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Kapil Gudala  (Gudala K) 2 Articles
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
Diabetes Metab J. 2015;39(4):321-327.   Published online July 8, 2015
DOI: https://doi.org/10.4093/dmj.2015.39.4.321
  • 3,566 View
  • 33 Download
  • 11 Web of Science
  • 12 Crossref
AbstractAbstract PDFPubReader   
Background

The 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.

Methods

A 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.

Results

The 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.

Conclusion

The 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.

Citations

Citations to this article as recorded by  
  • Endocan is Related to Increased Cardiovascular Risk in Type 2 Diabetes Mellitus Patients
    Aleksandra Klisic, Jelena Kotur-Stevuljevic, Ana Ninic
    Metabolic Syndrome and Related Disorders.2023; 21(7): 362.     CrossRef
  • Estimated risk of cardiovascular events and long-term complications: The projected future of diabetes patients in Delhi from the DEDICOM-II survey
    Swapnil Rawat, Ramasheesh Yadav, Siddhi Goyal, Jitender Nagpal
    Diabetes & Metabolic Syndrome: Clinical Research & Reviews.2023; 17(11): 102880.     CrossRef
  • Cardiovascular Biomarkers and Calculated Cardiovascular Risk in Orally Treated Type 2 Diabetes Patients: Is There a Link?
    Aleksandra Markova, Mihail Boyanov, Deniz Bakalov, Atanas Kundurdjiev, Adelina Tsakova
    Hormone and Metabolic Research.2021; 53(01): 41.     CrossRef
  • Risk of coronary heart disease and stroke based on United Kingdom prospective diabetes study in type 2 DM patients in Medan
    R Amelia, J Harahap, H Wijaya, I I Fujiati
    IOP Conference Series: Earth and Environmental Science.2021; 912(1): 012081.     CrossRef
  • Cardiovascular/stroke risk prevention: A new machine learning framework integrating carotid ultrasound image-based phenotypes and its harmonics with conventional risk factors
    Ankush Jamthikar, Deep Gupta, Narendra N. Khanna, Luca Saba, John R. Laird, Jasjit S. Suri
    Indian Heart Journal.2020; 72(4): 258.     CrossRef
  • Current Data Regarding the Relationship between Type 2 Diabetes Mellitus and Cardiovascular Risk Factors
    Cosmin Mihai Vesa, Loredana Popa, Amorin Remus Popa, Marius Rus, Andreea Atena Zaha, Simona Bungau, Delia Mirela Tit, Raluca Anca Corb Aron, Dana Carmen Zaha
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  • Performance evaluation of 10-year ultrasound image-based stroke/cardiovascular (CV) risk calculator by comparing against ten conventional CV risk calculators: A diabetic study
    Narendra N. Khanna, Ankush D. Jamthikar, Deep Gupta, Andrew Nicolaides, Tadashi Araki, Luca Saba, Elisa Cuadrado-Godia, Aditya Sharma, Tomaz Omerzu, Harman S. Suri, Ajay Gupta, Sophie Mavrogeni, Monika Turk, John R. Laird, Athanasios Protogerou, Petros P.
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    Open Medicine.2018; 13(1): 610.     CrossRef
  • Differential Association of Metabolic Risk Factors with Open Angle Glaucoma according to Obesity in a Korean Population
    Hyun-Ah Kim, Kyungdo Han, Yun-Ah Lee, Jin A Choi, Yong-Moon Park
    Scientific Reports.2016;[Epub]     CrossRef
  • The Association between Diabetic Retinopathy and Framingham Risk Score in Koreans with Type II Diabetes
    Da Yeong Kim, Su Jeong Song, Jeong Hun Bae, Cheol-Young Park, Eun-Jung Rhee
    Journal of the Korean Ophthalmological Society.2016; 57(5): 779.     CrossRef
Ruboxistaurin for the Treatment of Diabetic Peripheral Neuropathy: A Systematic Review of Randomized Clinical Trials
Dipika Bansal, Yogesh Badhan, Kapil Gudala, Fabrizio Schifano
Diabetes Metab J. 2013;37(5):375-384.   Published online October 17, 2013
DOI: https://doi.org/10.4093/dmj.2013.37.5.375
  • 7,049 View
  • 72 Download
  • 51 Crossref
AbstractAbstract PDFPubReader   
Background

Diabetic peripheral neuropathy (DPN) is a common complication of diabetes mellitus. Protein kinase C (PKC) inhibitor's has been thought to be a potential disease modifying drug's in DPN as it slows or reverse neuropathy's progression. To assesses the efficacy and safety of ruboxistaurin on the progression of symptoms, signs, or functional disability in DPN.

Methods

A systematic review of the literature databases like PubMed, ProQuest, EBSCO, EMBASE, and Cochrane Central was performed up to August 2012. We included randomized controlled trials (RCTs) comparing PKC inhibitor ruboxistaurin (RBX) with control and lasting at least 6 months. Our primary outcome measure was change in neurological examination, measured by neurological total symptom score (NTSS) and vibration detection threshold (VDT). Secondary outcome measures were total quality of life (QoL), skin microvascular blood flow and others.

Results

Six RCTs were included in review. Change in neurological function assessed by NTSS was reported in six studies, out of which significant difference between the RBX and placebo group seen in four studies favouring treatment group while remaining two studies reported no significant difference. VDT was assessed in only one study in which no significant difference seen between RBX and placebo group. Two studies reported significant improvement in QoL data. Safety data was reported in only two studies in which none of side effect was related to RBX.

Conclusion

RBX had effects on DPN in some studies, but the evidence is not enough for meta-analysis and firm conclusion.

Citations

Citations to this article as recorded by  
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    Journal of Diabetes Science and Technology.2024; 18(1): 159.     CrossRef
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    Current Diabetes Reviews.2024;[Epub]     CrossRef
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    Kopal Agarawal, Yogesh Anant Kulkarni, Sarika Wairkar
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    Bushra T. AlQuadeib, Fadilah S. Aleanizy, Fulwah Y. Alqahtani, Rehab A. Alshammari, Amal Aldarwesh, Ibrahim Alsarra
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    Che Aishah Nazariah Ismail
    World Journal of Diabetes.2023; 14(6): 741.     CrossRef
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    Diabetes & Metabolic Syndrome: Clinical Research & Reviews.2021; 15(1): 287.     CrossRef
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