The use of nondirected donor organs in living donor liver transplantation: Perspectives and guidance.

Interest in anonymous nondirected living organ donation is increasing in the United States and a small number of transplantation centers are accumulating an experience regarding nondirected donation in living donor liver transplantation. Herein, we review current transplant policy, discuss emerging data, draw parallels from nondirected kidney donation, and examine relevant considerations in nondirected living liver donation. We aim to provide a consensus guidance to ensure safe evaluation and selection of nondirected living liver donors and a schema for just allocation of nondirected grafts.

Machine Learning Models for Prediction of Posttreatment Recurrence in Early-Stage Hepatocellular Carcinoma Using Pretreatment Clinical and MRI Features: A Proof-of-Concept Study.

BACKGROUND. Posttreatment recurrence is an unpredictable complication after liver transplant for hepatocellular carcinoma (HCC) that is associated with poor survival. Biomarkers are needed to estimate recurrence risk before organ allocation. OBJECTIVE. This proof-of-concept study evaluated the use of machine learning (ML) to predict recurrence from pretreatment laboratory, clinical, and MRI data in patients with early-stage HCC initially eligible for liver transplant. METHODS. This retrospective study included 120 patients (88 men, 32 women; median age, 60.0 years) with early-stage HCC diagnosed who were initially eligible for liver transplant and underwent treatment by transplant, resection, or thermal ablation between June 2005 and March 2018. Patients underwent pretreatment MRI and posttreatment imaging surveillance. Imaging features were extracted from postcontrast phases of pretreatment MRI examinations using a pretrained convolutional neural network. Pretreatment clinical characteristics (including laboratory data) and extracted imaging features were integrated to develop three ML models (clinical model, imaging model, combined model) for predicting recurrence within six time frames ranging from 1 through 6 years after treatment. Kaplan-Meier analysis with time to recurrence as the endpoint was used to assess the clinical relevance of model predictions. RESULTS. Tumor recurred in 44 of 120 (36.7%) patients during follow-up. The three models predicted recurrence with AUCs across the six time frames of 0.60-0.78 (clinical model), 0.71-0.85 (imaging model), and 0.62-0.86 (combined model). The mean AUC was higher for the imaging model than the clinical model (0.76 vs 0.68, respectively; p = .03), but the mean AUC was not significantly different between the clinical and combined models or between the imaging and combined models (p > .05). Kaplan-Meier curves were significantly different between patients predicted to be at low risk and those predicted to be at high risk by all three models for the 2-, 3-, 4-, 5-, and 6-year time frames (p < .05). CONCLUSION. The findings suggest that ML-based models can predict recurrence before therapy allocation in patients with early-stage HCC initially eligible for liver transplant. Adding MRI data as model input improved predictive performance over clinical parameters alone. The combined model did not surpass the imaging model's performance. CLINICAL IMPACT. ML-based models applied to currently underutilized imaging features may help design more reliable criteria for organ allocation and liver transplant eligibility.

Outcomes of Simultaneous Liver-Kidney Transplantation Using Kidneys of Deceased Donors With Acute Kidney Injury.

Outcomes from simultaneous liver-kidney transplantation (SLKT) when using kidneys from donors with acute kidney injury (AKI) have not been studied. We studied 5344 SLKTs between May 1, 2007, and December 31, 2019, by using Organ Procurement and Transplantation Network registry data supplemented with United Network for Organ Sharing-DonorNet data. Designating a donor as having AKI required by definition that the following criteria were met: (1) the donor's condition aligned with the Kidney Disease: Improving Global Outcomes (KDIGO) international consensus guidelines and the terminal serum creatinine (Scr) level was ≥1.5 times the minimum Scr level for deceased donors before organ recovery and (2) the terminal Scr level was ≥1.5 mg/dL (a clinically meaningful and intuitive Scr threshold for defining AKI for transplant providers). The primary outcomes were liver transplant all-cause graft failure (ACGF; defined as graft failures and deaths) and kidney transplant death-censored graft failure (DCGF) at 1 year after transplant. The donors with AKI were young, had good organ quality, and had a short cold ischemia time. In the study cohort, 4482 donors had no AKI, whereas 862 had AKI (KDIGO AKI stages: 1, n = 521; 2, n = 202; and 3, n = 138). In the group with AKI and the group with no AKI, respectively, liver ACGF at 1 year (11.1% versus 12.9% [P = 0.13]; hazard ratio [HR], 1.20; 95% confidence interval [CI], 0.97-1.49) and kidney DCGF at 1 year (4.6% versus 5.7% [P = 0.18]; HR, 1.27; 95% CI, 0.95-1.70) did not differ in the full multivariable Cox proportional hazard models. Selected kidneys from deceased donors with AKI can be considered for SLKT.

