ArticleBMC infectious diseases2026
Development and validation of a prediction model for predicting invasive Klebsiella pneumoniae liver abscess syndrome in patients with pyogenic liver abscess.
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.
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Abstract
backgroundInvasive Klebsiella pneumoniae liver abscess syndrome (IKLAS) increases the risk of mortality and length of hospital stay in patients with pyogenic liver abscess (PLA). This study aimed to construct a nomogram capable of accurately predicting the occurrence of IKLAS in PLA patients.
methodsThis study retrospectively analyzed data from pyogenic liver abscess (PLA) patients admitted to Tianjin Medical University General Hospital between January 2022 and May 2024. We aimed to develop a nomogram predicting IKLAS, which defines as a liver abscess with metastatic Klebsiella pneumoniae infections at other sites. Data were collected from the inpatient management system. To address class imbalance, the dataset was augmented using Synthetic Minority Over-sampling Technique (SMOTE) and Random Over-Sampling Examples (ROSE). The enhanced dataset was split into a training set and validation set using R software. To improve the model’s explanatory power and stability, Least Absolute Shrinkage and Selection Operator (LASSO) regression was used. A logistic regression-based nomogram was developed. It was evaluated using receiver operating characteristic (ROC) curves, calibration diagrams, and decision curve analysis (DCA). Internal validation was performed on the holdout dataset.
resultsThis study included 160 PLA patients, 24 with IKLAS and 136 with non-IKLAS. Using SMOTE and ROSE, IKLAS cases were augmented to 120. Patients were divided into training (n = 180) and validation (n = 76) sets. Statistical analysis identified 12 significant factors, such as diabetes mellitus (DM), septic shock (SS), white blood cell (WBC), neutrophil percentage (N%), lymphocyte percentage (L%), N%: L% ratio, C-reactive protein (CRP), alkaline phosphatase (ALKP), blood urea nitrogen (BUN), total cholesterol (TC), and lipoproteins (HDL, LDL). LASSO regression selected eight parameters for nomogram: SS, WBC, L%, CRP, ALKP, BUN, and LDL. TC was excluded due to its compositional relationship with LDL. The model demonstrated excellent predictive performance. It achieved an AUC of 0.813 (95% CI: 0.752–0.834) in the training set and 0.853 (95% CI: 0.769–0.937) in the validation set. Calibration curves and DCA confirmed accuracy and strong clinical utility, proving its reliability for predicting IKLAS.
conclusionWe developed a risk prediction model for IKLAS in patients with PLA. The influencing factors include SS, WBC, L%, CRP, ALKP, BUN and LDL. This model provides preliminary evidence for IKLAS risk stratification. CLINICAL TRIAL NUMBER: Not applicable.
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