Abstract
Purpose
Hip arthroscopy (HAS) is technically demanding, and surgeon's experience may influence outcomes. However, the magnitude and domain‐specific effects of the learning curve remain unclear.
Methods
A systematic review and meta‐analysis were conducted according to Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) and registered in PROSPERO (CRD420251185937). PubMed/MEDLINE, Embase and Scopus were searched through 31 December 2025 for studies comparing early versus late experience phases in HAS. Random‐effects meta‐analyses (Sidik–Jonkman estimator with Hartung–Knapp adjustment) were performed for operative and traction time, patient‐reported outcomes, complications, revision arthroscopy and conversion to total hip arthroplasty (THA). Risk of bias was assessed using ROBINS‐I and certainty of evidence using Grading of Recommendations, Assessment, Development and Evaluation (GRADE).
Results
Eighteen studies (2624 procedures; 1291 early and 1333 late cases) were included. Late cases showed significantly shorter operative time (−31.77 min; 95% confidence interval [CI] −51.40 to −12.14) and traction time (−15.67 min; 95% CI −25.42 to −5.92), with substantial heterogeneity. Functional outcomes favoured late experience, including higher improvement in minimal clinically important difference units (0.58; 95% CI 0.17 to 0.99) and modest gains in modified Harris Hip Score (2.78 points; 95% CI 0.16 to 5.41) and Non‐Arthritic Hip Score (8.41 points; 95% CI 1.99 to 14.83). Complication rates and revision arthroscopy did not differ significantly, whereas conversion to THA was lower in late cases (odds ratio [OR] 0.10; 95% CI 0.01 to 0.98). Most studies were at moderate to serious risk of bias, and certainty of evidence was low to very low.
Conclusion
Surgeon experience in HAS is associated with improved operative efficiency and modest functional gains, while safety outcomes remain largely unchanged. The lower THA conversion rate in late cases may reflect improved patient selection and decision‐making, although confidence is limited by study design and heterogeneity.
Level of Evidence
Level III systematic review and meta‐analysis of predominantly retrospective cohort studies.
Keywords: hip arthroscopy, learning curve, meta‐analysis, surgeon experience
Abbreviations
- CI
confidence interval
- GRADE
Grading of Recommendations, Assessment, Development and Evaluation
- HHS
Harris Hip Score
- HOOS
Hip Disability and Osteoarthritis Outcome Score
- HOS‐SSS
Hip Outcome Score—Sports‐Specific Subscale
- I 2
inconsistency index
- MCID
minimal clinically important difference
- MD
mean difference
- mHHS
modified Harris Hip Score
- NAHS
Non‐Arthritic Hip Score
- OR
odds ratio
- PRISMA
Preferred Reporting Items for Systematic Reviews and Meta‐Analyses
- PROM
patient‐reported outcome measure
- ROBINS‐I
Risk Of Bias In Non‐randomized Studies—of Interventions
- SD
standard deviation
- THA
total hip arthroplasty
INTRODUCTION
Hip arthroscopy (HAS) has become a cornerstone procedure for diagnosing and managing intra‐articular hip disorders such as femoroacetabular impingement syndrome (FAIS), labral tears, and chondral lesions. With expanding indications and increasing procedural complexity, surgeon experience and the associated learning curve have emerged as key determinants of safety and outcome.
Across orthopaedic surgery, procedural mastery follows a measurable trajectory. A recent systematic review of learning curves in sports orthopaedics reported substantial variability in case numbers required for competence, ranging from 22 for shoulder arthroscopy to >70 for hip procedures, underscoring the technical demands of HAS [31]. In HAS specifically, prior reviews have demonstrated reductions in complication rates, operative and traction times, and revision rates with increasing experience, while patient‐reported outcomes remained favourable [9, 14]. However, these studies primarily described trends qualitatively without providing pooled estimates across key domains [9, 14, 31]. Consequently, the magnitude and clinical relevance of experience‐related differences remain unclear. Recent evidence from hip preservation surgery suggests that learning‐curve effects may primarily influence operative efficiency, while safety and radiological outcomes remain relatively stable, although data remain limited [27].
Simulator‐based training may improve technical performance, although transferability to operative proficiency remains uncertain [8]. While overall complication rates in HAS are low, outcomes remain dependent on surgeon experience [10]. Despite these insights, the quantitative characteristics of the HAS learning curve—its magnitude and domain‐specific effects—are insufficiently defined and likely vary depending on procedure type and context.
Therefore, the present systematic review and meta‐analysis aims to quantify the learning curve in HAS by comparing early versus late experience phases. Specifically, this study aims to (1) determine the magnitude of improvement across operative and clinical outcomes, (2) identify domains most affected by experience and (3) provide an evidence‐based framework for training and quality assurance in HAS. We hypothesized that increasing surgeon experience would be associated with improved operative efficiency and functional outcomes, while complication rates would remain unchanged.
METHODS
Protocol and registration
This systematic review and meta‐analysis were conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) guidelines [23] and prospectively registered in PROSPERO (CRD420251185937). The PRISMA checklist is provided in the Supporting Information (Table S1).
