Symptom scores and clinical measures in benign prostatic hyperplasia

Abstract

BACKGROUND

Benign prostatic hyperplasia commonly causes lower urinary tract symptoms and impaired quality of life (QoL). The International Prostate Symptom Score (IPSS), prostate volume (PV), and post-void residual (PVR) urine are routinely assessed together, but their correlations have not been comprehensively pooled. We hypothesized that symptom scores would correlate strongly with QoL but only moderately with anatomical and functional measures.

AIM

To determine pooled correlations between symptom scores, QoL, PV, and PVR urine in men with benign prostatic hyperplasia.

METHODS

PubMed, Scopus, Web of Science, and Lens.org were searched through December 2025. Eligible studies reported Pearson or Spearman correlation coefficients between the IPSS and QoL, PV, or PVR urine. Correlations were Fisher’s Z-transformed and pooled using restricted maximum likelihood random-effects meta-analysis. Heterogeneity, publication bias, subgroup patterns, and leave-one-out sensitivity were assessed.

RESULTS

Twenty-eight studies involving 4814 participants from 14 countries were included. The pooled correlation between the IPSS and QoL was strong [r = 0.713, 95% confidence interval (CI): 0.557-0.820; k = 5; I² = 3.26%; P < 0.001]. PV showed a moderate-to-large correlation with symptom severity (r = 0.457, 95%CI: 0.257-0.619; k = 12; I² = 38.37%; P < 0.001). PVR urine showed a moderate correlation with symptom severity (r = 0.363, 95%CI: 0.150-0.543; k = 11; I² = 51.18%; P = 0.001). No compelling publication bias was detected.

CONCLUSION

Symptom scores strongly reflect QoL impairment, whereas PV and PVR urine provide complementary, non-redundant clinical information.

Key Words: Benign prostatic hyperplasia; International Prostate Symptom Score; Lower urinary tract symptoms; Meta-analysis; Prostate volume; Post-void residual urine; Quality of life

Core Tip: Benign prostatic hyperplasia causes lower urinary tract symptoms and impaired quality of life. This meta-analysis shows that the International Prostate Symptom Score is strongly associated with quality-of-life impairment, while prostate volume and post-void residual urine are moderately and incompletely associated with symptom severity. The findings support integrated assessment using symptom scoring, prostate volume, post-void residual urine, intravesical prostatic protrusion grading, and uroflowmetry, where available, rather than reliance on one parameter.

INTRODUCTION

Benign prostatic hyperplasia (BPH) is one of the most common urological conditions affecting aging men worldwide, characterized by non-malignant proliferation of prostatic stromal and epithelial cells resulting in progressive glandular enlargement[1]. The prevalence of BPH rises sharply with age, affecting approximately 50% of men in their fifth decade of life and reaching up to 90% in those over 80 years of age[2].

The primary clinical manifestation of BPH is lower urinary tract symptoms (LUTS), which encompasses storage symptoms (urgency, frequency, nocturia) and voiding symptoms (weak stream, straining, intermittency, and the sensation of incomplete emptying[3,4]. LUTS imposes a substantial burden on individuals and healthcare systems through medical management costs, surgical intervention, and complications, including acute urinary retention (AUR), urinary tract infection (UTI), and bladder decompensation[5,6].

Critically, LUTS significantly impair health-related quality of life (QoL) by disrupting sleep, reducing social participation, causing psychological distress, and impairing occupational functioning[6,7]. A global analysis[8] confirmed that the burden of BPH and LUTS is greatest in regions of low-to-middle sociodemographic development, including Sub-Saharan Africa and South Asia, where population ageing is accelerating the fastest.

The clinical evaluation of BPH relies on an integrated approach combining subjective symptom assessment with objective anatomical and functional measurements. The International Prostate Symptom Score (IPSS) is the most widely adopted and internationally validated instrument for assessing LUTS severity[9,10]. It comprises seven symptom items yielding a total score from 0 to 35 (mild: 0-7; moderate: 8-19; severe: 20-35). A separate QoL item asks how a patient would feel living with current symptoms for the rest of their life (scored 0-6), serving as a critical measure of symptom bother guiding patient-centred treatment decisions[7].

Objective parameters are obtained primarily through transabdominal or transrectal ultrasonography. Prostate volume (PV) is an essential anatomical determinant for treatment planning and risk stratification, including guidance for 5-alpha reductase inhibitor use and prediction of disease progression, including AUR risk[11]. Post-void residual (PVR) urine volume is a critical functional indicator of bladder emptying efficiency and bladder outlet obstruction (BOO), carrying independent prognostic value as a risk factor for UTIs, bladder stones, and renal impairment[12,13]. Both the American Urological Association (AUA) and the European Association of Urology (EAU) recommend PV and PVR assessment in the standard initial evaluation of men with LUTS[14]. More recently, intravesical prostatic protrusion (IPP) - a transabdominal ultrasound measure of the extent to which the enlarged prostate protrudes into the bladder lumen - has emerged as a promising adjunctive parameter that, in some series, predicts BOO and voiding dysfunction more strongly than total PV[1,15].

While IPSS, QoL index, PV, and PVR are routinely used together in clinical practice, the exact nature and strength of the relationships between these subjective and objective variables remains a subject of ongoing clinical debate[12,16]. Clinical decision-making in BPH, including decisions to initiate pharmacotherapy, refer for surgery, or monitor conservatively, frequently assumes that these parameters co-vary in a predictable way. If correlations are strong and consistent, this assumption is justified. If they are weak or highly variable, clinicians must be cautious about inferring one parameter from another, and both objective and subjective assessments must be made independently.

Existing primary studies report highly inconsistent and conflicting findings. Regarding PV-IPSS, some studies demonstrate strong statistically significant positive correlations[11], while others report weak or non-significant associations[2,4,17]. These contradictions complicate clinical decision-making and hinder the development of evidence-based, resource-appropriate LUTS care pathways, particularly in low- and middle-income settings where access to uroflowmetry and urodynamic testing is limited. The IPSS-QoL correlation, while generally strong in individual studies[7,18], has not been systematically pooled across an international evidence base. Despite numerous primary studies, no prior systematic review and meta-analysis has simultaneously pooled IPSS-QoL, PV-IPSS, and IPSS-PVR correlation coefficients within a single analytical framework.

This study was conducted to address the existing evidence gap by evaluating and pooling correlation coefficients between subjective LUTS scores and objective sonographic parameters in men with LUTS/BPH, with the aim of determining the magnitude, direction, and consistency of these relationships across diverse settings. The review specifically examined three outcome pairs: The correlation between IPSS and QoL, PV and IPSS, and IPSS and PVR. Secondary objectives included assessing between-study heterogeneity, evaluating potential publication bias, exploring sources of variation through subgroup and sensitivity analyses, and contextualizing the findings within a structured narrative synthesis to inform evidence-based clinical practice and policy.

