Du Weiwei, Ji Wentao, Luo Tian, Zhang Yinying, Guo Weihong, Liang Jianping, Lv Yanhua
Department of Respiratory and Critical Care Medicine, Zhongshan City People's Hospital, Zhongshan, Guangdong Province, People's Republic of China.
J Inflamm Res. 2024 Nov 26;17:9823-9835. doi: 10.2147/JIR.S499008. eCollection 2024.
The incidence of invasive pulmonary aspergillosis (IPA) is progressively rising in the nonneutropenic population, but studies investigating relevant prognostic factors remain scarce.
Participants who were hospitalized at Zhongshan City People's Hospital from January 2018 to May 2023 and diagnosed with nonneutropenic deficient IPA were included in this study. The least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression methods were used to select variables for constructing the predictive model. The performance of the predictive model was evaluated using the concordance index (C-index), calibration curve, time-dependent receiver operating characteristic (T-ROC) curve, area under the curve (AUC), and decision curve analysis (DCA). Finally, prognostic risk stratification was performed for nonneutropenic IPA patients, transforming the nomogram into a risk-stratified prognostic model.
A total of 210 participants were included in this study, divided into training and validation cohorts at a ratio of 7.5:2.5. Lasso regression identified seven potential predictive factors, including age, comorbid bacterial pneumonia, pleural effusion, neutrophil to lymphocyte ratio (NLR), lactate dehydrogenase (LDH), invasive mechanical ventilation and ICU treatment. Multivariate Cox regression analysis showed age (HR=1.02), comorbid bacterial pneumonia (HR=3.36), NLR (HR=1.02), LDH (HR=1.001), and invasive mechanical ventilation (HR=4.86) as independent predictive factors and constructed nomogram. The calibration curves show that the nomogram performs well in terms of consistency between predictions and actual observations. The T-ROC curves and DCA of the nomogram model show that the recognition ability of the nomogram model was outstanding. Participants could be classified into high and low-risk groups based on the final score of this nomogram, demonstrating the excellent risk stratification performance of our model.
The nomogram model developed in this study is an effective tool for predicting mortality risk in nonneutropenic IPA patients, aiding clinicians in identifying high-risk patients and optimizing early treatment strategies.
侵袭性肺曲霉病（IPA）在非中性粒细胞减少人群中的发病率正在逐步上升，但针对相关预后因素的研究仍然较少。
纳入2018年1月至2023年5月在中山市人民医院住院并诊断为非中性粒细胞减少性IPA的患者。采用最小绝对收缩和选择算子（LASSO）回归和多变量Cox回归方法选择变量以构建预测模型。使用一致性指数（C指数）、校准曲线、时间依赖性受试者工作特征（T-ROC）曲线、曲线下面积（AUC）和决策曲线分析（DCA）评估预测模型的性能。最后，对非中性粒细胞减少性IPA患者进行预后风险分层，将列线图转化为风险分层预后模型。
本研究共纳入210名参与者，按7.5:2.5的比例分为训练队列和验证队列。Lasso回归确定了七个潜在预测因素，包括年龄、合并细菌性肺炎、胸腔积液、中性粒细胞与淋巴细胞比值（NLR）、乳酸脱氢酶（LDH）、有创机械通气和ICU治疗。多变量Cox回归分析显示年龄（HR=1.02）、合并细菌性肺炎（HR=3.36）、NLR（HR=1.02）、LDH（HR=1.001）和有创机械通气（HR=4.86）为独立预测因素并构建了列线图。校准曲线表明列线图在预测与实际观察结果的一致性方面表现良好。列线图模型的T-ROC曲线和DCA表明该列线图模型的识别能力出色。根据该列线图的最终得分，参与者可分为高风险组和低风险组，表明我们模型具有出色的风险分层性能。
本研究开发的列线图模型是预测非中性粒细胞减少性IPA患者死亡风险的有效工具，有助于临床医生识别高危患者并优化早期治疗策略。
