Development and validation of a clinically applicable diagnostic model for invasive pulmonary aspergillosis in patients with structural lung diseases DOI

Dengrong Hao,

Luyu Yang,

Zhimin Cao

et al.

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: May 19, 2025

Abstract Background Patients with structural lung disease are prone to develop lower respiratory tract infections, especially those caused by Aspergillus, due irreversible damage the parenchyma and interstitium. Early diagnosis of invasive Aspergillus infection is difficult, delayed treatment associated a high risk mortality. Therefore, in this study, we established diagnostic prediction model for patients aim providing foundation early detection. Methods We conducted retrospective cohort study analyzing inpatients diseases admitted Beijing Chest Hospital between January 1, 2020, December 31, 2024. Data were randomly divided into training (70%) validation sets (30%) using stratified random sampling maintain proportional representation key demographics.For variable selection, performed univariate analysis identify potential predictors pulmonary aspergillosis (IPA) diseases. Variables achieving significance at P < 0.1 retained further analysis. Subsequently, applied Lasso regression 10-fold cross-validation determine feature importance weights. Based on combined criteria (P 0.05) odds ratio magnitude, top five candidate selected inclusion stepwise multivariate logistic model.The final was visualized through nomogram incorporating factors. Model performance comprehensively evaluated using:Discrimination: Receiver operating characteristic (ROC) curve area under (AUC);Calibration: Hosmer-Lemeshow goodness-of-fit test;Clinical Utility: Decision (DCA) clinical impact (CIC);Diagnostic Metrics: Sensitivity, specificity, positive predictive value (PPV), negative (NPV).To enhance generalizability, six machine learning algorithms including Naive Bayes (NB), Tree (DT), K-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Machine (SVM), XGBoost employed comparative validation. Ensemble techniques implemented optimize across different algorithms. Results A total 204 eligible included (84 IAI 120 without IAI). After selection via LASSO regression, multiple performed, following four independent factors ultimately identified: coexisting diabetes, radiological cavitary manifestations, blood IgG antibody, BALF-mNGS. The AUC 0.88 (95% CI 0.82–0.94), visual created. At optimal cutoff (0.431), sensitivity specificity set reached 0.81 CI: 0.68–0.93) 0.92 0.81–1.00), respectively, (PPV) as 0.94 0.85–1.00), demonstrating good performance. validated classifiers showed stable performance: 0.977 0.960–0.994), GNB 0.890 0.841–0.939), decision tree 0.987 0.976–0.998), SVM 0.884 0.828–0.939), KNN 0.909 0.860–0.946), forest 0.979 0.963–0.996). Conclusions multimodal that integrates clinical, imaging microbiological data, after being verified classification methods, can effectively

Language: Английский

Development and validation of a clinically applicable diagnostic model for invasive pulmonary aspergillosis in patients with structural lung diseases DOI

Dengrong Hao,

Luyu Yang,

Zhimin Cao

et al.

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: May 19, 2025

Abstract Background Patients with structural lung disease are prone to develop lower respiratory tract infections, especially those caused by Aspergillus, due irreversible damage the parenchyma and interstitium. Early diagnosis of invasive Aspergillus infection is difficult, delayed treatment associated a high risk mortality. Therefore, in this study, we established diagnostic prediction model for patients aim providing foundation early detection. Methods We conducted retrospective cohort study analyzing inpatients diseases admitted Beijing Chest Hospital between January 1, 2020, December 31, 2024. Data were randomly divided into training (70%) validation sets (30%) using stratified random sampling maintain proportional representation key demographics.For variable selection, performed univariate analysis identify potential predictors pulmonary aspergillosis (IPA) diseases. Variables achieving significance at P < 0.1 retained further analysis. Subsequently, applied Lasso regression 10-fold cross-validation determine feature importance weights. Based on combined criteria (P 0.05) odds ratio magnitude, top five candidate selected inclusion stepwise multivariate logistic model.The final was visualized through nomogram incorporating factors. Model performance comprehensively evaluated using:Discrimination: Receiver operating characteristic (ROC) curve area under (AUC);Calibration: Hosmer-Lemeshow goodness-of-fit test;Clinical Utility: Decision (DCA) clinical impact (CIC);Diagnostic Metrics: Sensitivity, specificity, positive predictive value (PPV), negative (NPV).To enhance generalizability, six machine learning algorithms including Naive Bayes (NB), Tree (DT), K-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Machine (SVM), XGBoost employed comparative validation. Ensemble techniques implemented optimize across different algorithms. Results A total 204 eligible included (84 IAI 120 without IAI). After selection via LASSO regression, multiple performed, following four independent factors ultimately identified: coexisting diabetes, radiological cavitary manifestations, blood IgG antibody, BALF-mNGS. The AUC 0.88 (95% CI 0.82–0.94), visual created. At optimal cutoff (0.431), sensitivity specificity set reached 0.81 CI: 0.68–0.93) 0.92 0.81–1.00), respectively, (PPV) as 0.94 0.85–1.00), demonstrating good performance. validated classifiers showed stable performance: 0.977 0.960–0.994), GNB 0.890 0.841–0.939), decision tree 0.987 0.976–0.998), SVM 0.884 0.828–0.939), KNN 0.909 0.860–0.946), forest 0.979 0.963–0.996). Conclusions multimodal that integrates clinical, imaging microbiological data, after being verified classification methods, can effectively

Language: Английский

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