Glucagon-like peptide-1 receptor agonists and sodium-glucose cotransporter-2 inhibitors for the treatment of diabetes mellitus in liver transplant recipients.

AIM: To investigate the efficacy and safety of glucagon-like peptide-1 receptor agonists (GLP-1RAs) and sodium-glucose cotransporter-2 (SGLT2) inhibitors in liver transplant (LT) recipients with diabetes. METHODS: A single-centre, retrospective analysis of prospectively collected data from an LT recipient database (1990-2023) was conducted. We included adults with pre-existing diabetes and post-transplant diabetes, newly started on GLP-1RAs and/or SGLT2 inhibitors after LT. Metabolic and biochemical parameters and outcomes were collected for up to 12 months after starting medications and were compared to those in patients receiving dipeptidyl peptidase-4 (DPP-4) inhibitors. Statistical analysis included descriptive statistics and linear mixed models. RESULTS: We included participants on GLP-1RAs (n = 46), SGLT2 inhibitors (n = 87), combination therapy (n = 12), and a DPP-4 inhibitor comparator (n = 217). Both GLP-1RAs and combination therapy decreased mean glycated haemoglobin (HbA1c) levels, and combination therapy remained significant when adjusted for DPP-4 inhibitor treatment (-3.5%, 95% CI [-6.1, -0.95]; p = 0.0089) at 12 months. All three groups had significant decreases in mean weight and body mass index, but these remained significant in the GLP-1RA (-5.2 kg, 95% CI [-8.7, -1.7], p = 0.0039 and 1.99 kg/m2, 95% CI [-3.4, -0.6], p = 0.0048) and combination therapy groups (-5.4 kg, 95% CI [-10.5, -0.36], p = 0.04 and -3.4 kg/m2, 95% CI [-5.5, -1.3], p = 0.0015) when adjusted for DPP-4 inhibitor treatment at 12 months. Alanine aminotransferase levels decreased with GLP-1RA and combination therapy. There were two (1.4%) cases of graft rejection. CONCLUSION: We found that GLP-1RAs, SGLT2 inhibitors, and their combination, led to significant weight loss in LT recipients with diabetes. Combination therapy, in particular, lowered HbA1c and alanine aminotransferase levels compared to DPP-4 inhibitors. Further studies are needed to assess long-term safety and efficacy.

Impact of living donor liver transplantation on long-term cardiometabolic and graft outcomes in cirrhosis due to nonalcoholic steatohepatitis.

BACKGROUND AND AIM: Non-alcoholic steatohepatitis (NASH) is a leading indication for liver transplantation (LT). This study aimed to determine whether living donor LT (LDLT) recipients experienced less recurrent NASH, cirrhosis, and cardiometabolic complications compared to deceased donor LT (DDLT). METHOD: Patients with LDLT and DDLT for NASH between February 2002 and May 2018 at University Health Network (UHN) were compared. Cox Proportional Hazard model was used to analyze overall survival (OS), Fine and Gray's Competing Risk models were conducted to analyze cumulative incidence of post LT outcomes. RESULTS: One hundred and ninety-nine DDLTs and 66 LDLTs were performed for NASH cirrhosis. Time and rate of recurrence of NAFLD and NASH were comparable in both groups. Graft cirrhosis was more common in DDLT recipients (n = 14) versus LDLT (n = 0) (p < .0001). Significant fibrosis (Fibrosis ≥ F2) developed in 50 recipients (12 LDLT and 38 DDLT) post LT (DDLT vs. LDLT: HR = 1.00, 95% CI = (.52-1.93), p = .91) and there was no difference in time to significant fibrosis (p = .57). There was no difference in development of post-transplant diabetes, dyslipidemia, metabolic syndrome, cardiovascular disease, and cancers. LDLT group had better renal function at 10 years (MDRD eGFR of 57.0 mL/min vs. 48.5 mL/min, p = .047). Both groups had a comparable OS (HR = 1.83 (95% CI = .92-3.62), p = .08). CONCLUSION: Overall, LDLT recipients had significantly better renal function by virtue of having early transplantation in their disease course. LDLT was also associated with significantly less graft cirrhosis, although OS and cardiometabolic outcomes were comparable between LDLT and DDLT.

Development of a Radiomics-Based Model to Predict Graft Fibrosis in Liver Transplant Recipients: A Pilot Study.

