Cardiac indicator CK-MB might be a predictive marker for severity and organ failure development of acute pancreatitis

Identification and analysis of chemokine-related and NETosis-related genes in acute pancreatitis to develop a predictive model

Background: Chemokines and NETosis are significant contributors to the inflammatory response, yet there still needs to be a more comprehensive understanding regarding the specific molecular characteristics and interactions of NETosis and chemokines in the context of acute pancreatitis (AP) and severe AP (SAP). Methods: To address this gap, the mRNA expression profile dataset GSE194331 was utilized for analysis, comprising 87 AP samples (77 non-SAP and 10 SAP) and 32 healthy control samples. Enrichment analyses were conducted for differentially expressed chemokine-related genes (DECRGs) and NETosis-related genes (DENRGs). Three machine-learning algorithms were used for the identification of signature genes, which were subsequently utilized in the development and validation of nomogram diagnostic models for the prediction of AP and SAP. Furthermore, single-gene Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) were performed. Lastly, an interaction network for the identified signature genes was constructed. Results: We identified 12 DECRGs and 7 DENRGs, and enrichment analyses indicated they were primarily enriched in cytokine-cytokine receptor interaction, chemokine signaling pathway, TNF signaling pathway, and T cell receptor signaling pathway. Moreover, these machine learning algorithms finally recognized three signature genes (S100A8, AIF1, and IL18). Utilizing the identified signature genes, we developed nomogram models with high predictive accuracy for AP and differentiation of SAP from non-SAP, as demonstrated by area under the curve (AUC) values of 0.968 (95% CI 0.937-0.990) and 0.862 (95% CI 0.742-0.955), respectively, in receiver operating characteristic (ROC) curve analysis. Subsequent single-gene GESA and GSVA indicated a significant positive correlation between these signature genes and the proteasome complex. At the same time, a negative association was observed with the Th1 and Th2 cell differentiation signaling pathways. Conclusion: We have identified three genes (S100A8, AIF1, and IL18) related to chemokines and NETosis, and have developed accurate diagnostic models that might provide a novel method for diagnosing AP and differentiating between severe and non-severe cases.

Development and validation of a multimodal model in predicting severe acute pancreatitis based on radiomics and deep learning

OBJECTIVE

Aim to establish a multimodal model for predicting severe acute pancreatitis (SAP) using machine learning (ML) and deep learning (DL).

METHODS

In this multicentre retrospective study, patients diagnosed with acute pancreatitis at admission were enrolled from January 2017 to December 2021. Clinical information within 24 h and CT scans within 72 h of admission were collected. First, we trained Model α based on clinical features selected by least absolute shrinkage and selection operator analysis. Second, radiomics features were extracted from 3D-CT scans and Model β was developed on the features after dimensionality reduction using principal component analysis. Third, Model γ was trained on 2D-CT images. Lastly, a multimodal model, namely PrismSAP, was constructed based on aforementioned features in the training set. The predictive accuracy of PrismSAP was verified in the validation and internal test sets and further validated in the external test set. Model performance was evaluated using area under the curve (AUC), accuracy, sensitivity, specificity, recall, precision and F1-score.

RESULTS

A total of 1,221 eligible patients were randomly split into a training set (n = 864), a validation set (n = 209) and an internal test set (n = 148). Data of 266 patients were for external testing. In the external test set, PrismSAP performed best with the highest AUC of 0.916 (0.873-0.960) among all models [Model α: 0.709 (0.618-0.800); Model β: 0.749 (0.675-0.824); Model γ: 0.687 (0.592-0.782); MCTSI: 0.778 (0.698-0.857); RANSON: 0.642 (0.559-0.725); BISAP: 0.751 (0.668-0.833); SABP: 0.710 (0.621-0.798)].

CONCLUSION

The proposed multimodal model outperformed any single-modality models and traditional scoring systems.

