PCOM Library / Archive for "Hot Topics in Research"

Category: Hot Topics in Research

Hot Topics: Tobacco plus Cannabis Lowers Functioning

Jackie Werner Hot Topics in Research, Substance Use Disorders

Types of cannabis and tobacco/nicotine co-use and associated outcomes in young adulthood

Tucker JS, Pedersen ER, Seelam R, Dunbar MS, Shih RA, D’Amico EJ. Types of cannabis and tobacco/nicotine co-use and associated outcomes in young adulthood. Psychology of Addictive Behaviors. 2019. https://doi.org/10.1037/adb0000464

Cannabis and tobacco/nicotine use are highly comorbid. Given expanding access to cannabis through legalization for recreational use, it is important to understand how patterns of cannabis and tobacco/nicotine co-use are associated with young adult outcomes. A predominantly California-based sample of 2,429 young adults (mean age = 20.7) completed an online survey. Based on past-year reports of cannabis and tobacco/nicotine use, we defined 5 mutually exclusive groups: (a) single-product use; (b) concurrent use only (using both products, but only on separate occasions); (c) sequential use only (using both products on the same occasion, one right after the other, but not mixing them together); (d) coadministration only (using both products on the same occasion by mixing them in the same delivery device); and (e) both sequential use and coadministration. We examined group differences in use patterns, dependence, consequences of use, and psychosocial functioning. Fifty percent of respondents reported cannabis use, 43% tobacco/nicotine use, and 37% co-use of both substances. The most prevalent method of co-use involved smoking combustible products. Overall, individuals who co-used both substances on the same occasion in some way reported heavier use and greater problematic behaviors than those who did not. Sequential use (especially among those that also engaged in coadministration) was typically associated with worse physical and mental functioning overall compared to using each substance separately. Findings illuminate both prevalence and risks associated with co-use of cannabis and tobacco/nicotine products and can inform policies for states considering regulation of cannabis and tobacco/nicotine products.

Hot Topics: Anti-Stress Receptors Linked to PTSD

Jackie Werner Hot Topics in Research, Psychology and Psychiatry

Decreased Nociceptin Receptors Are Related to Resilience and Recovery in College Women Who Have Experienced Sexual Violence: Therapeutic Implications for Posttraumatic Stress Disorder

Narendran R, Tollefson S, Fasenmyer K, et al. Decreased nociceptin receptors are related to resilience and recovery in college women who have experienced sexual violence: Therapeutic implications for posttraumatic stress disorder. Biological Psychiatry. 2019. https://doi.org/10.1016/j.biopsych.2019.02.017.

Background

Posttraumatic stress disorder (PTSD) is a stress disorder that develops in only some individuals following a traumatic event. Data suggest that a substantial fraction of women recover after sexual violence. Thus, the investigation of stress and antistress neuropeptides in this sample has the potential to inform the neurochemistry of resilience following trauma. Nociceptin is an antistress neuropeptide in the brain that promotes resilience in animal models of PTSD.

Methods

[11C]NOP-1A positron emission tomography was used to measure the in vivobinding to nociceptin receptors in 18 college women who had experienced sexual violence irrespective of whether they met DSM-5 diagnostic criteria for PTSD. [11C]NOP-1A data from 18 healthy control subjects were also included to provide a contrast with the sexual violence group. [11C]NOP-1A total distribution volume (VT) in the regions of interest were measured with kinetic analysis using the arterial input function. The relationships between regional VT and Clinician-Administered PTSD Scale for DSM-5 total symptom and subscale severity were examined using correlational analyses.

Results

No differences in [11C]NOP-1A VT were noted between the sexual violence and control groups. VT in the midbrain and cerebellum were positively correlated with PTSD total symptom severity in the past month before positron emission tomography. Intrusion/re-experiencing and avoidance subscale symptoms drove this relationship. Stratification of subjects by a DSM-5 PTSD diagnosis and contrasting their VT with that in control subjects showed no group differences.

