Risk stratification with the risk chart from the European Society of Hypertension compared with SCORE in the general population

This article is the report of an International Symposium endorsed by the European Society of Cardiology, held within the Venice Arrhythmias 2007: 10th International Workshop on Cardiac Arrhythmias (Venice, October 2007). The topics of the Symposium are the following: how to stratify the risk of sudden death in the athletes; the role of different diagnostic examinations in the risk stratification of sudden death in the athletes; controversies on arrhythmias and sport; and exercise prescription in patients with arrhythmias. Eur J Cardiovasc Prev Rehabil14:707-714 © 2007 The European Society of Cardiology

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Acute pulmonary embolism: mortality prediction by the 2014 European Society of Cardiology risk stratification model

European Respiratory Journal ◽

10.1183/13993003.00024-2016 ◽

2016 ◽

Vol 48 (3) ◽

pp. 780-786 ◽

Cited By ~ 90

Author(s):

Cecilia Becattini ◽

Giancarlo Agnelli ◽

Mareike Lankeit ◽

Luca Masotti ◽

Piotr Pruszczyk ◽

...

Keyword(s):

Pulmonary Embolism ◽

High Risk ◽

Risk Stratification ◽

Negative Predictive Value ◽

European Society ◽

Predictive Value ◽

Acute Pulmonary Embolism ◽

Risk Stratification Model ◽

Risk Patients ◽

European Society Of Cardiology

The European Society of Cardiology (ESC) has proposed an updated risk stratification model for death in patients with acute pulmonary embolism based on clinical scores (Pulmonary Embolism Severity Index (PESI) or simplified PESI (sPESI)), right ventricle dysfunction (RVD) and elevated serum troponin (2014 ESC model).We assessed the ability of the 2014 ESC model to predict 30-day death after acute pulmonary embolism. Consecutive patients with symptomatic, confirmed pulmonary embolism included in prospective cohorts were merged in a collaborative database. Patients’ risk was classified as high (shock or hypotension), intermediate-high (RVD and elevated troponin), intermediate-low (RVD or increased troponin or none) and low (sPESI 0). Study outcomes were death and pulmonary embolism-related death at 30 days.Among 906 patients (mean±sd age 68±16, 489 females), death and pulmonary embolism-related death occurred in 7.2% and 4.1%, respectively. Death rate was 22% in “high-risk” (95% CI 14.0–29.8), 7.7% in “intermediate-high-risk” (95% CI 4.5–10.9) and 6.0% in “intermediate-low-risk” patients (95% CI 3.4–8.6). One of the 196 “low-risk” patients died (0.5%, 95% CI 0–1.0; negative predictive value 99.5%).By using the 2014 ESC model, RVD or troponin tests would be avoided in about 20% of patients (sPESI 0), preserving a high negative predictive value. Risk stratification in patients at intermediate risk requires further improvement.

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Practical prediction model for the risk of 2-year mortality of individuals in the general population

Journal of Investigative Medicine ◽

10.1136/jim-2015-000042 ◽

2016 ◽

Vol 64 (4) ◽

pp. 848-853 ◽

Cited By ~ 3

Author(s):

Alexander Goldfarb-Rumyantzev ◽

Shiva Gautam ◽

Robert S Brown

Keyword(s):

General Population ◽

Risk Stratification ◽

Prediction Model ◽

Mortality Rates ◽

Risk Indicator ◽

Intermediate Risk ◽

Data Set ◽

Health And Nutrition ◽

Risk Stratification Tool ◽

Potential Use

This study proposed to validate a prediction model and risk-stratification tool of 2-year mortality rates of individuals in the general population suitable for office practice use. A risk indicator (R) derived from data in the literature was based on only 6 variables: to calculate R for an individual, starting with 0, for each year of age above 60, add 0.14; for a male, add 0.9; for diabetes mellitus, add 0.7; for albuminuria >30 mg/g of creatinine, add 0.7; for stage ≥3 chronic kidney disease (CKD), add 0.9; for cardiovascular disease (CVD), add 1.4; or for both CKD and CVD, add 1.7. We developed a univariate logistic regression model predicting 2-year individual mortality rates. The National Health and Nutrition Examination Survey (NHANES) data set (1999–2004 with deaths through 2006) was used as the target for validation. These 12,515 subjects had a mean age of 48.9±18.1 years, 48% males, 9.5% diabetes, 11.7% albuminuria, 6.8% CVD, 5.4% CKD, and 2.8% both CKD and CVD. Using the risk indicator R alone to predict mortality demonstrated good performance with area under the receiver operating characteristic (ROC) curve of 0.84. Dividing subjects into low-risk (R=0–1.0), low intermediate risk (R>1.0–3.0), high intermediate risk (R>3.0–5.0) or high-risk (R>5.0) categories predicted 2-year mortality rates of 0.52%, 1.44%, 5.19% and 15.24%, respectively, by the prediction model compared with actual mortality rates of 0.29%, 2.48%, 5.13% and 13.40%, respectively. We have validated a model of risk stratification using easily identified clinical characteristics to predict 2-year mortality rates of individuals in the general population. The model demonstrated performance adequate for its potential use for clinical practice and research decisions.

