PMID- 27788800
OWN - NLM
STAT- MEDLINE
DCOM- 20170417
LR  - 20191210
IS  - 1873-2623 (Electronic)
IS  - 0041-1345 (Linking)
VI  - 48
IP  - 8
DP  - 2016 Oct
TI  - Cox Regression Model Analysis of Infection in Renal Transplants After Operation.
PG  - 2678-2683
LID - S0041-1345(16)30454-7 [pii]
LID - 10.1016/j.transproceed.2016.08.014 [doi]
AB  - BACKGROUND: The objective of this study was to explore the factors that affect 
      infections after renal transplant, establishing the Cox model to forecast 
      infection for patients of renal transplant. METHODS: Data were collected from 
      patients who had renal transplantation in Nanking Jinlin Hospital from January 
      2011 to April 2015 (n = 305 transplants). There were 296 individual data that 
      could be used after deleting the people who were lacking some data, changing the 
      main immunosuppressants during the first year, losing follow-up, and data writing 
      that was not fully 1 year after the operation; 296 individuals were divided by 
      3:7. The 206 data of patients (7/10 of the total individuals) were used to 
      analyze and build a model, and the rest of the data were used to verify the 
      model, analyzing the 206 data with Cox regression, discovering the factors that 
      affect the infection after renal transplant independently, building the model, 
      and verification. RESULTS: Cox regression showed that there are three independent 
      factors that affect infections after renal transplant: X3, the donor type 
      (relative risk [RR] = 1.929, P = .037); X9, dialysis time (RR = 1.017, P = .032); 
      and X13, human leukocyte antigen (HLA) match (RR = 0.257, P = .013). The model 
      is: PI = 0.657X3 + 0.017X9 - 1.359X13. All PI for the 206 individuals were 
      calculated and then divided into three groups: the low-risk group, the 
      median-risk group, and the high-risk group. The model was verified by calculating 
      the PI for all 90 people. The log-rank test showed that the survival rates among 
      these groups were significantly different (P < .001). CONCLUSIONS: Donor type, 
      dialysis time, and HLA match are all factors that affect infection after renal 
      transplant. Donor type and dialysis time were the dangerous factors for 
      infection, but HLA match was the protecting factor. The model depends on these 
      three factors and could forecast infection after renal transplant.
CI  - Copyright (c) 2016. Published by Elsevier Inc.
FAU - Junchen, Z
AU  - Junchen Z
AD  - Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University 
      Medical School, Nanjing City, People's Republic of China; Nanjing University, 
      Nanjing City, People's Republic of China.
FAU - Houjing, Z
AU  - Houjing Z
AD  - China Pharmaceutical University, Nanjing City, People's Republic of China.
FAU - Yun, F
AU  - Yun F
AD  - Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University 
      Medical School, Nanjing City, People's Republic of China. Electronic address: 
      njglfy@163.com.
LA  - eng
PT  - Journal Article
PL  - United States
TA  - Transplant Proc
JT  - Transplantation proceedings
JID - 0243532
RN  - 0 (HLA Antigens)
RN  - 0 (Immunosuppressive Agents)
SB  - IM
MH  - Adult
MH  - Female
MH  - HLA Antigens/immunology
MH  - Histocompatibility
MH  - Humans
MH  - Immunosuppressive Agents/therapeutic use
MH  - Infections/*epidemiology/*etiology
MH  - Kidney Transplantation/*adverse effects
MH  - Male
MH  - Middle Aged
MH  - Regression Analysis
MH  - *Renal Dialysis
MH  - Survival Rate
MH  - *Tissue Donors
EDAT- 2016/10/30 06:00
MHDA- 2017/04/18 06:00
CRDT- 2016/10/30 06:00
PHST- 2016/06/26 00:00 [received]
PHST- 2016/08/03 00:00 [accepted]
PHST- 2016/10/30 06:00 [pubmed]
PHST- 2017/04/18 06:00 [medline]
PHST- 2016/10/30 06:00 [entrez]
AID - S0041-1345(16)30454-7 [pii]
AID - 10.1016/j.transproceed.2016.08.014 [doi]
PST - ppublish
SO  - Transplant Proc. 2016 Oct;48(8):2678-2683. doi: 
      10.1016/j.transproceed.2016.08.014.