Breitwieser Martin, Moore Vanessa, Wiesner Teresa, Wichlas Florian, Deininger Christian
Department for Orthopedic Surgery and Traumatology, Paracelsus Medical University, 5020 Salzburg, Austria.
Diagnostics (Basel). 2024 Dec 12;14(24):2792. doi: 10.3390/diagnostics14242792.
: This study presents a systematic approach using a natural language processing (NLP) algorithm to assess the necessity of routine imaging after central venous catheter (CVC) placement and removal. With pneumothorax being a key complication of CVC procedures, this research aims to provide evidence-based recommendations for optimizing imaging protocols and minimizing unnecessary imaging risks. We analyzed electronic health records from four university hospitals in Salzburg, Austria, focusing on X-rays performed between 2012 and 2021 following CVC procedures. A custom-built NLP algorithm identified cases of pneumothorax from radiologists' reports and clinician requests, while excluding cases with contraindications such as chest injuries, prior pneumothorax, or missing data. Chi-square tests were used to compare pneumothorax rates between CVC insertion and removal, and multivariate logistic regression identified risk factors, with a focus on age and gender. : This study analyzed 17,175 cases of patients aged 18 and older, with 95.4% involving CVC insertion and 4.6% involving CVC removal. Pneumothorax was observed in 106 cases post-insertion (1.3%) and in 3 cases post-removal (0.02%), with no statistically significant difference between procedures ( = 0.5025). The NLP algorithm achieved an accuracy of 93%, with a sensitivity of 97.9%, a specificity of 87.9%, and an area under the ROC curve (AUC) of 0.9283. : The findings indicate no significant difference in pneumothorax incidence between CVC insertion and removal, supporting existing recommendations against routine imaging post-removal for asymptomatic patients and suggesting that routine imaging after CVC insertion may also be unnecessary in similar cases. This study demonstrates how advanced NLP techniques can support value-based medicine by enhancing clinical decision making and optimizing resources.
本研究提出了一种系统方法，使用自然语言处理（NLP）算法来评估中心静脉导管（CVC）置入和拔除后进行常规成像的必要性。气胸是CVC操作的关键并发症，本研究旨在为优化成像方案和最小化不必要的成像风险提供循证建议。我们分析了奥地利萨尔茨堡四家大学医院的电子健康记录，重点关注2012年至2021年CVC操作后进行的X线检查。一个定制的NLP算法从放射科医生的报告和临床医生的请求中识别气胸病例，同时排除有胸部损伤、既往气胸或数据缺失等禁忌证的病例。使用卡方检验比较CVC置入和拔除后的气胸发生率，多因素逻辑回归确定风险因素，重点关注年龄和性别。本研究分析了17175例18岁及以上患者的病例，其中95.4%涉及CVC置入，4.6%涉及CVC拔除。置入后观察到106例气胸（1.3%），拔除后观察到3例气胸（0.02%），两种操作之间无统计学显著差异（P = 0.5025）。NLP算法的准确率为93%，灵敏度为97.9%，特异度为87.9%，ROC曲线下面积（AUC）为0.9283。研究结果表明，CVC置入和拔除后的气胸发生率无显著差异，支持现有针对无症状患者拔除后不进行常规成像的建议，并表明在类似情况下，CVC置入后进行常规成像可能也不必要。本研究展示了先进的NLP技术如何通过加强临床决策和优化资源来支持基于价值的医学。
