Staff Profile

@article{Marrero2021a,

title = {A machine learning approach for the prediction of overall deceased donor organ yield.},

author = {Wesley J. Marrero and Mariel S Lavieri and Seth D Guikema and David W Hutton and Neehar D Parikh},

doi = {10.1016/j.surg.2021.06.004},

issn = {1532-7361},

year  = {2021},

date = {2021-06-01},

urldate = {2021-06-01},

journal = {Surgery},

abstract = {Optimizing organ yield (number of organs transplanted per donor) is a potentially modifiable way to increase the number of organs available for transplant. Models to predict the expected deceased donor organ yield have been developed based on ordinary least squares regression and logistic regression. However, alternative modeling methodologies incorporating machine learning may have superior performance compared with conventional approaches. We evaluated the predictive accuracy of 14 machine learning models for predicting overall organ yield in a cross-validation procedure. The models were parameterized using data from the Organ Procurement and Transplantation Network database from 2000 to 2018. The inclusion criteria for the study were adult deceased donors between 18 and 84 years of age that had at least 1 organ procured for transplantation. A total of 89,520 donors met the inclusion criteria. Their mean (standard deviation) age was 44 (15) years, and approximately 58% were male. Our cross-validation analysis showed that a tree-based gradient boosting model outperformed the remaining 13 models. Compared with the currently used prediction models, the gradient boosting model improves prediction accuracy by reducing the mean absolute error between 3 and 11 organs per 100 donors. Our analysis demonstrated that the gradient boosting methodology had the best performance in predicting overall deceased donor organ yield and can potentially serve as an aid to assess organ procurement organization performance.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Marrero2021,

title = {Optimal cholesterol treatment plans and genetic testing strategies for cardiovascular diseases},

author = {Wesley J. Marrero and Mariel S. Lavieri and Jeremy B. Sussman},

url = {http://link.springer.com/10.1007/s10729-020-09537-x},

doi = {10.1007/s10729-020-09537-x},

issn = {1386-9620},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {Health Care Management Science},

volume = {170},

pages = {1561--1567},

publisher = {Health Care Management Science},

abstract = {Optimizing organ yield (number of organs transplanted per donor) is a potentially modifiable way to increase the number of organs available for transplant. Models to predict the expected deceased donor organ yield have been developed based on ordinary least squares regression and logistic regression. However, alternative modeling methodologies incorporating machine learning may have superior performance compared with conventional approaches. We evaluated the predictive accuracy of 14 machine learning models for predicting overall organ yield in a cross-validation procedure. The models were parameterized using data from the Organ Procurement and Transplantation Network database from 2000 to 2018. The inclusion criteria for the study were adult deceased donors between 18 and 84 years of age that had at least 1 organ procured for transplantation. A total of 89,520 donors met the inclusion criteria. Their mean (standard deviation) age was 44 (15) years, and approximately 58% were male. Our cross-validation analysis showed that a tree-based gradient boosting model outperformed the remaining 13 models. Compared with the currently used prediction models, the gradient boosting model improves prediction accuracy by reducing the mean absolute error between 3 and 11 organs per 100 donors. Our analysis demonstrated that the gradient boosting methodology had the best performance in predicting overall deceased donor organ yield and can potentially serve as an aid to assess organ procurement organization performance.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DeRoos2020,

title = {Assessment of National Organ Donation Rates and Organ Procurement Organization Metrics},

author = {Luke J. DeRoos and Yuhang Zhou and Wesley J. Marrero and Elliot B Tapper and Christopher J Sonnenday and Mariel S Lavieri and David W Hutton and Neehar D Parikh},

url = {https://jamanetwork.com/journals/jamasurgery/fullarticle/2773525},

doi = {10.1001/jamasurg.2020.5395},

issn = {2168-6254},

year  = {2020},

date = {2020-12-01},

urldate = {2020-12-01},

journal = {JAMA Surgery},

volume = {48109},

pages = {1--8},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DeRoos2019,

title = {Estimated Association Between Organ Availability and Presumed Consent in Solid Organ Transplant},

author = {Luke J. DeRoos and Wesley J. Marrero and Elliot B. Tapper and Christopher J. Sonnenday and Mariel S. Lavieri and David W. Hutton and Neehar D. Parikh},

doi = {10.1001/jamanetworkopen.2019.12431},

issn = {25743805},

year  = {2019},

date = {2019-01-01},

urldate = {2019-01-01},

journal = {JAMA Network Open},

volume = {2},

number = {10},

pages = {e1912431},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Marrero2018,

title = {Development of a Predictive Model for Deceased Donor Organ Yield},

author = {Wesley J. Marrero and Mariel S. Lavieri and Seth D. Guikema and David W. Hutton and Neehar D. Parikh},

url = {http://insights.ovid.com/crossref?an=00007890-201808000-00013},

doi = {10.1097/TP.0000000000002274},

issn = {0041-1337},

year  = {2018},

date = {2018-08-01},

journal = {Transplantation},

volume = {102},

number = {8},

pages = {e364},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Parikh2018,

title = {Projected increase in obesity and non-alcoholic-steatohepatitis-related liver transplantation waitlist additions in the United States},

