Decoding the Clavien-Dindo Classification: Artificial Intelligence (AI) as a Novel Tool to Grade Postoperative Complications.

OBJECTIVE: To assess ChatGPT's capability of grading postoperative complications using the Clavien-Dindo classification (CDC) via Artificial Intelligence (AI) with Natural Language Processing (NLP). BACKGROUND: The CDC standardizes grading of postoperative complications. However, consistent, and precise application in dynamic clinical settings is challenging. AI offers a potential solution for efficient automated grading. METHODS: ChatGPT's accuracy in defining the CDC, generating clinical examples, grading complications from existing scenarios, and interpreting complications from fictional clinical summaries, was tested. RESULTS: ChatGPT 4 precisely mirrored the CDC, outperforming version 3.5. In generating clinical examples, ChatGPT 4 showcased 99% agreement with minor errors in urinary catheterization. For single complications, it achieved 97% accuracy. ChatGPT was able to accurately extract, grade, and analyze complications from free text fictional discharge summaries. It demonstrated near perfect performance when confronted with real-world discharge summaries: comparison between the human and ChatGPT4 grading showed a κ value of 0.92 (95% CI 0.82-1) (P<0.001). CONCLUSIONS: ChatGPT 4 demonstrates promising proficiency and accuracy in applying the CDC. In the future, AI has the potential to become the mainstay tool to accurately capture, extract, and analyze CDC data from clinical datasets.

Assessment of resectability of pancreatic cancer using novel immersive high-performance virtual reality rendering of abdominal computed tomography and magnetic resonance imaging.

PURPOSE: Virtual reality (VR) allows for an immersive and interactive analysis of imaging data such as computed tomography (CT) and magnetic resonance imaging (MRI). The aim of this study is to assess the comprehensibility of VR anatomy and its value in assessing resectability of pancreatic ductal adenocarcinoma (PDAC). METHODS: This study assesses exposure to VR anatomy and evaluates the potential role of VR in assessing resectability of PDAC. Firstly, volumetric abdominal CT and MRI data were displayed in an immersive VR environment. Volunteering physicians were asked to identify anatomical landmarks in VR. In the second stage, experienced clinicians were asked to identify vascular involvement in a total of 12 CT and MRI scans displaying PDAC (2 resectable, 2 borderline resectable, and 2 locally advanced tumours per modality). Results were compared to 2D standard PACS viewing. RESULTS: In VR visualisation of CT and MRI, the abdominal anatomical landmarks were recognised by all participants except the pancreas (30/34) in VR CT and the splenic (31/34) and common hepatic artery (18/34) in VR MRI, respectively. In VR CT, resectable, borderline resectable, and locally advanced PDAC were correctly identified in 22/24, 20/24 and 19/24 scans, respectively. Whereas, in VR MRI, resectable, borderline resectable, and locally advanced PDAC were correctly identified in 19/24, 19/24 and 21/24 scans, respectively. Interobserver agreement as measured by Fleiss κ was 0.7 for CT and 0.4 for MRI, respectively (p < 0.001). Scans were significantly assessed more accurately in VR CT than standard 2D PACS CT, with a median of 5.5 (IQR 4.75-6) and a median of 3 (IQR 2-3) correctly assessed out of 6 scans (p < 0.001). CONCLUSION: VR enhanced visualisation of abdominal CT and MRI scan data provides intuitive handling and understanding of anatomy and might allow for more accurate staging of PDAC and could thus become a valuable adjunct in PDAC resectability assessment in the future.

Reliability of Medical Information Provided by ChatGPT: Assessment Against Clinical Guidelines and Patient Information Quality Instrument.

BACKGROUND: ChatGPT-4 is the latest release of a novel artificial intelligence (AI) chatbot able to answer freely formulated and complex questions. In the near future, ChatGPT could become the new standard for health care professionals and patients to access medical information. However, little is known about the quality of medical information provided by the AI. OBJECTIVE: We aimed to assess the reliability of medical information provided by ChatGPT. METHODS: Medical information provided by ChatGPT-4 on the 5 hepato-pancreatico-biliary (HPB) conditions with the highest global disease burden was measured with the Ensuring Quality Information for Patients (EQIP) tool. The EQIP tool is used to measure the quality of internet-available information and consists of 36 items that are divided into 3 subsections. In addition, 5 guideline recommendations per analyzed condition were rephrased as questions and input to ChatGPT, and agreement between the guidelines and the AI answer was measured by 2 authors independently. All queries were repeated 3 times to measure the internal consistency of ChatGPT. RESULTS: Five conditions were identified (gallstone disease, pancreatitis, liver cirrhosis, pancreatic cancer, and hepatocellular carcinoma). The median EQIP score across all conditions was 16 (IQR 14.5-18) for the total of 36 items. Divided by subsection, median scores for content, identification, and structure data were 10 (IQR 9.5-12.5), 1 (IQR 1-1), and 4 (IQR 4-5), respectively. Agreement between guideline recommendations and answers provided by ChatGPT was 60% (15/25). Interrater agreement as measured by the Fleiss κ was 0.78 (P<.001), indicating substantial agreement. Internal consistency of the answers provided by ChatGPT was 100%. CONCLUSIONS: ChatGPT provides medical information of comparable quality to available static internet information. Although currently of limited quality, large language models could become the future standard for patients and health care professionals to gather medical information.

