Cerebral vasospasm due to Fusarium solani meningitis: A complication from medical tourism. Case report and literature review.

OBJECTIVES: Medical tourism is expanding globally, with patients seeking cosmetic procedures abroad. To date, little information is known regarding the risks and outcomes of cosmetic tourism, especially potential stroke complications. Here, we present a case of fungal meningitis in the setting of medical tourism leading to ischemic strokes and vasospasm. MATERIAL AND METHODS: We describe an immunocompetent 29-year-old female patient who initially presented with intractable headaches and an abnormal cerebrospinal fluid (CSF) profile who was eventually diagnosed with Fusarium solani meningitis as a part of a common source outbreak in Matamoros, Mexico. These patients were part of a cohort who underwent cosmetic procedures requiring spinal anesthesia. This report also highlights the unusual clinical course leading to poor outcomes in such conditions. RESULTS: The patient initially presented with headaches, papilledema, elevated opening pressure on the spinal tap, abnormal CSF studies, and eventually developed ischemic strokes and hydrocephalus. CSF showed positive beta D-Glucan with repeated negative CSF fungal cultures. A cerebral angiogram revealed extensive basilar artery vasospasm that led to ischemic strokes. Continued clinical worsening and lack of response to antifungal treatment prompted further imaging that revealed significant non-obstructive hydrocephalus subsequently complicated by spontaneous intracranial hemorrhage. CSF PCR for Fusarium solani species was positive days after her passing. CONCLUSION: This novel case highlights fungal meningitis caused by Fusarium solani complicated by bilateral ischemic strokes stemming from basilar artery vasospasm. Complications from medical tourism impact not only individual patients but also the health systems of both countries. Professional and regulatory entities for cosmetic surgeries must highlight and educate patients on the risks and complications of cosmetic surgeries happening abroad. Physicians should be aware of ongoing outbreaks and possible complications of these procedures.

Acculturation and Disparities in Telemedicine Readiness: A National Study.

Telemedicine provided older adults the ability to safely seek care during the coronavirus disease (COVID-19) pandemic. This study aimed to evaluate the potential impact of acculturation factors in telemedicine uptake between ethnic groups. As part of the National Health and Aging Trends Study 2018 survey, 303 participants (≥65 years) were interviewed. We assessed the impact of acculturation on telemedicine readiness by race and ethnicity. Compared to the white non-Hispanic immigrant population, Hispanic and Asian/Pacific Islander (API) populations had significantly lower telemedicine readiness and uptake. Limited English proficiency or older age at the time of migration was associated with telemedicine unreadiness and uptake in the Hispanic and API populations. Our findings suggested that acculturation factors play a substantial role in telemedicine uptake among older adult immigrants in the United States. Therefore, acculturation factors should be considered when promoting and adopting telemedicine technologies in older adults.

Predicting clinical outcomes among hospitalized COVID-19 patients using both local and published models.

BACKGROUND: Many models are published which predict outcomes in hospitalized COVID-19 patients. The generalizability of many is unknown. We evaluated the performance of selected models from the literature and our own models to predict outcomes in patients at our institution. METHODS: We searched the literature for models predicting outcomes in inpatients with COVID-19. We produced models of mortality or criticality (mortality or ICU admission) in a development cohort. We tested external models which provided sufficient information and our models using a test cohort of our most recent patients. The performance of models was compared using the area under the receiver operator curve (AUC). RESULTS: Our literature review yielded 41 papers. Of those, 8 were found to have sufficient documentation and concordance with features available in our cohort to implement in our test cohort. All models were from Chinese patients. One model predicted criticality and seven mortality. Tested against the test cohort, internal models had an AUC of 0.84 (0.74-0.94) for mortality and 0.83 (0.76-0.90) for criticality. The best external model had an AUC of 0.89 (0.82-0.96) using three variables, another an AUC of 0.84 (0.78-0.91) using ten variables. AUC's ranged from 0.68 to 0.89. On average, models tested were unable to produce predictions in 27% of patients due to missing lab data. CONCLUSION: Despite differences in pandemic timeline, race, and socio-cultural healthcare context some models derived in China performed well. For healthcare organizations considering implementation of an external model, concordance between the features used in the model and features available in their own patients may be important. Analysis of both local and external models should be done to help decide on what prediction method is used to provide clinical decision support to clinicians treating COVID-19 patients as well as what lab tests should be included in order sets.

