Mohammad Z I Chowdhury1, Fahmida Yeasmin2, Doreen M Rabi3, Paul E Ronksley4, Tanvir C Turin5. 1. Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, 3280 Hospital Drive NW, Calgary, AB T2N 4Z6, Canada. Electronic address: mohammad.chowdhury@ucalgary.ca. 2. Department of Mathematics and Statistics, University of Calgary, 2500 University Drive NW, Calgary, AB T2N 1N4, Canada. Electronic address: fyeasmin@ucalgary.ca. 3. Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, 3280 Hospital Drive NW, Calgary, AB T2N 4Z6, Canada; Department of Medicine, Cumming School of Medicine, University of Calgary, 3330 Hospital Drive NW, Calgary, AB T2N 4N1, Canada. Electronic address: drabi@ucalgary.ca. 4. Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, 3280 Hospital Drive NW, Calgary, AB T2N 4Z6, Canada. Electronic address: peronksl@ucalgary.ca. 5. Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, 3280 Hospital Drive NW, Calgary, AB T2N 4Z6, Canada; Department of Family Medicine, Cumming School of Medicine, University of Calgary, 3330 Hospital Drive NW, Calgary, AB T2N 4N1, Canada. Electronic address: chowdhut@ucalgary.ca.
Abstract
BACKGROUND: Diabetes is associated with an increased risk for cardiovascular diseases (CVD). Risk prediction models are tools widely used to identify individuals at particularly high-risk of adverse events. Many CVD risk prediction models have been developed but their accuracy and consistency vary. OBJECTIVE: This study reviews the literature on available CVD risk prediction models specifically developed or validated in patients with diabetes and performs a meta-analysis of C-statistics to assess and compare their predictive performance. METHODS: The online databases and manual reference checks of all identified relevant publications were searched. RESULTS: Fifteen CVD prediction models developed for patients with diabetes and 11 models developed in a general population but later validated in diabetes patients were identified. Meta-analysis of C-statistics showed an overall pooled C-statistic of 0.67 and 0.64 for validated models developed in diabetes patients and in general populations respectively. This small difference in the C-statistic suggests that CVD risk prediction for diabetes patients depends little on the population the model was developed in (p = 0.068). CONCLUSIONS: The discriminative ability of diabetes-specific CVD prediction models were modest. Improvements in the predictive ability of these models are required to understand both short and long-term risk before implementation into clinical practice.
BACKGROUND:Diabetes is associated with an increased risk for cardiovascular diseases (CVD). Risk prediction models are tools widely used to identify individuals at particularly high-risk of adverse events. Many CVD risk prediction models have been developed but their accuracy and consistency vary. OBJECTIVE: This study reviews the literature on available CVD risk prediction models specifically developed or validated in patients with diabetes and performs a meta-analysis of C-statistics to assess and compare their predictive performance. METHODS: The online databases and manual reference checks of all identified relevant publications were searched. RESULTS: Fifteen CVD prediction models developed for patients with diabetes and 11 models developed in a general population but later validated in diabetespatients were identified. Meta-analysis of C-statistics showed an overall pooled C-statistic of 0.67 and 0.64 for validated models developed in diabetespatients and in general populations respectively. This small difference in the C-statistic suggests that CVD risk prediction for diabetespatients depends little on the population the model was developed in (p = 0.068). CONCLUSIONS: The discriminative ability of diabetes-specific CVD prediction models were modest. Improvements in the predictive ability of these models are required to understand both short and long-term risk before implementation into clinical practice.
Authors: Wendy A Davis; Valentina Hellbusch; Michael L Hunter; David G Bruce; Timothy M E Davis Journal: J Clin Med Date: 2020-05-11 Impact factor: 4.241
Authors: Mohammad Ziaul Islam Chowdhury; Iffat Naeem; Hude Quan; Alexander A Leung; Khokan C Sikdar; Maeve O'Beirne; Tanvir C Turin Journal: PLoS One Date: 2022-04-07 Impact factor: 3.240
Authors: Mohammad Ziaul Islam Chowdhury; Alexander A Leung; Khokan C Sikdar; Maeve O'Beirne; Hude Quan; Tanvir C Turin Journal: Sci Rep Date: 2022-07-27 Impact factor: 4.996
Authors: Mohammad Ziaul Islam Chowdhury; Fahmida Yeasmin; Doreen M Rabi; Paul E Ronksley; Tanvir C Turin Journal: BMJ Open Date: 2019-08-30 Impact factor: 2.692