Eligibility criteria
Eligible studies included patients undergoing HAS for intra‐articular pathologies (e.g., FAIS, labral tears, chondral lesions), comparing different stages of surgeon experience (e.g., chronological grouping or cumulative sum [CUSUM]‐defined early vs. late phases), and reporting at least one relevant outcome (operative or traction time, complications, revision, total hip arthroplasty [THA] conversion or patient‐reported outcome measures [PROMs]). Prospective or retrospective cohort studies, registry studies and case series (>20 procedures) were included. Technical notes, reviews, case reports, cadaveric or simulation studies and studies without clear experience stratification were excluded.
Search strategy
PubMed/MEDLINE, Embase and Scopus were searched from inception to 31 December 2025 using terms related to HAS and learning curves. Reference lists of included studies and relevant reviews were screened. Two reviewers independently performed study selection, with disagreements resolved by consensus or a third reviewer.
Study selection
Records were managed in Rayyan for duplicate removal and blinded screening. Two reviewers independently assessed titles, abstracts and full texts against predefined criteria.
Data extraction
Data were extracted using a standardized, piloted form. Extracted variables included study characteristics, cohort size, patient demographics, definition of the learning curve and outcome data for early versus late phases. When multiple temporal groups were reported, early and late phases were selected or pooled to enable direct comparison. Graphical data were extracted using established methods [12, 29], and authors were contacted if necessary.
Minimal clinically important difference (MCID)‐based outcome normalization
PROMs were harmonized using MCID‐based normalization as previously described [26], enabling pooling across instruments on a unified scale.
Quality assessment
Risk of bias was assessed using Risk Of Bias In Non‐randomized Studies—of Interventions (ROBINS‐I) [34], and certainty of evidence using Grading of Recommendations, Assessment, Development and Evaluation (GRADE) [11]. Publication bias was evaluated using funnel plots and Egger's test [6]. Sensitivity analyses excluded outliers and high‐risk studies.
Data synthesis and statistical analysis
Random‐effects meta‐analyses (Sidik–Jonkman estimator with Hartung–Knapp adjustment) were performed when ≥3 studies reported comparable outcomes [28]. Continuous outcomes were analysed as mean differences (MDs) and dichotomous outcomes as odds ratios (ORs), both with 95% confidence intervals. Heterogeneity was assessed using I 2, τ 2 and Cochran's Q.
RESULTS
Systematic literature search
The database search identified 379 records (PubMed 115, Embase 124 and Scopus 140). After removal of duplicates (n = 237), 21 full‐text articles [2, 3, 4, 5, 7, 13, 15, 16, 17, 18, 19, 20, 21, 24, 25, 30, 32, 33, 36, 37, 39] were assessed, with excellent inter‐reviewer agreement (κ = 0.98–1.0). Three studies were excluded due to unclear stratification of experience phases [13, 20, 33]. Ultimately, 18 studies [2, 3, 4, 5, 7, 15, 16, 17, 18, 19, 21, 24, 25, 30, 32, 36, 37, 39] were included (Figure 1). Outcome harmonization was applied in studies with multiple phases [2, 5, 15, 17, 32] and graphical data extraction [36, 39].
Figure 1.
PRISMA flow diagram of study selection. Flow diagram illustrating the identification, screening, eligibility assessment and inclusion of studies. A total of 379 records were identified, 237 duplicates were removed, and 21 full‐text articles were assessed for eligibility. Ultimately, 18 primary studies met all criteria and were included in the quantitative synthesis. HAS, hip arthroscopy; PRISMA, Preferred Reporting Items for Systematic Reviews and Meta‐Analyses.
Descriptive patient characteristics
A total of 2624 procedures (1291 early; 1333 late) were analysed (Table 1). Sample sizes ranged from 40 to 360. Age and sex distributions were generally comparable between early and late cohorts. Mean age ranged from approximately 27 to 45 years, and the proportion of male patients ranged from 29% to 66%. BMI was less consistently reported but showed similar distributions between groups when available (approximately 22.6–29.0 kg/m2).
Table 1.
Characteristics of included primary studies.
