PMID- 32993687
OWN - NLM
STAT- MEDLINE
DCOM- 20210514
LR  - 20210514
IS  - 1479-5876 (Electronic)
IS  - 1479-5876 (Linking)
VI  - 18
IP  - 1
DP  - 2020 Sep 29
TI  - The study of the association between immune monitoring and pneumonia in kidney 
      transplant recipients through machine learning models.
PG  - 370
LID - 10.1186/s12967-020-02542-2 [doi]
LID - 370
AB  - BACKGROUND: Kidney transplantation is the optimal treatment to cure the patients 
      with end-stage renal disease (ESRD). However, the infectious complication, 
      especially pneumonia, is the main cause of mortality in the early stage. Immune 
      monitoring by relevant biomarkers provides direct evidence of immune status. We 
      aimed to study the association between immune monitoring and pneumonia in kidney 
      transplant patients through machine learning models. METHODS: A total of 146 
      patients receiving the immune monitoring panel in our center, including 46 
      pneumonia recipients and 100 stable recipients, were retrospectively reviewed to 
      develop the models. All the models were validated by external data containing 10 
      pneumonia recipients and 32 stable recipients. The immune monitoring panel 
      consisted of the percentages and absolute cell counts of CD3(+)CD4(+) T cells, 
      CD3(+)CD8(+) T cells, CD19(+) B cells and natural killer (NK) cells, and median 
      fluorescence intensity (MFI) of human leukocyte antigen (HLA)-DR on monocytes and 
      CD64 on neutrophils. The machine learning models including support vector machine 
      (SVM), logistic regression (LR), multi-layer perceptron (MLP) and random forest 
      (RF) were applied for analysis. RESULTS: The pneumonia and stable groups showed 
      significant difference in cell counts of each subpopulation and MFI of monocyte 
      HLA-DR and neutrophil CD64. The SVM model by monocyte HLA-DR (MFI), neutrophil 
      CD64 (MFI), CD8(+) T cells (cells/mul), NK cells (cell/mul) and TBNK (T cells, B 
      cells and NK cells, cells/mul) had the best performance with the average area 
      under the curve (AUC) of 0.940. The RF model best predicted the patients who 
      would progress into severe pneumonia, with the average AUC of 0.760. All the 
      models had good performance validated by external data. CONCLUSIONS: The immune 
      monitoring panel was tightly associated with pneumonia in kidney transplant 
      recipients. The models developed by machine learning techniques identified 
      patients at risk and predicted the prognosis. Based on the results of immune 
      monitoring, better individualized therapy might be achieved.
FAU - Peng, Bo
AU  - Peng B
AD  - Transplantation Center, The Third Xiangya Hospital, Central South University, No. 
      138 Tongzipo Road, Changsha, Hunan, 410013, P. R. China.
FAU - Gong, Hang
AU  - Gong H
AD  - Transplantation Center, The Third Xiangya Hospital, Central South University, No. 
      138 Tongzipo Road, Changsha, Hunan, 410013, P. R. China.
FAU - Tian, Han
AU  - Tian H
AD  - SING Lab, The Hong Kong University of Science and Technology, Hong Kong, P. R. 
      China.
FAU - Zhuang, Quan
AU  - Zhuang Q
AD  - Transplantation Center, The Third Xiangya Hospital, Central South University, No. 
      138 Tongzipo Road, Changsha, Hunan, 410013, P. R. China.
FAU - Li, Junhui
AU  - Li J
AD  - Transplantation Center, The Third Xiangya Hospital, Central South University, No. 
      138 Tongzipo Road, Changsha, Hunan, 410013, P. R. China.
FAU - Cheng, Ke
AU  - Cheng K
AD  - Transplantation Center, The Third Xiangya Hospital, Central South University, No. 
      138 Tongzipo Road, Changsha, Hunan, 410013, P. R. China.
FAU - Ming, Yingzi
AU  - Ming Y
AUID- ORCID: 0000-0002-3004-4405
AD  - Transplantation Center, The Third Xiangya Hospital, Central South University, No. 
      138 Tongzipo Road, Changsha, Hunan, 410013, P. R. China. mingyz_china@csu.edu.cn.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20200929
PL  - England
TA  - J Transl Med
JT  - Journal of translational medicine
JID - 101190741
SB  - IM
MH  - CD8-Positive T-Lymphocytes
MH  - Humans
MH  - *Kidney Transplantation/adverse effects
MH  - Machine Learning
MH  - Monitoring, Immunologic
MH  - *Pneumonia/complications
MH  - Retrospective Studies
MH  - Transplant Recipients
PMC - PMC7526199
OTO - NOTNLM
OT  - Immune monitoring
OT  - Immunosuppression
OT  - Kidney transplant
OT  - Machine learning
OT  - Pneumonia
COIS- The authors declare that they have no competing interests.
EDAT- 2020/10/01 06:00
MHDA- 2021/05/15 06:00
PMCR- 2020/09/29
CRDT- 2020/09/30 05:47
PHST- 2020/02/27 00:00 [received]
PHST- 2020/09/21 00:00 [accepted]
PHST- 2020/09/30 05:47 [entrez]
PHST- 2020/10/01 06:00 [pubmed]
PHST- 2021/05/15 06:00 [medline]
PHST- 2020/09/29 00:00 [pmc-release]
AID - 10.1186/s12967-020-02542-2 [pii]
AID - 2542 [pii]
AID - 10.1186/s12967-020-02542-2 [doi]
PST - epublish
SO  - J Transl Med. 2020 Sep 29;18(1):370. doi: 10.1186/s12967-020-02542-2.