Development and validation of a practical machine learning model to predict sepsis after liver transplantation

Multidrug-resistant bacterial infections after liver transplantation: Prevalence, impact, and risk factors

BACKGROUND & AIMS

Infections by multidrug-resistant bacteria (MDRB) are an increasing healthcare problem worldwide. This study analyzes the incidence, burden, and risk factors associated with MDRB infections after liver transplant(ation) (LT).

METHODS

This retrospective, multicenter cohort study included adult patients who underwent LT between January 2017 and January 2020. Risk factors related to pre-LT disease, surgical procedure, and postoperative stay were analyzed. Multivariate logistic regression analysis was performed to identify independent predictors of MDRB infections within the first 90 days after LT.

RESULTS

We included 1,045 LT procedures (960 patients) performed at nine centers across Spain. The mean age of our cohort was 56.8 ± 9.3 years; 75.4% (n = 782) were male. Alcohol-related liver disease was the most prevalent underlying etiology (43.2.%, n = 451). Bacterial infections occurred in 432 patients (41.3%) who presented with a total of 679 episodes of infection (respiratory infections, 19.3%; urinary tract infections, 18.5%; bacteremia, 13.2% and cholangitis 11%, among others). MDRB were isolated in 227 LT cases (21.7%) (348 episodes). Enterococcus faecium (22.1%), Escherichia coli (18.4%), and Pseudomonas aeruginosa (15.2%) were the most frequently isolated microorganisms. In multivariate analysis, previous intensive care unit admission (0-3 months before LT), previous MDRB infections (0-3 months before LT), and an increasing number of packed red blood cell units transfused during surgery were identified as independent predictors of MDRB infections. Mortality at 30, 90, 180, and 365 days was significantly higher in patients with MDRB isolates.

CONCLUSION

MDRB infections are highly prevalent after LT and have a significant impact on prognosis. Enterococcus faecium is the most frequently isolated multi-resistant microorganism. New pharmacological and surveillance strategies aimed at preventing MDRB infections after LT should be considered for patients with risk factors.

IMPACT AND IMPLICATIONS

Multidrug-resistant bacterial infections have a deep impact on morbidity and mortality after liver transplantation. Strategies aimed at improving prophylaxis, early identification, and empirical treatment are paramount. Our study unveiled the prevalence and main risk factors associated with these infections, and demonstrated that gram-positive bacteria, particularly Enterococcus faecium, are frequent in this clinical scenario. These findings provide valuable insights for the development of prophylactic and empirical antibiotic treatment protocols after liver transplantation.

Bacterial infections in solid organ transplant recipients

PURPOSE OF REVIEW

Bacteria are the leading cause of infections in solid organ transplant (SOT) recipients, significantly impacting patient outcome. Recently detailed and comprehensive epidemiological data have been published.

RECENT FINDING

This literature review aims to provide an overview of bacterial infections affecting different types of SOT recipients, emphasizing underlying risk factors and pathophysiological mechanisms.

SUMMARY

Lung transplantation connects two microbiotas: one derived from the donor's lower respiratory tract with one from the recipient's upper respiratory tract. Similarly, liver transplantation involves a connection to the digestive tract and its microbiota through the bile ducts. For heart transplant recipients, specific factors are related to the management strategies for end-stage heart failure based with different circulatory support tools. Kidney and kidney-pancreas transplant recipients commonly experience asymptomatic bacteriuria, but recent studies have suggested the absence of benefice of routine treatment. Bloodstream infections (BSI) are frequent and affect all SOT recipients. Nonorgan-related risk factors as age, comorbidity index score, and leukopenia contribute to BSI development. Bacterial opportunistic infections have become rare in the presence of efficient prophylaxis. Understanding the epidemiology, risk factors, and pathophysiology of bacterial infections in SOT recipients is crucial for effective management and improved patient outcomes.