| First author | Origin | Journal (ISSN) | Study design | Level of evidence | Early cases, N | Late cases, N | Age, years ± SD (range) | Male sex, N (%) | BMI, kg/m2 ± SD (range) | Follow‐up period (months) |
|---|---|---|---|---|---|---|---|---|---|---|
| Boden et al. 2014 [2] | United Kingdom | Acta Orthopaedica Belgica (ISSN 0001‐6462) | Prospective cohort study | IV | 40 | 40 | 39 (14–67) | NR | NR | 23 (12–43) |
| Dietrich et al. 2014 [3] | Germany | Knee Surgery, Sports Traumatology, Arthroscopy (ISSN 0942‐2056) | Retrospective comparative cohort study | III | 85 | 86 | NR | NR | NR | NR |
| Domb et al. 2020 [4] | United States | Arthroscopy – The Journal of Arthroscopic and Related Surgery (ISSN 0749‐8063) | Retrospective comparative cohort study | III | 200 | 200 |
Early: 36.7 ± 13.9 Late: 37.2 ± 15.6 |
Early: 63/187 (33.7%) Late: 55/189 (29.1%) |
Early: 25.3 ± 4.8 Late: 25.9 ± 5.2 |
≥24 |
| Dumont et al. 2020 [5] | United States | Arthroscopy – The Journal of Arthroscopic and Related Surgery (ISSN 0749‐8063) | Retrospective cohort study | IV | 75 | 75 | NR | NR | NR | NR |
| Flores et al. 2018 [7] | United States | The Orthopaedic Journal of Sports Medicine (ISSN 2325‐9671) | Prospective cohort study | II | 30 | 30 |
Early: 37.2 ± 11.5 Late: 35.3 ± 10.8 |
Early: 15/30 (50%) Late: 17/30 (56.7%) |
Early: 25.6 ± 4.0 Late: 25.1 ± 4.5 |
|
| Kautzner et al. 2017 [15] | Czech Republic | International Orthopaedics (SICOT) (ISSN 0341‐2695) | Prospective cohort study | II | 50 | 50 | 37 (16–69) | 64/150 (42.7%) | NR | 24 |
| Kern MJ et al. 2018 [16] | United States | Journal of the American Academy of Orthopaedic Surgeons (ISSN 1067‐151X) | Prospective case series | IV | 50 | 50 | 30.1 ± 11.8 (13–62) | 37/100 (37%) | 25.1 ± 4.8 | ≤9 |
| Konan et al. 2011 [17] | United Kingdom | The Journal of Bone and Joint Surgery – American Volume (ISSN 0021‐9355) | Prospective case series | IV | 30 | 30 | 32 (19–55) | 39/100 (39%) | 29 (21–39) | ≥24 |
| Kuschnaroff Contreras et al. 2010 [18] | Brazil | Revista Brasileira de Ortopedia (ISSN 0102‐3616) | Retrospective case series | IV | 75 | 75 | 37.3 (12–58) | 69/150 (46%) | NR | ≥24 |
| Lee et al. 2013 [19] | South Korea | Knee Surgery, Sports Traumatology, Arthroscopy (ISSN 0942‐2056) | Retrospective case series | IV | 20 | 20 | Early: 40.9 ± 17.1 Late: 42.0 ± 15.3 | Early: 12/20 (60%) Late: 8/20 (40%) | Early: 23.0 ± 2.7 Late: 23.4 ± 2.9 | 6 |
| Nossa et al. 2014 [21] | Colombia | Current Orthopaedic Practice (ISSN 1940‐7041) | Prospective cohort study | II | 150 | 210 | 40.4 ± 12.0 (15–79) | 147/362 (40.6%) | NR | ≥6 |
| Park et al. 2014 [24] | South Korea | Arthroscopy – The Journal of Arthroscopic and Related Surgery (ISSN 0749‐8063) | Retrospective case series | IV | 100 | 97 | 44.6 (19–70) | 97/200 (48.5%) | NR | 28.2 (19–42) |
| Philippon et al. 2020 [25] | United States | Knee Surgery, Sports Traumatology, Arthroscopy (ISSN 0942‐2056) | Retrospective cohort study | IV | 100 | 100 | Early: 37.1 ± 12 Late: 33.5 ± 12 | Early: 62/100 (62%) Late: 56/100 (56%) | NR | ≥24 |
| Schüttler KF et al. 2018 [30] | Germany | Archives of Orthopaedic and Trauma Surgery (ISSN 0936‐8051) | Retrospective cohort study | III | 60 | 60 | 43.9 | 254/529 (48.0%) | 26.9 | ≥1.5 |
| Smith KM et al. 2017 [32] | United States | Arthroscopy – The Journal of Arthroscopic and Related Surgery (ISSN 0749‐8063) | Retrospective case series | IV | 50 | 50 | 33.0 ± 11.5 | 48/100 (48%) | 26.0 ± 4.2 | NR |
| Tassinari E et al. 2025 [36] | Italy | Journal of Experimental Orthopaedics (ISSN 2198‐1964) | Retrospective case series | IV | 36 | 35 | NR | 42/71 (59.2%) | 22.6 ± 2.2 (18.7–26.5) | 90 ± 22 (60–132) |
| Vilchez F et al. 2010 [37] | Mexico/Spain | Acta Ortopédica Mexicana (ISSN 2306‐4102) | Prospective cohort study | V | 30 | 67 |
Early: 27 (25–52) Late: 29 (15–51) |
Early: 19/30 (63%) Late: 44/67 (66%) |
NR | ≥6 |
| You JS et al. 2020 [39] | United States | Orthopaedic Journal of Sports Medicine (ISSN 2325‐9671) | Prospective cohort study | II | 110 | 58 | 35.3 ± 9.6 | 77/168 (46%) | 25.07 ± 3.98 | 24 |
Abbreviations: BMI, body mass index; NR, not reported; SD, standard deviation.
Risk of bias
Most studies were rated at moderate to serious risk of bias, primarily due to confounding and participant selection. Classification of interventions was generally low risk, while missing data, outcome measurement and reporting were mostly moderate. No study was rated as low risk across all domains (Table 2).
Table 2.