MATERIALS AND METHODS

This study was conducted and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines[19] and the Meta-Analysis Reporting Standards. The protocol was pre-registered in the PROSPERO International Prospective Register of Systematic Reviews (No. 420261347619).

Literature search and study selection

A comprehensive electronic literature search was conducted in four databases: PubMed, Scopus, Web of Science, and Lens.org. Search terms combined MeSH headings and free-text keywords, including “International Prostate Symptom Score”, “IPSS”, “lower urinary tract symptoms”, “prostate volume”, “post-void residual”, “quality of life”, “BPH”, and “correlation”, using Boolean operators (AND, OR). No initial language restrictions were applied. Searches were conducted through December 2025. Complete, reproducible search strings are provided in Supplementary material.

Studies were eligible if they: (1) Were original peer-reviewed articles; (2) Enrolled adult men (aged ≥ 18 years) with LUTS; (3) Reported a Pearson or Spearman correlation coefficient between at least one specified variable pair (IPSS-QoL, PV-IPSS, or IPSS-PVR); and (4) Provided sample size and r for Fisher’s Z transformation. Studies were excluded if they were reviews, meta-analyses, case reports, conference abstracts without full text, or if they failed to report a numerical r with sample size. Two reviewers independently screened titles and abstracts, then full texts; discrepancies were resolved by consensus.

Data extraction

Two reviewers independently extracted: Publication details; study design and setting; sample size (n); participant characteristics; measurement instruments; and the reported correlation coefficient (r) with corresponding sample size. Where bivariate and adjusted coefficients were both reported, the bivariate (unadjusted) coefficient was extracted for methodological consistency. Discrepancies were resolved by consensus.

Quality assessment and risk of bias

Methodological quality was independently assessed by two reviewers using an adapted Joanna Briggs Institute Critical Appraisal Checklist, supplemented by elements of the Newcastle-Ottawa Scale. Disagreements were resolved by consensus. Four domains were evaluated: Selection bias (clear eligibility criteria), measurement validity (use of standardized tools such as IPSS and ultrasound for PV/PVR), confounding control, and statistical appropriateness (use of Pearson’s r or Spearman’s ρ). Each domain was scored as 1 (yes) or 0 (no/unclear), with total scores ranging from 0 to 4. Studies were classified as high quality (total score = 4), moderate quality (total score = 3), or low quality (total score ≤ 2). These four domains (labelled D1-D4), the 0-4 scoring scheme, and the corresponding high/moderate/low thresholds were pre-specified and are reported for every included study in Supplementary Table 1.

Effect size metric and Fisher’s Z transformation

Pearson’s r was the primary effect size. Because raw correlation coefficients have non-normal sampling distributions, particularly near ± 1, each r was transformed to Fisher’s Z using:

Z = 0.5 × ln[(1 + r) / (1 − r)]

The standard error (SE) was calculated as SE (Z) = 1 / √(N − 3). All pooled estimates and confidence interval bounds were back-transformed to r using:

r = (e^2z − 1) / (e^2z + 1)

Meta-analytic model

Three separate random-effects meta-analyses were conducted (one per outcome pair) using Restricted Maximum Likelihood (REML) estimation, implemented in the metafor package[20] within jamovi version 2.6.44 (The jamovi project, 2024). A random-effects model was selected a priori because the included studies were conducted across heterogeneous geographic settings, populations, and healthcare systems, conditions under which genuine between-study variability in true effect sizes is expected[21].

Heterogeneity assessment

Heterogeneity was quantified using four complementary indices: (1) Cochran’s Q statistic (P < 0.10 threshold due to low power with small k); (2) I² (proportion of total variability attributable to true between-study heterogeneity; classified as < 25% = low, 25%-50% = moderate, 50%-75% = substantial, > 75% = considerable); (3) τ² (estimated between-study variance); and (4) τ (standard deviation of true effects).

Publication bias assessment

Publication bias was assessed using three complementary methods: (1) Egger’s weighted regression test examining funnel plot asymmetry; (2) Kendall’s Tau assessing rank correlation between effect size and precision; and (3) Rosenthal’s Fail-Safe N (FSN) estimating how many null studies would be needed to nullify the pooled effect (threshold: 5k + 10). Given that publication bias tests have low power with fewer than ten studies[22], results were interpreted with appropriate caution.

Sensitivity analysis

Robustness was examined via leave-one-out (LOO) sensitivity analysis, sequentially removing each study and recalculating the pooled correlation on the remaining k - 1 studies. Stability was defined as no single study whose removal reversed the direction, significance, or substantive interpretation of the pooled finding.

Subgroup analysis

Exploratory subgroup analyses were stratified by geographic region and sample size. Pooled r values within each subgroup were estimated using variance-weighted random-effects pooling with τ² carried from the full model. Formal between-subgroup Q-tests were not generated automatically for every stratification because each subgroup pool was obtained by variance-weighted random-effects pooling with τ² carried from the full model, rather than by fitting a single categorical-moderator (meta-regression) model in metafor, which is the procedure that returns the omnibus between-subgroup Q statistic. For the principal pre-specified regional contrast (Africa vs South/Southeast Asia in the PV-IPSS analysis), a formal two-group comparison was therefore performed directly on Fisher’s Z scale using a Wald-type Z-test of the difference between the two independent pooled estimates (equivalent to a between-subgroup Q-test on 1 degree of freedom). The remaining subgroup results are reported descriptively and should be interpreted as hypothesis-generating.

Clinical magnitude benchmark

To support clinical interpretation, pooled correlations were interpreted using Cohen’s conventional benchmarks: Small r = 0.10-0.29, moderate r = 0.30-0.49, and large r ≥ 0.50. The threshold of r = 0.50 was used a priori as a benchmark for a large correlation, not as evidence of causality and not as a formal equivalence claim. Statistical significance was assessed from the pooled Fisher’s Z model, while clinical magnitude was interpreted from the back-transformed r and its 95% confidence interval (CI).

Statistical analysis

All computations were performed in jamovi version 2.6.44 (The jamovi project, 2024) using the metafor package for R with REML estimation. Effect sizes were back-transformed manually and verified analytically with α = 0.05 (two-tailed) for all tests unless otherwise specified.

RESULTS

Study selection and characteristics

A search of four electronic databases yielded 1450 records (PubMed: 273; Scopus: 468; Web of Science: 396; Lens.org: 313). Following the removal of 631 duplicate records, 819 unique records were screened at the title and abstract stage, with 731 excluded as irrelevant. Of 88 reports sought for full-text retrieval, 14 were inaccessible. The remaining 74 reports were assessed against eligibility criteria, and 46 were excluded (non-original studies: 12; no correlation coefficient reported: 14; insufficient quantitative data: 10; wrong population or outcomes: 10). The PRISMA flow diagram is provided in Figure 1.

Figure 1  PRISMA flow diagram for study identification and selection.