Control of angiogenesis and host response by modulating the cell adhesion properties of an Elastin-Like Recombinamer-based hydrogel.

The control of the in vivo vascularization of engineered tissue substitutes is essential in order to obtain either a rapid induction or a complete inhibition of the process (e.g. in muscles and hyaline-cartilage, respectively). Among the several polymers available, Elastin-Like Recombinamers (ELR)-based hydrogel stands out as a promising material for tissue engineering thanks to its viscoelastic properties, non-toxicity, and non-immunogenicity. In this study, we hypothesized that varying the cell adhesion properties of ELR-hydrogels could modulate the high angiogenic potential of adipose tissue-derived stromal vascular fraction (SVF) cells, predominantly composed of endothelial/mural and mesenchymal cells. Human SVF cells, embedded in RGD-REDV-bioactivated or unmodified ELR-hydrogels, were implanted in rat subcutaneous pockets either immediately or upon 5-day-culture in perfusion-bioreactors. Perfusion-based culture enhanced the endothelial cell cord-like-organization and the release of pro-angiogenic factors in functionalized constructs. While in vivo vascularization and host cell infiltration within the bioactivated gels were highly enhanced, the two processes were strongly inhibited in non-functionalized SVF-based hydrogels up to 28 days. ELR-based hydrogels showed a great potential to determine the successful integration of engineered substitutes thanks to their capacity to finely control the angiogenic/inflammation process at the recipient site, even in presence of SVF cells.

Assessing the severity of acute pancreatitis (ASAP) in Switzerland: A nationwide survey on severity assessment in daily clinical practice.

BACKGROUND/OBJECTIVES: We aim to assess which tools for severity stratification in acute pancreatitis are used in today's daily clinical practice and to what extent the new Atlanta classification is being implemented by the medical community in Switzerland. METHODS: The heads of surgical, medical and emergency departments of Swiss hospitals (n = 83) that directly treat patients with acute pancreatitis were given access to an online survey and asked to forward the questionnaire to their team. The questionnaire consisted of 16 items, including questions about the specialty background of the participants, the allocation of patients with AP, severity assessment, patient management, the role of imaging procedures, and future perspectives. RESULTS: A total of 233 participants from 63 hospitals responded (response rate, 74%). A vast majority of participants [198 (87%)] does assess severity. The most frequently used tools are the Ranson [108 (87%)] and APACHE II scores [28 (23%)]. A majority of the participants were not satisfied with the currently available tools to assess severity [130 (59%)]. A minority [15 (12%)] use the revised Atlanta classification to assess the degree of severity in AP. CONCLUSIONS: The Ranson score remains the dominant risk stratification tool in clinical practice in Switzerland, followed by the APACHE II score. Other modern instruments, such as the Atlanta 2012 classification, have not yet earned broad recognition and have not reached daily practice. Further efforts must be made to expand physicians' awareness of their existence and significance.

Laboratory markers predicting severity of acute pancreatitis.

Acute pancreatitis (AP) is an inflammatory disease of highly variable severity, ranging from mild cases with low mortality to severe cases with high mortality. Numerous biomarkers have been studied as potential early predictors of the severity of this disease so that treatment can be optimally tailored to prevent complications. We aim to present and discuss the most relevant biomarkers for early severity assessment in AP that have been studied to date. We review the current literature on biomarkers that have been used to predict the severity in AP. C-reactive protein (CRP) is still considered to be the gold standard, with a cut-off value of 150 mg/ml 48 h after disease onset. Other markers, including procalcitonin (PCT) and interleukin 6 (IL-6) have been implemented in some hospitals, but are not used on a routine basis. Most other markers, including acute phase proteins (LBP, SAA, PTX3), cytokines (Il-8, TNF-a, MIF), activation peptides of pancreatic proteases (TAP, CAPAP, PLAP), antiproteases (AAT, a2M), adhesion molecules (ICAM-1, selectins, E-cadherin) and leukocyte-derived enzymes (PA2, PMN-E) have shown some promising results but have not been routinely implemented. Furthermore, new and interesting biomarkers (Copeptin, TRX-1, Ang-2, E-2) have shown good results, but more research is needed to determine if they could play a role in the future. Various reasons why new markers for disease severity have not been adopted in daily routine include low accuracy, cumbersome laboratory techniques and high cost. Despite these difficulties, research is still very active in finding new markers to predict the severity of AP.