Persistently High Procalcitonin and C-Reactive Protein Are Good Predictors of Infection in Acute Necrotizing Pancreatitis: A Systematic Review and Meta-Analysis.

Infected necrotizing pancreatitis (INP) is associated with an increased risk of organ failure and mortality. Its early recognition and timely initiation of antibiotic therapy can save patients' lives. We systematically searched three databases on 27 October 2022. In the eligible studies, the presence of infection in necrotizing pancreatitis was confirmed via a reference test, which involved either the identification of gas within the necrotic collection through computed tomography imaging or the examination of collected samples, which yielded positive results in Gram staining or culture. Laboratory biomarkers compared between sterile necrotizing pancreatitis and INP were used as the index test, and our outcome measures included sensitivity, specificity, the receiver operating characteristic (ROC) curve and area under the ROC curve (AUC). Within the first 72 hours (h) after admission, the AUC of C-reactive protein (CRP) was 0.69 (confidence interval (CI): 0.62-0.76), for procalcitonin (PCT), it was 0.69 (CI: 0.60-0.78), and for white blood cell count, it was 0.61 (CI: 0.47-0.75). After the first 72 h, the pooled AUC of CRP showed an elevated level of 0.88 (CI: 0.75-1.00), and for PCT, it was 0.86 (CI: 0.60-1.11). The predictive value of CRP and PCT for infection is poor within 72 h after hospital admission but seems good after the first 72 h. Based on these results, infection is likely in case of persistently high CRP and PCT, and antibiotic initiation may be recommended.

Fatty Pancreas Is a Risk Factor for Pancreatic Cancer: A Systematic Review and Meta-Analysis of 2956 Patients.

Pancreatic cancer (PC) is one of the most lethal cancers worldwide. Recently, fatty pancreas (FP) has been studied thoroughly, and although its relationship to PC is not fully understood, FP is suspected to contribute to the development of PC. We aimed to assess the association between PC and FP by conducting a systematic review and meta-analysis. We systematically searched three databases, MEDLINE, Embase, and CENTRAL, on 21 October 2022. Case-control and cross-sectional studies reporting on patients where the intra-pancreatic fat deposition was determined by modern radiology or histology were included. As main outcome parameters, FP in patients with and without PC and PC in patients with and without FP were measured. Proportion and odds ratio (OR) with a 95% confidence interval (CI) were used for effect size measure. PC among patients with FP was 32% (OR 1.32; 95% CI 0.42-4.16). However, the probability of having FP among patients with PC was more than six times higher (OR 6.13; 95% CI 2.61-14.42) than in patients without PC, whereas the proportion of FP among patients with PC was 0.62 (95% CI 0.42-0.79). Patients identified with FP are at risk of developing PC. Proper screening and follow-up of patients with FP may be recommended.

Acute Pancreatitis Severity Prediction: It Is Time to Use Artificial Intelligence.

The clinical course of acute pancreatitis (AP) can be variable depending on the severity of the disease, and it is crucial to predict the probability of organ failure to initiate early adequate treatment and management. Therefore, possible high-risk patients should be admitted to a high-dependence unit. For risk assessment, we have three options: (1) There are univariate biochemical markers for predicting severe AP. One of their main characteristics is that the absence or excess of these factors affects the outcome of AP in a dose-dependent manner. Unfortunately, all of these parameters have low accuracy; therefore, they cannot be used in clinical settings. (2) Score systems have been developed to prognosticate severity by using 4-25 factors. They usually require multiple parameters that are not measured on a daily basis, and they often require more than 24 h for completion, resulting in the loss of valuable time. However, these scores can foresee specific organ failure or severity, but they only use dichotomous parameters, resulting in information loss. Therefore, their use in clinical settings is limited. (3) Artificial intelligence can detect the complex nonlinear relationships between multiple biochemical parameters and disease outcomes. We have recently developed the very first easy-to-use tool, EASY-APP, which uses multiple continuous variables that are available at the time of admission. The web-based application does not require all of the parameters for prediction, allowing early and easy use on admission. In the future, prognostic scores should be developed with the help of artificial intelligence to avoid information loss and to provide a more individualized risk assessment.