Pulmonary arterial hypertension treatment: an individual participant data network meta-analysis.

BACKGROUND AND AIMS: Effective therapies that target three main signalling pathways are approved to treat pulmonary arterial hypertension (PAH). However, there are few large patient-level studies that compare the effectiveness of these pathways. The aim of this analysis was to compare the effectiveness of the treatment pathways in PAH and to assess treatment heterogeneity. METHODS: A network meta-analysis was performed using individual participant data of 6811 PAH patients from 20 Phase III randomized clinical trials of therapy for PAH that were submitted to the US Food and Drug Administration. Individual drugs were grouped by the following treatment pathways: endothelin, nitric oxide, and prostacyclin pathways. RESULTS: The mean (±standard deviation) age of the sample was 49.2 (±15.4) years; 78.4% were female, 59.7% had idiopathic PAH, and 36.5% were on background PAH therapy. After covariate adjustment, targeting the endothelin + nitric oxide pathway {ß: 43.7 m [95% confidence interval (CI): 32.9, 54.4]}, nitric oxide pathway [ß: 29.4 m (95% CI: 22.6, 36.3)], endothelin pathway [ß: 25.3 m (95% CI: 19.8, 30.8)], and prostacyclin pathway [oral/inhaled ß: 19.1 m (95% CI: 14.2, 24.0), intravenous/subcutaneous ß: 24.4 m (95% CI: 15.1, 33.7)] significantly increased 6 min walk distance at 12 or 16 weeks compared with placebo. Treatments also significantly reduced the likelihood of having clinical worsening events. There was significant heterogeneity of treatment effects by age, body mass index, hypertension, diabetes, and coronary artery disease. CONCLUSIONS: Drugs targeting the three traditional treatment pathways significantly improve outcomes in PAH, with significant treatment heterogeneity in patients with some comorbidities. Randomized clinical trials are warranted to identify the most effective treatment strategies in a personalized approach.

Minimal Clinically Important Difference in the 6-minute-walk Distance for Patients with Pulmonary Arterial Hypertension.

Rationale: The 6-minute-walk distance (6MWD) is an important clinical and research metric in pulmonary arterial hypertension (PAH); however, there is no consensus about what minimal change in 6MWD is clinically significant. Objectives: We aimed to determine the minimal clinically important difference in the 6MWD. Methods: We performed a meta-analysis using individual participant data from eight randomized clinical trials of therapy for PAH submitted to the U.S. Food and Drug Administration to derive minimal clinically important differences in the 6MWD. The estimates were externally validated using the Pulmonary Hypertension Association Registry. We anchored the change in 6MWD to the change in the Medical Outcomes Survey Short Form physical component score. Measurements and Main Results: The derivation (clinical trial) and validation (Pulmonary Hypertension Association Registry) samples were comprised of 2,404 and 537 adult patients with PAH, respectively. The mean ± standard deviation age of the derivation sample was 50.5 ± 15.2 years, and 1,849 (77%) were female, similar to the validation sample. The minimal clinically important difference in the derivation sample was 33 meters (95% confidence interval, 27-38), which was almost identical to that in the validation sample (36 m [95% confidence interval, 29-43]). The minimal clinically important difference did not differ by age, sex, race, pulmonary hypertension etiology, body mass index, use of background therapy, or World Health Organization functional class. Conclusions: We estimated a 6MWD minimal clinically important difference of approximately 33 meters for adults with PAH. Our findings can be applied to the design of clinical trials of therapies for PAH.

Preoperative Depression Status and 5 Year Metabolic and Bariatric Surgery Outcomes in the PCORnet Bariatric Study Cohort.

