Value of assessment of pretest probability of deep-vein thrombosis in clinical management.

By P. Wells, D. Anderson, J. Bormanis, F. Guy, M. Mitchell, L. Gray, Cathy Clement, K. Robinson, Bernard J. Lewandowski. 1997

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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.,…”
Evaluation of D-dimer in the diagnosis of suspected deep-vein thrombosis. (2003, background): “Positive, 1-week ultrasonography (100) Negative, DVT ruled out (81) Positive for DVT (1) Negative, DVT ruled out (181)”
Supersparse Linear Integer Models for Interpretable Classification (2013, methodology): “…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 eﬀusions [42].”
Risk prediction models: I. Development, internal validation, and assessing the incremental value of a new (bio)marker (2012, methodology): “In the cardiovascular domain, well known prediction models are the Framingham,(6) SCORE,(7) ASSIGN,(8) EUROSCORE,(9) PROCAM(10) and Wells’ scores.(11) 12 Prediction models are developed, in most cases, to guide healthcare professionals and individuals in their decision making regarding further manag”
Translating Clinical Research into Clinical Practice: Impact of Using Prediction Rules To Make Decisions (2006, background): “For example, in advancing from a level 3 rule for predicting venous thrombosis (Table 1) to a level 5 decision rule, developers dichotomized the rule’s predictions into 2 rather than 3 probability levels, further stratified these probabilities after bedside D-dimer testing, and then analyzed the imp”
Minimum sample size for developing a multivariable prediction model: PART II ‐ binary and time‐to‐event outcomes (2018, methodology): “Applying Equation (5) within Equation (3), the Van Houwelingen and Le Cessie shrinkage factor becomes”
Soya food intake and risk of endometrial cancer among Chinese women in Shanghai: population based case-control study (2004, methodology): “Descriptive data, including a score of pretest probability of DVT calculated by using a validated scoring system (19), were collected for each patient.”
Prognosis and prognostic research: application and impact of prognostic models in clinical practice (2009, background): “One example is the diagnostic model to predict deep vein thrombosis, which had a negative predictive value of 97% (95% confidence interval 95% to 99%) and sensitivity 90% (83% to 96%) in Canadian secondary care patients.”
A Bayesian Approach to Learning Scoring Systems (2015, unknown): “The Wells’ criteria for pulmonary embolism 3,4 quantiﬁes the risk of pulmonary embolism (PE) in clinical settings.”