The risk factors for acute respiratory distress syndrome in patients with severe acute pancreatitis: A retrospective analysis

Comparison of different intensive triglyceride-lowering therapies in patients with hyperlipidemic acute pancreatitis

OBJECTIVES

The goal of this study was to investigate the clinical value of emergent triglyceride (TG)-lowering therapies for hyperlipidemic acute pancreatitis (HLAP).

METHODS

126 HLAP patients were assigned randomly to receive either conventional treatment (CT), normal saline (NS) alone, or continuous veno-venous hemofiltration (CVVH) as an intensive TG-lowering therapy. TG levels, clinical outcomes, and inflammatory biomarkers were compared among the three groups.

RESULTS

Baseline characteristics did not differ significantly among the groups. CVVH removed TG from the plasma and achieved its target TG (<500 mg/dL) in approximately 25 h, compared to 40 h in the NS alone group and no targeted effect within 48 h in the CT group (P < 0.05). Although the majority of clinical outcomes did not differ significantly, an unexpectedly higher incidence of organ failure occurred in the CVVH group compared to the others. Hospital costs, severe AP patients and length of stay were significantly higher in the CVVH group compared to the other groups (P < 0.005).

CONCLUSIONS

Early CVVH lowers TG levels more efficiently than NS alone or CT therapy, but is not superior in terms of clinical outcomes and costs. NS also lowers TG levels and is significantly less costly than the other two treatments. Further multicenter studies are needed to determine the feasibility of NS alone treatment for HLAP patients.

Effectiveness of neuromuscular electrical stimulation in severe acute pancreatitis complicated patients with acute respiratory distress syndrome: study protocol for a randomized controlled trial

BACKGROUND

Severe acute pancreatitis complicated by acute respiratory distress is a common cause of intensive care unit (ICU) admission. These patients are at risk of a decline in physical activity due to bed rest. Neuromuscular electrical stimulation (NMES) has been recommended for ICU patients to strengthen muscles, but its effects on muscle atrophy, respiratory function, multiple organ dysfunction, and functional status of these patients remain to be proven.

METHODS

Patients (n = 80) will be prospectively randomized into an NMES group and a control group. The NMES group will receive NMES for 1 h per day for 7 days, and both the control and NMES groups will receive usual care. The efficacy will be assessed by an experienced physiotherapist and sonographer who will be blinded to the patient's group assignment. Muscle power assessment (MRC scale), lower extremity circumference, grip strength, activities of daily living (Barthel index), and Marshall scores will be measured at baseline and posttreatment. The functions of the diaphragm assessments will be measured daily. Barthel index measurements will be followed up in the 1st month, 3rd month, and 6th month after discharge.

DISCUSSION

The trial will explore the effectiveness of NMES in functional status and diaphragm function in patients with SAP complicated with ARDS. The results of this trial will provide strong evidence of the efficacy of NMES in treating SAP patients with ARDS.

TRIAL REGISTRATION

This trial has been registered at the Chinese Clinical Trial Registry, and the registry name is "Effectiveness of neuromuscular electrical stimulation in severe acute pancreatitis complicated patients with acute respiratory distress syndrome: study protocol for a randomized controlled trial," URL: https://www.chictr.org.cn , numbered ChiCTR2300068995. Date of Registration: 2023-03-03.

The role of artificial neural networks in prediction of severe acute pancreatitis associated acute respiratory distress syndrome: A retrospective study

