Risk stratification of spontaneous bacterial peritonitis recurrence: integrating acute kidney injury, biomarkers, composite scores, and machine learning models
A composite score, combined with machine-learning models like random forest, enhances risk assessment for SBP recurrence. Clinical predictors such as AKI, CRP, bilirubin, MELD, PPI use, and β-blockers non-use help in targeted prevention.
Authors
- Mostafa Abdelsalam (ORCID: https://orcid.org/0000-0002-4682-2777)
- Sherif Mourad (ORCID: https://orcid.org/0000-0001-6399-2279)
- Dina Nour
- Alaa Elmetwalli (ORCID: https://orcid.org/0000-0001-5372-4297)
- Muhammad Diasty
- Eman Abdelkader
- Mohamed Wahba (ORCID: https://orcid.org/0000-0002-5569-3727)
- Eman Mousa (ORCID: https://orcid.org/0000-0003-0486-5661)
- Ahmed Taha (ORCID: https://orcid.org/0000-0003-1597-8303)
- Amany Hasson
- Ahmed El-Eraky
- Nasser Mousa (ORCID: https://orcid.org/0000-0001-8329-2587)
- Nader Elmalky
- Marwa Mansour
- Mohamed Selim (ORCID: https://orcid.org/0000-0002-5539-7274)
- Ahmed El‐Assmy (ORCID: https://orcid.org/0000-0002-0349-892X)
- Mohammed Abdelaziz (ORCID: https://orcid.org/0000-0002-2521-8642)
- Sherif Shiha
Institutions
- Mansoura University (EG)
- Taif University (SA)
- Jouf University (SA)
- Mansoura University Hospital (EG)
- Soliman Fakeeh Hospital (SA)
Publication Details
- Journal
- Gut Pathogens
- Published
- 2025-12-07
- DOI
- https://doi.org/10.1186/s13099-025-00774-5
- Primary Topic
- Sepsis Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00