Big Data, Predictive Analytics, and Quality Improvement in Kidney Transplantation: A Proof of Concept.
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.
Division of Nephrology, Medical University of South Carolina, Charleston, SC.
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Srinivas
Division of Transplant Surgery, Medical University of South Carolina, Charleston, SC.
D J
DJ
Taber
Division of General Internal Medicine & Geriatrics, Medical University of South Carolina, Charleston, SC.
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Z
Su
Division of General Internal Medicine & Geriatrics, Medical University of South Carolina, Charleston, SC.
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J
Zhang
Division of Nephrology, Medical University of South Carolina, Charleston, SC.
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Mour
Office of the Chief Information Officer, Medical University of South Carolina, Charleston, SC.
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Northrup
IBM Corporation, Armonk, NY.
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A
Tripathi
Division of General Internal Medicine & Geriatrics, Medical University of South Carolina, Charleston, SC.
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Marsden
Division of General Internal Medicine & Geriatrics, Medical University of South Carolina, Charleston, SC.
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WP
Moran
Division of General Internal Medicine & Geriatrics, Medical University of South Carolina, Charleston, SC.
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PD
Mauldin
D016208:Databases, Factual; D057286:Electronic Health Records; D005260:Female; D005500:Follow-Up Studies; D006085:Graft Survival; D006801:Humans; D007676:Kidney Failure, Chronic / Q000601:surgery*; D016030:Kidney Transplantation / Q000401:mortality* / Q000592:standards* / Q000706:statistics & numerical data; D008297:Male; D008875:Middle Aged; D058996:Quality Improvement; D012189:Retrospective Studies; D015996:Survival Rate; D009927:Tissue and Organ Procurement / Q000706:statistics & numerical data*; D016896:Treatment Outcome
business/management; clinical decision-making; clinical research/practice; epidemiology; health services and outcomes research; informatics; kidney transplantation/nephrology; quality of care/care delivery; risk assessment/risk stratification