■本研究旨在构建和验证基于临床指标的列线图以识别CPTB。
■本研究回顾性招募2018年11月至2023年9月中国嘉善县第一人民医院收治的肺结核患者。根据胸部计算机断层扫描结果将PTB患者分为CPTB组和非CPTB组。并随机分配到训练集(70%)和验证队列(30%).训练集和验证集用于建立和验证列线图,分别。采用多因素logistic回归分析(MLSA)确定PTB患者发生CPTB的独立危险因素。然后使用MLSA中具有统计学意义的变量来构建预测PTB患者CPTB的列线图。接收机工作特性(ROC)曲线,校准曲线分析(CCA),和决策曲线分析(DCA)用于评估列线图。
■共有293名PTB患者,包括训练集中的208(85CPTB)和验证集中的85(33CPTB\),包括在这项研究中。逐步MLSA显示痰涂片(≥2+),吸烟(是),糖化血红蛋白A1c(HbA1c),血红蛋白(HB),全身炎症反应指数(SIRI)是PTB患者发生空化的独立危险因素。成功建立了确定高危CPTB患者的列线图,显示出很强的预测能力,训练集和验证集的曲线下面积(AUC)分别为0.875(95%CI:0.806-0.909)和0.848(95%CI:0.751-0.946)。此外,CCA和DCA证实了列线图在训练集和验证集内的高水平准确性和临床适用性。
■构造的列线图,包括痰涂片阳性,吸烟,HbA1C,HB,和SIRI,作为CPTB早期识别和个性化管理的实用有效工具。
UNASSIGNED: The present study aimed to construct and validate a nomogram based on clinical metrics to identify CPTB.
UNASSIGNED: The present study retrospectively recruited pulmonary tuberculosis (PTB) patients admitted to Jiashan County First People\'s Hospital in China from November 2018 to September 2023. PTB patients were classified into the CPTB group and the non-CPTB group based on chest computed tomography findings, and were randomly allocated to the training set (70%) and the validation cohort (30%). The training set and validation set were used to establish and validate nomogram, respectively. Multivariate logistic regression analysis (MLSA) was used to identify the independent risk factors for CPTB in patients with PTB. Statistically significant variables in the MLSA were then used to construct a nomogram predicting CPTB in patients with PTB. The receiver operating characteristic (ROC) curve, calibration curve analysis (CCA), and decision curve analysis (DCA) were used for the evaluation of the nomogram.
UNASSIGNED: A total of 293 PTB patients, including 208 in the training set (85 CPTB) and 85 in the validation set (33 CPTB\\), were included in this study. Stepwise MLSA showed that sputum smear (≥2+), smoking(yes), glycosylated hemoglobin A1c(HbA1c), hemoglobin (HB), and systemic inflammatory response index (SIRI) were independent risk factors for the development of
cavitation in patients with PTB. The nomogram identifying the high-risk CPTB patients was successfully established and showed a strong predictive capacity, with area under the curves (AUCs) of 0.875 (95% CI:0.806-0.909) and 0.848 (95% CI:0.751-0.946) in the training set and validation set respectively. In addition, the CCA and DCA corroborated the nomogram\'s high level of accuracy and clinical applicability within both the training and validation sets.
UNASSIGNED: The constructed nomogram, consisting of sputum smear positivity, smoking, HbA1C, HB, and SIRI, serves as a practical and effective tool for early identification and personalized management of CPTB.