Liver Transplantation is complicated by recurrent fibrosis in 40% of recipients. We evaluated the ability of clinical and radiomic features to flag patients at risk of developing future graft fibrosis. CT scans of 254 patients at 3-6 months post-liver transplant were retrospectively analyzed. Volumetric radiomic features were extracted from the portal phase using an Artificial Intelligence-based tool (PyRadiomics). The primary endpoint was clinically significant (≥F2) graft fibrosis. A 10-fold cross-validated LASSO model using clinical and radiomic features was developed. In total, 75 patients (29.5%) developed ≥F2 fibrosis by a median of 19 (4.3-121.8) months. The maximum liver attenuation at the venous phase (a radiomic feature reflecting venous perfusion), primary etiology, donor/recipient age, recurrence of disease, brain-dead donor, tacrolimus use at 3 months, and APRI score at 3 months were predictive of ≥F2 fibrosis. The combination of radiomics and the clinical features increased the AUC to 0.811 from 0.793 for the clinical-only model (p = 0.008) and from 0.664 for the radiomics-only model (p < 0.001) to predict future ≥F2 fibrosis. This pilot study exploring the role of radiomics demonstrates that the addition of radiomic features in a clinical model increased the model's performance. Further studies are required to investigate the generalizability of this experimental tool.

Characterization and predictive functional profiles on metagenomic 16S rRNA data of liver transplant recipients: A longitudinal study.

Long-term survival after Liver Transplantation (LT) is often compromised by infectious and metabolic complications. We aimed to delineate alterations in intestinal microbiome (IM) over time that could contribute to medical complications compromising long-term survival following LT. Fecal samples from LT recipients were collected at 3 months (3 M) and 6 months (6 M) post-LT. The bacterial DNA was extracted using E.Z.N.A. Stool DNA Kit and 16S rRNA gene sequencing at V4 hypervariable region was performed. DADA2 and Phyloseq was implemented to analyze the taxonomic composition. Differentially abundant taxa were identified by metagenomeSeq and LEfSe. Piphillin, an Inferred functional metagenomic analysis tool was used to study the bacterial functional content. For comparison, healthy samples were extracted from NCBI and analyzed similarly. The taxonomic & functional profiles of LT recipients were validated with metagenomic sequencing data from animals exposed to immunosuppressants using Venny. Our findings provide a new perspective on longitudinal increase in specific IM communities post-LT along with an increase in bacterial genes associated with metabolic and infectious disease.

Phase II clinical trial of cabozantinib for the treatment of recurrent hepatocellular carcinoma after liver transplantation.

Recurrent hepatocellular carcinoma (HCC) develops in 15-20% of liver transplant recipients, and it tends to be more aggressive due to underlying immunosuppression. The multikinase inhibitor cabozantinib has been shown to be effective for the treatment of advanced HCC. However, there is no study evaluating this medication in patients with recurrent HCC. Adult patients with measurable biopsy-proven recurrent HCC are eligible for enrollment provided they are not amenable to curative treatments and no prior treatment with cabozantinib. In this study, 60 mg once daily cabozantinib will be administered orally. Participants will receive study treatment as long as they continue to experience clinical benefit or until there is unacceptable toxicity. Tumor measurements will be repeated every 8 weeks to evaluate response. The primary end point of this study will be the disease control rate at 4 months after treatment. The secondary end points will be overall survival, progression-free survival and safety profile of cabozantinib. Furthermore, potential biomarkers will be evaluated to identify their role in tumor progression. The total duration of this trial is expected to be 3 years. We anticipate that this trial will show the effectiveness and safety of cabozantinib in the treatment of post-liver transplant recurrent HCC. Cabozantinib is expected to be an effective treatment due to its activity against many protein kinases, including MET and AXL which are not inhibited by sorafenib.

Liver cancer is the sixth most diagnosed cancer worldwide with few available curative treatments. Liver transplantation (LT) is considered as one of the treatments for liver cancer especially in earlier stages of cancer. However, after LT, cancer develops again in 15­20% of the patients who undergo transplant for liver cancer. Compared with liver cancer in the nontransplant population, recurrent cancer grows faster and spreads in the body very quickly. Therefore, unfortunately, to date there are limited treatment options for these patients without significant effect on their survival. In this study, we aim to evaluate the effect of a new medication called cabozantinib on patients who develop recurrent liver cancer after their LT. Cabozantinib has been already tested in patients with liver cancer and was shown to be effective and safe in nontransplant patients. However, this is the first study to evaluate the effect of cabozantinib in liver transplant recipients with recurrent liver cancer. Clinical Trial Registration: NCT04204850 (ClinicalTrials.gov).

