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. 2019 Aug 6;71(4):350–355. doi: 10.1016/j.ihj.2019.07.004

Table 2.

Predictive factors for postoperative complications after cardiac surgery (n = 362).

Variable Complication
Absent
Present
Univariate
Multivariate
n (%)/mean (SD) n (%)/mean (SD) p value aOR 95% CI P value
Age (years)
≤50 44 (28.6) 110 (71.4) 0.651
>50 64 (30.8) 144 (69.2)
Sex
Male 58 (26.5) 161 (73.5) 0.085 1.79 1.08–2.96 0.024
Female 50 (35.0) 93 (65.0) 1
Type of surgery
CABG 52 (33.8) 102 (66.2) 0.094 0.25 0.09–0.69 0.008
Valve 50 (29.4) 120 (70.6) 0.32 0.12–0.85 0.023
Combined 06 (15.8) 32 (84.2) 1
DM 43 (34.7) 81 (65.3) 0.146 1.29 0.77–2.15 0.338
Hypertension 31 (28.7) 77 (71.3) 0.759
IHD 53 (32.5) 110 (67.5) 0.313
VHD 54 (28.9) 133 (71.1) 0.681
Asthma/COPD 02 (28.6) 05 (71.4) 0.651
CKD 04 (36.4) 07 (63.6) 0.425
Weight (kg) 58.4 (12.9) 62.2 (11.8) 0.008 1.03 1.01–1.05 0.010
LVEF 54.3 (3.5) 54.3 (3.4) 0.877
Preop Hb 12.4 (1.7) 12.4 (1.7) 0.851
Creatinine 1.19 (0.27) 1.18 (0.26) 0.593
CPB time 89.1 (30.9) 87.3 (26.9) 0.579
ACC time 50.9 (20.4) 52.2 (18.5) 0.571
Pump flow rate 3.8 (0.6) 3.7 (0.6) 0.553
CPB lactate 4.4 (2.3) 4.1 (0.6) 0.058 0.73 0.49–1.09 0.122
ACC lactate 4.0 (2.9) 3.9 (1.3) 0.763
On-pump Hb 6.7 (1.2) 6.9 (1.3) 0.142 1.18 0.97–1.44 0.105
ICU lactate 4.1 (1.7) 4.6 (1.7) 0.004 1.19 1.02–1.39 0.031
6-hr lactate 4.2 (1.9) 4.5 (2.1) 0.287
12-hr lactate 2.7 (1.4) 3.3 (1.8) 0.002 1.21 1.03–1.43 0.018
24-hr lactate 2.1 (1.0) 2.3 (1.4) 0.188

ICU: intensive care unit, SD: standard deviation, aOR: adjusted odds ratio, CI: confidence interval, CABG: coronary artery bypass grafting, DM: diabetes mellitus, IHD: ischemic heart disease, VHD: valvular heart disease, COPD: chronic obstructive pulmonary disease, CKD: chronic kidney disease, LVEF: left ventricular ejection fraction, Preop Hb: preoperative hemoglobin, CPB: cardiopulmonary bypass, ACC: aortic cross-clamp.

Model χ2 = 40.496, p < 0.001 and Hosmer and Lemeshow p = 0.809 indicates that the model fits the data. The classification table reports that overall expected model performance is 71%, that is, 71% of the cases can be expected to be classified correctly by the model.