Risk of bias assessment using the ROBINS‐I tool.
| Study | D1 Confounding | D2 Selection | D3 Classification | D4 Deviations | D5 Missing data | D6 Outcome measurement | D7 Reporting bias | Overall |
|---|---|---|---|---|---|---|---|---|
| Boden et al. [2] | Serious | Moderate | Low | Low | Moderate | Moderate | Moderate | Serious |
| Dietrich et al. [3] | Serious | Serious | Low | Low | Moderate | Moderate | Moderate | Serious |
| Domb et al. 2020 [4] | Moderate | Moderate | Low | Low | Low | Moderate | Moderate | Moderate |
| Dumont et al. 2020 [5] | Serious | Moderate | Low | Low | Low | Moderate | Moderate | Serious |
| Flores et al. 2018 [7] | Moderate | Moderate | Low | Low | Low | Low | Moderate | Moderate |
| Kautzner et al. 2017 [15] | Moderate | Moderate | Low | Low | Low | Low | Moderate | Moderate |
| Kern et al. 2018 [16] | Serious | Moderate | Low | Low | Low | Moderate | Moderate | Serious |
| Konan et al. 2011 [17] | Serious | Moderate | Low | Low | Moderate | Moderate | Moderate | Serious |
| Kuschnaroff Contreras et al. 2010 [18] | Serious | Serious | Low | Low | Moderate | Moderate | Moderate | Serious |
| Lee et al. 2013 [19] | Serious | Moderate | Low | Low | Low | Moderate | Moderate | Serious |
| Nossa et al. 2014 [21] | Moderate | Moderate | Low | Low | Low | Moderate | Moderate | Moderate |
| Park MS et al. 2014 [24] | Serious | Moderate | Low | Low | Moderate | Moderate | Moderate | Serious |
| Philippon et al. 2020 [25] | Serious | Moderate | Low | Low | Low | Moderate | Moderate | Serious |
| Schüttler KF et al. 2018 [30] | Moderate | Moderate | Low | Low | Moderate | Moderate | Moderate | Moderate |
| Smith KM et al. 2017 [32] | Serious | Moderate | Low | Low | Low | Moderate | Moderate | Serious |
| Tassinari E et al. 2025 [36] | Serious | Moderate | Low | Low | Low | Moderate | Moderate | Serious |
| Vilchez F et al. 2010 [37] | Serious | Serious | Low | Low | Moderate | Serious | Serious | Serious |
| You et al. [39] | Moderate | Low | Low | Low | Low | Low | Low | Moderate |
Abbreviation: ROBINS‐I, Risk Of Bias In Non‐randomized Studies—of Interventions.
GRADE assessment of certainty of evidence
Certainty of evidence was low to very low across outcomes (Table 3), mainly due to risk of bias and inconsistency.
Table 3.
GRADE assessment.
| Outcome | No. of studies | Study design | Key limitations | Consistency | Certainty of evidence (GRADE) |
|---|---|---|---|---|---|
| Operative time | 7 | Observational (cohort/case series) | Serious risk of bias due to confounding and selection; heterogeneity in learning‐curve definitions | Inconsistent | Low |
| Traction time | 7 | Observational (cohort/case series) | Serious risk of bias; imprecision and heterogeneity across studies | Inconsistent | Very low |
| Functional MCID | 9 | Observational (cohort studies) | Serious risk of bias; indirectness due to varying MCID definitions | Inconsistent | Low |
| mHHS | 6 | Observational (cohort/case series) | Serious risk of bias; heterogeneity in reporting and follow‐up | Inconsistent | Low |
| HOS‐SSS | 3 | Observational (cohort studies) | Serious risk of bias; imprecision due to small number of studies | Inconsistent | Very low |
| NAHS | 4 | Observational (cohort/case series) | Serious risk of bias; heterogeneity in outcome reporting | Inconsistent | Low |
| VAS Pain | 2 | Observational (cohort studies) | Very serious imprecision due to few studies; risk of bias | Inconsistent | Very low |
| Overall complications | 6 | Observational (cohort/case series) | Serious risk of bias; inconsistent complication definitions | Moderately consistent | Low |
| Conversion to THA | 5 | Observational (cohort/registry‐based) | Serious risk of bias; rare events and imprecision | Inconsistent | Very low |
| Revision arthroscopy | 3 | Observational (cohort/case series) | Serious risk of bias; imprecision due to low event rates | Inconsistent | Very low |
Abbreviations: GRADE, Grading of Recommendations Assessment, Development and Evaluation; HOS‐SSS, Hip Outcome Score—Sports‐Specific Subscale; MCID, minimal clinically important difference; mHHS, modified Harris Hip Score; NAHS, Non‐Arthritic Hip Score; THA, total hip arthroplasty; VAS, Visual Analogue Scale.
Publication bias
Funnel plots showed no consistent evidence of major publication bias (Figures [Link], [Link], [Link], [Link], [Link], [Link], [Link], [Link], [Link], [Link]). Mild asymmetry for operative and traction time suggested small‐study effects. For several outcomes, interpretation was limited by the small number of studies.
Meta‐analysis
Operative time
Across seven studies [5, 15, 19, 30, 36, 37, 39] reporting operative time, mean differences between late and early cases ranged from –60.0 [37] to –8.6 min [39]. All but one study demonstrated shorter operative times in late cases.
The pooled estimate under the random‐effects model showed a statistically significant reduction in operative time of –31.77 min (95% confidence interval [CI] –51.40 to –12.14). Statistical heterogeneity was high (I 2 = 92%, τ 2 = 388.43, p < 0.01) (Figure 2, Table 4).
Figure 2.
Forest plot of operative time (min.) and traction time (min.). CI, confidence interval; MD, mean difference; SD, standard deviation.
Table 4.
