The health concerns related to hysterectomy among Taiwanese women with uterine fibroids- a mixed-methods study

Examining the changes in women's lives after the hysterectomy operation: Experiences of women from Turkey

PURPOSE

The aim of the study is to examine what kind of changes the operation has brought about in the social and psychological life of women.

METHODS

The study was a hermeneutic-phenomenological research conducted using qualitative research methods. It took place between May and July 2023 at a university hospital located in Istanbul, Turkey. Following ethical approval, the study included a total of 24 women who had undergone a 'total abdominal hysterectomy, bilateral salpingo-oophorectomy.' These participants were selected using a combination of maximum diversity sampling and random sampling methods. Data collection was carried out using a semi-structured interview form. The interviews were conducted using face-to-face interview techniques and in-depth interview methods. Qualitative data analysis involved using the coding paradigm of Grounded Theory and Straus and Corbin's coding framework. For the analysis of quantitative data, SPSS version 28.0 was employed, while qualitative data were analyzed using MaxQDA Analytics Pro 2022.

RESULTS

The study found that the participants had a mean age of 47 ± 7.53, and the majority, 66.7%, had not reached menopause before the operation. The qualitative analysis of the semi-structured interviews revealed five main themes, which were identified as follows: 'hysterectomy experiences', 'the impact of hysterectomy on sexual life', 'the significance attributed to the uterus', 'the significance attributed to femininity', and 'self-efficacy after hysterectomy'.

CONCLUSIONS

The study revealed that women experienced changes in self-perception, body image, a sense of organ loss, and developed negative attitudes towards menopause following a hysterectomy. Additionally, there was a noticeable connection between the meanings attributed to the uterus and femininity, which was often influenced by cultural and social factors. In light of these findings, it is recommended that women receive counseling from healthcare professionals before undergoing a hysterectomy. This guidance can help women better understand and cope with the physical, emotional, and cultural aspects of the procedure.

Adaptation to Hysterectomy: Design and Psychometric Properties Assessment of Hysterectomy Adaptation Scale

Background

Adaptation to complications of hysterectomy is one of the topics of concern for women and health care providers. There is no instrument for evaluating adaptation to hysterectomy. This study aimed to design the Hysterectomy Adaptation Scale (HAS) and assess its psychometric properties.

Materials and Methods

This methodological study was conducted from 2018 to 2020 in Mashhad, Iran. To develop the item pool, qualitative data from directed content analysis and data from the review of adaptation and coping instruments were used. The face, content, construct validity, internal consistency, and stability were used to evaluate the psychometric properties of HAS.

Results

The final version of the HAS consisted of 24 items with a reported content validity index of 0.9. Six factors were extracted from the principal component analysis, which explained 60.3 of the observed variance. Model fit indices in confirmatory factor analysis showed that the model was well fitted. The values of the alpha coefficient and intra-class coefficient were 0.86 and 0.95, respectively.

Conclusions

The HAS is a valid and reliable scale for evaluating the adaptation level of hysterectomized Iranian women. HAS can distinguish between hysterectomized women who have adapted to hysterectomy and those who have not. It can be used to assess the adaptation of hysterectomized women in research and clinical practice.

Using Clinician-Patient WeChat Group Communication Data to Identify Symptom Burdens in Patients With Uterine Fibroids Under Focused Ultrasound Ablation Surgery Treatment: Qualitative Study

BACKGROUND

Unlike research project-based health data collection (questionnaires and interviews), social media platforms allow patients to freely discuss their health status and obtain peer support. Previous literature has pointed out that both public and private social platforms can serve as data sources for analysis.

OBJECTIVE

This study aimed to use natural language processing (NLP) techniques to identify concerns regarding the postoperative quality of life and symptom burdens in patients with uterine fibroids after focused ultrasound ablation surgery.

METHODS

Screenshots taken from clinician-patient WeChat groups were converted into free texts using image text recognition technology and used as the research object of this study. From 408 patients diagnosed with uterine fibroids in Chongqing Haifu Hospital between 2010 and 2020, we searched for symptom burdens in over 900,000 words of WeChat group chats. We first built a corpus of symptoms by manually coding 30% of the WeChat texts and then used regular expressions in Python to crawl symptom information from the remaining texts based on this corpus. We compared the results with a manual review (gold standard) of the same records. Finally, we analyzed the relationship between the population baseline data and conceptual symptoms; quantitative and qualitative results were examined.

