Evidence map›Paper›PMID 32425801›Full record

ArticleFrontiers in pharmacology2020

Detection of Prostatic Inflammation From Peripheral Lymphocyte Count and Free/Total PSA Ratio in Men With LUTS/BPH.

Xinyang Liao, Zhuang Tang, Jianzhong Ai, Hang Xu, Shiyu Zhang, Liangren Liu, Shi Qiu, Ping Tan, Yu Fan, Lu Yang and 1 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in pharmacology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.1field-weighted citation impact, top 19% of its field
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 11 citations in OpenAlex.

  1. Chronic inflammation in benign prostatic hyperplasia: Pathophysiology and treatment options.International journal of urology : official journal of the Japanese Urological Association · 2024
    Review
  2. Observational
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors at 3 institutions in 1 country.

Xinyang LiaoDepartment of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, China.
Zhuang TangDepartment of Urology, Sun Yat-sen Memorial Hospital, Guangzhou, China.
Jianzhong AiDepartment of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, China.
Hang XuDepartment of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, China.
Shiyu ZhangDepartment of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, China.
Liangren LiuDepartment of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, China.
Shi QiuDepartment of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, China.
Ping TanDepartment of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, China.
Yu FanDepartment of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, China.
Lu YangDepartment of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, China.
Qiang WeiDepartment of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, China.
Sichuan University · CNWest China Hospital of Sichuan University · CNSun Yat-sen Memorial Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveIdentifying biomarkers of prostatic inflammation has been a question of great interest in the development of anti-inflammatory pharmacotherapy for lower urinary tract symptoms suggestive of benign prostatic hyperplasia (LUTS/BPH). Systemic inflammation and serum prostate-specific antigen (PSA) have been linked with prostatic inflammation. This study set out to develop a diagnostic model for prostatic inflammation using clinical and laboratory parameters.

methodsWe included LUTS/BPH patients undergoing transurethral resection of the prostate. The severity of prostatic inflammation was determined by pathological review. Clinical manifestations and preoperative laboratory test results were recorded. We used LASSO regression with 10-fold cross-validation to select variables with the most diagnostic value of prostatic inflammation. Furthermore, we used multivariable logistic regression analysis to develop the diagnostic model, presented in a nomogram. The discrimination, calibration of the post-LASSO diagnostic model, and the model supplemented with clinical parameters were assessed. Decision curve analysis was performed.

resultsA total of 164 patients were included. Of all patients, 97 (59.1%) had no or mild prostatic inflammation, and 67 (40.9%) had moderate to severe prostatic inflammation. A higher peripheral white blood cell count, higher peripheral lymphocyte count, lower free/total (f/t) PSA ratio, and acute urinary retention history were associated with a higher risk of moderate to severe prostatic inflammation. Peripheral lymphocyte count and f/t PSA ratio were selected by the LASSO method and entered into the nomogram. The post-LASSO diagnostic model had an AUC of 0.756 (95% CI: 0.684-0.829) and good calibration. The addition of clinical parameters failed to show incremental diagnostic value. The decision curve analysis demonstrated that the post-LASSO laboratory nomogram was clinically useful.

conclusionOur findings demonstrated that peripheral lymphocyte count and f/t PSA ratio appear to be reliable diagnostic markers, based on which we build a clinically useful nomogram for prostatic inflammation. This diagnostic model could facilitate the development of anti-inflammatory pharmacotherapy for LUTS/BPH. Before this model is adopted in clinical practice, future validation is needed to determine its clinical utility.

Indexed as

inflammationlower urinary tract symptomsprostate-specific antigenprostatic hyperplasiaprostatitis

Identifiers

PMID32425801
PMCPMC7204507
OpenAlexW3020947439

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Registered trials

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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.