Regional Variation in Pulmonary Arterial Hypertension in the United States: The Pulmonary Hypertension Association Registry.

Rationale: Pulmonary arterial hypertension (PAH) is a heterogeneous disease within a complex diagnostic and treatment environment. Other complex heart and lung diseases have substantial regional variation in characteristics and outcomes; however, this has not been previously described in PAH. Objectives: To identify baseline differences between U.S. census regions in the characteristics and outcomes for participants in the Pulmonary Hypertension Association Registry (PHAR). Methods: Adults with PAH were divided into regional groups (Northeast, South, Midwest, and West), and baseline differences between census regions were presented. Kaplan-Meier survival analyses and Cox proportional hazards were used to estimate the association between region and mortality in unadjusted and adjusted models. Results: Substantial differences by census regions were seen in age, race, ethnicity, marital status, employment, insurance payor breakdown, active smoking, and current alcohol use. Differences were also seen in PAH etiology and baseline 6-minute walk distance test results. Treatment characteristics varied by census region, and mortality appeared to be lower in PHAR participants in the West (hazard ratio, 0.60; 95% confidence interval, 0.43-0.83, P = 0.005). This difference was not readily explained by differences in demographic characteristics, PAH etiology, baseline severity, baseline medication regimen, or disease prevalence. Conclusions: The present study suggests significant regional variation among participants at accredited pulmonary vascular disease centers in multiple baseline characteristics and mortality. This variation may have implications for clinical research planning and represent an important focus for further study to better understand whether there are remediable care aspects that can be addressed in the pursuit of providing equitable care in the United States.

The Predictive Potential of Elevated Serum Inflammatory Markers in Determining the Need for Intubation in CoVID-19 Patients.

Introduction: The predictive potential of demographics, clinical characteristics, and inflammatory markers at admission to determine future intubation needs of hospitalised CoVID-19 patients is unknown. The study aimed to determine the predictive potential of elevated serum inflammatory markers in determining the need for intubation in CoVID-19 Patients. Methods: In a retrospective cohort study of hospitalised SARS-CoV2 positive patients, single and multivariable regression analyses were used to determine covariate effects on intubation odds, and a minimax concave penalty regularised logistic regression was used to build a predictive model. A second prospective independent cohort tested the model. Results: Systemic inflammatory markers obtained at admission were higher in patients that required subsequent intubation, and adjusted odds of intubation increased for every standard deviation above the mean for c-reactive protein (CRP) OR:2.8 (95% CI 1.8-4.5, p<0.001) and lactate dehydrogenase OR:2.1 (95% CI 1.33.3, p=0.002). A predictive model incorporating C-reactive protein, lactate dehydrogenase, and diabetes status at the time of admission predicted intubation status with an area under the curve (AUC) of 0.78 with corresponding sensitivity of 86%, specificity of 63%. This predictive model achieved an AUC of 0.83, 91% sensitivity, and 41% specificity on the validation cohort. Conclusion: In patients hospitalised with CoVID-19, elevated serum inflammatory markers measured within the first twenty-four hours of admission are associated with an increased need for intubation. Additionally, a model of C-reactive protein, lactate dehydrogenase, and the presence of diabetes may play a predictive role in determining the future need for intubation.

Elevated glycohemoglobin is linked to critical illness in CoVID-19: a retrospective analysis.