Clinical Significance of Serum CTRP3 Level in the Prediction of Cardiac and Intestinal Mucosal Barrier Dysfunction in Patients with Severe Acute Pancreatitis

C1q/tumor necrosis factor-related protein 3 (CTRP3) has been demonstrated to play a protective role in mice with severe acute pancreatitis (SAP). However, its clinical significance in SAP remains unknown. This study was conducted to explore the clinical values of serum C1q/tumor necrosis factor-related protein 3 (CTRP3) level in the diagnosis of cardiac dysfunction (CD) and intestinal mucosal barrier dysfunction (IMBD) in SAP. Through RT-qPCR, we observed decreased CTRP3 level in the serum of SAP patients. Serum CTRP3 level was correlated with C-reactive protein, procalcitonin, creatine, modified computed tomography severity index score, and Acute Physiology and Chronic Health Evaluation II score. The receiver-operating characteristic curve revealed that CTRP3 serum level < 1.005 was conducive to SAP diagnosis with 72.55% sensitivity and 60.00% specificity, CTRP3 < 0.8400 was conducive to CD diagnosis with 80.49% sensitivity and specificity 65.57%, CTRP3 < 0.8900 was conducive to IMBD diagnosis with 94.87% sensitivity and 63.49% specificity, and CTRP3 < 0.6250 was conducive to the diagnosis of CD and IMBD co-existence with 65.22% sensitivity and 89.87% specificity. Generally, CTRP3 was downregulated in the serum of SAP patients and served as a candidate biomarker for the diagnosis of SAP and SAP-induced CD and IMBD.

Elevated CK-MB levels are associated with adverse clinical outcomes in acute pancreatitis: a propensity score-matched study

Aim

Cardiac injury, reflected by the measured concentrations of chemicals released from injured cardiac muscle, is common in acute pancreatitis (AP). However, there is no adequate evidence assessing the impact of cardiac injury on AP-related outcomes. Creatine kinase-myocardial band (CK-MB) mainly exists in the myocardium. Therefore, we sought to evaluate the relationship between the increase in CK-MB and the adverse clinical outcomes of AP.

Methods

This propensity score-matched study analyzed AP patients admitted to the Department of Gastroenterology in the First Affiliated Hospital of Nanchang University from June 2017 to July 2022. Propensity score matching and multivariate logistic regression analysis were used to explore the relationship between CK-MB elevation and AP outcome variables.

Results

A total of 5,944 patients were screened for eligibility, of whom 4,802 were ultimately enrolled. Overall, 896 (18.66%) of AP patients had elevated (>24 U/ml) CK-MB levels, and 895 (99.89%) were paired with controls using propensity score matching. The propensity score-matched cohort analysis demonstrated that mortality (OR, 5.87; 95% CI, 3.89-8.84; P < 0.001), severe acute pancreatitis (SAP) (OR, 2.74; 95% CI, 2.23-3.35; P < 0.001), and infected necrotizing pancreatitis (INP) (OR, 3.40; 95% CI, 2.34-4.94; P < 0.001) were more frequent in the elevated CK-MB (>24 U/ml) group than in the normal CK-MB (≤ 24 U/ml) group. Using the multivariate logistic regression analysis, elevated CK-MB levels were independently associated with increased mortality (OR, 2.753, 95% CI, 2.095-3.617, P < 0.001), SAP incidence (OR, 2.223, CI, 1.870-2.643, P < 0.001), and INP incidence (OR, 1.913, 95% CI, 1.467-2.494, P < 0.001). CK-MB elevation was an independent risk factor for adverse clinical outcomes in AP patients.

Conclusion

CK-MB elevation was significantly related to adverse outcomes in AP patients, which makes it a potentially useful laboratory parameter for predicting adverse clinical outcomes of AP.

