Evidence map›Paper›PMID 30386737›Full record

ArticleFrontiers in oncology2018

Development and Internal Validation of Novel Nomograms Based on Benign Prostatic Obstruction-Related Parameters to Predict the Risk of Prostate Cancer at First Prostate Biopsy.

Luigi Cormio, Luca Cindolo, Francesco Troiano, Michele Marchioni, Giuseppe Di Fino, Vito Mancini, Ugo Falagario, Oscar Selvaggio, Francesca Sanguedolce, Francesca Fortunato and 2 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in oncology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed, 3 pooled it
3.8field-weighted citation impact, top 6% 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

22 citing papers in PubMed, 3 syntheses or guidelines pooled it, 30 citations in OpenAlex.

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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

12 authors at 1 institution in 1 country.

Luigi CormioDepartment of Urology and Renal Transplantation, University of Foggia, Foggia, Italy.
Luca CindoloDepartment of Urology, ASL, Chieti, Italy.
Francesco TroianoDepartment of Urology and Renal Transplantation, University of Foggia, Foggia, Italy.
Michele MarchioniDepartment of Urology, SS Annunziata Hospital, "G.D'Annunzio" University of Chieti, Chieti, Italy.
Giuseppe Di FinoDepartment of Urology and Renal Transplantation, University of Foggia, Foggia, Italy.
Vito ManciniDepartment of Urology and Renal Transplantation, University of Foggia, Foggia, Italy.
Ugo FalagarioDepartment of Urology and Renal Transplantation, University of Foggia, Foggia, Italy.
Oscar SelvaggioDepartment of Urology and Renal Transplantation, University of Foggia, Foggia, Italy.
Francesca SanguedolceDepartment of Pathology, University of Foggia, Foggia, Italy.
Francesca FortunatoDepartment of Medical and Surgical Sciences, University of Foggia, Foggia, Italy.
Luigi SchipsDepartment of Urology, ASL, Chieti, Italy.
Giuseppe CarrieriDepartment of Urology and Renal Transplantation, University of Foggia, Foggia, Italy.
University of Foggia · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The present study aimed to determine the ability of novel nomograms based onto readily-available clinical parameters, like those related to benign prostatic obstruction (BPO), in predicting the outcome of first prostate biopsy (PBx). To do so, we analyzed our Internal Review Board-approved prospectively-maintained PBx database. Patients with PSA>20 ng/ml were excluded because of their high risk of harboring prostate cancer (PCa). A total of 2577 were found to be eligible for study analyses. The ability of age, PSA, digital rectal examination (DRE), prostate volume (PVol), post-void residual urinary volume (PVR), and peak flow rate (PFR) in predicting PCa and clinically-significant PCa (CSPCa)was tested by univariable and multivariable logistic regression analysis. The predictive accuracy of the multivariate models was assessed using receiver operator characteristic curves analysis, calibration plot, and decision-curve analyses (DCA). Nomograms predicting PCa and CSPCa were built using the coefficients of the logit function. Multivariable logistic regression analysis showed that all variables but PFR significantly predicted PCA and CSPCa. The addition of the BPO-related variables PVol and PVR to a model based on age, PSA and DRE findings increased the model predictive accuracy from 0.664 to 0.768 for PCa and from 0.7365 to 0.8002 for CSPCa. Calibration plot demonstrated excellent models' concordance. DCA demonstrated that the model predicting PCa is of value between ~15 and ~80% threshold probabilities, whereas the one predicting CSPCa is of value between ~10 and ~60% threshold probabilities. In conclusion, our novel nomograms including PVR and PVol significantly increased the accuracy of the model based on age, PSA and DRE in predicting PCa and CSPCa at first PBx. Being based onto parameters commonly assessed in the initial evaluation of men "prostate health," these novel nomograms could represent a valuable and easy-to-use tool for physicians to help patients to understand their risk of harboring PCa and CSPCa.

Indexed as

lower urinary tract symptomsnomogramprostate biopsyprostate cancerprostate volume

Identifiers

PMID30386737
PMCPMC6198078
OpenAlexW2897880553

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.