Predictive values of neutrophil-lymphocyte ratio as an early indicator for severe acute pancreatitis in the emergency department patients

Chronic liver disease is an important risk factor for worse outcomes in acute pancreatitis: a systematic review and meta-analysis

Chronic liver diseases (CLD) affect 1.5 billion patients worldwide, with dramatically increasing incidence in recent decades. It has been hypothesized that the chronic hyperinflammation associated with CLD may increase the risk of a more severe course of acute pancreatitis (AP). This study aims to investigate the underlying impact of CLD on the outcomes of AP. A systematic search was conducted in Embase, Medline, and Central databases until October 2022. Studies investigating patients with acute pancreatitis and CLD, were included in the meta-analysis. A total of 14,963 articles were screened, of which 36 were eligible to be included. CLD was a risk factor for increased mortality with an odds ratio (OR) of 2.53 (CI 1.30 to 4.93, p = 0.01). Furthermore, renal, cardiac, and respiratory failures were more common in the CLD group, with ORs of 1.92 (CI 1.3 to 2.83, p = 0.01), 2.11 (CI 0.93 to 4.77, p = 0.062) and 1.99 (CI 1.08 to 3.65, p = 0.033), respectively. Moreover, the likelihood of developing Systemic Inflammatory Response Syndrome (SIRS) was significantly higher, with an OR of 1.95 (CI 1.03 to 3.68, p = 0.042). CLD is an important risk factor for worse outcomes in AP pancreatitis, leading to higher mortality and increased rates of local and systemic complications.

Clinical value of BISAP score combined with CRP and NLR in evaluating the severity of acute pancreatitis

To investigate the clinical value of bedside index for severity in acute pancreatitis (BISAP) score combined with serum C-reactive protein (CRP) and neutrophil-to-lymphocyte ratio (NLR) in predicting the severity of early acute pancreatitis. A total of 113 patients with acute pancreatitis admitted to the Department of Gastroenterology, Second Affiliated Hospital of Nantong University from September 2019 to September 2022 were retrospectively collected and divided into mild acute pancreatitis group (51 cases), moderately severe acute pancreatitis group (32 cases) and severe acute pancreatitis group (30 cases) according to the severity of the disease. The general clinical data, laboratory test indicators, and imaging data within 72 hours were collected and compared among the 3 groups. The sensitivity, specificity, and accuracy of BISAP score, BISAP combined with CRP, BISAP combined with NLR, and BISAP combined with CRP and NLR in predicting the severity of acute pancreatitis were analyzed by receiver operating characteristic curve. 1. BISAP score (0.9608 ± 0.1119, 1.688 ± 0.1225, 2.6 ± 0.1135), CRP (74.77 ± 8.336, 142.9 ± 11.44, 187.6 ± 13.04), and NLR (8.063 ± 0.7781, 13.69 ± 1.023, 18.06 ± 1.685) increased sequentially in mild acute pancreatitis group, moderately severe acute pancreatitis group, and severe acute pancreatitis group, and the differences in BISAP score, CRP and NLR among the 3 groups were statistically significant (P < .05). BISAP score was positively correlated with CRP and NLR (R = 0.5062, 0.5247, P < .05). The area under the receiver operating characteristic curve of BISAP score, CRP, NLR, BISAP combined with NLR, and BISAP combined with CRP in predicting the severity of acute pancreatitis were 0.885, 0.814, 0.714, 0.953, respectively. The specificity and sensitivity of combined diagnosis were higher than those of BISAP score or CRP and NLR alone. BISAP score combined with CRP and NLR can effectively evaluate the severity of acute pancreatitis, and their combination has a higher predictive value for early severity assessment.

Construction and validation of machine learning models for sepsis prediction in patients with acute pancreatitis

BACKGROUND

This study aimed to construct predictive models for the risk of sepsis in patients with Acute pancreatitis (AP) using machine learning methods and compared optimal one with the logistic regression (LR) model and scoring systems.

