临床预测模型在慢性硬膜下血肿术后复发风险预测中的研究进展

Research Progress of Clinical Prediction Models in Predicting Postoperative Recurrence Risk in Chronic Subdural Hematoma

摘要: 慢性硬膜下血肿(Chronic Subdural Hematoma, CSDH)是一种常见的神经外科疾病，其治疗和管理一直是神经外科医生和研究人员关注的焦点。近年来，针对CSDH患者术后复发的影响因素研究逐渐成为热点。随着科学技术的发展，对于CSDH患者术后复发的风险预测模型研究逐渐成为热点。这一研究方向可以通过系统整合患者的临床和影像学资料，致力于更准确地评估患者术后复发的风险，为医生制定个体化治疗方案提供一定的科学依据。首先，术后复发风险预测模型通过收集入院时的基本信息、临床特征和影像学资料，建立了对CSDH患者术后复发的风险的预测模型，能够更全面地评估患者的临床特征，为术后复发的风险评估提供更为精准的数据。为医生提供了更详尽的信息，有助于全面了解患者的病情，从而更好地制定治疗计划。其次，风险预测模型在CSDH患者术后复发的临床诊疗中为临床医生提供了一定的决策支持。通过分析患者的临床特点和影像学表现，模型能够辅助医生判断患者术后复发的风险，为患者家属和医生选择不同的治疗策略提供科学依据。这种个性化的决策支持有助于医生更加精准地制定治疗方案，提高治疗效果和患者的康复率。我们通过对风险预测模型在CSDH患者术后复发临床诊疗中的应用的综述，旨在加深其理解并促进相关风险预测模型在今后临床实际应用中的可能。

Abstract: Chronic Subdural Hematoma (CSDH) is a common neurosurgical condition, and its treatment and management have been a focus for neurosurgeons and researchers. In recent years, the study of factors influencing postoperative recurrence in CSDH patients has gradually become a hotspot. With the advancement of science and technology, research on risk prediction models for postoperative recurrence in CSDH patients has gained increasing attention. This research direction aims to systematically integrate clinical and imaging data of patients to more accurately assess the risk of postoperative recurrence, providing certain scientific basis for doctors to develop individualized treatment plans. Firstly, the postoperative recurrence risk prediction model collects basic information, clinical characteristics, and imaging data at admission to establish a predictive model for the risk of recurrence in CSDH patients, allowing for a more comprehensive assessment of patients’ clinical features and providing more precise data for postoperative recurrence risk evaluation. This offers doctors more detailed information, helping them to fully understand the patient’s condition and better formulate treatment plans. Secondly, risk prediction models provide certain decision support for clinicians in the clinical diagnosis and treatment of postoperative recurrence in CSDH patients. By analyzing patients’ clinical characteristics and imaging findings, the model can assist doctors in assessing the risk of postoperative recurrence, providing a scientific basis for families and doctors to choose different treatment strategies. This personalized decision support helps doctors formulate treatment plans more accurately, improving treatment outcomes and patient recovery rates. Through a review of the application of risk prediction models in the clinical management of postoperative recurrence in CSDH patients, we aim to deepen understanding and promote the potential future clinical application of relevant risk prediction models.