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Table 1 Association of demographic and clinical factors with vaginal cuff dehiscence following total hysterectomy

From: Obesity and older age as protective factors for vaginal cuff dehiscence following total hysterectomy

Factor

Cases (n = 31)

Controls (n = 155)

Unadjusted OR (95 % CI)

p valuea

Age (years)

45.0 ± 13.4

47.9 ± 12.5

0.98 (0.95–1.01)

0.23

Body mass index (kg/m2)

 <25

14 (45.2)

45 (29.2)

1.0 (referent)

 

 ≥25 to <30

10 (32.3)

40 (26.0)

0.81 (0.33–1.99)

0.64

 ≥30

7 (22.6)

69 (44.8)

0.29 (0.10–0.83)

0.02*

Race

 African-American

6 (19.4)

20 (12.9)

1.69 (0.59–4.91)

0.33

Parity

  ≥ 1

26 (83.9)

119 (78.3)

1.47 (0.50–4.30)

0.48

Tobacco

11 (35.5)

38 (24.7)

1.92 (0.76–4.88)

0.17

Menopause

9 (30.0)

50 (32.5)

0.83 (0.34–2.01)

0.68

Hypertension

8 (25.8)

39 (25.16)

1.03 (0.43–2.49)

0.94

Diabetes

3 (9.7)

10 (6.5)

1.58 (0.40–6.27)

0.52

Indication for surgery

 Pelvic pain

12 (38.7)

61 (39.4)

0.97 (0.43–2.20)

0.95

 Abnormal uterine bleeding

14 (45.2)

70 (45.2)

1.00 (0.45–2.24)

>0.99

 Fibroids

7 (22.6)

36 (23.2)

0.96 (0.34–2.65)

0.93

 Endometrial cancer

4 (12.9)

24 (15.5)

0.81 (0.26–2.52)

0.71

 Endometriosis

3 (9.7)

12 (7.7)

1.30 (0.32–5.13)

0.71

 Prolapse

2 (6.5)

8 (5.2)

1.34 (0.22–8.05)

0.75

Malignant pathology

7 (22.6)

32 (20.8)

1.15 (0.42–3.16)

0.79

Monopolar colpotomy

16 (51.6)

75 (66.4)

1.33 (0.33–5.30)

0.69

Suture for cuff closure

 Polysorb

18 (78.3)

84 (65.6)

2.53 (0.58–11.09)

0.22

 PDS

2 (8.7)

11 (8.6)

0.86 (0.13–5.53)

0.88

 Barbed

1 (4.4)

8 (6.3)

0.47 (0.04–5.68)

0.56

Estimated blood loss (cc)

 ≥200

14 (45.2)

64 (41.3)

1.28 (0.49–3.39)

0.62

Length of case (minutes)

 ≥120

11 (35.5)

58 (37.4)

0.91 (0.40–2.10)

0.91

Uterine weight (g)

 ≥200

9 (30.0)

37 (24.2)

1.35 (0.55–3.27)

0.51

  1. Data presented as mean ± SD for continuous variables or n (%) for dichotomous variables
  2. OR odds ratio, CI confidence interval
  3. *p < 0.05, statistically significant
  4. a p value from unadjusted conditional logistic regression model