坏死
医学
急性胰腺炎
内科学
接收机工作特性
逻辑回归
胃肠病学
白细胞
胰腺炎
前瞻性队列研究
外科
作者
Žilvinas Dambrauskas,Antanas Gulbinas,Juozas Pundzius,Giedrius Barauskas
标识
DOI:10.1080/00365520701391613
摘要
Objective. Fine-needle aspiration (FNA) is the procedure of choice for accurate diagnosis of infected necrosis. However, invasive procedures increase the risk of secondary pancreatic infection and the timing of FNA is still a matter for debate. Our objective was to assess the value of routine clinical tests to determine the minimal risk for infected necrosis, thereby optimizing timing and selection of patients for image-guided FNA. Material and methods. This prospective, non-randomized study comprised 90 patients with acute necrotizing pancreatitis. The data of 52 patients were used for discriminant function analysis to determine the differences between patients with infected necrosis and those with sterile necrosis. Cut-off points for variables were established using receiver operating characteristic (ROC) curve analysis and logistic regression was performed to determine the risk of infected necrosis. The clinical relevance of the defined diagnostic system was prospectively tested in a further 38 consecutive patients with acute necrotizing pancreatitis (ANP). Results. Discriminant function analysis showed that C-reactive protein (CRP) and white blood cell (WBC) values were significant discriminators between patients with sterile necrosis and those with infected necrosis. Cut-off values of 81 mg/l for CRP and 13×109/l for WBC were established. The predicted risk for infected necrosis is approx. 1.4% if both tests are below the defined cut-off values. Consequently, we found FNA unnecessary in this subset of patients, unless otherwise indicated, as this invasive procedure per se carries a certain risk of bacterial contamination. Conclusions. Routine clinical tests are helpful in diagnosing the development of infected necrosis. Based on the application of classification functions, the timing and selection of patients for image-guided FNA can be optimized.
科研通智能强力驱动
Strongly Powered by AbleSci AI