Cognitive Decline, Body Mass Index, and Waist Circumference in Community-Dwelling Elderly Participants.

OBJECTIVE: The aim of this study was to explore the association of body mass index (BMI), waist circumference (WC), and BMI and WC changes over time with cognitive decline in a nationally representative sample. METHODS: A total of 5239 participants (≥65 years) were followed for 3 years as part of the National Health and Aging Trends Study. Cox proportional hazard regression was applied to model the risk of cognitive decline. RESULTS: BMI, after adjusting for WC and main confounders, was associated with reduced risk of cognitive decline (hazard ratio [HR] 0.97 for each unit BMI increase, 0.95-0.99). After stratifying by gender and age, this effect remained significant among females and young elders ≤80 years. A BMI decrease and WC increase >10% over the study period were associated with increased risk of cognitive decline (HR 1.98, 1.16-3.38; HR 1.30, 1.04-1.62, respectively). CONCLUSION: In the elderly individuals, lean mass, as measured by BMI adjusted for WC, was associated with reduced risk of cognitive decline. Loss of lean mass and gain of fat mass, as measured by WC adjusted for BMI, were associated with elevated risk of cognitive decline.

Lessons learned: Development of COVID-19 clinical staging models at a large urban research institution.

Background/Objective: The University of Illinois at Chicago (UIC), along with many academic institutions worldwide, made significant efforts to address the many challenges presented during the COVID-19 pandemic by developing clinical staging and predictive models. Data from patients with a clinical encounter at UIC from July 1, 2019 to March 30, 2022 were abstracted from the electronic health record and stored in the UIC Center for Clinical and Translational Science Clinical Research Data Warehouse, prior to data analysis. While we saw some success, there were many failures along the way. For this paper, we wanted to discuss some of these obstacles and many of the lessons learned from the journey. Methods: Principle investigators, research staff, and other project team members were invited to complete an anonymous Qualtrics survey to reflect on the project. The survey included open-ended questions centering on participants' opinions about the project, including whether project goals were met, project successes, project failures, and areas that could have been improved. We then identified themes among the results. Results: Nine project team members (out of 30 members contacted) completed the survey. The responders were anonymous. The survey responses were grouped into four key themes: Collaboration, Infrastructure, Data Acquisition/Validation, and Model Building. Conclusion: Through our COVID-19 research efforts, the team learned about our strengths and deficiencies. We continue to work to improve our research and data translation capabilities.

Deep Learning-Based Digitally Reconstructed Tomography of the Chest in the Evaluation of Solitary Pulmonary Nodules: A Feasibility Study.

RATIONALE AND OBJECTIVES: Computed tomography (CT) is preferred for evaluating solitary pulmonary nodules (SPNs) but access or availability may be lacking, in addition, overlapping anatomy can hinder detection of SPNs on chest radiographs. We developed and evaluated the clinical feasibility of a deep learning algorithm to generate digitally reconstructed tomography (DRT) images of the chest from digitally reconstructed frontal and lateral radiographs (DRRs) and use them to detect SPNs. METHODS: This single-institution retrospective study included 637 patients with noncontrast helical CT of the chest (mean age 68 years, median age 69 years, standard deviation 11.7 years; 355 women) between 11/2012 and 12/2020, with SPNs measuring 10-30 mm. A deep learning model was trained on 562 patients, validated on 60 patients, and tested on the remaining 15 patients. Diagnostic performance (SPN detection) from planar radiography (DRRs and CT scanograms, PR) alone or with DRT was evaluated by two radiologists in an independent blinded fashion. The quality of the DRT SPN image in terms of nodule size and location, morphology, and opacity was also evaluated, and compared to the ground-truth CT images RESULTS: Diagnostic performance was higher from DRT plus PR than from PR alone (area under the receiver operating characteristic curve 0.95-0.98 versus 0.80-0.85; p < 0.05). DRT plus PR enabled diagnosis of SPNs in 11 more patients than PR alone. Interobserver agreement was 0.82 for DRT plus PR and 0.89 for PR alone; and interobserver agreement for size and location, morphology, and opacity of the DRT SPN was 0.94, 0.68, and 0.38, respectively. CONCLUSION: For SPN detection, DRT plus PR showed better diagnostic performance than PR alone. Deep learning can be used to generate DRT images and improve detection of SPNs.

