An explainable supervised machine learning predictor of acute kidney injury after adult deceased donor liver transplantation

Neutrophil gelatinase-associated lipocalin for predicting acute kidney injury in orthotopic liver transplantation: a systematic review and meta-analysis

Acute kidney injury (AKI) is associated with poor prognosis. New biomarkers, like neutrophil gelatinase-associated lipocalin (NGAL), are helpful for early warning of AKI. This study aims to investigate the accuracy of NGAL in evaluating the perioperative AKI of liver transplantation. The four databases, PubMed, Web of Science, Embase, and Cochrane Library, were searched for relevant studies published from database inception to August 2023. Results were pooled using random-effects models, and heterogeneity was examined. A total of 16 case-control studies with 1271 patients were included. The results showed that both preoperative [standardized mean difference (SMD) = 0.53; 95% confidence interval (CI): 0.15, 0.91; P  < 0.001] and postoperative NGAL levels (SMD = 0.63; 95% CI: 0.24, 1.03; P  < 0.001) were higher in the AKI group compared with the non-AKI group. Subgroup analysis by continents showed higher preoperative NGAL levels in AKI patients in the European population (SMD = 1.63; 95% CI: 0.55, 0.27; P  = 0.003), but no differences in Asian, African, North American, and South American. Subgroup analysis by continents revealed higher postoperative NGAL levels in the European (SMD = 1.63; 95% CI: 0.55, 0.27; P  = 0.002) and Asian populations (SMD = 0.42; 95% CI: 0.04, 0.81; P  = 0.039). Higher postoperative NGAL levels in plasma and urine were observed in AKI patients compared with non-AKI patients [plasma (SMD = 1.29; 95% CI: 0.21, 2.38; P  = 0.011), urine (SMD = 0.88; 95% CI: 0.18, 1.59; P  = 0.035)], while there was no difference in African, North American, South American, and serum NGAL. NGAL level may be an important biomarker for early detection of AKI in the perioperative period of liver transplantation.

Optimization and Protection of Kidney Health in Liver Transplant Recipients: Intra- and Postoperative Approaches

Postoperative acute kidney injury after liver transplant (LT) has long-term implications for kidney health. LT recipients are at risk of acute kidney injury due to a number of factors related to the donor liver, intraoperative factors including surgical technique, as well as recipient factors, such as pre-LT kidney function and postoperative complications. This review discusses these factors in detail and their impact on posttransplant kidney function. Long-term risk factors such as calcineurin inhibitors have also been discussed. Additionally, the impact of liver allocation policies on pre- and post-LT kidney health is discussed.

Development and Validation of Ultrasound Hemodynamic-based Prediction Models for Acute Kidney Injury After Renal Transplantation

RATIONALE AND OBJECTIVES

Acute kidney injury (AKI) post-renal transplantation often has a poor prognosis. This study aimed to identify patients with elevated risks of AKI after kidney transplantation.

MATERIALS AND METHODS

A retrospective analysis was conducted on 422 patients who underwent kidney transplants from January 2020 to April 2023. Participants from 2020 to 2022 were randomized to training group (n=261) and validation group 1 (n=113), and those in 2023, as validation group 2 (n=48). Risk factors were determined by employing logistic regression analysis alongside the least absolute shrinkage and selection operator, making use of ultrasound hemodynamic, clinical, and laboratory information. Models for prediction were developed using logistic regression analysis and six machine-learning techniques. The evaluation of the logistic regression model encompassed its discrimination, calibration, and applicability in clinical settings, and a nomogram was created to illustrate the model. SHapley Additive exPlanations were used to explain and visualize the best of the six machine learning models.

RESULTS

The least absolute shrinkage and selection operator combined with logistic regression identified and incorporated five risk factors into the predictive model. The logistic regression model (AUC=0.927 in the validation set 1; AUC=0.968 in the validation set 2) and the random forest model (AUC=0.946 in the validation set 1;AUC=0.996 in the validation set 2) showed good performance post-validation, with no significant difference in their predictive accuracy.

CONCLUSION

These findings can assist clinicians in the early identification of patients at high risk for AKI, allowing for timely interventions and potentially enhancing the prognosis following kidney transplantation.

AI Innovations in Liver Transplantation: From Big Data to Better Outcomes

Artificial intelligence (AI) has emerged as a transformative field in computational research with diverse applications in medicine, particularly in the field of liver transplantation (LT) given its ability to analyze and build upon complex and multidimensional data. This literature review investigates the application of AI in LT, focusing on its role in pre-implantation biopsy evaluation, development of recipient prognosis algorithms, imaging analysis, and decision-making support systems, with the findings revealing that AI can be applied across a variety of fields within LT, including diagnosis, organ allocation, and surgery planning. As a result, algorithms are being developed to assess steatosis in pre-implantation biopsies and predict liver graft function, with AI applications displaying great accuracy across various studies included in this review. Despite its relatively recent introduction to transplantation, AI demonstrates potential in delivering cost and time-efficient outcomes. However, these tools cannot replace the role of healthcare professionals, with their widespread adoption demanding thorough clinical testing and oversight.

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.

Prediction models for acute kidney injury following liver transplantation: A systematic review and critical appraisal

OBJECTIVE

This study aims to systematically review and critical evaluation of the risk of bias and the applicability of existing prediction models for acute kidney injury post liver transplantation.

DATA SOURCE

A comprehensive literature search up until February 7, 2024, was conducted across nine databases: PubMed, Web of Science, EBSCO CINAHL Plus, Embase, Cochrane Library, CNKI, Wanfang, CBM, and VIP.

STUDY DESIGN

Systematic review of observational studies.

EXTRACTION METHODS

Literature screening and data extraction were independently conducted by two researchers using a standardized checklist designed for the critical appraisal of prediction modelling studies in systematic reviews. The prediction model risk of bias assessment tool was utilized to assess both the risk of bias and the models' applicability.

PRINCIPAL FINDINGS

Thirty studies were included, identifying 34 prediction models. External validation was conducted in seven studies, while internal validation exclusively took place in eight studies. Three models were subjected to both internal and external validation, the area under the curve ranging from 0.610 to 0.921. A meta-analysis of high-frequency predictors identified several statistically significant factors, including recipient body mass index, Model for End-stage Liver Disease score, preoperative albumin levels, international normalized ratio, and surgical-related factors such as cold ischemia time. All studies were demonstrated a high risk of bias, mainly due to the use of unsuitable data sources and inadequate detail in the analysis reporting.

CONCLUSIONS

The evaluation with prediction model risk of bias assessment tool indicated a considerable bias risk in current predictive models for acute kidney injury post liver transplantation.

IMPLICATIONS FOR CLINICAL PRACTICE

The recognition of high bias in existing models calls for future research to employ rigorous methodologies and robust data sources, aiming to develop and validate more accurate and clinically applicable predictive models for acute kidney injury post liver transplantation.

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.

Development and validation of a risk prediction model for acute kidney injury in coronary artery disease

BACKGROUND

Acute Kidney Injury (AKI) is a sudden and often reversible condition characterized by rapid kidney function reduction, posing significant risks to coronary artery disease (CAD) patients. This study focuses on developing accurate predictive models to improve the early detection and prognosis of AKI in CAD patients.

