Machine-Learning Algorithms Predict Graft Failure After Liver Transplantation

Lau, Lawrence; Kankanige, Yamuna; Rubinstein, Benjamin I. P.; Jones, Robert M.; Christophi, Christopher; Muralidharan, Vijayaragavan; Bailey, James

doi:10.1097/tp.0000000000001600

Cited by 117 publications

(137 citation statements)

References 48 publications

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“…Each model was trained with an initial feature set that included all available covariates, using 10‐fold cross‐validation to select optimal tuning parameters. For each ML model, up to the 15 most important variables for outcome prediction were retained for inclusion in the final feature set . For CART, importance was defined by how much a variable's inclusion improved model accuracy.…”

Section: Methodsmentioning

confidence: 99%

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Prediction of mortality following pediatric heart transplant using machine learning algorithms

Miller

Tumin

Cooper

et al. 2019

Pediatric Transplantation

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Background Optimizing transplant candidates’ priority for donor organs depends on the accurate assessment of post‐transplant outcomes. Due to the complexity of transplantation and the wide range of possible serious complications, recipient outcomes are difficult to predict accurately using conventional multivariable regression. Therefore, we evaluated the utility of 3 ML algorithms for predicting mortality after pediatric HTx. Methods We identified patients <18 years of age receiving HTx in 2006‐2015 in the UNOS Registry database. Mortality within 1, 3, or 5 years was predicted using classification and regression trees, RFs, and ANN. Each model was trained using cross‐validation, then validated in a separate testing set. Model performance was primarily evaluated by the area under the receiver operating characteristic (AUC) curve. Results The training set included 2802 patients, whereas 700 were included in the testing set. RF achieved the best fit to the training data with AUCs of 0.74, 0.68, and 0.64 for 1‐, 3‐, and 5‐year mortality, respectively, and performed best in the testing data, with AUCs of 0.72, 0.61, and 0.60, respectively. Nevertheless, sensitivity was poor across models (training: 0.22‐0.58; testing: 0.07‐0.49). Discussion ML algorithms demonstrated fair predictive utility in both training and testing data, but the sensitivity of these algorithms was generally poor. With the registry missing data on many determinants of long‐term survival, the ability of ML methods to predict mortality after pediatric HTx may be fundamentally limited.

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Section: Methodsmentioning

confidence: 99%

“…For each ML model, up to the 15 most important variables for outcome prediction were retained for inclusion in the final feature set. 24 For CART, importance was defined by how much a variable's inclusion improved model accuracy.…”

Section: Me Thodsmentioning

confidence: 99%

Prediction of mortality following pediatric heart transplant using machine learning algorithms

Miller

Tumin

Cooper

et al. 2019

Pediatric Transplantation

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“…A separate study combined donor-, recipient-, and transplant-related variables in predicting 30-day risk of graft failure. (57) RF applied on 15 donor and recipient characteristics at transplant resulted in an excellent AUC of 0.818 (95% CI, 0.812-0.824) in predicting risk of graft failure, further confirming the utility of ML tools in donor-recipient matching. (57) In addition to this, a more recent study used an ML tool, known as the optimal classification tool (OCT), to predict a given individual's 3-month mortality on the waiting list or risk of delisting.…”

Section: Screening and Selection Of Lt Recipientsmentioning

confidence: 66%

“…(57) RF applied on 15 donor and recipient characteristics at transplant resulted in an excellent AUC of 0.818 (95% CI, 0.812-0.824) in predicting risk of graft failure, further confirming the utility of ML tools in donor-recipient matching. (57) In addition to this, a more recent study used an ML tool, known as the optimal classification tool (OCT), to predict a given individual's 3-month mortality on the waiting list or risk of delisting. (58) This method is based on the principle that these hierarchically organized structures of nodes (classification trees) can make predictions by sequentially splitting the data.…”

Section: Screening and Selection Of Lt Recipientsmentioning

confidence: 66%

“…When compared to several current scoring systems that use either isolated donor/recipient scores or combined donor/recipient factors, the ANNs more accurately predicted 3‐month graft survival (AUC, 0.81) and graft loss (AUC, 0.82). A separate study combined donor‐, recipient‐, and transplant‐related variables in predicting 30‐day risk of graft failure . RF applied on 15 donor and recipient characteristics at transplant resulted in an excellent AUC of 0.818 (95% CI, 0.812‐0.824) in predicting risk of graft failure, further confirming the utility of ML tools in donor‐recipient matching …”

Section: In Ltmentioning

confidence: 77%

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Applying Machine Learning in Liver Disease and Transplantation: A Comprehensive Review

et al. 2020

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Machine learning (ML) utilizes artificial intelligence to generate predictive models efficiently and more effectively than conventional methods through detection of hidden patterns within large data sets. With this in mind, there are several areas within hepatology where these methods can be applied. In this review, we examine the literature pertaining to machine learning in hepatology and liver transplant medicine. We provide an overview of the strengths and limitations of ML tools and their potential applications to both clinical and molecular data in hepatology. ML has been applied to various types of data in liver disease research, including clinical, demographic, molecular, radiological, and pathological data. We anticipate that use of ML tools to generate predictive algorithms will change the face of clinical practice in hepatology and transplantation. This review will provide readers with the opportunity to learn about the ML tools available and potential applications to questions of interest in hepatology.

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“Real‐time” risk models of postoperative morbidity and mortality for liver transplants

Marubashi

Ichihara²,

Kakeji

et al. 2018

Annals of Gastroent Surgery

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AimA comprehensive description of morbidity and mortality risk factors for post liver transplant has not been available to date. In this study, we established real‐time risk models of postoperative morbidities and mortality in liver transplant recipients using two Japanese nationwide databases.MethodsData from two Japanese nationwide databases were combined and used for this study. We developed real‐time prognostic models for morbidity and mortality from a derivation cohort (n = 1472) and validated the findings with an independent cohort (n = 395). Preoperative variables (C1), preoperative and intraoperative variables (C2), and all variables including postoperative morbidities within 30 days (C3) were analyzed to evaluate the independent risk factors for postoperative morbidity and mortality.ResultsWe established real‐time risk models for morbidity and mortality. Areas under the curve (AUC) of C1 and C2 risk models for mortality were 0.74 (0.63‐0.82) and 0.79 (0.69‐0.86), respectively. Multivariate logistic analysis using C3 showed that hemoglobin <10 g/dL, operative time (hours), and five postoperative morbidities (prolonged ventilation >48 hours, coma >24 hours, renal dysfunction, postoperative systemic sepsis, and serum total bilirubin ≥10 mg/dL) represented independent risk factors for mortality (AUC = 0.87, 95% confidence interval [CI]: 0.78‐0.93).ConclusionsReal‐time risk models of postoperative morbidities and mortality at various perioperative time points in liver transplant recipients were established. These novel approaches may improve postoperative outcomes of liver transplant recipients. Furthermore, these real‐time risk models may be applicable to other surgical procedures.

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Machine-Learning Algorithms Predict Graft Failure After Liver Transplantation

Abstract: Using donor, transplant, and recipient characteristics known at the decision time of a transplant, high accuracy in matching donors and recipients can be achieved, potentially providing assistance with clinical decision making.

Cited by 117 publications

References 48 publications

Prediction of mortality following pediatric heart transplant using machine learning algorithms

Prediction of mortality following pediatric heart transplant using machine learning algorithms

Applying Machine Learning in Liver Disease and Transplantation: A Comprehensive Review

“Real‐time” risk models of postoperative morbidity and mortality for liver transplants

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