Evidence map›Paper›PMID 26301194›Full record

ArticleDiabetes & metabolism journal2015

Agreement between Framingham Risk Score and United Kingdom Prospective Diabetes Study Risk Engine in Identifying High Coronary Heart Disease Risk in North Indian Population.

Dipika Bansal, Ramya S R Nayakallu, Kapil Gudala, Rajavikram Vyamasuni, Anil Bhansali

Abstract read
In one paragraph

Article in Diabetes & metabolism journal, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
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1 · What the graph read from 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.

2 · The registry

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3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Dipika BansalDepartment of Pharmacy Practice, National Institute of Pharmaceutical Education and Research, Mohali, India.
Ramya S R NayakalluDepartment of Pharmacy Practice, National Institute of Pharmaceutical Education and Research, Mohali, India.
Kapil GudalaDepartment of Pharmacy Practice, National Institute of Pharmaceutical Education and Research, Mohali, India.
Rajavikram VyamasuniDepartment of Pharmacy Practice, National Institute of Pharmaceutical Education and Research, Mohali, India.
Anil BhansaliDepartment of Endocrinology, Postgraduate Institute of Medical Education and Research, Chandigarh, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe aim of the study is to evaluate the concurrence between Framingham Risk score (FRS) and United Kingdom Prospective Diabetes Study (UKPDS) risk engine in identifying coronary heart disease (CHD) risk in newly detected diabetes mellitus patients and to explore the characteristics associated with the discrepancy between them.

methodsA cross-sectional study involving 489 subjects newly diagnosed with type 2 diabetes mellitus was conducted. Agreement between FRS and UKPDS in classifying patients as high risk was calculated using kappa statistic. Subjects with discrepant scores between two algorithms were identified and associated variables were determined.

resultsThe FRS identified 20.9% subjects (range, 17.5 to 24.7) as high-risk while UKPDS identified 21.75% (range, 18.3 to 25.5) as high-risk. Discrepancy was observed in 17.9% (range, 14.7 to 21.7) subjects. About 9.4% had high risk by UKPDS but not FRS, and 8.6% had high risk by FRS but not UKPDS. The best agreement was observed at high-risk threshold of 20% for both (κ=0.463). Analysis showed that subjects having high risk on FRS but not UKPDS were elderly females having raised systolic and diastolic blood pressure. Patients with high risk on UKPDS but not FRS were males and have high glycosylated hemoglobin.

conclusionThe FRS and UKPDS (threshold 20%) identified different populations as being at high risk, though the agreement between them was fairly good. The concurrence of a number of factors (e.g., male sex, low high density lipoprotein cholesterol, and smoking) in both algorithms should be regarded as increasing the CHD risk. However, longitudinal follow-up is required to form firm conclusions.

Indexed as

Coronary heart diseaseDiabetes mellitusPredictive value of testsRisk assessment

Identifiers

PMID26301194
PMCPMC4543196

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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.