OBJECTIVE: To examine whether depression status before metabolic and bariatric surgery (MBS) influenced 5-year weight loss, diabetes, and safety/utilization outcomes in the PCORnet Bariatric Study. SUMMARY OF BACKGROUND DATA: Research on the impact of depression on MBS outcomes is inconsistent with few large, long-term studies. METHODS: Data were extracted from 23 health systems on 36,871 patients who underwent sleeve gastrectomy (SG; n=16,158) or gastric bypass (RYGB; n=20,713) from 2005-2015. Patients with and without a depression diagnosis in the year before MBS were evaluated for % total weight loss (%TWL), diabetes outcomes, and postsurgical safety/utilization (reoperations, revisions, endoscopy, hospitalizations, mortality) at 1, 3, and 5 years after MBS. RESULTS: 27.1% of SG and 33.0% of RYGB patients had preoperative depression, and they had more medical and psychiatric comorbidities than those without depression. At 5 years of follow-up, those with depression, versus those without depression, had slightly less %TWL after RYGB, but not after SG (between group difference = 0.42%TWL, P = 0.04). However, patients with depression had slightly larger HbA1c improvements after RYGB but not after SG (between group difference = - 0.19, P = 0.04). Baseline depression did not moderate diabetes remission or relapse, reoperations, revision, or mortality across operations; however, baseline depression did moderate the risk of endoscopy and repeat hospitalization across RYGB versus SG. CONCLUSIONS: Patients with depression undergoing RYGB and SG had similar weight loss, diabetes, and safety/utilization outcomes to those without depression. The effects of depression were clinically small compared to the choice of operation.

Heterogeneity of treatment effects by risk in pulmonary arterial hypertension.

BACKGROUND: It is currently unknown if disease severity modifies response to therapy in pulmonary arterial hypertension (PAH). We aimed to explore if disease severity, as defined by established risk-prediction algorithms, modified response to therapy in randomised clinical trials in PAH. METHODS: We performed a meta-analysis using individual participant data from 18 randomised clinical trials of therapy for PAH submitted to the United States Food and Drug Administration to determine if predicted risk of 1-year mortality at randomisation modified the treatment effect on three outcomes: change in 6-min walk distance (6MWD), clinical worsening at 12 weeks and time to clinical worsening. RESULTS: Of 6561 patients with a baseline US Registry to Evaluate Early and Long-Term PAH Disease Management (REVEAL 2.0) score, we found that individuals with higher baseline risk had higher probabilities of clinical worsening but no difference in change in 6MWD. We detected a significant interaction of REVEAL 2.0 risk and treatment assignment on change in 6MWD. For every 3-point increase in REVEAL 2.0 score, there was a 12.49 m (95% CI 5.86-19.12 m; p=0.001) greater treatment effect in change in 6MWD. We did not detect a significant risk by treatment interaction on clinical worsening with most of the risk-prediction algorithms. CONCLUSIONS: We found that predicted risk of 1-year mortality in PAH modified treatment effect as measured by 6MWD, but not clinical worsening. Our findings highlight the importance of identifying sources of treatment heterogeneity by predicted risk to tailor studies to patients most likely to have the greatest treatment response.

Informative missingness: What can we learn from patterns in missing laboratory data in the electronic health record?

BACKGROUND: In electronic health records, patterns of missing laboratory test results could capture patients' course of disease as well as ​​reflect clinician's concerns or worries for possible conditions. These patterns are often understudied and overlooked. This study aims to identify informative patterns of missingness among laboratory data collected across 15 healthcare system sites in three countries for COVID-19 inpatients. METHODS: We collected and analyzed demographic, diagnosis, and laboratory data for 69,939 patients with positive COVID-19 PCR tests across three countries from 1 January 2020 through 30 September 2021. We analyzed missing laboratory measurements across sites, missingness stratification by demographic variables, temporal trends of missingness, correlations between labs based on missingness indicators over time, and clustering of groups of labs based on their missingness/ordering pattern. RESULTS: With these analyses, we identified mapping issues faced in seven out of 15 sites. We also identified nuances in data collection and variable definition for the various sites. Temporal trend analyses may support the use of laboratory test result missingness patterns in identifying severe COVID-19 patients. Lastly, using missingness patterns, we determined relationships between various labs that reflect clinical behaviors. CONCLUSION: In this work, we use computational approaches to relate missingness patterns to hospital treatment capacity and highlight the heterogeneity of looking at COVID-19 over time and at multiple sites, where there might be different phases, policies, etc. Changes in missingness could suggest a change in a patient's condition, and patterns of missingness among laboratory measurements could potentially identify clinical outcomes. This allows sites to consider missing data as informative to analyses and help researchers identify which sites are better poised to study particular questions.