Early identification and intervention of acute respiratory distress syndrome (ARDS) are particularly important. This study aimed to construct predictive models for ARDS following severe acute pancreatitis (SAP) by artificial neural networks and logistic regression. The artificial neural networks model was constructed using clinical data from 214 SAP patients. The patient cohort was randomly divided into a training set and a test set, with 149 patients allocated to the training set and 65 patients assigned to the test set. The artificial neural networks and logistic regression models were trained by the training set, and then the performance of both models was evaluated using the test set. The sensitivity, specificity, PPV, NPV, accuracy, and AUC value of artificial neural networks model were 68.0%, 87.5%, 77.3%, 81.4%, 80.0%, 0.853 ± 0.054 (95% CI: 0.749-0.958). The sensitivity, specificity, PPV, NPV, accuracy and AUC value of logistic regression model were 48.7%, 85.3%, 65.5%, 74.4%, 72.0%, 0.799 ± 0.045 (95% CI: 0.710-0.888). There were no significant differences between the artificial neural networks and logistic regression models in predictive performance. Bedside Index of Severity in Acute Pancreatitis score, procalcitonin, prothrombin time, and serum calcium were the most important predictive variables in the artificial neural networks model. The discrimination abilities of logistic regression and artificial neural networks models in predicting SAP-related ARDS were similar. It is advisable to choose the model according to the specific research purpose.

Early prediction of acute respiratory distress syndrome complicated by acute pancreatitis based on four machine learning models

BACKGROUND

Acute Respiratory Distress syndrome (ARDS) is a common complication of Acute Pancreatitis (AP) and is associated with high mortality. This study used Machine Learning (ML) to predict ARDS in patients with AP at admission.

METHODS

The authors retrospectively analyzed the data from patients with AP from January 2017 to August 2022. Clinical and laboratory parameters with significant differences between patients with and without ARDS were screened by univariate analysis. Then, Support Vector Machine (SVM), Ensembles of Decision Trees (EDTs), Bayesian Classifier (BC), and nomogram models were constructed and optimized after feature screening based on these parameters. Five-fold cross-validation was used to train each model. A test set was used to evaluate the predictive performance of the four models.

RESULTS

A total of 83 (18.04%) of 460 patients with AP developed ARDS. Thirty-one features with significant differences between the groups with and without ARDS in the training set were used for modeling. The Partial Pressure of Oxygen (PaO₂), C-reactive protein, procalcitonin, lactic acid, Ca²⁺, the neutrophil:lymphocyte ratio, white blood cell count, and amylase were identified as the optimal subset of features. The BC algorithm had the best predictive performance with the highest AUC value (0.891) than SVM (0.870), EDTs (0.813), and the nomogram (0.874) in the test set. The EDT algorithm achieved the highest accuracy (0.891), precision (0.800), and F1 score (0.615), but the lowest FDR (0.200) and the second-highest NPV (0.902).

CONCLUSIONS

A predictive model of ARDS complicated by AP was successfully developed based on ML. Predictive performance was evaluated by a test set, for which BC showed superior predictive performance and EDTs could be a more promising prediction tool for larger samples.

To Establish an Early Prediction Model for Acute Respiratory Distress Syndrome in Severe Acute Pancreatitis Using Machine Learning Algorithm

OBJECTIVE

To develop binary and quaternary classification prediction models in patients with severe acute pancreatitis (SAP) using machine learning methods, so that doctors can evaluate the risk of patients with acute respiratory distress syndrome (ARDS) and severe ARDS at an early stage.

METHODS

A retrospective study was conducted on SAP patients hospitalized in our hospital from August 2017 to August 2022. Logical Regression (LR), Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), and eXtreme Gradient Boosting (XGB) were used to build the binary classification prediction model of ARDS. Shapley Additive explanations (SHAP) values were used to interpret the machine learning model, and the model was optimized according to the interpretability results of SHAP values. Combined with the optimized characteristic variables, four-class classification models, including RF, SVM, DT, XGB, and Artificial Neural Network (ANN), were constructed to predict mild, moderate, and severe ARDS, and the prediction effects of each model were compared.

RESULTS

The XGB model showed the best effect (AUC = 0.84) in the prediction of binary classification (ARDS or non-ARDS). According to SHAP values, the prediction model of ARDS severity was constructed with four characteristic variables (PaO2/FiO2, APACHE II, SOFA, AMY). Among them, the overall prediction accuracy of ANN is 86%, which is the best.

CONCLUSIONS

Machine learning has a good effect in predicting the occurrence and severity of ARDS in SAP patients. It can also provide a valuable tool for doctors to make clinical decisions.