There were 28 studies that met all eligibility criteria: Five for IPSS-QoL (n = 705), 12 for PV-IPSS (n = 1446), and 11 for IPSS-PVR (n = 2663). The combined sample comprised 4814 men from 14 countries across Africa, South Asia, Southeast Asia, East Asia, and North America, with publication years between 2010 and 2025. Three studies[3,6,11] contributed to more than one meta-analysis, each reporting eligible correlations for multiple outcome pairs. Individual study characteristics are presented in Table 1[1,3-13,16-18,23-32].

Table 1 Study characteristics, correlation coefficients, and Fisher’s Z Transformation values for all 28 included studies¹.

Meta-analysis	Ref.	Country	n	r	Fisher’s Z	SE	95%CI lower (r)	95%CI upper (r)
Meta-analysis 1: IPSS-quality of life (k = 5; n = 705)
IPSS-QoL	Roy et al[18], 2016	India	100	0.693	0.854	0.102	0.575	0.783
	Taneja et al[7], 2017	India	121	0.656	0.786	0.092	0.541	0.747
	Anyimba et al[3], 2023	Nigeria	170	0.760	0.996	0.077	0.688	0.817
	Nketiah et al[6], 2024	Ghana	256	0.541	0.606	0.063	0.448	0.622
	Timilsina et al[23], 2024	Nepal	58	0.910	1.528	0.135	0.852	0.946
Meta-analysis 2: Prostate volume-IPSS (k = 12; n = 1446)
PV-IPSS	Udeh et al[17], 2012	Nigeria	120	-0.004	-0.004	0.092	-0.183	0.176
	Awaisu et al[9], 2021	Nigeria	290	0.179	0.181	0.059	0.065	0.288
	Ngwa-Ebogo et al[24], 2023	Cameroon	45	0.410	0.436	0.154	0.132	0.628
	Hossain et al[25], 2020	Bangladesh	60	0.585	0.670	0.132	0.389	0.730
	Yadav et al[26], 2021	India	154	0.560	0.633	0.081	0.441	0.660
	Rananda et al[4], 2021	Indonesia	86	0.048	0.048	0.110	-0.166	0.257
	Ng et al[2], 2015	India	126	0.400	0.424	0.090	0.242	0.537
	Sadiq et al[27], 2024	Pakistan	45	0.437	0.469	0.154	0.165	0.647
	Maghfira et al[28], 2024	Indonesia	93	0.853	1.267	0.105	0.786	0.900
	Shah et al[11], 2024	Pakistan	158	0.779	1.043	0.080	0.709	0.834
	Almaasah et al[29], 2025	Indonesia	19	0.617	0.720	0.250	0.226	0.837
	Rehman et al[30], 2024	Pakistan	250	0.250	0.255	0.064	0.130	0.363
Meta-analysis 3: IPSS-post-void residual (k = 11; n = 2663)
IPSS-PVR	Ko et al[13], 2010	Korea	309	0.200	0.203	0.057	0.090	0.305
	Cakiroglu[31], 2013	Turkey	152	0.441	0.473	0.082	0.303	0.561
	Aisuodionoe-Shadrach et al[32], 2020	Nigeria	100	0.350	0.365	0.102	0.165	0.511
	Hamza et al[10], 2021	Nigeria	167	0.285	0.293	0.078	0.139	0.419
	Mbouché et al[1], 2022	Cameroon	103	0.000	0.000	0.100	-0.194	0.194
	Anyimba et al[3], 2023	Nigeria	170	0.490	0.536	0.077	0.367	0.597
	Apata et al[5], 2023	Nigeria	150	0.118	0.119	0.082	-0.043	0.273
	Kohler and Kausik[16], 2023	United States	1014	0.151	0.152	0.031	0.090	0.211
	Nketiah et al[6], 2024	Ghana	256	0.085	0.085	0.063	-0.038	0.205
	Shah et al[11], 2024	Pakistan	158	0.599	0.692	0.080	0.489	0.691
	Fazal et al[12], 2025	Pakistan	84	0.920	1.589	0.111	0.879	0.948

¹Anyimba et al[3], Nketiah et al[6], and Shah et al[11] appear in more than one meta-analysis, each reporting separate correlation coefficients for different outcome pairs within the same study.

R: Pearson/Spearman correlation coefficient; Fisher’s Z: Variance-stabilizing transformation computed as 0.5 × ln[(1 + r)/(1 − r)]; SE: Standard error of Fisher’s Z computed as 1/√(N − 3); 95% confidence interval bounds are on the r scale, back-transformed from Fisher’s Z. Negative r values indicate inverse correlations. CI: Confidence interval; IPSS: International Prostate Symptom Score; PV: Prostate volume; PVR: Post-void residual; QoL: Quality of life.

Pooled effect sizes

Results of the three random-effects meta-analyses are summarized in Table 2. All three pooled correlations were positive and statistically significant (all P ≤ 0.001).

Table 2 Summary of pooled meta-analysis results: Correlation coefficients, Fisher’s Z, and statistical significance for all three outcome pairs¹.

Outcome pair	k	Total n	Pooled r	95%CI (r scale)	Fisher’s Z	SE	Z-statistic	P value	Effect size classification
IPSS-quality of life	5	705	0.713	0.557-0.820	0.893	0.135	6.62	< 0.001	Large (Cohen, 1988)
Prostate volume-IPSS	12	1,446	0.457	0.257-0.619	0.493	0.117	4.20	< 0.001	Moderate-to-Large
IPSS-post-void residual	11	2,663	0.363	0.150-0.543	0.380	0.117	3.25	0.001	Moderate

¹Effect size benchmarks per Cohen: Small r = 0.10-0.29; medium r = 0.30-0.49; large r ≥ 0.50. Back-transformation formula: R = (e^2z - 1)/(e^2z + 1).

k: Number of included studies; n: Total participants; Pooled r: Back-transformed Pearson correlation from Restricted Maximum Likelihood random-effects model; 95% confidence interval on r scale obtained by back-transforming Fisher’s Z interval bounds; SE: Standard error of pooled Fisher’s Z; Z-statistic: Wald test of H₀; IPSS: International Prostate Symptom Score; CI: Confidence interval.

IPSS-QoL

The random-effects meta-analysis of five studies yielded a pooled Fisher’s Z = 0.893 (SE = 0.135; 95%CI: 0.629-1.158; Z = 6.62, P < 0.001). Back-transformation produced a pooled r = 0.713 (95%CI: 0.557-0.820), reflecting a large, positive association between IPSS and QoL impairment. By Cohen’ s conventions, r = 0.71 constitutes a large effect (r ≥ 0.50). The coefficient of determination (r² approximately 0.508) indicates that approximately 50.8% of QoL variance is explained by IPSS severity, although this estimate should be interpreted cautiously because only five studies contributed to this analysis.

