Evidence map›Paper›PMID 42222036›Full record

ArticleInfection and drug resistance2026

Development and Internal Validation of a Nomogram Model to Predict Invasive Pulmonary Aspergillosis Occurrence Risk in ICU Patients with Sepsis.

Hui Li, Yao Liang, Wenyan Xiao, Yao Zheng, Tianfeng Hua, Min Yang

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Article in Infection and drug resistance, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

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6 authors.

Hui LiThe Second Department of Critical Care Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, People's Republic of China.
Yao LiangThe Second Department of Critical Care Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, People's Republic of China.ORCID 0009-0001-1152-0728
Wenyan XiaoThe Second Department of Critical Care Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, People's Republic of China.
Yao ZhengThe Second Department of Critical Care Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, People's Republic of China.
Tianfeng HuaThe Second Department of Critical Care Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, People's Republic of China.
Min YangThe Second Department of Critical Care Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Sepsis is characterized by immune dysregulation and increased susceptibility to secondary infections. Invasive pulmonary aspergillosis (IPA) is an important complication in intensive care unit (ICU) patients with sepsis and is associated with poor outcomes. Identifying its predictors and developing a prediction model may facilitate early risk assessment and identification of high-risk patients. Methods: A single-centre retrospective cohort of 574 adult ICU patients with sepsis (January 2020-December 2024) was randomly divided into a training set (n=459) and a validation set (n=115) in an 8:2 ratio. IPA was diagnosed according to international consensus criteria. In the training set, candidate predictors were screened by least absolute shrinkage and selection operator (LASSO) regression, followed by multivariable logistic regression. A nomogram-based prediction model was developed and internally validated in the validation set. Model discrimination, calibration, and clinical utility were assessed using the area under the receiver operating characteristic curve (ROC), calibration curves, and decision curve analysis (DCA), respectively. Results: Among 574 ICU patients with sepsis, 86 (14.98%) developed IPA. LASSO followed by multivariable logistic regression showed that smoking history, corticosteroid therapy, coexisting SFTS or COVID-19, duration of mechanical ventilation, NRS 2002 score, and SOFA score were independently associated with IPA. The area under the ROC curve (AUC) for the training set was 0.883 (95% CI 0.842-0.924), and for the validation set, it was 0.844 (95% CI 0.766-0.921). The nomogram constructed from these variables suggested good discrimination and calibration in both the training and validation cohorts. DCA indicated a net clinical benefit across clinically relevant threshold probabilities. Conclusion: We developed and internally validated a nomogram for predicting secondary IPA occurrence risk in ICU patients with sepsis. The inclusion of NRS 2002 suggests that nutritional risk may contribute to IPA occurrence risk. External validation is warranted before broader clinical application.

Indexed as

ICUIPAnomogramprediction modelsepsis

Identifiers

PMID42222036
PMCPMC13220839

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