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

Identification of a postoperative survival scoring index for adult liver transplantation

BACKGROUND

In addition to surgical technology, successful liver transplantation (LT) depends on perioperative management, which needs an effective prognostic index. Therefore, a simplified and sensitive postoperative index for adult LT should be developed.

METHODS

In total, 906 patients who underwent LT were included in this cross-sectional study. Univariate analysis was used to identify the independent risk factors for recipient survival. Multivariate logistic and stepwise regression analyses were used to construct and simplify the model design. Area under the curve (AUC) and Kaplan-Meier's (K-M) analysis demonstrated superiority of the new index. The postoperative survival score (POSS) index was further simplified via restricted cubic spline (RCS) analysis. Finally, the interpretation of the long-term mortality and subgroup analyses extended the application of the POSS index.

RESULTS

Finally, a total of five factors (donor sex, recipient body mass index (BMI), total bilirubin (Tbil), international normalized ratio (INR) and total operative time) were identified as independent risk parameters and included in our POSS index. The AUCs of the original and simplified POSS indices were 0.764 and 0.723, respectively. Patients with high scores had poor short-term survival. Our index also functioned well in predicting long-term mortality, and it was more effective for patients with hepatitis B cirrhosis or hepatocellular carcinoma (HCC).

CONCLUSIONS

We constructed a simplified and effective postoperative survival scoring index to predict short-term complications and survival in adult LT patients.

Comparative Efficacy of Non-Pharmacological Interventions on Anxiety, Depression, Sleep Disorder, and Quality of Life in Patients With Liver Transplantation: A Systematic Review and Network Meta-Analysis

AIMS

To compare and rank the efficacy of different non-pharmacological interventions on anxiety, depression, sleep disorders, and the quality of life in liver transplantation patients.

BACKGROUND

In recent years, numerous non-pharmacological interventions have been developed to address anxiety, depression, sleep disorders, and the quality of life in liver transplantation patients. However, it remains unclear which non-pharmacological intervention serves as the most effective and preferred approach.

DESIGN

A systematic review and network meta-analysis in accordance with the PRISMA guidelines.

METHODS

Relevant randomised controlled trials were extracted from eight electronic databases. A network meta-analysis was then performed to evaluate the relative efficacy of the non-pharmacological interventions for liver transplantation patients. The quality of the data was assessed using the Cochrane Risk of Bias tool. We registered this study in PROSPERO, number CRD42023450346.

RESULTS

A total of 25 randomised controlled trials were included. Spouse support education combined with mindfulness training, individualised psychological intervention, and cognitive behavioural therapy were found to be significantly effective for both anxiety and depression. The top three interventions against anxiety were spouse support education combined with mindfulness training, individualised psychological intervention, and exercise rehabilitation training. Meanwhile, individualised psychological intervention, spouse support education combined with mindfulness training, and cognitive behavioural therapy were the top-ranked three interventions for reducing depression. Sleep hygiene education was the most effective to improve sleep disorders. Continuous care based on a mobile medical platform emerged as the most effective intervention in improving the quality of life.

CONCLUSION

Several non-pharmacological interventions appeared to be effective in treating anxiety, depression, sleep disorders, and improving the quality of life among liver transplantation patients. More high-quality clinical trials should be incorporated in the future to investigate the reliability of existing findings.

RELEVANCE TO CLINICAL PRACTICE

Healthcare professionals should be encouraged to apply these promising non-pharmacological interventions during clinical care.

NO PATIENT OR PUBLIC CONTRIBUTION

This study did not directly involve patients or public contributions to the manuscript.