SurvMaximin: Robust federated approach to transporting survival risk prediction models.

OBJECTIVE: For multi-center heterogeneous Real-World Data (RWD) with time-to-event outcomes and high-dimensional features, we propose the SurvMaximin algorithm to estimate Cox model feature coefficients for a target population by borrowing summary information from a set of health care centers without sharing patient-level information. MATERIALS AND METHODS: For each of the centers from which we want to borrow information to improve the prediction performance for the target population, a penalized Cox model is fitted to estimate feature coefficients for the center. Using estimated feature coefficients and the covariance matrix of the target population, we then obtain a SurvMaximin estimated set of feature coefficients for the target population. The target population can be an entire cohort comprised of all centers, corresponding to federated learning, or a single center, corresponding to transfer learning. RESULTS: Simulation studies and a real-world international electronic health records application study, with 15 participating health care centers across three countries (France, Germany, and the U.S.), show that the proposed SurvMaximin algorithm achieves comparable or higher accuracy compared with the estimator using only the information of the target site and other existing methods. The SurvMaximin estimator is robust to variations in sample sizes and estimated feature coefficients between centers, which amounts to significantly improved estimates for target sites with fewer observations. CONCLUSIONS: The SurvMaximin method is well suited for both federated and transfer learning in the high-dimensional survival analysis setting. SurvMaximin only requires a one-time summary information exchange from participating centers. Estimated regression vectors can be very heterogeneous. SurvMaximin provides robust Cox feature coefficient estimates without outcome information in the target population and is privacy-preserving.

Distinguishing Admissions Specifically for COVID-19 From Incidental SARS-CoV-2 Admissions: National Retrospective Electronic Health Record Study.

BACKGROUND: Admissions are generally classified as COVID-19 hospitalizations if the patient has a positive SARS-CoV-2 polymerase chain reaction (PCR) test. However, because 35% of SARS-CoV-2 infections are asymptomatic, patients admitted for unrelated indications with an incidentally positive test could be misclassified as a COVID-19 hospitalization. Electronic health record (EHR)-based studies have been unable to distinguish between a hospitalization specifically for COVID-19 versus an incidental SARS-CoV-2 hospitalization. Although the need to improve classification of COVID-19 versus incidental SARS-CoV-2 is well understood, the magnitude of the problems has only been characterized in small, single-center studies. Furthermore, there have been no peer-reviewed studies evaluating methods for improving classification. OBJECTIVE: The aims of this study are to, first, quantify the frequency of incidental hospitalizations over the first 15 months of the pandemic in multiple hospital systems in the United States and, second, to apply electronic phenotyping techniques to automatically improve COVID-19 hospitalization classification. METHODS: From a retrospective EHR-based cohort in 4 US health care systems in Massachusetts, Pennsylvania, and Illinois, a random sample of 1123 SARS-CoV-2 PCR-positive patients hospitalized from March 2020 to August 2021 was manually chart-reviewed and classified as "admitted with COVID-19" (incidental) versus specifically admitted for COVID-19 ("for COVID-19"). EHR-based phenotyping was used to find feature sets to filter out incidental admissions. RESULTS: EHR-based phenotyped feature sets filtered out incidental admissions, which occurred in an average of 26% of hospitalizations (although this varied widely over time, from 0% to 75%). The top site-specific feature sets had 79%-99% specificity with 62%-75% sensitivity, while the best-performing across-site feature sets had 71%-94% specificity with 69%-81% sensitivity. CONCLUSIONS: A large proportion of SARS-CoV-2 PCR-positive admissions were incidental. Straightforward EHR-based phenotypes differentiated admissions, which is important to assure accurate public health reporting and research.