Deep learning-assisted differentiation of pathologically proven atypical and typical hepatocellular carcinoma (HCC) versus non-HCC on contrast-enhanced MRI of the liver.

OBJECTIVES: To train a deep learning model to differentiate between pathologically proven hepatocellular carcinoma (HCC) and non-HCC lesions including lesions with atypical imaging features on MRI. METHODS: This IRB-approved retrospective study included 118 patients with 150 lesions (93 (62%) HCC and 57 (38%) non-HCC) pathologically confirmed through biopsies (n = 72), resections (n = 29), liver transplants (n = 46), and autopsies (n = 3). Forty-seven percent of HCC lesions showed atypical imaging features (not meeting Liver Imaging Reporting and Data System [LI-RADS] criteria for definitive HCC/LR5). A 3D convolutional neural network (CNN) was trained on 140 lesions and tested for its ability to classify the 10 remaining lesions (5 HCC/5 non-HCC). Performance of the model was averaged over 150 runs with random sub-sampling to provide class-balanced test sets. A lesion grading system was developed to demonstrate the similarity between atypical HCC and non-HCC lesions prone to misclassification by the CNN. RESULTS: The CNN demonstrated an overall accuracy of 87.3%. Sensitivities/specificities for HCC and non-HCC lesions were 92.7%/82.0% and 82.0%/92.7%, respectively. The area under the receiver operating curve was 0.912. CNN's performance was correlated with the lesion grading system, becoming less accurate the more atypical imaging features the lesions showed. CONCLUSION: This study provides proof-of-concept for CNN-based classification of both typical- and atypical-appearing HCC lesions on multi-phasic MRI, utilizing pathologically confirmed lesions as "ground truth." KEY POINTS: • A CNN trained on atypical appearing pathologically proven HCC lesions not meeting LI-RADS criteria for definitive HCC (LR5) can correctly differentiate HCC lesions from other liver malignancies, potentially expanding the role of image-based diagnosis in primary liver cancer with atypical features. • The trained CNN demonstrated an overall accuracy of 87.3% and a computational time of < 3 ms which paves the way for clinical application as a decision support instrument.

Current status: meeting the regulatory goals of your liver transplant program.

PURPOSE OF REVIEW: The regulatory framework set by the Organ Procurement and Transplantation Network (OPTN) and Center for Medicare and Medicaid Services (CMS) for practice of liver transplantation in US is periodically updated and risk adjusted. Therefore, it is prudent for transplant centers to know the rules of engagement as it pertains to their practice. RECENT FINDINGS: OPTN besides providing the regulatory oversight for safe and continued practice of transplant centers, provides necessary tools like: advanced statistical models and technological platforms to aid, and guide transplant centers including the necessary safeguards for high-quality transplant care.CMS regulations although had different thresholds to flag underperformance, often covered common grounds similar to the OPTN, therefore considered duplicative and unnecessary. But with much deliberation and consideration CMS undertook a major overhaul to the final rule for re-approval applications, a giant leap in the positive direction for transplant innovation and growth. SUMMARY: The duplicative regulatory framework of OPTN and CMS has although achieved the goal of improving 1-year patient outcomes, it has proven costly in terms of slowing innovation, increasing organ discard and stunting growth of transplant volume. But the new updates in effect and also in the pipeline are a long-awaited opportunity for waiting transplant patients.

Liver Transplant Costs and Activity After United Network for Organ Sharing Allocation Policy Changes.

