Establishment Of Standardized Dataset For Acute Pancreatitis And Research On Metabolomics And Late Severe Complication Prediction Models

Acute pancreatitis(AP)is a common emergency in the digestive system,accounting for over 20% of severe acute pancreatitis cases with a high mortality rate.Currently,there is considerable heterogeneity in the diagnostic criteria and treatment modalities for AP across different regions and levels of hospitals in China.Particularly,the lack of interconnected hospital information systems hinders the formation of high-quality big data for clinical reference,such as etiology,proportion of severe cases,treatment measures and efficacy,prognosis,etc.,thereby impacting the clinical management of AP.Additionally,there is limited research on the metabolomics and proteomics of AP,and a lack of predictive factor analysis based on big data models for severe fatal complications of AP in the later stages,such as abdominal bleeding,intestinal fistula,pancreatic infection,etc.,leading to high occurrence rates of related complications,which are the main causes of death in the later stages of AP.Although most hospitals have adopted medical record management systems,they are insufficient to meet the deeper research requirements.Nowadays,relying on medical big data to stratify diseases enables a more refined and in-depth focus on major diseases with high incidence,mortality,significantly affecting public health.Clinical diagnosis and treatment data,along with omics data,exhibit inherently heterogeneous characteristics,and the lack of unified standards for the standardized collection,storage,and processing of specialized disease data in AP poses technical challenges.AP is a common acute abdominal emergency in the digestive system,characterized by a high incidence and mortality rate.The specific pathogenesis of AP,strategies for preventing the progression to severe acute pancreatitis,clinical management of severe cases,early detection and treatment of organ failure,and the management of late complications such as infected pancreatic necrosis(IPN),bleeding,and intestinal fistula remain formidable challenges in the diagnosis and treatment of AP.Therefore,there is an urgent need to establish an AP database and biobank,utilizing techniques such as data mining,deep learning,and integration of omics data to conduct multimodal data and knowledge fusion analysis,aiming to further enhance and improve the management of AP.This project is supported by the Development Center of Shanghai Shenkang Hospital(Project SHDC2020CR5013)and focuses on the construction of the Acute Pancreatitis(AP)Cohort Database and Biobank Platform.Drawing upon national electronic medical record standards(ICD 10.0/11.0)and clinical pathway standards,as well as industry information standards,and after multiple rounds of clinical expert consultation,we have established the "Standard Dataset for Acute Pancreatitis(2022)",comprising 9 modules,70 sub-modules,and 613 field names,which can cover 90% of clinical research needs.Building on this foundation,with assistance from the Shanghai Institute of Computing Technology,we have networked AP structured medical records at 9 hospitals specializing in acute pancreatitis care in Shanghai.This enables real-time collection and automatic auditing of AP diagnosis and treatment data and covers over 70% of AP cases treated in the region annually.Through real-world big data analysis from January 2021 to July 2023,involving 2836 AP patients,our findings revealed that gallstone etiology remains the leading cause of AP in Shanghai and surrounding areas.Hyperlipidemia ranks as the top cause among individuals aged 30-49,with a higher incidence rate of severe acute pancreatitis(SAP)compared to gallstone-induced SAP.The incidence rate of post-endoscopic retrograde cholangiopancreatography pancreatitis(PEP)was 0.9%,lower than previously reported rates,indicating the maturation and improvement of ERCP techniques and widespread adoption of preventive measures.The mortality rate of SAP in this study was 19.4%,lower than previously reported rates,suggesting a significant improvement in SAP management in recent years due to multidisciplinary cooperation in diagnosis and treatment.The aforementioned survey findings have provided clear insights into the clinical characteristics of AP patients in Shanghai.This serves as reliable background information and research entry points for future targeted randomized controlled trials(RCTs).Results from metabolomics and proteomics studies have identified three metabolites(L-methionine sulfoxide,Ala-Cys-Glu,3,7-dimethyluric acid)and two proteins(RPLP2,VMO1)as potential biomarkers for SAP.IPN and intra-abdominal hemorrhage are significant late-stage complications of AP and major contributors to AP-related mortality.A nomogram based on heart rate,platelet count,hematocrit(HCT),blood urea nitrogen(BUN),albumin/globulin ratio,and triglyceride levels demonstrated high accuracy in predicting IPN occurrence.Risk factor analysis revealed that intra-abdominal hypertension/abdominal