Medical imaging for pancreatic diseases: Prediction of severe acute pancreatitis complicated with acute respiratory distress syndrome

In this editorial we comment on the article published in the recent issue of the World Journal of Gastroenterology [2022; 28 (19): 2123-2136]. We pay attention to how to construct a simpler and more reliable new clinical predictive model to early identify patients at high risk of acute respiratory distress syndrome (ARDS) associated with severe acute pancreatitis (SAP), and to early predict the severity of organ failure from chest computed tomography (CT) findings in SAP patients. As we all know, SAP has a sudden onset, is a rapidly changing condition, and can be complicated with ARDS and even multiple organ dysfunction syndrome, and its mortality rate has remained high. At present, there are many clinical scoring systems for AP, including the bedside index for severity in AP, acute physiology and chronic health evaluation II, systemic inflammatory response syndrome, Japanese severe score, quick sepsis-related organ failure assessment, etc. However, some of these scoring systems are complex and require multiple and difficult clinical parameters for risk stratification. Although the aforementioned biomarkers are readily available, their ability to predict ARDS varies. Accor-dingly, it is extremely necessary to establish a simple and valuable novel model to predict the development of ARDS in AP. In addition, the extra-pancreatic manifestations of AP patients often involve the chest, among which pleural effusion and pulmonary consolidation are the more common complications. Therefore, by measuring the semi-quantitative indexes of chest CT in AP patients, such as the amount of pleural effusion and the number of lobes involved as pulmonary consolidation, it has important reference value for the early diagnosis of SAP complicated with ARDS and is expected to provide a basis for the early treatment of ARDS.

Diagnosis, severity stratification and management of adult acute pancreatitis-current evidence and controversies

Acute pancreatitis (AP) is a disease spectrum ranging from mild to severe with an unpredictable natural course. Majority of cases (80%) are mild and self-limiting. However, severe AP (SAP) has a mortality risk of up to 30%. Establishing aetiology and risk stratification are essential pillars of clinical care. Idiopathic AP is a diagnosis of exclusion which should only be used after extended investigations fail to identify a cause. Tenets of management of mild AP include pain control and management of aetiology to prevent recurrence. In SAP, patients should be resuscitated with goal-directed fluid therapy using crystalloids and admitted to critical care unit. Routine prophylactic antibiotics have limited clinical benefit and should not be given in SAP. Patients able to tolerate oral intake should be given early enteral nutrition rather than nil by mouth or parenteral nutrition. If unable to tolerate per-orally, nasogastric feeding may be attempted and routine post-pyloric feeding has limited evidence of clinical benefit. Endoscopic retrograde cholangiopancreatogram should be selectively performed in patients with biliary obstruction or suspicion of acute cholangitis. Delayed step-up strategy including percutaneous retroperitoneal drainage, endoscopic debridement, or minimal-access necrosectomy are sufficient in most SAP patients. Patients should be monitored for diabetes mellitus and pseudocyst.

The Clinical Characteristics and Outcome of Elderly Patients With Acute Pancreatitis

OBJECTIVES

This study aimed to identify the risk factors for the progression of acute pancreatitis (AP) to severe acute pancreatitis (SAP) and death in elderly patients.

METHODS

This was a single-center retrospective study conducted in a tertiary teaching hospital. Data on patient demographics, comorbidities, duration of hospitalization, complications, interventions, and mortality rates were collected.

RESULTS

Between January 2010 and January 2021, 2084 elderly patients with AP were included in this study. The mean age of the patients was 70.0 years (standard deviation, 7.1 years). Among them, 324 (15.5%) had SAP and 105 died (5.0%). The 90-day mortality rate in the SAP group was significantly higher than that in the AP group (P < 0.0001). Multivariate regression analysis revealed that trauma, hypertension, and smoking were risk factors for SAP. After multivariate adjustment, acute respiratory distress syndrome, acute kidney injury, sepsis, organ perforation, and abdominal hemorrhage were associated with higher 90-day mortality.