Summary of pooled outcomes from the meta‐analysis.
| Parameter | Primary studies (N) | Hips (N) | Treatment effect | CIs | p value | I 2 | τ 2 | Egger bias | Egger p value |
|---|---|---|---|---|---|---|---|---|---|
| Operative time | 7 | 746 | −31.77 | −51.4; −12.14 | 0.0075** | 0.92 | 388.43 | −1.91 | 0.6721 |
| Traction time | 7 | 1133 | −15.67 | −25.42; −5.92 | 0.0077** | 0.91 | 99.44 | 4.53 | 0.3336 |
| Functional MCID | 9 | 1257 | 0.58 | 0.17; 0.99 | 0.0119* | 0.68 | 0.2 | 1.33 | 0.2965 |
| mHHS | 6 | 1017 | 2.78 | 0.16; 5.41 | 0.0416* | 0 | 1.84 | −0.22 | 0.7764 |
| HOS‐SSS | 3 | 720 | 2.54 | −7.41; 12.48 | 0.387 | 0 | 3.76 | −2.59 | 0.3731 |
| NAHS | 4 | 640 | 8.41 | 1.99; 14.83 | 0.0251* | 0.47 | 7.15 | 2.32 | 0.25 |
| VAS Pain | 2 | 460 | −0.83 | −3.97; 2.3 | 0.1831 | 0 | 0.01 | N/A | N/A |
| Complication | 6 | 1078 | 0.42 | 0.12; 1.51 | 0.1416 | 0.81 | 1.17 | 2.53 | 0.3575 |
| THA conversion | 4 | 440 | 0.1 | 0.01; 0.98 | 0.0487* | 0.49 | 1.16 | −1.46 | 0.5696 |
| Revision | 3 | 340 | 0.31 | 0.01; 10.95 | 0.2919 | 0.44 | 1.1 | −2.18 | 0.2633 |
Note: *significant; **more significant.
Abbreviations: CI, confidence interval; HOS‐SSS, Hip Outcome Score—Sports‐Specific Subscale; MCID, minimal clinically important difference; mHHS, modified Harris Hip Score; N/A, not applicable; NAHS, Non‐Arthritic Hip Score; THA, total hip arthroplasty; VAS, Visual Analogue Scale.
Traction time
Across seven studies [4, 15, 16, 24, 36, 37, 39] reporting traction time, mean differences between late and early cases ranged from –27.6 [36] to 1.0 min [15]. Most studies favoured shorter traction times in late cases.
The pooled random‐effects estimate demonstrated a statistically significant reduction in traction time of –15.67 min (95% CI –25.42 to –5.92). Heterogeneity was substantial (I 2 = 91%, τ 2 = 99.44, p < 0.01) (Figure 2, Table 4).
Functional MCID
Across nine studies [2, 4, 7, 15, 17, 19, 24, 25, 30] reporting functional MCID, mean differences between late and early cases ranged from 0.0 [25] to 1.5 [15]. Most studies demonstrated higher rates of achieving MCID in late cases.
The pooled random‐effects estimate demonstrated a statistically significant difference favouring late cases (MD 0.58; 95% CI 0.17 to 0.99). Heterogeneity was substantial (I 2 = 68%, τ 2 = 0.20, p < 0.01) (Figure 3, Table 4).
Figure 3.
Forest plot of functional MCID, mHHS, HOS‐SSS and NAHS. CI, confidence interval; HOS‐SSS, Hip Outcome Score—Sports‐Specific Subscale; MCID, minimal clinically important difference; MD, mean difference; mHHS, modified Harris Hip Score; NAHS, Non‐Arthritic Hip Score; SD, standard deviation.
mHHS
Across six studies [4, 7, 19, 24, 25, 30] reporting mHHS, mean differences between late and early cases ranged from 0.0 [25] to 7.2 points [7]. The majority of studies reported higher postoperative scores in late cases.
The pooled random‐effects estimate showed a statistically significant improvement in mHHS for late cases (MD 2.78; 95% CI 0.16 to 5.41). No relevant heterogeneity was observed (I 2 = 0%, τ 2 = 1.84, p = 0.69) (Figure 3, Table 4).
HOS‐SSS
Across three studies [4, 25, 30] reporting HOS‐SSS, individual study effects were small and inconsistent.
The pooled random‐effects estimate did not demonstrate a statistically significant difference between late and early cases (MD 2.54; 95% CI –7.41 to 12.48). No statistical heterogeneity was observed (I 2 = 0%, τ 2 = 3.76, p = 0.39) (Figure 3, Table 4).
NAHS
Across four studies [2, 4, 15, 17] reporting NAHS, all studies demonstrated higher postoperative scores in late cases, with mean differences ranging from 5.2 to 13.0 points.
The pooled random‐effects estimate demonstrated a statistically significant improvement in NAHS favouring late cases (MD 8.41; 95% CI 1.99 to 14.83). Moderate heterogeneity was observed (I 2 = 47%, τ 2 = 7.15, p = 0.13) (Figure 3, Table 4).
Complications
Across six studies [3, 15, 16, 18, 21, 24] reporting overall complications, individual odds ratios ranged from 0.09 to 2.15. Most studies demonstrated lower complication rates in late cases compared with early cases.
