ArticleBMC medical research methodology2011
Multivariate modeling to identify patterns in clinical data: the example of chest pain.
Article in BMC medical research methodology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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.
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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.
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Who cites it
5 citing papers in PubMed, 12 citations in OpenAlex.
- Visualizing adverse events in clinical trials using correspondence analysis with R-package visae.BMC medical research methodology · 2021Trial
- Unboxing cluster heatmaps.BMC bioinformatics · 2017Article
- Patterns of motor signs in spinocerebellar ataxia type 3 at the start of follow-up in a reference unit.Cerebellum & ataxias · 2016Article
- Wild and Domestic Pig Interactions at the Wildlife-Livestock Interface of Murchison Falls National Park, Uganda, and the Potential Association with African Swine Fever Outbreaks.Frontiers in veterinary science · 2016Article
- Development and validation of a clinical prediction rule for chest wall syndrome in primary care.BMC family practice · 2012Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn chest pain, physicians are confronted with numerous interrelationships between symptoms and with evidence for or against classifying a patient into different diagnostic categories. The aim of our study was to find natural groups of patients on the basis of risk factors, history and clinical examination data which should then be validated with patients' final diagnoses.
methodsWe conducted a cross-sectional diagnostic study in 74 primary care practices to establish the validity of symptoms and findings for the diagnosis of coronary heart disease. A total of 1199 patients above age 35 presenting with chest pain were included in the study. General practitioners took a standardized history and performed a physical examination. They also recorded their preliminary diagnoses, investigations and management related to the patient's chest pain. We used multiple correspondence analysis (MCA) to examine associations on variable level, and multidimensional scaling (MDS), k-means and fuzzy cluster analyses to search for subgroups on patient level. We further used heatmaps to graphically illustrate the results.
resultsA multiple correspondence analysis supported our data collection strategy on variable level. Six factors emerged from this analysis: "chest wall syndrome", "vital threat", "stomach and bowel pain", "angina pectoris", "chest infection syndrome", and " self-limiting chest pain". MDS, k-means and fuzzy cluster analysis on patient level were not able to find distinct groups. The resulting cluster solutions were not interpretable and had insufficient statistical quality criteria.
conclusionsChest pain is a heterogeneous clinical category with no coherent associations between signs and symptoms on patient level.
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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.