Choice of Machine Learning Models Is Important to Predict Post-Operative Infections in Surgical Patients

Background: Surgical quality datasets are critical to decision-making tools including surgical infection (SI). Machine learning models (MLMs), a branch of artificial intelligence (AI), are increasingly being ingrained within surgical decision-making algorithms. However, given the unique and distinct functioning of individual models, not all models may be suitable for acutely ill surgical patients. Patients and Methods: This is a 5-year retrospective review of National Surgical Quality Improvement Program (NSQIP) patients who underwent an operation. The data were reviewed for demographics, medical comorbidities, rates, and sites of infection. To generate the MLMs, data were imported into Python, and four common MLMs, extreme gradient boosting, K-nearest neighbor (KNN), random forest, and logistic regression, as well as two novel models (flexible discriminant analysis and generalized additive model) and ensemble modeling, were generated to predict post-operative SIs. Outputs included area under the receiver-operating characteristic curve (AUC ROC) including recall curves. Results: Overall, 624,625 urgent and emergent NSQIP patients were included. The overall infection rate was 8.6%. Patients who sustained a post-operative infection were older, more likely geriatric, male, diabetic, had chronic obstructive pulmonary disease, were smokers, and were less likely White race. With respect to MLMs, all four MLMs had reasonable accuracy. However, a hierarchy of MLMs was noted with predictive abilities (XGB AUC = 0.85 and logistic regression = 0.82), wherein KNN has the lowest performance (AUC = 0.62). With respect to the ability to detect an infection, precision recall of XGB performed well (AUC = 0.73), whereas KNN performed poorly (AUC = 0.16). Conclusions: MLMs are not created nor function similarly. We identified differences with MLMs to predict post-operative infections in surgical patients. Before MLMs are incorporated into surgical decision making, it is critical that surgeons are at the fore of understanding the role and functioning of MLMs.

Machine learning in solid organ transplantation: Charting the evolving landscape

BACKGROUND

Machine learning (ML), a major branch of artificial intelligence, has not only demonstrated the potential to significantly improve numerous sectors of healthcare but has also made significant contributions to the field of solid organ transplantation. ML provides revolutionary opportunities in areas such as donor-recipient matching, post-transplant monitoring, and patient care by automatically analyzing large amounts of data, identifying patterns, and forecasting outcomes.

AIM

To conduct a comprehensive bibliometric analysis of publications on the use of ML in transplantation to understand current research trends and their implications.

METHODS

On July 18, a thorough search strategy was used with the Web of Science database. ML and transplantation-related keywords were utilized. With the aid of the VOS viewer application, the identified articles were subjected to bibliometric variable analysis in order to determine publication counts, citation counts, contributing countries, and institutions, among other factors.

RESULTS

Of the 529 articles that were first identified, 427 were deemed relevant for bibliometric analysis. A surge in publications was observed over the last four years, especially after 2018, signifying growing interest in this area. With 209 publications, the United States emerged as the top contributor. Notably, the "Journal of Heart and Lung Transplantation" and the "American Journal of Transplantation" emerged as the leading journals, publishing the highest number of relevant articles. Frequent keyword searches revealed that patient survival, mortality, outcomes, allocation, and risk assessment were significant themes of focus.

CONCLUSION

The growing body of pertinent publications highlights ML's growing presence in the field of solid organ transplantation. This bibliometric analysis highlights the growing importance of ML in transplant research and highlights its exciting potential to change medical practices and enhance patient outcomes. Encouraging collaboration between significant contributors can potentially fast-track advancements in this interdisciplinary domain.

Perspectives and Tools in Liver Graft Assessment: A Transformative Era in Liver Transplantation