Unsupervised modeling and genome-wide association identify novel features of allergic march trajectories.

BACKGROUND: The allergic march refers to the natural history of allergic conditions during infancy and childhood. However, population-level disease incidence patterns do not necessarily reflect the development of allergic disease in individuals. A better understanding of the factors that predispose to different allergic trajectories is needed. OBJECTIVE: Our aim was to determine the demographic and genetic features that are associated with the major allergic march trajectories. METHODS: Presence or absence of common allergic conditions (atopic dermatitis [AD], IgE-mediated food allergy [IgE-FA], asthma, and allergic rhinitis [AR]) was ascertained in a pediatric primary care birth cohort of 158,510 subjects. Hierarchic clustering and decision tree modeling were used to associate demographic features with allergic outcomes. Genome-wide association study was used to test for risk loci associated with specific allergic trajectories. RESULTS: We found an association between self-identified black race and progression from AD to asthma. Conversely, Asian or Pacific Islander race was associated with progression from AD to IgE-mediated food allergy, and white race was associated with progression from AD to AR. Genome-wide association study of trajectory groups identified risk loci associated with progression from AD to asthma (rs60242841) and from AD to AR (rs9565267, rs151041509, and rs78171803). Consistent with our epidemiologic associations, rs60242841 was more common in individuals of African ancestry than in individuals of European ancestry, whereas rs9565267 and rs151041509 were more common in individuals of European ancestry than in individuals of African ancestry. CONCLUSION: We have identified novel associations between race and progression along distinct allergic trajectories. Ancestral genetic differences may contribute to these associations. These results uncover important health disparities, refine the concept of the allergic march, and represent a step toward developing individualized medical approaches for these conditions.

What Every Reader Should Know About Studies Using Electronic Health Record Data but May Be Afraid to Ask.

Coincident with the tsunami of COVID-19-related publications, there has been a surge of studies using real-world data, including those obtained from the electronic health record (EHR). Unfortunately, several of these high-profile publications were retracted because of concerns regarding the soundness and quality of the studies and the EHR data they purported to analyze. These retractions highlight that although a small community of EHR informatics experts can readily identify strengths and flaws in EHR-derived studies, many medical editorial teams and otherwise sophisticated medical readers lack the framework to fully critically appraise these studies. In addition, conventional statistical analyses cannot overcome the need for an understanding of the opportunities and limitations of EHR-derived studies. We distill here from the broader informatics literature six key considerations that are crucial for appraising studies utilizing EHR data: data completeness, data collection and handling (eg, transformation), data type (ie, codified, textual), robustness of methods against EHR variability (within and across institutions, countries, and time), transparency of data and analytic code, and the multidisciplinary approach. These considerations will inform researchers, clinicians, and other stakeholders as to the recommended best practices in reviewing manuscripts, grants, and other outputs from EHR-data derived studies, and thereby promote and foster rigor, quality, and reliability of this rapidly growing field.

International Changes in COVID-19 Clinical Trajectories Across 315 Hospitals and 6 Countries: Retrospective Cohort Study.