Metabolic Complications in Liver Transplantation Recipients: How We Can Optimize Long-Term Survival.

Liver transplantation (LT) recipients have experienced a significant improvement in short-term survival during the past 3 decades attributed to advancements in surgical techniques, perioperative management, and effective immunosuppressive regimens. However, long-term survival is affected by a high incidence of metabolic disorders and their consequences, including cardiovascular disease (CVD) and malignancies. Pretransplant metabolic impairments especially in those with nonalcoholic steatohepatitis cirrhosis are aggravated by the addition of posttransplant weight gain, physical inactivity, and reversal from catabolic to anabolic state. Moreover, although immunosuppressants are vital to avoid graft rejection, long-term exposure to these medications is implicated in metabolic impairments after LT. In this review, we summarize the molecular pathogenesis of different metabolic disorders after LT, including diabetes mellitus, dyslipidemia, and nonalcoholic fatty liver disease. Furthermore, CVD, malignancies, and graft rejections were provided as significant complications of post-LT metabolic conditions threatening both the patient and graft survival. Ultimately, emerging preventive and treatment strategies for posttransplant diabetes mellitus are summarized. This review highlights the significant need for more clinical trials of antihyperglycemic agents in LT recipients. Also, translational studies will help us to better understand the molecular and genetic factors underlying these metabolic complications and could lead to more personalized management in this high-risk population.

Identifying Modifiable Predictors of Long-Term Survival in Liver Transplant Recipients With Diabetes Mellitus Using Machine Learning.

Diabetes mellitus (DM) significantly impacts long-term survival after liver transplantation (LT). We identified survival factors for LT recipients who had DM to inform preventive care using machine-learning analysis. We analyzed risk factors for mortality in patients from across the United States using the Scientific Registry of Transplant Recipients (SRTR). Patients had undergone LT from 1987 to 2019, with a follow-up of 6.47 years (standard deviation [SD] 5.95). Findings were validated on a cohort from the University Health Network (UHN) from 1989 to 2014 (follow-up 8.15 years [SD 5.67]). Analysis was conducted with Cox proportional hazards and gradient boosting survival. The training set included 84.67% SRTR data (n = 15,289 patients), and the test set included 15.33% SRTR patients (n = 2769) and data from UHN patients (n = 1290). We included 18,058 adults (12,108 [67.05%] men, average age 54.21 years [SD 9.98]) from the SRTR who had undergone LT and had complete data for investigated features. A total of 4634 patients had preexisting DM, and 3158 had post-LT DM. The UHN data consisted of 1290 LT recipients (910 [70.5%] men, average age 54.0 years [SD 10.4]). Increased serum creatinine and hypertension significantly impacted mortality with preexisting DM 1.36 (95% confidence interval [CI], 1.21-1.54) and 1.20 (95% CI, 1.06-1.35) times, respectively. Sirolimus use increased mortality 1.36 times (95% CI, 1.18-1.58) in nondiabetics and 1.33 times (95% CI, 1.09-1.63) in patients with preexisting DM. A similar effect was found in post-LT DM, although it was not statistically significant (1.38 times; 95% CI, 1.07-1.77; P = 0.07). Survival predictors generally achieved a 0.60 to 0.70 area under the receiver operating characteristic for 5-year mortality. LT recipients who have DM have a higher mortality risk than those without DM. Hypertension, decreased renal function, and sirolimus for maintenance immunosuppression compound this mortality risk. These predisposing factors must be intensively treated and modified to optimize long-term survival after transplant.

Combined proteomic/transcriptomic signature of recurrence post-liver transplantation for hepatocellular carcinoma beyond Milan.

