scholarly journals A machine learning prediction model for waiting time to kidney transplant

PLoS ONE ◽  
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.

scholarly journals Exploring Survival Models Associated with MCI to AD Conversion: A Machine Learning Approach

2019 ◽  
Author(s):  
Jorge Orozco-Sanchez ◽  
Victor Trevino ◽  
Emmanuel Martinez-Ledesma ◽  
Joshua Farber ◽  
Jose Tamez-Peña
Keyword(s):  
Machine Learning ◽  
Regression Models ◽  
Cox Regression ◽  
Early Stage ◽  
Final Analysis ◽  
Structural Mri ◽  
Survival Models ◽  
Mri Findings ◽  
Cox Models ◽  
Mri Features

AbstractSeveral studies have documented that structural MRI findings are associated with the presence of early-stage Alzheimer Disease (AD). However, the association of each MRI feature with the rate of conversion from mild cognitive impairment (MCI) to AD in a multivariate setting has not been studied fully. The objective of this work is the comprehensive exploration of four different machine learning (ML) strategies to build MRI-based multivariate Cox regression models. These models evaluated the association of MRI features with the time of MCI to clinical AD conversion. We used 442 MCI subjects from the Alzheimer’s disease Neuroimaging Initiative (ADNI) study. Each subject was described by 346 MRI features and time to AD conversion. Cox regression models then estimated the rate of conversion. Models were built using four ML methodologies in a cross-validation (CV) setting. All the ML methods returned successful Cox models with different CV performances. The best model exhibited a concordance index of 0.84 (95% CI: 0.82-0.86). The final analysis described the hazard ratios (HR) of the top ten MRI features associated with MCI to AD conversion. Our results suggest ML exploration is a viable strategy for building and analyzing survival models that predict subjects at risk of AD conversion.

scholarly journals Impact of deceased donor with acute kidney injury on subsequent kidney transplant outcomes–an ANZDATA registry analysis

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0249000
Author(s):  
Juan Pei ◽  
Yeoungjee Cho ◽  
Yong Pey See ◽  
Elaine M. Pascoe ◽  
Andrea K. Viecelli ◽  
...  
Keyword(s):  
Acute Kidney Injury ◽  
Kidney Transplantation ◽  
Acute Rejection ◽  
Kidney Transplant ◽  
Graft Failure ◽  
Cox Regression ◽  
Graft Function ◽  
Kidney Injury ◽  
Deceased Donor ◽  
Hazard Ratio

Background The need for kidney transplantation drives efforts to expand organ donation. The decision to accept organs from donors with acute kidney injury (AKI) can result in a clinical dilemma in the context of conflicting reports from published literature. Material and methods This observational study included all deceased donor kidney transplants performed in Australia and New Zealand between 1997 and 2017. The association of donor-AKI, defined according to KDIGO criteria, with all-cause graft failure was evaluated by multivariable Cox regression. Secondary outcomes included death-censored graft failure, death, delayed graft function (DGF) and acute rejection. Results The study included 10,101 recipients of kidneys from 5,774 deceased donors, of whom 1182 (12%) recipients received kidneys from 662 (11%) donors with AKI. There were 3,259 (32%) all-cause graft failures, which included 1,509 deaths with functioning graft. After adjustment for donor, recipient and transplant characteristics, donor AKI was not associated with all-cause graft failure (adjusted hazard ratio [HR] 1.11, 95% CI 0.99–1.26), death-censored graft failure (HR 1.09, 95% CI 0.92–1.28), death (HR 1.15, 95% CI 0.98–1.35) or graft failure when death was evaluated as a competing event (sub-distribution hazard ratio [sHR] 1.07, 95% CI 0.91–1.26). Donor AKI was not associated with acute rejection but was associated with DGF (adjusted odds ratio [OR] 2.27, 95% CI 1.92–2.68). Conclusion Donor AKI stage was not associated with any kidney transplant outcome, except DGF. Use of kidneys with AKI for transplantation appears to be justified.

scholarly journals Artificial Intelligence—A Tool for Risk Assessment of Delayed-Graft Function in Kidney Transplant

2021 ◽  
Vol 10 (22) ◽  
pp. 5244
Author(s):  
Andrzej Konieczny ◽  
Jakub Stojanowski ◽  
Klaudia Rydzyńska ◽  
Mariusz Kusztal ◽  
Magdalena Krajewska
Keyword(s):  
Machine Learning ◽  
Risk Assessment ◽  
Kidney Transplant ◽  
Graft Function ◽  
Deceased Donor ◽  
Delayed Graft Function ◽  
Clinical Tool ◽  
Kidney Donor ◽  
Common Input ◽  
Discriminant Ability

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.