The Impact of Type 2 Myocardial Infarction in Acute Pancreatitis: Analysis of 1.1 Million Hospitalizations and Review of the Literature

Introduction  Acute pancreatitis (AP) is a common inflammatory disorder with acute onset and rapid progression. Studies have reported cardiac injury in patients with AP. It is often thought that stress cardiomyopathy can induce these changes leading to type 2 myocardial infarction (type 2 MI) in AP. Our study aims to assess the prevalence as well as the impact of type 2 MI on outcomes in patients with AP.  Methods National Inpatient Sample (NIS) 2016-2020 was used to identify adult patients (age>18) with acute pancreatitis. We excluded patients with STEMI, NSTEMI, pancreatic cancer, or chronic pancreatitis. Patients with missing demographics and mortality were also excluded. Patients were stratified into two groups, based on the presence of type 2 MI. Multivariate logistic regression analysis was performed to assess the impact of concomitant type 2 MI on mortality, sepsis, acute kidney injury (AKI), ICU admission, deep venous thrombosis (DVT), and pulmonary embolism (PE) after adjusting for patient demographics, hospital characteristics, etiology of AP and the Elixhauser comorbidities.  Results Of the 1.1 million patients in the study population, only 2315 patients had type 2 MI. The majority of the patients in the type 2 MI group were aged >65 years (49.2%, p<0.001), males (54.6%, p=0.63), White (67.6%, p=0.19), had Medicare insurance (55.5%, p<0.001), and were in the lowest income quartile (34.8%, p=0.12). Patients in the type 2 MI group had a higher incidence of mortality (5.4% vs 0.6%, p<0.001), sepsis (7.1% vs 3.7%, p<0.001), shock (9.3% vs 0.9%, p<0.001), AKI (42.9% vs. 11.8%, p<0.001) and ICU admission (12.1% vs 1.4%, p<0.001). After adjusting for confounding factors, patients in the type 2 MI group were noted to be at higher odds of mortality (aOR=2.4; 95% CI 1.5-3.8, p<0.001). Patients in the type 2 MI group had a longer length of stay (adjusted coefficient=2.1 days; 95% CI 1.4-2.8; p<0.001) and higher total hospitalization charges (adjusted coefficient=$45,088; 95% CI $30,224-$59,952; p<0.001).  Conclusion Although the prevalence of type 2 MI in AP is low, the presence of type 2 MI is associated with increased mortality and worse outcomes. Physicians should be aware of this association and these patients should be monitored carefully to prevent worse outcomes.

Could extracellular vesicles derived from mesenchymal stem cells be a potential therapy for acute pancreatitis-induced cardiac injury?

Acute pancreatitis (AP) often leads to a high incidence of cardiac injury, posing significant challenges in the treatment of severe AP and contributing to increased mortality rates. Mesenchymal stem cells (MSCs) release bioactive molecules that participate in various inflammatory diseases. Similarly, extracellular vesicles (EVs) secreted by MSCs have garnered extensive attention due to their comparable anti-inflammatory effects to MSCs and their potential to avoid risks associated with cell transplantation. Recently, the therapeutic potential of MSCs-EVs in various inflammatory diseases, including sepsis and AP, has gained increasing recognition. Although preclinical research on the utilization of MSCs-EVs in AP-induced cardiac injury is limited, several studies have demonstrated the positive effects of MSCs-EVs in regulating inflammation and immunity in sepsis-induced cardiac injury and cardiovascular diseases. Furthermore, clinical studies have been conducted on the therapeutic application of MSCs-EVs for some other diseases, wherein the contents of these EVs could be deliberately modified through prior modulation of MSCs. Consequently, we hypothesize that MSCs-EVs hold promise as a potential therapy for AP-induced cardiac injury. This paper aims to discuss this topic. However, additional research is essential to comprehensively elucidate the underlying mechanisms of MSCs-EVs in treating AP-induced cardiac injury, as well as to ascertain their safety and efficacy.

Analysis of the Application Value of Echocardiography Combined with CK-MB, Alb, and CysC in the Prognosis Assessment of Patients with Chronic HF

In order to evaluate the diagnostic and prognostic value of echocardiography combined with serum creatine kinase-MB (CK-MB), albumin (Alb), and cystatin C (CysC) in patients with chronic heart failure (HF), 93 patients diagnosed with chronic HF in our hospital from March 2019 to January 2020 are retrospectively analyzed and included in the HF group. Another 100 healthy subjects who come to our hospital for general physical examination are selected as the control group. Echocardiography is used to detect the cardiac parameters of each group. The experimental results show that echocardiography parameters combined with CK-MB, Alb, and CysC have high application value in diagnosis and evaluation of patients with chronic HF, which can provide theoretical basis for improving the prognosis of patients with chronic HF through real-time monitoring of the above indicators.