BACKGROUND AND AIMS: Liver transplantation (LT) can be offered to patients with Hepatocellular carcinoma (HCC) beyond Milan criteria. However, there are currently limited molecular markers on HCC explant histology to predict recurrence, which arises in up to 20% of LT recipients. The goal of our study was to derive a combined proteomic/transcriptomic signature on HCC explant predictive of recurrence post-transplant using unbiased, high-throughput approaches. METHODS: Patients who received a LT for HCC beyond Milan criteria in the context of hepatitis B cirrhosis were identified. Tumor explants from patients with post-transplant HCC recurrence (N = 7) versus those without recurrence (N = 4) were analyzed by mass spectrometry and gene expression array. Univariate analysis was used to generate a combined proteomic/transcriptomic signature linked to recurrence. Significantly predictive genes and proteins were verified and internally validated by immunoblotting and immunohistochemistry. RESULTS: Seventy-nine proteins and 636 genes were significantly differentially expressed in HCC tumors with subsequent recurrence (p < 0.05). Univariate survival analysis identified Aldehyde Dehydrogenase 1 Family Member A1 (ALDH1A1) gene (HR = 0.084, 95%CI 0.01-0.68, p = 0.0152), ALDH1A1 protein (HR = 0.039, 95%CI 0.16-0.91, p = 0.03), Galectin 3 Binding Protein (LGALS3BP) gene (HR = 7.14, 95%CI 1.20-432.96, p = 0.03), LGALS3BP protein (HR = 2.6, 95%CI 1.1-6.1, p = 0.036), Galectin 3 (LGALS3) gene (HR = 2.89, 95%CI 1.01-8.3, p = 0.049) and LGALS3 protein (HR = 2.6, 95%CI 1.2-5.5, p = 0.015) as key dysregulated analytes in recurrent HCC. In concordance with our proteome findings, HCC recurrence was linked to decreased ALDH1A1 and increased LGALS3 protein expression by Western Blot. LGALS3BP protein expression was validated in 29 independent HCC samples. CONCLUSIONS: Significantly increased LGALS3 and LGALS3BP gene and protein expression on explant were associated with post-transplant recurrence, whereas increased ALDH1A1 was associated with absence of recurrence in patients transplanted for HCC beyond Milan criteria. This combined proteomic/transcriptomic signature could help in predicting HCC recurrence risk and guide post-transplant surveillance.

Harnessing Metabolites as Serum Biomarkers for Liver Graft Pathology Prediction Using Machine Learning.

Graft injury affects over 50% of liver transplant (LT) recipients, but non-invasive biomarkers to diagnose and guide treatment are currently limited. We aimed to develop a biomarker of graft injury by integrating serum metabolomic profiles with clinical variables. Serum from 55 LT recipients with biopsy confirmed metabolic dysfunction-associated steatohepatitis (MASH), T-cell mediated rejection (TCMR) and biliary complications was collected and processed using a combination of LC-MS/MS assay. The metabolomic profiles were integrated with clinical information using a multi-class Machine Learning (ML) classifier. The model's efficacy was assessed through the Out-of-Bag (OOB) error estimate evaluation. Our ML model yielded an overall accuracy of 79.66% with an OOB estimate of the error rate at 19.75%. The model exhibited a maximum ability to distinguish MASH, with an OOB error estimate of 7.4% compared to 22.2% for biliary and 29.6% for TCMR. The metabolites serine and serotonin emerged as the topmost predictors. When predicting binary outcomes using three models: Biliary (biliary vs. rest), MASH (MASH vs. rest) and TCMR (TCMR vs. rest); the AUCs were 0.882, 0.972 and 0.896, respectively. Our ML tool integrating serum metabolites with clinical variables shows promise as a non-invasive, multi-class serum biomarker of graft pathology.

Transcriptomic changes in liver transplant recipients with non-alcoholic steatohepatitis indicate dysregulation of wound healing.

Background: Non-alcoholic steatohepatitis (NASH) has become a leading indication for liver transplantation. However, it often recurs in the graft and can also arise de novo in individuals transplanted for other indications. Post-transplant NASH (PT-NASH) is more aggressive and leads to accelerated fibrosis. The mechanistic basis of PT-NASH has not yet been defined and no specific therapeutic strategies are currently available. Methods: Here, we profiled the transcriptomes of livers with PT-NASH from liver transplant recipients to identify dysregulated genes, pathways, and molecular interaction networks. Results: Transcriptomic changes in the PI3K-Akt pathway were observed in association with metabolic alterations in PT-NASH. Other significant changes in gene expression were associated with DNA replication, cell cycle, extracellular matrix organization, and wound healing. A systematic comparison with non-transplant NASH (NT-NASH) liver transcriptomes indicated an increased activation of wound healing and angiogenesis pathways in the post-transplant condition. Conclusion: Beyond altered lipid metabolism, dysregulation of wound healing and tissue repair mechanisms may contribute to the accelerated development of fibrosis associated with PT-NASH. This presents an attractive therapeutic avenue to explore for PT-NASH to optimize the benefit and survival of the graft.

