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Peer-reviewedCC BY 4.0PeerJ

Inflammatory and nutritional indices as predictors of poor prognosis in pyogenic liver abscesses: a retrospective cohort study

  • Bihong Sun1
  • Zhelong Liu1,2,3
  • Shaohua Liu4
  • Tian Yu1
  • Li Liu1,2,3
4 affiliations
  1. 1Department of Endocrinology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
  2. 2Hubei Provincial Clinical Medical Research Center for Endocrinology and Metabolic Diseases, Wuhan, Hubei, China
  3. 3Branch of National Clinical Research Center for Metabolic Diseases, Wuhan, Hubei, China
  4. 4Department of Endocrinology, Chongqing Hospital, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
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Abstract

Objective: This retrospective study of 458 pyogenic liver abscesses (PLA) patients explored the link between inflammation/nutrition-based indices and poor prognosis in PLA, aiming to enhance the prediction of poor prognosis such as mortality, septic shock, or intensive care unit (ICU) admission.

Methods: Patient data, including demographics, comorbidities, clinical presentations, laboratory results, and treatment outcomes, were extracted from medical records. Multivariable logistic regression and receiver operating characteristic (ROC) curve analyses were performed to identify independent predictors and evaluate their discriminative power. A simplified joint prediction model was developed based on rigorous variable selection criteria, and its performance was assessed using internal validation.

Results: Patients with poor prognosis ( n = 33) exhibited lower lymphocyte counts, hemoglobin, platelets, and albumin, alongside elevated inflammation markers. Multivariable analysis identified the neutrophil-to-albumin ratio (NAR) (odds ratio (OR) 6.87 per standard deviation (SD), 95% confidence interval (CI) [2.13–22.17], p = 0.001), prognostic nutritional index (PNI) (OR 0.99 per SD, 95% CI [0.894–0.997], p = 0.038), and controlling nutritional status (CONUT) score (OR 1.37 per SD, 95% CI [1.164–1.602], p < 0.001) as independent predictors. ROC analysis showed these indices had good discriminative ability (PNI: area under the curve (AUC) = 0.757, p < 0.001; PLR: AUC = 0.704, p < 0.001; CONUT: AUC = 0.803, p < 0.001; PLT: AUC = 0.740, p < 0.001). Notably, a joint prediction model combining platelet count (PLT) and CONUT achieved superior prediction with an AUC of 0.822.

Conclusion: This investigation reveals that easily obtainable inflammatory and nutritional indices (NAR, PNI, CONUT and PLT) serve as reliable predictors of poor prognosis in PLA. A combined model integrating PLT and CONUT demonstrates promising discriminative performance, enabling early risk stratification.

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Cite

BibTeXRIS
BibTeX
@article{sun2026inflammatory,
  title = {{Inflammatory and nutritional indices as predictors of poor prognosis in pyogenic liver abscesses: a retrospective cohort study}},
  author = {Bihong Sun and Zhelong Liu and Shaohua Liu and Tian Yu and Li Liu},
  journal = {PeerJ},
  year = {2026},
  month = oct,
  doi = {10.7717/peerj.21852},
  url = {https://peerj.com/articles/21852},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Inflammatory and nutritional indices as predictors of poor prognosis in pyogenic liver abscesses: a retrospective cohort study
AU  - Bihong Sun
AU  - Zhelong Liu
AU  - Shaohua Liu
AU  - Tian Yu
AU  - Li Liu
PY  - 2026
DA  - 2026/10/27
JO  - PeerJ
DO  - 10.7717/peerj.21852
UR  - https://peerj.com/articles/21852
AB  - Objective: This retrospective study of 458 pyogenic liver abscesses (PLA) patients explored the link between inflammation/nutrition-based indices and poor prognosis in PLA, aiming to enhance the prediction of poor prognosis such as mortality, septic shock, or intensive care unit (ICU) admission.  Methods: Patient data, including demographics, comorbidities, clinical presentations, laboratory results, and treatment outcomes, were extracted from medical records. Multivariable logistic regression and receiver operating characteristic (ROC) curve analyses were performed to identify independent predictors and evaluate their discriminative power. A simplified joint prediction model was developed based on rigorous variable selection criteria, and its performance was assessed using internal validation.  Results: Patients with poor prognosis ( n = 33) exhibited lower lymphocyte counts, hemoglobin, platelets, and albumin, alongside elevated inflammation markers. Multivariable analysis identified the neutrophil-to-albumin ratio (NAR) (odds ratio (OR) 6.87 per standard deviation (SD), 95% confidence interval (CI) [2.13–22.17], p = 0.001), prognostic nutritional index (PNI) (OR 0.99 per SD, 95% CI [0.894–0.997], p = 0.038), and controlling nutritional status (CONUT) score (OR 1.37 per SD, 95% CI [1.164–1.602], p < 0.001) as independent predictors. ROC analysis showed these indices had good discriminative ability (PNI: area under the curve (AUC) = 0.757, p < 0.001; PLR: AUC = 0.704, p < 0.001; CONUT: AUC = 0.803, p < 0.001; PLT: AUC = 0.740, p < 0.001). Notably, a joint prediction model combining platelet count (PLT) and CONUT achieved superior prediction with an AUC of 0.822.  Conclusion: This investigation reveals that easily obtainable inflammatory and nutritional indices (NAR, PNI, CONUT and PLT) serve as reliable predictors of poor prognosis in PLA. A combined model integrating PLT and CONUT demonstrates promising discriminative performance, enabling early risk stratification.
SN  - 2167-8359
ER  -