METHODS

We used Electronic Health Records (EHRs) from a nationwide CAD registry including 54 429 patients. Initially, univariate analysis identified potential predictors. Subsequently, a stepwise multivariate logistic model integrated clinical significance and data distribution. To refine predictor selection, we applied a random forest algorithm. The top 10 variables, including admission to the surgical department, EGFR, hemoglobin, and others, were incorporated into a logistic regression-based prediction model. Model performance was assessed using the area under the curve (AUC) and calibration analysis, and a nomogram was developed for practical application.

RESULTS

During hospitalization, 2,112 (3.88%) patients in the overall population of both the development and validation groups experienced AKI within 30 days. The final prediction model exhibited strong discrimination with an AUC of 0.867 (95% CI: 0.858 to 0.876) and well calibration capability in both the development and validation groups. Key predictors included surgical department admission, eGFR, hemoglobin, chronic kidney disease history, male sex, white blood cell count, age, left ventricular ejection fraction, acute myocardial infarction at admission, and congestive heart failure history. Bootstrap resampling confirmed model stability (Harrell's optimism-correct AUC = 0.866). The nomogram provided a practical tool for AKI risk assessment.

CONCLUSION

This study introduced a refined AKI risk prediction model for CAD patients. This model showed adaptability to subgroups and held the potential for early AKI alerts and personalized interventions, thereby enhancing patient care.

An explainable and supervised machine learning model for prediction of red blood cell transfusion in patients during hip fracture surgery

AIM

The study aimed to develop a predictive model with machine learning (ML) algorithm, to predict and manage the need for red blood cell (RBC) transfusion during hip fracture surgery.

METHODS

Data of 2785 cases that underwent hip fracture surgery from April 2016 to May 2022 were collected, covering demographics, medical history and comorbidities, type of surgery and preoperative laboratory results. The primary outcome was the intraoperative RBC transfusion. The predicting performance of six algorithms were respectively evaluated with the area under the receiver operating characteristic (AUROC). The SHapley Additive exPlanations (SHAP) package was applied to interpret the Random Forest (RF) model. Data from 122 patients at The Third Affiliated Hospital of Sun Yat-sen University were collected for external validation.

RESULTS

1417 patients (50.88%) were diagnosed with preoperative anemia (POA) and 209 patients (7.5%) received intraoperative RBC transfusion. Longer estimated duration of surgery, POA, older age, hypoproteinemia, and surgery of internal fixation were revealed as the top 5 important variables contributing to intraoperative RBC transfusion. Among the six ML models, the RF model performed the best, which achieved the highest AUC (0.887, CI 0.838 to 0.926) in the internal validation set. Further, it achieved a comparable AUC of 0.834(0.75, 0.911) in the external validation set.

CONCLUSION

Our study firstly demonstrated that the RF model with 10 common variables might predict intraoperative RBC transfusion in hip fracture patients.

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.

Artificial intelligence and predictive models for early detection of acute kidney injury: transforming clinical practice

Acute kidney injury (AKI) presents a significant clinical challenge due to its rapid progression to kidney failure, resulting in serious complications such as electrolyte imbalances, fluid overload, and the potential need for renal replacement therapy. Early detection and prediction of AKI can improve patient outcomes through timely interventions. This review was conducted as a narrative literature review, aiming to explore state-of-the-art models for early detection and prediction of AKI. We conducted a comprehensive review of findings from various studies, highlighting their strengths, limitations, and practical considerations for implementation in healthcare settings. We highlight the potential benefits and challenges of their integration into routine clinical care and emphasize the importance of establishing robust early-detection systems before the introduction of artificial intelligence (AI)-assisted prediction models. Advances in AI for AKI detection and prediction are examined, addressing their clinical applicability, challenges, and opportunities for routine implementation.

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.

Clinical Deployment of Machine Learning Tools in Transplant Medicine: What Does the Future Hold?

Medical applications of machine learning (ML) have shown promise in analyzing patient data to support clinical decision-making and provide patient-specific outcomes. In transplantation, several applications of ML exist which include pretransplant: patient prioritization, donor-recipient matching, organ allocation, and posttransplant outcomes. Numerous studies have shown the development and utility of ML models, which have the potential to augment transplant medicine. Despite increasing efforts to develop robust ML models for clinical use, very few of these tools are deployed in the healthcare setting. Here, we summarize the current applications of ML in transplant and discuss a potential clinical deployment framework using examples in organ transplantation. We identified that creating an interdisciplinary team, curating a reliable dataset, addressing the barriers to implementation, and understanding current clinical evaluation models could help in deploying ML models into the transplant clinic setting.

Comparative Analysis of Logistic Regression, Gradient Boosted Trees, SVM, and Random Forest Algorithms for Prediction of Acute Kidney Injury Requiring Dialysis After Cardiac Surgery

Purpose

This study aimed to identify the best-performing algorithm for predicting Acute Kidney Injury (AKI) necessitating dialysis following cardiac surgery.

Patients and Methods

The dataset encompassed patient data from a tertiary cardiothoracic center in Malaysia between 2011 and 2015, sourced from electronic health records. Extensive preprocessing and feature selection ensured data quality and relevance. Four machine learning algorithms were applied: Logistic Regression, Gradient Boosted Trees, Support Vector Machine, and Random Forest. The dataset was split into training and validation sets and the hyperparameters were tuned. Accuracy, Area Under the ROC Curve (AUC), precision, F-measure, sensitivity, and specificity were some of the evaluation criteria. Ethical guidelines for data use and patient privacy were rigorously followed throughout the study.

Results

With the highest accuracy (88.66%), AUC (94.61%), and sensitivity (91.30%), Gradient Boosted Trees emerged as the top performance. Random Forest displayed strong AUC (94.78%) and accuracy (87.39%). In contrast, the Support Vector Machine showed higher sensitivity (98.57%) with lower specificity (59.55%), but lower accuracy (79.02%) and precision (70.81%). Sensitivity (87.70%) and specificity (87.05%) were maintained in balance via Logistic Regression.

Conclusion

These findings imply that Gradient Boosted Trees and Random Forest might be an effective method for identifying patients who would develop AKI following heart surgery. However specific goals, sensitivity/specificity trade-offs, and consideration of the practical ramifications should all be considered when choosing an algorithm.

Explainable Boosting Machine approach identifies risk factors for acute renal failure

BACKGROUND

Risk stratification and outcome prediction are crucial for intensive care resource planning. In addressing the large data sets of intensive care unit (ICU) patients, we employed the Explainable Boosting Machine (EBM), a novel machine learning model, to identify determinants of acute kidney injury (AKI) in these patients. AKI significantly impacts outcomes in the critically ill.

METHODS

An analysis of 3572 ICU patients was conducted. Variables such as average central venous pressure (CVP), mean arterial pressure (MAP), age, gender, and comorbidities were examined. This analysis combined traditional statistical methods with the EBM to gain a detailed understanding of AKI risk factors.

RESULTS

Our analysis revealed chronic kidney disease, heart failure, arrhythmias, liver disease, and anemia as significant comorbidities influencing AKI risk, with liver disease and anemia being particularly impactful. Surgical factors were also key; lower GI surgery heightened AKI risk, while neurosurgery was associated with a reduced risk. EBM identified four crucial variables affecting AKI prediction: anemia, liver disease, and average CVP increased AKI risk, whereas neurosurgery decreased it. Age was a progressive risk factor, with risk escalating after the age of 50 years. Hemodynamic instability, marked by a MAP below 65 mmHg, was strongly linked to AKI, showcasing a threshold effect at 60 mmHg. Intriguingly, average CVP was a significant predictor, with a critical threshold at 10.7 mmHg.