METHODS

In this retrospective cohort study, data were collected from the Medical Information Mart for Intensive Care III (MIMIC III) database between 2001 and 2012 and the MIMIC IV database between 2008 and 2019. Patients were randomly divided into training and test sets (8:2). The least absolute shrinkage and selection operator (LASSO) regression plus 5-fold cross-validation were used to screen and confirm the predictive factors. Based on the selected predictive factors, 6 machine learning models were constructed, including support vector machine (SVM), K-nearest neighbour (KNN), multi-layer perceptron (MLP), LR, gradient boosting decision tree (GBDT) and adaptive enhancement algorithm (AdaBoost). The models and scoring systems were evaluated and compared using sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and the area under the curve (AUC).

RESULTS

A total of 1, 672 patients were eligible for participation. In the training set, 261 AP patients (19.51%) were diagnosed with sepsis. The predictive factors for the risk of sepsis in AP patients included age, insurance, vasopressors, mechanical ventilation, Glasgow Coma Scale (GCS), heart rate, respiratory rate, temperature, SpO2, platelet, red blood cell distribution width (RDW), International Normalized Ratio (INR), and blood urea nitrogen (BUN). The AUC of the GBDT model for sepsis prediction in the AP patients in the testing set was 0.985. The GBDT model showed better performance in sepsis prediction than the LR, systemic inflammatory response syndrome (SIRS) score, bedside index for severity in acute pancreatitis (BISAP) score, sequential organ failure assessment (SOFA) score, quick-SOFA (qSOFA), and simplified acute physiology score II (SAPS II).

CONCLUSION

The present findings suggest that compared to the classical LR model and SOFA, qSOFA, SAPS II, SIRS, and BISAP scores, the machine learning model-GBDT model had a better performance in predicting sepsis in the AP patients, which is a useful tool for early identification of high-risk patients and timely clinical interventions.

High Triglyceride-Glucose Index Is Associated with Poor Prognosis in Patients with Acute Pancreatitis

BACKGROUND

Acute pancreatitis (AP) is a common gastrointestinal disease worldwide. Severe acute pancreatitis (SAP) is characterized as persistent organ failure with a mortality rate as high as 20-30%. Early assessment of the severity and screening out possible SAP is of great significance. Given that there is still a lack of both convenient and practical tools for evaluating SAP, we conducted this study to explore the association between TyG index and acute pancreatitis prognosis.

METHODS

A total of 353 in-patients diagnosed with acute pancreatitis in the Second Hospital of Shandong University were retrospectively enrolled from January 2018 to November 2021 in this study. According to the Atlanta Classification, they were divided into two groups based on the AP severity. Demographic information and clinical materials were retrospectively collected. The TyG index calculation formula is as follows: ln [fasting triglycerides (mg/dL) × fasting plasma glucose (mg/dL)/2]. Statistical analyses were performed using SPSS software (IBM version 22.0) and Medcalc software. Multivariable logistic regression analyses were used to investigate independent predictors for SAP. ROC curve was plotted to assess the predictive ability and cutoffs of TyG index.

RESULTS

A total of 353 AP patients were respectively enrolled in this study, of which 47 suffered from SAP. Compared with the non-SAP group, TyG index was significantly higher in the SAP group (10.44 ± 1.55 vs 9.33 ± 1.44, P < 0.001). Multivariate logistic regression analysis showed that TyG index was an independent risk factor for SAP (OR 1.835, 95% CI 1.380-2.442 P < 0.001), with a cutoff of 8.76 for non-HTG/AAP and 11.81 for HTG/AAP by ROC curve. TyG index of patients who suffered from SIRS, OF, APFC, and ANC was higher than those without (P < 0.05).

CONCLUSIONS

The triglyceride-glucose index is an independent risk factor for SAP. High TyG index is closely related to SAP and AP-related complications.

