Acute-on-chronic liver failure is independently associated with higher mortality for cirrhotic patients with acute esophageal variceal hemorrhage: Retrospective cohort study

Assessment of nomogram model for the prediction of esophageal variceal hemorrhage in hepatitis B-induced hepatic cirrhosis

BACKGROUND

Esophageal variceal (EV) hemorrhage is a life-threatening consequence of portal hypertension in hepatitis B virus (HBV) -induced cirrhotic patients. Screening upper endoscopy and endoscopic variceal ligation to find EVs for treatment have complications, contraindications, and high costs. We sought to identify the nomogram models (NMs) as alternative predictions for the risk of EV hemorrhage.

METHODS

In this case-control study, we retrospectively analyzed 241 HBV-induced liver cirrhotic patients treated for EVs at the Second People's Hospital of Fuyang City, China from January 2021 to April 2023. We applied univariate analysis and multivariate logistic regression to assess the accuracy of various NMs in EV hemorrhage. The area under the curve (AUC) and calibration curves of the receiver's operating characteristics were used to evaluate the predictive accuracy of the nomogram. Decision curve analysis (DCA) was used to determine the clinically relevant of nomograms.

RESULTS

In the prediction group, multivariate logistic regression analysis identified platelet distribution and spleen length as independent risk factors for EVs. We applied NMs as the independent risk factors to predict EVs risk. The NMs fit well with the calibration curve and have good discrimination ability. The AUC and DCA demonstrated that NMs with a good net benefit. The above results were validated in the validation cohort.

CONCLUSION

Our non-invasive NMs based on the platelet distribution width and spleen length may be used to predict EV hemorrhage in HBV-induced cirrhotic patients. NMs can help clinicians to increase diagnostic performance leading to improved treatment measures.

Use of machine learning models for the prognostication of liver transplantation: A systematic review

BACKGROUND

Liver transplantation (LT) is a life-saving intervention for patients with end-stage liver disease. However, the equitable allocation of scarce donor organs remains a formidable challenge. Prognostic tools are pivotal in identifying the most suitable transplant candidates. Traditionally, scoring systems like the model for end-stage liver disease have been instrumental in this process. Nevertheless, the landscape of prognostication is undergoing a transformation with the integration of machine learning (ML) and artificial intelligence models.

AIM

To assess the utility of ML models in prognostication for LT, comparing their per formance and reliability to established traditional scoring systems.

METHODS

Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines, we conducted a thorough and standardized literature search using the PubMed/MEDLINE database. Our search imposed no restrictions on publication year, age, or gender. Exclusion criteria encompassed non-English stu dies, review articles, case reports, conference papers, studies with missing data, or those exhibiting evident methodological flaws.

RESULTS

Our search yielded a total of 64 articles, with 23 meeting the inclusion criteria. Among the selected studies, 60.8% originated from the United States and China combined. Only one pediatric study met the criteria. Notably, 91% of the studies were published within the past five years. ML models consistently demonstrated satisfactory to excellent area under the receiver operating characteristic curve values (ranging from 0.6 to 1) across all studies, surpassing the performance of traditional scoring systems. Random forest exhibited superior predictive capa bilities for 90-d mortality following LT, sepsis, and acute kidney injury (AKI). In contrast, gradient boosting excelled in predicting the risk of graft-versus-host disease, pneumonia, and AKI.

CONCLUSION

This study underscores the potential of ML models in guiding decisions related to allograft allocation and LT, marking a significant evolution in the field of prognostication.

Management of sepsis in a cirrhotic patient admitted to the intensive care unit: A systematic literature review

BACKGROUND

Sepsis is a severe medical condition that occurs when the body's immune system overreacts to an infection, leading to life-threatening organ dysfunction. The "Third international consensus definitions for sepsis and septic shock (Sepsis-3)" defines sepsis as an increase in sequential organ failure assessment score of 2 points or more, with a mortality rate above 10%. Sepsis is a leading cause of intensive care unit (ICU) admissions, and patients with underlying conditions such as cirrhosis have a higher risk of poor outcomes. Therefore, it is critical to recognize and manage sepsis promptly by administering fluids, vasopressors, steroids, and antibiotics, and identifying and treating the source of infection.

AIM

To conduct a systematic review and meta-analysis of existing literature on the management of sepsis in cirrhotic patients admitted to the ICU and compare the management of sepsis between cirrhotic and non-cirrhotic patients in the ICU.

METHODS

This study is a systematic literature review that followed the PRISMA statement's standardized search method. The search for relevant studies was conducted across multiple databases, including PubMed, Embase, Base, and Cochrane, using predefined search terms. One reviewer conducted the initial search, and the eligibility criteria were applied to the titles and abstracts of the retrieved articles. The selected articles were then evaluated based on the research objectives to ensure relevance to the study's aims.

RESULTS

The study findings indicate that cirrhotic patients are more susceptible to infections, resulting in higher mortality rates ranging from 18% to 60%. Early identification of the infection source followed by timely administration of antibiotics, vasopressors, and corticosteroids has been shown to improve patient outcomes. Procalcitonin is a useful biomarker for diagnosing infections in cirrhotic patients. Moreover, presepsin and resistin have been found to be reliable markers of bacterial infection in patients with decompensated liver cirrhosis, with similar diagnostic performance compared to procalcitonin.

CONCLUSION

This review highlights the importance of early detection and management of infections in cirrhosis patients to reduce mortality. Therefore, early detection of infection using procalcitonin test and other biomarker as presepsin and resistin, associated with early management with antibiotics, fluids, vasopressors and low dose corticosteroids might reduce the mortality associated with sepsis in cirrhotic patients.