The study of the association between immune monitoring and pneumonia in kidney transplant recipients through machine learning models

Abstract

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/μl), NK cells (cell/μl) and TBNK (T cells, B cells and NK cells, cells/μl) 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.

Multi-omics network characterization reveals novel microRNA biomarkers and mechanisms for diagnosis and subtyping of kidney transplant rejection

“…It is widely known that immune response is a leading factor affecting the function of allograft after kidney transplantation [ 29 , 30 ]. In this study immune-related BP terms, e.g., interferon-gamma-mediated signaling pathway, type I interferon signaling pathway, immune response, antigen processing and presentation, etc.…”

The Expansion of Myeloid-Derived Suppressor Cells Correlates With the Severity of Pneumonia in Kidney Transplant Patients

“…Previously, the expansion of MDSCs has been reported in patients with sepsis or coronavirus disease 2019 (COVID-19), and it was correlated with the severity of the diseases (17)(18)(19). Our previous work also found a lower expression of HLA-DR on monocytes in KTRs with pneumonia, indicating the possibility of MDSC expansion in these patients (9). However, the detailed association between MDSCs and pneumonia in KTRs has not been elucidated.…”

“…A notable characteristic of KTRs diagnosed with pneumonia is that patients are generally in a state of significant immunosuppression, manifesting consistent lymphopenia, and low immune response to pathogens even when withdrawing all of the immunosuppressive drugs ( 8 ). Our group previously found that, compared with stable KTRs, patients with pneumonia had significantly lower absolute cell counts of CD3 + CD4 + T cells, CD3 + CD8 + T cells, CD19 + B cells, and natural killer cells, as well as lower expression of human leukocyte antigen (HLA)-DR on monocytes but higher expression of CD64 on neutrophils ( 9 ). A composite equation with these immune biomarkers better assessed the association between immune status and pneumonia than any single parameter did and could predict the prognosis of pneumonia ( 9 ).…”

“…Our group previously found that, compared with stable KTRs, patients with pneumonia had significantly lower absolute cell counts of CD3 + CD4 + T cells, CD3 + CD8 + T cells, CD19 + B cells, and natural killer cells, as well as lower expression of human leukocyte antigen (HLA)-DR on monocytes but higher expression of CD64 on neutrophils ( 9 ). A composite equation with these immune biomarkers better assessed the association between immune status and pneumonia than any single parameter did and could predict the prognosis of pneumonia ( 9 ). However, the current criteria used to evaluate the severity of pneumonia, such as the Infectious Diseases Society of America/American Thoracic Society (IDSA/ATS) criteria for severe community-acquired pneumonia (CAP) ( 10 ), the CURB-65 score ( 11 ), the pneumonia severity index (PSI) ( 12 ) and the sequential organ failure assessment (SOFA) score ( 12 ), only focus on immunocompetent patients and involve few parameters to assess the immune status of the patients.…”

Artificial intelligence and kidney transplantation

“…In addition to the above-mentioned areas, AI techniques are used in kidney transplantation for different purposes. We located different articles in the following topics: Assessment of risk for various complications such as cardiovascular risk[ 58 ], pneumonia[ 59 , 60 ], and CMV infection[ 61 ], prediction of changes in lipid parameters[ 62 ], prediction of HLA response[ 63 - 65 ], and assessment of the risk of kidney transplantation during the coronavirus disease 2019 pandemic[ 66 ].…”