A deep learning framework for personalised dynamic diagnosis of graft fibrosis after liver transplantation: a retrospective, single Canadian centre, longitudinal study.

BACKGROUND: Recurrent graft fibrosis after liver transplantation can threaten both graft and patient survival. Therefore, early detection of fibrosis is essential to avoid disease progression and the need for retransplantation. Non-invasive blood-based biomarkers of fibrosis are limited by moderate accuracy and high cost. We aimed to evaluate the accuracy of machine learning algorithms in detecting graft fibrosis using longitudinal clinical and laboratory data. METHODS: In this retrospective, longitudinal study, we trained machine learning algorithms, including our novel weighted long short-term memory (LSTM) model, to predict the risk of significant fibrosis using follow-up data from 1893 adults who had a liver transplantation between Feb 1, 1987, and Dec 30, 2019, with at least one liver biopsy post transplantation. Liver biopsy samples with indefinitive fibrosis stage and those from patients with multiple transplantations were excluded. Longitudinal clinical variables were collected from transplantation to the date of last available liver biopsy. Deep learning models were trained on 70% of the patients as the training set and 30% of the patients as the test set. The algorithms were also separately tested on longitudinal data from patients in a subgroup of patients (n=149) who had transient elastography within 1 year before or after the date of liver biopsy. Weighted LSTM model performance for diagnosing significant fibrosis was compared against LSTM, other deep learning models (recurrent neural network and temporal convolutional network), and machine learning models (Random Forest, Support vector machines, Logistic regression, Lasso regression, and Ridge regression) and aspartate aminotransferase-to-platelet ratio index (APRI), fibrosis-4 index (FIB-4), and transient elastography. FINDINGS: 1893 people who had a liver transplantation (1261 [67%] men and 632 [33%] women) with at least one liver biopsy between Jan 1, 1992, and June 30, 2020, were included in the study (591 [31%] cases and 1302 [69%] controls). The median age at liver transplantation was 53·7 years (IQR 47·3-59·0) for cases and 55·3 years (48·0 to 61·2) for controls. The median time interval between transplant and liver biopsy was 21 months (5 to 71). The weighted LSTM model (area under the curve 0·798 [95% CI 0·790 to 0·810]) consistently outperformed other methods, including unweighted LSTM (0·761 [0·750 to 0·769]; p=0·031) Recurrent Neural Network (0·736 [0·721 to 0·744]), Temporal Convolutional Networks (0·700 [0·662 to 0·747], and Random Forest 0·679 [0·652 to 0·707]), FIB-4 (0·650 [0·636 to 0·663]) and APRI (0·682 [0·671 to 0·694]) when diagnosing F2 or worse stage fibrosis. In a subgroup of patients with transient elastography results, weighted LSTM was not significantly better at detecting fibrosis (≥F2; 0·705 [0·687 to 0·724]) than transient elastography (0·685 [0·662 to 0·704]). The top ten variables predictive for significant fibrosis were recipient age, primary indication for transplantation, donor age, and longitudinal data for creatinine, alanine aminotransferase, aspartate aminotransferase, total bilirubin, platelets, white blood cell count, and weight. INTERPRETATION: Deep learning algorithms, particularly weighted LSTM, outperform other routinely used non-invasive modalities and could help with the earlier diagnosis of graft fibrosis using longitudinal clinical and laboratory variables. The list of most important predictive variables for the development of fibrosis will enable clinicians to modify their management accordingly to prevent onset of graft cirrhosis. FUNDING: Canadian Institute of Health Research, American Society of Transplantation, Toronto General and Western Hospital Foundation, and Paladin Labs.

Predictors of patient survival following liver transplant in non-alcoholic steatohepatitis: A systematic review and meta-analysis.

