| Literature DB >> 35330431 |
Milan Terzic1,2,3, Gulzhanat Aimagambetova4, Talshyn Ukybassova2, Gauri Bapayeva2, Aiym Kaiyrlykyzy5, Faye Foster1, Faina Linkov6.
Abstract
OBJECTIVES: Abnormal uterine bleeding (AUB) is a common complaint of women in different age groups, and endometrial biopsy is widely used to investigate the underlying causes. The aim of this observational study was to assess factors influencing pain in patients undergoing endometrial biopsy for AUB.Entities:
Keywords: Pipelle; abnormal uterine bleeding; endometrial biopsy; pain
Year: 2022 PMID: 35330431 PMCID: PMC8950507 DOI: 10.3390/jpm12030431
Source DB: PubMed Journal: J Pers Med ISSN: 2075-4426
Pain scores by patient characteristics.
| N(%) | Pain Score before | Pain Score during | Pain Score after Biopsy | ||||
|---|---|---|---|---|---|---|---|
| Age group | 0.2866 | 0.7676 | 0.16 | ||||
| ≤44 | 94 (58.75%) | 0 (0–2) | 3 (1–5) | 2 (1–4) | |||
| 45–54 | 47 (29.4%) | 1 (0–2.5) | 3 (1–5) | 1 (1–3) | |||
| ≥55 | 19 (11.9%) | 0 (0–2.5) | 3 (1.5–5.5) | 2 (0.5–4.5) | |||
| Menopausal status | 0.4755 | 0.8933 | 0.6017 | ||||
| Premenopausal | 131 (81.9%) | 0 (0–2) | 3 (1–5) | 2 (1–4) | |||
| Postmenopausal | 29 (18.1%) | 0 (0–2) | 3 (1–5) | 2 (1–3) | |||
| Body mass index | 0.216 | 0.8147 | 0.9082 | ||||
| Normal | 70 (43.7%) | 0 (0–1.75) | 3 (1–5) | 2 (1–3) | |||
| Overweight and obese | 90 (56.3%) | 1 (0–2) | 3 (1–5) | 2 (1–4) | |||
| Type of provider | 0.0087 | <0.0001 | 0.15 | ||||
| Senior OBGYN | 100 (62.5%) | 1 (0–3) | 2 (0–5) | 2 (1–3) | |||
| Junior OBGYN | 60 (37.5%) | 0 (0–1) | 5 (3–6.25) | 2.5 (1–4) | |||
| Indication for biopsy | 0.7038 | 0.6526 | 0.4174 | ||||
| Abnormal bleeding in reproductive age | 97 (60.6%) | 0 (0–2) | 3 (1–5) | 2 (1–4) | |||
| Premenopausal bleeding | 29 (18.1%) | 1 (0–2) | 3 (1.5–5.5) | 1 (1–3) | |||
| Postmenopausal bleeding | 34 (21.3%) | 0 (0–0.25) | 3 (0.75–5) | 1.5 (0.75–3.25) | |||
| Parity | 0.6513 | 0.3791 | 0.1083 | ||||
| None | 48 (30%) | 0 (0–2) | 3 (0–5.25) | 2 (1–4.25) | |||
| One and more | 112 (70%) | 0 (0–2) | 3 (1–5) | 2 (1–3) | |||
| Education | 0.6854 | 0.6854 | 0.6309 | ||||
| None, technical and college | 65 (40.6%) | 0 (0–2) | 3 (1–5) | 2 (1–3) | |||
| High, postgraduate | 87 (54.4%) | 0 (0–2) | 3 (0.5–5) | 2 (1–4) | |||
| NA (missing values) | 8 (5%) | ||||||
| Income | 0.6715 | 0.5148 | 0.2308 | ||||
| Not satisfactory | 29 (18.1%) | 1 (0–2) | 3 (0–5) | 1 (1–3) | |||
| Satisfactory | 91 (56.9%) | 0 (1–0.25) | 3 (1–5) | 2 (1–4) | |||
| NA (missing values) | 40 (25%) | ||||||
| Residency | 0.7065 | 0.3226 | 0.08217 | ||||
| Urban | 128 (80%) | 0 (0–2) | 3.5 (1–5) | 2 (1–4) | |||
| Rural | 30 (18.8%) | 1 (0–1.75) | 3 (0.25–4.5) | 1 (1–2) | |||
| NA (missing values) | 2 (1.2%) |
Data presented (Q1–Q3) for continuous and N (%) for categorical variables.