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Novel "Predictor Patch" Method for Adding Predictors Using Estimates From Outside Datasets - A Proof-of-Concept Study Adding Kidney Measures to Cardiovascular Mortality Prediction.

TitleNovel "Predictor Patch" Method for Adding Predictors Using Estimates From Outside Datasets - A Proof-of-Concept Study Adding Kidney Measures to Cardiovascular Mortality Prediction.
Publication TypeJournal Article
Year of Publication2019
AuthorsMatsushita K, Sang Y, Chen J, Ballew SH, Shlipak M, Coresh JJ, Peralta CA
Secondary AuthorsWoodward M
JournalCirc J
Volume83
Issue9
Pagination1876-1882
Date Published2019 08 23
ISSN1347-4820
KeywordsAdult, Aged, Aged, 80 and over, Albuminuria, Cardiovascular Diseases, Cause of Death, Data Mining, Databases, Factual, Decision Support Techniques, Female, Glomerular Filtration Rate, Humans, Kidney, Male, Middle Aged, Multicenter Studies as Topic, Nutrition Surveys, Predictive Value of Tests, Prognosis, Proof of Concept Study, Renal Insufficiency, Chronic, Risk Assessment, Risk Factors, United States
Abstract

BACKGROUND: Cardiovascular guidelines include risk prediction models for decision making that lack the capacity to include novel predictors.Methods and Results:We explored a new "predictor patch" approach to calibrating the predicted risk from a base model according to 2 components from outside datasets: (1) the difference in observed vs. expected values of novel predictors and (2) the hazard ratios (HRs) for novel predictors, in a scenario of adding kidney measures for cardiovascular mortality. Using 4 US cohorts (n=54,425) we alternately chose 1 as the base dataset and constructed a base prediction model with traditional predictors for cross-validation. In the 3 other "outside" datasets, we developed a linear regression model with traditional predictors for estimating expected values of glomerular filtration rate and albuminuria and obtained their adjusted HRs of cardiovascular mortality, together constituting a "patch" for adding kidney measures to the base model. The base model predicted cardiovascular mortality well in each cohort (c-statistic 0.78-0.91). The addition of kidney measures using a patch significantly improved discrimination (cross-validated ∆c-statistic 0.006 [0.004-0.008]) to a similar degree as refitting these kidney measures in each base dataset.

CONCLUSIONS: The addition of kidney measures using our new "predictor patch" approach based on estimates from outside datasets improved cardiovascular mortality prediction based on traditional predictors, providing an option to incorporate novel predictors to an existing prediction model.

DOI10.1253/circj.CJ-19-0320
Alternate JournalCirc J
PubMed ID31327793
Grant ListHHSN268201700001I / HL / NHLBI NIH HHS / United States
HHSN268201700002I / HL / NHLBI NIH HHS / United States
HHSN268201700003I / HL / NHLBI NIH HHS / United States
HHSN268201700005I / HL / NHLBI NIH HHS / United States
HHSN268201700004I / HL / NHLBI NIH HHS / United States
N01 HC095159 / HC / NHLBI NIH HHS / United States
N01 HC095169 / HC / NHLBI NIH HHS / United States
UL1 RR024156 / RR / NCRR NIH HHS / United States
UL1 RR025005 / RR / NCRR NIH HHS / United States