PV-IPSS

The 12-study meta-analysis yielded a pooled Fisher’s Z = 0.493 (SE = 0.117; 95%CI: 0.263-0.723; Z = 4.20, P < 0.001), corresponding to a pooled r = 0.457 (95%CI: 0.257-0.619). This represents a moderate-to-large positive association between PV and IPSS. The coefficient of determination (r² approximately 0.209) indicates that PV explains approximately 20.9% of IPSS variance, underscoring the multifactorial nature of LUTS. Equivalence testing confirmed the effect is non-trivial (Z-lower = 8.470, P < 0.001).

IPSS-PVR

The 11-study meta-analysis produced a pooled Fisher’s Z = 0.380 (SE = 0.117; 95%CI: 0.151-0.609; Z = 3.25, P = 0.001), corresponding to a pooled r = 0.363 (95%CI: 0.150-0.543). This reflects a statistically significant moderate positive association between IPSS and PVR. The coefficient of determination (r² approximately 0.132) indicates approximately 13.2% of PVR variance is explained by IPSS scores, confirming that IPSS and PVR provide partially overlapping but non-redundant information.

Forest plots

Forest plots for all three meta-analyses are presented in Figure 2. Each plot displays study-level effect sizes (r) as squares scaled to inverse-variance weight, with horizontal lines representing 95%CIs. The pooled random-effects estimate is shown as a diamond at the bottom, with diamond width representing the 95%CI. All values are on the back-transformed Pearson’s r scale.

Figure 2 Forest plots. A: Forest plot for the International Prostate Symptom Score (IPSS)-quality of life meta-analysis (k = 5, n = 705). Pooled random-effects r = 0.713 [95% confidence interval (CI): 0.557-0.820; I² = 3.26%]. Studies ordered by publication year; B: Forest plot for the prostate volume-IPSS meta-analysis (k = 12, n = 1446). Pooled random-effects r = 0.457 (95%CI: 0.257-0.619; I² = 38.37%). Studies ordered by publication year; C: Forest plot for the IPSS-post-void residual meta-analysis (k = 11, n = 2663). Pooled random-effects r = 0.363 (95%CI: 0.150-0.543; I² = 51.18%). Studies ordered by publication year.

Heterogeneity assessment

Heterogeneity statistics are presented in Table 3. The degree of between-study heterogeneity differed markedly across the three outcome pairs.

Table 3 Heterogeneity statistics for all three random-effects meta-analyses¹.

Outcome pair	τ2 (SE)	τ	I2 (%)	H2	Cochran’s Q	df	Q P value
IPSS-quality of life	0.003 (0.064)	0.055	3.26	1.034	4.578	4	0.333
Prostate volume-IPSS	0.062 (0.069)	0.248	38.37	1.623	17.196	11	0.102
IPSS-post-void residual	0.075 (0.067)	0.274	51.18	2.048	20.558	10	0.024

¹I² classification: < 25% low; 25%-50% moderate; 50%-75% substantial; > 75% considerable. Q P < 0.10 indicates significant heterogeneity; Q has limited power with small k.

τ²: Between-study variance estimated by Restricted Maximum Likelihood; τ: √τ² (standard deviation of true effects); I²: Proportion of total variability attributable to between-study heterogeneity; H²: Ratio of total variability to expected sampling variability; Cochran’s Q: χ² heterogeneity test; df: k-1; IPSS: International Prostate Symptom Score; SE: Standard error.

IPSS-QoL

Heterogeneity was low (I² = 3.26%, τ² = 0.003, τ = 0.055). Cochran’s Q = 4.578 (df = 4, P = 0.333) was non-significant, confirming that observed variability is consistent with sampling error alone. The H² statistic of 1.034 confirmed minimal excess variability. These five studies were highly consistent despite spanning four countries[3,6,7,18,23]. Nonetheless, with only five studies, the I² estimate is itself imprecise, and the Q test is underpowered; the low value of I² should therefore be read as consistent with, rather than firm proof of, true homogeneity, and is interpreted with corresponding caution.

PV-IPSS

Moderate heterogeneity was observed (I² = 38.37%, τ² = 0.062, τ = 0.248; Q = 17.196, df = 11, P = 0.102). Approximately 38% of total variability is attributable to genuine between-study differences, likely reflecting variation in PV thresholds, LUTS etiology (BOO vs detrusor overactivity), and geographic/ethnic differences in BPH epidemiology.

IPSS-PVR

Substantial heterogeneity was demonstrated (I² = 51.18%, τ² = 0.075, τ = 0.274; Q = 20.558, df = 10, P = 0.024). The statistically significant Q confirms real between-study differences, reflecting the wide range from r = 0.000[1] to r = 0.920[12], attributable to differences in disease severity, patient selection, PVR measurement methodology, and IPSS administration mode.

Publication bias assessment

Publication bias results are presented in Table 4. Funnel plots are provided in Figure 3. For IPSS-QoL: Egger’s test P = 0.076 (non-significant); Kendall’s τ = 0.600 (P = 0.233, non-significant); FSN = 83, exceeding the threshold of 5k + 10 = 35. For PV-IPSS: All three tests were non-significant (Egger’s P = 0.510; Kendall’s P = 0.630; FSN = 118, threshold = 70). For IPSS-PVR: Egger’s result was borderline (P = 0.087); Kendall’s τ = 0.309 (P = 0.218, non-significant); FSN = 78 (threshold = 65). No compelling evidence of publication bias was detected in any analysis, although the IPSS-PVR analysis showed borderline funnel-plot asymmetry and one influential small study.

Figure 3 Funnel plots. A: Funnel plot for the International Prostate Symptom Score (IPSS)-quality of life meta-analysis. No significant asymmetry detected (Egger’s P = 0.076; Kendall’s P = 0.233); B: Funnel plot for the prostate volume-IPSS meta-analysis. No evidence of publication bias (Egger’s P = 0.510; Kendall’s P = 0.630); C: Funnel plot for the IPSS-post-void residual meta-analysis. Borderline asymmetry noted (Egger’s P = 0.087; Kendall’s P = 0.218). Fazal et al[12] is a high-Fisher’s-Z outlier consistent with a small-study effect.

Table 4 Publication bias assessment: Egger’s regression test, Kendall’s Tau (Begg’s Test), and Rosenthal’s Fail-Safe N for all three meta-analyses¹.

Outcome pair	Egger’s intercept	Egger’s P value	Kendall’s τ	Kendall’s P value	Fail-Safe n	Overall bias assessment
IPSS-quality of life	1.774	0.076	0.600	0.233	83	No compelling evidence of bias
Prostate volume-IPSS	0.659	0.510	0.107	0.630	118	No evidence of publication bias
IPSS-post-void residual	1.710	0.0872	0.309	0.218	78	No significant bias; borderline Egger’s2

¹Fail-Safe N threshold = 5k + 10; International Prostate Symptom Score-quality of life = 35; Prostate volume-International Prostate Symptom Score = 70; International Prostate Symptom Score-post-void residual = 65. Values exceeding the threshold indicate robustness against publication bias.

²Borderline Egger’s result (P = 0.087) for International Prostate Symptom Score-post-void residual, with k = 11. Kendall’s Tau (Begg’s test): P < 0.05 indicates significant rank correlation between effect size and standard error. Egger’s test: P < 0.05 indicates statistically significant funnel plot asymmetry.

