Optimizing care for LUTS in older patients: Diagnostics, challenges of frailty and patient priorities—NOPIA meeting (ICI-RS 2024)
Bibliographic record
Abstract
As the global population ages, the prevalence of lower urinary tract symptoms (LUTS) in older adults is increasing, with frailty adding complexity to this challenge. There is a recognized paucity of data addressing LUTS in older adults with frailty. This literature review, summarizing insights from a panel of experts at the 2024 NOPIA — Nocturia and Polyuria research group meeting at the International Consultation on Incontinence-Research Society (ICI-RS) congress in Bristol, addresses the need to optimize care for LUTS in older adults by refining diagnostics, adapting approaches for frail patients, considering patient priorities and identify indicators and predictors for LUTS. The concept of lifelong LUTS underscores the persistence of urinary dysfunction across life stages, highlighting the importance of distinguishing age-related changes from pathology for accurate diagnosis. Due to age-related physiological changes, comorbidities, and cognitive decline, traditional diagnostics like frequency-volume charts may be challenging, emphasizing the value of a holistic, patient-centered approach. Treatments in frail populations require caution, as common medications can pose increased risks, often lacking safety data for this group, emphasizing the need for tailored approaches. Recognizing predictors and indicators of LUTS helps to prevent complications, understand progression and to aid in early intervention. Key patient priorities include effective symptom relief, low-risk management, and clear communication, with a strong preference for shared decision-making to accommodate individual needs, considering possible health illiteracy. In conclusion, managing LUTS in older, frail adults requires a comprehensive approach that includes improving diagnostics, recognizing frailty’s impact, and aligning treatment with patient expectations. Future research should develop frailty-specific strategies to enhance quality of life, while a multidisciplinary, patient-centered approach can better address the complex needs of this population.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.017 | 0.008 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".