Introduction and Validation of a Novel Acute Pancreatitis Digital Tool: Interrogating Large Pooled Data From 2 Prospectively Ascertained Cohorts

Machine Learning-Guided Fluid Resuscitation for Acute Pancreatitis Improves Outcomes

INTRODUCTION

Ariel Dynamic Acute Pancreatitis Tracker (ADAPT) is an artificial intelligence tool using mathematical algorithms to predict severity and manage fluid resuscitation needs based on the physiologic parameters of individual patients. Our aim was to assess whether adherence to ADAPT fluid recommendations vs standard management impacted clinical outcomes in a large prospective cohort.

METHODS

We analyzed patients consecutively admitted to the Los Angeles General Medical Center between June 2015 and November 2022 whose course was richly characterized by capturing more than 100 clinical variables. We inputted these data into the ADAPT system to generate resuscitation fluid recommendations and compared with the actual fluid resuscitation within the first 24 hours from presentation. The primary outcome was the difference in organ failure in those who were over-resuscitated (>500 mL) vs adequately resuscitated (within 500 mL) with respect to the ADAPT fluid recommendation. Additional outcomes included intensive care unit admission, systemic inflammatory response syndrome (SIRS) at 48 hours, local complications, and pancreatitis severity.

RESULTS

Among the 1,083 patients evaluated using ADAPT, 700 were over-resuscitated, 196 were adequately resuscitated, and 187 were under-resuscitated. Adjusting for pancreatitis etiology, gender, and SIRS at admission, over-resuscitation was associated with increased respiratory failure (odd ratio [OR] 2.73, 95% confidence interval [CI] 1.06-7.03) as well as intensive care unit admission (OR 2.40, 1.41-4.11), more than 48 hours of hospital length of stay (OR 1.87, 95% CI 1.19-2.94), SIRS at 48 hours (OR 1.73, 95% CI 1.08-2.77), and local pancreatitis complications (OR 2.93, 95% CI 1.23-6.96).

DISCUSSION

Adherence to ADAPT fluid recommendations reduces respiratory failure and other adverse outcomes compared with conventional fluid resuscitation strategies for acute pancreatitis. This validation study demonstrates the potential role of dynamic machine learning tools in acute pancreatitis management.

Identification of Potential Hub Genes Related to Acute Pancreatitis and Chronic Pancreatitis via Integrated Bioinformatics Analysis and In Vitro Analysis

Acute pancreatitis (AP) and chronic pancreatitis (CP) are considered to be two separate pancreatic diseases in most studies, but some clinical retrospective analyses in recent years have found some degree of correlation between the two in actual treatment, however, the exact association is not clear. In this study, bioinformatics analysis was utilized to examine microarray sequencing data in mice, with the aim of elucidating the critical signaling pathways and genes involved in the progression from AP to CP. Differential gene expression analyses on murine transcriptomes were conducted using the R programming language and the R/Bioconductor package. Additionally, gene network analysis was performed using the STRING database to predict correlations among genes in the context of pancreatic diseases. Functional enrichment and gene ontology pathways common to both diseases were identified using Metascape. The hub genes were screened in the cytoscape algorithm, and the mRNA levels of the hub genes were verified in mice pancreatic tissues of AP and CP. Then the drugs corresponding to the hub genes were obtained in the drug-gene relationship. A set of hub genes, including Jun, Cd44, Epcam, Spp1, Anxa2, Hsp90aa1, and Cd9, were identified through analysis, demonstrating their pivotal roles in the progression from AP to CP. Notably, these genes were found to be enriched in the Helper T-cell factor (Th17) signaling pathway. Up-regulation of these genes in both AP and CP mouse models was validated through quantitative real-time polymerase chain reaction (qRT-PCR) results. The significance of the Th17 signaling pathway in the transition from AP to CP was underscored by our findings. Specifically, the essential genes driving this progression were identified as Jun, Cd44, Epcam, Spp1, Anxa2, Hsp90aa1, and Cd9. Crucial insights into the molecular mechanisms underlying pancreatitis progression were provided by this research, offering promising avenues for the development of targeted therapeutic interventions.

Predictive and Prognostic Potentials of Lymphocyte-C-Reactive Protein Ratio Upon Hospitalization in Adult Patients with Acute Pancreatitis

Purpose

In this study, our objective was to investigate the potential utility of lymphocyte-C-reactive protein ratio (LCR) as a predictor of disease progression and a screening tool for intensive care unit (ICU) admission in adult patients with acute pancreatitis (AP).

Methods

We included a total of 217 adult patients with AP who were admitted to the First Affiliated Hospital of Harbin Medical University between July 2019 and June 2022. These patients were categorized into three groups: mild AP (MAP), moderately severe AP (MSAP), and severe AP (SAP), based on the presence and duration of organ dysfunction. Various demographic and clinical data were collected and compared among different disease severity groups.