compartment syndrome(IAH/ACS),acute kidney injury on admission,IPN,platelet count(>340.5x10^9/L),and lactate dehydrogenase(LDH)(>542.5U/L)are independent risk factors for late-stage intra-abdominal hemorrhage in AP patients.Based on the analysis of these risk factors,patients with intra-abdominal bleeding could be categorized into high-risk and low-risk groups.Patients in the high-risk group exhibited significantly higher APACHE II and MCTSI scores compared to those in the low-risk group.In summary,this study has established the first integrated AP database and biobank that combine clinical data and sample information of AP patients.Utilizing this database,we conducted an investigation into the etiology,severity,and current management status of AP in Shanghai.Through proteomic and metabolomic analyses,we explored the pathophysiological changes occurring during the onset of AP and identified potential biomarkers that aid in predicting the severity of AP.Additionally,we constructed risk prediction models and conducted risk factor analyses for severe fatal complications that occur during the course of AP.These efforts provide evidence-based support for the early recognition and timely management of late-stage complications in AP patients.Part Ⅰ: Establishment of Standardized Dataset for Acute PancreatitisObjectiveTo construct a multicenter database and biospecimen bank for acute pancreatitis with uniform collection specificationsMethodsFirstly,based on the national electronic medical record standards(ICD 10.0/11.0),clinical pathway guidelines,information technology industry standards,as well as the《China Acute Pancreatitis Diagnosis and Treatment Guidelines(2019,Shenyang)》and the《Multidisciplinary Diagnostic and Treatment(MDT)Consensus Opinions on Acute Pancreatitis in China(2015)》 we delineated recommendations for the classification,etiology,diagnosis,and treatment of acute pancreatitis(AP).We integrated these recommendations with the electronic medical record systems of various levels of hospitals in Shanghai and the actual clinical practices in AP diagnosis and treatment.Through multiple rounds of expert consultations,considering the unique characteristics of acute pancreatitis,we finalized a structured AP specialty dataset standard based on diagnosis and treatment norms.This ensured consistency in data format,value range,and expression standards among participating institutions.We established a network for structured AP medical records at nine hospitals in Shanghai,enabling real-time data collection and automatic auditing of AP diagnosis and treatment data.The integration of the AP database and biospecimen database will be facilitated by the in-hospital biospecimen database,utilizing unique patient identification codes.Finally,we relied on the big data platform of Shenkang Medical Union to construct an AP database covering over 70% of AP cases treated annually in the Shanghai region.Results:We have successfully established the first integrated AP database and biobank,consolidating clinical diagnostic and treatment information with biological sample data.This database comprises 9 modules,70 sub-modules,and 613 fields of data.It enables real-time collection of AP diagnostic and treatment data from multiple hospital systems such as Hospital Information Systems(HIS),Laboratory Information Systems(LIS),and Picture Archiving and Communication Systems(PACS).Additionally,it automates the calculation of AP-related scoring forms.Part Ⅱ: Real-World Study on the Diagnosis and Treatment Status of Acute Pancreatitis in Shanghai RegionObjectiveBased on the database constructed in the first part,we conducted a real-world study on the diagnosis and treatment status of acute pancreatitis in the Shanghai region.MethodsAfter establishing the AP database,a total of 7836 hospitalized AP patients’ data from January 1,2021,to July 31,2023,were collected in the Shanghai area.In this section of the study,research subjects were selected from the constructed database based on the completeness and accuracy of the data.Patients without imaging examination data,those lacking laboratory information within 48 hours of onset,and those missing clinical prognosis information were excluded.Finally,2836 AP patients were included for analysis.ResultsThe average age of onset of AP was(52.33±16.99)years old,and the incidence of male AP was significantly higher than that of female AP.Biliary etiology is still the first cause of AP in Shanghai,and biliary acute pancreatitis occurs more often in people over 50 years old,and hyperlipidemic acute pancreatitis occurs more often in people aged 30-49 years old,and it is the first etiology in the younger group.The incidence of PEP was 0.9%and the incidence of SAP was 4.5%.Acute peripancreatic fluid accumulation and systemic inflammatory response were the most common local and systemic complications of AP.Percutaneous catheter drainage(PCD)is the most common minimally invasive intervention for IPN.The overall mortality rate for AP is 1%,with a mortality rate of19.4% for SAP.Abdominal hemorrhage