IPSS: International Prostate Symptom Score.

Sensitivity analysis (LOO)

Complete LOO results are presented in Table 5. For IPSS-QoL, pooled r ranged from 0.653-0.751 across all five iterations; no removal reversed direction or significance. For PV-IPSS, Shah et al[11] produced the largest change (Δr = -0.100; new pooled r = 0.357) and Maghfira et al[28] the second largest (Δr = -0.085; r = 0.372); both remain moderate-to-large. For IPSS-PVR, Fazal et al[12] was the single influential study (Δr = -0.080; new pooled r = 0.283), consistent with a small-study effect from a highly selected tertiary referral population. The pooled correlation remained positive and significant across all 31 LOO iterations (Supplementary Table 1).

Table 5 Complete leave-one-out sensitivity analysis results for all 28 studies across all three meta-analyses¹.

Meta-analysis	Ref.	New Fisher’s Z	New Pooled r	Δr	95%CI	Stability
Meta-analysis 1: IPSS-quality of life (Full model r = 0.713; 95%CI: 0.557-0.820)
	None (Full model)	0.893	0.713	-	0.557-0.820	Reference
	Roy et al[18], 2016	0.837	0.684	-0.029	-	Stable
	Taneja et al[7], 2017	0.850	0.691	-0.022	-	Stable
	Anyimba et al[3], 2023	0.789	0.658	-0.055	-	Stable
	Nketiah et al[6], 2024	0.975	0.751	+0.038	-	Stable
	Timilsina et al[23], 2024	0.780	0.653	-0.060	-	Stable
Meta-analysis 2: Prostate volume-IPSS (Full model r = 0.457; 95%CI: 0.257-0.619)
	None (Full model)	0.493	0.457	-	0.257-0.619	Reference
	Udeh et al[17], 2012	0.487	0.452	-0.005	-	Stable
	Hossain et al[25], 2020	0.437	0.411	-0.046	-	Stable
	Rananda et al[4], 2021	0.449	0.421	-0.036	-	Stable
	Awaisu et al[9], 2021	0.514	0.473	+0.016	-	Stable
	Yadav et al[26], 2021	0.424	0.401	-0.056	-	Stable
	Ng et al[2], 2015	0.449	0.421	-0.036	-	Stable
	Ngwa-Ebogo et al[24], 2023	0.447	0.419	-0.038	-	Stable
	Sadiq et al[27], 2024	0.446	0.419	-0.038	-	Stable
	Rehman et al[30], 2024	0.487	0.452	-0.005	-	Stable
	Maghfira et al[28], 2024	0.391	0.372	-0.085	-	Influential
	Almaasah et al[29], 2025	0.443	0.417	-0.040	-	Stable
	Shah et al[11], 2024	0.373	0.357	-0.100	-	Influential
Meta-analysis 3: IPSS-post-void residual (Full model r = 0.363; 95%CI: 0.150-0.543)
	None (Full model)	0.380	0.363	-	0.150-0.543	Reference
	Ko et al[13], 2010	0.398	0.381	+0.018	-	Stable
	Cakiroglu[31], 2013	0.375	0.359	-0.004	-	Stable
	Aisuodionoe-Shadrach et al[32], 2020	0.384	0.367	+0.004	-	Stable
	Hamza et al[10], 2021	0.390	0.373	+0.010	-	Stable
	Mbouché et al[1], 2022	0.413	0.393	+0.030	-	Stable
	Anyimba et al[3], 2023	0.369	0.354	-0.009	-	Stable
	Apata et al[5], 2023	0.405	0.386	+0.023	-	Stable
	Kohler and Kausik[16], 2023	0.404	0.386	+0.023	-	Stable
	Nketiah et al[6], 2024	0.409	0.390	+0.027	-	Stable
	Shah et al[11], 2024	0.356	0.342	-0.021	-	Stable
	Fazal et al[12], 2025	0.291	0.283	-0.080	0.105-0.444	Influential

¹Δr: Change in pooled r relative to the full model (reference row). New pooled r was calculated using the Restricted Maximum Likelihood random-effects model on the remaining k - 1 studies. 95% confidence intervals marked - were positive across all iterations; full values available in Supplementary Table 1. Stable:|Δr| < 0.05 and no change in direction or significance; Influential: |Δr| ≥ 0.05.

IPSS: International Prostate Symptom Score; CI: Confidence interval.

Subgroup analysis

Exploratory subgroup results stratified by geographic region and sample size are presented in Table 6 and should be interpreted as hypothesis-generating. For IPSS-QoL: South Asian studies (k = 3, pooled r approximately 0.75) showed numerically higher correlations than African studies (k = 2, pooled r approximately 0.65); both subgroups remain in the large-effect range. For PV-IPSS: African studies yielded a markedly lower pooled r (approximately 0.22, 95%CI: 0.01-0.41) compared to South/Southeast Asian (r approximately 0.52) and Pakistani (r approximately 0.51) studies, consistent with storage-symptom dominance in African BPH populations. A formal between-subgroup comparison confirmed that this difference between the African and South/Southeast Asian pools was statistically significant (Fisher’s Z difference = 0.353; Wald Z = 2.29, P = 0.022; equivalent to a between-subgroup Q = 5.25, df = 1), rather than reflecting sampling variation alone. Sample size stratification produced consistent estimates (small: r approximately 0.44; large: r approximately 0.46). For IPSS-PVR, Pakistani studies demonstrated the strongest association (r approximately 0.74), likely reflecting tertiary referral bias. Western studies (United States, Turkey) showed the weakest correlation (r approximately 0.22). A pronounced small-study effect was identified: Large-sample studies (n > 200) yielded a pooled r approximately 0.18 vs r approximately 0.45 for smaller studies (n ≤ 200).

Table 6 Exploratory subgroup analysis by geographic region and sample size: Pooled correlation coefficients across all three meta-analyses.

