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Record W3014346033 · doi:10.1111/bju.15036

Chronic prostatitis/chronic pelvic pain syndrome: it is time to change our management and research strategy

2020· letter· en· W3014346033 on OpenAlexaffabout
J. Curtis Nickel

Bibliographic record

VenueBritish Journal of Urology · 2020
Typeletter
Languageen
FieldMedicine
TopicUrinary Bladder and Prostate Research
Canadian institutionsQueen's University
Fundersnot available
KeywordsMedicineProstatitisChronic prostatitis/chronic pelvic pain syndromePsychological interventionPelvic painPlaceboPhysical therapyClinical trialPopulationCohortInternal medicineAlternative medicineIntensive care medicineProstateSurgeryPathologyPsychiatry

Abstract

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A urologist who manages patients with prostatitis (or for that matter, a patient with the condition) would read the latest comprehensive review on pharmacological interventions for chronic prostatitis/chronic pelvic pain syndrome (CP/CPPS) with despair. In the Cochrane Systemic review examining the available clinical evidence for the efficacy of pharmacological interventions for treating CP/CPPS, Franco et al. [1] clearly show that low- to very-low-quality evidence suggests that some treatments may confer at best, only a small and perhaps clinically insignificant benefit for patients. Are we doing something wrong? To start with, we do not need to despair. We are now managing men with CP/CPPS much better, achieving clinically significant improvement in over 80% of patients [2, 3]. This real-world management success story, which continues to evolve, clearly shows much greater benefit than that suggested by all the clinical trials assessed in this review. Our similar independent patient data meta-analysis and comprehensive review of CP/CPPS management strategies [4] described very similar findings to those of Franco et al. [1]. What intrigued us was the difference or the lack of correlation between overall symptom improvement (based on mean symptom score changes from baseline in the treated cohort of subjects compared to the placebo-treated subjects) and the responder analyses which clearly showed some subjects had very significant responses despite the overall dismal mean symptom score differences in the entire population evaluated. We saw this consistently in our clinical trials and we see this in our day-to-day practice; some patients do well with an intervention and others fail miserably. Some of the problem lies in what we are measuring as outcomes in clinical treatment trials. The National Institute of Health (NIH) Chronic Prostatitis Symptom Index (CPSI) is a composite score evaluating many different variables (e.g. location, frequency and severity) and domains (pain, urinary and impact/quality of life) and, while very useful for looking at the clinical picture in each individual patient, should not be used as a primary endpoint or outcome of a clinical trial. The CPSI pain domain is better but it still examines too many variables (location, frequency and severity of pain). The NIH CPSI question #4, which is an numerical rating score measurement of only pain severity, is in fact a validated outcome that can be compared between groups. However, CP/CPPS is much more complicated than just pain and that is why a patient-driven subjective global assessment may be a more appropriate outcome, certainly in clinical practice. We need more CP/CPPS patient-directed specific measurement tools to really assess the benefits of our treatments in individual patients, or at least in intervention-specific domains. We now know the reason for this discrepancy between the overall population symptom score difference and the individual responder rate. We have learned that we cannot treat or manage CP/CPPS patients as a homogenous group and hope that one treatment will benefit them all. We now know that men with CP/CPPS are a clinically heterogeneous group with different mechanisms of disease, a spectrum of clinical symptoms and physical examination parameters. We have learned to identify the various clinical phenotypes based on a UPOINT categorization [5]. By assessing the contribution of urinary factors, psychosocial factors, organ specificity (e.g. prostate, penis, testes), infection, neurogenic/neuropathic factors and tenderness of skeletal muscles (e.g. pelvic floor) in each individual, we can identify targets of intervention. These individualized multimodal treatment plans that we develop for each patient have led to clinical success in managing the majority of patients with CP/CPPS [3, 6]. In future, we hope to understand the mechanisms for these phenotypes and develop biomarkers to better differentiate them. What I have learned from the comprehensive review of CP/CPPS treatments by Franco et al. [1] is that we must stop designing and performing these monotherapy treatment trials in which we enroll all subjects with a diagnosis of CP/CPPS. This type of clinical study has been mainly driven by government regulatory rules in attempts to have drugs approved for CP/CPPS treatment. We should consider trial designs for which the patient eligibility criteria are definitive and clear enough so that we enroll only patients with a phenotype and/or mechanism towards which the specific therapy is directed, that is, a domain-specific trial design. Better yet, we must discover CP/CPPS biomarkers (urine, serum and/or prostate fluid) that will allow us to differentiate mechanisms and allow more effective directed therapy. We must consider more complicated and novel trial designs in which multimodal therapies can be assessed in different populations. I would propose a multi-intervention for pelvic pain study be designed and considered for CP/CPPS in which multimodal treatments designed for specific phenotype domains or disease mechanisms are evaluated in specific individuals. It is anticipated that such a real-world experience study (designed to mimic real-life clinical practice) would result in much better outcomes for patients. Going forward, it is time to not only change our management approach, but also our research strategies. Dr Nickel reports grants from the Canadian Institute for Health Research, grants from the National Institute of Health/National Institute of Diabetes and Digestive and Kidney Diseases, other from the AUA, personal fees from Farr Labs, personal fees from Seikagaku Corp, personal fees from Redleaf Medical, personal fees from Inmunotek, personal fees from Urogen, personal fees from Kanglaite, personal fees from Alivio, personal fees from MicroGenDx, and personal fees from Valensa, outside the submitted work.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.197
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.006
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.061
GPT teacher head0.342
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations33
Published2020
Admission routes2
Has abstractyes

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