author = {Neehar D. Parikh and Wesley J. Marrero and Jingyuan Wang and Justin Steuer and Elliot B. Tapper and Monica Konerman and Amit G. Singal and David W. Hutton and Eunshin Byon and Mariel S. Lavieri},

url = {http://doi.wiley.com/10.1002/hep.29473},

doi = {10.1002/hep.29473},

issn = {02709139},

year  = {2018},

date = {2018-05-01},

journal = {Hepatology},

volume = {00},

number = {00},

pages = {1--9},

abstract = {Non-alcoholic steatohepatitis (NASH) cirrhosis is the fastest growing indication for liver transplantation (LT) in the US. We aimed to determine the temporal trend behind the rise in obesity and NASH‐related additions to the LT waitlist in the US and make projections for future NASH burden on the LT waitlist. We used data from the Organ Procurement and Transplantation Network database from 2000‐2014 to obtain the number of NASH‐related LT waitlist additions. The obese population in the US from 2000‐2014 was estimated using data from the US Census Bureau and the National Health and Nutrition Examination Survey. Based on obesity trends, we established a time lag between obesity prevalence and NASH‐related waitlist additions. We used data from the US Census Bureau on population projections from 2016‐2030 to forecast obesity estimates and NASH‐related LT waitlist additions. From 2000‐2014, the proportion of obese individuals significantly increased 44.9% and the number of NASH‐related annual waitlist additions increased from 391 to 1605. Increase in obesity prevalence was strongly associated with LT waitlist additions 9 years later in derivation and validation cohorts (R2=0.9). Based on these data, annual NASH‐related waitlist additions are anticipated to increase by 55.4% (1,354 to 2,104) between 2016 and 2030. There is significant regional variation in obesity rates and in the anticipated increase in NASH‐related waitlist additions (ptextless0.01). Conclusion: We project a marked increase in demand for LT for NASH given population obesity trends. Continued public health efforts to curb obesity prevalence are needed to reduce the projected future burden of NASH. This article is protected by copyright. All rights reserved.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Parikh2017a,

title = {Population-Based Analysis and Projections of Liver Supply Under Redistricting},

author = {Neehar D. Parikh and Wesley J. Marrero and Christopher J. Sonnenday and Anna S. Lok and David W. Hutton and Mariel S. Lavieri},

url = {http://insights.ovid.com/crossref?an=00007890-201709000-00024},

doi = {10.1097/TP.0000000000001785},

issn = {0041-1337},

year  = {2017},

date = {2017-01-01},

journal = {Transplantation},

volume = {101},

number = {9},

pages = {2048--2055},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Schell2016,

title = {Data-driven Markov decision process approximations for personalized hypertension treatment planning},

author = {G. J. Schell and Wesley J. Marrero and M. S. Lavieri and J. B. Sussman and R. A. Hayward},

doi = {10.1177/2381468316674214},

issn = {2381-4683},

year  = {2016},

date = {2016-10-01},

urldate = {2016-10-01},

journal = {MDM Policy \& Practice},

volume = {1},

number = {1},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Marrero2016,

title = {Predictors of Deceased Donor Kidney Discard in the United States.},

author = {Wesley J. Marrero and Abhijit S Naik and John J Friedewald and Yongcai Xu and David W Hutton and Mariel S Lavieri and Neehar D Parikh},

url = {http://www.ncbi.nlm.nih.gov/pubmed/27163541},

doi = {10.1097/TP.0000000000001238},

issn = {1534-6080},

year  = {2016},

date = {2016-05-01},

urldate = {2016-05-01},

journal = {Transplantation},

volume = {00},

number = {00},

pages = {1--8},

abstract = {BACKGROUND Renal transplantation is a lifesaving intervention for end-stage renal disease. The demand for renal transplantation outweighs the availability of organs; however, up to 20% of recovered kidneys are discarded before transplantation. We aimed to better characterize the risk factors for deceased donor kidney discard. METHODS We performed a secondary analysis of the Organ Procurement and Transplantation Network database from 2000 to 2012 of all solid organ donors. The cohort was split into training (80%) and validation (20%) subsets. We performed a stepwise logistic regression to develop a multivariate risk prediction model for kidney graft discard and validated the model. The performance of the models was evaluated with respect to calibration, and area under the curve (AUC) of receiver operating characteristic curves. RESULTS There were no significant baseline differences between the training (n = 57 474) and validation (n = 14 368) cohorts. The multivariate model validation showed very good discriminant function in predicting kidney discard (AUC = 0.84). Predictors of increased discard included age older than 50 years, performance of a kidney biopsy, cytomegalovirus seropositive status, donation after cardiac death, hepatitis B and C seropositive status, cigarette use, diabetes, hypertension, terminal creatinine greater than 1.5 mg/dL and AB blood type. The model outperformed the Kidney Donor Risk Index in predicting discard (P textless 0.001). Subgroup analysis of expanded criteria donor kidneys demonstrated good discrimination with an AUC of 0.70. CONCLUSIONS We have characterized several important predictors of deceased donor kidney discard. Better understanding of factors that lead to increased deceased donor kidney discard can allow for targeted interventions to reduce discard.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}