Mary Angelyn Bethel1, Kristen A Hyland2, Antonio R Chacra3, Prakash Deedwania4, Gregory R Fulcher5, Rury R Holman6, Trond Jenssen7, Naomi S Levitt8, John J V McMurray9, Eleni Boutati10, Laine Thomas11, Jie-Lena Sun12, Steven M Haffner13. 1. Diabetes Trials Unit, Oxford Centre for Diabetes, Endocrinology, and Metabolism, University of Oxford, Oxford, UK. Electronic address: angelyn.bethel@dtu.ox.ac.uk. 2. Wilmington VA Medical Center, Wilmington, DE, USA. Electronic address: kristen.hyland@va.gov. 3. Federal University of São Paulo, São Paulo, Brazil. Electronic address: chacra@unifesp.br. 4. University of California-San Francisco Program at Fresno and the Veterans Affairs Central California Health Care System, Fresno, CA, USA. Electronic address: deed@fresno.ucsf.edu. 5. Royal North Shore Hospital, University of Sydney, Sydney, New South Wales, Australia. Electronic address: gfulcher@med.usyd.edu.au. 6. Diabetes Trials Unit, Oxford Centre for Diabetes, Endocrinology, and Metabolism, University of Oxford, Oxford, UK. Electronic address: rury.holman@dtu.ox.ac.uk. 7. Oslo University Hospital Rikshospitalet, Oslo Institute of Clinical Medicine, and the University of Tromsø, Tromsø, Norway. Electronic address: trond.jenssen@rikshospitalet.no. 8. Groote Schuur Hospital, University of Cape Town, Cape Town, South Africa. Electronic address: naomi.levitt@uct.ac.za. 9. British Heart Foundation, University of Glasgow, Glasgow, UK. Electronic address: john.mcmurray@glasgow.ac.uk. 10. National and Kapodistrian University of Athens, Athens, Greece. Electronic address: boutati@otenet.gr. 11. Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC, USA. Electronic address: laine.thomas@dm.duke.edu. 12. Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC, USA. Electronic address: jielena.sun@dm.duke.edu. 13. San Antonio, TX, USA. Electronic address: traffic15@satx.rr.com.
Abstract
AIMS: Predicting incident diabetes could inform treatment strategies for diabetes prevention, but the incremental benefit of recalculating risk using updated risk factors is unknown. We used baseline and 1-year data from the Nateglinide and Valsartan in Impaired Glucose Tolerance Outcomes Research (NAVIGATOR) Trial to compare diabetes risk prediction using historical or updated clinical information. METHODS: Among non-diabetic participants reaching 1year of follow-up in NAVIGATOR, we compared the performance of the published baseline diabetes risk model with a "landmark" model incorporating risk factors updated at the 1-year time point. The C-statistic was used to compare model discrimination and reclassification analyses to demonstrate the relative accuracy of diabetes prediction. RESULTS: A total of 7527 participants remained non-diabetic at 1year, and 2375 developed diabetes during a median of 4years of follow-up. The C-statistic for the landmark model was higher (0.73 [95% CI 0.72-0.74]) than for the baseline model (0.67 [95% CI 0.66-0.68]). The landmark model improved classification to modest (<20%), moderate (20%-40%), and high (>40%) 4-year risk, with a net reclassification index of 0.14 (95% CI 0.10-0.16) and an integrated discrimination index of 0.01 (95% CI 0.003-0.013). CONCLUSIONS: Using historical clinical values to calculate diabetes risk reduces the accuracy of prediction. Diabetes risk calculations should be routinely updated to inform discussions about diabetes prevention at both the patient and population health levels.
AIMS: Predicting incident diabetes could inform treatment strategies for diabetes prevention, but the incremental benefit of recalculating risk using updated risk factors is unknown. We used baseline and 1-year data from the Nateglinide and Valsartan in Impaired Glucose Tolerance Outcomes Research (NAVIGATOR) Trial to compare diabetes risk prediction using historical or updated clinical information. METHODS: Among non-diabeticparticipants reaching 1year of follow-up in NAVIGATOR, we compared the performance of the published baseline diabetes risk model with a "landmark" model incorporating risk factors updated at the 1-year time point. The C-statistic was used to compare model discrimination and reclassification analyses to demonstrate the relative accuracy of diabetes prediction. RESULTS: A total of 7527 participants remained non-diabetic at 1year, and 2375 developed diabetes during a median of 4years of follow-up. The C-statistic for the landmark model was higher (0.73 [95% CI 0.72-0.74]) than for the baseline model (0.67 [95% CI 0.66-0.68]). The landmark model improved classification to modest (<20%), moderate (20%-40%), and high (>40%) 4-year risk, with a net reclassification index of 0.14 (95% CI 0.10-0.16) and an integrated discrimination index of 0.01 (95% CI 0.003-0.013). CONCLUSIONS: Using historical clinical values to calculate diabetes risk reduces the accuracy of prediction. Diabetes risk calculations should be routinely updated to inform discussions about diabetes prevention at both the patient and population health levels.
Authors: Michael J Wheeler; Daniel J Green; Ester Cerin; Kathryn A Ellis; Ilkka Heinonen; Jaye Lewis; Louise H Naylor; Neale Cohen; Robyn Larsen; Paddy C Dempsey; Bronwyn A Kingwell; Neville Owen; David W Dunstan Journal: Int J Behav Nutr Phys Act Date: 2020-12-14 Impact factor: 6.457