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Related Concept Videos

Prediction Intervals01:03

Prediction Intervals

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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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Frequency-dependent Selection01:21

Frequency-dependent Selection

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When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.
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Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification ADCI and Dose Estimation
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Selecting DCD Recipients Using Predictive Indices.

Saif Ganni1, Greta Handing2,3, Adrish Anand2,3

  • 1Division of Abdominal Transplant, Department of Surgery, Michael E DeBakey, Houston, TX.

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|April 3, 2023
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Summary
This summary is machine-generated.

A new Recipient Selector Index (RSI) for Donation after Circulatory Death (DCD) liver transplants identifies optimal candidates. This tool improves recipient selection and increases the use of DCD liver allografts by predicting survival.

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Area of Science:

  • Hepatology
  • Transplantation Surgery
  • Organ Donation

Background:

  • Donation after Circulatory Death (DCD) liver allografts represent a significant, underutilized resource.
  • Identifying optimal candidates for DCD liver transplantation is crucial for improving outcomes.

Purpose of the Study:

  • To identify independent recipient risk factors predicting mortality in DCD liver allograft recipients.
  • To develop and validate a novel DCD Recipient Selector Index (RSI) for pre-transplant candidate selection.
  • To compare the predictive performance of the DCD RSI against existing risk scores.

Main Methods:

  • Retrospective analysis of 4228 DCD liver allograft recipients from the Organ Procurement and Transplantation Network database.
  • Univariate and multivariate analyses to identify significant recipient risk factors.
  • Construction and validation of the DCD RSI, comparing its C-statistic to established models.

Main Results:

  • Identified 8 significant factors, incorporating them into the weighted DCD RSI to predict 3-month survival (C-statistic = 0.6971).
  • Key predictors included recipient serum sodium >150 mEq/L, albumin <2.0 g/dL, and history of portal vein thrombosis.
  • The DCD RSI demonstrated superior predictive ability compared to Balance of Risk, Renal Risk Index, and Patient-Survival Outcomes Following Liver Transplantation scores.

Conclusions:

  • The DCD RSI is a validated tool for preselecting optimal candidates for DCD liver transplantation.
  • Implementing the DCD RSI can enhance patient outcomes and increase the utilization of DCD liver donors.
  • Improved recipient selection through the DCD RSI is key to maximizing the potential of DCD allografts.