Opportunistic detection of type 2 diabetes using deep learning from frontal chest radiographs.

Deep learning (DL) models can harness electronic health records (EHRs) to predict diseases and extract radiologic findings for diagnosis. With ambulatory chest radiographs (CXRs) frequently ordered, we investigated detecting type 2 diabetes (T2D) by combining radiographic and EHR data using a DL model. Our model, developed from 271,065 CXRs and 160,244 patients, was tested on a prospective dataset of 9,943 CXRs. Here we show the model effectively detected T2D with a ROC AUC of 0.84 and a 16% prevalence. The algorithm flagged 1,381 cases (14%) as suspicious for T2D. External validation at a distinct institution yielded a ROC AUC of 0.77, with 5% of patients subsequently diagnosed with T2D. Explainable AI techniques revealed correlations between specific adiposity measures and high predictivity, suggesting CXRs' potential for enhanced T2D screening.

It's time to change our documentation philosophy: writing better neurology notes without the burnout.

Succinct clinical documentation is vital to effective twenty-first-century healthcare. Recent changes in outpatient and inpatient evaluation and management (E/M) guidelines have allowed neurology practices to make changes that reduce the documentation burden and enhance clinical note usability. Despite favorable changes in E/M guidelines, some neurology practices have not moved quickly to change their documentation philosophy. We argue in favor of changes in the design, structure, and implementation of clinical notes that make them shorter yet still information-rich. A move from physician-centric to team documentation can reduce work for physicians. Changing the documentation philosophy from "bigger is better" to "short but sweet" can reduce the documentation burden, streamline the writing and reading of clinical notes, and enhance their utility for medical decision-making, patient education, medical education, and clinical research. We believe that these changes can favorably affect physician well-being without adversely affecting reimbursement.

Telemedicine Readiness Across Medical Conditions in a US National Representative Sample of Older Adults.

Telemedicine has provided older adults the ability to seek care remotely during the coronavirus disease (COVID-19) pandemic. However, it is unclear how diverse medical conditions play a role in telemedicine uptake. A total of 3379 participants (≥65 years) were interviewed in 2018 as part of the National Health and Aging Trends Study. We assessed telemedicine readiness across multiple medical conditions. Most chronic medical conditions and mood symptoms were significantly associated with telemedicine unreadiness, for physical or technical reasons or both, while cancer, hypertension, and arthritis were significantly associated with telemedicine readiness. Our findings suggest that multiple medical conditions play a substantial role in telemedicine uptake among older adults in the US. Therefore, comorbidities should be taken into consideration when promoting and adopting telemedicine technologies among older adults.

Detecting Racial/Ethnic Health Disparities Using Deep Learning From Frontal Chest Radiography.