Optimizing the Clinical Direction of Artificial Intelligence With Health Policy: A Narrative Review of the Literature

Artificial intelligence (AI) has the ability to completely transform the healthcare industry by enhancing diagnosis, treatment, and resource allocation. To ensure patient safety and equitable access to healthcare, it also presents ethical and practical issues that need to be carefully addressed. Its integration into healthcare is a crucial topic. To realize its full potential, however, the ethical issues around data privacy, prejudice, and transparency, as well as the practical difficulties posed by workforce adaptability and statutory frameworks, must be addressed. While there is growing knowledge about the advantages of AI in healthcare, there is a significant lack of knowledge about the moral and practical issues that come with its application, particularly in the setting of emergency and critical care. The majority of current research tends to concentrate on the benefits of AI, but thorough studies that investigate the potential disadvantages and ethical issues are scarce. The purpose of our article is to identify and examine the ethical and practical difficulties that arise when implementing AI in emergency medicine and critical care, to provide solutions to these issues, and to give suggestions to healthcare professionals and policymakers. In order to responsibly and successfully integrate AI in these important healthcare domains, policymakers and healthcare professionals must collaborate to create strong regulatory frameworks, safeguard data privacy, remove prejudice, and give healthcare workers the necessary training.

Prediction of Complications and Prognostication in Perioperative Medicine: A Systematic Review and PROBAST Assessment of Machine Learning Tools

BACKGROUND

The utilization of artificial intelligence and machine learning as diagnostic and predictive tools in perioperative medicine holds great promise. Indeed, many studies have been performed in recent years to explore the potential. The purpose of this systematic review is to assess the current state of machine learning in perioperative medicine, its utility in prediction of complications and prognostication, and limitations related to bias and validation.

METHODS

A multidisciplinary team of clinicians and engineers conducted a systematic review using the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) protocol. Multiple databases were searched, including Scopus, Cumulative Index to Nursing and Allied Health Literature (CINAHL), the Cochrane Library, PubMed, Medline, Embase, and Web of Science. The systematic review focused on study design, type of machine learning model used, validation techniques applied, and reported model performance on prediction of complications and prognostication. This review further classified outcomes and machine learning applications using an ad hoc classification system. The Prediction model Risk Of Bias Assessment Tool (PROBAST) was used to assess risk of bias and applicability of the studies.

RESULTS

A total of 103 studies were identified. The models reported in the literature were primarily based on single-center validations (75%), with only 13% being externally validated across multiple centers. Most of the mortality models demonstrated a limited ability to discriminate and classify effectively. The PROBAST assessment indicated a high risk of systematic errors in predicted outcomes and artificial intelligence or machine learning applications.

CONCLUSIONS

The findings indicate that the development of this field is still in its early stages. This systematic review indicates that application of machine learning in perioperative medicine is still at an early stage. While many studies suggest potential utility, several key challenges must be first overcome before their introduction into clinical practice.

EDITOR’S PERSPECTIVE

The use of probabilistic graphical models in pediatric sepsis: a feasibility and scoping review

Background

Recent research has demonstrated that machine learning (ML) has the potential to improve several aspects of medical application for critical illness, including sepsis. This scoping review aims to evaluate the feasibility of probabilistic graphical model (PGM) methods in pediatric sepsis application and describe the use of pediatric sepsis definition in these studies.

Methods

Literature searches were conducted in PubMed, Scopus, Cumulative Index to Nursing and Allied Health Literature (CINAHL+), and Web of Sciences from 2000-2023. Keywords included "pediatric", "neonates", "infants", "machine learning", "probabilistic graphical model", and "sepsis".

Results

A total of 3,244 studies were screened, and 72 were included in this scoping review. Sepsis was defined using positive microbiology cultures in 19 studies (26.4%), followed by the 2005's international pediatric sepsis consensus definition in 11 studies (15.3%), and Sepsis-3 definition in seven studies (9.7%). Other sepsis definitions included: bacterial infection, the international classification of diseases, clinicians' assessment, and antibiotic administration time. Among the most common ML approaches used were logistic regression (n=27), random forest (n=24), and Neural Network (n=18). PGMs were used in 13 studies (18.1%), including Bayesian classifiers (n=10), and the Markov Model (n=3). When applied on the same dataset, PGMs show a relatively inferior performance to other ML models in most cases. Other aspects of explainability and transparency were not examined in these studies.

Conclusions

Current studies suggest that the performance of probabilistic graphic models is relatively inferior to other ML methods. However, its explainability and transparency advantages make it a potentially viable method for several pediatric sepsis studies and applications.