Wang Wei, Li Yan, Wang Hua, Du Yumeng, Cheng Mengyuan, Tang Jinyan, Wu Mingliang, Chen Chaomin, Lv Qingwen, Cheng Weibin
Institute for Healthcare Artificial Intelligence Application, The Affiliated Guangdong Second Provincial General Hospital of Jinan University, Guangzhou, 510317, China.
School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou, 511442, China.
BMC Infect Dis. 2025 May 7;25(1):673. doi: 10.1186/s12879-025-11030-1.
Invasive fungal disease (IFD) is characterized by its capacity to rapidly escalate to life-threatening conditions, even when patients are hospitalized. However, the precise prognostic significance of baseline clinical characteristics related to the progression outcome of IFD remains elusive.
A retrospective cohort study spanning a duration of 10 years was conducted at two prominent tertiary teaching hospitals in Southern China. Patients with proven IFD were queried and divided into serious and non-serious groups based on the disease deterioration. To establish robust predictive models, patients from the first hospital were randomly assigned to either a training set or an internal validation set, while patients from the second hospital constituted an external test set. To analyze the potential predictors of IFD deterioration and identify independent predictors, the study employed the least absolute shrinkage and selection operator (LASSO) method in conjunction with binary logistic regressions. Based on the outcomes of this analysis, a predictive nomogram was constructed. The performance of the developed model was thoroughly evaluated using the training set, internal validation set, and external test set.
A total of 480 cases from the first hospital and 256 cases from the second hospital were included in the study. Among the 480 patients, 81 cases (16.9%) experienced deterioration, and out of those, 45 (55.6%) cases resulted in mortality. Seven independent predictors were identified and utilized to construct a predictive nomogram. The nomogram exhibited excellent predictive performance in all three sets: the training set, internal validation set, and external test set. The area under the receiver operating characteristic curve (AUC) for the training set was 0.88, for the internal validation set was 0.91, and for the external test set was 0.90. The Hosmer-Lemeshow test and Brier score indicated a high goodness of fit for the model. Furthermore, the calibration curve demonstrated a strong agreement between the predicted outcomes from the nomogram and the actual observations. Additionally, the decision curve analysis exhibited that the nomogram provided significant clinical net benefits in predicting IFD deterioration.
The study successfully identified seven independent predictors and developed a predictive nomogram for early assessment of the likelihood of IFD deterioration.
侵袭性真菌病（IFD）的特点是即使患者住院，也有迅速发展为危及生命状况的可能。然而，与IFD进展结局相关的基线临床特征的确切预后意义仍不明确。
在中国南方两家著名的三级教学医院进行了一项为期10年的回顾性队列研究。对确诊为IFD的患者进行调查，并根据疾病恶化情况分为严重组和非严重组。为建立强大的预测模型，将第一家医院的患者随机分配到训练集或内部验证集，而第二家医院的患者构成外部测试集。为分析IFD恶化的潜在预测因素并识别独立预测因素，该研究采用最小绝对收缩和选择算子（LASSO）方法结合二元逻辑回归。基于该分析结果，构建了预测列线图。使用训练集、内部验证集和外部测试集对所开发模型的性能进行了全面评估。
该研究纳入了第一家医院的480例病例和第二家医院的256例病例。在480例患者中，81例（16.9%）病情恶化，其中45例（55.6%）死亡。确定了七个独立预测因素并用于构建预测列线图。该列线图在训练集、内部验证集和外部测试集这三个集合中均表现出优异的预测性能。训练集的受试者操作特征曲线（AUC）下面积为0.88，内部验证集为0.91，外部测试集为0.90。Hosmer-Lemeshow检验和Brier评分表明该模型具有良好的拟合优度。此外，校准曲线显示列线图的预测结果与实际观察结果之间具有很强的一致性。此外，决策曲线分析表明，该列线图在预测IFD恶化方面提供了显著的临床净效益。
该研究成功识别出七个独立预测因素，并开发了一个预测列线图，用于早期评估IFD恶化的可能性。