CONCLUSION

Using an Explainable Boosting Machine enhance the precision in AKI risk factors in ICU patients, providing a more nuanced understanding of known AKI risks. This approach allows for refined predictive modeling of AKI, effectively overcoming the limitations of traditional statistical models.

Interpretable machine learning-based individual analysis of acute kidney injury in immune checkpoint inhibitor therapy

BACKGROUND

Acute kidney injury (AKI) is a critical complication of immune checkpoint inhibitor therapy. Since the etiology of AKI in patients undergoing cancer therapy varies, clarifying underlying causes in individual cases is critical for optimal cancer treatment. Although it is essential to individually analyze immune checkpoint inhibitor-treated patients for underlying pathologies for each AKI episode, these analyses have not been realized. Herein, we aimed to individually clarify the underlying causes of AKI in immune checkpoint inhibitor-treated patients using a new clustering approach with Shapley Additive exPlanations (SHAP).

METHODS

We developed a gradient-boosting decision tree-based machine learning model continuously predicting AKI within 7 days, using the medical records of 616 immune checkpoint inhibitor-treated patients. The temporal changes in individual predictive reasoning in AKI prediction models represented the key features contributing to each AKI prediction and clustered AKI patients based on the features with high predictive contribution quantified in time series by SHAP. We searched for common clinical backgrounds of AKI patients in each cluster, compared with annotation by three nephrologists.

RESULTS

One hundred and twelve patients (18.2%) had at least one AKI episode. They were clustered per the key feature, and their SHAP value patterns, and the nephrologists assessed the clusters' clinical relevance. Receiver operating characteristic analysis revealed that the area under the curve was 0.880. Patients with AKI were categorized into four clusters with significant prognostic differences (p = 0.010). The leading causes of AKI for each cluster, such as hypovolemia, drug-related, and cancer cachexia, were all clinically interpretable, which conventional approaches cannot obtain.

CONCLUSION

Our results suggest that the clustering method of individual predictive reasoning in machine learning models can be applied to infer clinically critical factors for developing each episode of AKI among patients with multiple AKI risk factors, such as immune checkpoint inhibitor-treated patients.

Explainable artificial intelligence model for mortality risk prediction in the intensive care unit: a derivation and validation study

BACKGROUND

The lack of transparency is a prevalent issue among the current machine-learning (ML) algorithms utilized for predicting mortality risk. Herein, we aimed to improve transparency by utilizing the latest ML explicable technology, SHapley Additive exPlanation (SHAP), to develop a predictive model for critically ill patients.

METHODS

We extracted data from the Medical Information Mart for Intensive Care IV database, encompassing all intensive care unit admissions. We employed nine different methods to develop the models. The most accurate model, with the highest area under the receiver operating characteristic curve, was selected as the optimal model. Additionally, we used SHAP to explain the workings of the ML model.

RESULTS

The study included 21 395 critically ill patients, with a median age of 68 years (interquartile range, 56-79 years), and most patients were male (56.9%). The cohort was randomly split into a training set (N = 16 046) and a validation set (N = 5349). Among the nine models developed, the Random Forest model had the highest accuracy (87.62%) and the best area under the receiver operating characteristic curve value (0.89). The SHAP summary analysis showed that Glasgow Coma Scale, urine output, and blood urea nitrogen were the top three risk factors for outcome prediction. Furthermore, SHAP dependency analysis and SHAP force analysis were used to interpret the Random Forest model at the factor level and individual level, respectively.

CONCLUSION

A transparent ML model for predicting outcomes in critically ill patients using SHAP methodology is feasible and effective. SHAP values significantly improve the explainability of ML models.

Quantitative methods for optimizing patient outcomes in liver transplantation

Liver transplantation (LT) is a lifesaving yet complex intervention with considerable challenges impacting graft and patient outcomes. Despite best practices, 5-year graft survival is only 70%. Sophisticated quantitative techniques offer potential solutions by assimilating multifaceted data into insights exceeding human cognition. Optimizing donor-recipient matching and graft allocation presents additional intricacies, involving the integration of clinical and laboratory data to select the ideal donor and recipient pair. Allocation must balance physiological variables with geographical and logistical constraints and timing. Quantitative methods can integrate these complex factors to optimize graft utilization. Such methods can also aid in personalizing treatment regimens, drawing on both pretransplant and posttransplant data, possibly using continuous immunological monitoring to enable early detection of graft injury or infected states. Advanced analytics is thus poised to transform management in LT, maximizing graft and patient survival. In this review, we describe quantitative methods applied to organ transplantation, with a focus on LT. These include quantitative methods for (1) utilizing and allocating donor organs equitably and optimally, (2) improving surgical planning through preoperative imaging, (3) monitoring graft and immune status, (4) determining immunosuppressant doses, and (5) establishing and maintaining the health of graft and patient after LT.

Identification and validation of an explainable prediction model of acute kidney injury with prognostic implications in critically ill children: a prospective multicenter cohort study

Background

Acute kidney injury (AKI) is a common and serious organ dysfunction in critically ill children. Early identification and prediction of AKI are of great significance. However, current AKI criteria are insufficiently sensitive and specific, and AKI heterogeneity limits the clinical value of AKI biomarkers. This study aimed to establish and validate an explainable prediction model based on the machine learning (ML) approach for AKI, and assess its prognostic implications in children admitted to the pediatric intensive care unit (PICU).

Methods

This multicenter prospective study in China was conducted on critically ill children for the derivation and validation of the prediction model. The derivation cohort, consisting of 957 children admitted to four independent PICUs from September 2020 to January 2021, was separated for training and internal validation, and an external data set of 866 children admitted from February 2021 to February 2022 was employed for external validation. AKI was defined based on serum creatinine and urine output using the Kidney Disease: Improving Global Outcome (KDIGO) criteria. With 33 medical characteristics easily obtained or evaluated during the first 24 h after PICU admission, 11 ML algorithms were used to construct prediction models. Several evaluation indexes, including the area under the receiver-operating-characteristic curve (AUC), were used to compare the predictive performance. The SHapley Additive exPlanation method was used to rank the feature importance and explain the final model. A probability threshold for the final model was identified for AKI prediction and subgrouping. Clinical outcomes were evaluated in various subgroups determined by a combination of the final model and KDIGO criteria.

Findings

The random forest (RF) model performed best in discriminative ability among the 11 ML models. After reducing features according to feature importance rank, an explainable final RF model was established with 8 features. The final model could accurately predict AKI in both internal (AUC = 0.929) and external (AUC = 0.910) validations, and has been translated into a convenient tool to facilitate its utility in clinical settings. Critically ill children with a probability exceeding or equal to the threshold in the final model had a higher risk of death and multiple organ dysfunctions, regardless of whether they met the KDIGO criteria for AKI.

Interpretation

Our explainable ML model was not only successfully developed to accurately predict AKI but was also highly relevant to adverse outcomes in individual children at an early stage of PICU admission, and it mitigated the concern of the "black-box" issue with an undirect interpretation of the ML technique.