The pooled random‐effects model did not demonstrate a statistically significant difference in overall complication rates between late and early cases (OR 0.42; 95% CI 0.12 to 1.51). Statistical heterogeneity was substantial (I 2 = 81%, τ 2 = 1.17, p < 0.01) (Figure 4, Table 4).
Figure 4.
Forest plot of complication, THA conversion and revision rates. CI, confidence interval; OR, odds ratio; THA, total hip arthroplasty.
Total hip arthroplasty (THA) conversion
Across four studies [2, 15, 17, 25] reporting conversion to THA, odds ratios consistently favoured late cases, ranging from 0.01 to 0.31.
The pooled random‐effects estimate demonstrated a statistically significant reduction in the risk of conversion to THA for late cases (OR 0.10; 95% CI 0.01 to 0.98). Moderate heterogeneity was observed (I 2 = 49%, τ 2 = 1.16, p = 0.12) (Figure 4, Table 4).
Revision
Across three studies [2, 17, 25] reporting revision surgery, individual odds ratios ranged from 0.04 to 0.70, with all studies favouring late cases.
The pooled random‐effects estimate did not demonstrate a statistically significant difference in revision rates between late and early cases (OR 0.31; 95% CI 0.01 to 10.95). Heterogeneity was moderate (I 2 = 44%, τ 2 = 1.10, p = 0.17) (Figure 4, Table 4).
DISCUSSION
The main finding of this meta‐analysis is that increasing surgeon experience in HAS is associated with substantial improvements in operative efficiency, reflected by significantly reduced operative and traction times and modest gains in functional outcomes. In contrast, complication and revision rates remained unchanged, whereas conversion to THA was lower in late cases.
Operative efficiency and learning‐curve dynamics
The most consistent findings relate to operative efficiency, with reductions of approximately 32 min in operative time and 16 min in traction time. These improvements reflect increasing procedural familiarity and optimization of intraoperative workflow, although operative time should be interpreted as a marker of efficiency rather than surgical quality.
Prior studies suggest that initial proficiency may be achieved after 50–100 cases, with higher thresholds for complex procedures [9, 14, 22]. Volume–outcome relationships indicate that learning effects may extend further, with improved outcomes observed at higher cumulative case volumes [20]. Substantial heterogeneity likely reflects variability in case mix, procedural complexity, surgeon experience and institutional factors.
HAS presents specific technical challenges, including constrained joint access and complex pathology management [35], which may limit generalizability. Overall, these findings are consistent with previous reviews [9, 14, 31] and extend them by providing quantitative estimates across outcome domains.
Functional outcomes
Improvements in PROMs were smaller but consistently favoured late cases. Higher MCID achievement and modest gains in mHHS and NAHS suggest incremental functional benefits with experience. However, effect sizes were limited and approached thresholds of clinical relevance. Functional outcomes are likely influenced more strongly by patient selection and underlying pathology than by surgical experience alone. The absence of differences in HOS‐SSS supports this interpretation. These findings are consistent with recent evidence indicating that improved surgical planning and technical execution may further enhance clinical outcomes in HAS, underscoring the role of experience‐dependent factors in optimizing results [38].
Safety outcomes
Complication and revision rates were comparable between early and late phases, indicating an acceptable safety profile even during early experience. In contrast, conversion to THA was lower in late cases, although this finding should be interpreted cautiously, given limited data and low certainty of evidence. This effect may reflect improved patient selection and decision‐making rather than technical factors alone.
Publication bias
No consistent evidence of substantial publication bias was observed. Mild asymmetry for operative and traction time likely reflects small‐study effects. Interpretation was limited for several outcomes due to the small number of studies.
Methodological limitations
All included studies were non‐randomized and subject to confounding and selection bias. Substantial heterogeneity and non‐standardized definitions of the learning curve limit comparability. Additional uncertainty arises from outcome harmonization, graphical data extraction and MCID‐based normalization. Radiographic outcomes were inconsistently reported and could not be analysed. Overall certainty of evidence was low to very low, and findings should be interpreted as indicative rather than causal.
Clinical implications
From a clinical perspective, these findings suggest that HAS can be performed safely during the early learning phase under appropriate supervision, while continued experience leads to substantial gains in operative efficiency. This supports structured training pathways, careful case selection during early practice and more informed patient counselling regarding expected outcomes and surgeon experience. Future research should focus on modifiable factors influencing learning, including simulation‐based training and standardized surgical pathways [1]. Prospective multicenter studies may further define experience thresholds associated with optimal outcomes.
CONCLUSION
Advancing surgical experience in HAS is associated with improved operative efficiency and modest functional gains, while safety outcomes remain largely unchanged. The reduced rate of THA conversion in late cases should be interpreted cautiously, given the observational nature and heterogeneity of the data. Overall, these findings support structured learning while highlighting the need for higher‐quality evidence.
AUTHOR CONTRIBUTIONS
Nikolai Ramadanov and Maximilian Heinz performed the literature search, the data extraction and the risk of bias assessment. Robert Hable and Nikolai Ramadanov conducted the statistical calculations. Nikolai Ramadanov and Robert Hable created all figures and tables. Nikolai Ramadanov wrote the manuscript. Ingo J. Banke and Roland Becker supervised the work.
CONFLICT OF INTEREST STATEMENT
The authors declare no conflicts of interest.