Importance: A new liver allocation policy was implemented by United Network for Organ Sharing (UNOS) in February 2020 with the stated intent of improving access to liver transplant (LT). There are growing concerns nationally regarding the implications this new system may have on LT costs, as well as access to a chance for LT, which have not been captured at a multicenter level. Objective: To characterize LT volume and cost changes across the US and within specific center groups and demographics after the policy implementation. Design, Setting, and Participants: This cross-sectional study collected and reviewed LT volume from multiple centers across the US and cost data with attention to 8 specific center demographics. Two separate 12-month eras were compared, before and after the new UNOS allocation policy: March 4, 2019, to March 4, 2020, and March 5, 2020, to March 5, 2021. Data analysis was performed from May to December 2022. Main Outcomes and Measures: Center volume, changes in cost. Results: A total of 22 of 68 centers responded comparing 1948 LTs before the policy change and 1837 LTs postpolicy, resulting in a 6% volume decrease. Transplants using local donations after brain death decreased 54% (P < .001) while imported donations after brain death increased 133% (P = .003). Imported fly-outs and dry runs increased 163% (median, 19; range, 1-75, vs 50, range, 2-91; P = .009) and 33% (median, 3; range, 0-16, vs 7, range, 0-24; P = .02). Overall hospital costs increased 10.9% to a total of $46 360 176 (P = .94) for participating centers. There was a 77% fly-out cost increase postpolicy ($10 600 234; P = .03). On subanalysis, centers with decreased LT volume postpolicy observed higher overall hospital costs ($41 720 365; P = .048), and specifically, a 122% cost increase for liver imports ($6 508 480; P = .002). Transplant centers from low-income states showed a significant increase in hospital (12%) and import (94%) costs. Centers serving populations with larger proportions of racial and ethnic minority candidates and specifically Black candidates significantly increased costs by more than 90% for imported livers, fly-outs, and dry runs despite lower LT volume. Similarly, costs increased significantly (>100%) for fly-outs and dry runs in centers from worse-performing health systems. Conclusions and Relevance: Based on this large multicenter effort and contrary to current assumptions, the new liver distribution system appears to place a disproportionate burden on populations of the current LT community who already experience disparities in health care. The continuous allocation policies being promoted by UNOS could make the situation even worse.

Predicting tumor recurrence on baseline MR imaging in patients with early-stage hepatocellular carcinoma using deep machine learning.

Tumor recurrence affects up to 70% of early-stage hepatocellular carcinoma (HCC) patients, depending on treatment option. Deep learning algorithms allow in-depth exploration of imaging data to discover imaging features that may be predictive of recurrence. This study explored the use of convolutional neural networks (CNN) to predict HCC recurrence in patients with early-stage HCC from pre-treatment magnetic resonance (MR) images. This retrospective study included 120 patients with early-stage HCC. Pre-treatment MR images were fed into a machine learning pipeline (VGG16 and XGBoost) to predict recurrence within six different time frames (range 1-6 years). Model performance was evaluated with the area under the receiver operating characteristic curves (AUC-ROC). After prediction, the model's clinical relevance was evaluated using Kaplan-Meier analysis with recurrence-free survival (RFS) as the endpoint. Of 120 patients, 44 had disease recurrence after therapy. Six different models performed with AUC values between 0.71 to 0.85. In Kaplan-Meier analysis, five of six models obtained statistical significance when predicting RFS (log-rank p < 0.05). Our proof-of-concept study indicates that deep learning algorithms can be utilized to predict early-stage HCC recurrence. Successful identification of high-risk recurrence candidates may help optimize follow-up imaging and improve long-term outcomes post-treatment.

Fibronodular hepatocellular carcinoma-a new variant of liver cancer: clinical, pathological and radiological correlation.

