BACKGROUND Uterine fibroids (UF), common benign tumors in women of reproductive age, confer substantial costs. The epidemiological characteristics of UF are unclear in China and Asia. OBJECTIVE This study investigated UF-associated imaging changes, and their prevalence, incidence, and risks in the Chinese population. METHODS This population-based retrospective analysis of multiyear (2017–2020) health examination data (n=33,915 female participants; age ≥15 years) from Nanchong, China included two expert consultation rounds to identify entries of UF-associated imaging changes and calculate prevalence and incidence of UF-associated imaging changes. Logistic regression estimated the association (odds ratio [OR], 95% confidence interval [CI]) of body mass index, high blood pressure, blood lipid profile, and fasting blood glucose level with UF-associated imaging changes. Age-stratified (≤40 and >40 years) risks were ascertained. RESULTS Besides “considering UF”, 17 entries of UF-associated imaging changes screened by the expert consultation were included, involving a total of 46,864 records (n=33,915), and crude prevalence=25.18%; crude incidence density/1000-woman-years=63.28. Incidence and prevalence increased with age during reproductive age (15–49 years) and decreased thereafter. The greatest burden was in 40–54 years women, the prevalence is 38.60%–45.38% and the incidence is 14.73%–17.96%. In the incident younger population (age ≤40 years), risk factors (OR [95% CI]) for UF-associated imaging changes were overweight, high blood pressure (1.48 [1.03–2.14] and 2.16 [1.10–4.24], respectively); in the >40 group, no association was observed. CONCLUSIONS UF incidence and prevalence in Asians were higher than previously reported, showed age-related increase in reproductive age, and UF incidence increased with overweight, high blood pressure in ≤40-year-old participants. Variation in UF burden and risk factors was noted in different age range, and risk factors identified in the younger women population make it possible to be early preventive measures for women with a higher risk of UF.
BACKGROUND Unlike research project-based health data collections, such as questionnaires, interviews, and social media platforms, which allow patients to freely discuss their health status and obtain peer support, previous literature has pointed out that both public-facing websites and private Facebook can serve as data sources for patient-reported outcomes. OBJECTIVE This study aimed to use natural language processing (NLP) techniques based on machine learning 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. The mixed method was used to access 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 23.47±29.37 (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 and slimmer (mean (SD), P<.05), with lower fertility and parity (P<.05), and tended to stay longer in the hospital (P<.05). Logistic regression models identified menstrual duration (odds ratios (OR) (95%CI)), age at menarche (OR (95%CI)), reported symptoms before surgery (OR (95%CI)), and the number and size of fibroids as significant risk factors for postoperative symptoms. CONCLUSIONS Unstructured free texts from social media platforms extracted by NLP technology can be used for analysis, to capture the conceptual information about patients' HRQol, screen out high-risk groups, and track the reporting time of certain symptoms, 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.
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.
BACKGROUND Longitudinal patient-reported outcomes studies require questionnaire assessments to be administered remotely multiple times, burdening research staff. OBJECTIVE To define and quantify the burden that researcher may experience during patient follow-up. METHODS Data were collected via interviews and a questionnaire. This study is an exploratory sequential mixed-methods study. Traditional content analysis was used for the qualitative data. Quantitative data were analyzed using Spearman’s correlation, and significance was tested using the chi-square test. Learning curves of healthcare staff regarding follow-up calls were generated using cumulative summation analysis. RESULTS We constructed a three-dimension conceptual framework for staff burden: (a) time-related burden, (b) technical-related burden, and (c) emotional-related burden. The quantitative analysis found that follow-up time was significantly correlated with staff experience, workload, and learning curve periods. There was a significant difference between the lost-to-follow-up rate of staff with and without follow-up experience with this program. Staff working on a daily assessment schedule had a higher lost-to-follow-up rate than those on a twice-a-week schedule. Additionally, inexperienced follow-up staff needed 113 calls to achieve stable follow-up time and quality, while experienced staff needed only 55 calls. CONCLUSIONS Researchers in longitudinal PROs projects suffer from a multidimensional burden during remote follow-up. Our results may help establish a proper PROs follow-up protocol to reduce the burden on research staff without sacrificing data quality.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.