Dataset Information
Prognostic nomogram for 30-day mortality of deep vein thrombosis patients in intensive care unit.
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ABSTRACT: Background
We aimed to use the Medical Information Mart for Intensive Care III database to build a nomogram to identify 30-day mortality risk of deep vein thrombosis (DVT) patients in intensive care unit (ICU).Methods
Stepwise logistic regression and logistic regression with least absolute shrinkage and selection operator (LASSO) were used to fit two prediction models. Bootstrap method was used to perform internal validation.Results
We obtained baseline data of 535 DVT patients, 91 (17%) of whom died within 30 days. The discriminations of two new models were better than traditional scores. Compared with simplified acute physiology score II (SAPSII), the predictive abilities of two new models were improved (Net reclassification improvement [NRI]?>?0; Integrated discrimination improvement [IDI]?>?0; P?ConclusionsThe nomogram developed by logistic regression with LASSO model can provide an accurate prognosis for DVT patients in ICU.
SUBMITTER: Shen R
PROVIDER: S-EPMC7788873 | biostudies-literature | 2021 Jan
REPOSITORIES: biostudies-literature
Publications
Prognostic nomogram for 30-day mortality of deep vein thrombosis patients in intensive care unit.
BMC cardiovascular disorders 20210106 1
<h4>Background</h4>We aimed to use the Medical Information Mart for Intensive Care III database to build a nomogram to identify 30-day mortality risk of deep vein thrombosis (DVT) patients in intensive care unit (ICU).<h4>Methods</h4>Stepwise logistic regression and logistic regression with least absolute shrinkage and selection operator (LASSO) were used to fit two prediction models. Bootstrap method was used to perform internal validation.<h4>Results</h4>We obtained baseline data of 535 DVT pa ...[more]
