Wang Xinyu, Lu Yajie, Sun Chao, Zhong Huanhuan, Cai Yuchen, Cao Min, Cui Xuefan, Sun Wenkui, Wang Li, Lu Xin, Chen Cheng, Chen Yanbin, Feng Chunlai, Tao Yujian, Zhou Jun, Shi Jiaxin, Ma Guoer, Li Yuanqin, Su Xin
Department of Respiratory and Critical Care Medicine, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.
Department of Respiratory and Critical Care Medicine, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.
Microbiol Spectr. 2025 Jul;13(7):e0060725. doi: 10.1128/spectrum.00607-25. Epub 2025 May 22.
This study aims to develop and validate an optimized diagnostic model for nonneutropenic invasive pulmonary aspergillosis (IPA) among suspected cases. A cohort of 344 nonneutropenic suspected cases from 13 medical centers (August 2020 to February 2024) was analyzed. The cohort was divided into a training data set (70%) and a testing data set (30%) using stratified sampling based on the IPA diagnosis. Three machine learning models (a regularized logistic regression model, a support vector machine model, and a weighted ensemble model) were developed. SHapley Additive explanation (SHAP) method was used for model interpretation. Six predictor variables were finally selected: sputum culture, -specific IgG, imaging feature of cavity, serum galactomannan, critical condition, and plasma pentraxin 3. The weighted ensemble model, exhibiting the significantly higher specificity of 95.1% in internal cross-validation and 95.7% in testing among the three models, was selected as the optimal prediction model despite comparable discrimination capacity, calibration ability, and clinical applicability across all models. The risk score derived from SHAP values showed a highly significant correlation with the predicted probability of the weighted ensemble model (Spearman = 0.974), achieving an area under the curve of 0.857 in internal cross-validation and 0.871 in external testing. Using the optimal cut-off value of 3, the risk score demonstrated sensitivity (68.8%) and specificity (87.5%) comparable to those of bronchoalveolar lavage fluid galactomannan (cut-off = 1.0). The diagnostic model and risk score could assist in identifying nonneutropenic IPA from suspected cases independently of invasive procedures, thereby enhancing clinical applicability.
Although clinicians can screen out suspected cases through medical history inquiries, the diagnosis of nonneutropenic invasive pulmonary aspergillosis (IPA) from suspected cases remains a significant challenge. The study developed a novel diagnostic framework by integrating clinical parameters, imaging features, and laboratory biomarkers using machine learning techniques. The risk score, derived from SHapley Additive explanation values, exhibited a highly significant correlation with the predicted probability of the weighted ensemble model, demonstrating robust discrimination capacity and generalizability. The diagnostic model and risk score could assist in identifying nonneutropenic IPA from suspected cases independently of invasive procedures, thereby enhancing clinical applicability.
本研究旨在开发并验证一种针对疑似非中性粒细胞减少侵袭性肺曲霉病（IPA）的优化诊断模型。分析了来自13个医疗中心（2020年8月至2024年2月）的344例非中性粒细胞减少疑似病例队列。基于IPA诊断，采用分层抽样将该队列分为训练数据集（70%）和测试数据集（30%）。开发了三种机器学习模型（正则化逻辑回归模型、支持向量机模型和加权集成模型）。使用夏普利值法（SHapley Additive explanation，SHAP）进行模型解释。最终选择了六个预测变量：痰培养、特异性IgG、空洞影像学特征、血清半乳甘露聚糖、病情严重程度和血浆五聚素3。尽管所有模型的区分能力、校准能力和临床适用性相当，但加权集成模型在内部交叉验证中的特异性显著更高，为95.1%，在测试中的特异性为95.7%，因此被选为最佳预测模型。从SHAP值得出的风险评分与加权集成模型的预测概率显示出高度显著的相关性（斯皮尔曼相关系数 = 0.974），在内部交叉验证中的曲线下面积为0.857，在外部测试中的曲线下面积为0.871。使用最佳截断值3时，风险评分显示出与支气管肺泡灌洗 fluid半乳甘露聚糖（截断值 = 1.0）相当的敏感性（68.8%）和特异性（87.5%）。该诊断模型和风险评分可以独立于侵入性操作，从疑似病例中识别非中性粒细胞减少IPA，从而提高临床适用性。
尽管临床医生可以通过病史询问筛选出疑似病例，但从疑似病例中诊断非中性粒细胞减少侵袭性肺曲霉病（IPA）仍然是一项重大挑战。该研究通过使用机器学习技术整合临床参数、影像学特征和实验室生物标志物，开发了一种新型诊断框架。从夏普利值得出的风险评分与加权集成模型的预测概率显示出高度显著的相关性，表明具有强大的区分能力和通用性。该诊断模型和风险评分可以独立于侵入性操作，从疑似病例中识别非中性粒细胞减少IPA，从而提高临床适用性。
