AIMC Topic: Waiting Lists

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Machine Learning for Predicting Waitlist Mortality in Pediatric Heart Transplantation.

Pediatric transplantation
BACKGROUND: Waitlist mortality remains a critical issue for pediatric heart transplant (HTx) candidates, particularly for candidates with congenital heart disease. Listing center organ offer acceptance practices have been identified as a factor influ...

Analysis of the most influential factors affecting outcomes of lung transplant recipients: a multivariate prediction model based on UNOS Data.

BMJ open
OBJECTIVES: In lung transplantation (LTx), a priority is assigned to each candidate on the waiting list. Our primary objective was to identify the key factors that influence the allocation of priorities in LTx using machine learning (ML) techniques t...

Applying Artificial Intelligence to Quantify Body Composition on Abdominal CTs and Better Predict Kidney Transplantation Wait-List Mortality.

Journal of the American College of Radiology : JACR
BACKGROUND: Prekidney transplant evaluation routinely includes abdominal CT for presurgical vascular assessment. A wealth of body composition data are available from these CT examinations, but they remain an underused source of data, often missing fr...

Kidney Allocation Policy: Past, Present, and Future.

Advances in chronic kidney disease
Despite an increase in the number of kidney transplants performed annually, there remain more than 90,000 individuals awaiting transplantation in the United States. As kidney transplantation has evolved, so has kidney allocation policies. The Kidney ...

Effect of a Predictive Model on Planned Surgical Duration Accuracy, Patient Wait Time, and Use of Presurgical Resources: A Randomized Clinical Trial.

JAMA surgery
IMPORTANCE: Accurate surgical scheduling affects patients, clinical staff, and use of physical resources. Although numerous retrospective analyses have suggested a potential for improvement, the real-world outcome of implementing a machine learning m...

The rise and fall of the model for end-stage liver disease score and the need for an optimized machine learning approach for liver allocation.

Current opinion in organ transplantation
PURPOSE OF REVIEW: The Model for End-Stage Liver Disease (MELD) has been used to rank liver transplant candidates since 2002, and at the time bringing much needed objectivity to the liver allocation process. However, and despite numerous revisions to...

Validation of artificial neural networks as a methodology for donor-recipient matching for liver transplantation.

Liver transplantation : official publication of the American Association for the Study of Liver Diseases and the International Liver Transplantation Society
In 2014, we reported a model for donor-recipient (D-R) matching in liver transplantation (LT) based on artificial neural networks (ANNs) from a Spanish multicenter study (Model for Allocation of Donor and Recipient in EspaƱa [MADR-E]). The aim is to ...

WAITING DAAS LIST MORTALITY IMPACT IN HCV CIRRHOTIC PATIENTS.

Arquivos de gastroenterologia
BACKGROUND: The infection for the hepatitis C virus (HCV) is a leading cause of liver-related morbidity and mortality through its evolution to liver cirrhosis, end-stage liver complications and hepatocellular carcinoma. Currently, the new drugs for t...