Palmer A J, Brandt A, Gozzoli V, Weiss C, Stock H, Wenzel H
IMIB, Bachtelenweg 3, 4125, Riehen, Switzerland.
Diabetes Res Clin Pract. 2000 Nov;50 Suppl 3:S47-56. doi: 10.1016/s0168-8227(00)00216-3.
A complex interactive computer model was developed to determine the health outcomes and economic consequences of different diabetes interventions for user-defined observation periods. The interventions include intensive or conventional insulin therapy, different oral hypoglycaemic medications, different screening and treatment strategies for micro-vascular complications, different treatment strategies for end-stage complications, or multi-factorial interventions. The analyses can be performed on different sub-groups of type 1 and 2 diabetic patients, defined in terms of age, gender, baseline risk factors and pre-existing complications. The model performs real-time simulations. Full on-screen documentation of the model structure, logic, calculations and data sources is available to maximize the model's transparency. Economic and clinical data used in the disease management model are editable by the user, allowing the input of new data as they become available, the creation of country-specific, HMO-specific, or provider-specific versions of the model, and the exploration of new hypotheses ('what-if' analyses). The approach used allows maximum flexibility, adaptability, and transparency within the model structure. For the user-defined patient cohorts and intervention strategies the diabetes disease management model compares life expectancy, expected incidence and prevalence of complications as well as expected life-time (or shorter) treatment cost. Diabetes and complication management strategies can be compared in different patient populations in a variety of realistic clinical settings. The model allows extrapolation of results obtained from relatively short-term clinical trials to longer-term medical outcomes, and from trial populations to real-life populations providing a tangible yardstick to judge the quality of diabetes care. The model was used to evaluate diabetes care options in Germany, France, Switzerland, UK and US.
开发了一个复杂的交互式计算机模型，以确定在用户定义的观察期内不同糖尿病干预措施的健康结果和经济后果。这些干预措施包括强化或常规胰岛素治疗、不同的口服降糖药物、微血管并发症的不同筛查和治疗策略、终末期并发症的不同治疗策略或多因素干预。分析可以在1型和2型糖尿病患者的不同亚组上进行，这些亚组根据年龄、性别、基线风险因素和既往并发症来定义。该模型进行实时模拟。提供模型结构、逻辑、计算和数据源的完整屏幕文档，以最大限度地提高模型的透明度。疾病管理模型中使用的经济和临床数据可由用户编辑，允许在新数据可用时输入新数据，创建特定国家、特定健康维护组织或特定提供者版本的模型，并探索新的假设（“如果……会怎样”分析）。所采用的方法在模型结构内允许最大程度的灵活性、适应性和透明度。对于用户定义的患者队列和干预策略，糖尿病疾病管理模型比较预期寿命、并发症的预期发病率和患病率以及预期终身（或更短）治疗成本。糖尿病和并发症管理策略可以在各种现实临床环境中的不同患者群体中进行比较。该模型允许将从相对短期临床试验获得 的结果外推到长期医疗结果，并从试验人群外推到现实生活人群，提供一个切实的标准来评判糖尿病护理的质量。该模型被用于评估德国、法国、瑞士、英国和美国的糖尿病护理选择。
