Category Ranking

98%

Total Visits

921

Avg Visit Duration

2 minutes

Citations

20

Article Abstract

Purpose: With a growing number of treatment options for localized kidney cancer, patients and health care professionals have both the opportunity and the burden of selecting the most suitable management option. This mixed method systematic review aims to understand the barriers and facilitators of the treatment decision making process in localized kidney cancer.

Materials And Methods: We searched PubMed®, Embase® and Cochrane Central databases between January 1, 2004 and April 23, 2020 using the Joanna Briggs Manual for Evidence Synthesis and the Preferred Reporting Items for Systematic Review and Meta-analysis statement. We identified 553 unique citations; of these, 511 were excluded resulting in 42 articles included for synthesis. The Purpose, Respondents, Explanation, Findings and Significance and the Strengthening the Reporting of Observational Studies in Epidemiology checklist was applied.

Results: The key themes describing barriers and facilitators to treatment decision making were identified and categorized into 3 domains: 1) kidney cancer specific characteristics, 2) decision maker related criteria and 3) contextual factors. The main facilitators identified within these domains were size at diagnosis, age, comorbidities, body mass index, gender, nephrometry scoring systems, biopsy, socioeconomic status, family history of cancer, year of diagnosis, geographic region and practice pattern. The key barriers were race, gender, patient anxiety, low confidence in diagnostic and treatment options, cost of procedure, and practice patterns.

Conclusions: Future interventions designed to improve the decision making process for localized kidney cancer should consider these barriers and facilitators to ensure a better patient experience.

Download full-text PDF

Source
http://dx.doi.org/10.1097/JU.0000000000001901DOI Listing

Publication Analysis

Top Keywords

decision making
16
localized kidney
16
kidney cancer
16
treatment decision
12
systematic review
12
barriers facilitators
12
treatment options
8
facilitators treatment
8
making process
8
process localized
8

Similar Publications

Background: A clear understanding of minimal clinically important difference (MCID) and substantial clinical benefit (SCB) is essential for effectively implementing patient-reported outcome measurements (PROMs) as a performance measure for total knee arthroplasty (TKA). Since not achieving MCID and SCB may reflect suboptimal surgical benefit, the primary aim of this study was to use machine learning to predict patients who may not achieve the threshold-based outcomes (i.e.

View Article and Find Full Text PDF

Background: Soil salinization represents a critical global challenge to agricultural productivity, profoundly impacting crop yields and threatening food security. Plant salt-responsive is complex and dynamic, making it challenging to fully elucidate salt tolerance mechanism and leading to gaps in our understanding of how plants adapt to and mitigate salt stress.

Results: Here, we conduct high-resolution time-series transcriptomic and metabolomic profiling of the extremely salt-tolerant maize inbred line, HLZY, and the salt-sensitive elite line, JI853.

View Article and Find Full Text PDF

Objective: This study aimed to identify key predictors of uterine fibroid (UF) recurrence following laparoscopic myomectomy (LM) in reproductive-age women and to construct a predictive nomogram to support individualized clinical decision-making.

Methods: This retrospective cohort study included 459 women who underwent LM. Recurrence of UFs and risk of recurrence were analyzed.

View Article and Find Full Text PDF

Bariatric surgery is an effective treatment for morbid obesity, but patient outcomes differ greatly because of a variety of phenotypes, comorbidities, and postoperative adherence. In bariatric care, artificial intelligence (AI) and machine learning (ML) are becoming revolutionary tools because traditional predictive models based on BMI and demographic variables are unable to account for these complexities. To put it simply, AI is a branch of computer science that enables machines to perform tasks that typically require human intelligence.

View Article and Find Full Text PDF

Background: The SHARE Approach Model and training curriculum was developed by the Agency for Healthcare Research and Quality (AHRQ) to teach clinicians practicing in diverse settings how to engage in more effective Shared Decision Making (SDM).

Objective: To determine the effectiveness of the SHARE Approach at improving SDM in practices located across Colorado, USA.

Design: A longitudinal study with pre- and post-intervention observations.

View Article and Find Full Text PDF