Predictive tools to determine risk of infection in kidney transplant recipients

Fernández‐Ruiz, Mario; López‐Medrano, Francisco; Paz‐Artal, Estela

doi:10.1080/14787210.2020.1733976

Cited by 17 publications

(12 citation statements)

References 146 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent years, a quantity of immune biomarkers have been found to diagnose or predict infection in solid organ transplant recipients, making them as parameters for immune monitoring. These include cell counts of lymphocytes, CD4 + T cells, CD8 + T cells and natural killer (NK) cells, CD4/CD8 ratio, molecule expression on specific cells such as human leukocyte antigen (HLA)-DR on monocytes, CD64 on neutrophils and programmed cell death protein 1 (PD-1) on lymphocytes, immunoglobulins, complements, soluble CD30 and immune cell response to stimuli [ 11 – 13 ]. However, in majority of these studies, parameters are usually analysed separately, or patients are simply classified by scoring according to the parameters.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Important to note that while more data supports a positive relation between hypogammaglobulinemia and infections [19,20], some studies concluded otherwise [21]. Nonetheless, there is still no consensus on preventive measures for infections regarding hypogammaglobulinemia in kidney transplant recipients [21,22,23]. In our case of secondary HGG the decision was made to administer IV IgG in order to correct a substantial deficiency and thus possibly improve patient's outcome.…”

Section: Discussionmentioning

confidence: 88%

Pneumocystis Jirovecii Pneumonia in a Kidney Transplant Recipient 13 Months after Transplantation: A Case Report and Literature Review

Varnas

Jankauskienė

2021

AML

View full text Add to dashboard Cite

Background. Pneumocystis jirovecii pneumonia (PCP) is an opportunistic and prevalent fungal infection in immunocompromised hosts, including patients after kidney transplantation (KTx). It is a life threatening infection. While with effective prophylaxis it became less common, it still remains an issue among solid organ transplant (SOT) recipients during the first year. There are no specific clinical signs for PCP. Computed tomography (CT) is a better method for detecting PCP, but definite diagnosis can only be made by identification of the microorganism either by a microscopy or by a polymerase chain reaction (PCR).Clinical case. We present a case of a 17 year old with severe PCP 13 months after KTx followed by reduction in kidney function and respiratory compromise. The pathogen was detected by PCR from bronchoalveolar lavage fluid (BALF) and patient was treated successfully with trimethoprim-sulfamethoxazole (TMP-SMX). Patient’s condition, respiratory status and kidney function gradually improved. Our presented case is unusual because patient had no known risk factors for PCP and he was more than one year after KTx, what is considered rare. In addition patient and his parents delayed in notifying the treating physician about ongoing symptoms because did not deem them important enough.Conclusions. Clinicians treating patients in risk groups for PCP must always remain vigilant even in era of effective prophylaxis. The vigilance should also extend to the patient and patient’s family.

show abstract

“…The vulnerability of KTRs to infection has prompted development of predictive models of likelihood of infection based on novel biomarkers of immune function and allograft integrity [13]. However, in the presence of a novel viral pandemic with unknown biological behavior, prior experience and modeling are of limited value.…”

Section: Discussionmentioning

confidence: 99%

Remote monitoring using donor-derived, cell-free DNA after kidney transplantation during the coronavirus disease 2019 pandemic

Potter

Hinojosa²,

Miles

et al. 2020

Kidney Res Clin Pract

View full text Add to dashboard Cite

Predictive tools to determine risk of infection in kidney transplant recipients

Cited by 17 publications

References 146 publications

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

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

Pneumocystis Jirovecii Pneumonia in a Kidney Transplant Recipient 13 Months after Transplantation: A Case Report and Literature Review

Remote monitoring using donor-derived, cell-free DNA after kidney transplantation during the coronavirus disease 2019 pandemic

Contact Info

Product

Resources

About