Elsevier

Transplantation Proceedings

Volume 48, Issue 8, October 2016, Pages 2678-2683
Transplantation Proceedings

Current Topics in Transplantation
Renal transplantation
Cox Regression Model Analysis of Infection in Renal Transplants After Operation

https://doi.org/10.1016/j.transproceed.2016.08.014Get rights and content

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    The Cox proportional hazards model that depends on donor type, dialysis time, and HLA match could forecast infection after renal transplant.

Abstract

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.

Section snippets

Study Population

For this research, we considered the data of renal transplants from January 2011 to April 2015 in JinLin Hospital (n = 305). After deleting the individuals who lacked clinical data, changed the main immunosuppressive in 1 year, and data writing not full at 1 year (n = 9), we had the total number of 296 people who could be observed.

End Point and Data Dividing

We defined infection as the end point. The infection end point is the first-time infection event after kidney transplantation within 1 year, which included all of the

Patients

The baseline characteristics of the 206 patients are shown in Table 1.

Evaluation of the factors that are relevant to infections after renal transplantation are shown in Table 2.

Single-Factor Analysis by Cox Regression

The 13 parameters were first investigated by means of single Cox regression (P < .05); the relevant factors were X1, recipient age (P = .005); X3, donor type (P = .001); X9, dialysis time (P = .004); and X13, HLA match (P = .001) (Table 3).

Multiple-Factor Analysis by Cox Regression

The four relevant factors above were disposed by means of Cox multiple regression

Discussion

After renal transplantation, patients, especially the recipients, have lower immunity function because of drugs, operation, and living in an unclear environment, so they become the people who are susceptible to infections and relevant disease. These postoperative complications decrease life quality and have the possibility to lead to death. Thus, it is important to forecast infection after operation. Nowadays, researchers have experimented on risk factors of severe pulmonary infection after

Conclusions

Donor type, dialysis time, and HLA match are the factors that affect infection after renal transplant. Donor type and dialysis time are the dangerous factors for infection, but HLA match is the protecting factor. The model that depends on these three factors could forecast infection after renal transplant.

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