CONCLUSIONS

Traumatic pancreatitis, hypertension, and smoking are independent risk factors for SAP in elderly patients. Acute respiratory distress syndrome, acute kidney injury, sepsis, organ perforation, and abdominal hemorrhage are independent risk factors for death in elderly patients with AP.

Follistatin-like 1 ameliorates severe acute pancreatitis associated lung injury via inhibiting the activation of NLRP3 inflammasome and NF-κB pathway

OBJECTIVE

Severe acute pancreatitis (SAP) is one of the most common abdominal conditions of digestive system that usually causes acute lung injury through systemic inflammation. Follistatin-like 1 (FSTL-1) has been reported to have anti-inflammatory and anti-apoptotic effects in a variety of diseases. The aim of this study was to investigate the effects of FSTL-1 on SAP-associated lung injury (SAPALI) and the underlying mechanism.

METHODS

SAP model was induced by intraperitoneal injection of the L-arginine in C57BL/6 mice. The haematoxylin and eosin (H&E) staining was applied to determine the severity of lung and pancreatic injury. ELISA kits were used to determine serum amylase and inflammatory cytokines levels. TUNEL staining was carried out to measure cell apoptosis. Western blotting was applied to analyze the related proteins of NLRP3 inflammasome and NF-κB pathways.

RESULTS

FSTL-1 was significantly increased in the lung of SAP mice. Knockout of FSTL-1 ameliorated pancreatic injury, lung injury, inflammation and apoptosis in mice with SAP. Moreover, the protein levels of NLRP3, ASC, Caspase-1, p-p65 and p-IκBα were obviously reduced in the FSTL-1 KO+SAP group in comparison with SAP group, suggesting that inhibition of FSTL-1 repressed the activation of the NLRP3 inflammasome and NF-κB pathway.

CONCLUSION

This study helps us understand the mechanism of FSTL-1 in SAPALI and might provide a potential new strategy for the treatment of SAPALI.

Impact of early thoracic epidural analgesia in patients with severe acute pancreatitis

OBJECTIVE

This study was designed to assess the impact of thoracic epidural analgesia (TEA) in patients with severe acute pancreatitis (SAP).

METHODS

This is a single-centre retrospective study. In this study, the outcomes of SAP patients were compared between patients received TEA (TEA group) and without TEA (NTEA group). Early TEA was defined as TEA performed within 48 hours after onset. The main outcome was the mortality at 30 days after ICU admission, and secondary outcomes included the incidence of acute respiratory distress syndrome (ARDS), the acute renal injury (AKI) and sepsis, the hospital stay and hospitalization expenses.

RESULTS

The mortality of SAP patients in TEA versus NTEA was 8.0% and 13.3% (p = .1520). Multivariate regression analysis showed significant difference in mortality between the TEA and NTEA groups (OR, 0.387; 95% CI, 0.168-0.892; p = .026). The incidence of ARDS in TEA versus NTEA was 46.0% and 62.4% (p = .0044); the proportion of patients requiring invasive ventilator assisted ventilation in TEA, and NTEA was 22.6% and 39.2% (p = .0016). The incidence of AKI in TEA versus NTEA was 27.7% and 45.3% (p = .0044); the proportion of patients needing for continuous renal replacement therapy (CRRT) in TEA and NTEA was 48.2% and 74.0% (p < .0001). The mortality of SAP patients in early TEA versus NTEA was 4.8% and 15.3% (p = .0263).

CONCLUSIONS

TEA was associated with low incidence of ARDS and AKI in patients with SAP. Early TEA may benefit mortality in SAP patients and is a possible protective factor for the mortality of SAP patients.

Development and validation of a prediction model for moderately severe and severe acute pancreatitis in pregnancy

BACKGROUND

The severity of acute pancreatitis in pregnancy (APIP) is correlated with higher risks of maternal and fetal death.

AIM

To develop a nomogram that could predict moderately severe and severe acute pancreatitis in pregnancy (MSIP).

