Derivation and external validation of a risk prediction algorithm to estimate future risk of cardiovascular death among patients with type 2 diabetes and incident diabetic nephropathy: prospective cohort study.

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  • Additional Information
    • Source:
      Publisher: Published by BMJ in partnership with the American Diabetes Association Country of Publication: England NLM ID: 101641391 Publication Model: eCollection Cited Medium: Internet ISSN: 2052-4897 (Electronic) Linking ISSN: 20524897 NLM ISO Abbreviation: BMJ Open Diabetes Res Care Subsets: MEDLINE
    • Publication Information:
      Original Publication: London : Published by BMJ in partnership with the American Diabetes Association
    • Subject Terms:
      Algorithms* ; Diabetes Mellitus, Type 2*/complications ; Diabetes Mellitus, Type 2*/diagnosis ; Diabetes Mellitus, Type 2*/mortality ; Diabetes Mellitus, Type 2*/pathology; Cardiovascular Diseases/*mortality ; Diabetic Angiopathies/*mortality ; Diabetic Nephropathies/*mortality; Aged ; Biopsy ; Cardiovascular Diseases/complications ; Cardiovascular Diseases/diagnosis ; Cardiovascular Diseases/pathology ; Cohort Studies ; Diabetic Angiopathies/diagnosis ; Diabetic Angiopathies/epidemiology ; Diabetic Angiopathies/pathology ; Diabetic Nephropathies/complications ; Diabetic Nephropathies/epidemiology ; Diabetic Nephropathies/pathology ; Female ; Humans ; Incidence ; Male ; Middle Aged ; Prognosis ; Prospective Studies ; Risk Assessment ; Risk Factors
    • Abstract:
      Objective: To derive, and externally validate, a risk score for cardiovascular death among patients with type 2 diabetes and newly diagnosed diabetic nephropathy (DN).
      Research Design and Methods: Two independent prospective cohorts with type 2 diabetes were used to develop and externally validate the risk score. The derivation cohort comprised 2282 patients with an incident, clinical diagnosis of DN. The validation cohort includes 950 patients with incident, biopsy-proven diagnosis of DN. The outcome was cardiovascular death within 2 years of the diagnosis of DN. Logistic regression was applied to derive the risk score for cardiovascular death from the derivation cohort, which was externally validated in the validation cohort. The score was also estimated by applying the United Kingdom Prospective Diabetes Study (UKPDS) risk score in the external validation cohort.
      Results: The 2-year cardiovascular mortality was 12.05% and 11.79% in the derivation cohort and validation cohort, respectively. Traditional predictors including age, gender, body mass index, blood pressures, glucose, lipid profiles alongside novel laboratory test items covering five test panels (liver function, serum electrolytes, thyroid function, blood coagulation and blood count) were included in the final model.C-statistics was 0.736 (95% CI 0.731 to 0.740) and 0.747 (95% CI 0.737 to 0.756) in the derivation cohort and validation cohort, respectively. The calibration slope was 0.993 (95% CI 0.974 to 1.013) and 1.000 (95% CI 0.981 to 1.020) in the derivation cohort and validation cohort, respectively.The UKPDS risk score substantially underestimated cardiovascular mortality.
      Conclusions: A new risk score based on routine clinical measurements that quantified individual risk of cardiovascular death was developed and externally validated. Compared with the UKPDS risk score, which underestimated the cardiovascular disease risk, the new score is a more specific tool for patients with type 2 diabetes and DN. The score could work as a tool to identify individuals at the highest risk of cardiovascular death among those with DN.
      Competing Interests: Competing interests: None declared.
      (© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY. Published by BMJ.)
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    • Contributed Indexing:
      Keywords: cardiovascular mortality; prediction; prognostic models; statistical
    • Publication Date:
      Date Created: 20191205 Date Completed: 20200831 Latest Revision: 20200831
    • Publication Date:
      20231215
    • Accession Number:
      PMC6861120
    • Accession Number:
      10.1136/bmjdrc-2019-000735
    • Accession Number:
      31798896