Liver transplantation is a critical and evolving field in modern medicine, offering life-saving treatment for patients with end-stage liver disease and other hepatic conditions. Despite its transformative potential, transplantation faces persistent challenges, including a global organ shortage, increasing liver disease prevalence, and significant waitlist mortality rates. Current donor evaluation practices often discard potentially viable livers, underscoring the need for refined graft assessment tools. This review explores advancements in graft evaluation and utilization aimed at expanding the donor pool and optimizing outcomes. Emerging technologies, such as imaging techniques, dynamic functional tests, and biomarkers, are increasingly critical for donor assessment, especially for marginal grafts. Machine learning and artificial intelligence, exemplified by tools like LiverColor, promise to revolutionize donor-recipient matching and liver viability predictions, while bioengineered liver grafts offer a future solution to the organ shortage. Advances in perfusion techniques are improving graft preservation and function, particularly for donation after circulatory death (DCD) grafts. While challenges remain-such as graft rejection, ischemia-reperfusion injury, and recurrence of liver disease-technological and procedural advancements are driving significant improvements in graft allocation, preservation, and post-transplant outcomes. This review highlights the transformative potential of integrating modern technologies and multidisciplinary approaches to expand the donor pool and improve equity and survival rates in liver transplantation.

Comparison of time-to-event machine learning models in predicting biliary complication and mortality rate in liver transplant patients

Post-Liver transplantation (LT) survival rates stagnate, with biliary complications (BC) as a major cause of death. We analyzed longitudinal data with a median 19-month follow-up. BC was diagnosed with ultrasounds and MRCP. Missing data was imputed using mean and median. Data preprocessing involved feature scaling and one-hot encoding. Survival analysis used filter (Cox-P, Cox-c) and embedded (RSF, LASSO) feature selection methods. Seven survival machine learning algorithms were used: LASSO, Ridge, RSF, E-NET, GBS, C-GBS, and FS-SVM. Model development employed 5-fold cross-validation, random oversampling, and hyperparameter tuning. Random oversampling addressed data imbalance. Optimal hyperparameters were determined based on average C-index. Features importance was assessed using standardized regression coefficients and permutation importance for top models. Stability was evaluated using 5-fold cross-validation standard deviation. Finally, 1799 observations with 40 outcome predictors were included. RSF with Ridge achieved the highest performance (C-index: 0.699) for BC prediction, while RSF with RSF had the highest performance (C-index: 0.784) for mortality prediction. Top BC predictors were LT graft types, IBD in recipients, recipient's BMI, recipient's history of PVT, and previous LT history. For mortality, they were post-transplant AST, creatinine, recipient's age, post-transplant ALT, and tacrolimus consumption. We identified BC and mortality risk factors, improving decision-making and outcomes.

A Supervised Explainable Machine Learning Model for Perioperative Neurocognitive Disorder in Liver-Transplantation Patients and External Validation on the Medical Information Mart for Intensive Care IV Database: Retrospective Study

BACKGROUND

Patients undergoing liver transplantation (LT) are at risk of perioperative neurocognitive dysfunction (PND), which significantly affects the patients' prognosis.

OBJECTIVE

This study used machine learning (ML) algorithms with an aim to extract critical predictors and develop an ML model to predict PND among LT recipients.

METHODS

In this retrospective study, data from 958 patients who underwent LT between January 2015 and January 2020 were extracted from the Third Affiliated Hospital of Sun Yat-sen University. Six ML algorithms were used to predict post-LT PND, and model performance was evaluated using area under the receiver operating curve (AUC), accuracy, sensitivity, specificity, and F1-scores. The best-performing model was additionally validated using a temporal external dataset including 309 LT cases from February 2020 to August 2022, and an independent external dataset extracted from the Medical Information Mart for Intensive Care Ⅳ (MIMIC-Ⅳ) database including 325 patients.

RESULTS

In the development cohort, 201 out of 751 (33.5%) patients were diagnosed with PND. The logistic regression model achieved the highest AUC (0.799) in the internal validation set, with comparable AUC in the temporal external (0.826) and MIMIC-Ⅳ validation sets (0.72). The top 3 features contributing to post-LT PND diagnosis were the preoperative overt hepatic encephalopathy, platelet level, and postoperative sequential organ failure assessment score, as revealed by the Shapley additive explanations method.

CONCLUSIONS

A real-time logistic regression model-based online predictor of post-LT PND was developed, providing a highly interoperable tool for use across medical institutions to support early risk stratification and decision making for the LT recipients.