Conclusions

Decreased midbrain and cerebellum nociceptin receptors are associated with less severe PTSD symptoms. Medications that target nociceptin should be explored to prevent and treat PTSD.

Hot Topics: Steroid Implant Restores Sight

Jackie Werner Hot Topics in Research, Oncology, Surgery

Outcomes Associated With Sustained-Release Intraocular Fluocinolone Implants in a Case of Melanoma-Associated Retinopathy Treated Without Systemic Immunosuppression

Karatsai E, Robson AG, Taylor SRJ. Outcomes associated with sustained-release intraocular fluocinolone implants in a case of melanoma-associated retinopathy treated without systemic immunosuppression. 2019. https://doi.org/10.1001/jamaophthalmol.2019.0284.

Importance  Melanoma-associated retinopathy (MAR) is a paraneoplastic syndrome in which antiretinal antibodies crossreact with retinal ON-bipolar cells, resulting in night blindness and progressive visual field loss. Current therapeutic options include cytoreductive surgery in combination with immunoglobulin, corticosteroids, or plasmapheresis, but their effectiveness is limited and may be contraindicated, given the possible protective role of circulating autoantibodies against metastatic spread. We report 3-year follow-up of the first case (to our knowledge) of MAR treated with intravitreal long-acting steroid implants.

Objective  To report on a patient with MAR who was treated with intravitreal fluocinolone acetonide implants in the absence of systemic immunosuppression.

Design, Setting, and Participants  This is a 3-year follow-up of a 73-year-old woman with a history of surgical excision of a malignant melanoma of the left pinna who presented with visual symptoms of shimmering and nyctalopia. Fundus examination, fundus autofluorescence, and optical coherence tomography were normal, with no evidence of cystoid macular edema. Automated perimetry showed a reduction in visual field and full-field electroretinography (ERG) demonstrated findings consistent with generalized ON-bipolar cell dysfunction, typical of MAR. The patient was treated with bilateral fluocinolone acetonide intravitreal implants.

Main Outcomes and Measures  Visual acuity, visual field, and electroretinography testing for 3 years after treatment.

Results  Visual fields improved in this 73-year-old patient from 20/30 (Snellen measured as 6/9) OD and 20/16 (6/5) OS at baseline to 20/20 OU within 1 week of treatment. Detailed electroretinography monitoring indicated characteristic abnormalities that partly resolved after treatment, consistent with improved inner retinal ON-bipolar cell function. Bilateral cataracts developed approximately 2 years after injection; cataract surgery was performed uneventfully. At 3 years posttreatment, the patient remained visually stable and in systemic disease remission, with best-corrected visual acuity remaining at 20/20 OU.

Conclusions and Relevance  We report what is, to our knowledge, the first case of MAR treated with intravitreal slow-release corticosteroid implants, which shows improvements in visual symptoms, visual fields, and retinal function. Sustained-release intraocular steroid implants may offer an effective and safe alternative to systemic immunosuppression in MAR, although results from 1 case should be generalized with abundant caution.

Hot Topics: Algorithm Predicts Irregular Heartbeats

Jackie Werner Cardiology, Hot Topics in Research

A New Prediction Model for Ventricular Arrhythmias in Arrhythmogenic Right Ventricular Cardiomyopathy

Bhonsale A, Murray B, Tichnell C, et al. A new prediction model for ventricular arrhythmias in arrhythmogenic right ventricular cardiomyopathy. . 2019. https://doi.org/10.1093/eurheartj/ehz103.

Aims: Arrhythmogenic right ventricular dysplasia/cardiomyopathy (ARVC) is characterized by ventricular arrhythmias (VAs) and sudden cardiac death (SCD). We aimed to develop a model for individualized prediction of incident VA/SCD in ARVC patients.