Automated Machine Learning for the Early Prediction of the Severity of Acute Pancreatitis in Hospitals

Background

Machine learning (ML) algorithms are widely applied in building models of medicine due to their powerful studying and generalizing ability. This study aims to explore different ML models for early identification of severe acute pancreatitis (SAP) among patients hospitalized for acute pancreatitis.

Methods

This retrospective study enrolled patients with acute pancreatitis (AP) from multiple centers. Data from the First Affiliated Hospital and Changshu No. 1 Hospital of Soochow University were adopted for training and internal validation, and data from the Second Affiliated Hospital of Soochow University were adopted for external validation from January 2017 to December 2021. The diagnosis of AP and SAP was based on the 2012 revised Atlanta classification of acute pancreatitis. Models were built using traditional logistic regression (LR) and automated machine learning (AutoML) analysis with five types of algorithms. The performance of models was evaluated by the receiver operating characteristic (ROC) curve, the calibration curve, and the decision curve analysis (DCA) based on LR and feature importance, SHapley Additive exPlanation (SHAP) Plot, and Local Interpretable Model Agnostic Explanation (LIME) based on AutoML.

Results

A total of 1,012 patients were included in this study to develop the AutoML models in the training/validation dataset. An independent dataset of 212 patients was used to test the models. The model developed by the gradient boost machine (GBM) outperformed other models with an area under the ROC curve (AUC) of 0.937 in the validation set and an AUC of 0.945 in the test set. Furthermore, the GBM model achieved the highest sensitivity value of 0.583 among these AutoML models. The model developed by eXtreme Gradient Boosting (XGBoost) achieved the highest specificity value of 0.980 and the highest accuracy of 0.958 in the test set.

Conclusions

The AutoML model based on the GBM algorithm for early prediction of SAP showed evident clinical practicability.

Elevation of creatine kinase in acute pancreatitis: A case report

Creatine kinase (CK/CK‐MB) testing is an essential laboratory test approaching a patient with chest or epigastric pain. We report a 38‐year‐old man with acute pancreatitis and elevated CK/CK‐MB level without myocardial involvement. Acute pancreatitis may be considered as a false‐positive cause of CK/CK‐MB test in patients presenting with chest pain.

Leukocyte cell population data from the blood cell analyzer as a predictive marker for severity of acute pancreatitis

BACKGROUND

The prediction for severe acute pancreatitis (SAP) is the key to give timely targeted treatment. Leukocyte cell population data (CPD) have been widely applied in early prediction and diagnosis of many diseases, but their predictive ability for SAP remains unexplored. We aim to testify whether CPD could be an indicator of AP severity in the early stage of the disease.

METHODS

The prospective observational study was conducted in the emergency department ward of a territory hospital in Shanghai. The enrolled AP patients should meet 2012 Atlanta guideline.

RESULTS

Totally, 103 AP patients and 62 healthy controls were enrolled and patients were classified into mild AP (n = 30), moderate SAP (n = 42), and SAP (n = 31). Forty-two CPD parameters were examined in first 3 days of admission. Four CPD parameters were highest in SAP on admission and were constantly different among 3 groups during first 3 days of hospital stay. Eighteen CPD parameters were found correlated with the occurrence of SAP. Stepwise multivariate logistic regression analysis identified a scoring system of 4 parameters (SD_LALS_NE, MN_LALS_LY, SD_LMALS_MO, and SD_AL2_MO) with a sensitivity of 96.8%, specificity of 65.3%, and AUC of 0.87 for diagnostic accuracy on early identification of SAP. AUC of this scoring system was comparable with MCTSI, SOFA, APACHE II, MMS, BISAP, or biomarkers as CRP, PCT, and WBC in prediction of SAP and ICU transfer or death.

CONCLUSIONS

Several leukocyte CPD parameters have been identified different among MAP, MSAP, and SAP. They might be ultimately incorporated into a predictive system marker for severity of AP.