Derivation of a Simple Clinical Model to Categorize Patients Probability of Pulmonary Embolism: Increasing the Models Utility with the SimpliRED D-dimer

By P. Wells, D. Anderson, M. Rodger, Jeffrey S. Ginsberg, C. Kearon, M. Gent, A. Turpie, J. Bormanis, J. Weitz, M. Chamberlain and 3 more. 2000

1,672 citations recorded by the source corpus.

Subfields: Cardiology and Cardiovascular Medicine, Emergency Medical Services, Internal Medicine

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Citations in the corpus

Supersparse linear integer models for optimized medical scoring systems (2015, background): “…1985, 1991); • CHADS2, to assess the risk of stroke in patients with atrial fibrillation (Gage et al., 2001); • Wells Criteria for pulmonary embolisms (Wells et al., 2000) and deep vein thrombosis (Wells et al., 1997); • TIMI, to assess the risk of death and ischemic events (Antman et al.,…”
Guidelines on the Diagnosis and Management of Acute Pulmonary Embolism (2008, methodology): “The most frequently used clinical prediction rule is the Canadian rule, by Wells et al.(65) (Table 7).”
Minimum sample size for developing a multivariable prediction model: PART II ‐ binary and time‐to‐event outcomes (2018, methodology): “In this situation, the researcher should calculate lnLnull based on other information in the article, and then calculate LR using equation (4), thus allowing RCS_app 2 and RCS_adj 2 to be derived using equations (6) and (8), respectively.”
ESC Guidelines on the diagnosis and management of acute pulmonary embolism (2014, methodology): “(Table 4).(95) This rule has been validated extensively using both a three-category scheme (low, moderate, or high clinical probability of PE) and a twocategoryscheme (PE likelyorunlikely).”
Supersparse Linear Integer Models for Interpretable Classification (2013, background): “…certain types of heart problems [3, 53]; – SIRS, to detect Systemic Inﬂammatory Response Syndrome [8]; – Wells Criteria for pulmonary embolisms [75], and deep vein thrombosis [74]; – Ranson Criteria for acute pancreatitis [57]; – Light’s Criteria for transudative from exudative pleural…”
Effectiveness of managing suspected pulmonary embolism using an algorithm combining clinical probability, D-dimer testing, and computed tomography. (2006, background): “Pulmonary embolism diagnosed by CT 674 20 (3) [1.”
Prediction of Pulmonary Embolism in the Emergency Department: The Revised Geneva Score (2006, background): “Of interest, the clinical characteristics entering in our score are very similar to those of the Canadian prediction rule for pulmonary embolism developed by Wells and colleagues (13), although we derived our score in an entirely distinct population from western Europe, hence adding content validity”
A Bayesian Approach to Learning Scoring Systems (2015, neutral): “The Wells’ criteria for pulmonary embolism 3,4 quantiﬁes the risk of pulmonary embolism (PE) in clinical settings.”
External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis: opportunities and challenges (2016, background): “Well-known examples are the Framingham risk score and QRISK2,(4) 5 which estimate the 10-year risk of developing cardiovascular disease; the Nottingham Prognostic Index, which predicts the 5-year survival probability of a woman with newly diagnosed breast cancer;(6) 7 and the Wells score for predict”
Excluding Pulmonary Embolism at the Bedside without Diagnostic Imaging: Management of Patients with Suspected Pulmonary Embolism Presenting to the Emergency Department by Using a Simple Clinical Model and d-dimer (2001, neutral): “org probability of pulmonary embolism (5).”
[2014 ESC Guidelines on the diagnosis and management of acute pulmonary embolism]. (2014, positive): “(Table 4).(95) This rule has been validated extensively using both a three-category scheme (low, moderate, or high clinical probability of PE) and a twocategoryscheme (PE likelyorunlikely).”