PURPOSE: The aim of this study was to assess racial/ethnic and socioeconomic disparities in the difference between atherosclerotic vascular disease prevalence measured by a multitask convolutional neural network (CNN) deep learning model using frontal chest radiographs (CXRs) and the prevalence reflected by administrative hierarchical condition category codes in two cohorts of patients with coronavirus disease 2019 (COVID-19). METHODS: A CNN model, previously published, was trained to predict atherosclerotic disease from ambulatory frontal CXRs. The model was then validated on two cohorts of patients with COVID-19: 814 ambulatory patients from a suburban location (presenting from March 14, 2020, to October 24, 2020, the internal ambulatory cohort) and 485 hospitalized patients from an inner-city location (hospitalized from March 14, 2020, to August 12, 2020, the external hospitalized cohort). The CNN model predictions were validated against electronic health record administrative codes in both cohorts and assessed using the area under the receiver operating characteristic curve (AUC). The CXRs from the ambulatory cohort were also reviewed by two board-certified radiologists and compared with the CNN-predicted values for the same cohort to produce a receiver operating characteristic curve and the AUC. The atherosclerosis diagnosis discrepancy, Δvasc, referring to the difference between the predicted value and presence or absence of the vascular disease HCC categorical code, was calculated. Linear regression was performed to determine the association of Δvasc with the covariates of age, sex, race/ethnicity, language preference, and social deprivation index. Logistic regression was used to look for an association between the presence of any hierarchical condition category codes with Δvasc and other covariates. RESULTS: The CNN prediction for vascular disease from frontal CXRs in the ambulatory cohort had an AUC of 0.85 (95% confidence interval, 0.82-0.89) and in the hospitalized cohort had an AUC of 0.69 (95% confidence interval, 0.64-0.75) against the electronic health record data. In the ambulatory cohort, the consensus radiologists' reading had an AUC of 0.89 (95% confidence interval, 0.86-0.92) relative to the CNN. Multivariate linear regression of Δvasc in the ambulatory cohort demonstrated significant negative associations with non-English-language preference (ß = -0.083, P < .05) and Black or Hispanic race/ethnicity (ß = -0.048, P < .05) and positive associations with age (ß = 0.005, P < .001) and sex (ß = 0.044, P < .05). For the hospitalized cohort, age was also significant (ß = 0.003, P < .01), as was social deprivation index (ß = 0.002, P < .05). The Δvasc variable (odds ratio [OR], 0.34), Black or Hispanic race/ethnicity (OR, 1.58), non-English-language preference (OR, 1.74), and site (OR, 0.22) were independent predictors of having one or more hierarchical condition category codes (P < .01 for all) in the combined patient cohort. CONCLUSIONS: A CNN model was predictive of aortic atherosclerosis in two cohorts (one ambulatory and one hospitalized) with COVID-19. The discrepancy between the CNN model and the administrative code, Δvasc, was associated with language preference in the ambulatory cohort; in the hospitalized cohort, this discrepancy was associated with social deprivation index. The absence of administrative code(s) was associated with Δvasc in the combined cohorts, suggesting that Δvasc is an independent predictor of health disparities. This may suggest that biomarkers extracted from routine imaging studies and compared with electronic health record data could play a role in enhancing value-based health care for traditionally underserved or disadvantaged patients for whom barriers to care exist.

Validation of a deep learning, value-based care model to predict mortality and comorbidities from chest radiographs in COVID-19.

We validate a deep learning model predicting comorbidities from frontal chest radiographs (CXRs) in patients with coronavirus disease 2019 (COVID-19) and compare the model's performance with hierarchical condition category (HCC) and mortality outcomes in COVID-19. The model was trained and tested on 14,121 ambulatory frontal CXRs from 2010 to 2019 at a single institution, modeling select comorbidities using the value-based Medicare Advantage HCC Risk Adjustment Model. Sex, age, HCC codes, and risk adjustment factor (RAF) score were used. The model was validated on frontal CXRs from 413 ambulatory patients with COVID-19 (internal cohort) and on initial frontal CXRs from 487 COVID-19 hospitalized patients (external cohort). The discriminatory ability of the model was assessed using receiver operating characteristic (ROC) curves compared to the HCC data from electronic health records, and predicted age and RAF score were compared using correlation coefficient and absolute mean error. The model predictions were used as covariables in logistic regression models to evaluate the prediction of mortality in the external cohort. Predicted comorbidities from frontal CXRs, including diabetes with chronic complications, obesity, congestive heart failure, arrhythmias, vascular disease, and chronic obstructive pulmonary disease, had a total area under ROC curve (AUC) of 0.85 (95% CI: 0.85-0.86). The ROC AUC of predicted mortality for the model was 0.84 (95% CI,0.79-0.88) for the combined cohorts. This model using only frontal CXRs predicted select comorbidities and RAF score in both internal ambulatory and external hospitalized COVID-19 cohorts and was discriminatory of mortality, supporting its potential use in clinical decision making.