scholarly journals Long-Term Risk of Ischemic Stroke among Elderly Survivors of Non-Traumatic Subarachnoid Hemorrhage

2021 ◽  
pp. 1-6
Author(s):  
Melvin Parasram ◽  
Neal S. Parikh ◽  
Alexander E. Merkler ◽  
Judy H. Ch’ang ◽  
Babak B . Navi ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Subarachnoid Hemorrhage ◽  
Cox Regression ◽  
Sensitivity Analyses ◽  
Medicare Beneficiaries ◽  
Cox Models ◽  
Cox Regression Analysis ◽  
Increased Risk ◽  
Traumatic Subarachnoid Hemorrhage

<b><i>Introduction:</i></b> Non-traumatic subarachnoid hemorrhage (SAH) is associated with poor long-term functional outcomes, but the risk of ischemic stroke among SAH survivors is poorly understood. <b><i>Objectives:</i></b> The aim of this study was to evaluate the risk of ischemic stroke among survivors of SAH. <b><i>Methods:</i></b> We performed a retrospective cohort study using claims data from Medicare beneficiaries from 2008 to 2015. The exposure was a diagnosis of SAH, while the outcome was an acute ischemic stroke, both identified using previously validated <i>ICD-9-CM</i> diagnosis codes. We used Cox regression analysis adjusting for demographics and stroke risk factors to evaluate the association between SAH and long-term risk of ischemic stroke. <b><i>Results:</i></b> Among 1.7 million Medicare beneficiaries, 912 were hospitalized with non-traumatic SAH. During a median follow-up of 5.2 years (IQR, 2.7–6.7), the cumulative incidence of ischemic stroke was 22 per 1,000 patients per year among patients with SAH, and 7 per 1,000 patients per year in those without SAH. In adjusted Cox models, SAH was associated with an increased risk of ischemic stroke (HR, 2.0; 95% confidence interval, 1.4–2.8) as compared to beneficiaries without SAH. Similar results were obtained in sensitivity analyses, when treating death as a competing risk (sub HR, 3.0; 95% CI, 2.8–3.3) and after excluding ischemic stroke within 30 days of SAH discharge (HR, 1.5; 95% CI, 1.1–2.3). <b><i>Conclusions:</i></b> In a large, heterogeneous national cohort of elderly patients, survivors of SAH had double the long-term risk of ischemic stroke. SAH survivors should be closely monitored and risk stratified for ischemic stroke.

The Cardiac Waiting Game: Are Patients Prioritized on the Basis of Clinical Need?

1997 ◽  
Vol 10 (4) ◽  
pp. 216-224 ◽  
Author(s):  
S. Langham ◽  
M. Soljak ◽  
B. Keogh ◽  
M. Gill ◽  
M. Thorogood ◽  
...  
Keyword(s):  
Coronary Artery ◽  
Coronary Artery Bypass ◽  
Waiting Time ◽  
Smoking Status ◽  
Current System ◽  
Artery Bypass ◽  
Waiting Times ◽  
Waiting Lists ◽  
Waiting List ◽  
Use Of Resources

Waiting lists for coronary artery bypass grafting (CABG) have been a recurring problem for many hospitals, putting pressure on hospitals to manage waiting lists more effectively. In this study, we audited the records of 1594 patients who had coronary artery bypass surgery in 1992 and 1993 in three London hospitals, to assess their waiting time experience. Patients' actual waiting times were compared with an appropriate waiting time defined using an adapted version of a Canadian urgency scoring system. Influence of other factors (sex, age, smoking, hypertension, diabetes and obesity) on actual waiting time was assessed. A comparison of patients' actual waiting times with an appropriate waiting time, defined by the urgency score, showed that only 38% were treated within the appropriate period. Thirty-four per cent were treated earlier than their ischaemic risk indicated, and 28% with high ischaemic risk were delayed. Actual waiting time was associated with a patient's sex and smoking status but not with the other factors studied. The current system of prioritizing patients awaiting CABG is not concordant with a measure of appropriate waiting time. This could have arisen due to a number of factors, including the contracting process, waiting list initiatives, and methods of waiting list administration and patient pressures. The use of a standard method for prioritizing patients would enable a more appropriate use of resources.

Detection of MICA Antibodies in Kidney Transplant Candidates on the Deceased Donor Waiting List in Panama.

2014 ◽  
Vol 98 ◽  
pp. 692-693
Author(s):  
A. Vernaza ◽  
G. Gutierrez ◽  
C. Cuero ◽  
J. Manzanarez ◽  
M. Askar
Keyword(s):  
Kidney Transplant ◽  
Deceased Donor ◽  
Waiting List ◽  
Transplant Candidates
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