Methods and Results: Five hundred and twenty-eight patients with a definite diagnosis and no history of sustained VAs/SCD at baseline, aged 38.2 ± 15.5 years, 44.7% male, were enrolled from five registries in North America and Europe. Over 4.83 (interquartile range 2.44–9.33) years of follow-up, 146 (27.7%) experienced sustained VA, defined as SCD, aborted SCD, sustained ventricular tachycardia, or appropriate implantable cardioverter-defibrillator (ICD) therapy. A prediction model estimating annual VA risk was developed using Cox regression with internal validation. Eight potential predictors were pre-specified: age, sex, cardiac syncope in the prior 6 months, non-sustained ventricular tachycardia, number of premature ventricular complexes in 24 h, number of leads with T-wave inversion, and right and left ventricular ejection fractions (LVEFs). All except LVEF were retained in the final model. The model accurately distinguished patients with and without events, with an optimism-corrected C-index of 0.77 [95% confidence interval (CI) 0.73–0.81] and minimal over-optimism [calibration slope of 0.93 (95% CI 0.92–0.95)]. By decision curve analysis, the clinical benefit of the model was superior to a current consensus-based ICD placement algorithm with a 20.6% reduction of ICD placements with the same proportion of protected patients (P < 0.001).

Conclusion: Using the largest cohort of patients with ARVC and no prior VA, a prediction model using readily available clinical parameters was devised to estimate VA risk and guide decisions regarding primary prevention ICDs (www.arvcrisk.com).

Hot Topics: Training Muscles Before Surgery Eases Autotransplantation

Jackie Werner Hot Topics in Research, Pediatrics, Sports Medicine, Surgery

Perspectives for the Use of Neurotechnologies in Conjunction With Muscle Autotransplantation in Children

Blagovechtchenski E, Agranovich O, Kononova Y, Nazarova M, Nikulin VV. Perspectives for the use of neurotechnologies in conjunction with muscle autotransplantation in children. Frontiers in Neuroscience. 2019;13:99. https://doi.org/10.3389/fnins.2019.00099

Muscles autotransplantation is an important way to restore motor activity in case of injury or diseases associated with a loss of muscles ability. One of the typical examples of such pathology is arthrogryposis multiplex congenita (AMC). Arthrogryposis is one of the most serious congenital malformations of the musculoskeletal system. It is characterized by the presence of two or more major joint contractures, muscle damage, and motoneuronal dysfunction in the anterior horns of the spinal cord. One of the main problems that determines the limitation or even impossibility of self-care of patients suffering from arthrogryposis is the lack of active movements in the upper limb joints, which can be restored by autotransplantation of the muscles of various donor areas (Hall, 1997Bamshad et al., 2009Loeffler and Lewis, 2016).

Hot Topics: Loan Forgiveness Drives DOs to Primary Care

Jackie Werner Family Medicine, Hot Topics in Research, Osteopathic Manipulative Medicine

Role of Debt and Loan Forgiveness/Repayment Programs in Osteopathic Medical Graduates’ Plans to Enter Primary Care

Scheckel CJ, Richards J, Newman JR, et al. Role of debt and loan forgiveness/repayment programs in osteopathic medical graduates’ plans to enter primary care. JAOA. 2019;119(4):227-235. https://doi.org/10.7556/jaoa.2019.038.

Context: Osteopathic medicine emphasizes partnering with patients to help them attain or maintain health. This philosophy encourages physicians to practice primary care and a mission of improving community health. However, there is currently a shortage of primary care physicians in many areas of the United States.

Objective: To determine whether intended practice patterns of recent graduates of colleges of osteopathic medicine favor primary care and whether practice patterns correlate with medical education debt.

Methods: Responses were analyzed from the American Association of Colleges of Osteopathic Medicine survey of pending medical school graduates from 2007 through 2016 regarding indebtedness and specialty selection.

