ArticlePeerJ2016
A method to construct a points system to predict cardiovascular disease considering repeated measures of risk factors.
Article in PeerJ, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
3 citing papers in PubMed.
- A Machine Learning Based Discharge Prediction of Cardiovascular Diseases Patients in Intensive Care Units.Healthcare (Basel, Switzerland) · 2022Article
- Harnessing repeated measurements of predictor variables for clinical risk prediction: a review of existing methods.Diagnostic and prognostic research · 2020Review
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Current predictive models for cardiovascular disease based on points systems use the baseline situation of the risk factors as independent variables. These models do not take into account the variability of the risk factors over time. Predictive models for other types of disease also exist that do consider the temporal variability of a single biological marker in addition to the baseline variables. However, due to their complexity these other models are not used in daily clinical practice. Bearing in mind the clinical relevance of these issues and that cardiovascular diseases are the leading cause of death worldwide we show the properties and viability of a new methodological alternative for constructing cardiovascular risk scores to make predictions of cardiovascular disease with repeated measures of the risk factors and retaining the simplicity of the points systems so often used in clinical practice (construction, statistical validation by simulation and explanation of potential utilization). We have also applied the system clinically upon a set of simulated data solely to help readers understand the procedure constructed.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.