METHODS

Patients with APIP admitted to West China Hospital between January 2012 and December 2018 were included in this study. They were divided into mild acute pancreatitis in pregnancy (MAIP) and MSIP. Characteristic parameters and laboratory results were collected. The training set and test set were randomly divided at a ratio of 7:3. Least absolute shrinkage and selection operator regression was used to select potential prognostic factors. A nomogram was developed by logistic regression. A random forest model was used to validate the stability of the prediction factors. Receiver operating characteristic curves and calibration curves were used to evaluate the model’s predictive performance.

RESULTS

A total of 190 patients were included in this study. A total of 134 patients (70.5%) and 56 patients (29.5%) were classified as having MAIP and MSIP, respectively. Four independent predictors (lactate dehydrogenase, triglyceride, cholesterol, and albumin levels) were identified for MSIP. A nomogram prediction model based on these factors was established. The model had areas under the curve of 0.865 and 0.853 in the training and validation sets, respectively. The calibration curves showed that the nomogram has a good consistency.

CONCLUSION

A nomogram including lactate dehydrogenase, triglyceride, cholesterol, and albumin levels as independent predictors was built with good performance for MSIP prediction.

A prediction model for acute respiratory distress syndrome among patients with severe acute pancreatitis: a retrospective analysis

Background:

Acute respiratory distress syndrome (ARDS) is a severe complication among patients with severe acute pancreatitis (SAP), which may be associated with increased mortality in hospitalized patients. Thus, an effective model to predict ARDS in patients with SAP is urgently required.

Methods:

We retrospectively analyzed the data from the patients with SAP who recruited in Xiangya Hospital between April 2017 and May 2021. Patients meeting the Berlin definition of ARDS were categorized into the ARDS group. Logistic regression models and a nomogram were utilized in the study. Descriptive statistics, logistic regression models, and a nomogram were used in the current study.

Results:

Comorbidity of ARDS occurred in 109 (46.58%) of 234 patients with SAP. The SAP patients with ARDS group had a higher 60-day mortality rate, an increased demand for invasive mechanical ventilation, and a longer intensive care unit (ICU) stay than those without ARDS (p < .001 for all). Partial pressure of oxygen (PaO2): fraction of inspired oxygen (FiO2) < 200, platelets <125 × 109/L, lactate dehydrogenase >250 U/L, creatinine >111 mg/dL, and procalcitonin >0.5 ng/mL were independent risk variables for development of ARDS in SAP patients. The area under the curve for the model was 0.814, and the model fit was acceptable [p = .355 (Hosmer–Lemeshow)]. Incorporating these 5 factors, a nomogram was established with sufficient discriminatory power (C-index 0.814). Calibration curve indicated the proper discrimination and good calibration in the predicting nomogram model.

Conclusion:

The prediction nomogram for ARDS in patients with SAP can be applied using clinical common variables after the diagnosis of SAP. Future studies would be warranted to verify the potential clinical benefits of this model.

Assessment of Prophylactic Carbapenem Antibiotics Administration for Severe Acute Pancreatitis: An Updated Systematic Review and Meta-Analysis

BACKGROUND

The effectiveness of prophylactic antibiotics in severe acute pancreatitis (SAP) remains a debatable issue. This meta-analysis aimed to determine the efficacy of prophylactic carbapenem antibiotics in SAP.

METHODS

This meta-analysis of prophylactic carbapenem antibiotics for SAP was conducted in PubMed, EMBASE, Web of Science, MEDLINE, and Cochrane Library up to February 2021. The related bibliographies were manually searched. The primary outcomes involved infected pancreatic or peripancreatic necrosis, mortality, complications, infections, and organ failure.

