| Literature DB >> 28269892 |
Che Ngufor1, Dennis Murphree1, Sudhi Upadhyaya1, Nageswar Madde1, Jyotishman Pathak2, Rickey Carter1, Daryl Kor1.
Abstract
In blood transfusion studies, plasma transfusion (PPT) and bleeding are known to be associated with risk of prolonged ICU length of stay (ICU-LOS). However, as patients can show significant heterogeneity in response to a treatment, there might exists subgroups with differential effects. The existence and characteristics of these subpopulations in blood transfusion has not been well-studied. Further, the impact of bleeding in patients offered PPT on prolonged ICU-LOS is not known. This study presents a causal and predictive framework to examine these problems. The two-step approach first estimates the effect of bleeding in PPT patients on prolonged ICU-LOS and then estimates risks of bleeding and prolonged ICU-LOS. The framework integrates a classification model for risks prediction and a regression model to predict actual LOS. Results showed that the effect of bleeding in PPT patients significantly increases risk of prolonged ICU-LOS (55%, p=0.00) while no bleeding significantly reduces ICU-LOS (4%, p=0.046).Entities:
Keywords: Blood transfusion; bleeding; classification; machine learning; perioperative
Mesh:
Year: 2017 PMID: 28269892 PMCID: PMC5333266
Source DB: PubMed Journal: AMIA Annu Symp Proc ISSN: 1559-4076