Meta-analysis	Subgroup factor	Category	k	Pooled r	95%CI	I2	Notes
IPSS-QoL	Geographic region	South Asia (India, Nepal)	3	Approximately 0.75	0.66-0.91	-	Higher upper bound; includes study with r = 0.91
		Africa (Nigeria, Ghana)	2	Approximately 0.65	0.54-0.76	-	Numerically lower; both estimates are still large-effect
	Sample size	Large (n > 100)	4	Approximately 0.68	0.54-0.76	-	More stable; consistent effect
		Small (n ≤ 100)	1	0.910	-	N/A	Single study[23]; interpret with caution
PV-IPSS	Geographic region	Africa (Nigeria, Cameroon)	3	0.22	0.01-0.41	-	Substantially lower; storage-symptom dominance
		South/Southeast Asia (India, Bangladesh, Indonesia)	6	0.52	0.35-0.66	-	Consistent moderate-to-large effect
		Pakistan	3	0.51	0.32-0.66	-	Comparable to South/Southeast Asia
	Sample size	Small (n < 100)	5	0.44	0.22-0.63	-	Wider CI; includes influential study[29]
		Large (n ≥ 100)	7	0.46	0.30-0.61	-	More stable and representative
IPSS-PVR	Geographic Region	Africa (Nigeria, Ghana, Cameroon)	6	0.262	0.079-0.425	54.3%	Moderate heterogeneity within subgroup
		Pakistan	2	0.738	0.461-0.882	High	Only k = 2; tertiary referral bias likely
		East Asia (Korea)	1	0.200	0.090-0.305	N/A	Single study[31]
		Western (United States, Turkey)	2	0.220	0.121-0.313	Low	Earlier disease detection; diverse LUTS aetiology
	Sample Size	Large (n > 200)	4	0.178	0.094-0.259	31.2%	Large, representative samples; more conservative
		Small (n ≤ 200)	7	0.446	0.261-0.604	68.1%	Small-study effect; higher and more variable estimates

k: Number of studies in subgroup; Pooled r: Back-transformed correlation from variance-weighted random-effects pooling (τ² carried from full model); 95% confidence interval on r scale; I²: Within-subgroup heterogeneity. Subgroup analyses are exploratory; formal between-subgroup Q-tests were unavailable for all analyses. -: Insufficient k for meaningful I² estimation. Subgroup estimates should be interpreted with caution, given the small k per subgroup. An omnibus between-subgroup Q-test was not generated by the variance-weighted pooling procedure; however, the principal pre-specified regional contrast (Africa vs South/Southeast Asia for Prostate volume-International Prostate Symptom Score) was tested formally on Fisher’s Z scale and was statistically significant (Z = 2.29, P = 0.022).

IPSS-QoL: International Prostate Symptom Score-quality of life; PV-IPSS: Prostate volume-International Prostate Symptom Score; IPSS-PVR: International Prostate Symptom Score-post-void residual; CI: Confidence interval.

Summary of results

Across all three meta-analyses (combined k = 28, n = 4,814), statistically significant, positive pooled correlations were demonstrated. The strongest and most consistent finding was IPSS-QoL (r = 0.713; I² = 3.26%; P < 0.001). A moderate-to-large association was found for PV-IPSS (r = 0.457; I² = 38.37%; P < 0.001), and a moderate association for IPSS-PVR (r = 0.363; I² = 51.18%; P = 0.001). No evidence of publication bias was detected. All estimates were stable across sensitivity analyses.

Narrative synthesis: Contextualizing statistical correlations within clinical and practice frameworks

The quantitative findings demonstrated above are statistically robust, but a structured appraisal of the primary literature reveals important pathophysiological and health-system nuances that directly inform clinical practice. Key clinical findings and practice implications by study are synthesized in Table 7.

Table 7 Narrative synthesis: Key clinical findings and practice implications derived from primary studies included in the meta-analysis¹.

Ref.	Country	Key clinical findings	Practice implications
Kohler and Kausik[16], 2023	United States	IPSS has statistically significant but weak correlations with Qmax, voided volume, and PVR in an unselected population (n = 1014); subjective symptom severity does not reliably predict objective voiding parameters	Holistic assessment mandate: Clinical and reimbursement pathways must integrate subjective symptom scores with objective voiding profiles; surgical decision-making must not be based on symptom scores alone
Mbouché et al[1], 2022	Cameroon	IPP correlates more strongly with low Qmax and AUR risk than with overall prostate volume; IPP grade > 10 mm confers significantly elevated obstruction risk, even with modest total PV	Diagnostic standardization: Ultrasound protocols for LUTS/BPH should mandate systematic IPP measurement and grading alongside PV and PVR; IPP is non-invasive, adds no equipment cost, and improves prognostic accuracy
Taneja et al[7], 2017; Timilsina et al[23], 2024	India/Nepal	High illiteracy rates render the IPSS difficult to complete without clinician assistance; the pictogram-based VPSS correlates strongly with IPSS and uroflowmetry parameters and takes less time to complete unaided	Tool adaptation: VPSS should be adopted as a first-line or co-administered assessment tool in developing nations and low-literacy populations to eliminate interviewer bias and improve the accuracy of symptom scoring
Anyimba et al[3], 2023	Nigeria	Interviewer-assisted IPSS administration introduces systematic reporting bias, attenuating the observed correlation between reported symptoms and objective Qmax/PVR in low-literacy settings	Minimizing measurement bias: Healthcare policies in low-resource settings must account for interviewer-assisted questionnaire subjectivity; objective ultrasonographic imaging and VPSS adoption should be prioritized as complementary tools
Awaisu et al[9], 2021	Nigeria	Patients present late because LUTS are falsely attributed to normal ageing; PV correlates significantly with IPSS at presentation (r = 0.179), reflecting delayed healthcare-seeking and low BPH community literacy	Public health and early triage: Community education campaigns are needed to decouple LUTS from “normal agein” where uroflowmetry is unavailable, PV combined with IPSS serves as a practical triage tool for urgency of referral
Fazal et al[12], 2025	Pakistan	Strong IPSS-PVR correlation (r = 0.920) in a tertiary referral population with advanced disease; UTIs are highly prevalent in older BPH patients with severe LUTS and elevated residual urine volumes	Comorbidity management: BPH protocols must incorporate routine urine culture and sensitivity testing for all patients with PVR ≥ 100 mL, even without overt infective symptoms; microbiological surveillance alongside ultrasonography is clinically imperative
Ngwa-Ebogo et al[24], 2023	Cameroon	Weak-to-moderate PV-IPSS correlation (r = 0.410); confirms that prostate size is a poor standalone predictor of symptom bother; large volume does not reliably indicate severe or treatment-requiring LUTS	Surgical criteria refinement: PV alone is insufficient for clinical or surgical decision-making; guidelines should require composite assessment - incorporating symptom score, uroflowmetry, PVR, and IPP grade - rather than volume thresholds in isolation
Shah et al[11], 2024	Pakistan	Strong correlations between IPSS and both PV (r = 0.779) and PVR (r = 0.599) in a tertiary urology population with advanced disease burden at presentation	Tertiary care pathway: The strength of IPSS correlations in tertiary settings reflects advanced-stage disease, not baseline population parameters; policies should prioritize earlier primary care intervention to reduce disproportionate burden on tertiary facilities

¹Practice implications derived from primary study findings within the context of current European Association of Urology/American Urological Association guidelines. All studies were cross-sectional unless otherwise stated. AUR: Acute Urinary Retention; BPH: Benign prostatic hyperplasia; IPP: Intravesical prostatic protrusion; IPSS: International Prostate Symptom Score; PV: Prostate volume; PVR: Post-void residual; Qmax: Maximum urinary flow rate; QoL: Quality of life; UTI: Urinary tract infection; VPSS: Visual Prostate Symptom Score; LUTS: Lower urinary tract symptoms.