Results: The percentage of graduating osteopathic medical students who chose a primary care specialty increased from 28.1% (676 students) in 2007 to 33.2% (1377 students) in 2016. Among graduates, those above the 75th percentile of debt had a general move toward more non–primary care positions, with a value of 74.4% in 2007 and 79.9% in 2016. Graduates below the 25th percentile had a gradual increase in primary care representation, moving from 24.6% in 2007 to 29.4% in 2016. In 2007, graduates with a loan forgiveness/repayment program were more likely to choose primary care over graduates without such a program (OR, 0.681 [95% CI, 0.505-0.920]; P=.02). Analysis of subsequent years showed a declining OR with increasing significance.

Conclusions: Results of this analysis indicated that increased educational debt loan directly influenced physician practice choice. Graduates with high debt burden were more likely to enter primary care fields and use loan forgiveness/repayment programs. Graduates with high debt burden who did not use loan forgiveness/repayment programs were more likely to enter non–primary care specialty fields, with this trend increasing as mean medical school debt increased. This association has implications for policies that could affect choice of primary care. However, further research is needed to fully understand the primary care choice by graduates of colleges of osteopathic medicine.

Hot Topics: New Schizophrenia Model May Aid Prevention

Jackie Werner Hot Topics in Research, Psychology and Psychiatry, Schizophrenia

Towards Artificial Intelligence in Mental Health by Improving Schizophrenia Prediction with Multiple Brain Parcellation Ensemble-Learning

Kalmady SV, Greiner R, Agrawal R, et al. Towards artificial intelligence in mental health by improving schizophrenia prediction with multiple brain parcellation ensemble-learning. npj Schizophrenia. 2019;5(1):2. https://doi.org/10.1038/s41537-018-0070-8.

In the literature, there are substantial machine learning attempts to classify schizophrenia based on alterations in resting-state (RS) brain patterns using functional magnetic resonance imaging (fMRI). Most earlier studies modelled patients undergoing treatment, entailing confounding with drug effects on brain activity, and making them less applicable to real-world diagnosis at the point of first medical contact. Further, most studies with classification accuracies >80% are based on small sample datasets, which may be insufficient to capture the heterogeneity of schizophrenia, limiting generalization to unseen cases. In this study, we used RS fMRI data collected from a cohort of antipsychotic drug treatment-naive patients meeting DSM IV criteria for schizophrenia (N = 81) as well as age- and sex-matched healthy controls (N = 93). We present an ensemble model — EMPaSchiz (read as ‘Emphasis’; standing for ‘Ensemble algorithm with Multiple Parcellations for Schizophrenia prediction’) that stacks predictions from several ‘single-source’ models, each based on features of regional activity and functional connectivity, over a range of different a priori parcellation schemes. EMPaSchiz yielded a classification accuracy of 87% (vs. chance accuracy of 53%), which out-performs earlier machine learning models built for diagnosing schizophrenia using RS fMRI measures modelled on large samples (N > 100). To our knowledge, EMPaSchiz is first to be reported that has been trained and validated exclusively on data from drug-naive patients diagnosed with schizophrenia. The method relies on a single modality of MRI acquisition and can be readily scaled-up without needing to rebuild parcellation maps from incoming training images.

Hot Topics: First Risk Genes for Autism Discovered

Jackie Werner Developmental Disorders, Hot Topics in Research, Psychology and Psychiatry

Identification of common genetic risk variants for autism spectrum disorder

Grove J, Ripke S, Als TD, et al. Identification of common genetic risk variants for autism spectrum disorder. Nat Genet. 2019;51(3):431-444. https://doi.org/10.1038/s41588-019-0344-8.

Autism spectrum disorder (ASD) is a highly heritable and heterogeneous group of neurodevelopmental phenotypes diagnosed in more than 1% of children. Common genetic variants contribute substantially to ASD susceptibility, but to date no individual variants have been robustly associated with ASD. With a marked sample-size increase from a unique Danish population resource, we report a genome-wide association meta-analysis of 18,381 individuals with ASD and 27,969 controls that identified five genome-wide-significant loci. Leveraging GWAS results from three phenotypes with significantly overlapping genetic architectures (schizophrenia, major depression, and educational attainment), we identified seven additional loci shared with other traits at equally strict significance levels. Dissecting the polygenic architecture, we found both quantitative and qualitative polygenic heterogeneity across ASD subtypes. These results highlight biological insights, particularly relating to neuronal function and corticogenesis, and establish that GWAS performed at scale will be much more productive in the near term in ASD.

