Quantitative ultrasound for non-invasive evaluation of subclinical rejection in renal transplantation

Kim, Deok Gie; Lee, Jun Young; Ahn, Jhii-Hyun; Lee, Taesic; Eom, Minseob; Cho, Hyun‐Seok; Ku, Jihye

doi:10.1007/s00330-022-09260-x

Cited by 4 publications

(3 citation statements)

References 41 publications

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“…This could be attributed to poor graft function (survival), medication nonadherence, or the presence of comorbidities. This result is supported by previous studies [36,37]. Regular monitoring of BUN levels in kidney transplant recipients can help detect graft dysfunction early and prompt intervention, such as medication adjustments, patient education, lifestyle modifications, and management of comorbidity, leading to better graft survival.…”

Section: Discussionsupporting

confidence: 84%

Developing Clinical Prognostic Models to Predict Graft Survival after Renal Transplantation: Comparison of Statistical and Machine Learning Models

Mulugeta,

Zewotir,

Tegegne

et al. 2024

Preprint

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Introduction: Renal transplantation is a critical treatment that can save the lives of individuals who are suffering from end-stage renal disease (ESRD), but graft failure remains a significant concern. Accurate prediction of graft survival after renal transplantation is crucial as it enables clinicians to identify patients at higher risk of graft failure. This study aimed to develop clinical prognostic models for predicting graft survival after renal transplantation and compare the performance of various statistical and machine learning models. Methodology: The study utilized data from a retrospective cohort of renal transplant recipients at the Ethiopian National Kidney Transplantation Center from September 2015 to February 2022. Various statistical and machine learning models were evaluated based on their discrimination, calibration, and interpretability. The comparison of models included standard Cox, Lasso-Cox, Ridge-Cox, Elastic net-Cox, Random Survival Forest, and Stochastic Gradient Boosting. The prognostic predictors of graft survival were selected based on the significance and relative importance of variables in different models. Results The study analyzed a total of 278 completed cases and observed the event of graft failure in 21 patients. The median graft survival time was 33 months, and the mean hazard of graft failure was 0.0755. The results revealed that the 1-year, 3-year, and 5-year graft survival rates are 0.936, 0.924, and 0.914 respectively. The study found that the Random Survival Forest and Stochastic Gradient Boosting models demonstrated the best calibration and discrimination performance shown by an equal AUC of 0.97 and the overlapped calibration plots. On the other hand, the Cox proportional hazards model has the highest interpretability and established superior accuracy in estimating survival probabilities, as evidenced by its lowest Brier score of 0.000071. The current study indicates that an episode of chronic rejection, recipient residence, an episode of acute rejection, post-transplant urological complications, post-transplant nonadherence, blood urea nitrogen level, and number of post-transplant admissions were consistently identified as the top significant prognostic predictors of renal graft survival. Conclusions The Random Survival Forest and Stochastic Gradient Boosting models demonstrated superior calibration and discrimination performance, while the Cox proportional hazards model offered accurate estimation of survival probabilities and interpretability. Clinicians should consider the trade-off between performance and interpretability when choosing a model. Incorporating these findings into clinical practice can improve risk stratification, enable early interventions, and inform personalized management strategies for kidney transplant recipients.

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Section: Discussionsupporting

confidence: 84%

Developing Clinical Prognostic Models to Predict Graft Survival after Renal Transplantation: Comparison of Statistical and Machine Learning Models

Mulugeta,

Zewotir,

Tegegne

et al. 2024

Preprint

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“…Lastly, the variable selection was only performed on the training dataset, and the statistical technique used in the intermediate stage of the variable selection process utilized all datasets, which may have affected the reliability of the results. Although previous studies have reported prediction models constructed without additional verification, it is still important to address the limitation of variable selection being performed only on the training dataset [ 41 ].…”

Section: Discussionmentioning

confidence: 99%

Classification Model for Epileptic Seizure Using Simple Postictal Laboratory Indices

