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Big Data, Predictive Analytics, and Quality Improvement in Kidney Transplantation: A Proof of Concept
Author(s) -
Srinivas T. R.,
Taber D. J.,
Su Z.,
Zhang J.,
Mour G.,
Northrup D.,
Tripathi A.,
Marsden J. E.,
Moran W. P.,
Mauldin P. D.
Publication year - 2017
Publication title -
american journal of transplantation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.89
H-Index - 188
eISSN - 1600-6143
pISSN - 1600-6135
DOI - 10.1111/ajt.14099
Subject(s) - medicine , confidence interval , database , big data , kidney transplantation , unstructured data , transplantation , emergency medicine , data mining , computer science
We sought proof of concept of a Big Data Solution incorporating longitudinal structured and unstructured patient‐level data from electronic health records ( EHR ) to predict graft loss ( GL ) and mortality. For a quality improvement initiative, GL and mortality prediction models were constructed using baseline and follow‐up data (0–90 days posttransplant; structured and unstructured for 1‐year models; data up to 1 year for 3‐year models) on adult solitary kidney transplant recipients transplanted during 2007–2015 as follows: Model 1: United Network for Organ Sharing ( UNOS ) data; Model 2: UNOS & Transplant Database (Tx Database) data; Model 3: UNOS , Tx Database & EHR comorbidity data; and Model 4: UNOS , Tx Database, EHR data, Posttransplant trajectory data, and unstructured data. A 10% 3‐year GL rate was observed among 891 patients (2007–2015). Layering of data sources improved model performance; Model 1: area under the curve ( AUC ), 0.66; (95% confidence interval [ CI ]: 0.60, 0.72); Model 2: AUC , 0.68; (95% CI : 0.61–0.74); Model 3: AUC , 0.72; (95% CI : 0.66–077); Model 4: AUC , 0.84, (95 % CI : 0.79–0.89). One‐year GL ( AUC , 0.87; Model 4) and 3‐year mortality ( AUC , 0.84; Model 4) models performed similarly. A Big Data approach significantly adds efficacy to GL and mortality prediction models and is EHR deployable to optimize outcomes.

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