PDF(Pubmed)
Abstract：
UNASSIGNED: Malignant pleural effusion (MPE) is prevalent among cancer patients, indicating pleural metastasis and predicting poor prognosis. However, accurately identifying MPE in clinical settings is challenging. The aim of this study was to establish an innovative nomogram-derived model based on clinical indicators and serum metal ion levels to identify MPE.
UNASSIGNED: From July 2020 to May 2022, 428 patients diagnosed with pleural effusion (PE) were consecutively recruited. Comprehensive demographic details, clinical symptoms, imaging data, pathological information, and laboratory results, including serum metal ion levels, were systematically collected. The nomogram was created by incorporating the most significant predictors identified through LASSO and multivariate logistic regression analysis. The predictors were assigned weighted points based on their respective regression coefficients, allowing for the calculation of a total score that corresponds to the probability of MPE. Internal validation using bootstrapping techniques assessed the nomogram\'s performance, including calibration, discrimination, and clinical applicability.
UNASSIGNED: Seven key variables were identified using LASSO regression and multiple regression analysis, including dyspnea, fever, X-ray/CT compatible with malignancy, pleural carcinoembryonic antigen(pCEA), serum neuron-specific enolase(sNSE), serum carcinoembryonic antigen(sCEA), and pleural lactate dehydrogenase(pLDH). Internal validation underscored the superior performance of our model (AUC=0.940). Decision curve analysis (DCA) analysis demonstrated substantial net benefit across a probability threshold range > 1%. Additionally, serum calcium and copper levels were significantly higher, while serum zinc levels were significantly lower in MPE patients compared to benign pleural effusion (BPE) patients.
UNASSIGNED: This study effectively developed a user-friendly and reliable MPE identification model incorporating seven markers, aiding in the classification of PE subtypes in clinical settings. Furthermore, our study highlights the clinical value of serum metal ions in distinguishing malignant pleural effusion from BPE. This significant advancement provides essential tools for physicians to accurately diagnose and treat patients with MPE.
UNASSIGNED: From July 2020 to May 2022, 428 patients diagnosed with pleural effusion (PE) were consecutively recruited. Comprehensive demographic details, clinical symptoms, imaging data, pathological information, and laboratory results, including serum metal ion levels, were systematically collected. The nomogram was created by incorporating the most significant predictors identified through LASSO and multivariate logistic regression analysis. The predictors were assigned weighted points based on their respective regression coefficients, allowing for the calculation of a total score that corresponds to the probability of MPE. Internal validation using bootstrapping techniques assessed the nomogram\'s performance, including calibration, discrimination, and clinical applicability.
UNASSIGNED: Seven key variables were identified using LASSO regression and multiple regression analysis, including dyspnea, fever, X-ray/CT compatible with malignancy, pleural carcinoembryonic antigen(pCEA), serum neuron-specific enolase(sNSE), serum carcinoembryonic antigen(sCEA), and pleural lactate dehydrogenase(pLDH). Internal validation underscored the superior performance of our model (AUC=0.940). Decision curve analysis (DCA) analysis demonstrated substantial net benefit across a probability threshold range > 1%. Additionally, serum calcium and copper levels were significantly higher, while serum zinc levels were significantly lower in MPE patients compared to benign pleural effusion (BPE) patients.
UNASSIGNED: This study effectively developed a user-friendly and reliable MPE identification model incorporating seven markers, aiding in the classification of PE subtypes in clinical settings. Furthermore, our study highlights the clinical value of serum metal ions in distinguishing malignant pleural effusion from BPE. This significant advancement provides essential tools for physicians to accurately diagnose and treat patients with MPE.
摘要：
■恶性胸腔积液（MPE）在癌症患者中普遍存在，提示胸膜转移并预测不良预后。然而,在临床环境中准确识别MPE具有挑战性。本研究的目的是建立基于临床指标和血清金属离子水平的创新列线图衍生模型，以识别MPE。
■从2020年7月至2022年5月，连续招募了428例诊断为胸腔积液（PE）的患者。全面的人口统计细节，临床症状,成像数据,病理信息，和实验室结果，包括血清金属离子水平，被系统地收集。通过合并通过LASSO和多变量逻辑回归分析确定的最重要的预测因子来创建列线图。预测因子根据各自的回归系数分配加权点，允许计算对应于MPE概率的总分。使用自举技术的内部验证评估了列线图的性能，包括校准,歧视,和临床适用性。
■使用LASSO回归和多元回归分析确定了七个关键变量，包括呼吸困难,发烧,与恶性肿瘤相容的X线/CT，胸膜癌胚抗原(pCEA),血清神经元特异性烯醇化酶（sNSE），血清癌胚抗原（sCEA），和胸膜乳酸脱氢酶（pLDH）。内部验证强调了我们模型的优异性能(AUC=0.940)。决策曲线分析（DCA）分析表明，在概率阈值范围>1％的情况下，净收益很大。此外,血清钙和铜水平明显升高，与良性胸腔积液（BPE）患者相比，MPE患者的血清锌水平显着降低。
■这项研究有效地开发了一种包含七个标记的用户友好且可靠的MPE识别模型，在临床环境中帮助PE亚型的分类。此外，我们的研究强调了血清金属离子在鉴别恶性胸腔积液和BPE中的临床价值.这一重大进步为医生准确诊断和治疗患有MPE的患者提供了必要的工具。
■从2020年7月至2022年5月，连续招募了428例诊断为胸腔积液（PE）的患者。全面的人口统计细节，临床症状,成像数据,病理信息，和实验室结果，包括血清金属离子水平，被系统地收集。通过合并通过LASSO和多变量逻辑回归分析确定的最重要的预测因子来创建列线图。预测因子根据各自的回归系数分配加权点，允许计算对应于MPE概率的总分。使用自举技术的内部验证评估了列线图的性能，包括校准,歧视,和临床适用性。
■使用LASSO回归和多元回归分析确定了七个关键变量，包括呼吸困难,发烧,与恶性肿瘤相容的X线/CT，胸膜癌胚抗原(pCEA),血清神经元特异性烯醇化酶（sNSE），血清癌胚抗原（sCEA），和胸膜乳酸脱氢酶（pLDH）。内部验证强调了我们模型的优异性能(AUC=0.940)。决策曲线分析（DCA）分析表明，在概率阈值范围>1％的情况下，净收益很大。此外,血清钙和铜水平明显升高，与良性胸腔积液（BPE）患者相比，MPE患者的血清锌水平显着降低。
■这项研究有效地开发了一种包含七个标记的用户友好且可靠的MPE识别模型，在临床环境中帮助PE亚型的分类。此外，我们的研究强调了血清金属离子在鉴别恶性胸腔积液和BPE中的临床价值.这一重大进步为医生准确诊断和治疗患有MPE的患者提供了必要的工具。
