Content Analysis and Characterization of Medical Tweets During the Early Covid-19 Pandemic.

Objective The novel coronavirus disease 2019 (Covid-19) has infected millions worldwide and impacted the lives of many folds more. Many clinicians share new Covid-19-related resources, research, and ideas within the online Free Open Access to Medical Education (FOAM) community of practice. This study provides a detailed content and contributor analysis of Covid-19-related tweets among the FOAM community during the first months of the pandemic. Design, Setting, and Participants In this social media content analysis study, Twitter was searched from November 1, 2019, to March 21, 2020, for English tweets discussing Covid-19 in the FOAM community. Tweets were classified into one of 13 pre-specified content categories: original research, editorials, FOAM resource, public health, podcast or video, learned experience, refuting false information, policy discussion, emotional impact, blatantly false information, other Covid-19, and non-Covid-19. Further analysis of linked original research and FOAM resources was performed. One-thousand (1000) randomly selected contributor profiles and those deemed to have contributed false information were analyzed. Results The search yielded 8541 original tweets from 4104 contributors. The number of tweets in each content category were: 1557 other Covid-19 (18.2%), 1190 emotional impact (13.9%), 1122 FOAM resources (13.1%), 1111 policy discussion (13.0%), 928 advice (10.9%), 873 learned experience (10.2%), 424 non-Covid-19 (5.0%), 410 podcast or video (4.8%), 304 editorials (3.6%), 275 original research (3.2%), 245 public health (2.9%), 83 refuting false information (1.0%), and 19 blatantly false (0.2%). Conclusions Early in the Covid-19 pandemic, the FOAM community used Twitter to share Covid-19 learned experiences, online resources, crowd-sourced advice, and research and to discuss the emotional impact of Covid-19. Twitter also provided a forum for post-publication peer review of new research. Sharing blatantly false information within this community was infrequent. This study highlights several potential benefits from engaging with the FOAM community on Twitter.

Utility of the Seattle Heart Failure Model for palliative care referral in advanced ambulatory heart failure.

BACKGROUND: Physicians face uncertainty when predicting death in heart failure (HF) leading to underutilisation of palliative care. To facilitate decision-making, we assessed the Seattle Heart Failure Model (SHFM) as a referral tool by evaluating its performance in predicting 1-year event-free survival from death, heart transplant (HTx), and ventricular assist device (VAD) implantation. METHODS: We retrospectively reviewed the charts of consecutive patients with advanced ambulatory HF with New York Heart Association Class III/IV HF and a left ventricular ejection fraction of ≤40% from 2000 to 2016. We evaluated SHFM's performance by using the Cox proportional hazards model, its discrimination using the c-statistic, its calibration by comparing the observed and predicted survival and its clinical utility by hypothetically assessing the proportion of patients adequately or inadequately referred to palliative care. RESULTS: We included 612 patients in our study. During the 1-year follow-up, there were 83 deaths, 4 HTx and 1 VAD. Although SHFM showed very good discrimination (c-statistic=0.71) and adequate calibration in medium to low-risk patients, it underestimated event-free survival by 12% in high-risk patients. SHFM's clinical utility was limited: 33% of eligible patients would have missed the opportunity for referral and only 27% of referred patients would have benefited. CONCLUSION: Use of SHFM could result in a high proportion of referrals while capturing the majority of patients who may benefit from palliative care. Though this may be a more encompassing and safer alternative than current referral practices, it could lead to many early referrals.

Incidence and predictors of sudden cardiac death after heart transplantation: A systematic review and meta-analysis.

PURPOSE: Sudden cardiac death (SCD) is an important post-transplant problem being responsible for ~10% of deaths. We conducted a systematic review and meta-analysis to evaluate incidence and predictors of post-heart transplant SCD and the use of implantable cardiac defibrillator (ICD). METHODS: Citations were identified in electronic databases and references of included studies. Observational studies on adults reporting on incidence and predictors of post-transplant SCD and ICD use were selected. We meta-analyzed SCD in person-years using random effects models. We qualitatively summarized predictors. RESULTS: This study includes 55 studies encompassing 47 901 recipients. The pooled incidence rate of SCD was 1.30 per 100 person-years (95% CI: 1.08-1.52). Cardiac allograft vasculopathy (CAV) was associated with higher SCD risk (2.40 per 100 patient-years, 95% CI: 1.46-3.34). Independent predictors of SCD identified by two moderate-quality studies were older donor age, younger recipient age, non-Caucasian race, reduced left ventricular ejection fraction, rejection, infection, and cancer. Authors rarely reported on ICD use. CONCLUSION: This meta-analysis found that post-transplant SCD risk in heart transplant recipients is higher than that in the general population. CAV was associated with increased SCD risk. Observational studies reporting on absolute risk of SCD are needed to better identify populations at a clinically significant increased risk.

Predictors of 1-year mortality in heart transplant recipients: a systematic review and meta-analysis.

OBJECTIVE: A systematic summary of the observational studies informing heart transplant guideline recommendations for selection of candidates and donors has thus far been unavailable. We performed a meta-analysis to better understand the impact of such known risk factors. METHODS: We systematically searched and meta-analysed the association between known pretransplant factor and 1-year mortality identified by multivariable regression models. Our review used the Grading of Recommendations, Assessment, Development and Evaluation for assessing the quality of assessment. We pooled risk estimates by using random effects models. RESULTS: Recipient variables including age (HR 1.16 per 10-year increase, 95% CI 1.10-1.22, high quality), congenital aetiology (HR 2.35, 95% CI 1.62 to 3.41, moderate quality), diabetes (HR 1.37, 95% CI 1.15 to 1.62, high quality), creatinine (HR 1.11 per 1 mg/dL increase, 95% CI 1.06 to 1.16, high quality), mechanical ventilation (HR 2.46, 95% CI 1.48 to 4.09, low quality) and short-term mechanical circulatory support (MCS) (HR 2.47, 95% CI 1.04 to 5.87, low quality) were significantly associated with 1-year mortality. Donor age (HR 1.20 per 10-year increase, 95% CI 1.14 to 1.26, high quality) and female donor to male recipient sex mismatch (HR 1.38, 95% CI 1.06 to 1.80, high quality) were significantly associated with 1-year mortality. None of the operative factors proved significant predictors. CONCLUSION: High-quality and moderate-quality evidence demonstrates that recipient age, congenital aetiology, creatinine, pulsatile MCS, donor age and female donor to male recipient sex mismatch are associated with 1-year mortality post heart transplant. The results of this study should inform future guideline and predictive model development.