Integrating StEP-COMPAC definition and enhanced recovery after surgery status in a machine-learning-based model for postoperative pulmonary complications in laparoscopic hepatectomy

BACKGROUND

Postoperative pulmonary complications (PPCs) contribute to high mortality rates and impose significant financial burdens. In this study, a machine learning-based prediction model was developed to identify patients at high risk of developing PPCs following laparoscopic hepatectomy.

METHODS

Data were collected from 1022 adult patients who underwent laparoscopic hepatectomy at two centres between January 2015 and February 2021. The dataset was divided into a development set and a temporal external validation set based on the year of surgery. A total of 42 factors were extracted for pre-modelling, including the implementation status of Enhanced Recovery after Surgery (ERAS). Feature selection was performed using the least absolute shrinkage and selection operator (LASSO) method. Model performance was assessed using the area under the receiver operating characteristic curve (AUC). The model with the best performance was externally validated using temporal data.

RESULTS

The incidence of PPCs was 8.7%. Lambda.1se was selected as the optimal lambda for LASSO feature selection. For implementation of ERAS, serum gamma-glutamyl transferase levels, malignant tumour presence, total bilirubin levels, and age-adjusted Charleston Comorbidities Index were the selected factors. Seven models were developed. Among them, logistic regression demonstrated the best performance, with an AUC of 0.745 in the internal validation set and 0.680 in the temporal external validation set.

CONCLUSIONS

Based on the most recent definition, a machine learning model was employed to predict the risk of PPCs following laparoscopic hepatectomy. Logistic regression was identified as the best-performing model. ERAS implementation was associated with a reduction in the number of PPCs.

Early detection of sepsis using machine learning algorithms: a systematic review and network meta-analysis

Background

With machine learning (ML) carving a niche in diverse medical disciplines, its role in sepsis prediction, a condition where the 'golden hour' is critical, is of paramount interest. This study assesses the factors influencing the efficacy of ML models in sepsis prediction, aiming to optimize their use in clinical practice.

Methods

We searched Medline, PubMed, Google Scholar, and CENTRAL for studies published from inception to October 2023. We focused on studies predicting sepsis in real-time settings in adult patients in any hospital settings without language limits. The primary outcome was area under the curve (AUC) of the receiver operating characteristic. This meta-analysis was conducted according to PRISMA-NMA guidelines and Cochrane Handbook recommendations. A Network Meta-Analysis using the CINeMA approach compared ML models against traditional scoring systems, with meta-regression identifying factors affecting model quality.

Results

From 3,953 studies, 73 articles encompassing 457,932 septic patients and 256 models were analyzed. The pooled AUC for ML models was 0.825 and it significantly outperformed traditional scoring systems. Neural Network and Decision Tree models demonstrated the highest AUC metrics. Significant factors influencing AUC included ML model type, dataset type, and prediction window.

Conclusion

This study establishes the superiority of ML models, especially Neural Network and Decision Tree types, in sepsis prediction. It highlights the importance of model type and dataset characteristics for prediction accuracy, emphasizing the necessity for standardized reporting and validation in ML healthcare applications. These findings call for broader clinical implementation to evaluate the effectiveness of these models in diverse patient groups.

Systematic review registration

https://inplasy.com/inplasy-2023-12-0062/, identifier, INPLASY2023120062.

