Summary Background Since December, 2019, Wuhan, China, has experienced an outbreak of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Epidemiological and clinical characteristics of patients with COVID-19 have been reported but risk factors for mortality and a detailed clinical course of illness, including viral shedding, have not been well described. Methods In this retrospective, multicentre cohort study, we included all adult inpatients (≥18 years old) with laboratory-confirmed COVID-19 from Jinyintan Hospital and Wuhan Pulmonary Hospital (Wuhan, China) who had been discharged or had died by Jan 31, 2020. Demographic, clinical, treatment, and laboratory data, including serial samples for viral RNA detection, were extracted from electronic medical records and compared between survivors and non-survivors. We used univariable and multivariable logistic regression methods to explore the risk factors associated with in-hospital death. Findings 191 patients (135 from Jinyintan Hospital and 56 from Wuhan Pulmonary Hospital) were included in this study, of whom 137 were discharged and 54 died in hospital. 91 (48%) patients had a comorbidity, with hypertension being the most common (58 [30%] patients), followed by diabetes (36 [19%] patients) and coronary heart disease (15 [8%] patients). Multivariable regression showed increasing odds of in-hospital death associated with older age (odds ratio 1·10, 95% CI 1·03–1·17, per year increase; p=0·0043), higher Sequential Organ Failure Assessment (SOFA) score (5·65, 2·61–12·23; p Interpretation The potential risk factors of older age, high SOFA score, and d-dimer greater than 1 μg/mL could help clinicians to identify patients with poor prognosis at an early stage. Prolonged viral shedding provides the rationale for a strategy of isolation of infected patients and optimal antiviral interventions in the future. Funding Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences; National Science Grant for Distinguished Young Scholars; National Key Research and Development Program of China; The Beijing Science and Technology Project; and Major Projects of National Science and Technology on New Drug Creation and Development.

Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study

/pdf/2021-esc-guidelines-for-the-diagnosis-and-treatment-of-acute-4pqhe8c49t.pdf

2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure.

https://dro.deakin.edu.au/eserv/DU:30146218/sharifulislam-globalburden-2020.pdf

Global Burden of Cardiovascular Diseases and Risk Factors, 1990–2019: Update From the GBD 2019 Study

Background: The American Heart Association, in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascul...

Heart Disease and Stroke Statistics-2021 Update: A Report From the American Heart Association.

Objective To review and appraise the validity and usefulness of published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at increased risk of covid-19 infection or being admitted to hospital with the disease. Design Living systematic review and critical appraisal by the COVID-PRECISE (Precise Risk Estimation to optimise covid-19 Care for Infected or Suspected patients in diverse sEttings) group. Data sources PubMed and Embase through Ovid, up to 1 July 2020, supplemented with arXiv, medRxiv, and bioRxiv up to 5 May 2020. Study selection Studies that developed or validated a multivariable covid-19 related prediction model. Data extraction At least two authors independently extracted data using the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist; risk of bias was assessed using PROBAST (prediction model risk of bias assessment tool). Results 37 421 titles were screened, and 169 studies describing 232 prediction models were included. The review identified seven models for identifying people at risk in the general population; 118 diagnostic models for detecting covid-19 (75 were based on medical imaging, 10 to diagnose disease severity); and 107 prognostic models for predicting mortality risk, progression to severe disease, intensive care unit admission, ventilation, intubation, or length of hospital stay. The most frequent types of predictors included in the covid-19 prediction models are vital signs, age, comorbidities, and image features. Flu-like symptoms are frequently predictive in diagnostic models, while sex, C reactive protein, and lymphocyte counts are frequent prognostic factors. Reported C index estimates from the strongest form of validation available per model ranged from 0.71 to 0.99 in prediction models for the general population, from 0.65 to more than 0.99 in diagnostic models, and from 0.54 to 0.99 in prognostic models. All models were rated at high or unclear risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, high risk of model overfitting, and unclear reporting. Many models did not include a description of the target population (n=27, 12%) or care setting (n=75, 32%), and only 11 (5%) were externally validated by a calibration plot. The Jehi diagnostic model and the 4C mortality score were identified as promising models. Conclusion Prediction models for covid-19 are quickly entering the academic literature to support medical decision making at a time when they are urgently needed. This review indicates that almost all pubished prediction models are poorly reported, and at high risk of bias such that their reported predictive performance is probably optimistic. However, we have identified two (one diagnostic and one prognostic) promising models that should soon be validated in multiple cohorts, preferably through collaborative efforts and data sharing to also allow an investigation of the stability and heterogeneity in their performance across populations and settings. Details on all reviewed models are publicly available at https://www.covprecise.org/. Methodological guidance as provided in this paper should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, prediction model authors should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline. Systematic review registration Protocol https://osf.io/ehc47/, registration https://osf.io/wy245. Readers’ note This article is a living systematic review that will be updated to reflect emerging evidence. Updates may occur for up to two years from the date of original publication. This version is update 3 of the original article published on 7 April 2020 (BMJ 2020;369:m1328). Previous updates can be found as data supplements (https://www.bmj.com/content/369/bmj.m1328/related#datasupp). When citing this paper please consider adding the update number and date of access for clarity.