Funding

The National Natural Science Foundation of China, Jiangsu Province Science and Technology Support Program, Key talent of women's and children's health of Jiangsu Province, and Postgraduate Research & Practice Innovation Program of Jiangsu Province.

Network pharmacology, molecular docking and experimental verification of the mechanism of huangqi-jixuecao herb pair in treatment of peritoneal fibrosis

ETHNOPHARMACOLOGICAL RELEVANCE

The Huangqi-Jixuecao herb pair (HQJXCHP) is a traditional herbal formula composed of two widely applied TCM prescriptions, Huangqi (Astragalus membranaceus (Fisch.) Bunge) and Jixuecao (Centella asiatica (L.) Urb.), used for hundreds of years to replenish qi and clear away heat. However, the therapeutic effects of HQJXCHP against peritoneal fibrosis (PF) and potential targets are currently unclear.

AIMS OF THE STUDY

The main objective of this study was preliminary prediction and validation of the effects and molecular mechanisms of action of HQJXCHP against PF based on network pharmacology analysis and experimental verification.

MATERIALS AND METHODS

The ingredients of HQJXCHP were analyzed via HPLC-Q-TOF/MS. Bioactive compounds of HQJXCHP used for network pharmacology analysis were obtained from the TCMSP database. HQJXCHP-related therapeutic targets in PF were obtained from the GeneCards, OMIM, Therapeutic Targets and PharmGkb databases. Therapeutic target-related signaling pathways were predicted via GO and KEGG pathway enrichment analyses. The targets of HQJXCHO were further validated in a PDS-induced PF mouse model in vivo and PMCs MMT model in vitro.

RESULTS

A total of 23 bioactive compounds of HQJXCHP related 188 target genes were retrieved. The HQJXCHP compound-target and PF-related target networks identified 131 common target genes. Subsequent protein-protein interaction (PPI) network analysis results disclosed Akt1, TP53, TNF, VEGFA and CASP3 as the top five key targets of HQJXCHP. Further molecular docking data revealed strong affinity of the two key compounds of HQJXCHP, quercetin and kaempferol, for these key targets. GO and KEGG pathway enrichment analyses further showed that PI3K/Akt, IL-17, TNF and TLR pathways contribute to the therapeutic effects of HQJXCHP on PF. An in vivo PDS-induced PF mouse model and in vitro PMCs mesothelial-to-mesenchymal transition (MMT) model with or without HQJXCHP intervention were used to confirm the effects and mechanisms of action of HQJXCHP. Western blot and qRT-PCR results showed that HQ, JXC and HQJXCHP reduced PDS-induced inflammatory cell aggregation and peritoneal thickening through suppressing the MMT process, among which HQJXCHP exerted the greatest therapeutic effect. Moreover, HQJXCHP inhibited activation of the PI3K/Akt, IL-17, TNF and TLR signaling pathways induced by PDS.

CONCLUSIONS

This is the first study to employ network pharmacology and molecular docking analyses to predict the targets of HQJXCHP with therapeutic effects on PDS-related PF. Data from in vivo and in vitro validation experiments collectively showed that HQJXCHP delays the PF process through inhibiting PI3K/Akt, IL-17, TNF and TLR signaling pathways. Overall, our findings highlight the successful application of network pharmacology theory to provide a scientific basis for clinical utility of HQJXCHP against PF.

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

Development of a nomogram for the prediction of acute kidney injury after liver transplantation: a model based on clinical parameters and postoperative cystatin C level

BACKGROUND

Acute kidney injury (AKI) is common after liver transplantation (LT). We developed a nomogram model to predict post-LT AKI.

METHODS

A total of 120 patients were eligible for inclusion in the study. Clinical information was extracted from the institutional electronic medical record system. Blood samples were collected prior to surgery and immediately after surgery. Univariable and multivariate logistic regression were used to identify independent risk factors. Finally, a nomogram was developed based on the final multivariable logistic regression model.

RESULTS

In total, 58 (48.3%) patients developed AKI. Multivariable logistic regression revealed four independent risk factors for post-LT AKI: operation duration [odds ratio (OR) = 1.728, 95% confidence interval (CI) = 1.121-2.663, p = 0.013], intraoperative hypotension (OR = 3.235, 95% CI = 1.316-7.952, p = 0.011), postoperative cystatin C level (OR = 1.002, 95% CI = 1.001-1.004, p = 0.005) and shock (OR = 4.002, 95% CI = 0.893-17.945, p = 0.070). Receiver operating characteristic curve analysis was used to evaluate model discrimination. The area under the curve value was 0.815 (95% CI = 0.737-0.894).

CONCLUSION

The model based on combinations of clinical parameters and postoperative cystatin C levels had a higher predictive performance for post-LT AKI than the model based on clinical parameters or postoperative cystatin C level alone. Additionally, we developed an easy-to-use nomogram based on the final model, which could aid in the early detection of AKI and improve the prognosis of patients after LT.

Artificial intelligence in anesthesiology

Artificial intelligence (AI) is rapidly evolving and gaining attention in the medical world. Our aim is to provide readers with insights into this quickly changing medical landscape and the role of clinicians in the middle of this popular technology. In this review, our aim is to explain some of the increasingly frequently used AI terminology explicitly for physicians. Next, we give a summation, an overview of currently existing applications, future possibilities for AI in the medical field of anesthesiology and thoroughly highlight possible problems that could arise from implementing this technology in daily practice. Therefore, we conducted a literature search, including all types of articles published between the first of January 2010 and the 1st of May 2023, written in English, and having a free full text available. We searched Pubmed, Medline, and Embase using “artificial intelligence”, “machine learning”, “deep learning”, “neural networks” and “anesthesiology” as MESH terms. To structure these findings, we divided the results into five categories: preoperatively, perioperatively, postoperatively, AI in the intensive care unit and finally, AI used for teaching purposes. In the first category, we found AI applications for airway assessment, risk prediction, and logistic support. Secondly, we made a summation of AI applications used during the operation. AI can predict hypotensive events, delivering automated anesthesia, reducing false alarms, and aiding in the analysis of ultrasound anatomy in locoregional anesthesia and echocardiography. Thirdly, namely postoperatively, AI can be applied in predicting acute kidney injury, pulmonary complications, postoperative cognitive dysfunction and can help to diagnose postoperative pain in children. At the intensive care unit, AI tools discriminate acute respiratory distress syndrome (ARDS) from pulmonary oedema in pleural ultrasound, predict mortality and sepsis more accurately, and predict survival rates in severe Coronavirus-19 (COVID-19). Finally, AI has been described in training residents in spinal ultrasound, simulation, and plexus block anatomy. Several concerns must be addressed regarding the use of AI. Firstly, this software does not explain its decision process (i.e., the ‘black box problem’). Secondly, to develop AI models and decision support systems, we need big and accurate datasets, unfortunately with potential unknown bias. Thirdly, we need an ethical and legal framework before implementing this technology. At the end of this paper, we discuss whether this technology will be able to replace the clinician one day. This paper adds value to already existing literature because it not only offers a summation of existing literature on AI applications in anesthesiology but also gives clear definitions of AI itself and critically assesses implementation of this technology.