BACKGROUND: Several studies have explored hospitalization risk factors with the novel coronavirus disease 2019 (COVID-19) infection. Our goal was to identify clinical characteristics outside of laboratory or radiologic data associated with intubation or death within 7 days of admission. METHODS: The first 436 patients admitted to the University of Colorado Hospital (Denver metropolitan area) with confirmed COVID-19 were included. Demographics, comorbidities, and select medications were collected by chart abstraction. Missing height for calculating body mass index (BMI) was imputed using the median height for patients' sex and race/ethnicity. Adjusted odds ratios (aOR) were estimated using multivariable logistic regression and a minimax concave penalty (MCP) regularized logistic regression explored prediction. RESULTS: Participants had a mean [standard deviation (SD)] age 55 (17), BMI 30.9 (8.2), 55% were male and 80% were ethnic/racial minorities. Increasing age [aOR: 1.24 (1.07, 1.45) per 10 years], higher BMI (aOR 1.03 (1.00, 1.06), and poorly controlled diabetes [hemoglobin A1C (HbA1c) ⩾ 8] (aOR 2.26 (1.24, 4.12) were significantly (p < 0.05) associated with greater odds of intubation or death. Female sex [aOR: 0.63, 95% CI (0.40, 0.98); p value = 0.043] was associated with lesser odds of intubation or death. The odds of death and/or intubation increased 19% for every 1 unit increase in HbA1c value [OR: 1.19 (1.01, 1.43); p = 0.04]. Our final MCP model included indicators of A1C ⩾ 8, age > 65, sex, and minority status, but predicted intubation/death only slightly better than random chance [area under the receiver operating characteristic curve (AUC) = 0.61 (0.56, 0.67)]. CONCLUSION: In a hospitalized patient cohort with COVID-19, worsening control of diabetes as evidenced by higher HbA1c was associated with increased risk of intubation or death within 7 days of admission. These results complement and help clarify previous associations found between diabetes and acute disease in COVID-19. Importantly, our analysis is missing some known predictors of severity in COVID-19. Our predictive model had limited success, suggesting unmeasured factors contribute to disease severity differences.

Resting-state functional connectivity predicts longitudinal pain symptom change in urologic chronic pelvic pain syndrome: a MAPP network study.

Chronic pain symptoms often change over time, even in individuals who have had symptoms for years. Studying biological factors that predict trends in symptom change in chronic pain may uncover novel pathophysiological mechanisms and potential therapeutic targets. In this study, we investigated whether brain functional connectivity measures obtained from resting-state functional magnetic resonance imaging at baseline can predict longitudinal symptom change (3, 6, and 12 months after scan) in urologic chronic pelvic pain syndrome. We studied 52 individuals with urologic chronic pelvic pain syndrome (34 women, 18 men) who had baseline neuroimaging followed by symptom tracking every 2 weeks for 1 year as part of the Multidisciplinary Approach to the Study of Chronic Pelvic Pain (MAPP) Research Network study. We found that brain functional connectivity can make a significant prediction of short-term (3 month) pain reduction with 73.1% accuracy (69.2% sensitivity and 75.0% precision). In addition, we found that the brain regions with greatest contribution to the classification were preferentially aligned with the left frontoparietal network. Resting-state functional magnetic resonance imaging measures seemed to be less informative about 6- or 12-month symptom change. Our study provides the first evidence that future trends in symptom change in patients in a state of chronic pain may be linked to functional connectivity within specific brain networks.

Sex-based differences in brain alterations across chronic pain conditions.

Common brain mechanisms are thought to play a significant role across a multitude of chronic pain syndromes. In addition, there is strong evidence for the existence of sex differences in the prevalence of chronic pain and in the neurobiology of pain. Thus, it is important to consider sex when developing general principals of pain neurobiology. The goal of the current Mini-Review is to evaluate what is known about sex-specific brain alterations across multiple chronic pain populations. A total of 15 sex difference and 143 single-sex articles were identified from among 412 chronic pain neuroimaging articles. Results from sex difference studies indicate more prominent primary sensorimotor structural and functional alterations in female chronic pain patients compared with male chronic pain patients: differences in the nature and degree of insula alterations, with greater insula reactivity in male patients; differences in the degree of anterior cingulate structural alterations; and differences in emotional-arousal reactivity. Qualitative comparisons of male-specific and female-specific studies appear to be consistent with the results from sex difference studies. Given these differences, mixed-sex studies of chronic pain risk creating biased data or missing important information and single-sex studies have limited generalizability. The advent of large-scale neuroimaging databases will likely aid in building a more comprehensive understanding of sex differences and commonalities in brain mechanisms underlying chronic pain. © 2016 Wiley Periodicals, Inc.

