Machine learning‐based method for tacrolimus dose predictions in Chinese kidney transplant perioperative patients

Delayed-graft function (DGF) might be responsible for shorter graft survival. Therefore, a clinical tool predicting its occurrence is vital for the risk assessment of transplant outcomes. In a single-center study, we conducted data mining and machine learning experiments, resulting in DGF predictive models based on random forest classifiers (RF) and an artificial neural network called multi-layer perceptron (MLP). All designed models had four common input parameters, determining the best accuracy and discriminant ability: donor’s eGFR, recipient’s BMI, donor’s BMI, and recipient–donor weight difference. RF and MLP designs, using these parameters, achieved an accuracy of 84.38% and an area under curve (AUC) 0.84. The model additionally implementing a donor’s age, gender, and Kidney Donor Profile Index (KDPI) accomplished an accuracy of 93.75% and an AUC of 0.91. The other configuration with the estimated post-transplant survival (EPTS) and the kidney donor risk profile (KDRI) achieved an accuracy of 93.75% and an AUC of 0.92. Using machine learning, we were able to assess the risk of DGF in recipients after kidney transplant from a deceased donor. Our solution is scalable and can be improved during subsequent transplants. Based on the new data, the models can achieve better outcomes.

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The Study of the Association between Immune Monitoring and Pneumonia in Kidney Transplant Recipients Through Machine Learning Models

10.21203/rs.3.rs-15932/v2 ◽

2020 ◽

Author(s):

Bo Peng ◽

Hang Gong ◽

Han Tian ◽

Quan Zhuang ◽

Junhui Li ◽

...

Keyword(s):

Machine Learning ◽

T Cells ◽

Nk Cells ◽

Kidney Transplant ◽

Immune Monitoring ◽

Learning Models ◽

Kidney Transplant Recipients ◽

Cell Counts ◽

Hla Dr ◽

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 of 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.

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275 Predicting Deceased Donor Kidney Transplant Outcomes: Comparing KDRI/KDPI with Machine Learning

American Journal of Kidney Diseases ◽

10.1053/j.ajkd.2019.03.277 ◽

2019 ◽

Vol 73 (5) ◽

pp. 714

Keyword(s):

Machine Learning ◽

Kidney Transplant ◽

Deceased Donor ◽

Donor Kidney ◽

Transplant Outcomes ◽

Deceased Donor Kidney ◽

Deceased Donor Kidney Transplant

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

Journal of Translational Medicine ◽

10.1186/s12967-020-02542-2 ◽

2020 ◽

Vol 18 (1) ◽

Cited By ~ 1

Author(s):

Bo Peng ◽

Hang Gong ◽

Han Tian ◽

Quan Zhuang ◽

Junhui Li ◽

...

Keyword(s):

Machine Learning ◽

T Cells ◽

Nk Cells ◽

Kidney Transplant ◽

Immune Monitoring ◽

Learning Models ◽

Kidney Transplant Recipients ◽

Cell Counts ◽

Hla Dr ◽

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.

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Which Kidney Transplant Recipients Can Benefit from the Initial Tacrolimus Dose Reduction?

BioMed Research International ◽

10.1155/2018/4573452 ◽

2018 ◽

Vol 2018 ◽

pp. 1-9 ◽

Cited By ~ 1

Author(s):

Kinga Krzyżowska ◽

Aureliusz Kolonko ◽

Piotr Giza ◽

Jerzy Chudek ◽

Andrzej Więcek

Keyword(s):

Risk Factors ◽

Dose Reduction ◽

Kidney Transplant ◽

Multiple Logistic Regression Analysis ◽

Hemoglobin Concentration ◽

Blood Levels ◽

Transplant Recipients ◽

Kidney Transplant Recipients ◽

Related Factors ◽

Tacrolimus Dose

Background. Observational data suggest that the fixed initial recommended tacrolimus (Tc) dosing (0.2 mg/kg/day) results in supratherapeutic drug levels in some patients during the early posttransplant period. The aim of the study was to analyze a wide panel of patient-related factors and their interactions which increase the risk for first Tc blood level > 15 ng/ml. Materials and Methods. We performed a retrospective analysis of 488 consecutive adult kidney transplant recipients who were initially treated with triple immunosuppressive regimen containing tacrolimus twice daily. The analysis included the first assessment of Tc trough blood levels and several demographic, anthropometric, laboratory, and comedication data. Results. The multiple logistic regression analysis showed that age > 55 years, BMI > 24.6 kg/m2, blood hemoglobin concentration > 9.5 g/dl, and the presence of anti-HCV antibodies independently increased the risk for first Tc level > 15 ng/ml. The relative risk (RR) for first tacrolimus level > 15 ng/ml was 1.88 (95% CI 1.35–2.64, p<0.001) for patients with one risk factor and 2.81 (2.02–3.89, p<0.001) for patients with two risk factors. Conclusions. Initial tacrolimus dose reduction should be considered in older, overweight, or obese kidney transplant recipients and in subjects with anti-HCV antibodies. Moreover, dose reduction of tacrolimus is especially important in patients with coexisting multiple risk factors.

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A machine learning prediction model for waiting time to kidney transplant

PLoS ONE ◽

10.1371/journal.pone.0252069 ◽

2021 ◽

Vol 16 (5) ◽

pp. e0252069

Author(s):

Juliana Feiman Sapiertein Silva ◽

Gustavo Fernandes Ferreira ◽

Marcelo Perosa ◽

Hong Si Nga ◽

Luis Gustavo Modelli de Andrade

Keyword(s):

Machine Learning ◽

Kidney Transplant ◽

Waiting Time ◽

Cox Regression ◽

Final Analysis ◽

Deceased Donor ◽

Waiting List ◽

Sensitivity Analyses ◽

Cox Models ◽

Use Of Resources

Background Predicting waiting time for a deceased donor kidney transplant can help patients and clinicians to discuss management and contribute to a more efficient use of resources. This study aimed at developing a predictor model to estimate time on a kidney transplant waiting list using a machine learning approach. Methods A retrospective cohort study including data of patients registered, between January 1, 2000 and December 31, 2017, in the waiting list of São Paulo State Organ Allocation System (SP-OAS) /Brazil. Data were randomly divided into two groups: 75% for training and 25% for testing. A Cox regression model was fitted with deceased donor transplant as the outcome. Sensitivity analyses were performed using different Cox models. Cox hazard ratios were used to develop the risk-prediction equations. Results Of 54,055 records retrieved, 48,153 registries were included in the final analysis. During the study period, approximately 1/3 of the patients were transplanted with a deceased donor. The major characteristics associated with changes in the likelihood of transplantation were age, subregion, cPRA, and frequency of HLA-DR, -B and -A. The model developed was able to predict waiting time with good agreement in internal validation (c-index = 0.70). Conclusion The kidney transplant waiting time calculator developed shows good predictive performance and provides information that may be valuable in assisting candidates and their providers. Moreover, it can significantly improve the use of economic resources and the management of patient care before transplant.

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