Predicting Prolonged Hospitalization and Supplemental Oxygenation in Patients with COVID-19 Infection from Ambulatory Chest Radiographs using Deep Learning.

RATIONALE AND OBJECTIVES: The clinical prognosis of outpatients with coronavirus disease 2019 (COVID-19) remains difficult to predict, with outcomes including asymptomatic, hospitalization, intubation, and death. Here we determined the prognostic value of an outpatient chest radiograph, together with an ensemble of deep learning algorithms predicting comorbidities and airspace disease to identify patients at a higher risk of hospitalization from COVID-19 infection. MATERIALS AND METHODS: This retrospective study included outpatients with COVID-19 confirmed by reverse transcription-polymerase chain reaction testing who received an ambulatory chest radiography between March 17, 2020 and October 24, 2020. In this study, full admission was defined as hospitalization within 14 days of the COVID-19 test for > 2 days with supplemental oxygen. Univariate analysis and machine learning algorithms were used to evaluate the relationship between the deep learning model predictions and hospitalization for > 2 days. RESULTS: The study included 413 patients, 222 men (54%), with a median age of 51 years (interquartile range, 39-62 years). Fifty-one patients (12.3%) required full admission. A boosted decision tree model produced the best prediction. Variables included patient age, frontal chest radiograph predictions of morbid obesity, congestive heart failure and cardiac arrhythmias, and radiographic opacity, with an internally validated area under the curve (AUC) of 0.837 (95% CI: 0.791-0.883) on a test cohort. CONCLUSION: Deep learning analysis of single frontal chest radiographs was used to generate combined comorbidity and pneumonia scores that predict the need for supplemental oxygen and hospitalization for > 2 days in patients with COVID-19 infection with an AUC of 0.837 (95% confidence interval: 0.791-0.883). Comorbidity scoring may prove useful in other clinical scenarios.

Temporal sequencing of nicotine dependence and major depressive disorder: A U.S. national study.

Major Depressive Disorder (MDD) and Nicotine dependence (ND) often co-occur. However, little attention has been given to the temporal order between the two disorders. We compared the sociodemographic and clinical characteristics of individuals whose onset of ND preceded (ND-prior) or followed the onset of MDD (MDD-prior). Binary logistic regression models were computed to compare ND-prior (n=546) and MDD-prior (n=801) individuals from the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC, n=43,093). We found that MDD-prior were more likely to have a history of suicide attempts and a family history of both depression and antisocial behavior, to have had psychiatric hospitalization, and to have an earlier age of onset of the first depressive episode; but a later age of onset for both daily smoking and ND. On average, MDD-prior individuals showed a significantly longer transition time from daily smoking to ND (15.6±0.6 vs. 6.9±0.4 years, P<0.001). In contrast, ND-prior subjects had a significantly greater proportion of withdrawal symptoms, and of lifetime alcohol use or alcohol use disorder. We conclude that the phenomenology and course of ND and MDD vary significantly, depending on which disorder had earlier onset.

Generalizability of Pharmacologic and Psychotherapy Clinical Trial Results for Posttraumatic Stress Disorder to Community Samples.

