Best Practice & Research Clinical Gastroenterology最新文献

Adequate bowel preparation is of paramount importance for the effectiveness of preventive colonoscopy as it allows visualization of the mucosal surface and adenomas detection, the pre-malignant lesions leading to colon cancer. Still, a considerable portion of patients fail to achieve adequate bowel cleansing, with predictors of inadequate bowel preparation being at the focal point of several studies, so far. Incorporation of these factors within predictive models has been implemented in an effort to promptly identify patients at risk for inadequate bowel preparation and thus, timely adopt practices that have the potential to improve bowel cleansing. Ultimately, this could lead to improved procedural outcomes not only in terms of neoplastic detection rate but also interval repeat procedures, expenses, patient convenience and adverse events risk. Aim of this manuscript is to present an up to date overview of all predictive scores/models addressing bowel cleansing adequacy in everyday clinical practice.

Predictive models (PMs) in endoscopic retrograde cholangiopancreatography (ERCP) and endoscopic ultrasound (EUS) have the potential to improve patient outcomes, enhance diagnostic accuracy, and guide therapeutic interventions. This review aims to summarize the current state of predictive models in ERCP and EUS and their clinical implications. To be considered useful in clinical practice a PM should be accurate, easy to perform, and may consider objective variables. PMs in ERCP estimate correct indication, probability of success, and the risk of developing adverse events. These models incorporate patient-related factors and technical aspects of the procedure. In the field of EUS, these models utilize clinical and imaging data to predict the likelihood of malignancy, presence of specific lesions, or risk of complications related to therapeutic interventions. Further research, validation, and refinement are necessary to maximize the utility and impact of these models in routine clinical practice.

Cirrhosis is a major cause of death worldwide, and is associated with significant health care costs. Even if milestones have been recently reached in understanding and managing end-stage liver disease (ESLD), the disease course remains somewhat difficult to prognosticate. These difficulties have already been acknowledged already in the past, when scores instead of single parameters have been proposed as valuable tools for short-term prognosis. These standard scores, like Child Turcotte Pugh (CTP) and model for end-stage liver disease (MELD) score, relying on biochemical and clinical parameters, are still widely used in clinical practice to predict short- and medium-term prognosis. The MELD score, which remains an accurate, easy-to-use, objective predictive score, has received significant modifications over time, in order to improve its performance especially in the liver transplant (LT) setting, where it is widely used as prioritization tool. Although many attempts to improve prognostic accuracy have failed because of lack of replicability or poor benefit with the comparator (often the MELD score or its variants), few scores have been recently proposed and validated especially for subgroups of patients with ESLD, as those with acute-on-chronic liver failure. Artificial intelligence will probably help hepatologists in the near future to fill the current gaps in predicting disease course and long-term prognosis of such patients.

Several scoring systems have been developed for both upper and lower GI bleeding to predict the bleeding severity and discriminate between low-risk patients, who may be suitable for outpatient management, and those who would likely need hospital-based interventions and are at high risk for adverse outcomes. Risk scores created to identify low-risk patients (namely the Glasgow Blatchford Score and the Oakland score) showed very good discriminative performances and their implementation has proven to be effective in reducing hospital admissions and healthcare burden. Conversely, the performances of risk scores in identifying specific adverse events to define high-risk patients are less accurate, and whether their integration into routine clinical practice has a tangible impact on patient management remains unproven.

This review describes the existing risk score systems for GI bleeding, emphasizes key research findings, elucidates the circumstances in which their utilization can be beneficial, examines their constraints when considering routine clinical application, and discuss future development.

Autoimmune liver diseases (AILDs) are complex diseases with unknown causes and immune-mediated pathophysiology. In primary biliary cholangitis (PBC) and autoimmune hepatitis (AIH) disease modifying drugs are available which improve patient quality and quantity of life. In primary sclerosing cholangitis (PSC) no medical therapy is available and the only accepted treatment is liver transplantation (LT). PBC, PSC and AIH possess features that describe the archetype of patients within each disorder. On the other hand, the classical disorders are not homogeneous, and patients within each diagnosis may present with a range of clinical, biochemical, serological, and histological findings.

Singularly, they are considered rare diseases, but together, they account for approximately 20% of LTs in Europe and USA. Management of these patients is complex, as AILDs are relatively uncommon in clinical practice with challenges in developing expertise, disease presentation can be sneaky, clinical phenotypes and disease course are heterogeneous. Prognostic models are key tools for clinicians to assess patients’ risk and to provide personalized care to patients. Aim of this review is to discuss challenges of the management of AILDs and how the available prognostic models can help. We will discuss the prognostic models developed in AILDs, with a special focus on the prognostic models that can support the clinical management of patients with AILDs: in PBC models based on ursodeoxycholic acid (UDCA) response and markers of liver fibrosis; in PSC several markers including biochemistry, disease stage and radiological semiquantitative markers; and finally in AIH, markers of disease stage and disease activity.

In the ideal world prognostication or predicting disease course in any chronic condition would allow the clinician to anticipate disease behaviour, providing crucial information for the patient and data regarding best use of resources. Prognostication also allows an understanding of likely response to treatment and the risk of adverse effects of a treatment leading to withdrawal in any individual patient. Therefore, the ability to predict outcomes from the onset of disease is the key step to developing precision personalised medicine, which is the design of medical care to optimise efficiency or therapeutic benefit based on careful profiling of patients. An important corollary is to prevent unnecessary healthcare costs. This paper outlines currently available predictors of disease outcome in IBD and looks to the future which will involve the use of artificial intelligence to interrogate big data derived from various important ‘omes’ to tease out a more holistic approach to IBD.

Alcohol-associated liver disease (ALD) and alcohol-associated hepatitis (AH) are dynamic disorders whose prognosis can be challenging to determine. A number of prognostic models have been developed to determine likelihood of death, when to refer for liver transplant (LT) and the role for glucocorticoids. Often these models were created with a specific application in mind but were found to have additional applications with further study. Those prognostic models that have stood the test of time are easy to use, have clear interpretations and employ objective parameters. These parameters most often include total bilirubin, INR and creatinine among other data points. Ideally, these models could be utilized at all phases of disease but in most, it is important for clinicians to consider drinking history and how it might alter the determined scores. Herein we provide a brief review of prognostic models in ALD and AH and provide practical tips and considerations to successfully make use of these tools in a clinical setting.

Prognostic model building is a process that begins much earlier than data analysis and ends later than when a model is reached. It requires careful delineation of a clinical question, methodical planning of the approach and attentive exploration of the data before attempting model building. Once following these important initial steps, the researcher may postulate a model to describe the process of interest and build such model. Once built, the model will need to be checked, validated and the exercise may take the researcher back a few steps - for instance, to adapt the model to fit a variable that displays a ‘curved’ pattern - to then return to check and validate the model again. To interpret and report the results it is vital to relate the output to the original question, to be transparent in the methodology followed and to understand the limitations of the data and the approach.