Trial reportQuality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation2021
Health-related quality of life among Chinese primary care patients with different lower urinary tract symptoms: a latent class analysis.
Trial report in Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Short-term postoperative recovery for patients after Thulium Laser Vapoenucleation of Prostate: evaluation of the impact of lower urinary tract symptoms on quality of life.World journal of urology · 2024Article
- Patterns of neurogenic lower urinary tract dysfunction management and associated factors among Chinese community-dwelling individuals with spinal cord injury.Scientific reports · 2024Article
- Evaluation of Lower Urinary Tract Symptoms in Males and Urinary Incontinence in Females in Primary Health Care in Greece.Medicina (Kaunas, Lithuania) · 2024Article
- Heterogeneity of symptoms and functions among women receiving chemotherapy for breast cancer in China: A multicentre, cross-sectional study.Frontiers in public health · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
purposeNo previous study has used a data-driven approach to explore symptom subclasses among patients with lower urinary tract symptoms (LUTS). The objectives of this study were to use latent class analysis (LCA) to identify distinct classes of LUTS among primary care patients and to assess the class differences in health-related quality of life (HRQOL).
methodsIn this cross-sectional study, 500 patients were randomly recruited, and 18 symptoms according to the International Continence Society 2002 criteria were assessed. Classes were identified by LCA. Patient HRQOL was measured using the 12-item Short Form Health Survey (version 2), the modified Incontinence Impact Questionnaire-Short Form and the HRQOL item from the International Prostate Symptom Score.
resultsSix distinct LUTS classes were identified: "asymptomatic" (26.0%), "mild symptoms" (22.6%), "moderate multiple symptoms" (17.0%), "urgency symptoms" (13.8%), "urinary incontinence" (12.0%) and "severe multiple symptoms" (8.6%). Multinomial regression analysis found differences in the gender distribution and prevalence of heart diseases across classes, and multiple linear regression found that patients with "severe multiple symptoms" and "urinary incontinence" had the poorest HRQOL.
conclusionAlmost three quarters of the primary care patients in this study were suffering from varying degrees of LUTS. The poor HRQOL in "severe multiple symptoms" and "urinary incontinence" implies that patients in these classes require additional attention and treatments.
Indexed as
Identifiers
33447962What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.