The Accuracy of Neutrophil-to-Lymphocyte Ratio and Abdominal Computed Tomography to Predict the Severity of Acute Cholecystitis

Pericholecystic Fat Stranding as a Predictive Factor of Length of Stays of Patients with Acute Cholecystitis: A Novel Scoring Model

Background: The 2018 Tokyo Guidelines (TG18) are used to classify the severity of acute cholecystitis (AC) but insufficient to predict the length of hospital stay (LOS). Methods: For patients with AC, clinical factors and computed tomography features, including our proposed grading system of pericholecystic fat stranding were used for predicting an LOS of ≥7 days in the logistic regression models. Results: Our multivariable model showed age ≥ 65 years (OR: 2.56, p < 0.001), C-reactive protein (CRP) ≥ 2 mg/dL (OR: 1.97, p = 0.013), gamma-glutamyltransferase levels (OR: 2.460, p = 0.001), TG18 grade (OR: 2.89 per grade, p < 0.001), and moderate to severe pericholecystic fat stranding (OR: 2.14, p = 0.012) exhibited prolonged LOS ≥ 7 days. Conclusions: We developed a scoring model, including TG18 grades (score of 1-3 per grade), our grading system of fat stranding (score of 1), CRP (score of 1), and gamma-glutamyltransferase (score of 1), and a cutoff of >3 had highest diagnostic performance.

The Role of Different Systemic Inflammatory Indexes Derived from Complete Blood Count in Differentiating Acute from Chronic Calculus Cholecystitis and Predicting Its Severity

Purpose

This study aimed to evaluate the diagnostic value of the different Complete blood count-derived systemic inflammation indexes, including neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), and neutrophil to lymphocyte × platelet ratio (NLPR) in differential diagnosing the chronic calculus cholecystitis (CCC) and the acute calculus cholecystitis (ACC), as well as determining the severity of ACC.

Patients and Methods

105 CCC and 88 ACC patients were enrolled. NLR, PLR, SII, and NLPR were evaluated in both cohorts as well as in different severity levels of ACC. The severity of ACC was determined based on the Tokyo Guidelines. Receiver operating characteristic (ROC) curve and Univariate/multivariate regression analyses were conducted.

Results

The levels of NLR, PLR, SII, and NLPR were significantly higher in the ACC group compared to the CCC group. The optimal cutoff values for NLR, PLR, SII, and NLPR were determined to be 3.89, 144.7, 896.8, and 0.031 respectively. NLR>3.89 demonstrates the highest predictive capability with an AUC of 0.801 and a sensitivity of 72.73%. Multivariate analysis showed that NLR>3.89 (OR: 4.169, p = 0.004) and NLPR>0.031 (OR: 4.304, p = 0.005) were dominant in distinguishing ACC from CCC. In ACC patients, the levels of NLR, SII, and NLPR were significantly higher in the Moderate to Severe-degree ACC (MS-ACC) group than in Mild-Degree ACC (M-ACC). NLPR > 0.044 exhibited the highest predictive ability with an AUC of 0.778 and a specificity of 91.67%. Multivariate analysis showed that NLR>6.399 (OR: 10.308, p = 0.000) was a possible independent prognostic factor for accessing the severity of ACC.

Conclusion

Systemic inflammation indexes can be useful in predicting the risk of ACC and MS-ACC. NLR demonstrates the best distinguishing power and sensitivity for distinguishing ACC from CCC, while NLPR shows the best predictive power and specificity for predicting the severity of ACC.