OBJECTIVE: The present study sought to quantify the generalizability of pharmacologic and psychotherapy clinical trial results in individuals with a DSM-IV diagnosis of posttraumatic stress disorder (PTSD) to a large representative community sample. METHODS: Data were derived from the 2004-2005 National Epidemiologic Survey on Alcohol and Related Conditions (NESARC), a large nationally representative sample of the adult US population. We applied a standard set of exclusion criteria representative of pharmacologic and psychotherapy clinical trials to all adults with a DSM-IV diagnosis of PTSD in the previous 12 months (n = 1,715) and then to a subsample of participants seeking treatment (n = 366). Our aim was to assess how many participants with PTSD would fulfill typical eligibility criteria. RESULTS: We found that more than 6 of 10 respondents from the overall PTSD sample and more than 7 of 10 respondents seeking treatment for PTSD would have been excluded by 1 exclusion criterion or more in a typical pharmacologic trial. In contrast, about 2 of 10 participants in the full sample and about 3 of 10 participants seeking treatment for PTSD would have been excluded in a typical psychotherapy efficacy trial. CONCLUSIONS: We found that psychotherapy trial results may be applied to most patients with PTSD in routine clinical practice. The designers of pharmacologic clinical trials should carefully consider the trade-offs between the application of each exclusion criterion and its impact on representativeness. Specification a priori of the goals of the study, better justification for each exclusion criterion, and estimation of the proportion of individuals ineligible for the trial would assist study design. Developing integrated forms of pharmacotherapy and psychotherapy that simultaneously target commonly overlapping psychiatric disorders may yield more informative results for mental health care providers and research funding agencies.

Generalizability of Neuroimaging Studies in 5 Common Psychiatric Disorders Based on the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC).

OBJECTIVE: Although neuroimaging studies have an important role in psychiatric nosology and treatment development, little is known about the representativeness of participants in neuroimaging research. We estimated the effects of commonly used study eligibility criteria on the representativeness of neuroimaging research participants in relation to the general population with the psychiatric disorders of interest. METHODS: Common eligibility criteria were applied from 112 published neuroimaging studies of DSM-IV nicotine dependence (13 studies), alcohol dependence (12 studies), drug use disorders (13 studies), major depressive disorder (MDD) (37 studies), and posttraumatic stress disorder (PTSD) (36 studies) to representative US samples with these conditions from the 2001-2002 National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) (n = 43,093). The analyses were repeated with NESARC respondents with the disorders and substantial psychosocial impairment. RESULTS: Most NESARC respondents with nicotine dependence (64.1%), alcohol dependence (57.7%), drug use disorders (86.6%), and PTSD (66.9%), though not with MDD (18.2%), would have been excluded by eligibility criteria used in at least half of the relevant neuroimaging studies. Across the diagnostic groups, comorbid psychiatric and general medical conditions resulted in the largest percentages of exclusions. Corresponding analyses limited to respondents with substantial impairment excluded larger percentages with nicotine dependence (77.6%), alcohol dependence (75.8%), drug use disorders (93.5%), and PTSD (76.8%), though not MDD (18.3%). CONCLUSIONS: Neuroimaging studies tend to recruit highly selected samples with the psychiatric disorders of interest that markedly underrepresent individuals with common comorbid conditions. Larger studies with less restrictive eligibility criteria may promote translation of advances in neuroimaging research to populations commonly encountered in clinical practice.

Impaired consciousness in patients with absence seizures investigated by functional MRI, EEG, and behavioural measures: a cross-sectional study.