A novel explainable online calculator for contrast-induced AKI in diabetics: a multi-centre validation and prospective evaluation study

BACKGROUND

In patients undergoing percutaneous coronary intervention (PCI), contrast-induced acute kidney injury (CIAKI) is a frequent complication, especially in diabetics, and is connected with severe mortality and morbidity in the short and long term. Therefore, we aimed to develop a CIAKI predictive model for diabetic patients.

METHODS

3514 patients with diabetes from four hospitals were separated into three cohorts: training, internal validation, and external validation. We developed six machine learning (ML) algorithms models: random forest (RF), gradient-boosted decision trees (GBDT), logistic regression (LR), least absolute shrinkage and selection operator with LR, extreme gradient boosting trees (XGBT), and support vector machine (SVM). The area under the receiver operating characteristic curve (AUC) of ML models was compared to the prior score model, and developed a brief CIAKI prediction model for diabetes (BCPMD). We also validated BCPMD model on the prospective cohort of 172 patients from one of the hospitals. To explain the prediction model, the shapley additive explanations (SHAP) approach was used.

RESULTS

In the six ML models, XGBT performed best in the cohort of internal (AUC: 0.816 (95% CI 0.777-0.853)) and external validation (AUC: 0.816 (95% CI 0.770-0.861)), and we determined the top 15 important predictors in XGBT model as BCPMD model variables. The features of BCPMD included acute coronary syndromes (ACS), urine protein level, diuretics, left ventricular ejection fraction (LVEF) (%), hemoglobin (g/L), congestive heart failure (CHF), stable Angina, uric acid (umol/L), preoperative diastolic blood pressure (DBP) (mmHg), contrast volumes (mL), albumin (g/L), baseline creatinine (umol/L), vessels of coronary artery disease, glucose (mmol/L) and diabetes history (yrs). Then, we validated BCPMD in the cohort of internal validation (AUC: 0.819 (95% CI 0.783-0.855)), the cohort of external validation (AUC: 0.805 (95% CI 0.755-0.850)) and the cohort of prospective validation (AUC: 0.801 (95% CI 0.688-0.887)). SHAP was constructed to provide personalized interpretation for each patient. Our model also has been developed into an online web risk calculator. MissForest was used to handle the missing values of the calculator.

CONCLUSION

We developed a novel risk calculator for CIAKI in diabetes based on the ML model, which can help clinicians achieve real-time prediction and explainable clinical decisions.

TMT quantitative proteomics and network pharmacology reveal the mechanism by which asiaticoside regulates the JAK2/STAT3 signaling pathway to inhibit peritoneal fibrosis

ETHNOPHARMACOLOGICAL RELEVANCE

Traditional Chinese medicine, Centella asiatica (L.) Urb., has been extensively utilized in clinics to treat a variety of fibrotic disorders. Asiaticoside (ASI), as an important active ingredient, has attracted much attention in this field. However, the effect of ASI on peritoneal fibrosis (PF) is still unclear. Therefore, we evaluated the benefits of ASI for PF and mesothelial-mesenchymal transition (MMT) and revealed the underlying mechanisms.

AIM OF STUDY

The objective of this investigation was to anticipate the potential molecular mechanism of ASI against peritoneal mesothelial cells (PMCs) MMT employing proteomics and network pharmacology, and to confirm it using in vivo and in vitro studies.

MATERIALS AND METHODS

The mesentery of peritoneal fibrosis mice and normal mice were analyzed quantitatively for proteins that were differentially expressed using a technique tandem mass tag (TMT). Next, the core target genes of ASI against PF were screened through network pharmacology analysis, and PPI and C-P‒T networks were constructed by Cytoscape Version 3.7.2. According to the findings of a GO and KEGG enrichment analysis of differential proteins and core target genes, the signaling pathway with a high correlation degree was selected as the key signaling pathway of ASI inhibiting the PMCs MMT for further molecular docking analysis and experimental verification.

RESULTS

TMT-based quantitative proteome analysis revealed the identification of 5727 proteins, of which 70 were downregulated and 178 were upregulated. Among them, the levels of STAT1, STAT2, and STAT3 in the mesentery of mice with peritoneal fibrosis were considerably lower than in the control group, indicating a role for the STAT family in the pathogenesis of peritoneal fibrosis. Then, a total of 98 ASI-PF-related targets were identified by network pharmacology analysis. JAK2 is one of the top 10 core target genes representing a potential therapeutic target. JAK/STAT signaling may represent a core pathway mediating PF effects by ASI. Molecular docking studies showed that ASI had the potential to interact favorably with target genes involved in the JAK/STAT signaling pathway, such as JAK2 and STAT3. The experimental results showed that ASI could significantly alleviate Chlorhexidine Gluconate (CG)-induced peritoneal histopathological changes and increase JAK2 and STAT3 phosphorylation levels. In TGF-β1-stimulated HMrSV5 cells, E-cadherin expression levels were dramatically reduced whereas Vimentin, p-JAK2, α-SMA, and p-STAT3 expression levels were considerably increased. ASI inhibited the TGF-β1-induced HMrSV5 cell MMT, decreased the activation of JAK2/STAT3 signaling, and increased the nuclear translocation of p-STAT3, which was consistent with the effect of the JAK2/STAT3 pathway inhibitor AG490.

CONCLUSION

ASI can inhibit PMCs MMT and alleviate PF by regulating the JAK2/STAT3 signaling pathway.

Artificial intelligence, machine learning, and deep learning in liver transplantation

Liver transplantation (LT) is a life-saving treatment for individuals with end-stage liver disease. The management of LT recipients is complex, predominantly because of the need to consider demographic, clinical, laboratory, pathology, imaging, and omics data in the development of an appropriate treatment plan. Current methods to collate clinical information are susceptible to some degree of subjectivity; thus, clinical decision-making in LT could benefit from the data-driven approach offered by artificial intelligence (AI). Machine learning and deep learning could be applied in both the pre- and post-LT settings. Some examples of AI applications pre-transplant include optimising transplant candidacy decision-making and donor-recipient matching to reduce waitlist mortality and improve post-transplant outcomes. In the post-LT setting, AI could help guide the management of LT recipients, particularly by predicting patient and graft survival, along with identifying risk factors for disease recurrence and other associated complications. Although AI shows promise in medicine, there are limitations to its clinical deployment which include dataset imbalances for model training, data privacy issues, and a lack of available research practices to benchmark model performance in the real world. Overall, AI tools have the potential to enhance personalised clinical decision-making, especially in the context of liver transplant medicine.

Grading the Severity of Damage to the Perijunctional Actomyosin Ring and Zonula Occludens-1 of the Corneal Endothelium by Ensemble Learning Methods

Purpose: In many epithelia, including the corneal endothelium, intracellular/extracellular stresses break down the perijunctional actomyosin ring (PAMR) and zonula occludens-1 (ZO-1) at the apical junctions. This study aims to grade the severity of damage to PAMR and ZO-1 through machine learning. Methods: Immunocytochemical images of PAMR and ZO-1 were drawn from recent studies on the corneal endothelium subjected to hypothermia and oxidative stress. The images were analyzed for their morphological (e.g., Hu moments) and textural features (based on gray-level co-occurrence matrix [GLCM] and Gabor filters). The extracted features were ranked by SHapley analysis and analysis of variance. Then top features were used to grade the severity of damage using a suite of ensemble classifiers, including random forest, bagging classifier (BC), AdaBoost, extreme gradient boosting, and stacking classifier. Results: A partial set of features from GLCM, along with Hu moments and the number of hexagons, enabled the classification of damage to PAMR into Control, Mild, Moderate, and Severe with the area under the receiver operating characteristics curve (AUC) = 0.92 and F1 score = 0.77 with BC. In contrast, a bank of Gabor filters provided a partial set of features that could be combined with Hu moments, branch length, and sharpness for the classification of ZO-1 images into four levels with AUC = 0.95 and F1 score of 0.8 with BC. Conclusions: We have developed a workflow that enables the stratification of damage to PAMR and ZO-1. The approach can be applied to similar data during drug discovery or pathophysiological studies of epithelia.