Background: Non-alcoholic steatohepatitis (NASH) is the second-leading indication for liver transplantation (LT) worldwide and is projected to become the leading indication. Our study aimed to determine clinical variables that predict post-LT survival in NASH. Methods: A systematic review and meta-analysis was performed. On June 18, 2020 and April 28, 2022, Ovid MEDLINE ALL, Ovid Embase, Cochrane Database of Systematic Reviews, and Cochrane Central Register of Controlled Trials were searched. No date limits were applied. Inclusion criteria specified the type of study and our study's population/comparison and outcome/timepoints. Pediatric, animal, retransplantation-only, and studies classifying cryptogenic cirrhosis patients with body mass index (BMI) <30 as NASH were excluded. Studies with duplicate cohorts and missing information were excluded from the meta-analysis. Studies were appraised using the Newcastle-Ottawa Scale. This study was preregistered in PROSPERO (CRD42020196915). Findings: Out of 8583 studies identified, 25 studies were included in the systematic review, while 5 studies were included in the meta-analysis. Our quantitative review suggested that the following variables were predictive of post-LT NASH patient survival: recipient age, functional status, pre-LT hepatoma, model for end-stage liver disease (MELD) score, diabetes mellitus (DM), pre-LT dialysis, hepatic encephalopathy, portal vein thrombosis, hospitalization/ICU at LT, and year of LT. Predictors of graft survival included recipient age, BMI, pre-LT dialysis, and DM. Our pooled meta-analyses included five predictors of patient survival. Increased patient mortality was associated with older recipient age (HR=2·07, 95%CI: 1·71-2·50, I2=0, τ2=0, p=0·40) and pretransplant DM (HR=1·18, 95%CI: 1·08-1·28, I2=0, τ2=0, p=0·76). Interpretation: Our systematic review and meta-analysis aimed to synthesise predictive variables of mortality in LT NASH patients. Clinically, this might help to identify modifiable risk factors that can be optimized in the post-transplant setting to improve patient outcomes and optimises decision making in the resource-limited LT setting. Funding: Toronto General and Western Hospital Foundation.

Divergent trajectories of lean vs obese non-alcoholic steatohepatitis patients from listing to post-transplant: A retrospective cohort study.

BACKGROUND: Non-alcoholic steatohepatitis (NASH) cirrhosis is the second most common indication for liver transplantation (LT). The role of body mass index (BMI) on outcomes of NASH cirrhosis has been conflicting. AIM: To compare the longitudinal trajectories of patients with lean vs obese NASH cirrhosis, from listing up to post-transplant, having adjusted their BMI for ascites. METHODS: We retrospectively reviewed all adult NASH patients listed for LT in our program from 2012 to 2019. Fine-Gray Competing Risk analyses and Cox Proportional-Hazard Models were performed to examine the cumulative incidence of transplant and survival outcomes respectively. RESULTS: Out of 265 NASH cirrhosis listed patients, 176 were included. Median age was 61.0 years; 46% were females. 111 patients underwent LT. Obese robust patients had better waitlist survival [hazard ratio (HR): 0.12; 95%CI: 0.05-0.29, P < 0.0001] with higher instantaneous rate of transplant (HR: 5.71; 95%CI: 1.26-25.9, P = 0.02). Lean NASH patients had a substantially higher risk of graft loss within 90 d post-LT (1.2% vs 13.8%, P = 0.032) and death post-LT (2.4% vs 17.2%, P = 0.029). 1- 3- and 5-year graft survival was poor for lean NASH (78.6%, 77.3% and 41.7% vs 98.6%, 96% and 85% respectively). Overall patient survival post-LT was significantly worse in lean NASH (HR: 0.17; 95%CI: 0.03-0.86, P = 0.0142) with 83% lower instantaneous rate of death in obese group. CONCLUSION: Although lean NASH is considered to be more benign than obese NASH, our study suggests a paradoxical correlation of lean NASH with waitlist outcomes, and graft and patient survival post-LT.

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.

Development and validation of an ensemble machine learning framework for detection of all-cause advanced hepatic fibrosis: a retrospective cohort study.

