ArticleFrontiers in medicine2025
Development and validation of a prediction model for invasive syndrome in liver abscess patients based on LASSO regression: a multi-center retrospective cohort study in China.
Article in Frontiers in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 1 paper.
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
1 citing paper in PubMed.
- Clinical characteristics of pyogenic liver abscess patients with multiple organ dysfunction syndrome and development and validation of a predictive model.Frontiers in cellular and infection microbiology · 2026Article
Corrections and comments
- Erratum issued
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Patients with liver abscess are at high risk of developing invasive K. pneumonia liver abscess syndrome (IKPLAS), which can worsen survival and quality of life. Early identification of high-risk patients is crucial. This study aimed to identify risk factors for IKPLAS and develop a predictive model to guide early intervention. Methods: We retrospectively collected data from 1,762 liver abscess patients at the First Hospital of Jilin University between 2015 and 2024. Patients were randomly divided into a training set and an internal validation set at a 7:3 ratio, and 203 patients from another hospital served as an external validation cohort. The SMOTE algorithm was applied to address data imbalance. Independent risk factors were identified using LASSO and logistic regression analyses, and the performance of different models was compared. Ultimately, a LASSO-based logistic regression model was used to construct a predictive nomogram. Model performance was comprehensively evaluated using the area under the receiver operating characteristic curve (AUC), decision curve analysis (DCA), clinical impact curve (CIC), and calibration curve. An online risk calculator was also developed for clinical use. Results: Among 1,965 patients (1,304 males, 661 females; mean age 58.96 ± 13.07 years), 548 (28.9%) developed IKPLAS. Independent risk factors included CRP (OR = 1.005, 95% CI: 1.003-1.007), PLT (OR = 0.995, 95% CI: 0.994-0.997), Prior biliary disease (OR = 1.137, 95% CI: 1.025-2.571), Fever (OR = 2.196, 95% CI: 1.292-3.824), Pleural effusion (OR = 7.355, 95% CI: 4.883-14.761), Ascites (OR = 8.786, 95% CI: 5.141-9.342), Broth culture (OR = 2.264, 95% CI: 1.186-3.371), DM (OR = 2.516, 95% CI: 1.757-3.63), and TBIL (OR = 1.006, 95% CI: 1.002-1.010). The nomogram achieved AUCs of 0.960, 0.920, and 0.892 in the training, internal, and external validation sets, respectively, with good calibration and clinical utility. Conclusion: We developed a nine-factor nomogram to predict individualized IKPLAS risk, demonstrating high discrimination and calibration, supporting early identification of high-risk patients and personalized management.
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.