ETHICS STATEMENT
The authors have nothing to report.
Supporting information
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ACKNOWLEDGEMENTS
During manuscript preparation, ChatGPT was used solely for language refinement and stylistic editing; it did not contribute to data analysis, interpretation or modification of the scientific content. The Graphical Abstract was created by ChatGPT. Open Access funding enabled and organized by Projekt DEAL.
DATA AVAILABILITY STATEMENT
Data are available from the corresponding author upon reasonable request.
REFERENCES
- 1. Arevalo A, Keller R, Szukics P, Olsen C, Arevalo I, Yagnik G, et al. Variation in reported learning outcomes and measurement instruments in hip arthroscopy simulation training: a systematic review. Arthroscopy. 2024;40:176–186. [DOI] [PubMed] [Google Scholar]
- 2. Boden RA, Wall AP, Fehily MJ. Results of the learning curve for interventional hip arthroscopy: a prospective study. Acta Orthop Belg. 2014;80:39–44. [PubMed] [Google Scholar]
- 3. Dietrich F, Ries C, Eiermann C, Miehlke W, Sobau C. Complications in hip arthroscopy: necessity of supervision during the learning curve. Knee Surg Sports Traumatol Arthrosc. 2014;22:953–958. [DOI] [PubMed] [Google Scholar]
- 4. Domb BG, Chen SL, Shapira J, Maldonado DR, Lall AC, Rosinsky PJ. The evolution of hip arthroscopy: what has changed since 2008—a single surgeon's experience. Arthroscopy. 2020;36:761–772. [DOI] [PubMed] [Google Scholar]
- 5. Dumont GD, Cohn RM, Gross MM, Menge TJ, Battle NC, Thier ZT. The learning curve in hip arthroscopy: effect on surgical times in a single‐surgeon cohort. Arthroscopy. 2020;36:1293–1298. [DOI] [PubMed] [Google Scholar]
- 6. Egger M, Smith GD, Schneider M, Minder C. Bias in meta‐analysis detected by a simple, graphical test. BMJ. 1997;315:629–634. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. Flores SE, Borak KR, Zhang AL. Hip arthroscopic surgery for femoroacetabular impingement: a prospective analysis of the relationship between surgeon experience and patient outcomes. Orthop J Sports Med. 2018;6:2325967118755048. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8. Frank RM, Erickson B, Frank JM, Bush‐Joseph CA, Bach BR Jr., Cole, BJ , et al. Utility of modern arthroscopic simulator training models. Arthroscopy. 2013;29:2220–2233. [DOI] [PubMed] [Google Scholar]
- 9. Go CC, Kyin C, Maldonado DR, Domb BG. Surgeon experience in hip arthroscopy affects surgical time, complication rate, and reoperation rate: a systematic review on the learning curve. Arthroscopy. 2020;36:3092–3105. [DOI] [PubMed] [Google Scholar]
- 10. Gupta A, Redmond JM, Hammarstedt JE, Schwindel L, Domb BG. Safety measures in hip arthroscopy and their efficacy in minimizing complications: a systematic review of the evidence. Arthroscopy. 2014;30:1342–1348. [DOI] [PubMed] [Google Scholar]
- 11. Guyatt GH, Oxman AD, Vist GE, Kunz R, Falck‐Ytter Y, Alonso‐Coello P, et al. GRADE: an emerging consensus on rating quality of evidence and strength of recommendations. BMJ. 2008;336:924–926. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. Guyot P, Ades A, Ouwens MJ, Welton NJ. Enhanced secondary analysis of survival data: reconstructing the data from published Kaplan–Meier survival curves. BMC Med Res Methodol. 2012;12:9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Haipeng L, Ji L, Juanli Z, Lijun S, Yujie L, Zhongli L, et al. Portal setup: the key point in the learning curve for hip arthroscopy technique. Orthop Surg. 2021;13:1781–1786. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Hoppe DJ, de Sa D, Simunovic N, Bhandari M, Safran MR, Larson CM, et al. The learning curve for hip arthroscopy: a systematic review. Arthroscopy. 2014;30:389–397. [DOI] [PubMed] [Google Scholar]
- 15. Kautzner J, Zeman P, Stančák A, Havlas V. Hip arthroscopy learning curve: a prospective single‐surgeon study. Int Orthop. 2018;42:777–782. [DOI] [PubMed] [Google Scholar]
- 16. Kern MJ, Murray RS, Sherman TI, Postma WF. Incidence of nerve injury after hip arthroscopy. J Am Acad Orthop Surg. 2018;26:773–778. [DOI] [PubMed] [Google Scholar]
- 17. Konan S, Rhee SJ, Haddad FS. Hip arthroscopy: analysis of a single surgeon's learning experience. J Bone Jt Surg. 2011;93(Suppl 2):52–56. [DOI] [PubMed] [Google Scholar]
- 18. Kuschnaroff Contreras ME, Hoffmann RB, de Araújo LC, Dani WS, José Berral F. Complications in hip arthroscopy. Rev Bras Ortop. 2015;45:61–66. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. Lee YK, Ha YC, Hwang DS, Koo KH. Learning curve of basic hip arthroscopy technique: CUSUM analysis. Knee Surg Sports Traumatol Arthrosc. 2013;21:1940–1944. [DOI] [PubMed] [Google Scholar]