BACKGROUND: Cirrhosis is the result of advanced scarring (or fibrosis) of the liver, and is often diagnosed once decompensation with associated complications has occurred. Current non-invasive tests to detect advanced liver fibrosis have limited performance, with many indeterminate classifications. We aimed to identify patients with advanced liver fibrosis of all-causes using machine learning algorithms (MLAs). METHODS: In this retrospective study of routinely collected laboratory, clinical, and demographic data, we trained six MLAs (support vector machine, random forest classifier, gradient boosting classifier, logistic regression, artificial neural network, and an ensemble of all these algorithms) to detect advanced fibrosis using 1703 liver biopsies from patients seen at the Toronto Liver Clinic (TLC) between Jan 1, 2000, and Dec 20, 2014. Performance was validated using five datasets derived from patient data provided by the TLC (n=104 patients with a biopsy sample taken between March 24, 2014, and Dec 31, 2017) and McGill University Health Centre (MUHC; n=404). Patients with decompensated cirrhosis were excluded. Performance was benchmarked against aspartate aminotransferase-to-platelet ratio index (APRI), fibrosis-4 index (FIB-4), non-alcoholic fatty liver disease fibrosis score (NFS), transient elastography, and an independent panel of five hepatology experts (MB, GS, HK, KP, and RSK). MLA performance was evaluated using the area under the receiver operating characteristic curve (AUROC) and the percentage of determinate classifications. FINDINGS: The best MLA was an ensemble algorithm of support vector machine, random forest classifier, gradient boosting classifier, logistic regression, and neural network algorithms, which achieved 100% determinate classifications (95% CI 100·0-100·0), an AUROC score of 0·870 (95% CI 0·797-0·931) on the TLC validation set (fibrosis stages F0 and F1 vs F4), and an AUROC of 0·716 (95% CI 0·664-0·766) on the MUHC validation set (fibrosis stages F0, F1, and F2 vs F3 and F4). The ensemble MLA outperformed all routinely used biomarkers and achieved comparable performance to hepatologists as measured by AUROC and percentage of indeterminate classifications in both the TLC validation dataset (APRI AUROC score 0·719 [95% CI 0·611-0·820], 83·7% determinate [95% CI 76·0-90·4]; FIB-4 AUROC score 0·825 [95% CI 0·730-0·912], 72·1% determinate [95% CI 63·5-80·8]) and the MUHC validation dataset (APRI AUROC score 0·618 [95% CI 0·548-0·691], 75·5% determinate [95% CI 71·5-79·2]; FIB-4 AUROC score 0·717 (95% CI 0·652-0·776), 75·5% determinate [95% CI 0·713-0·797]), and achieving only slightly lower AUROC than transient elastography (0·773 [95% CI 0·699-0·834] vs 0·826 [95% CI 0·758-0·889]). INTERPRETATION: We have shown that an ensemble MLA outperforms non-imaging-based methods in detecting advanced fibrosis across different causes of liver disease. Our MLA was superior to APRI, FIB-4, and NFS with no indeterminate classifications, while achieving performance comparable to an independent panel of experts. MLAs using routinely collected data could identify patients at high-risk of advanced hepatic fibrosis and cirrhosis among patients with chronic liver disease, allowing intervention before onset of decompensation. FUNDING: Toronto General Hospital Foundation.

Feasibility of a Home-Based Exercise Program for Managing Posttransplant Metabolic Syndrome in Lung and Liver Transplant Recipients: Protocol for a Pilot Randomized Controlled Trial.

BACKGROUND: Posttransplant metabolic syndrome (PTMS) is a common contributor to morbidity and mortality among solid organ transplant recipients in the late posttransplant period (≥1 year). Patients diagnosed with PTMS are at a higher risk of cardiovascular disease and frequently experience decreased physical function and health-related quality of life (HRQL). Studies in the early posttransplant period (<1 year) have shown the benefits of facility-based exercise training on physical function and HRQL, but have not evaluated the effects on metabolic risk factors. It remains unclear whether home-based exercise programs are feasible and can be delivered at a sufficient exercise dose to have effects on PTMS. This protocol outlines the methodology of a randomized controlled trial of a partly supervised home-based exercise program in lung transplant (LTx) and orthotopic liver transplant (OLT) recipients. OBJECTIVE: This study aims to evaluate the feasibility (ie, recruitment rate, program adherence, attrition, safety, and participant satisfaction) of a 12-week individualized, home-based aerobic and resistance training program in LTx and OLT recipients initiated 12 to 18 months after transplantation, and to assess estimates of intervention efficacy on metabolic risk factors, exercise self-efficacy, and HRQL. METHODS: In total, 20 LTx and 20 OLT recipients with ≥2 cardiometabolic risk factors at 12 to 18 months after transplantation will be randomized to an intervention (home-based exercise training) or control group. The intervention group will receive an individualized exercise prescription comprising aerobic and resistance training, 3 to 5 times a week for 12 weeks. Participants will meet on a weekly basis (via videoconference) with a qualified exercise professional who will supervise exercise progression, provide support, and support exercise self-efficacy. Participants in both study groups will receive a counseling session on healthy eating with a dietitian at the beginning of the intervention. For the primary aim, feasibility will be assessed through recruitment rate, program adherence, satisfaction, attrition, and safety parameters. Secondary outcomes will be measured at baseline and 12 weeks, including assessments of metabolic risk factors (ie, insulin resistance, abdominal obesity, blood pressure, and cholesterol), HRQL, and exercise self-efficacy. Descriptive statistics will be used to summarize program feasibility and effect estimates (means and 95% CIs) for sample size calculations in future trials. RESULTS: Enrollment started in July 2021. It is estimated that the study period will be 18 months, with data collection to be completed by December 2022. CONCLUSIONS: A partly supervised home-based, individually tailored exercise program that promotes aerobic and resistance training and exercise self-efficacy may be an important intervention for improving the metabolic profile of LTx and OLT recipients with cardiometabolic risk factors. Thus, characterizing the feasibility and effect estimates of home-based exercise constitutes the first step in developing future clinical trials designed to reduce the high morbidity associated with PTMS. TRIAL REGISTRATION: ClinicalTrials.gov NCT04965142; https://clinicaltrials.gov/ct2/show/NCT04965142. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/35700.