The clinical disconnect between PV and symptom severity

Although the pooled PV-IPSS correlation (r = 0.457) is significant, PV explains approximately 20.9% of IPSS variance. Prostate size is not a universally reliable predictor of LUTS severity. The subgroup data (Table 6) illustrate this clearly: African studies yielded a pooled r approximately 0.22 vs r approximately 0.51-0.52 in Asian cohorts[9,24]. These differences likely reflect genuine variation in prostate morphology, LUTS etiology, and the relative contributions of detrusor overactivity vs BOO. PV cannot serve as a standalone criterion for treatment or surgical decisions; integrated multiparametric assessment incorporating IPSS, uroflowmetry, PVR, and IPP grading is required[16].

Literacy barriers, interviewer bias, and the Visual Prostate Symptom Score

Heterogeneity across studies is partly attributable to variation in IPSS administration modes. In Sub-Saharan African and South Asian settings, a considerable proportion of participants require clinician assistance to complete the IPSS[3,7], introducing directional information bias that attenuates correlation coefficients toward the null. The Visual Prostate Symptom Score (VPSS), using pictograms for urinary frequency, stream strength, nocturia, and QoL, enables accurate unaided self-reporting without literacy requirements and eliminates interviewer subjectivity[7,33]. Formal adoption of the VPSS as a first-line or co-administered instrument in low-literacy settings is directly supported by these findings.

IPP as an emerging prognostic parameter

While this review examined total PV, the primary literature increasingly identifies IPP as a superior predictor of voiding dysfunction and clinical outcomes[1]. IPP, graded as < 5 mm (grade I), 5-10 mm (grade II), and > 10 mm (grade III), correlates more strongly with reduced maximum urinary flow rate (Qmax), elevated detrusor pressure, and AUR risk than total PV. Patients with IPP > 10 mm face a substantially elevated risk of disease progression even with modest total PV. Standard ultrasound protocols should mandate systematic IPP measurement alongside PV and PVR, as it requires no additional equipment and adds critical prognostic value.

Late presentation, detrusor decompensation, and comorbid UTIs

The higher-end IPSS-PVR correlations in Pakistani tertiary referral cohorts[11,12] represent patients presenting at an advanced, decompensated stage, driven by cultural normalization of LUTS as ageing[9]. Chronic incomplete emptying leads to progressive detrusor decompensation, escalating PVR and UTI risk. Studies in this review confirm substantially higher UTI prevalence in patients with severe LUTS and high PVR[12]. Two evidence-based interventions are supported: (1) Community campaigns to decouple LUTS from normal ageing; and (2) Routine urine culture for all patients with PVR ≥ 100 mL to 150 mL regardless of overt infective symptoms.

DISCUSSION

This study pooled data from 28 studies (n = 4814) across three clinically important outcome pairs. All three analyses produced statistically significant positive correlations: IPSS-QoL (r = 0.713, 95%CI: 0.557-0.820), PV-IPSS (r = 0.457, 95%CI: 0.257-0.619), and IPSS-PVR (r = 0.363, 95%CI: 0.150-0.543). The strongest finding, IPSS-QoL, was also the most consistent (I² = 3.26%), confirming that IPSS is not merely a symptom counter but a valid indicator of overall disease burden on QoL. The moderate PV-IPSS and IPSS-PVR correlations confirm real, clinically meaningful associations while underscoring that no single parameter fully characterizes LUTS severity.

The pooled IPSS-QoL correlation (r = 0.713) closely matches a recent scoping review by Faraon et al[34], which reported r = 0.72 overall and r = 0.79 in medically treated patients across 31 BPH studies. Our slightly lower estimate likely reflects the more diverse, unselected populations from low- and middle-income settings. For PV-IPSS, our pooled r = 0.457 is consistent with the broader literature but higher than the weak associations reported in single African studies: Obiesie et al[35] reported r = + 0.109 in Nigeria and Awaisu et al[9] reported r = 0.179, both consistent with the low African subgroup estimate (r approximately 0.22) in this review. Studys emphasize that BPH burden is greatest in middle socio-demographic index regions, contextualizing the predominantly low-and middle-income country sample base of this review[8,36].

No prior meta-analysis has specifically pooled IPSS-PVR correlation coefficients, making this the first pooled estimate in the literature. The wide range from r = 0.000[1] to r = 0.920[12] and the substantial heterogeneity are consistent with the observation by Tan et al[15], that the predictive strength of objective urological parameters varies substantially by disease severity, patient selection, and clinical context. The AUA guidelines recommend PVR assessment prior to any planned intervention, a recommendation supported by the complementary, non-redundant relationship between IPSS and PVR demonstrated in this review[14].

Biological and clinical explanation

The strong IPSS-QoL correlation reflects the direct relationship between urinary symptom severity and daily functional impairment[37-39]. Because the IPSS was specifically designed to capture subjective bother rather than mere symptom frequency, it is not surprising that it correlates strongly with patient-reported QoL[40]. The approximately 50% explained QoL variance is a clinically substantial proportion for a single questionnaire in a condition with multiple contributing factors[41].

The moderate PV-IPSS correlation (r = 0.457; r² approximately 0.209) reflects the dual-component nature of LUTS: While prostatic enlargement contributes to BOO by narrowing the urethral lumen, many men develop significant storage symptoms driven by secondary detrusor overactivity rather than static outlet obstruction[42,43]. IPSS scores can thus be high even when PV is modest, explaining why volume explains only 20.9% of symptom variance[4,43]. The AUA and EAU guidelines acknowledge this multifactorial etiology and recommend assessment of both anatomical and functional bladder parameters[14].

The IPSS-PVR correlation (r = 0.363; r² approximately 0.132) is biologically plausible but modest because PVR is elevated primarily by impaired detrusor contractility or severe outlet obstruction, neither of which is fully captured by the IPSS[16,43]. The IPSS “sensation of incomplete emptying” item is a subjective perception that may not reflect actual residual volume[6]. Men with storage-dominant LUTS may have high IPSS but normal PVR; men with detrusor failure may have high PVR but few subjective symptoms[16,43]. This partial overlap explains the moderate, imperfect correlation[16,44].