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Development and presentation of an objective risk stratification tool for healthcare workers when dealing with the COVID-19 pandemic in the UK: risk modelling based on hospitalisation and mortality statistics compared with epidemiological data

BMJ Open ◽

10.1136/bmjopen-2020-042225 ◽

2021 ◽

Vol 11 (9) ◽

pp. e042225

Author(s):

W David Strain ◽

Janusz Jankowski ◽

Angharad P Davies ◽

Peter English ◽

Ellis Friedman ◽

...

Keyword(s):

General Population ◽

Risk Stratification ◽

Healthcare Workers ◽

Epidemiological Data ◽

Assessment Process ◽

White Female ◽

Increased Risk ◽

Objective Risk ◽

Risk Stratification Tool ◽

Line Manager

ObjectivesHealthcare workers have greater exposure to SARS-CoV-2 and an estimated 2.5-fold increased risk of contracting COVID-19 than the general population. We wished to explore the predictive role of basic demographics to establish a simple tool that could help risk stratify healthcare workers.SettingWe undertook a review of the published literature (including multiple search strategies in MEDLINE with PubMed interface) and critically assessed early reports on preprint servers. We explored the relative risk of mortality from readily available demographics to identify the population at the highest risk.ResultsThe published studies specifically assessing the risk of healthcare workers had limited demographics available; therefore, we explored the general population in the literature. Clinician demographics: Mortality increased with increasing age from 50 years onwards. Male sex at birth, and people of black and minority ethnicity groups had higher susceptibility to both hospitalisation and mortality. Comorbid disease. Vascular disease, renal disease, diabetes and chronic pulmonary disease further increased risk. Risk stratification tool: A risk stratification tool was compiled using a white female aged <50 years with no comorbidities as a reference. A point allocated to risk factors was associated with an approximate doubling in risk. This tool provides numerical support for healthcare workers when determining which team members should be allocated to patient facing clinical duties compared with remote supportive roles.ConclusionsWe generated a tool that provides a framework for objective risk stratification of doctors and healthcare professionals during the COVID-19 pandemic, without requiring disclosure of information that an individual may not wish to share with their direct line manager during the risk assessment process. This tool has been made freely available through the British Medical Association website and is widely used in the National Health Service and other external organisations.

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A Bayesian Framework for Estimating the Risk Ratio of Hospitalization for People with Comorbidity Infected by the SARS-CoV-2 Virus

10.1101/2020.07.25.20162131 ◽

2020 ◽

Author(s):

Xiang Gao ◽

Qunfeng Dong

Keyword(s):

Public Health ◽

Cardiovascular Diseases ◽

General Population ◽

Risk Stratification ◽

Large Scale ◽

Chronic Respiratory Disease ◽

Health Policies ◽

Public Health Policies ◽

Infected People ◽

The U.S

Estimating the hospitalization risk for people with certain comorbidities infected by the SARS-CoV-2 virus is important for developing public health policies and guidance based on risk stratification. Traditional biostatistical methods require knowing both the number of infected people who were hospitalized and the number of infected people who were not hospitalized. However, the latter may be undercounted, as it is limited to only those who were tested for viral infection. In addition, comorbidity information for people not hospitalized may not always be readily available for traditional biostatistical analyses. To overcome these limitations, we developed a Bayesian approach that only requires the observed frequency of comorbidities in COVID-19 patients in hospitals and the prevalence of comorbidities in the general population. By applying our approach to two different large-scale datasets in the U.S., our results consistently indicated that cardiovascular diseases carried the highest hospitalization risk for COVID-19 patients, followed by diabetes, chronic respiratory disease, hypertension, and obesity, respectively.

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