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).
Original language | English (US) |
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Pages (from-to) | 954-963 |
Number of pages | 10 |
Journal | AMIA ... Annual Symposium proceedings. AMIA Symposium |
Volume | 2016 |
State | Published - 2016 |
Keywords
- Blood transfusion
- bleeding
- classification
- machine learning
- perioperative
ASJC Scopus subject areas
- General Medicine