SynthesisClinics (Sao Paulo, Brazil)2017
Diagnostic models of the pre-test probability of stable coronary artery disease: A systematic review.
Synthesis in Clinics (Sao Paulo, Brazil), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Coronary Computed Tomographic Angiography to Optimize the Diagnostic Yield of Invasive Angiography for Low-Risk Patients Screened With Artificial Intelligence: Protocol for the CarDIA-AI Randomized Controlled Trial.JMIR research protocols · 2025Article
- Machine learning models using symptoms and clinical variables to predict coronary artery disease on coronary angiography.Postepy w kardiologii interwencyjnej = Advances in interventional cardiology · 2024Article
- Construction and Validation of a Predictive Model for Coronary Artery Disease Using Extreme Gradient Boosting.Journal of inflammation research · 2024Article
- Predictive value of machine learning algorithm of coronary artery calcium score and clinical factors for obstructive coronary artery disease in hypertensive patients.BMC medical informatics and decision making · 2023Article
- Predictive value of CAC score combined with clinical features for obstructive coronary heart disease on coronary computed tomography angiography: a machine learning method.BMC cardiovascular disorders · 2022Article
- Prediction of 2-year major adverse cardiac events from myocardial perfusion scintigraphy and clinical risk factors.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2022Article
- Adding value to myocardial perfusion scintigraphy: A prediction tool to predict adverse cardiac outcomes and risk stratify.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2021Article
- Lipid biomarkers in statin users with coronary artery disease annotated by coronary computed tomography angiography.Scientific reports · 2021Article
- A Risk-Stratification Machine Learning Framework for the Prediction of Coronary Artery Disease Severity: Insights From the GESS Trial.Frontiers in cardiovascular medicine · 2021Article
- Machine learning of clinical variables and coronary artery calcium scoring for the prediction of obstructive coronary artery disease on coronary computed tomography angiography: analysis from the CONFIRM registry.European heart journal · 2020Observational
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A comprehensive search of PubMed and Embase was performed in January 2015 to examine the available literature on validated diagnostic models of the pre-test probability of stable coronary artery disease and to describe the characteristics of the models. Studies that were designed to develop and validate diagnostic models of pre-test probability for stable coronary artery disease were included. Data regarding baseline patient characteristics, procedural characteristics, modeling methods, metrics of model performance, risk of bias, and clinical usefulness were extracted. Ten studies involving the development of 12 models and two studies focusing on external validation were identified. Seven models were validated internally, and seven models were validated externally. Discrimination varied between studies that were validated internally (C statistic 0.66-0.81) and externally (0.49-0.87). Only one study presented reclassification indices. The majority of better performing models included sex, age, symptoms, diabetes, smoking, and hyperlipidemia as variables. Only two diagnostic models evaluated the effects on clinical decision making processes or patient outcomes. Most diagnostic models of the pre-test probability of stable coronary artery disease have had modest success, and very few present data regarding the effects of these models on clinical decision making processes or patient outcomes.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.