and infected pancreatic necrosis are the main causes of late-stage mortality in AP.Part Ⅲ: Research on the correlation between Metabolomics,Proteomics,and the Severity of Acute PancreatitisObjectiveAnalyzing the plasma metabolomics and proteomics of MAP and SAP patients aims to explore the pathophysiological processes occurring in the body during AP and to identify potential biomarkers that can predict the severity of AP.Methods:A comprehensive analysis of proteomics and metabolomics was conducted on MAP and SAP patients(total of 68 cases),screening for endogenous differential metabolites and proteins.We then performed bioinformatics analyses,including GO and KEGG pathway analyses,on the differential metabolites and proteins.Using machine learning methods,we identified a risk prediction model for predicting the occurrence of SAP.We compared this model with clinical blood indicators to further clarify its performance.The samples were divided into training and validation cohort,and the efficacy of the model was assessed using the area under the receiver operating characteristic curve(AUC).Results:Compared to MAP,SAP shows significant changes in metabolites such as 3-In doleacrylic acid,Ser-Val-Asn-Glu,Thr-Ser-Phe-Asp,Indole-3-carboxaldehyde,PC(8:0/8:0),Carnitine C5:1,N6-Succinyl Adenosine.KEGG pathway analysis primarily enr iches in the biosynthesis of valine,leucine,and isoleucine,phenylalanine metabolis m,and glycolysis/gluconeogenesis pathways.Differential protein KEGG pathways ar e enriched in protein processing in the endoplasmic reticulum,vitamin B6 metabolis m,and the IL-17 signaling pathway and neuroactive ligand-receptor interaction.The se findings suggest that the pathogenesis of SAP involves multiple proteins and imp ortant metabolic pathways.Additionally,three metabolites(L-methionine sulfoxide,A la-Cys-Glu,3,7-dimethyluric acid)and two proteins(RPLP2,VMO1)are identified as candidate biomarkers for SAP.These candidate biomarkers may participate in APprocesses,providing new insights into the occurrence and development of SAP.Part Ⅳ:Construction and validation of risk prediction models for late severe complications of acute pancreatitisObjective:Infected pancreatic necrosis(IPN)and abdominal hemorrhage are the severe complications in the late stage of MSAP and SAP with high mortality rate.Early identification and timely intervention can significantly improve the prognosis of patients with AP.This part of the study analyzes the risk factors of IPN and abdominal hemorrhage in MSAP and SAP patients and constructs risk prediction models.Methods:The subjects of this part were selected from the large AP database established in the previous stage,and the inclusion criteria were a diagnosis consistent with MASP or SAP and complete abdominal imaging during hospitalization;those who were in pregnancy,or who had undergone abdominal surgery,decompression of abdominal septal compartment syndrome,or who had IPN,or abdominal hemorrhage prior to hospitalization were excluded.To determine the risk factors for IPN and abdominal hemorrhage,one-way logistic regression was performed on the potential risk factors of MSAP and SAP patients,and then the statistically different factors among the one-way factors were included in the multivariate logistic regression analysis,after which the screened independent risk factors were presented in a nomogram,and the model efficacy test was performed on the constructed nomogram model.Results:The incidence of IPN in patients with MSAP and SAP was 19.2%,with a mortality rate of 15.8%.Patients with MSAP and SAP who developed IPN had significantly longer hospitalization days and higher mortality rates compared to those who did not develop IPN(P<0.001).Hematocrit(HCT),blood urea nitrogen(BUN),platelet count,serum triglyceride levels,albumin/globulin ratio,and heart rate were identified as independent risk factors for the development of IPN in MSAP and SAP patients.The incidence of intra-abdominal hemorrhage(IAH)in MSAP patients is 3%,while in SAP patients,it is24.7%.The overall mortality rate is 41.46%.Patients with MSAP and SAP who experienced intra-abdominal hemorrhage had significantly higher hospitalization days and mortality rates compared to those without intra-abdominal hemorrhage.Risk factors for intra-abdominal hemorrhage in MSAP and SAP patients included presence of IAH/ACS upon admission,acute renal failure,concurrent infected pancreatic necrosis,platelet count(>340.5x10^9/L),and lactate dehydrogenase(>542.5U/L).APACHE II score and MCTSI score of patients in the high risk group of intra-abdominal hemorrhage were significantly higher than those of patients in the low risk group of intra-abdominal hemorrhage(P<0.001),and the hospitalization days of patients in the high risk group of intra-abdominal hemorrhage were significantly longer than that of the low risk group of intra-abdominal hemorrhage(P<0.001).The predictive models constructed based on these independent risk factors for infected pancreatic necrosis and intra-abdominal hemorrhage demonstrate good clinical utility.