RESULTS

Seven articles comprised 5 randomized controlled trials and 2 retrospective observational studies, including 3,864 SAP participants. Prophylactic carbapenem antibiotics in SAP were associated with a statistically significant reduction in the incidence of infections (odds ratio [OR]: 0.27; p = 0.03) and complications (OR: 0.48; p = 0.009). Nevertheless, no statistically significant difference was demonstrated in the incidence of infected pancreatic or peripancreatic necrosis (OR: 0.74; p = 0.24), mortality (OR: 0.69; p = 0.17), extrapancreatic infection (OR: 0.64, p = 0.54), pulmonary infection (OR: 1.23; p = 0.69), blood infection (OR: 0.60; p = 0.35), urinary tract infection (OR: 0.97; p = 0.97), pancreatic pseudocyst (OR: 0.59; p = 0.28), fluid collection (OR: 0.91; p = 0.76), organ failure (OR: 0.63; p = 0.19), acute respiratory distress syndrome (OR: 0.80; p = 0.61), surgical intervention (OR: 0.97; p = 0.93), dialysis (OR: 2.34; p = 0.57), use of respirator or ventilator (OR: 1.90; p = 0.40), intensive care unit treatment (OR: 2.97; p = 0.18), and additional antibiotics (OR: 0.59; p = 0.28) between the experimental and control groups.

CONCLUSIONS

It is not recommended to administer routine prophylactic carbapenem antibiotics in SAP.

Nomogram for the prediction of in-hospital incidence of acute respiratory distress syndrome in patients with acute pancreatitis

OBJECTIVE

Acute respiratory distress syndrome (ARDS) associated with high mortality is the common complication in acute pancreatitis (AP). The aim of this study was to formulate and validate an individualized predictive nomogram for in-hospital incidence of ARDS in Patients with AP.

METHOD

From January 2017 to December 2018, 779 individuals with AP were involved in this study. They were randomly distributed into primary cohort (n=560) and validation cohort (n=219). Based on the primary cohort, risk factors were identified by logistic regression model and a nomogram was performed. The nomogram was validated in the primary and validation cohort by the bootstrap validation method. The calibration curve was applied to evaluate the consistency between the nomogram and the ideal observation.

RESULTS

There were 728 patients in the non-ARDS group and 51 in the ARDS group, with an incidence of about 6.55%. Five independent factors including white blood cell counts (WBC), prothrombin time (PT), albumin (ALB), serum creatinine (SCR) and triglyceride (TG) were associated with in-hospital incidence of ARDS in Patients with AP. A nomogram was constructed based on the five independent factors with primary cohort of AUC=0.821 and validation cohort of AUC=0.823. Calibration curve analysis indicated that the predicted probability was in accordance with the observed probability in both primary and validation cohorts.

CONCLUSIONS

The study developed an intuitive nomogram with easily available laboratory parameters for the prediction of in-hospital incidence of ARDS in patients with AP. The incidence of ARDS for an individual patient can be fast and conveniently evaluated by our nomogram.

The impact of fluid resuscitation via colon on patients with severe acute pancreatitis

Severe acute pancreatitis (SAP) is a life-threatening disease. Fluid Resuscitation Via Colon (FRVC) may be a complementary therapy for early controlled fluid resuscitation. But its clinical application has not been reported. This study aims to explore the impact of FRVC on SAP. All SAP patients with the first onset within 72 h admitted to the hospital were included from January 2014 to December 2018 through electronic databases of Ruijin hospital and were divided into FRVC group (n = 103) and non-FRVC group (n = 78). The clinical differences before and after the therapy between the two groups were analyzed. Of the 181 patients included in the analysis, the FRVC group received more fluid volume and reached the endpoint of blood volume expansion ahead of the non-FRVC group. After the early fluid resuscitation, the inflammation indicators in the FRVC group were lower. The rate of mechanical ventilation and the incidence of hypernatremia also decreased significantly. Using pure water for FRVC was more helpful to reduce hypernatremia. However, Kaplan-Meier 90-day survival between the two groups showed no difference. These results suggest that the combination of FRVC might benefit SAP patients in the early stage of fluid resuscitation, but there is no difference between the prognosis of SAP patients and that of conventional fluid resuscitation. Further prospective study is needed to evaluate the effect of FRVC on SAP patients.