Hot Topics: Online Intervention May Decrease HIV Risk

Jackie Werner Hot Topics in Research, Infectious Disease, Psychology and Psychiatry

Acceptability and Preliminary Efficacy of an Online HIV Prevention Intervention for Single Young Men Who Have Sex with Men Seeking Partners Online: The myDEx Project

Bauermeister JA, Tingler RC, Demers M, et al. Acceptability and preliminary efficacy of an online HIV prevention intervention for single young men who have sex with men seeking partners online: The myDEx project. AIDS and Behavior. 2019. https://doi.org/10.1007/s10461-019-02426-7.

Prevention of new cases of HIV among young gay, bisexual and other men who have sex with men (YGBMSM; ages 18–24) remains a priority. We developed and pilot tested an online intervention (myDEx) using a pilot randomized trial design with 180 online-recruited single YGBMSM who reported recent unprotected anal intercourse, self-reporting as HIV negative or status-unaware, and who met sexual partners through online dating applications. myDEx participants reported higher overall satisfaction (d = 0.46) and willingness to recommend the intervention to friends (d = 0.48) than controls. myDEx participants were less likely to report foregoing condoms to achieve an emotional connection with a partner (d =0 .43), and more likely to report greater emotional regulation during their partner-seeking behaviors (d = 0.44). myDEx participants reported fewer partners with whom they had condomless receptive anal sex (d = 0.48). Our pilot results demonstrate the potential of the myDEx intervention, suggesting that a larger efficacy trial may be warranted in the future.

Hot Topics: Routine Data Can Quickly Detect Sepsis in Newborns

Jackie Werner Critical Care, Hot Topics in Research, Pediatrics

Machine learning models for early sepsis recognition in the neonatal intensive care unit using readily available electronic health record data

Masino AJ, Harris MC, Forsyth D, et al. Machine learning models for early sepsis recognition in the neonatal intensive care unit using readily available electronic health record data. PLOS ONE. 2019;14(2):e0212665. https://doi.org/10.1371/journal.pone.0212665.

Background

Rapid antibiotic administration is known to improve sepsis outcomes, however early diagnosis remains challenging due to complex presentation. Our objective was to develop a model using readily available electronic health record (EHR) data capable of recognizing infant sepsis at least 4 hours prior to clinical recognition.

Methods and findings

We performed a retrospective case control study of infants hospitalized ≥48 hours in the Neonatal Intensive Care Unit (NICU) at the Children’s Hospital of Philadelphia between September 2014 and November 2017 who received at least one sepsis evaluation before 12 months of age. We considered two evaluation outcomes as cases: culture positive–positive blood culture for a known pathogen (110 evaluations); and clinically positive–negative cultures but antibiotics administered for ≥120 hours (265 evaluations). Case data was taken from the 44-hour window ending 4 hours prior to evaluation. We randomly sampled 1,100 44-hour windows of control data from all times ≥10 days removed from any evaluation. Model inputs consisted of up to 36 features derived from routine EHR data. Using 10-fold nested cross-validation, 8 machine learning models were trained to classify inputs as sepsis positive or negative. When tasked with discriminating culture positive cases from controls, 6 models achieved a mean area under the receiver operating characteristic (AUC) between 0.80–0.82 with no significant differences between them. Including both culture and clinically positive cases, the same 6 models achieved an AUC between 0.85–0.87, again with no significant differences.

Conclusions

Machine learning models can identify infants with sepsis in the NICU hours prior to clinical recognition. Learning curves indicate model improvement may be achieved with additional training examples. Additional input features may also improve performance. Further research is warranted to assess potential performance improvements and clinical efficacy in a prospective trial.