Metabolic Consequences of Solid Organ Transplantation.

Metabolic complications affect over 50% of solid organ transplant recipients. These include posttransplant diabetes, nonalcoholic fatty liver disease, dyslipidemia, and obesity. Preexisting metabolic disease is further exacerbated with immunosuppression and posttransplant weight gain. Patients transition from a state of cachexia induced by end-organ disease to a pro-anabolic state after transplant due to weight gain, sedentary lifestyle, and suboptimal dietary habits in the setting of immunosuppression. Specific immunosuppressants have different metabolic effects, although all the foundation/maintenance immunosuppressants (calcineurin inhibitors, mTOR inhibitors) increase the risk of metabolic disease. In this comprehensive review, we summarize the emerging knowledge of the molecular pathogenesis of these different metabolic complications, and the potential genetic contribution (recipient +/- donor) to these conditions. These metabolic complications impact both graft and patient survival, particularly increasing the risk of cardiovascular and cancer-associated mortality. The current evidence for prevention and therapeutic management of posttransplant metabolic conditions is provided while highlighting gaps for future avenues in translational research.

Long-term mortality risk stratification of liver transplant recipients: real-time application of deep learning algorithms on longitudinal data.

BACKGROUND: Survival of liver transplant recipients beyond 1 year since transplantation is compromised by an increased risk of cancer, cardiovascular events, infection, and graft failure. Few clinical tools are available to identify patients at risk of these complications, which would flag them for screening tests and potentially life-saving interventions. In this retrospective analysis, we aimed to assess the ability of deep learning algorithms of longitudinal data from two prospective cohorts to predict complications resulting in death after liver transplantation over multiple timeframes, compared with logistic regression models. METHODS: In this machine learning analysis, model development was done on a set of 42 146 liver transplant recipients (mean age 48·6 years [SD 17·3]; 17 196 [40·8%] women) from the Scientific Registry of Transplant Recipients (SRTR) in the USA. Transferability of the model was further evaluated by fine-tuning on a dataset from the University Health Network (UHN) in Canada (n=3269; mean age 52·5 years [11·1]; 1079 [33·0%] women). The primary outcome was cause of death, as recorded in the databases, due to cardiovascular causes, infection, graft failure, or cancer, within 1 year and 5 years of each follow-up examination after transplantation. We compared the performance of four deep learning models against logistic regression, assessing performance using the area under the receiver operating characteristic curve (AUROC). FINDINGS: In both datasets, deep learning models outperformed logistic regression, with the Transformer model achieving the highest AUROCs in both datasets (p<0·0001). The AUROC for the Transformer model across all outcomes in the SRTR dataset was 0·804 (99% CI 0·795-0·854) for 1-year predictions and 0·733 (0·729-0·769) for 5-year predictions. In the UHN dataset, the AUROC for the top-performing deep learning model was 0·807 (0·795-0·842) for 1-year predictions and 0·722 (0·705-0·764) for 5-year predictions. AUROCs ranged from 0·695 (0·680-0·713) for prediction of death from infection within 5 years to 0·859 (0·847-0·871) for prediction of death by graft failure within 1 year. INTERPRETATION: Deep learning algorithms can incorporate longitudinal information to continuously predict long-term outcomes after liver transplantation, outperforming logistic regression models. Physicians could use these algorithms at routine follow-up visits to identify liver transplant recipients at risk for adverse outcomes and prevent these complications by modifying management based on ranked features. FUNDING: Canadian Donation and Transplant Research Program, CIFAR AI Chairs Program.