AIMS: To establish and define a new, not previously reported hepatocellular carcinoma (HCC) variant, termed fibronodular HCC (FN-HCC). METHODS: We retrospectively reviewed 290 HCC cases and identified 29 FN-HCC and 24 scirrhous HCC (SCHCC). Clinical, pathological and radiological features of FN-HCC were reviewed and compared with 30 conventional HCCs (CV-HCC) and SC-HCC. RESULTS: FN-HCCs were more likely to arise in non-advanced fibrotic livers with lower advanced Barcelona Clinic Liver Cancer (BCLC) stage, had lower rates of progression and longer time to progression and were more likely to be surgically resected compared with CV-HCCs and SC-HCCs. Imaging analysis of FN-HCCs demonstrated higher rates of non-peripheral washout and a new distinct pattern of enhancement which is characterised by the presence of multiple rounded nodules within a lesion embedded in fibrotic-appearing parenchyma. CONCLUSIONS: FN-HCC may represent a specific variant of HCC with distinct pathological, radiological and clinical features with potential ramifications for outcome.

Inactive status is an independent predictor of liver transplant waitlist mortality and is associated with a transplant centers median meld at transplant.

BACKGROUND: Approximately 30% of patients on the liver transplant waitlist experience at least one inactive status change which makes them temporarily ineligible to receive a deceased donor transplant. We hypothesized that inactive status would be associated with higher mortality which may differ on a transplant centers' or donor service areas' (DSA) Median MELD at Transplant (MMaT). METHODS: Multi-state models were constructed (OPTN database;06/18/2013-06/08/2018) using DSA-level and transplant center-level data where MMaT were numerically ranked and categorized into tertiles. Hazards ratios were calculated between DSA and transplant center tertiles, stratified by MELD score, to determine differences in inactive to active transition probabilities. RESULTS: 7,625 (30.2% of sample registrants;25,216 total) experienced at least one inactive status change in the DSA-level cohort and 7,623 experienced at least one inactive status change in the transplant-center level cohort (30.2% of sample registrants;25,211 total). Inactive patients with MELD≤34 had a higher probability of becoming re-activated if they were waitlisted in a low or medium MMaT transplant center or DSA. Transplant rates were higher and lower re-activation probability was associated with higher mortality for the MELD 26-34 group in the high MMaT tertile. There were no significant differences in re-activation, transplant probability, or waitlist mortality for inactivated patients with MELD≥35 regardless of a DSA's or center's MMaT. CONCLUSION: This study shows that an inactive status change is independently associated with waitlist mortality. This association differs by a centers' and a DSAs' MMaT. Prioritization through care coordination to resolve issues of inactivity is fundamental to improving access.

Neural network predicts need for red blood cell transfusion for patients with acute gastrointestinal bleeding admitted to the intensive care unit.

Acute gastrointestinal bleeding is the most common gastrointestinal cause for hospitalization. For high-risk patients requiring intensive care unit stay, predicting transfusion needs during the first 24 h using dynamic risk assessment may improve resuscitation with red blood cell transfusion in admitted patients with severe acute gastrointestinal bleeding. A patient cohort admitted for acute gastrointestinal bleeding (N = 2,524) was identified from the Medical Information Mart for Intensive Care III (MIMIC-III) critical care database and separated into training (N = 2,032) and internal validation (N = 492) sets. The external validation patient cohort was identified from the eICU collaborative database of patients admitted for acute gastrointestinal bleeding presenting to large urban hospitals (N = 1,526). 62 demographic, clinical, and laboratory test features were consolidated into 4-h time intervals over the first 24 h from admission. The outcome measure was the transfusion of red blood cells during each 4-h time interval. A long short-term memory (LSTM) model, a type of Recurrent Neural Network, was compared to a regression-based models on time-updated data. The LSTM model performed better than discrete time regression-based models for both internal validation (AUROC 0.81 vs 0.75 vs 0.75; P < 0.001) and external validation (AUROC 0.65 vs 0.56 vs 0.56; P < 0.001). A LSTM model can be used to predict the need for transfusion of packed red blood cells over the first 24 h from admission to help personalize the care of high-risk patients with acute gastrointestinal bleeding.

Liver Transplantation in the Time of COVID19: Barriers and Ethical Considerations for Management and Next Steps.

