Logistic Regression Model in a Machine Learning Application to Predict Elderly Kidney Transplant Recipients with Worse Renal Function One Year after Kidney Transplant: Elderly KTbot

Júnior, Ubiraçé Fernando Elihimas; Couto, Jamila Pinho; Pereira, Wallace; Sá, Michel Pompeu Barros de Oliveira; França, Eduardo; Aguiar, Filipe Carrilho de; Cabral, Diogo Buarque Cordeiro; Alencar, Saulo Barbosa Vasconcelos de; Feitosa, Saulo José da Costa; Santos, Thais Oliveira Claizoni Dos; Elihimas, Helen Conceição dos Santos; Alves, Emilly Pereira; Lima, Marcio José de Carvalho; Cavalcanti, Frederico Castelo Branco; Schwingel, Paulo Adriano

doi:10.1155/2020/7413616

Cited by 6 publications

(4 citation statements)

References 53 publications

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“…Regarding the literature inherent to renal transplantation, it is possible to identify three possible applications of AI [123]: (i) diagnosis, using AI to diagnose the level of transplant risk by detecting parameters associated with renal transplant rejections, and identifying abnormal patterns within them, as in [104], with 68.4% accuracy, and in [106]; (ii) prescription, using AI to prescribe postoperative therapies [124] to prevent complications or rejection, or to prescribe diets that may improve quality of life after renal transplantation [125]; (iii) prediction, using AI to predict mortality, and possible rejection, as in [102], with 73.8% specificity and 88.2% sensitivity; in [103], with 56% accuracy over a 3-year timeframe from possible rejection, and in [105], with 85% accuracy. It is important to note that for this specific task, the main limitation for the application of AI is given by the fact that the type of database is very patient-specific [103][104][105][106], as the values are highly dependent on both the recipient and the donor(s) available, resulting in a limitation that makes it difficult to generalize the solutions devised [126].…”

Section: Discussionmentioning

confidence: 99%

“…Although, nowadays, there is a method that reduces the risk of rejection, in the case of mismatched HLA [99,100], approximately 40% of donated kidneys are rejected [101]. The ML techniques applied by [102][103][104][105][106] focus on predicting the probability of success and survival in these types of interventions using numerical features (e.g., age, sex, time in dialysis, donor type, donor age, HLA mismatches, delayed graft function, acute rejection episode, and chronic allograft nephropathy). Table 6 reports all the necessary information, analogously to the others.…”

Section: Kidney Transplantmentioning

confidence: 99%

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Machine Learning for Renal Pathologies: An Updated Survey

Magherini

Mussi

Volpe

et al. 2022

Sensors

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Within the literature concerning modern machine learning techniques applied to the medical field, there is a growing interest in the application of these technologies to the nephrological area, especially regarding the study of renal pathologies, because they are very common and widespread in our society, afflicting a high percentage of the population and leading to various complications, up to death in some cases. For these reasons, the authors have considered it appropriate to collect, using one of the major bibliographic databases available, and analyze the studies carried out until February 2022 on the use of machine learning techniques in the nephrological field, grouping them according to the addressed pathologies: renal masses, acute kidney injury, chronic kidney disease, kidney stone, glomerular disease, kidney transplant, and others less widespread. Of a total of 224 studies, 59 were analyzed according to inclusion and exclusion criteria in this review, considering the method used and the type of data available. Based on the study conducted, it is possible to see a growing trend and interest in the use of machine learning applications in nephrology, becoming an additional tool for physicians, which can enable them to make more accurate and faster diagnoses, although there remains a major limitation given the difficulty in creating public databases that can be used by the scientific community to corroborate and eventually make a positive contribution in this area.

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Section: Discussionmentioning

confidence: 99%

Section: Kidney Transplantmentioning

confidence: 99%

Machine Learning for Renal Pathologies: An Updated Survey

Magherini

Mussi

Volpe

et al. 2022

Sensors

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“…The regions experiencing the highest growth rates of T2DM are Asia, the Middle East and North Africa 1. According to the International Diabetes Federation, the total number of individuals with T2DM worldwide reached 537 million in 2021, and it is projected to reach 783 million by 2045 2. T2DM has emerged as one of the fastest-growing diseases globally.…”

Section: Introductionmentioning

confidence: 99%

“… 1 According to the International Diabetes Federation, the total number of individuals with T2DM worldwide reached 537 million in 2021, and it is projected to reach 783 million by 2045. 2 T2DM has emerged as one of the fastest-growing diseases globally. Implementing timely preventive measures and targeted treatments is crucial for improving the overall health of the global population.…”