Patterns of brain structural connectivity differentiate normal weight from overweight subjects.

BACKGROUND: Alterations in the hedonic component of ingestive behaviors have been implicated as a possible risk factor in the pathophysiology of overweight and obese individuals. Neuroimaging evidence from individuals with increasing body mass index suggests structural, functional, and neurochemical alterations in the extended reward network and associated networks. AIM: To apply a multivariate pattern analysis to distinguish normal weight and overweight subjects based on gray and white-matter measurements. METHODS: Structural images (N = 120, overweight N = 63) and diffusion tensor images (DTI) (N = 60, overweight N = 30) were obtained from healthy control subjects. For the total sample the mean age for the overweight group (females = 32, males = 31) was 28.77 years (SD = 9.76) and for the normal weight group (females = 32, males = 25) was 27.13 years (SD = 9.62). Regional segmentation and parcellation of the brain images was performed using Freesurfer. Deterministic tractography was performed to measure the normalized fiber density between regions. A multivariate pattern analysis approach was used to examine whether brain measures can distinguish overweight from normal weight individuals. RESULTS: 1. White-matter classification: The classification algorithm, based on 2 signatures with 17 regional connections, achieved 97% accuracy in discriminating overweight individuals from normal weight individuals. For both brain signatures, greater connectivity as indexed by increased fiber density was observed in overweight compared to normal weight between the reward network regions and regions of the executive control, emotional arousal, and somatosensory networks. In contrast, the opposite pattern (decreased fiber density) was found between ventromedial prefrontal cortex and the anterior insula, and between thalamus and executive control network regions. 2. Gray-matter classification: The classification algorithm, based on 2 signatures with 42 morphological features, achieved 69% accuracy in discriminating overweight from normal weight. In both brain signatures regions of the reward, salience, executive control and emotional arousal networks were associated with lower morphological values in overweight individuals compared to normal weight individuals, while the opposite pattern was seen for regions of the somatosensory network. CONCLUSIONS: 1. An increased BMI (i.e., overweight subjects) is associated with distinct changes in gray-matter and fiber density of the brain. 2. Classification algorithms based on white-matter connectivity involving regions of the reward and associated networks can identify specific targets for mechanistic studies and future drug development aimed at abnormal ingestive behavior and in overweight/obesity.

Disease-related differences in resting-state networks: a comparison between localized provoked vulvodynia, irritable bowel syndrome, and healthy control subjects.

Localized provoked vulvodynia (LPVD) affects approximately 16% of the female population, but biological mechanisms underlying symptoms remain unknown. Like in other often comorbid chronic pain disorders, altered sensory processing and modulation of pain, including central sensitization, dysregulation of endogenous pain modulatory systems, and attentional enhancement of pain perception, have been implicated. The aim of this study was to test whether regions of interest showing differences in LPVD compared to healthy control subjects (HCs) in structural and evoked-pain neuroimaging studies, also show alterations during rest when compared with HCs and a chronic pain control group (irritable bowel syndrome [IBS]). Functional magnetic resonance imaging was performed during resting state in 87 age-matched premenopausal females (29 LPVD, 29 HCs, and 29 IBS). Group-independent component analysis and general linear models were applied to investigate group differences in the intrinsic connectivity of regions comprising sensorimotor, salience, and default mode resting-state networks. Subjects with LPVD showed substantial alterations in the intrinsic connectivity of these networks compared with HCs and IBS. The intrinsic connectivity of many of the regions showing group differences during rest were moderately associated with clinical symptom reports in LPVD. Findings were robust to controlling for affect and medication usage. The current findings indicate that subjects with LPVD have alterations in the intrinsic connectivity of regions comprising the sensorimotor, salience, and default mode networks. Although shared brain mechanisms between different chronic pain disorders have been postulated, the current findings suggest that some alterations in functional connectivity may show disease specificity.