Hypertriglyceridaemia-Induced Acute Pancreatitis: A Different Disease Phenotype

Acute pancreatitis (AP) is the most common gastrointestinal indication requiring hospitalisation. Severe hypertriglyceridaemia (HTG) is the third most common aetiology of AP (HTGAP), with a complication rate and severity that are higher than those of other aetiologies (non-HTGAP). The aim of this study was to evaluate the supposedly higher complication rate of HTGAP compared to non-HTGAP. The secondary objectives were to find different biomarkers for predicting a severe form. This was a retrospective study that included patients admitted with AP in a tertiary department of gastroenterology and hepatology. The patients were divided into two groups: HTGAP and non-HTGAP. We searched for differences regarding age, gender, the presence of diabetes mellitus (DM), the severity of the disease, the types of complications and predictive biomarkers for severity, hospital stay and mortality. A total of 262 patients were included, and 11% (30/262) of the patients had HTGAP. The mean ages were 44.4 ± 9.2 in the HTGAP group and 58.2 ± 17.1 in the non-HTGAP group, p < 0.0001. Male gender was predominant in both groups, at 76% (23/30) in the HTGAP group vs. 54% (126/232) in non-HTGAP, p = 0.02; 53% (16/30) presented with DM vs. 18% (42/232), p < 0.0001. The patients with HTG presented higher CRP 48 h after admission: 207 mg/dL ± 3 mg/dL vs. non-HTGAP 103 mg/dL ± 107 mg/dL, p < 0.0001. Among the patients with HTGAP, there were 60% (18/30) with moderately severe forms vs. 30% (71/232), p = 0.001, and 16% (5/30) SAP vs. 11% (27/232) in non-HTGAP, p = 0.4 Among the predictive markers, only haematocrit (HT) and blood urea nitrogen (BUN) had AUCs > 0.8. According to a multiple regression analysis, only BUN 48 h was independently associated with the development of SAP (p = 0.05). Diabetes mellitus increased the risk of developing severe acute pancreatitis (OR: 1.3; 95% CI: 0.1963−9.7682; p = 0.7). In our cohort, HTGAP more frequently had local complications compared with non-HTGAP. A more severe inflammatory syndrome seemed to be associated with this aetiology; the best predictive markers for complicated forms of HTGAP were BUN 48 h and HT 48 h.

The BISAP score, NLR, CRP, or BUN: Which marker best predicts the outcome of acute pancreatitis?

Acute pancreatitis is a common disease, and the mortality rate can be high. Thus, a risk assessment should be performed early to optimize treatment. We compared simple prognostic markers with the bedside index for severity in acute pancreatitis (BISAP) scoring system to identify the best predictors of severity and mortality.This retrospective study stratified disease severity based on the revised Atlanta criteria. The accuracies of the markers for predicting severe AP (SAP) were assessed using receiver operating characteristic curves. The sensitivity, specificity, positive predictive value, and negative predictive value were calculated for each marker. Multivariate logistic regression analyses were used to identify independent predictors of SAP and mortality.The area under the curve (AUC) for the BISAP score was classified as fair for predicting SAP. The neutrophil-to-lymphocyte ratio at 48 hours (NLR48 h) and the C-reactive protein level at 48 hours (CRP48 h) had the best AUCs and were independently associated with SAP. When both criteria were met, the AUC was 0.89, sensitivity was 68%, and specificity was 92%. CRP48 h and hematocrit at 48 hours were independently associated with mortality.NLR48 h and CRP48 h were independently associated with SAP but not superior to the BISAP score at admission. Assessing NLR48 h and CRP48H together was most suitable for predicting SAP. The CRP level was a good predictive marker for mortality.

