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Assessment of Plasma Coagulation on Liver Tissue in a Large Animal Model In Vivo
Published on: August 4, 2018
Development of a nomogram to assess thromboembolic risk in patients with liver abscesses
Jaejun Lee1,2, Soon Woo Nam1,3, Jung Hyun Kwon1,3
1Department of Biomedicine & Health Sciences, College of Medicine, The Catholic University Liver Research Center, The Catholic University of Korea, Seoul, Republic of Korea.
Abstract:
Thromboembolism, a serious complication in patients with liver abscesses, can lead to organ deterioration if left untreated. However, factors associated with thromboembolism in these patients remain poorly understood. We aimed to identify the factors associated with liver abscess complications, with a focus on thromboembolism and metastatic infection. Data from 325 patients diagnosed with liver abscesses between March 2019 and June 2023 were retrospectively collected. Baseline clinical and laboratory variables associated with thromboembolic events and metastatic infections were analyzed using logistic regression. A nomogram for predicting thromboembolism was constructed using significant predictors. Among the 325 patients, the median age was 68.0 years, and included 129 women. Fifty patients experienced thromboembolic events and 44 had metastatic infections. Significant predictors for thromboembolic events included white blood cell (WBC) ≥ 20,000/µL (odds ratio [OR] 3.401, p = 0.002), platelet count < 100,000/µL (OR 3.291, p = 0.004), and abscess septation (OR 2.704, p = 0.007). Age ≥ 65 years (OR 0.457, p = 0.040), WBC ≥ 20,000/µL (OR 3.340, p = 0.005), and abscess septation (OR 2.909, p = 0.008) were identified as factors associated with metastatic infections. A nomogram was constructed to predict thromboembolism using the following four variables: WBC ≥ 20,000/µL platelet count < 100,000/µL, albumin < 2.8 g/dL, and abscess septation, and demonstrated an AUROC of 0.755. Our study is the first to develop a reliable nomogram for thromboembolism prediction, utilizing easily accessible clinical parameters. This innovative model enables clinicians to efficiently identify high-risk patients, facilitating the implementation of additional studies to mitigate the risk of overlooked thromboembolic events in these at-risk individuals.
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