PDF(Pubmed)
Abstract：
Background and Objectives: Soft tissue sarcomas represent a heterogeneous group of malignant mesenchymal tissues. Despite their low prevalence, soft tissue sarcomas present clinical challenges for orthopedic surgeons owing to their aggressive nature, and perioperative wound infections. However, the low prevalence of soft tissue sarcomas has hindered the availability of large-scale studies. This study aimed to analyze wound infections after wide resection in patients with soft tissue sarcomas by employing big data analytics from the Hub of the Health Insurance Review and Assessment Service (HIRA). Materials and Methods: Patients who underwent wide excision of soft tissue sarcomas between 2010 and 2021 were included. Data were collected from the HIRA database of approximately 50 million individuals\' information in the Republic of Korea. The data collected included demographic information, diagnoses, prescribed medications, and surgical procedures. Random forest has been used to analyze the major associated determinants. A total of 10,906 observations with complete data were divided into training and validation sets in an 80:20 ratio (8773 vs. 2193 cases). Random forest permutation importance was employed to identify the major predictors of infection and Shapley Additive Explanations (SHAP) values were derived to analyze the directions of associations with predictors. Results: A total of 10,969 patients who underwent wide excision of soft tissue sarcomas were included. Among the study population, 886 (8.08%) patients had post-operative infections requiring surgery. The overall transfusion rate for wide excision was 20.67% (2267 patients). Risk factors among the comorbidities of each patient with wound infection were analyzed and dependence plots of individual features were visualized. The transfusion dependence plot reveals a distinctive pattern, with SHAP values displaying a negative trend for individuals without blood transfusions and a positive trend for those who received blood transfusions, emphasizing the substantial impact of blood transfusions on the likelihood of wound infection. Conclusions: Using the machine learning random forest model and the SHAP values, the perioperative transfusion, male sex, old age, and low SES were important features of wound infection in soft-tissue sarcoma patients.
摘要：
背景与目的：软组织肉瘤是一组异质性的恶性间充质组织。尽管他们的患病率很低，软组织肉瘤由于其侵袭性，给整形外科医生带来了临床挑战，围手术期伤口感染。然而,软组织肉瘤的低患病率阻碍了大规模研究的可用性。这项研究旨在通过采用健康保险审查和评估服务中心（HIRA）的大数据分析，分析软组织肉瘤患者广泛切除后的伤口感染。材料和方法：纳入2010年至2021年间接受软组织肉瘤广泛切除术的患者。数据是从HIRA数据库中收集的，该数据库包含大韩民国大约5千万个人的信息。收集的数据包括人口统计信息，诊断,处方药,和外科手术。随机森林已用于分析主要的相关决定因素。共有10,906个具有完整数据的观察结果以80：20的比例分为训练集和验证集（8773vs.2193例）。采用随机森林排列重要性来确定感染的主要预测因子，并得出Shapley加法解释（SHAP）值，以分析与预测因子的关联方向。结果：共纳入10969例接受软组织肉瘤广泛切除术的患者。在研究人群中，886例（8.08％）患者发生术后感染，需要手术治疗。广泛切除术的总输血率为20.67%(2267例)。分析了每位伤口感染患者合并症的危险因素，并可视化了个体特征的依赖性图。输血依赖图揭示了一种独特的模式，SHAP值对没有输血的个体显示负趋势，对接受输血的个体显示正趋势，强调输血对伤口感染可能性的重大影响。结论：使用机器学习随机森林模型和SHAP值，围手术期输血，男性,老年,低SES是软组织肉瘤患者伤口感染的重要特征。