Monitoring Approach of Fatality Risk Factors for Patients with Severe Acute Pancreatitis Admitted to the Intensive Care Unit. A Retrospective, Monocentric Study

Acute pancreatitis is an unpredictable disease affecting the pancreas and it is characterized by a wide range of symptoms and modified lab tests, thus there is a continuing struggle to classify this disease and to find risk factors associated with a worse outcome. The main objective of this study was to identify the risk factors associated with the fatal outcome of the intensive care unit’s patients diagnosed and admitted for severe acute pancreatitis, the secondary objective was to investigate the prediction value for the death of different inflammatory markers at the time of their admission to the hospital. This retrospective study included all the patients with a diagnosis of acute pancreatitis admitted to the Intensive Care Unit of the Emergency County Hospital Timisoara between 1 January 2016 and 31 May 2021. The study included 53 patients diagnosed with severe acute pancreatitis, out of which 21 (39.6%) survived and 32 (60.4%) died. For the neutrophils/lymphocytes ratio, a cut-off value of 12.4 was found. When analyzing age, we found out that age above 52 years old can predict mortality, and for the platelets/lymphocytes ratio, a cut-off value of 127 was found. Combining the three factors we get a new model for predicting mortality, with an increased performance, AUROC = 0.95, p < 0.001. Multiple persistent organ failure, age over 50, higher values of C reactive protein, and surgery were risk factors for death in the patients with severe acute pancreatitis admitted to the intensive care unit. The model design from the neutrophils/lymphocytes ratio, platelets/lymphocytes ratio, and age proved to be the best in predicting mortality in severe acute pancreatitis.

New markers in predicting the severity of acute pancreatitis in the emergency department: Immature granulocyte count and percentage

Background:

Acute pancreatitis (AP) may vary in severity, from mild, self-limiting pancreatic inflammation to rapidly progressive life-threatening clinical course. If the severity of AP can be predicted early and treated quickly, it may lead to a decrease in morbidity and mortality rates. There?fore, we aimed to investigate the clinical utility of immature granulocyte count (IGC) and IGC percentage (IG%) in showing the severity of AP in this study.

Methods:

Two hundred and twenty-seven patients who were admitted to our emergency department and diagnosed with AP between March 1 and September 30, 2019, were included in the study. The patients were divided into two groups as mild and severe AP (MAP and SAP) according to the severity of the disease. Demographic characteristics of the patients, disease etiology, disease severity, and inflammation markers [white blood cell count (WBC), IGC, IG%, neutrophil–lymphocyte ratio (NLR), and C-reactive protein (CRP)] were recorded. Differences between the groups were statistically analyzed.

Results:

Of the patients included in the study, 183 (80.7%) were in the MAP group and 44 (19.3%) were in the SAP group. The mean WBC, NLR, CRP, IGC, and IG% levels were significantly higher in the SAP group compared to the MAP group. The power of IGC and IG% in predicting SAP was higher than other inflammation markers (WBC, NLR, and CRP) [(AUC for IGC: 0.902; sensitivity: 78.2%; specificity: 92.8%); (AUC for IG%: 0.843; sensitivity: 72.7%; specificity: 84.6%)].

Conclusion:

IGC and IG% show the severity of AP more effectively than WBC, NLR, and CRP, which are traditional inflammation markers.

Comparison of MPL-ANN and PLS-DA models for predicting the severity of patients with acute pancreatitis: An exploratory study

OBJECTIVE

Acute pancreatitis (AP) is a common inflammatory disorder that may develop into severe AP (SAP), resulting in life-threatening complications and even death. The purpose of this study was to explore two different machine learning models of multilayer perception-artificial neural network (MPL-ANN) and partial least squares-discrimination (PLS-DA) to diagnose and predict AP patients' severity.

METHODS

The MPL-ANN and PLS-DA models were established using candidate markers from 15 blood routine parameters and five serum biochemical indexes of 133 mild acute pancreatitis (MAP) patients, 167 SAP (including 88 moderately SAP) patients, and 69 healthy controls (HCs). The independent parameters and combined model's diagnostic efficiency in AP severity differentiation were analyzed using the area under the receiver operating characteristic curve (AUC).

