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Predictive Value of C-Reactive Protein/Albumin Ratio in Acute Pancreatitis
Nischal Khanal1, Amrit Baral2, Shristi Shrestha3
1General Surgery, James Paget University Hospitals NHS Foundation Trust, Lowestoft, GBR.
None:
Background Acute pancreatitis (AP) is a common and potentially life-threatening condition with a wide spectrum of severity. While most cases are mild, a subset progresses to severe disease with high mortality. This study evaluates the predictive value of the C-reactive protein (CRP)/albumin ratio (CAR), an inflammation-based biomarker, in determining AP severity. Methods A prospective, observational study was conducted among adult patients diagnosed with AP. CRP and albumin levels were measured at presentation, and CAR was calculated and categorized into three quantiles (≤6.7, 6.7-30.5, ≥30.5). Associations between CAR quantiles and disease severity were analyzed using Fisher's exact test. Pearson correlation assessed the relationship between CAR and hospital stay. Receiver operating characteristic (ROC) analysis was performed to evaluate diagnostic performance, with optimal cutoffs determined by Youden's index. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated at both the Youden cutoff and a lower pragmatic threshold. Results Among the 80 participants (55% male; median age 44.5 years), the median CAR was 12.7. No patients in the lowest CAR quantile developed severe AP. CAR demonstrated good discriminatory ability for severe AP (AUC 0.862). The optimal Youden cutoff of 18.39 yielded 100% sensitivity and 69.4% specificity. At a lower cutoff of 10.28, sensitivity remained 100%, while specificity decreased to 40.3%. CAR values were positively correlated with hospital stay duration (r = 0.568, p < 0.001). Conclusion CAR is a simple, inexpensive, and non-invasive marker that predicts AP severity. Low CAR values are associated with a reduced risk of severe AP. Using CAR, particularly at a lower threshold, may help identify high-risk patients early and guide management decisions in resource-limited settings.
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