The recent outbreak of the novel virus severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), which causes the corona virus disease of 2019 (COVID19), has spread globally and affects millions of people. This pandemic has taxed our health care system and disrupted normal operations, even life-saving procedures, such as liver transplants. During these unprecedented times, providers and patients are imperiled and resources for diagnosis and care may be limited. Continuing to perform resource-intense advanced procedures is challenging, as is caring for patients with end-stage liver disease or patients with urgent needs for liver tumor control. Liver transplantation, in particular, requires critical resources, like blood products and critical care beds, which are fairly limited in the COVID19 pandemic. The potential of COVID19 infections in posttransplant recipients on immunosuppression and staff contacts further adds to the complexity. Therefore, transplant programs must reevaluate the ethicality, feasibility, and safety of performing liver transplants during this pandemic. Herein, we discuss the clinical and ethical challenges posed by performing liver transplants and offer guidance for managing patients with end-stage liver disease during the COVID19 pandemic.

Public health safety and transplant with increased-risk organs: striking the balance.

There is significant variability amongst transplant centers, Organ Procurement Organizations (OPO), members of public, and patients about organs from Public Health Service increased risk donors. This has therefore required regulatory bodies like Centers for Disease Control and Prevention to formulate policies for transplant centers and OPOs to minimize risk of infectious transmission to recipients of solid-organ transplants from such donors.

Kidney Transplant Program at the Mayo Clinic in Arizona.

Since 1999, we have performed 2,302 kidney transplants at the Mayo Clinic in Arizona. Transplant volume has increased by 45% since 2010. Our center performed 269 kidney transplants in 2013. Our growth is related to multiple factors, including an experienced, committed team and strong support from our institution and referring nephrologists. Areas of program innovation at our center include: transplanting deceased donors with acute kidney injury, outcomes in older kidney transplant recipients, alemtuzumab induction with steroid avoidance, living donor paired kidney exchange-3 site experience, and other non-traditional deceased donor kidney transplants. Of the 162 acute kidney injury (AKI) donor transplants done at our program, 71% had severe AKI. The AKI donor kidneys had more delayed graft function; but graft survival, estimated glomerular filtration rate, and biopsy findings at 1 year were not different form the control group. We have transplanted 188 patients ≥ 70 years old at the time of transplantation. Graft survival at 1, 3, and 5 years was similar to that of patients < 70. Since 2008, 778 (37%) patients received alemtuzumab induction, therapy with excellent patient and graft survival. We have used steroid avoidance immunosuppression with excellent outcomes since 2003. Since starting kidney paired donation in 2009, it has resulted in 54 kidney transplants, including 4 compatible pairs. More than half of the deceased donor transplants done at our center are from non-traditional donors such as Public Health Service increased risk, donation after cardiac death, extended criteria donors/high kidney donor profile index, and pediatric en-bloc donors. One- and 3-year graft survival of the non-traditional deceased donor kidney transplants are not different than the traditional deceased donor kidney transplants.

Penoscrotal entrapment: a safe, innovative technique for removing metal constricting devices.

Penoscrotal entrapment is a rare urological emergency that requires urgent treatment to avoid penile ischaemia and subsequent erectile dysfunction. Non-metallic and thin metallic objects are easily removed via cutting, while thick metallic constricting devices require specialist saws or motorised cutters, all of which may be difficult to locate in the emergency setting. We report a case of a 45-year-old patient who presented with a 48-h history of gross penoscrotal oedema secondary to entrapment by a thick metal ring (internal diameter 55 mm, external diameter 74 mm and thickness 19 mm). The positioning, thickness of the ring, delayed presentation and extensive oedema meant its removal was especially challenging. We improvised a simple, three-step technique (aspiration, strapping and sling) which enabled the metal ring to be removed without the need for specialist equipment. We would encourage others to employ this safe technique in an emergency setting.

Therapeutic hypothermia in drowning induced hypoxic brain injury: a case report.

BACKGROUND: Although therapeutic hypothermia for neuroprotection has been in use for over half a century but its use has been controversial in absence of proper guidelines. However for over two decades there has been revived interest in mild therapeutic hypothermia (32 - 34 degrees C) for neuroprotection. CASE: A 17 year-old female tourist was rescued from sea. She received cardio-pulmonary resuscitation for about 16 minutes. But she had sustained significant neurological insult as a result of hypoxic brain injury. Therapeutic hypothermia was added to her regime of neuroprotection in intensive care unit, and her neurological status improved in just 8 hours with full correction of her coma score by day 4.