https://www.bmj.com/content/bmj/369/bmj.m1328.full.pdf

Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal

Heartmate 6: bridge to heart transplantation utilizing biventricular support for cardiogenic shock

The Universal Definition of Heart Failure: Perspectives from Diverse Stakeholders.

Advanced heart failure (HF) therapies improve survival in patients with stage D HF. We sought to evaluate differences by race/ethnicity and sex in advanced HF therapy referrals and decision-making across a multicenter survey. We performed a retrospective analysis of patients referred for evaluation for advanced HF therapies at 9 centers (n = 515) across the United States. The median age was 58 years, and 73% were male. White patients comprised 55.7% of referrals, whereas non-White patients comprised 44.3%. Non-ischemic etiology was more common in non-White patients (66.6% vs 47.4% p = 0.0005), and ischemic etiology was more common in men (37.8% vs 20.4% p = 0.0005). The primary reason for referral differed by race/ethnicity but not sex, with ventricular arrhythmias (7.6% vs 3%, p = 0.024) and pulmonary hypertension (3.4% vs 0.4% p = 0.018) being more common in White patients, whereas worsening HF was less common (25.4% vs 35.9%; p = 0.009). White patients were offered left ventricular assist devices (LVADs) (60.3% vs 54.7 p = 0.039) and heart transplants (51.8% vs 33.1% p = 0.0007) more often than non-White patients. The preference not to pursue LVAD therapy was more common in non-White patients (17.6% vs 9.6%; p = 0.049). Men were more often declined for a heart transplant because of psychosocial contraindications (34% vs 15%, p = 0.005). In conclusion, in this multicenter analysis of referrals for advanced HF therapies, we observed significant differences by race, ethnicity, and sex in both referral characteristics and evaluation outcomes. Further investigation is warranted to better understand why rates of LVAD and transplantation may be lower in non-White patients who are referred for advanced therapies. 

Disparities in Practice Patterns by Sex, Race, and Ethnicity in Patients Referred for Advanced Heart Failure Therapies.

SGLT-2 inhibitors have been shown to confer reduced risk of adverse cardiovascular events in patients with heart failure, and have also been studied preliminarily among heart transplant patients, with overall positive findings. Use of SGLT-2 inhibitors among patients with durable mechanical circulatory support has not been studied. Here we present our results from a combined retrospective cohort of LVAD patients on SGLT-2 inhibitors at two major academic centers, which showed a good safety profile but prompted questions for further investigation. We advocate for further research into the safety and impact of SGLT-2 inhibitors among LVAD patients. 

Anuradha Lala

Papers

Heartmate 6: bridge to heart transplantation utilizing biventricular support for cardiogenic shock

The Universal Definition of Heart Failure: Perspectives from Diverse Stakeholders.

Disparities in Practice Patterns by Sex, Race, and Ethnicity in Patients Referred for Advanced Heart Failure Therapies.

Safety and effects of SGLT-2 inhibitor use among LVAD patients with type 2 diabetes mellitus

Participating in the Peer Review Process: The Journal of Cardiac Failure Construct.