Section: Introductionmentioning

confidence: 99%

Associations between human cytomegalovirus infection and type 2 diabetes mellitus: a systematic review and meta-analysis

Wang,

Chen,

Cao

et al. 2023

BMJ Open

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ObjectiveMultiple studies have reported a potential contribution of human cytomegalovirus (HCMV) to the pathogenesis of type 1 diabetes and post-transplantation diabetes. However, the association between HCMV and type 2 diabetes mellitus (T2DM) remains unclear. In this paper, we employ the meta-analysis approach to investigate the potential correlation between HCMV infection and T2DM.MethodThe data of our study were collected from PubMed, Embase, Web of Science, Cochrane Library, China National Knowledge Infrastructure and WAN FANG databases from inception to November 2022. Using the Review Manager V.5.4 software, the meta-analysis was performed.ResultsA total of 18 139 patients from 22 studies were included in our analysis. In the Asian subgroup, the patients with T2DM group had a significantly higher frequency of HCMV infection and older age compared with the healthy group. In the European, the frequency of HCMV infection in the T2DM was lower than the healthy group, although this difference was not statistically significant. After adjusting for demographic factors, the adjusted OR of T2DM for risk of by HCMV status was not found to be significant (adjusted OR=1.19, 95% CI=0.88 to 1.62, p>0.05). Additionally, T2DM with vasculopathy had a significantly higher rate of HCMV infection compared with those without vasculopathy (OR=1.87, 95% CI=1.24 to 2.83, p<0.05). Among T2DM with HCMV infection, there were significant increases in fasting blood glucose levels and the proportion of CD8+ T lymphocytes. Conversely, fasting blood insulin levels, the proportion of CD4+ T lymphocyte and the CD4+/CD8+ ratio were significantly decreased compared with the healthy group.ConclusionAt present, the available evidence does not provide a clear understanding of whether there is a significant association between T2DM and HCMV infection. Additionally, T2DM with HCMV infection exhibited significantly worse blood glucose regulation and immune markers, as well as a higher frequency of vasculopathy.PROSPERO registration numberCRD42022342066.

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Machine learning models in predicting graft survival in kidney transplantation: meta-analysis

et al. 2023

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Background The variations in outcome and frequent occurrence of kidney allograft failure continue to pose important clinical and research challenges despite recent advances in kidney transplantation. The aim of this systematic review was to examine the current application of machine learning models in kidney transplantation and perform a meta-analysis of these models in the prediction of graft survival. Methods This review was registered with the PROSPERO database (CRD42021247469) and all peer-reviewed original articles that reported machine learning model-based prediction of graft survival were included. Quality assessment was performed by the criteria defined by Qiao and risk-of-bias assessment was performed using the PROBAST tool. The diagnostic performance of the meta-analysis was assessed by a meta-analysis of the area under the receiver operating characteristic curve and a hierarchical summary receiver operating characteristic plot. Results A total of 31 studies met the inclusion criteria for the review and 27 studies were included in the meta-analysis. Twenty-nine different machine learning models were used to predict graft survival in the included studies. Nine studies compared the predictive performance of machine learning models with traditional regression methods. Five studies had a high risk of bias and three studies had an unclear risk of bias. The area under the hierarchical summary receiver operating characteristic curve was 0.82 and the summary sensitivity and specificity of machine learning-based models were 0.81 (95 per cent c.i. 0.76 to 0.86) and 0.81 (95 per cent c.i. 0.74 to 0.86) respectively for the overall model. The diagnostic odds ratio for the overall model was 18.24 (95 per cent c.i. 11.00 to 30.16) and 29.27 (95 per cent c.i. 13.22 to 44.46) based on the sensitivity analyses. Conclusion Prediction models using machine learning methods may improve the prediction of outcomes after kidney transplantation by the integration of the vast amounts of non-linear data.

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Logistic Regression Model in a Machine Learning Application to Predict Elderly Kidney Transplant Recipients with Worse Renal Function One Year after Kidney Transplant: Elderly KTbot

Cited by 6 publications

References 53 publications

Machine Learning for Renal Pathologies: An Updated Survey

Machine Learning for Renal Pathologies: An Updated Survey

Associations between human cytomegalovirus infection and type 2 diabetes mellitus: a systematic review and meta-analysis

Machine learning models in predicting graft survival in kidney transplantation: meta-analysis

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