BACKGROUND: Many countries have experienced 2 predominant waves of COVID-19-related hospitalizations. Comparing the clinical trajectories of patients hospitalized in separate waves of the pandemic enables further understanding of the evolving epidemiology, pathophysiology, and health care dynamics of the COVID-19 pandemic. OBJECTIVE: In this retrospective cohort study, we analyzed electronic health record (EHR) data from patients with SARS-CoV-2 infections hospitalized in participating health care systems representing 315 hospitals across 6 countries. We compared hospitalization rates, severe COVID-19 risk, and mean laboratory values between patients hospitalized during the first and second waves of the pandemic. METHODS: Using a federated approach, each participating health care system extracted patient-level clinical data on their first and second wave cohorts and submitted aggregated data to the central site. Data quality control steps were adopted at the central site to correct for implausible values and harmonize units. Statistical analyses were performed by computing individual health care system effect sizes and synthesizing these using random effect meta-analyses to account for heterogeneity. We focused the laboratory analysis on C-reactive protein (CRP), ferritin, fibrinogen, procalcitonin, D-dimer, and creatinine based on their reported associations with severe COVID-19. RESULTS: Data were available for 79,613 patients, of which 32,467 were hospitalized in the first wave and 47,146 in the second wave. The prevalence of male patients and patients aged 50 to 69 years decreased significantly between the first and second waves. Patients hospitalized in the second wave had a 9.9% reduction in the risk of severe COVID-19 compared to patients hospitalized in the first wave (95% CI 8.5%-11.3%). Demographic subgroup analyses indicated that patients aged 26 to 49 years and 50 to 69 years; male and female patients; and black patients had significantly lower risk for severe disease in the second wave than in the first wave. At admission, the mean values of CRP were significantly lower in the second wave than in the first wave. On the seventh hospital day, the mean values of CRP, ferritin, fibrinogen, and procalcitonin were significantly lower in the second wave than in the first wave. In general, countries exhibited variable changes in laboratory testing rates from the first to the second wave. At admission, there was a significantly higher testing rate for D-dimer in France, Germany, and Spain. CONCLUSIONS: Patients hospitalized in the second wave were at significantly lower risk for severe COVID-19. This corresponded to mean laboratory values in the second wave that were more likely to be in typical physiological ranges on the seventh hospital day compared to the first wave. Our federated approach demonstrated the feasibility and power of harmonizing heterogeneous EHR data from multiple international health care systems to rapidly conduct large-scale studies to characterize how COVID-19 clinical trajectories evolve.

Authorship Correction: International Changes in COVID-19 Clinical Trajectories Across 315 Hospitals and 6 Countries: Retrospective Cohort Study.

[This corrects the article DOI: 10.2196/31400.].

Determinants of stigma among patients with hepatitis C virus infection.

Stigma around hepatitis C virus (HCV) infection is an important and understudied barrier to HCV treatment and elimination. The determinants of HCV-related stigma, including the impacts of stage of HCV treatment (ie spontaneously cleared; diagnosed, untreated; previously treated, not cured; currently being treated; and treated, cured) and coinfection with human immunodeficiency virus (HIV), remain unknown. To address these gaps, we conducted a cross-sectional study among patients with a history of HCV infection (n = 270) at outpatient clinics in Philadelphia from July 2018 to May 2019. We evaluated stigma using the validated HCV Stigma Scale, adapted from the Berger HIV Stigma Scale. Associations among HCV-related stigma and hypothesized demographic, behavioural, and clinical risk factors were evaluated by multivariable linear regression. Most participants (95.5%) experienced HCV-related stigma. Mean stigma scores did not differ significantly between HCV-monoinfected and HIV/HCV-coinfected participants (P = .574). However, we observed significant interactions between HIV status and multiple determinants; therefore, we stratified analyses by HIV status. Among HIV/HCV-coinfected participants, previous HCV treatment without cure, female gender, Hispanic/Latinx ethnicity and some college education were significantly associated with higher HCV-stigma scores. An annual income of $10 000-$40 000 was associated with significantly lower stigma scores. No significant associations were observed among HCV-monoinfected participants. We found that most participants experienced stigma associated with HCV diagnosis. While stigma scores were similar between HCV-monoinfected and HIV/HCV-coinfected participants, the determinants associated with HCV stigma differed by HIV status. Understanding how experiences of stigma differ between HCV-monoinfected and HIV/HCV-coinfected patients may aid in the development of targeted interventions to address the HCV epidemic.

Factors Affecting Patient Prioritization Decisions at Admission to Home Healthcare: A Predictive Study to Develop a Risk Screening Tool.

