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Surgical volume is related to the rate of positive surgical margins at radical prostatectomy in European patients
Author(s) -
Chun Felix K.H.,
Briganti Alberto,
Antebi Elie,
Graefen Markus,
Currlin Eike,
Steuber Thomas,
Schlomm Thorsten,
Walz Jochen,
Haese Alexander,
Friedrich Martin G.,
Ahyai Sascha A.,
Eichelberg Christian,
Salomon Georg,
Gallina Andrea,
Erbersdobler Andreas,
Perrotte Paul,
Heinzer Hans,
Huland Hartwig,
Karakiewicz Pierre I.
Publication year - 2006
Publication title -
bju international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.773
H-Index - 148
eISSN - 1464-410X
pISSN - 1464-4096
DOI - 10.1111/j.1464-410x.2006.06442.x
Subject(s) - prostatectomy , medicine , multivariate statistics , multivariate analysis , urology , prostate cancer , prostate specific antigen , stage (stratigraphy) , surgery , cancer , statistics , mathematics , paleontology , biology
OBJECTIVE To assess the association between surgical volume (SV) and the rate of positive surgical margins (PSM) after radical prostatectomy (RP) in a large single‐institution European cohort of patients. PATIENTS AND METHODS In all, 2402 men had a RP by a group of 11 surgeons, all of whom were trained by the surgeon with the highest SV; all surgeons used the same surgical technique. Variables assessed before RP were prostate‐specific antigen (PSA) level, clinical stage and biopsy Gleason sum; variables assessed after RP were PSA level, extracapsular extension, seminal vesicle invasion, lymph node invasion and pathological Gleason sum. These were used to predict the rate of PSM in models before or after RP. Multivariate models were complemented with SV to test its independent and multivariate statistical significance and to quantify its impact on the model’s overall (and 200 bootstrap‐corrected) predictive accuracy. RESULTS The mean (range) SV was 201 (1–1293) RPs; the mean (median, range) rate of PSM was 20.2 (21.4, 0–32.9)%. In multivariate models, SV was a highly statistically significant independent predictor of PSM ( P < 0.001) and increased the predictive accuracy in multivariate models both before (2.0%) and after RP (1.5%, both P < 0.001). However, when the surgeon with the highest SV, who contributed to 1293 cases, was removed from the analyses, the multivariate independent prediction and the gains in predictive accuracy related to adding SV, disappeared in the models both before ( P = 0.9, accuracy gain 0.1%) and after ( P = 0.4, accuracy gain − 0.3%) RP. CONCLUSIONS These results indicate that patients treated by surgeons with a very high volume can expect to have a significantly lower rate of PSM, after accounting for clinical and pathological case‐mix differences. However, SV is not a predictor of PSM when analyses are restricted to intermediate‐ and low‐volume surgeons.