PMID- 20534243
OWN - NLM
STAT- MEDLINE
DCOM- 20101029
LR  - 20100610
IS  - 1873-2623 (Electronic)
IS  - 0041-1345 (Linking)
VI  - 42
IP  - 4
DP  - 2010 May
TI  - Decisional trees in renal transplant follow-up.
PG  - 1134-6
LID - 10.1016/j.transproceed.2010.03.061 [doi]
AB  - INTRODUCTION: The predictive potentialities of application of data mining 
      algorithms to medical research are well known. In this article, we have applied 
      to a transplant population classification trees to build predictive models of 
      graft failure, evaluating the interactions between body mass index (BMI) and 
      other risk factors. The decision trees have been widely used to represent 
      classification rules in a population by a hierarchical sequential structure. 
      PATIENTS AND METHODS: We retrospectively studied 194 renal transplant patients 
      with 5 years of follow-up (128 males, 66 females, mean age at time of transplant 
      of 43.9 +/- 12.5 years). Exclusion criteria were: age < 18 years, multiorgan 
      transplant, and retransplant. The BMI was calculated at the time of 
      transplantation. In the classification algorithm, we considered the following 
      parameters: age, sex, time on dialysis, donor type, donor age, HLA mismatches, 
      delayed graft function (DGF), acute rejection episode (ARE), and chronic 
      allograft nephropathy (CAN). The primary endpoint was graft loss within 5-years 
      follow-up. RESULTS: The classification algorithm produced a decision tree that 
      allowed us to evaluate the interactions between ARE, DGF, CAN, and BMI on graft 
      outcomes, producing a validation set with 88.2% sensitivity and 73.8% 
      specificity. Our model was able to highlight that subjects at risk of graft loss 
      experienced one or more events of ARE, developed DGF and CAN, or has a BMI > 24.8 
      kg/m(2) and CAN. CONCLUSIONS: The use of decision trees in clinical practice may 
      be a suitable alternative to the traditional statistical methods, since it may 
      allow one to analyze interactions between various risk factors beyond the 
      previous knowledge.
CI  - Copyright (c) 2010 Elsevier Inc. All rights reserved.
FAU - Greco, R
AU  - Greco R
AD  - Department of Nephrology, Annunziata Hospital, Cosenza, Italy.
FAU - Papalia, T
AU  - Papalia T
FAU - Lofaro, D
AU  - Lofaro D
FAU - Maestripieri, S
AU  - Maestripieri S
FAU - Mancuso, D
AU  - Mancuso D
FAU - Bonofiglio, R
AU  - Bonofiglio R
LA  - eng
PT  - Journal Article
PL  - United States
TA  - Transplant Proc
JT  - Transplantation proceedings
JID - 0243532
SB  - IM
MH  - Adult
MH  - Artificial Intelligence
MH  - *Decision Trees
MH  - Female
MH  - Follow-Up Studies
MH  - Graft Rejection/classification/epidemiology
MH  - Histocompatibility Testing
MH  - Humans
MH  - Kidney Transplantation/pathology/*physiology
MH  - Male
MH  - Middle Aged
MH  - Patient Selection
MH  - Predictive Value of Tests
MH  - Retrospective Studies
MH  - Time Factors
MH  - Tissue Donors/statistics & numerical data
MH  - Treatment Failure
EDAT- 2010/06/11 06:00
MHDA- 2010/10/30 06:00
CRDT- 2010/06/11 06:00
PHST- 2010/06/11 06:00 [entrez]
PHST- 2010/06/11 06:00 [pubmed]
PHST- 2010/10/30 06:00 [medline]
AID - S0041-1345(10)00349-0 [pii]
AID - 10.1016/j.transproceed.2010.03.061 [doi]
PST - ppublish
SO  - Transplant Proc. 2010 May;42(4):1134-6. doi: 10.1016/j.transproceed.2010.03.061.