BACKGROUND: The neural underpinnings of impaired consciousness and of the variable severity of behavioural deficits from one absence seizure to the next are not well understood. We aimed to measure functional MRI (fMRI) and electroencephalography (EEG) changes in absence seizures with impaired task performance compared with seizures in which performance was spared. METHODS: In this cross-sectional study done at the Yale School of Medicine, CT, USA, we recruited patients from 59 paediatric neurology practices in the USA. We did simultaneous EEG, fMRI, and behavioural testing in patients aged 6-19 years with childhood or juvenile absence epilepsy, and with an EEG with typical 3-4 Hz bilateral spike-wave discharges and normal background. The main outcomes were fMRI and EEG amplitudes in seizures with impaired versus spared behavioural responses analysed by t test. We also examined the timing of fMRI and EEG changes in seizures with impaired behavioural responses compared with seizures with spared responses. FINDINGS: 93 patients were enrolled between Jan 1, 2005, and Sept 1, 2013; we recorded 1032 seizures in 39 patients. fMRI changes during seizures occurred sequentially in three functional brain networks. In the default mode network, fMRI amplitude was 0·57% (SD 0·26) for seizures with impaired and 0·40% (0·16) for seizures with spared behavioural responses (mean difference 0·17%, 95% CI 0·11-0·23; p<0·0001). In the task-positive network, fMRI amplitude was 0·53% (SD 0·29) for seizures with impaired and 0·39% (0·15) for seizures with spared behavioral responses (mean difference 0·14%, 95% CI 0·08-0·21; p<0·0001). In the sensorimotor-thalamic network, fMRI amplitude was 0·41% (0·25) for seizures with impaired and 0·34% (0·14) for seizures with spared behavioural responses (mean difference 0·07%, 95% CI 0·01-0·13; p=0·02). Mean fractional EEG power in the frontal leads was 50·4 (SD 15·2) for seizures with impaired and 24·8 (6·5) for seizures with spared behavioural responses (mean difference 25·6, 95% CI 21·0-30·3); middle leads 35·4 (6·5) for seizures with impaired, 13·3 (3·4) for seizures with spared behavioural responses (mean difference 22·1, 95% CI 20·0-24·1); posterior leads 41·6 (5·3) for seizures with impaired, 24·6 (8·6) for seizures with spared behavioural responses (mean difference 17·0, 95% CI 14·4-19·7); p<0·0001 for all comparisons. Mean seizure duration was longer for seizures with impaired behaviour at 7·9 s (SD 6·6), compared with 3·8 s (3·0) for seizures with spared behaviour (mean difference 4·1 s, 95% CI 3·0-5·3; p<0·0001). However, larger amplitude fMRI and EEG signals occurred at the outset or even preceding seizures with behavioural impairment. INTERPRETATION: Impaired consciousness in absence seizures is related to the intensity of physiological changes in established networks affecting widespread regions of the brain. Increased EEG and fMRI amplitude occurs at the onset of seizures associated with behavioural impairment. These finding suggest that a vulnerable state might exist at the initiation of some absence seizures leading them to have more severe physiological changes and altered consciousness than other absence seizures. FUNDING: National Institutes of Health, National Institute of Neurological Disorders and Stroke, National Center for Advancing Translational Science, the Loughridge Williams Foundation, and the Betsy and Jonathan Blattmachr Family.

Age at onset of major depression and adulthood cardiovascular risk.

Childhood-onset compared to adulthood-onset of major depression is associated with increased rates of serious cardiovascular events, independently of cardiovascular risk factors. This could be explained by a longer duration of exposure to depression. Cardiovascular disease risk should be systematically assessed in individuals with long duration of major depression.

Probability and predictors of treatment-seeking for substance use disorders in the U.S.

BACKGROUND: Little is known about to what extent treatment-seeking behavior varies across individuals with alcohol abuse, alcohol dependence, drug abuse, and drug dependence. METHODS: The sample included respondents from the Wave 2 of the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) who reported a lifetime diagnosis alcohol abuse, alcohol dependence, drug abuse, or drug dependence. Unadjusted and adjusted hazard ratios are presented for time to first treatment contact by sociodemographic characteristics and comorbid psychiatric disorders. Individuals were censored from the analyses if their condition remitted prior to seeking treatment. RESULTS: In the first year after disorder onset, rates of treatment-seeking were 13% for drug dependence, 5% for alcohol dependence, 2% for drug abuse, and 1% for alcohol abuse. The lifetime probability of seeking treatment among individuals who did not remit was also highest for drug dependence (90%), followed by drug abuse (60%), alcohol dependence (54%), and alcohol abuse (16%). Having had previous treatment contact for a substance use disorder (SUD) increased the probability of seeking treatment for another SUD. By contrast, an early age of SUD onset, belonging to an older cohort, and a higher level of education decreased the lifetime probability of treatment contact for SUD. The role of comorbid mental disorders was more complex, with some disorders increasing and other decreasing the probability of seeking treatment. CONCLUSIONS: Given high rates of SUD and their substantial health and economic burden, these patterns suggest the need for innovative approaches to increase treatment access for individuals with SUD.