title: Predicting Prolonged Stay in the ICU Attributable to Bleeding in Patients Offered Plasma Transfusion.
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).
authors.affiliation: Mayo Clinic, Rochester, MN.
authors.forename: Che
authors.initials: C
authors.lastname: Ngufor
authors.affiliation: Mayo Clinic, Rochester, MN.
authors.forename: Dennis
authors.initials: D
authors.lastname: Murphree
authors.affiliation: Mayo Clinic, Rochester, MN.
authors.forename: Sudhi
authors.initials: S
authors.lastname: Upadhyaya
authors.affiliation: Mayo Clinic, Rochester, MN.
authors.forename: Nageswar
authors.initials: N
authors.lastname: Madde
authors.affiliation: Weill Cornell Medical College, NY, NY.
authors.forename: Jyotishman
authors.initials: J
authors.lastname: Pathak
authors.affiliation: Mayo Clinic, Rochester, MN.
authors.forename: Rickey
authors.initials: R
authors.lastname: Carter
authors.affiliation: Mayo Clinic, Rochester, MN.
authors.forename: Daryl
authors.initials: D
authors.lastname: Kor
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D001803:Blood Transfusion; D005260:Female; D006470:Hemorrhage; D006801:Humans; D007362:Intensive Care Units; D007902:Length of Stay; D000069550:Machine Learning; D008297:Male; D008875:Middle Aged; D012044:Regression Analysis; D012306:Risk
keywords: Blood transfusion; bleeding; classification; machine learning; perioperative