Results

Height, diabetes, lymphocyte count (LYMPH), lymphocyte percentage (LYM%), platelet count (PLT), D-Dimer, albumin (ALB), blood urea nitrogen (BUN), serum creatinine (SCr), glucose (GLU), calcium ion (Ca2+), C-reactive protein (CRP), procalcitonin (PCT), hospitalization duration, ICU admission, need for BP, LCR, sequential organ failure assessment (SOFA) score, bedside index for severity in AP (BISAP) score, and modified Marshall score showed significant differences across different disease severity groups upon hospitalization. Notably, there were significant differences in LCR between the MAP group and the MSAP and SAP combined group, and the MAP and MSAP combined group and the SAP group, and adult AP patients with ICU admission and those without ICU admission upon hospitalization.

Conclusion

In summary, LCR upon hospitalization can be utilized as a simple and reliable predictor of disease progression and a screening tool for ICU admission in adult patients with AP.

The Application of Knowledge-Based Clinical Decision Support Systems to Enhance Adherence to Evidence-Based Medicine in Chronic Disease

Among the technology-based solutions, clinical decision support systems (CDSSs) have the ability to keep up with clinicians with the latest evidence in a smart way. Hence, the main objective of our study was to investigate the applicability and characteristics of CDSSs regarding chronic disease. The Web of Science, Scopus, OVID, and PubMed databases were searched using keywords from January 2000 to February 2023. The review was completed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist. Then, an analysis was done to determine the characteristics and applicability of CDSSs. The quality of the appraisal was assessed using the Mixed Methods Appraisal Tool checklist (MMAT). A systematic database search yielded 206 citations. Eventually, 38 articles from sixteen countries met the inclusion criteria and were accepted for final analysis. The main approaches of all studies can be classified into adherence to evidence-based medicine (84.2%), early and accurate diagnosis (81.6%), identifying high-risk patients (50%), preventing medical errors (47.4%), providing up-to-date information to healthcare providers (36.8%), providing patient care remotely (21.1%), and standardizing care (71.1%). The most common features among the knowledge-based CDSSs included providing guidance and advice for physicians (92.11%), generating patient-specific recommendations (84.21%), integrating into electronic medical records (60.53%), and using alerts or reminders (60.53%). Among thirteen different methods to translate the knowledge of evidence into machine-interpretable knowledge, 34.21% of studies utilized the rule-based logic technique while 26.32% of studies used rule-based decision tree modeling. For CDSS development and translating knowledge, diverse methods and techniques were applied. Therefore, the development of a standard framework for the development of knowledge-based decision support systems should be considered by informaticians.

The harmless acute pancreatitis score (HAPS) identifies non-severe patients: A systematic review and meta-analysis

INTRODUCTION

We previously described a scoring system to identify patients with harmless acute pancreatitis as defined by absence of pancreatic necrosis, no need for artificial ventilation or dialysis, and non-fatal course. This scoring system, the Harmless Acute Pancreatitis Score (HAPS), can be quickly calculated from three parameters: absence of abdominal tenderness or rebound, normal hematocrit and normal creatinine level. We aim to assess the positive predictive value (PPV) of the HAPS by performing a meta-analysis of subsequently published studies.

METHODS

We performed a literature search using Pubmed, Web of Science^TM and Google Scholar. We used random effects models, with maximum likelihood estimates, to estimate the PPV of HAPS. We produced forest plots and used the I² statistic to quantify heterogeneity.

RESULTS

Twenty reports covering 6374 patients were identified. The overall PPV based on 16 studies that closely followed the original description of the HAPS system was 97% (95%CI 95-99%) with significant heterogeneity (I² = 76%; P < 0.01). For 11 studies in which HAPS was used to identify patients with mild AP, the overall PPV dropped to 83% (74-91%). For 8 studies in which HAPS was used to predict non-fatal course the overall PPV was 98% (97-100%).

CONCLUSION

The HAPS, if used as originally defined, accurately identifies patients with non-severe AP who will not require ICU care and facilitate selection of patients who can be discharged after a short stay on a general ward or can even be cared for at home. This could free hospital beds for other purposes and decrease healthcare costs.

Ranson score to stratify severity in Acute Pancreatitis remains valid - Old is gold

Introduction: Acute pancreatitis (AP) is a common gastrointestinal disease with a wide spectrum of severity and morbidity. Developed in 1974, the Ranson score was the first scoring system to prognosticate AP. Over the past decades, while the Ranson score remains widely used, it was identified to have certain limitations, such as having low predictive power. It has also been criticized for its 48-hour requirement for computation of the final score, which has been argued to potentially delay management. With advancements in our understanding of AP, is the Ranson score still relevant as an effective prognostication system for AP?Areas covered: This review summarizes the available evidence comparing Ranson score with other conventional and novel scoring systems, in terms of prognostic accuracy, benefits, limitations and clinical applicability. It also evaluates the effectiveness of Ranson score with regard to the Revised Atlanta Classification.Expert opinion: The Ranson score consistently exhibits comparable prognostic accuracy to other newer scoring systems, and the 48-hour timeframe for computing the full Ranson score is an inherent strength, not a weakness. These aspects, coupled with relative ease of use, practicality and universality of the score, advocate for the continued relevance of the Ranson score in modern clinical practice.

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.