopic submucosal dissection (ESD) training in the West. We evaluated the safety and efficacy of rectal ESD in North America. Methods This is a multicenter retrospective analysis of rectal ESD between January 2010 and September 2018 in 15 centers. End points included: rates of en bloc resection, R0 resection, adverse events, comparison of pre- and post-ESD histology, and factors associated with failed resection. Results In total, 171 patients (median age 63 years; 56 % men) underwent rectal ESD (median size 43 mm). En bloc resection was achieved in 141 cases (82.5 %; 95 %CI 76.8-88.2), including 24 of 27 (88.9 %) with prior failed endoscopic mucosal resection (EMR). R0 resection rate was 74.9 % (95 %CI 68.4-81.4). Post-ESD bleeding and perforation occurred in 4 (2.3 %) and 7 (4.1 %), respectively. Covert submucosal invasive cancer (SMIC) was identified in 8.6 % of post-ESD specimens. There was one case (1/120; 0.8 %) of recurrence at a median follow-up of 31 weeks; IQR: 19-76 weeks). Older age and higher body mass index (BMI) were predictors of failed R0 resection, whereas submucosal fibrosis was associated with a higher likelihood of both failed en bloc and R0 resection. Conclusion Rectal ESD in North America is safe and is associated with high en bloc and R0 resection rates. The presence of submucosal fibrosis was the main predictor of failed en bloc and R0 resection. ESD can be considered for select rectal lesions, and serves not only to establish a definitive tissue diagnosis but also to provide curative resection for lesions with covert advanced disease.

Neurodevelopmental outcomes at 9-14 months gestational age after treatment of neonatal seizures due to brain injury.

PURPOSE: Infants with brain injury are susceptible to developmental delays. Survivors of neonatal seizures are at risk for developmental delay, epilepsy, and further neurological comorbidities. Despite advances in neonatal critical care, the prevalence of adverse long-term outcomes and seizure recurrence remains unchanged. Our goal is to determine if early treatment of neonatal seizures with phenobarbital or levetiracetam is associated with worse neurodevelopmental outcomes in brain-injured infants. METHODS: We conducted a retrospective cohort study of 119 infants admitted between 2013 and 2017 who were at risk for developmental delay and assessed in our clinic. We compared brain injury infants with neonatal seizures to brain injury infants without neonatal seizures using Bayley scores (BSID III) at 9-14 months gestational age. A comparison of Bayley scores between those exposed to phenobarbital and levetiracetam was conducted. RESULTS: Twenty-two children with neonatal seizures scored lower than 53 children without seizures in all domains with significant values in composite scores for cognitive function (p = 0.003) and language (p = 0.031). We found no difference in scores at 9-14 months between infants exposed to phenobarbital versus levetiracetam. CONCLUSIONS: Our results suggest that in infants with brain injury, the occurrence of neonatal seizures has an adverse effect on neurodevelopmental outcomes. The choice of antiseizure medication may not play a significant role in their outcomes.