A simple four-factor preoperative recipient scoring model for prediction of 90-day mortality after adult liver Transplantation:A retrospective cohort study

A narrative review: predicting liver transplant graft survival using artificial intelligence modeling

Liver transplantation is the only treatment for patients with liver failure. As demand for liver transplantation grows, it remains a challenge to predict the short- and long-term survival of the liver graft. Recently, artificial intelligence models have been used to evaluate the short- and long-term survival of the liver transplant. To make the models more accurate, suitable liver transplantation characteristics must be used as input to train them. In this narrative review, we reviewed studies concerning liver transplantations published in the PubMed, Web of Science, and Cochrane databases between 2017 and 2022. We picked out 17 studies using our selection criteria and analyzed them, evaluating which medical characteristics were used as input for creation of artificial intelligence models. In eight studies, models estimating only short-term liver graft survival were created, while in five of the studies, models for the prediction of only long-term liver graft survival were built. In four of the studies, artificial intelligence algorithms evaluating both the short- and long-term liver graft survival were created. Medical characteristics that were used as input in reviewed studies and had the biggest impact on the accuracy of the model were the recipient's age, recipient's body mass index, creatinine levels in the recipient's serum, recipient's international normalized ratio, diabetes mellitus, and recipient's model of end-stage liver disease score. To conclude, in order to define important liver transplantation characteristics that could be used as an input for artificial intelligence algorithms when predicting liver graft survival, more models need to be created and analyzed, in order to fully support the results of this review.

Use of machine learning models for the prognostication of liver transplantation: A systematic review

BACKGROUND

Liver transplantation (LT) is a life-saving intervention for patients with end-stage liver disease. However, the equitable allocation of scarce donor organs remains a formidable challenge. Prognostic tools are pivotal in identifying the most suitable transplant candidates. Traditionally, scoring systems like the model for end-stage liver disease have been instrumental in this process. Nevertheless, the landscape of prognostication is undergoing a transformation with the integration of machine learning (ML) and artificial intelligence models.

AIM

To assess the utility of ML models in prognostication for LT, comparing their per formance and reliability to established traditional scoring systems.

METHODS

Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines, we conducted a thorough and standardized literature search using the PubMed/MEDLINE database. Our search imposed no restrictions on publication year, age, or gender. Exclusion criteria encompassed non-English stu dies, review articles, case reports, conference papers, studies with missing data, or those exhibiting evident methodological flaws.

RESULTS

Our search yielded a total of 64 articles, with 23 meeting the inclusion criteria. Among the selected studies, 60.8% originated from the United States and China combined. Only one pediatric study met the criteria. Notably, 91% of the studies were published within the past five years. ML models consistently demonstrated satisfactory to excellent area under the receiver operating characteristic curve values (ranging from 0.6 to 1) across all studies, surpassing the performance of traditional scoring systems. Random forest exhibited superior predictive capa bilities for 90-d mortality following LT, sepsis, and acute kidney injury (AKI). In contrast, gradient boosting excelled in predicting the risk of graft-versus-host disease, pneumonia, and AKI.

CONCLUSION

This study underscores the potential of ML models in guiding decisions related to allograft allocation and LT, marking a significant evolution in the field of prognostication.

Poor handling of continuous predictors in clinical prediction models using logistic regression: a systematic review

BACKGROUND AND OBJECTIVES

When developing a clinical prediction model, assuming a linear relationship between the continuous predictors and outcome is not recommended. Incorrect specification of the functional form of continuous predictors could reduce predictive accuracy. We examine how continuous predictors are handled in studies developing a clinical prediction model.

METHODS

We searched PubMed for clinical prediction model studies developing a logistic regression model for a binary outcome, published between July 01, 2020, and July 30, 2020.

RESULTS

In total, 118 studies were included in the review (18 studies (15%) assessed the linearity assumption or used methods to handle nonlinearity, and 100 studies (85%) did not). Transformation and splines were commonly used to handle nonlinearity, used in 7 (n = 7/18, 39%) and 6 (n = 6/18, 33%) studies, respectively. Categorization was most often used method to handle continuous predictors (n = 67/118, 56.8%) where most studies used dichotomization (n = 40/67, 60%). Only ten models included nonlinear terms in the final model (n = 10/18, 56%).

CONCLUSION

Though widely recommended not to categorize continuous predictors or assume a linear relationship between outcome and continuous predictors, most studies categorize continuous predictors, few studies assess the linearity assumption, and even fewer use methodology to account for nonlinearity. Methodological guidance is provided to guide researchers on how to handle continuous predictors when developing a clinical prediction model.

Clinical value and limitations of the preoperative C-reactive-protein-to-albumin ratio in predicting post-operative morbidity and mortality after deceased-donor liver transplantation: a retrospective single-centre study

Liver transplantation is still associated with a high risk of severe complications and post-operative mortality. This study examines the predictive value of the preoperative C-reactive-protein-to-albumin ratio (CAR) regarding perioperative morbidity and mortality in deceased-donor liver transplantation (DDLT) recipients. In total, 390 DDLT recipients between 05/2010 and 03/2020 were eligible. Predictive abilities of CAR were examined through receiver operating characteristic curve (ROC) analyses. Groups were compared using parametric and non-parametric tests as appropriate. Independent risk factors for morbidity and mortality were identified using uni- and multivariable logistic regression analyses. A good predictive ability for CAR was shown regarding perioperative morbidity (comprehensive complication index ≥75, Clavien-Dindo score ≥4a) and 12-month mortality, with an ideal cut-off of CAR = 26%. Patients with CAR>26% had significantly higher median CCI scores (60 vs. 43, P < 0.001), longer intensive care unit (ICU, 5 vs. 4 days, P < 0.001) and hospital (28 vs. 21 days, P < 0.001) stays and higher 12-month mortality rates (20% vs 6%, P < 0.001). Multivariable analyses identified CAR>26%, pre-OLT inpatient hospitalization (including ICU) and post-operative red blood cell transfusions as independent predictors of severe cumulative morbidity (CCI≥75). Preoperative CAR might be a reliable additional tool to predict perioperative morbidity and mortality in DDLT recipients.