There is a lack of evidence on how to identify high-risk patients admitted to home healthcare. This study aimed (1) to identify which disease characteristics, medications, patient needs, social support characteristics, and other factors are associated with patient priority for the first home health nursing visit; and (2) to construct and validate a predictive model of patient priority for the first home health nursing visit. This was a predictive study of home health visit priority decisions made by 20 nurses for 519 older adults. The study found that nurses were more likely to prioritize patients who had wounds (odds ratio = 1.88), comorbid condition of depression (odds ratio = 1.73), limitation in current toileting status (odds ratio = 2.02), higher number of medications (increase in odds ratio for each medication = 1.04), and comorbid conditions (increase in odds ratio for each condition = 1.04). This study developed one of the first clinical decision support tools for home healthcare called "PREVENT". (PRiority home health Visit Tool). Further work is needed to increase the specificity and generalizability of the tool and to test its effects on patient outcomes.

Text mining applied to electronic cardiovascular procedure reports to identify patients with trileaflet aortic stenosis and coronary artery disease.

BACKGROUND: Interrogation of the electronic health record (EHR) using billing codes as a surrogate for diagnoses of interest has been widely used for clinical research. However, the accuracy of this methodology is variable, as it reflects billing codes rather than severity of disease, and depends on the disease and the accuracy of the coding practitioner. Systematic application of text mining to the EHR has had variable success for the detection of cardiovascular phenotypes. We hypothesize that the application of text mining algorithms to cardiovascular procedure reports may be a superior method to identify patients with cardiovascular conditions of interest. METHODS: We adapted the Oracle product Endeca, which utilizes text mining to identify terms of interest from a NoSQL-like database, for purposes of searching cardiovascular procedure reports and termed the tool "PennSeek". We imported 282,569 echocardiography reports representing 81,164 individuals and 27,205 cardiac catheterization reports representing 14,567 individuals from non-searchable databases into PennSeek. We then applied clinical criteria to these reports in PennSeek to identify patients with trileaflet aortic stenosis (TAS) and coronary artery disease (CAD). Accuracy of patient identification by text mining through PennSeek was compared with ICD-9 billing codes. RESULTS: Text mining identified 7115 patients with TAS and 9247 patients with CAD. ICD-9 codes identified 8272 patients with TAS and 6913 patients with CAD. 4346 patients with AS and 6024 patients with CAD were identified by both approaches. A randomly selected sample of 200-250 patients uniquely identified by text mining was compared with 200-250 patients uniquely identified by billing codes for both diseases. We demonstrate that text mining was superior, with a positive predictive value (PPV) of 0.95 compared to 0.53 by ICD-9 for TAS, and a PPV of 0.97 compared to 0.86 for CAD. CONCLUSION: These results highlight the superiority of text mining algorithms applied to electronic cardiovascular procedure reports in the identification of phenotypes of interest for cardiovascular research.

Impact of store-and-forward (SAF) teledermatology on outpatient dermatologic care: A prospective study in an underserved urban primary care setting.

BACKGROUND: The clinical value of teledermatology in the primary care setting remains relatively unknown. OBJECTIVE: We sought to determine the impact of teledermatology on outpatient diagnosis, management, and access to dermatologic care in a resource-poor primary care setting. METHODS: We performed a prospective study of store-and-forward teledermatology consults submitted between January and November 2013 from 11 underserved clinics in Philadelphia to the University of Pennsylvania using mobile devices and the Internet. We assessed diagnostic and management concordance between primary care providers and dermatologists, time to consult completion, anticipated level of dermatology input in the absence of teledermatology, and number of consults managed with teledermatology alone. RESULTS: The study included 196 consults encompassing 206 dermatologic conditions. Diagnoses and management plans of primary care providers and dermatologists were fully concordant for 22% and 23% of conditions, respectively. The median time to consult completion was 14 (interquartile range 3-28) hours. At least 61% of consults would not otherwise have received dermatology input, and 77% of consults were managed with teledermatology alone. LIMITATIONS: Lack of a diagnostic gold standard, limited patient follow-up, and uncertain generalizability are limitations. CONCLUSION: Teledermatology is an innovative and impactful modality for delivering dermatologic care to outpatients in resource-poor primary care settings.