Single-cell sequencing of ascites fluid illustrates heterogeneity and therapy-induced evolution during gastric cancer peritoneal metastasis

Peritoneal metastasis is the leading cause of death for gastrointestinal cancers. The native and therapy-induced ascites ecosystems are not fully understood. Here, we characterize single-cell transcriptomes of 191,987 ascites cancer/immune cells from 35 patients with/without gastric cancer peritoneal metastasis (GCPM). During GCPM progression, an increase is seen of monocyte-like dendritic cells (DCs) that are pro-angiogenic with reduced antigen-presenting capacity and correlate with poor gastric cancer (GC) prognosis. We also describe the evolution of monocyte-like DCs and regulatory and proliferative T cells following therapy. Moreover, we track GC evolution, identifying high-plasticity GC clusters that exhibit a propensity to shift to a high-proliferative phenotype. Transitions occur via the recently described, autophagy-dependent plasticity program, paligenosis. Two autophagy-related genes (MARCKS and TXNIP) mark high-plasticity GC with poorer prognosis, and autophagy inhibitors induce apoptosis in patient-derived organoids. Our findings provide insights into the developmental trajectories of cancer/immune cells underlying GCPM progression and therapy resistance.

The Evolution of Single-Cell RNA Sequencing Technology and Application: Progress and Perspectives

As an emerging sequencing technology, single-cell RNA sequencing (scRNA-Seq) has become a powerful tool for describing cell subpopulation classification and cell heterogeneity by achieving high-throughput and multidimensional analysis of individual cells and circumventing the shortcomings of traditional sequencing for detecting the average transcript level of cell populations. It has been applied to life science and medicine research fields such as tracking dynamic cell differentiation, revealing sensitive effector cells, and key molecular events of diseases. This review focuses on the recent technological innovations in scRNA-Seq, highlighting the latest research results with scRNA-Seq as the core technology in frontier research areas such as embryology, histology, oncology, and immunology. In addition, this review outlines the prospects for its innovative application in traditional Chinese medicine (TCM) research and discusses the key issues currently being addressed by scRNA-Seq and its great potential for exploring disease diagnostic targets and uncovering drug therapeutic targets in combination with multiomics technologies.

A Novel Online Calculator Predicting Acute Kidney Injury After Liver Transplantation: A Retrospective Study

Acute kidney injury (AKI) after liver transplantation (LT) is a common complication, and its development is thought to be multifactorial. We aimed to investigate potential risk factors and build a model to identify high-risk patients. A total of 199 LT patients were enrolled and each patient data was collected from the electronic medical records. Our primary outcome was postoperative AKI as diagnosed and classified by the KDIGO criteria. A least absolute shrinkage and selection operating algorithm and multivariate logistic regression were utilized to select factors and construct the model. Discrimination and calibration were used to estimate the model performance. Decision curve analysis (DCA) was applied to assess the clinical application value. Five variables were identified as independent predictors for post-LT AKI, including whole blood serum lymphocyte count, RBC count, serum sodium, insulin dosage and anhepatic phase urine volume. The nomogram model showed excellent discrimination with an AUC of 0.817 (95% CI: 0.758-0.876) in the training set. The DCA showed that at a threshold probability between 1% and 70%, using this model clinically may add more benefit. In conclusion, we developed an easy-to-use tool to calculate the risk of post-LT AKI. This model may help clinicians identify high-risk patients.

Artificial Intelligence and Machine Learning in Perioperative Acute Kidney Injury

Acute kidney injury (AKI) is a common complication after a surgery, especially in cardiac and aortic procedures, and has a significant impact on morbidity and mortality. Early identification of high-risk patients and providing effective prevention and therapeutic approach are the main strategies for reducing the possibility of perioperative AKI. Consequently, several risk-prediction models and risk assessment scores have been developed for the prediction of perioperative AKI. However, a majority of these risk scores are only derived from preoperative data while the intraoperative time-series monitoring data such as heart rate and blood pressure were not included. Moreover, the complexity of the pathophysiology of AKI, as well as its nonlinear and heterogeneous nature, imposes limitations on the use of linear statistical techniques. The development of clinical medicine's digitization, the widespread availability of electronic medical records, and the increase in the use of continuous monitoring have generated vast quantities of data. Machine learning has recently shown promise as a method for automatically integrating large amounts of data in predicting the risk of perioperative outcomes. In this article, we discussed the development, limitations of existing work, and the potential future direction of models using machine learning techniques to predict AKI after a surgery.

Calcification of the visceral aorta and celiac trunk is associated with renal and allograft outcomes after deceased donor liver transplantation

PURPOSE

Atherosclerosis affects clinical outcomes in the setting of major surgery. Here we aimed to investigate the prognostic role of visceral aortic (VAC), extended visceral aortic (VAC+), and celiac artery calcification (CAC) in the assessment of short- and long-term outcomes following deceased donor orthotopic liver transplantation (OLT) in a western European cohort.

METHODS

We retrospectively analyzed the data of 281 consecutive recipients who underwent OLT at a German university medical center (05/2010-03/2020). The parameters VAC, VAC+, or CAC were evaluated by preoperative computed tomography-based calcium quantification according to the Agatston score.

RESULTS

Significant VAC or CAC were associated with impaired postoperative renal function (p = 0.0016; p = 0.0211). Patients with VAC suffered more frequently from early allograft dysfunction (EAD) (38 vs 26%, p = 0.031), while CAC was associated with higher estimated procedural costs (p = 0.049). In the multivariate logistic regression analysis, VAC was identified as an independent predictor of EAD (2.387 OR, 1.290-4.418 CI, p = 0.006). Concerning long-term graft and patient survival, no significant difference was found, even though patients with calcification showed a tendency towards lower 5-year survival compared to those without (VAC: 65 vs 73%, p = 0.217; CAC: 52 vs 72%, p = 0.105). VAC+ failed to provide an additional prognostic value compared to VAC.

CONCLUSION

This is the first clinical report to show the prognostic role of VAC/CAC in the setting of deceased donor OLT with a particular value in the perioperative phase. Further studies are warranted to validate these findings. CT computed tomography, OLT orthotopic liver transplantation.

Artificial intelligence for the prediction of acute kidney injury during the perioperative period: systematic review and Meta-analysis of diagnostic test accuracy

BACKGROUND

Acute kidney injury (AKI) is independently associated with morbidity and mortality in a wide range of surgical settings. Nowadays, with the increasing use of electronic health records (EHR), advances in patient information retrieval, and cost reduction in clinical informatics, artificial intelligence is increasingly being used to improve early recognition and management for perioperative AKI. However, there is no quantitative synthesis of the performance of these methods. We conducted this systematic review and meta-analysis to estimate the sensitivity and specificity of artificial intelligence for the prediction of acute kidney injury during the perioperative period.