Interpretation of heterogeneity

The three meta-analyses showed strikingly different heterogeneity levels, with important clinical implications. The low I² for the IPSS-QoL analysis (3.26%; Q = 4.578; P = 0.333) suggests consistency across the available studies; however, because only five studies contributed to this analysis, I² and Q estimates should be interpreted cautiously rather than as definitive proof of homogeneity or broad generalizability. The moderate PV-IPSS heterogeneity (I² = 38.37%; Q = 17.196; P = 0.102) likely reflects variation in PV enlargement thresholds across studies, differing proportions of obstructive vs storage symptoms, and ethnic differences in prostate morphology. The substantial IPSS-PVR heterogeneity (I² = 51.18%; Q = 20.558; P = 0.024) reflects the clinical diversity across the 11 included studies from a primary care cohort (r = 0.151)[16] to a tertiary referral center (r = 0.920)[12], driven by differences in disease severity, PVR measurement technique, and IPSS administration mode. Tan et al[15] similarly observed that objective urological parameter predictiveness varies substantially by clinical context. The complementary analyses point to two specific, identifiable contributors to this heterogeneity. First, the LOO analysis flagged Fazal et al[12] as the single influential study (Δr = -0.080), an extreme tertiary-referral estimate (r = 0.920) whose removal lowers the pool to r = 0.283. Second, the subgroup analysis showed a pronounced small-study effect: The two Pakistani tertiary-referral studies pooled to r approximately 0.74, and smaller studies (n ≤ 200) yielded r approximately 0.45 against r approximately 0.18 in larger studies (n > 200). Together, the influential Fazal et al[12] estimate and the small-sample Pakistani subgroup account for much of the observed I², linking the statistical heterogeneity directly to identifiable clinical and methodological sources.

Subgroup analysis

The most notable subgroup finding was the markedly lower PV-IPSS correlation in African studies (r approximately 0.22) compared to Asian studies (r approximately 0.51-0.52). Studies from Nigeria and Cameroon consistently show high IPSS scores despite relatively modest prostatic enlargement, consistent with storage-symptom dominance (nocturia, urgency, frequency) in African BPH populations rather than obstructive voiding symptoms linked to prostate size[35]. This may also partly reflect interviewer-assisted IPSS inflation in low-literacy settings. For IPSS-PVR, Pakistani studies yielded the highest pooled r (approximately 0.74), reflecting tertiary referral bias toward severely obstructed patients. Western studies (United States, Turkey) showed the weakest correlation (r approximately 0.22), consistent with earlier disease detection and a more diverse LUTS aetiology[16,31].

Clinical implications

The strong IPSS-QoL correlation (r = 0.713) confirms that the IPSS reliably captures overall disease burden on QoL. Clinicians in all settings can use IPSS with confidence, not only to quantify symptom severity but also to assess QoL impact, consistent with AUA and EAU guideline recommendations for IPSS use in the initial evaluation of all men with bothersome LUTS.

The moderate PV-IPSS correlation (r = 0.457, explaining approximately 20.9% of symptom variance) confirms that PV is a meaningful but partial contributor. Treatment decisions must integrate IPSS, PVR, uroflowmetry (where available), and IPP grading alongside volume[15,45]. In resource-limited settings without uroflowmetry, combining IPSS with ultrasound-derived PV and PVR provides a practical and evidence-based initial assessment.

The moderate IPSS-PVR correlation (r = 0.363) confirms that IPSS and PVR provide complementary, non-redundant information. PVR measurement should not be omitted because IPSS has been collected; elevated PVR may be present even with low IPSS and carries independent prognostic value, including as a marker of a UTI and detrusor dysfunction. PVR assessment before any planned intervention is recommended.

Strengths

This review has several notable strengths. First, to our knowledge, it is the first to simultaneously pool correlation coefficients across three distinct clinical parameter pairs in LUTS/BPH in a single meta-analytic framework, enabling direct comparison of relative association strengths. Second, the combined sample of 4814 participants from 14 countries across five global regions provides a geographically diverse evidence base. Third, all analyses followed rigorous methodological standards, including REML random-effects modelling, Fisher’s Z transformation, complementary heterogeneity indices (Q, I², τ²), triangulated publication bias testing, TOST equivalence testing, and complete LOO sensitivity analysis for all 28 studies. Fourth, the narrative synthesis translates quantitative findings into structured, actionable clinical recommendations grounded in primary study evidence.

Limitations

Several limitations should be acknowledged. First, the number of studies included was modest for IPSS-QoL (k = 5) and IPSS-PVR (k = 11), limiting the precision of pooled estimates and the power of bias detection tests. Second, all 28 studies employed cross-sectional designs, precluding causal inference. Third, pooled estimates are based on unadjusted bivariate correlation coefficients, with no control for confounders, including age, comorbidities, pharmacotherapy, or prostate-specific antigen level. Fourth, heterogeneity in IPSS administration mode (self-administered vs interviewer-assisted), QoL instrument selection (IPSS QoL item vs validated multi-domain tools), and PVR measurement methodology (ultrasound vs catheterization) introduces construct non-equivalence that statistical adjustment cannot fully address. Fifth, the IPSS-QoL pool in particular rests on only five studies, which constrains the generalizability of that estimate and means the accompanying low-heterogeneity finding should be regarded as provisional, since heterogeneity metrics are themselves unstable when k is small. Sixth, the cross-sectional design of every included study means the observed correlations cannot establish directionality, for example, whether greater symptom severity worsens QoL, or whether poorer baseline QoL amplifies symptom reporting. Seventh, although exploratory subgroup analyses were performed, formal meta-regression on key clinical covariates such as disease severity (mild/moderate/severe IPSS strata) and treatment status (untreated, on medical therapy, or post-intervention) could not be undertaken because these variables were inconsistently reported, and too few studies were available per stratum; these unmodelled covariates are themselves plausible sources of the residual heterogeneity. Finally, the primary studies reported a mixture of Pearson and Spearman coefficients, which were pooled together; because these statistics quantify slightly different (linear vs monotonic) relationships, their combination may introduce a minor degree of imprecision into the pooled estimates.

Future research directions

Future priorities include: (1) Prospective multicenter cohort studies from high-, middle-, and low-income settings using standardized protocols including IPSS/VPSS, transabdominal ultrasound with IPP grading, PVR, and uroflowmetry; (2) Individual patient data meta-analysis to enable covariate adjustment; (3) Head-to-head comparative IPSS-VPSS validation studies in low-literacy populations to quantify the magnitude of interviewer bias; (4) Meta-analyses specifically pooling IPP-based correlations to enable formal comparison of IPP vs PV as LUTS predictors; (5) Health economic analyses of routine IPP measurement and PVR-triggered urine culture screening in resource-limited BPH pathways; and (6) Prospective cohort studies employing structural equation modelling to investigate the direct, indirect, and mediating effects among IPSS, PV, PVR, and QoL, so as to characterize their interaction pathways more precisely than pairwise correlation allows.

CONCLUSION

This meta-analysis of 28 studies provides pooled evidence on three clinically important correlations in men with LUTS/BPH. IPSS was strongly associated with QoL impairment, although this finding is based on only five studies and should therefore be interpreted with appropriate caution. PV was moderately associated with IPSS, but the approximately 79% unexplained IPSS variance confirms that gland size alone does not determine symptom burden. IPSS was also moderately associated with PVR, indicating that symptom severity and bladder emptying dysfunction provide complementary, non-redundant clinical information. Overall, the findings support integrated multiparametric evaluation using IPSS or VPSS, PV, PVR, IPP grading, and uroflowmetry where available, rather than reliance on any single parameter. This approach is particularly relevant for low-resource settings where standardized, low-cost ultrasound-based LUTS/BPH assessment pathways may improve clinical decision-making.