Establishment and effect evaluation of risk prediction model for lung infection after kidney transplantation
10.3969/j.issn.1674-7445.2022.03.016
- VernacularTitle:肾移植受者术后肺部感染风险预测模型的构建及效果评价
- Author:
Jing GONG
1
;
Xiaoqing DOU
;
Liangcheng DING
;
Ya′nan SUN
;
Jingyu LI
;
Qinghui MENG
Author Information
1. School of Nursing, Weifang Medical College, Weifang 261053, China
- Publication Type:Research Article
- Keywords:
Lung infection;
Kidney transplantation;
Risk prediction model;
Risk factor;
Pulmonary disease;
Diabetes mellitus;
Albumin;
Smoking
- From:
Organ Transplantation
2022;13(3):385-
- CountryChina
- Language:Chinese
-
Abstract:
Objective To establish and evaluate the predictive value of the risk prediction model for lung infection within postoperative 1 year in kidney transplant recipients. Methods Clinical data of 197 kidney transplant recipients were retrospectively analyzed. All recipients were divided into the infection group (n=42) and non-infection group (n=155) according to the incidence of lung infection within postoperative 1 year. The incidence and risk factors of lung infection after kidney transplantation were analyzed. Risk prediction model was established by multiple logistic regression analysis. Forty-five kidney transplant recipients who met the inclusion criteria, including 8 cases in the infection group and 37 cases in the non-infection group, were selected to verify the predictive effect of the established model. Results The incidence of lung infection within 1 year after kidney transplantation was 21.3% (n=42), including 38 cases (90%) of pneumonia severity index (PSI) class Ⅰ, 1 case (2%) of PSI class Ⅲ and 3 cases (8%) of PSI class Ⅴ. Lung infection occurred within 1 month after operation in 13 cases, within postoperative 2-6 months in 22 cases and after postoperative 6 months in 7 cases. Nineteen recipients were diagnosed with bacterial infection, 7 cases of fungal infection, 10 cases of viral infection and 6 cases of mixed infection. Smoking history, diabetes mellitus history, pulmonary disease history and albumin level of < 35 g/L were the independent risk factors for lung infection after kidney transplantation (all P < 0.05). The equation of risk prediction model for postoperative lung infection in kidney transplant recipients was logit (lung infection within postoperative 1 year in kidney transplant recipients)=-1.891+1.063×smoking history (yes=1, no=0)+1.398×diabetes mellitus history (yes=1, no=0)+1.732×pulmonary disease history (yes=1, no=0)+1.269×albumin level (< 35 g/L=1, ≥35 g/L=0). The area under the curve (AUC) of receiver operating characteristic (ROC) was 0.788, the sensitivity was 0.786, the specificity was 0.645, and the Youden index was 0.431, respectively. Hosmer-Lemeshow goodness-of-fit test demonstrated that the predicted value of this model yielded relatively high consistency with the observed value. The AUC in the verification group was 0.834. Hosmer-Lemeshow goodness-of-fit test validated high degree of calibration of this model. Conclusions The risk prediction model, consisting of smoking history, diabetes mellitus history, pulmonary disease history and albumin level as predictors, may effectively predict the incidence of lung infection within postoperative 1 year in kidney transplant recipients.