METHODS

Pubmed, Embase, and Cochrane Library were searched to 2nd October 2021. Studies presenting diagnostic performance of artificial intelligence in the early detection of perioperative acute kidney injury were included. True positives, false positives, true negatives and false negatives were pooled to collate specificity and sensitivity with 95% CIs and results were portrayed in forest plots. The risk of bias of eligible studies was assessed using the PROBAST tool.

RESULTS

Nineteen studies involving 304,076 patients were included. Quantitative random-effects meta-analysis using the Rutter and Gatsonis hierarchical summary receiver operating characteristics (HSROC) model revealed pooled sensitivity, specificity, and diagnostic odds ratio of 0.77 (95% CI: 0.73 to 0.81),0.75 (95% CI: 0.71 to 0.80), and 10.7 (95% CI 8.5 to 13.5), respectively. Threshold effect was found to be the only source of heterogeneity, and there was no evidence of publication bias.

CONCLUSIONS

Our review demonstrates the promising performance of artificial intelligence for early prediction of perioperative AKI. The limitations of lacking external validation performance and being conducted only at a single center should be overcome.

TRIAL REGISTRATION

This study was not registered with PROSPERO.

Comprehensive single-cell transcriptional profiling defines shared and unique epithelial injury responses during kidney fibrosis

The underlying cellular events driving kidney fibrogenesis and metabolic dysfunction are incompletely understood. Here, we employed single-cell combinatorial indexing RNA sequencing to analyze 24 mouse kidneys from two fibrosis models. We profiled 309,666 cells in one experiment, representing 50 cell types/states encompassing epithelial, endothelial, immune, and stromal populations. Single-cell analysis identified diverse injury states of the proximal tubule, including two distinct early-phase populations with dysregulated lipid and amino acid metabolism, respectively. Lipid metabolism was defective in the chronic phase but was transiently activated in the very early stages of ischemia-induced injury, where we discovered increased lipid deposition and increased fatty acid β-oxidation. Perilipin 2 was identified as a surface marker of intracellular lipid droplets, and its knockdown in vitro disrupted cell energy state maintenance during lipid accumulation. Surveying epithelial cells across nephron segments identified shared and unique injury responses. Stromal cells exhibited high heterogeneity and contributed to fibrogenesis by epithelial-stromal crosstalk.

Application of explainable artificial intelligence for healthcare: A systematic review of the last decade (2011-2022)

BACKGROUND AND OBJECTIVES

Artificial intelligence (AI) has branched out to various applications in healthcare, such as health services management, predictive medicine, clinical decision-making, and patient data and diagnostics. Although AI models have achieved human-like performance, their use is still limited because they are seen as a black box. This lack of trust remains the main reason for their low use in practice, especially in healthcare. Hence, explainable artificial intelligence (XAI) has been introduced as a technique that can provide confidence in the model's prediction by explaining how the prediction is derived, thereby encouraging the use of AI systems in healthcare. The primary goal of this review is to provide areas of healthcare that require more attention from the XAI research community.

METHODS

Multiple journal databases were thoroughly searched using PRISMA guidelines 2020. Studies that do not appear in Q1 journals, which are highly credible, were excluded.

RESULTS

In this review, we surveyed 99 Q1 articles covering the following XAI techniques: SHAP, LIME, GradCAM, LRP, Fuzzy classifier, EBM, CBR, rule-based systems, and others.

CONCLUSION

We discovered that detecting abnormalities in 1D biosignals and identifying key text in clinical notes are areas that require more attention from the XAI research community. We hope this is review will encourage the development of a holistic cloud system for a smart city.

Development of a machine learning model to predict lateral hinge fractures by analyzing patient factors before open wedge high tibial osteotomy

PURPOSE

Several methods have been developed to prevent lateral hinge fractures (LHFs), using only classic statistical models. Machine learning is under the spotlight because of its ability to analyze various weights and model nonlinear relationships. The purpose of this study was to create a machine learning model that predicts LHF with high predictive performance.

METHODS

Data were collected from a total of 439 knees with medial osteoarthritis (OA) treated with Medial open wedge high tibial osteotomy (MOW-HTO) from March 2014 to February 2020. The patient data included age, sex, height, and weight. Preoperative, determined, and modifiable factors were categorized using X-ray and CT data to create ensemble models with better predictive performance. Among the 57 ensemble models, which is the total number of possible combinations with six models, the model with the highest area under curve (AUC) or F1-score was selected as the final ensemble model. Gain feature importance analysis and the Shapley additive explanations (SHAP) feature explanation were performed on the best models.

RESULTS

The ensemble model with the highest AUC was a combination of a light gradient boosting machine (LGBM) and multilayer perceptron (MLP) (AUC = 0.992). The ensemble model with the highest F1-score was the model that combined logistic regression (LR) and MLP (F1-score = 0.765). Distance X was the most predictive feature in the results of both model interpretation analyses.

CONCLUSION

Two types of ensemble models, LGBM with MLP and LR with MLP, were developed as machine learning models to predict LHF with high predictive performance. Using these models, surgeons can identify important features to prevent LHF and establish strategies by adjusting modifiable factors.

STUDY DESIGN

Retrospective cohort study.

The promise of machine learning applications in solid organ transplantation

Solid-organ transplantation is a life-saving treatment for end-stage organ disease in highly selected patients. Alongside the tremendous progress in the last several decades, new challenges have emerged. The growing disparity between organ demand and supply requires optimal patient/donor selection and matching. Improvements in long-term graft and patient survival require data-driven diagnosis and management of post-transplant complications. The growing abundance of clinical, genetic, radiologic, and metabolic data in transplantation has led to increasing interest in applying machine-learning (ML) tools that can uncover hidden patterns in large datasets. ML algorithms have been applied in predictive modeling of waitlist mortality, donor–recipient matching, survival prediction, post-transplant complications diagnosis, and prediction, aiming to optimize immunosuppression and management. In this review, we provide insight into the various applications of ML in transplant medicine, why these were used to evaluate a specific clinical question, and the potential of ML to transform the care of transplant recipients. 36 articles were selected after a comprehensive search of the following databases: Ovid MEDLINE; Ovid MEDLINE Epub Ahead of Print and In-Process & Other Non-Indexed Citations; Ovid Embase; Cochrane Database of Systematic Reviews (Ovid); and Cochrane Central Register of Controlled Trials (Ovid). In summary, these studies showed that ML techniques hold great potential to improve the outcome of transplant recipients. Future work is required to improve the interpretability of these algorithms, ensure generalizability through larger-scale external validation, and establishment of infrastructure to permit clinical integration.