Utilizing radiomics for differential diagnosis of inverted papilloma and chronic rhinosinusitis with polyps based on unenhanced CT scans

To evaluate whether radiomics models based on unenhanced paranasal sinuses CT images could be a useful tool for differentiating inverted papilloma (IP) from chronic rhinosinusitis with polyps (CRSwNP). This retrospective study recruited 240 patients with CRSwNP and 106 patients with IP from three centers. 253 patients from Qilu Hospital were randomly divided into the training set (n = 151) and the internal validation set (n = 102) with a ratio of 6:4. 93 patients from the other two centers were used as the external validation set. The patients with the unilateral disease (n = 115) from Qilu Hospital were selected to further develop a subgroup analysis. Lesion segmentation was manually delineated in CT images. Least absolute shrinkage and selection operator algorithm was performed for feature reduction and selection. Decision tree, support vector machine, random forest, and adaptive boosting regressor were employed to establish the differential diagnosis models. 43 radiomic features were selected for modeling. Among the models, RF achieved the best results, with an AUC of 0.998, 0.943, and 0.934 in the training set, the internal validation set, and the external validation set, respectively. In the subgroup analysis, RF achieved an AUC of 0.999 in the training set and 0.963 in the internal validation set. The proposed radiomics models offered a non-invasion and accurate differential approach between IP and CRSwNP and has some significance in guiding clinicians determining the best treatment plans, as well as predicting the prognosis.

Predictive ability of hypotension prediction index and machine learning methods in intraoperative hypotension: a systematic review and meta-analysis

INTRODUCTION

Intraoperative Hypotension (IOH) poses a substantial risk during surgical procedures. The integration of Artificial Intelligence (AI) in predicting IOH holds promise for enhancing detection capabilities, providing an opportunity to improve patient outcomes. This systematic review and meta analysis explores the intersection of AI and IOH prediction, addressing the crucial need for effective monitoring in surgical settings.

METHOD

A search of Pubmed, Scopus, Web of Science, and Embase was conducted. Screening involved two-phase assessments by independent reviewers, ensuring adherence to predefined PICOS criteria. Included studies focused on AI models predicting IOH in any type of surgery. Due to the high number of studies evaluating the hypotension prediction index (HPI), we conducted two sets of meta-analyses: one involving the HPI studies and one including non-HPI studies. In the HPI studies the following outcomes were analyzed: cumulative duration of IOH per patient, time weighted average of mean arterial pressure < 65 (TWA-MAP < 65), area under the threshold of mean arterial pressure (AUT-MAP), and area under the receiver operating characteristics curve (AUROC). In the non-HPI studies, we examined the pooled AUROC of all AI models other than HPI.

RESULTS

43 studies were included in this review. Studies showed significant reduction in IOH duration, TWA-MAP < 65 mmHg, and AUT-MAP < 65 mmHg in groups where HPI was used. AUROC for HPI algorithms demonstrated strong predictive performance (AUROC = 0.89, 95CI). Non-HPI models had a pooled AUROC of 0.79 (95CI: 0.74, 0.83).

CONCLUSION

HPI demonstrated excellent ability to predict hypotensive episodes and hence reduce the duration of hypotension. Other AI models, particularly those based on deep learning methods, also indicated a great ability to predict IOH, while their capacity to reduce IOH-related indices such as duration remains unclear.

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.

The transformative potential of artificial intelligence in solid organ transplantation

Solid organ transplantation confronts numerous challenges ranging from donor organ shortage to post-transplant complications. Here, we provide an overview of the latest attempts to address some of these challenges using artificial intelligence (AI). We delve into the application of machine learning in pretransplant evaluation, predicting transplant rejection, and post-operative patient outcomes. By providing a comprehensive overview of AI's current impact, this review aims to inform clinicians, researchers, and policy-makers about the transformative power of AI in enhancing solid organ transplantation and facilitating personalized medicine in transplant care.

Diagnostic performance of machine-learning algorithms for sepsis prediction: An updated meta-analysis

BACKGROUND

Early identification of sepsis has been shown to significantly improve patient prognosis.

OBJECTIVE

Therefore, the aim of this meta-analysis is to systematically evaluate the diagnostic efficacy of machine-learning algorithms for sepsis prediction.

METHODS

Systematic searches were conducted in PubMed, Embase and Cochrane databases, covering literature up to December 2023. The keywords included machine learning, sepsis and prediction. After screening, data were extracted and analysed from studies meeting the inclusion criteria. Key evaluation metrics included sensitivity, specificity and the area under the curve (AUC) for diagnostic accuracy.

