Erasmus School of Health Policy and Management, Erasmus University Rotterdam, Rotterdam, The Netherlands.
Institute for Medical Technology Assessment, Erasmus University Rotterdam, Rotterdam, The Netherlands.
Value Health. 2021 Oct;24(10):1435-1445. doi: 10.1016/j.jval.2021.04.004. Epub 2021 May 28.
Developing and validating a discrete event simulation model that is able to model patients with heart failure managed with usual care or an early warning system (with or without a diagnostic algorithm) and to account for the impact of individual patient characteristics in their health outcomes.
The model was developed using patient-level data from the Trans-European Network - Home-Care Management System study. It was coded using RStudio Version 1.3.1093 (version 3.6.2.) and validated along the lines of the Assessment of the Validation Status of Health-Economic decision models tool. The model includes 20 patient and disease characteristics and generates 8 different outcomes. Model outcomes were generated for the base-case analysis and used in the model validation.
Patients managed with the early warning system, compared with usual care, experienced an average increase of 2.99 outpatient visits and a decrease of 0.02 hospitalizations per year, with a gain of 0.81 life years (0.45 quality-adjusted life years) and increased average total costs of €11 249. Adding a diagnostic algorithm to the early warning system resulted in a 0.92 life year gain (0.57 quality-adjusted life years) and increased average costs of €9680. These patients experienced a decrease of 0.02 outpatient visits and 0.65 hospitalizations per year, while they avoided being hospitalized 0.93 times. The model showed robustness and validity of generated outcomes when comparing them with other models addressing the same problem and with external data.
This study developed and validated a unique patient-level simulation model that can be used for simulating a wide range of outcomes for different patient subgroups and treatment scenarios. It provides useful information for guiding research and for developing new treatment options by showing the hypothetical impact of these interventions on a large number of important heart failure outcomes.
开发和验证一个离散事件仿真模型，该模型能够对接受常规护理或早期预警系统（有或无诊断算法）管理的心力衰竭患者进行建模，并考虑患者个体特征对其健康结果的影响。
该模型使用来自 Trans-European Network - Home-Care Management System 研究的患者水平数据进行开发。它使用 RStudio Version 1.3.1093（版本 3.6.2）进行编码，并根据健康经济决策模型评估验证状态工具进行验证。该模型包含 20 个患者和疾病特征，并生成 8 种不同的结果。为基础案例分析生成模型结果，并将其用于模型验证。
与常规护理相比，接受早期预警系统管理的患者每年平均增加 2.99 次门诊就诊，减少 0.02 次住院治疗，获得 0.81 个生命年（0.45 个质量调整生命年），并增加平均总成本 11249 欧元。在早期预警系统中添加诊断算法可获得 0.92 个生命年的增益（0.57 个质量调整生命年）和增加平均成本 9680 欧元。这些患者每年减少 0.02 次门诊就诊和 0.65 次住院治疗，同时避免住院 0.93 次。当将生成的结果与解决同一问题的其他模型和外部数据进行比较时，该模型显示了结果的稳健性和有效性。
本研究开发并验证了一个独特的患者水平仿真模型，该模型可用于模拟不同患者亚组和治疗方案的广泛结果。通过显示这些干预措施对大量重要心力衰竭结果的假设影响，为指导研究和开发新的治疗选择提供了有用的信息。