RESULTS

A total of 408 patients with uterine fibroids were included in the study; 190,000 words of free text were obtained after data cleaning. The mean age of the patients was 39.94 (SD 6.81) years, and their mean BMI was 22.18 (SD 2.78) kg/m2. The median reporting times of the 7 major symptoms were 21, 26, 57, 2, 18, 30, and 49 days. Logistic regression models identified preoperative menstrual duration (odds ratio [OR] 1.14, 95% CI 5.86-6.37; P=.009), age of menophania (OR -1.02 , 95% CI 11.96-13.47; P=.03), and the number (OR 2.34, 95% CI 1.45-1.83; P=.04) and size of fibroids (OR 0.12, 95% CI 2.43-3.51; P=.04) as significant risk factors for postoperative symptoms.

CONCLUSIONS

Unstructured free texts from social media platforms extracted by NLP technology can be used for analysis. By extracting the conceptual information about patients' health-related quality of life, we can adopt personalized treatment for patients at different stages of recovery to improve their quality of life. Python-based text mining of free-text data can accurately extract symptom burden and save considerable time compared to manual review, maximizing the utility of the extant information in population-based electronic health records for comparative effectiveness research.

Using WeChat clinician-patient group communication data to identify symptom burdens in patients with uterine fibroids under focused ultrasound ablation surgery treatment :Qualitative Study (Preprint).. [DOI: 10.2196/preprints.43995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

BACKGROUND

Unlike research project-based health data collections(questionnaires, interviews), social media platforms, which allow patients to freely discuss their health status and obtain peer support.Previous literature has pointed out that both public and private social platforms can serve as data sources for analysing.

OBJECTIVE

This study aimed to use natural language processing (NLP) techniques to identify concerns regarding the postoperative quality of life and symptom burdens in uterine fibroids after focused ultrasound ablation surgery.

METHODS

Screenshots taken from the clinician-patient WeChat groups were converted into free texts using image text recognition technology and used as the research object of this study, which used regular expressions in Python to search for symptom burdens in over 900,000 words of WeChat group-chats associated with 408 patients in Chongqing Haifu Hospital diagnosed with uterine fibroids between 2010 and 2020. We first built a corpus of symptoms by manually coding 30% of the WeChat texts, and then used regular expressions to crawl symptom information from the remaining texts based on this corpus. We compared the results with a manual review (gold standard) of the same records. Then we analyzed the relationship between the population baseline data and conceptual symptoms, Quantitative and qualitative results were examined.

RESULTS

A total of 190,000 words of uterine fibroids patients' free text were finally obtained after data cleaning. A total of 408 patients were included in the study. The age of the patients was 39.94±6.81 years, and their BMI was 22.18±2.78 (kg/m^2). The median reporting times of the seven major symptoms were 21, 26, 57, 2, 18, 30, and 49 days. Results showed that patients with dysmenorrhea were younger(mean 38.26 (SD 7.05), P=.004) and slimmer (mean 22.37 (SD 3.81), P=.04), with lower fertility and parity (P<.05), and tended to stay longer in the hospital (P<.05). Logistic regression models identified preoperative menstrual duration (OR 1.14, 95% CI 5.86-6.37; P= .009), age of menophania (OR -1.02 ,95%CI 11.96-13.47,P=.03), and the number(OR 2.34,95% CI 1.45-1.83,P=.04) and size of fibroids(OR 0.12,95% CI 2.43-3.51,P=.04) as significant risk factors for postoperative symptoms.

CONCLUSIONS

Unstructured free texts from social media platforms extracted by NLP technology can be used for analysis, extracting the conceptual information about patients' HRQol,adopt personalized treatment for patients at different stages of recovery to improve the quality of life of patients. Python-based text mining of free-text data can accurately extract symptom burden administered and save considerable time compared to manual review, maximizing the utility of the extant information in population-based electronic health records for comparative effectiveness research.

CLINICALTRIAL

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