@phdthesis{Schiborn2020,
  author    = {Schiborn, Catarina},
  title     = {Extension of the German Diabetes Risk Score with regard to risk communication and cardiovascular outcomes},
  school      = {Universit{\"a}t Potsdam},
  pages     = {218},
  year      = {2020},
  language  = {en}
}
@article{SchibornSchulze2020,
  author    = {Schiborn, Catarina and Schulze, Matthias B.},
  title     = {Diabetes risk scores},
  series = {Der Diabetologe},
  volume    = {16},
  journal   = {Der Diabetologe},
  number    = {3},
  publisher = {Springer},
  address   = {Heidelberg},
  issn      = {1860-9716},
  doi       = {10.1007/s11428-020-00592-0},
  pages     = {226 -- 233},
  year      = {2020},
  abstract  = {Risk scores are used to identify high-risk individuals for type 2 diabetes (T2DM) who benefit from preventive measures. The DIfE-DEUTSCHER DIABETES-RISIKO-TEST (R) (DRT) is used to determine the absolute 5-year risk for T2DM. Since the calculation is based on non-clinical information, the test can be used independently of a doctor's visit. Data from prospective population-based long-term studies serve as the basis for the development of risk scores. As in the case of the DRT, the very good predictive quality of a score should be confirmed in independent populations. In addition to the use by doctors and for individual self-anamnesis, non-clinical risk scores can be used in the context of broader, population-based prevention concepts and information offers to reduce the risk of disease. Prevention services billable by health insurance companies should support the integration of health-promoting behavior into everyday life within the meaning of the German Prevention Act. Although obesity and diet are relevant lifestyle risk factors for T2DM, the proportion of preventive courses taken on this topic is only 3\% of the courses billed. Appropriate recommendations in medical examinations could promote more extensive use. The use of risk scores as the basis for systematic and targeted recommendations for behavioral prevention could also support this, as is already established in guidelines for cardiovascular prevention. The further development of implementation research is also important for the efficient use of risk scores.},
  language  = {de}
}
@article{SchibornSchulze2022,
  author    = {Schiborn, Catarina and Schulze, Matthias Bernd},
  title     = {Precision prognostics for the development of complications in diabetes},
  series = {Diabetologia : journal of the European Association for the Study of Diabetes (EASD)},
  journal   = {Diabetologia : journal of the European Association for the Study of Diabetes (EASD)},
  publisher = {Springer},
  address   = {New York},
  issn      = {0012-186X},
  doi       = {10.1007/s00125-022-05731-4},
  pages     = {16},
  year      = {2022},
  abstract  = {Individuals with diabetes face higher risks for macro- and microvascular complications than their non-diabetic counterparts. The concept of precision medicine in diabetes aims to optimise treatment decisions for individual patients to reduce the risk of major diabetic complications, including cardiovascular outcomes, retinopathy, nephropathy, neuropathy and overall mortality. In this context, prognostic models can be used to estimate an individual's risk for relevant complications based on individual risk profiles. This review aims to place the concept of prediction modelling into the context of precision prognostics. As opposed to identification of diabetes subsets, the development of prediction models, including the selection of predictors based on their longitudinal association with the outcome of interest and their discriminatory ability, allows estimation of an individual's absolute risk of complications. As a consequence, such models provide information about potential patient subgroups and their treatment needs. This review provides insight into the methodological issues specifically related to the development and validation of prediction models for diabetes complications. We summarise existing prediction models for macro- and microvascular complications, commonly included predictors, and examples of available validation studies. The review also discusses the potential of non-classical risk markers and omics-based predictors. Finally, it gives insight into the requirements and challenges related to the clinical applications and implementation of developed predictions models to optimise medical decision making.},
  language  = {en}
}