RESULTS

The meta-analysis included a total of 21 studies with a data sample size of 4,158,941. Overall, the pooled sensitivity was 0.82 (95% confidence interval [CI] = 0.70-0.90; P< 0.001; I2= 99.7%), the specificity was 0.91 (95% CI = 0.86-0.94; P< 0.001; I2= 99.9%), and the AUC was 0.94 (95% CI = 0.91-0.96). The subgroup analysis revealed that in the emergency department setting (6 studies), the pooled sensitivity was 0.79 (95% CI = 0.68-0.87; P< 0.001; I2= 99.6%), the specificity was 0.94 (95% CI 0.90-0.97; P< 0.001; I2= 99.9%), and the AUC was 0.94 (95% CI = 0.92-0.96). In the Intensive Care Unit setting (11 studies), the sensitivity was 0.91 (95% CI = 0.75-0.97; P< 0.001; I2= 98.3%), the specificity was 0.85 (95% CI = 0.75-0.92; P< 0.001; I2= 99.9%), and the AUC was 0.93 (95% CI = 0.91-0.95). Due to the limited number of studies in the in-hospital and mixed settings (n< 3), no pooled analysis was performed.

CONCLUSION

Machine-learning algorithms have demonstrated excellent diagnostic accuracy in predicting the occurrence of sepsis, showing potential for clinical application.

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

Managing a Prospective Liver Transplant Recipient on the Waiting List

The management of a patient in the peri-transplantation period is highly challenging, and it is even more difficult while the patient is on the transplantation waitlist. Keeping the patient alive during this period involves managing the complications of liver disease and preventing the disease's progression. Based on the pre-transplantation etiology and type of liver failure, there is a difference in the management protocol. The current review is divided into different sections, which include: the management of underlying cirrhosis and complications of portal hypertension, treatment and identification of infections, portal vein thrombosis management, and particular emphasis on the management of patients of hepatocellular carcinoma and acute liver failure in the transplantation waitlist. The review highlights special concerns in the management of patients in the Asian subcontinent also. The review also addresses the issue of delisting from the transplant waitlist to see that futility does not overtake the utility of organs. The treatment modalities are primarily expressed in tabular format for quick reference. The following review integrates the vast issues in this period concisely so that the management during this crucial period is taken care of in the best possible way.

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.

Radiobioinformatics: A novel bridge between basic research and clinical practice for clinical decision support in diffuse liver diseases

The liver is a multifaceted organ that is responsible for many critical functions encompassing amino acid, carbohydrate, and lipid metabolism, all of which make a healthy liver essential for the human body. Contemporary imaging methodologies have remarkable diagnostic accuracy in discerning focal liver lesions; however, a comprehensive understanding of diffuse liver diseases is a requisite for radiologists to accurately diagnose or predict the progression of such lesions within clinical contexts. Nonetheless, the conventional attributes of radiological features, including morphology, size, margin, density, signal intensity, and echoes, limit their clinical utility. Radiomics is a widely used approach that is characterized by the extraction of copious image features from radiographic depictions, which gives it considerable potential in addressing this limitation. It is worth noting that functional or molecular alterations occur significantly prior to the morphological shifts discernible by imaging modalities. Consequently, the explication of potential mechanisms by multiomics analyses (encompassing genomics, epigenomics, transcriptomics, proteomics, and metabolomics) is essential for investigating putative signal pathway regulations from a radiological viewpoint. In this review, we elaborate on the principal pathological categorizations of diffuse liver diseases, the evaluation of multiomics approaches pertaining to diffuse liver diseases, and the prospective value of predictive models. Accordingly, the overarching objective of this review is to scrutinize the interrelations between radiological features and bioinformatics as well as to consider the development of prediction models predicated on radiobioinformatics as integral components of clinical decision support systems for diffuse liver diseases.