Evaluation of a Mobile Health Approach to Tuberculosis Contact Tracing in Botswana.

Tuberculosis (TB) contact tracing is typically conducted in resource-limited settings with paper forms, but this approach may be limited by inefficiencies in data collection, storage, and retrieval and poor data quality. In Botswana, we developed, piloted, and evaluated a mobile health (mHealth) approach to TB contact tracing that replaced the paper form-based approach for a period of six months. For both approaches, we compared the time required to complete TB contact tracing and the quality of data collected. For the mHealth approach, we also administered the Computer System Usability Questionnaire to 2 health care workers who used the new approach, and we identified and addressed operational considerations for implementation. Compared to the paper form-based approach, the mHealth approach reduced the median time required to complete TB contact tracing and improved data quality. The mHealth approach also had favorable overall rating, system usefulness, information quality, and interface quality scores on the Computer System Usability Questionnaire. Overall, the mHealth approach to TB contact tracing improved on the paper form-based approach used in Botswana. This new approach may similarly benefit TB contact tracing efforts in other resource-limited settings.

Twitter response to the United States Preventive Services Task Force recommendations against screening with prostate-specific antigen.

OBJECTIVE: To examine public and media response to the draft (October 2011) and finalised (May 2012) recommendations of the United States Preventive Services Task Force (USPSTF) against prostate-specific antigen (PSA) testing via Twitter, a popular social network with over 200 million active users. MATERIALS AND METHODS: We used a mixed-methods design to analyse posts on Twitter, known as 'tweets'. Using the search term 'prostate cancer', we archived tweets in the 24-h periods following the release of both the draft and the finalised USPSTF recommendations. We recorded tweet rate per h and developed a coding system to assess the type of user and sentiment expressed in tweets and linked articles. RESULTS: After the draft and finalised USPSTF recommendations were released, 2042 and 5357 tweets focused on the USPSTF report, respectively. The tweet rate nearly doubled within 2 h of both announcements. Fewer than 10% of tweets expressed an opinion about screening, and the majority of these were pro-screening during both periods. By contrast, anti-screening articles were tweeted more frequently in both the draft and finalised study periods. Between the draft and the finalised recommendations, the proportion of anti-screening tweets and anti-screening article links increased (P = 0.03 and P < 0.01, respectively). CONCLUSIONS: There was increased Twitter activity surrounding the USPSTF draft and finalised recommendations. The percentage of anti-screening tweets and articles appeared to increase, perhaps due to the interval public comment period. Despite this, most tweets did not express an opinion, suggesting a missed opportunity in this important arena for advocacy.

Prediction of significant vasospasm in aneurysmal subarachnoid hemorrhage using automated data.

BACKGROUND: When vasospasm is detected after aneurysmal subarachnoid hemorrhage (aSAH), it is treated with hypertensive or endovascular therapy. Current classification methods are resource-intensive, relying on specialty-trained professionals (nursing exams, transcranial dopplers, and perfusion imaging). More passively obtained variables such as cerebrospinal fluid drainage volumes, sodium, glucose, blood pressure, intracranial pressure, and heart rate, have not been used to predict vasospasm. We hypothesize that these features may yield as much information as resource-intensive features to classify vasospasm. METHODS: We studied data from 81 aSAH patients presenting within two days of onset. Vasospasm class (VSP) was defined by angiographic vasospasm warranting endovascular treatment. Naïve Bayes (NB) and logistic regression (LR) classifiers were trained on selected variable feature sets from the first three days of illness. Performance of trained classifiers was evaluated using area under the receiver operator characteristic curve (AUC classifier) and F-measure (F classifier). Ablation analysis determined incremental utility of each variable and subsets. RESULTS: 43.2 % developed VSP. During feature selection, the only passively collected variable that did not yield a statistically significant summary statistic was CSF drainage volume. NB classifier trained on all passively obtained features (AUC NB 0.708 and F NB 0.636) outperformed NB classifier trained on resource-intensive features (AUC NB 0.501 and F NB 0.349). CONCLUSIONS: Data-driven analysis of passively obtained clinical data predicted VSP better than current targeted resource-intensive monitoring techniques after aSAH. Automated classification of VSP may be possible.

