| アブストラクト | BACKGROUND: Cardiovascular disease (CVD) risk prediction models for individuals with type 2 diabetes are important tools to guide intensification of interventions for CVD prevention. We aimed to assess the added value of incorporating risk factors variability in CVD risk prediction for people with type 2 diabetes. METHODS: We used electronic health records (EHRs) data from 83 910 adults with type 2 diabetes but without pre-existing CVD from the UK Clinical Practice Research Datalink for 2004-2017. Using a landmark-modelling approach, we developed and validated sex-specific Cox models, incorporating conventional predictors and trajectories plus variability of systolic blood pressure (SBP), total and high-density lipoprotein (HDL) cholesterol, and glycated haemoglobin (HbA1c). Such models were compared against simpler models using single last observed values or means. RESULTS: The standard deviations (SDs) of SBP, HDL cholesterol and HbA1c were associated with higher CVD risk (P < 0.05). Models incorporating trajectories and variability of continuous predictors demonstrated improvement in risk discrimination (C-index = 0.659, 95% CI: 0.654-0.663) as compared with using last observed values (C-index = 0.651, 95% CI: 0.646-0.656) or means (C-index = 0.650, 95% CI: 0.645-0.655). Inclusion of SDs of SBP yielded the greatest improvement in discrimination (C-index increase = 0.005, 95% CI: 0.004-0.007) in comparison to incorporating SDs of total cholesterol (C-index increase = 0.002, 95% CI: 0.000-0.003), HbA1c (C-index increase = 0.002, 95% CI: 0.000-0.003) or HDL cholesterol (C-index increase= 0.003, 95% CI: 0.002-0.005). CONCLUSION: Incorporating variability of predictors from EHRs provides a modest improvement in CVD risk discrimination for individuals with type 2 diabetes. Given that repeat measures are readily available in EHRs especially for regularly monitored patients with diabetes, this improvement could easily be achieved. |
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| ジャーナル名 | International journal of epidemiology |
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| Pubmed追加日 | 2022/7/2 |
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| 投稿者 | Xu, Zhe; Arnold, Matthew; Sun, Luanluan; Stevens, David; Chung, Ryan; Ip, Samantha; Barrett, Jessica; Kaptoge, Stephen; Pennells, Lisa; Di Angelantonio, Emanuele; Wood, Angela M |
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| 組織名 | British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public;Health and Primary Care, University of Cambridge, Cambridge, UK.;Medical Research Council Biostatistics Unit, Cambridge Institute of Public;Health, University of Cambridge, Cambridge, UK.;National Institute for Health Research Blood and Transplant Research Unit in;Donor Health and Genomics, University of Cambridge, Cambridge, UK.;British Heart Foundation Centre of Research Excellence, University of Cambridge,;Cambridge, UK.;Health Data Research UK Cambridge, Wellcome Genome Campus and University of;Cambridge, Cambridge, UK.;The Alan Turing Institute, London, UK. |
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| Pubmed リンク | https://www.ncbi.nlm.nih.gov/pubmed/35776101/ |
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