- 20. Mehta N, Chamberlin P, Marx RG, Hidaka C, Ge Y, Nawabi DH, et al. Defining the learning curve for hip arthroscopy: a threshold analysis of the volume‐outcomes relationship. Am J Sports Med. 2018;46:1284–1293. [DOI] [PubMed] [Google Scholar]
- 21. Nossa JM, Aguilera B, Márquez W, Aranzazu A, Alzate R, Rueda G, et al. Factors associated with hip arthroscopy complications in the treatment of femoroacetabular impingement. Curr Orthop Pract. 2014;25:362–366. [Google Scholar]
- 22. Oduguwa E, Price A, Awal A, Kraeutler M. EP238 Assessing the learning curve in hip arthroscopy: a prospective case series analysis of operative efficiency over time. J Hip Preserv Surg. 2025;12(Suppl 2):ii104. [Google Scholar]
- 23. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24. Park MS, Yoon SJ, Kim YJ, Chung WC. Hip arthroscopy for femoroacetabular impingement: the changing nature and severity of associated complications over time. Arthroscopy. 2014;30:957–963. [DOI] [PubMed] [Google Scholar]
- 25. Philippon MJ, Utsunomiya H, Locks R, Briggs KK. First 100 segmental labral reconstructions compared to the most recent 100: the role of surgeon experience in decreasing conversion to total hip arthroplasty. Knee Surg Sports Traumatol Arthrosc. 2020;28:2295–2301. [DOI] [PubMed] [Google Scholar]
- 26. Ramadanov N. MCID normalization: a methodological framework for harmonizing heterogeneous PROMs in hip arthroscopy research. J Exp Orthop. 2025;12:e70568. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27. Ramadanov N, Heinz M, Voss M, Hable R, Becker R, Ahmad SS. Learning curve in periacetabular osteotomy for developmental dysplasia of the hip: a systematic review and meta‐analysis. Bone Joint Open. 2026;7(4):574–583. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28. Ramadanov N, Voss M, Diallo RM, Lettner J, Hakam HT, Prill R, et al. Do meta‐analyses of total hip arthroplasty produce reliable results? A systematic review and meta‐epidemiological study of statistical methods. Orthop Surg. 2025;17:1936–1955. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Rohatgi A. WebPlotDigitizer, Version 4.6. https://automeris.io/WebPlotDigitizer (2022).
- 30. Schüttler KF, Schramm R, El‐Zayat BF, Schofer MD, Efe T, Heyse TJ. The effect of surgeon's learning curve: complications and outcome after hip arthroscopy. Arch Orthop Trauma Surg. 2018;138:1415–1421. [DOI] [PubMed] [Google Scholar]
- 31. Shultz C, Levine N, Curtis W, Christian RA, Hendren S, Lau BC. A systematic review of learning curves in orthopaedic sports surgery. Iowa Orthop J. 2025;45:161–178. [PMC free article] [PubMed] [Google Scholar]
- 32. Smith KM, Duplantier NL, Crump KH, Delgado DA, Sullivan SL, McCulloch PC, et al. Fluoroscopy learning curve in hip arthroscopy‐a single surgeon's experience. Arthroscopy. 2017;33:1804–1809. [DOI] [PubMed] [Google Scholar]
- 33. Souza BGS, Dani WS, Honda EK, Ricioli W Jr., Guimarães, RP , Guimarães RP, et al. Do complications in hip arthroscopy change with experience? Arthroscopy. 2010;26:1053–1057. [DOI] [PubMed] [Google Scholar]
- 34. Sterne JA, Hernán MA, Reeves BC, Savović J, Berkman ND, Viswanathan M, et al. ROBINS‐I: a tool for assessing risk of bias in non‐randomised studies of interventions. BMJ. 2016;355:i4919. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35. Suarez‐Ahedo C, Camacho‐Galindo J, López‐Reyes A, Martinez‐Gómez LE, Pineda C, Domb BG. A comprehensive review of hip arthroscopy techniques and outcomes. SAGE Open Med. 2024;12:20503121231222212. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36. Tassinari E, Caternicchia F, Rosa MD, Castagnini F, Angeletti E, Fantoni V, et al. Functional outcome improvement and surgical time reduction in a single‐surgeon consecutive case series of hip arthroscopy for femoroacetabular impingement: a minimum 5 years follow‐up study. J Exp Orthop. 2025;12:e70022. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37. Vilchez F, Erquicia J, Tey M. [Learning curve of arthroscopic hip surgery]. Acta Ortop Mex. 2010;24:177–181. [PubMed] [Google Scholar]
- 38. Zheng F, Daofeng W, Guanyang S, Yue L, Xuesong W. Quantitative evaluation and preoperative planning using three‐dimensional CT profiles improves the accuracy of bony correction under primary hip arthroscopy and clinical outcomes in the setting of cam‐type femoroacetabular impingement syndrome. Orthop Surg. 2025;17(8):2422–2434. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39. You JS, Flores SE, Friedman JM, Lansdown DA, Zhang AL. The learning curve for hip arthroscopic surgery: a prospective evaluation with 2‐year outcomes in patients with femoroacetabular impingement. Orthop J Sports Med. 2020;8:2325967120959140. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
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Data Availability Statement
Data are available from the corresponding author upon reasonable request.