ArticleBMC infectious diseases2026
Risk factors and a prediction model of poor prognosis in patients with invasive aspergillosis in a general hospital.
Article in BMC infectious diseases, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
backgroundThis study retrospectively analyzed the epidemiological trends of invasive aspergillosis (IA) at the Anhui Public Health Clinical Center. By evaluating the clinical risk factors associated with IA, an individualized nomogram prediction model was constructed. This study aims to improve early diagnosis and clinical management of severe invasive aspergillosis.
methodsA retrospective analysis was conducted on the clinical data of 307 patients diagnosed with IA between January 2019 and March 2024. Patients were categorized into two groups based on clinical outcomes: the favorable outcome group (n = 268) and the unfavorable outcome group (n = 39). Least absolute shrinkage and selection operator (LASSO) regression and multivariable logistic regression analyses were employed to identify independent risk factors associated with poor prognosis. Subsequently, a nomogram was constructed to develop a risk prediction model for IA.
resultsFour variables associated with poor prognosis were identified via LASSO regression: ICU length of stay, elevated C-reactive protein (CRP), elevated blood glucose, and renal dysfunction (all P < 0.05). Multivariable logistic regression revealed that ICU length of stay (OR = 1.049, 95%CI: 1.003-1.096, P = 0.028), C-reactive protein (OR = 1.007, 95%CI: 1.003-1.012, P = 0.001), blood glucose (OR = 1.119, 95%CI: 1.041-1.206, P = 0.003), and renal failure (yes vs. no, OR = 3.187, 95%CI: 1.412-7.048, P = 0.004) were independent risk factors for poor prognosis. The area under the receiver operating characteristic curve (AUC) was 0.810, with a bootstrap-validated AUC of 0.7962, demonstrating favourable discrimination and stability. Calibration curves indicated favourable agreement between predicted and observed probabilities, and the Hosmer-Lemeshow test showed no significant deviation (P = 0.615), confirming favorable model fit.
conclusionsIA detection increased significantly over the past two years. We developed and validated a risk-factor-based nomogram that demonstrates good predictive accuracy and robustness in identifying poor prognosis among IA patients. This individualized tool, accessible via an interactive web-based application, provides clinicians with a good basis for early risk stratification and timely therapeutic intervention, ultimately aiming to improve clinical outcomes in high-risk populations.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.