Jin

Lee

Moon

et al. 2023

JCM

Self Cite

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Distinguishing syncope from epileptic seizures in patients with sudden loss of consciousness is important. Various blood tests have been used to indicate epileptic seizures in patients with impaired consciousness. This retrospective study aimed to predict the diagnosis of epilepsy in patients with transient loss of consciousness using the initial blood test results. A seizure classification model was constructed using logistic regression, and predictors were selected from a cohort of 260 patients using domain knowledge and statistical methods. The study defined the diagnosis of seizures and syncope based on the consistency of the diagnosis made by an emergency medicine specialist at the first visit to the emergency room and the diagnosis made by an epileptologist or cardiologist at the first outpatient visit using the International Classification of Diseases 10th revision (ICD-10) code. Univariate analysis showed higher levels of white blood cells, red blood cells, hemoglobin, hematocrit, delta neutrophil index, creatinine kinase, and ammonia levels in the seizure group. The ammonia level had the highest correlation with the diagnosis of epileptic seizures in the prediction model. Therefore, it is recommended to be included in the first examination at the emergency room.

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“…An objective and quantitative diagnosis of perfusion characteristics is of particular relevance in the follow-up of cancer patients but can also be used for the diagnostic assessment of other pathological changes associated with alterations in tissue perfusion. This applies, for example, to the noninvasive diagnosis of the progression of parenchymal liver disease, liver cirrhosis, and portal hypertension [10,11,12,13,14,15,16] and for the noninvasive evaluation of chronic kidney disease [17,18,19] and subclinical kidney transplant rejection [17,20,21,22,23,24]. There are partially contradictory data regarding the evaluation of inflammatory activity and response to biologic therapy in inflammatory bowel disease [25,26,27,28,29,30,31,32,33,34].…”

Section: Why Do We Need Quantification?mentioning

confidence: 99%

EFSUMB Technical Review – Update 2023: Dynamic Contrast-Enhanced Ultrasound (DCE-CEUS) for the Quantification of Tumor Perfusion

Dietrich,

Correas,

Cui

et al. 2023

Ultraschall Med

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Dynamic contrast-enhanced ultrasound (DCE-US) is a technique to quantify tissue perfusion based on phase-specific enhancement after the injection of microbubble contrast agents for diagnostic ultrasound. The guidelines of the European Federation of Societies for Ultrasound in Medicine and Biology (EFSUMB) published in 2004 and updated in 2008, 2011, and 2020 focused on the use of contrast-enhanced ultrasound (CEUS), including essential technical requirements, training, investigational procedures and steps, guidance regarding image interpretation, established and recommended clinical indications, and safety considerations. However, the quantification of phase-specific enhancement patterns acquired with ultrasound contrast agents (UCAs) is not discussed here. The purpose of this EFSUMB Technical Review is to further establish a basis for the standardization of DCE-US focusing on treatment monitoring in oncology. It provides some recommendations and descriptions as to how to quantify dynamic ultrasound contrast enhancement, and technical explanations for the analysis of time-intensity curves (TICs). This update of the 2012 EFSUMB introduction to DCE-US includes clinical aspects for data collection, analysis, and interpretation that have emerged from recent studies. The current study not only aims to support future work in this research field but also to facilitate a transition to clinical routine use of DCE-US.

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Quantitative ultrasound for non-invasive evaluation of subclinical rejection in renal transplantation

Cited by 4 publications

References 41 publications

Developing Clinical Prognostic Models to Predict Graft Survival after Renal Transplantation: Comparison of Statistical and Machine Learning Models

Developing Clinical Prognostic Models to Predict Graft Survival after Renal Transplantation: Comparison of Statistical and Machine Learning Models

Classification Model for Epileptic Seizure Using Simple Postictal Laboratory Indices

EFSUMB Technical Review – Update 2023: Dynamic Contrast-Enhanced Ultrasound (DCE-CEUS) for the Quantification of Tumor Perfusion

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