Scholarlink

Background <p>Spontaneous bacterial peritonitis (SBP) remains a life-threatening complication of liver cirrhosis, requiring accurate and rapid prediction. Albumin and leukocyte count reflect systemic inflammation and liver dysfunction; combining them into an Albumin-to-Leukocyte Ratio (ALR) may enhance early risk stratification.</p> Aim <p>To evaluate the diagnostic value of the ALR for predicting SBP in patients with cirrhosis and develop an ALR-based risk score. We will compare its predictive accuracy with machine-learning models to establish a practical triage tool for SBP risk stratification.</p> Patient and methods <p>This cross-sectional study included 240 cirrhotic patients, categorized into SBP (<i>n</i> = 152) and non-SBP (<i>n</i> = 88). Demographic, clinical, hematological, and biochemical variables were analyzed. ALR was calculated, and its predictive accuracy was assessed using ROC curves. A risk score integrating ALR, hemoglobin, bilirubin, and Child-Pugh class was generated. Logistic regression identified independent predictors. Diagnostic performance of ALR was compared with decision tree and random forest models.</p> Results <p>SBP patients had significantly lower albumin, ALR, hemoglobin, and platelets, and higher leukocyte count, bilirubin, INR, and creatinine (all <i>p</i> ≤ 0.05). ALR demonstrated moderate diagnostic performance with fair discrimination; AUC of 0.707, achieving a sensitivity of 82% at a cutoff of ≤ 0.728. Multivariate analysis identified ALR (OR = 0.114, <i>p</i> = 0.001), hemoglobin (OR = 0.847, <i>p</i> = 0.024), bilirubin (OR = 1.319, <i>p</i> = 0.004), MELD score (OR = 0.998, <i>p</i> = 0.042) and Child-Pugh class C (OR = 4.86, <i>p</i> = 0.001) as independent predictors. The generated ALR risk score (0–7 points) categorized patients into low, intermediate, and high-risk groups with increasing diagnostic accuracy (AUC range 0.63–0.75). Machine-learning models outperformed ALR alone; random forest showed the highest discrimination (AUC = 0.901), followed by decision tree (AUC = 0.852).</p> Conclusion <p>ALR is a simple and accessible biomarker with moderate accuracy for predicting SBP in cirrhotic patients and may serve as a practical bedside triage tool when integrated into a composite risk score. However, machine-learning models, particularly random forest, demonstrated superior predictive performance, suggesting that combining conventional biomarkers with advanced algorithms may enhance SBP risk stratification.</p> Clinical trial number <p>Not applicable.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Diagnostic value of integrated albumin-to-leukocyte ratio risk modeling stratification and machine learning approaches for prediction of spontaneous bacterial peritonitis in cirrhotic patients

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月26日

查看全文
  • Nasser Mousa,
  • Alaa Elmetwalli,
  • Mostafa Abdelsalam,
  • Mohamed Wahba,
  • Adel El-Assmy,
  • Eman Mousa,
  • Ali El-Assmy,
  • Sherif Taha,
  • Marwa Mansour,
  • Dina Nour,
  • Muhammad Diasty,
  • Nader ElmalkI,
  • Ghada O. Elkhawaga,
  • Ahmed Alhawarey,
  • Ola El-Emam,
  • Nader AbdelRazek,
  • Niveen El-wakeel,
  • Mohamed Selim

摘要

Background

Spontaneous bacterial peritonitis (SBP) remains a life-threatening complication of liver cirrhosis, requiring accurate and rapid prediction. Albumin and leukocyte count reflect systemic inflammation and liver dysfunction; combining them into an Albumin-to-Leukocyte Ratio (ALR) may enhance early risk stratification.

Aim

To evaluate the diagnostic value of the ALR for predicting SBP in patients with cirrhosis and develop an ALR-based risk score. We will compare its predictive accuracy with machine-learning models to establish a practical triage tool for SBP risk stratification.

Patient and methods

This cross-sectional study included 240 cirrhotic patients, categorized into SBP (n = 152) and non-SBP (n = 88). Demographic, clinical, hematological, and biochemical variables were analyzed. ALR was calculated, and its predictive accuracy was assessed using ROC curves. A risk score integrating ALR, hemoglobin, bilirubin, and Child-Pugh class was generated. Logistic regression identified independent predictors. Diagnostic performance of ALR was compared with decision tree and random forest models.

Results

SBP patients had significantly lower albumin, ALR, hemoglobin, and platelets, and higher leukocyte count, bilirubin, INR, and creatinine (all p ≤ 0.05). ALR demonstrated moderate diagnostic performance with fair discrimination; AUC of 0.707, achieving a sensitivity of 82% at a cutoff of ≤ 0.728. Multivariate analysis identified ALR (OR = 0.114, p = 0.001), hemoglobin (OR = 0.847, p = 0.024), bilirubin (OR = 1.319, p = 0.004), MELD score (OR = 0.998, p = 0.042) and Child-Pugh class C (OR = 4.86, p = 0.001) as independent predictors. The generated ALR risk score (0–7 points) categorized patients into low, intermediate, and high-risk groups with increasing diagnostic accuracy (AUC range 0.63–0.75). Machine-learning models outperformed ALR alone; random forest showed the highest discrimination (AUC = 0.901), followed by decision tree (AUC = 0.852).

Conclusion

ALR is a simple and accessible biomarker with moderate accuracy for predicting SBP in cirrhotic patients and may serve as a practical bedside triage tool when integrated into a composite risk score. However, machine-learning models, particularly random forest, demonstrated superior predictive performance, suggesting that combining conventional biomarkers with advanced algorithms may enhance SBP risk stratification.

Clinical trial number

Not applicable.