Parent and Youth Athlete Perceptions of Concussion Injury: Establishing a Factor Structure.

OBJECTIVE: The first objective was to establish the respective factor structures of a concussion perceptions inventory that was adapted for youth athletes (ages 8-14 years) and their parents from the Perceptions of Concussion Inventory for Athletes. The second objective was to understand the associations between the concussion perceptions of youth athlete-parent dyads. METHOD: In this cross-sectional study, 329 parent-youth athlete dyads completed a respective concussion perception inventory. Mean age of youth respondents was 10.9 ± 1.8 years (70.1% male) and mean age of parent respondents was 40.5 ± 13.6 years (60.9% female). RESULTS: Exploratory factor analyses revealed unique 7-factor structures for both the youth athlete and parent inventories (youth athlete: anxiety, clarity, treatment, permanent injury, symptom variability, long-term outcomes, and personal control; parent: anxiety, clarity, treatment, permanent injury, symptom variability, and long-term outcomes, and affect others). Weak associations were found between dyads on the 5 factors that were composed of identical items (anxiety, clarity, treatment, permanent injury, and symptom variability). CONCLUSIONS: Findings suggest that this adapted inventory has adequate psychometric properties to be used in the study of the concussion perceptions of youth athletes and their parents. Weak correlations across the concussion perceptions in the dyads suggest that parents and children hold different concussion perceptions and this should be considered in instrument selection of future studies.

Electrocardiographic Abnormalities and Their Association with Outcomes in Randomized Clinical Trials of Pulmonary Arterial Hypertension.

Rationale: Pulmonary arterial hypertension (PAH) is a progressive disease with manifestations including right atrial enlargement, right ventricular dysfunction, dilation, and hypertrophy. Electrocardiography (ECG) is a noninvasive, inexpensive test that is routinely performed in clinical settings. Prior studies have described separate abnormal findings in the electrocardiograms of patients with PAH. However, the role of composite ECG findings reflective of right heart disease (RHD) for risk stratification, clinical trial enrichment, and management of patients with PAH has not been explored. Objectives: To describe a pattern of RHD on ECG in patients with PAH and to investigate the association of this pattern with clinical measures of disease severity and outcomes. Methods: We harmonized individual participant data from 18 phase III randomized clinical trials of therapies for PAH (1998-2013) submitted to the U.S. Food and Drug Administration. RHD was defined as the presence of right ventricular hypertrophy, right axis deviation, right atrial enlargement, or right bundle branch block on ECG. Random effects linear regression, multilevel ordinal regression (cumulative link model), and Cox proportional hazards models were used to assess the association of RHD by ECG with 6-minute walk distance (6MWD), World Health Organization (WHO) functional class, and clinical worsening after a priori adjustment for age, sex, body mass index, and PAH etiology. Effect modification of treatment and ECG abnormalities was assessed by including an interaction term. Results: A total of 4,439 patients had baseline ECG, and 68% of patients had evidence of RHD. RHD on ECG was associated with higher pulmonary vascular resistance (P < 0.001) and higher mean pulmonary artery pressures (P < 0.001). Patients with RHD on ECG had 10 meters shorter 6MWD (P = 0.005) and worse WHO functional class (P < 0.001) at baseline. RHD on baseline ECG was associated with increased risk of clinical worsening (hazard ratio, 1.42; 95% confidence interval; 1.21, 1.67; P < 0.001). Patients with RHD had greater treatment effect in terms of 6MWD, WHO functional class, and time to clinical worsening than those without (P for interaction = 0.03, 0.001, and 0.03, respectively). Conclusions: RHD by ECG may be associated with worse outcomes and potentially greater treatment effect. Electrocardiograms could be an inexpensive, widely available noninvasive method to enrich clinical trial populations in PAH.