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. 2020 Jul 31;46(5):754–771. doi: 10.1590/S1677-5538.IBJU.2019.0420

Table 2. Perioperative outcomes during hospitalization of patients treated with RARP and ORP.

Pre-propensity score-matched Post-propensity score-matched
RARP (N=55704) ORP (N=21350) RARP vs. ORP OR (95% CI)* p value RARP (N=20573) ORP (N=20573) ORP OR (95% CI)# RARP vs. p value
Homologous blood transfusion 896 (1.61) 2032 (9.52) 0.18 (0.16-0.19) <0.0001 403 (1.96) 1934 (9.40) 0.19 (0.17-0.21) <0.0001
Intraoperative complication 385 (0.69) 266 (1.25) 0.62 (0.52-0.73) <0.0001 151 (0.73) 257 (1.25) 0.58 (0.48-0.72) <0.0001
Postoperative complication
Overall 4502 (8.08) 2576 (12.07) 0.71 (0.68-0.75) <0.0001 1825 (8.87) 2463 (11.97) 0.72 (0.67-0.76) <0.0001
Cardiac 513 (0.92) 292 (1.37) 0.75 (0.65-0.88) 0.0003 203 (0.99) 281 (1.37) 0.72 (0.60-0.86) 0.0004
Respiratory 640 (1.15) 508 (2.38) 0.57 (0.50-0.64) <0.0001 271 (1.32) 477 (2.32) 0.56 (0.48-0.65) <0.0001
Genitourinary 491 (0.88) 259 (1.21) 0.78 (0.67-0.92) 0.0023 190 (0.92) 247 (1.20) 0.77 (0.63-0.93) 0.0063
Wound 198 (0.36) 133 (0.62) 0.66 (0.52-0.83) 0.0004 84 (0.41) 130 (0.63) 0.64 (0.49-0.85) 0.0018
Vascular 210 (0.38) 110 (0.52) 0.82 (0.64-1.04) 0.1052 82 (0.40) 106 (0.52) 0.77 (0.58-1.03) 0.0802
Miscellaneous medical 2678 (4.81) 1400 (6.56) 0.80 (0.74-0.85) <0.0001 1070 (5.20) 1338 (6.50) 0.79 (0.73-0.86) <0.0001
Miscellaneous surgical 917 (1.65) 591 (2.77) 0.67 (0.60-0.74) <0.0001 395 (1.92) 567 (2.76) 0.69 (0.61-0.79) <0.0001
Length of stay >2d 6610 (11.87) 7956 (37.26) 0.25 (0.24-0.26) <0.0001 2755 (13.39) 7551 (36.70) 0.27 (0.25-0.28) <0.0001
In-hospital mortality 11 (0.02) 14 (0.07) 0.28 (0.12-0.63) 0.0021 3 (0.01) 14 (0.07) 0.21 (0.06-0.75) 0.0154

RARP=Robot-assisted Radical Prostatectomy; ORP=Open Radical Prostatectomy; OR=Odds Ratio; CI=Confidence interval.

Note: * The ORs and corresponding 95% CI for perioperative outcomes were estimated by using multivariable Logistic regression adjusted for demographic characteristics.

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The ORs and corresponding 95% CI for perioperative outcomes were estimated by using univariate Logistic regression.