Lilienthal Patrick, Tetschke Manuel, Schalk Enrico, Fischer Thomas, Sager Sebastian
Institute for Mathematical Optimization, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany.
Department of Hematology and Oncology, Medical Center, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany.
Front Physiol. 2020 Apr 17;11:328. doi: 10.3389/fphys.2020.00328. eCollection 2020.
Polycythemia vera (PV) is a slow-growing type of blood cancer, where the production of red blood cells (RBCs) increase considerably. The principal treatment for targeting the symptoms of PV is bloodletting (phlebotomy) at regular intervals based on data derived from blood counts and physician assessments based on experience. Model-based decision support can help to identify optimal and individualized phlebotomy schedules to improve the treatment success and reduce the number of phlebotomies and thus negative side effects of the therapy. We present an extension of a simple compartment model of the production of RBCs in adults to capture patients suffering from PV. We analyze the model's properties to show the plausibility of its assumptions. We complement this with numerical results using exemplary PV patient data. The model is then used to simulate the dynamics of the disease and to compute optimal treatment plans. We discuss heuristics and solution approaches for different settings, which include constraints arising in real-world applications, where the scheduling of phlebotomies depends on appointments between patients and treating physicians. We expect that this research can support personalized clinical decisions in cases of PV.
真性红细胞增多症（PV）是一种生长缓慢的血癌，其中红细胞（RBC）的生成会大幅增加。针对PV症状的主要治疗方法是根据血细胞计数数据和医生的经验评估，定期进行放血（静脉切开术）。基于模型的决策支持有助于确定最佳的个体化放血时间表，以提高治疗成功率，减少放血次数，从而降低治疗的负面副作用。我们提出了一个成人红细胞生成简单隔室模型的扩展，以涵盖患有PV的患者。我们分析模型的属性以证明其假设的合理性。我们使用示例性PV患者数据的数值结果对此进行补充。然后，该模型用于模拟疾病的动态变化并计算最佳治疗方案。我们讨论了不同设置下的启发式方法和解决方案，其中包括实际应用中出现的约束条件，即放血的安排取决于患者与主治医生之间的预约。我们期望这项研究能够支持PV病例中的个性化临床决策。