RESULTS

The neutrophil to lymphocyte ratio (NLR) is the most useful marker in 20 parameters for screening AP patients [AUC = 0.990, 95% confidence interval (CI): 0.984-0.997, sensitivity 94.3%, specificity 98.6%]. The MPL-ANN model based on six optimal parameters exhibited better diagnostic and predict performance (AUC = 0.984, 95% CI: 0.960-1.00, sensitivity 92.7%, specificity 93.3%, accuracy 93.0%) than the PLS-DA model based on five optimal parameters (AUC = 0.912, 95% CI: 0.853-0.971, sensitivity 87.8%, specificity 84.4%, accuracy 84.8%) in discriminating MAP patients from SAP patients.

CONCLUSION

The results demonstrated that the MPL-ANN model based on routine blood and serum biochemical indexes provides a reliable and straightforward daily clinical practice tool to predict AP patients' severity.

Neutrophil to lymphocyte ratio and albumin bilirubin grade in hepatocellular carcinoma: A systematic review

BACKGROUND

Hepatocellular carcinoma (HCC) is a frequent cause of cancer related death globally. Neutrophil to lymphocyte ratio (NLR) and albumin bilirubin (ALBI) grade are emerging prognostic indicators in HCC.

AIM

To study published literature of NLR and ALBI over the last five years, and to validate NLR and ALBI locally in our centre as indicators of HCC survival.

METHODS

A systematic review of the published literature on PubMed of NLR and ALBI in HCC over the last five years. The search followed the guidelines of the preferred reporting items for systematic reviews and meta-analyses. Additionally, we also investigated HCC cases between December 2013 and December 2018 in our centre.

RESULTS

There were 54 studies describing the relation between HCC and NLR and 95 studies describing the relation between HCC and ALBI grade over the last five years. Our local cohort of patients showed NLR to have a significant negative relationship to survival (P = 0.011). There was also significant inverse relationship between the size of the largest HCC nodule and survival (P = 0.009). Median survival with alpha fetoprotein (AFP) < 10 KU/L was 20 mo and with AFP > 10 KU/L was 5 mo. We found that AFP was inversely related to survival, this relationship was not statically significant (P = 0.132). Mean survival for ALBI grade 1 was 37.7 mo, ALBI grade 2 was 13.4 months and ALBI grade 3 was 4.5 mo. ALBI grades performed better than Child Turcotte Pugh score in detecting death from HCC.

CONCLUSION

NLR and ALBI grade in HCC predict survival better than the conventional alpha fetoprotein. ALBI grade performs better than Child Turcotte Pugh score. These markers are done as part of routine clinical care and in cases of normal alpha fetoprotein, these markers could give a better understanding of the patient disease progression. NLR and ALBI grade could have a role in modified easier to learn staging and prognostic systems for HCC.

Evaluation of Prognostic Factors of Severity in Acute Biliary Pancreatitis

Acute pancreatitis (AP) is an inflammatory disorder of the pancreas that, when classified as severe, is associated with high morbidity and mortality. Promptly identifying the severity of AP is of extreme importance for improving clinical outcomes. The aim of this study was to compare the prognostic value of serological biomarkers, ratios, and multifactorial scores in patients with acute biliary pancreatitis and to identify the best predictors. In this observational and prospective study, the biomarkers, ratios and multifactorial scores were evaluated on admission and at 48 h of the symptom onset. On admission, regarding the AP severity, the white blood count (WBC) and neutrophil-lymphocyte ratio (NLR), and regarding the mortality, the WBC and the modified Marshall score (MMS) showed the best predictive values. At 48 h, regarding the AP severity, the hepcidin, NLR, systemic inflammatory response index (SIRI) and MMS and regarding the mortality, the NLR, hepcidin and the bedside index for severity in AP (BISAP) score, showed the best predictive values. The present study enabled the identification, for the first time, of SIRI as a new prognostic tool for AP severity, and validated hepcidin and the NLR as better prognostic markers than C-reactive protein (CRP) at 48 h of symptom onset.