PDF(Pubmed)
Abstract：
UNASSIGNED: The reciprocal promotion of cancer and stroke occurs due to changes in shared risk factors, such as metabolic pathways and molecular targets, creating a \"vicious cycle.\" Cancer plays a direct or indirect role in the pathogenesis of ischemic stroke (IS), along with the reactive medical approach used in the treatment and clinical management of IS patients, resulting in clinical challenges associated with occult cancer in these patients. The lack of reliable and simple tools hinders the effectiveness of the predictive, preventive, and personalized medicine (PPPM/3PM) approach. Therefore, we conducted a multicenter study that focused on multiparametric analysis to facilitate early diagnosis of occult cancer and personalized treatment for stroke associated with cancer.
UNASSIGNED: Admission routine clinical examination indicators of IS patients were retrospectively collated from the electronic medical records. The training dataset comprised 136 IS patients with concurrent cancer, matched at a 1:1 ratio with a control group. The risk of occult cancer in IS patients was assessed through logistic regression and five alternative machine-learning models. Subsequently, select the model with the highest predictive efficacy to create a nomogram, which is a quantitative tool for predicting diagnosis in clinical practice. Internal validation employed a ten-fold cross-validation, while external validation involved 239 IS patients from six centers. Validation encompassed receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis (DCA), and comparison with models from prior research.
UNASSIGNED: The ultimate prediction model was based on logistic regression and incorporated the following variables: regions of ischemic lesions, multiple vascular territories, hypertension, D-dimer, fibrinogen (FIB), and hemoglobin (Hb). The area under the ROC curve (AUC) for the nomogram was 0.871 in the training dataset and 0.834 in the external test dataset. Both calibration curves and DCA underscored the nomogram\'s strong performance.
UNASSIGNED: The nomogram enables early occult cancer diagnosis in hospitalized IS patients and helps to accurately identify the cause of IS, while the promotion of IS stratification makes personalized treatment feasible. The online nomogram based on routine clinical examination indicators of IS patients offered a cost-effective platform for secondary care in the framework of PPPM.
UNASSIGNED: The online version contains supplementary material available at 10.1007/s13167-024-00354-8.
UNASSIGNED: Admission routine clinical examination indicators of IS patients were retrospectively collated from the electronic medical records. The training dataset comprised 136 IS patients with concurrent cancer, matched at a 1:1 ratio with a control group. The risk of occult cancer in IS patients was assessed through logistic regression and five alternative machine-learning models. Subsequently, select the model with the highest predictive efficacy to create a nomogram, which is a quantitative tool for predicting diagnosis in clinical practice. Internal validation employed a ten-fold cross-validation, while external validation involved 239 IS patients from six centers. Validation encompassed receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis (DCA), and comparison with models from prior research.
UNASSIGNED: The ultimate prediction model was based on logistic regression and incorporated the following variables: regions of ischemic lesions, multiple vascular territories, hypertension, D-dimer, fibrinogen (FIB), and hemoglobin (Hb). The area under the ROC curve (AUC) for the nomogram was 0.871 in the training dataset and 0.834 in the external test dataset. Both calibration curves and DCA underscored the nomogram\'s strong performance.
UNASSIGNED: The nomogram enables early occult cancer diagnosis in hospitalized IS patients and helps to accurately identify the cause of IS, while the promotion of IS stratification makes personalized treatment feasible. The online nomogram based on routine clinical examination indicators of IS patients offered a cost-effective platform for secondary care in the framework of PPPM.
UNASSIGNED: The online version contains supplementary material available at 10.1007/s13167-024-00354-8.
摘要：
■由于共同危险因素的变化，癌症和中风的相互促进发生，如代谢途径和分子靶标，造成“恶性循环”。“癌症在缺血性卒中（IS）的发病机理中起着直接或间接的作用，以及在IS患者的治疗和临床管理中使用的反应性医疗方法，导致这些患者中与隐匿性癌症相关的临床挑战。缺乏可靠和简单的工具阻碍了预测的有效性，预防性,和个性化医疗(PPPM/3PM)方法。因此,我们进行了一项多中心研究,重点是多参数分析,以促进隐匿性癌症的早期诊断和癌症相关卒中的个性化治疗.
■对IS患者入院常规临床检查指标与电子病历进行回顾性整理。训练数据集包括136名患有并发癌症的IS患者，与对照组以1:1的比例匹配。通过逻辑回归和五种替代机器学习模型评估IS患者隐匿性癌症的风险。随后，选择预测效果最高的模型来创建列线图，它是临床实践中预测诊断的定量工具。内部验证采用了十倍的交叉验证，而外部验证涉及来自六个中心的239名IS患者。包括受试者工作特性(ROC)曲线的验证，校正曲线,决策曲线分析(DCA)，并与先前研究的模型进行了比较。
■最终预测模型基于逻辑回归，并包含以下变量：缺血性病变区域，多个血管区域，高血压,D-二聚体,纤维蛋白原（FIB），和血红蛋白（Hb）。列线图的ROC曲线下面积(AUC)在训练数据集中为0.871,在外部测试数据集中为0.834。校准曲线和DCA都强调了列线图的强劲表现。
■列线图使住院IS患者能够进行早期隐匿性癌症诊断，并有助于准确识别IS的病因，而IS分层的推广使个性化治疗变得可行。基于IS患者常规临床检查指标的在线列线图为PPPM框架下的二级护理提供了具有成本效益的平台。
■在线版本包含补充材料，可在10.1007/s13167-024-00354-8获得。
■对IS患者入院常规临床检查指标与电子病历进行回顾性整理。训练数据集包括136名患有并发癌症的IS患者，与对照组以1:1的比例匹配。通过逻辑回归和五种替代机器学习模型评估IS患者隐匿性癌症的风险。随后，选择预测效果最高的模型来创建列线图，它是临床实践中预测诊断的定量工具。内部验证采用了十倍的交叉验证，而外部验证涉及来自六个中心的239名IS患者。包括受试者工作特性(ROC)曲线的验证，校正曲线,决策曲线分析(DCA)，并与先前研究的模型进行了比较。
■最终预测模型基于逻辑回归，并包含以下变量：缺血性病变区域，多个血管区域，高血压,D-二聚体,纤维蛋白原（FIB），和血红蛋白（Hb）。列线图的ROC曲线下面积(AUC)在训练数据集中为0.871,在外部测试数据集中为0.834。校准曲线和DCA都强调了列线图的强劲表现。
■列线图使住院IS患者能够进行早期隐匿性癌症诊断，并有助于准确识别IS的病因，而IS分层的推广使个性化治疗变得可行。基于IS患者常规临床检查指标的在线列线图为PPPM框架下的二级护理提供了具有成本效益的平台。
■在线版本包含补充材料，可在10.1007/s13167-024-00354-8获得。
