A multivariate Bayesian model for assessing morbidity after coronary artery surgery

Table 5 Examples of application of the Bayesian model

Variables and model calculations	Case A	Case B
DO₂I (mL/minute/m²)	427.9	332.3
Inotropic support after CPB	NO	NO
PVD and/or CD	YES	NO
Preoperative creatinine (mg/dL)	1.0	1.2
IABP after CABG	NO	NO
Weight (kg)	74	52
REDO	NO	NO
Duration of CPB (minutes)	150	185
Age (years)	53	73
WBC (10³/mm³)	12	7.97
Preoperative IABP	NO	NO
Emergency operation	NO	NO
p(x\|N)	1.24 × 10^-11	4.29 × 10^-11
p(x\|M)	7.03 × 10^-12	4.80 × 10^-11
Bayes posterior probability of morbidity (DT = 0.427)	0.361 (LoRi)	0.528 (HiRi)
FC score (DT = 4)	5 (HiRi)	6 (HiRi)
PC score (DT = 4)	4 (HiRi)	4 (HiRi)
Higgins score (DT = 6)	10 (HiRi)	10 (HiRi)

CABG, coronary artery bypass graft; Case A, a patient without complications; Case B, a patient with a cardiovascular complication; CD, carotid disease; CPB, cardiopulmonary bypass; DO₂I, oxygen delivery index; DT, decision threshold; FC, fully customized; HiRi, classification at high risk of morbidity; IABP, intra-aortic balloon pump; LoRi, classification at low risk of morbidity; PVD, peripheral vascular disease; PC, partially customized; p(x|M), probability density function of morbidity; p(x|N), probability density function of normal condition; REDO, re-operation; WBC, white blood cells.

ISSN: 1364-8535