Machine Learning for Renal Pathologies: An Updated Survey

Within the literature concerning modern machine learning techniques applied to the medical field, there is a growing interest in the application of these technologies to the nephrological area, especially regarding the study of renal pathologies, because they are very common and widespread in our society, afflicting a high percentage of the population and leading to various complications, up to death in some cases. For these reasons, the authors have considered it appropriate to collect, using one of the major bibliographic databases available, and analyze the studies carried out until February 2022 on the use of machine learning techniques in the nephrological field, grouping them according to the addressed pathologies: renal masses, acute kidney injury, chronic kidney disease, kidney stone, glomerular disease, kidney transplant, and others less widespread. Of a total of 224 studies, 59 were analyzed according to inclusion and exclusion criteria in this review, considering the method used and the type of data available. Based on the study conducted, it is possible to see a growing trend and interest in the use of machine learning applications in nephrology, becoming an additional tool for physicians, which can enable them to make more accurate and faster diagnoses, although there remains a major limitation given the difficulty in creating public databases that can be used by the scientific community to corroborate and eventually make a positive contribution in this area.

Machine learning-based models for predicting clinical outcomes after surgery in unilateral primary aldosteronism

Unilateral subtype of primary aldosteronism (PA) is a common surgically curable form of endocrine hypertension. However, more than half of the patients with PA who undergo unilateral adrenalectomy suffer from persistent hypertension, which may discourage those with PA from undergoing adrenalectomy even when appropriate. The aim of this retrospective cross-sectional study was to develop machine learning-based models for predicting postoperative hypertensive remission using preoperative predictors that are readily available in routine clinical practice. A total of 107 patients with PA who achieved complete biochemical success after adrenalectomy were included and randomly assigned to the training and test datasets. Predictive models of complete clinical success were developed using supervised machine learning algorithms. Of 107 patients, 40 achieved complete clinical success after adrenalectomy in both datasets. Six clinical features associated with complete clinical success (duration of hypertension, defined daily dose (DDD) of antihypertensive medication, plasma aldosterone concentration (PAC), sex, body mass index (BMI), and age) were selected based on predictive performance in the machine learning-based model. The predictive accuracy and area under the curve (AUC) for the developed model in the test dataset were 77.3% and 0.884 (95% confidence interval: 0.737–1.000), respectively. In an independent external cohort, the performance of the predictive model was found to be comparable with an accuracy of 80.4% and AUC of 0.867 (95% confidence interval: 0.763–0.971). The duration of hypertension, DDD of antihypertensive medication, PAC, and BMI were non-linearly related to the prediction of complete clinical success. The developed predictive model may be useful in assessing the benefit of unilateral adrenalectomy and in selecting surgical treatment and antihypertensive medication for patients with PA in clinical practice.

Integrating machine learning with electronic health record data to facilitate detection of prolactin level and pharmacovigilance signals in olanzapine-treated patients

BACKGROUND AND AIM

Available evidence suggests elevated serum prolactin (PRL) levels in olanzapine (OLZ)-treated patients with schizophrenia. However, machine learning (ML)-based comprehensive evaluations of the influence of pathophysiological and pharmacological factors on PRL levels in OLZ-treated patients are rare. We aimed to forecast the PRL level in OLZ-treated patients and mine pharmacovigilance information on PRL-related adverse events by integrating ML and electronic health record (EHR) data.

METHODS

Data were extracted from an EHR system to construct an ML dataset in 672×384 matrix format after preprocessing, which was subsequently randomly divided into a derivation cohort for model development and a validation cohort for model validation (8:2). The eXtreme gradient boosting (XGBoost) algorithm was used to build the ML models, the importance of the features and predictive behaviors of which were illustrated by SHapley Additive exPlanations (SHAP)-based analyses. The sequential forward feature selection approach was used to generate the optimal feature subset. The co-administered drugs that might have influenced PRL levels during OLZ treatment as identified by SHAP analyses were then compared with evidence from disproportionality analyses by using OpenVigil FDA.

RESULTS

The 15 features that made the greatest contributions, as ranked by the mean (|SHAP value|), were identified as the optimal feature subset. The features were gender_male, co-administration of risperidone, age, co-administration of aripiprazole, concentration of aripiprazole, concentration of OLZ, progesterone, co-administration of sulpiride, creatine kinase, serum sodium, serum phosphorus, testosterone, platelet distribution width, α-L-fucosidase, and lipoprotein (a). The XGBoost model after feature selection delivered good performance on the validation cohort with a mean absolute error of 0.046, mean squared error of 0.0036, root-mean-squared error of 0.060, and mean relative error of 11%. Risperidone and aripiprazole exhibited the strongest associations with hyperprolactinemia and decreased blood PRL according to the disproportionality analyses, and both were identified as co-administered drugs that influenced PRL levels during OLZ treatment by SHAP analyses.

CONCLUSIONS

Multiple pathophysiological and pharmacological confounders influence PRL levels associated with effective treatment and PRL-related side-effects in OLZ-treated patients. Our study highlights the feasibility of integration of ML and EHR data to facilitate the detection of PRL levels and pharmacovigilance signals in OLZ-treated patients.

Feature Importance of Acute Rejection among Black Kidney Transplant Recipients by Utilizing Random Forest Analysis: An Analysis of the UNOS Database

Background: Black kidney transplant recipients have worse allograft outcomes compared to White recipients. The feature importance and feature interaction network analysis framework of machine learning random forest (RF) analysis may provide an understanding of RF structures to design strategies to prevent acute rejection among Black recipients. Methods: We conducted tree-based RF feature importance of Black kidney transplant recipients in United States from 2015 to 2019 in the UNOS database using the number of nodes, accuracy decrease, gini decrease, times_a_root, p value, and mean minimal depth. Feature interaction analysis was also performed to evaluate the most frequent occurrences in the RF classification run between correlated and uncorrelated pairs. Results: A total of 22,687 Black kidney transplant recipients were eligible for analysis. Of these, 1330 (6%) had acute rejection within 1 year after kidney transplant. Important variables in the RF models for acute rejection among Black kidney transplant recipients included recipient age, ESKD etiology, PRA, cold ischemia time, donor age, HLA DR mismatch, BMI, serum albumin, degree of HLA mismatch, education level, and dialysis duration. The three most frequent interactions consisted of two numerical variables, including recipient age:donor age, recipient age:serum albumin, and recipient age:BMI, respectively. Conclusions: The application of tree-based RF feature importance and feature interaction network analysis framework identified recipient age, ESKD etiology, PRA, cold ischemia time, donor age, HLA DR mismatch, BMI, serum albumin, degree of HLA mismatch, education level, and dialysis duration as important variables in the RF models for acute rejection among Black kidney transplant recipients in the United States.

Using Explainable Machine Learning to Improve Intensive Care Unit Alarm Systems

Due to the continuous monitoring process of critical patients, Intensive Care Units (ICU) generate large amounts of data, which are difficult for healthcare personnel to analyze manually, especially in overloaded situations such as those present during the COVID-19 pandemic. Therefore, the automatic analysis of these data has many practical applications in patient monitoring, including the optimization of alarm systems for alerting healthcare personnel. In this paper, explainable machine learning techniques are used for this purpose, with a methodology based on age-stratification, boosting classifiers, and Shapley Additive Explanations (SHAP) proposed. The methodology is evaluated using MIMIC-III, an ICU patient research database. The results show that the proposed model can predict mortality within the ICU with AUROC values of 0.961, 0.936, 0.898, and 0.883 for age groups 18–45, 45–65, 65–85 and 85+, respectively. By using SHAP, the features with the highest impact in predicting mortality for different age groups and the threshold from which the value of a clinical feature has a negative impact on the patient’s health can be identified. This allows ICU alarms to be improved by identifying the most important variables to be sensed and the threshold values at which the health personnel must be warned.