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.
■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。