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BACKGROUND: Local recurrence (LR) after radical nephrectomy (RN) for kidney cancer is uncommon. Our objectives were to analyse characteristics and therapeutic options of LR after RN and to identify survival prognostic factors. MATERIALS AND METHODS: From a multi-institutional retrospective database, we identified 72 patients who experienced LR after RN. RESULTS: Mean time to LR was 26.5 ± 3.3 months. The location of the recurrence was renal fossa, regional lymph node, homolateral adrenal and both renal fossa and regional lymph node for 43 (59.7%), 27 (37.5%), 9 (12.5%) and 7 (9.7%) patients, respectively. Patients were treated by surgery, systemic therapies, combination of therapies and palliative treatment in 24 (33.3%), 18 (25%), 24 (33.3%) and 6 (8.4%) cases, respectively. Within a mean follow-up of 26.4 ± 3.3 months from the date of local recurrence, 12 (16.6%) patients were alive without disease, 30 (41.7%) patients were alive with disease, 30 patients (41.6%) died including 28 (38.8%) from the disease. In multivariate analysis, time to recurrence \textless1 year (P \textless 0.001; HR: 4.81) and surgical treatment (P = 0.027; HR: 0.33) were predictive factors. CONCLUSIONS: Local recurrence after radical nephrectomy is associated with poor prognosis. The time to recurrence and the completeness of the surgical treatment are major prognostic factors.

Rationale In nephrology, the NEOERICA project assessed the feasibility of the diagnosis scheme based on a general practice database. This approach opened a new area where routinely collected data could be used for purposes other than patient management, such as epidemiological analysis and professional practice evaluation. In Lyon, the TIRCEL network is made up of a coordination team and an online database. In 2008, a total of 468 professionals participated and 983 patients were in the database corresponding to 4114 consultations and 9250 biological assessments. Objective To investigate the impact of a quality control process on the data from operational databases. Methods We set up a quality control process and we described the impact of this process on data. We also specifically investigated the role of measurement scales in error frequency and we studied the impact of data quality on variables which could be used for professional practice evaluation. Results Quality control allowed us to detect as inconsistent data 7.5% of tested data. This rate is linked to the parameters and varied from less than 1% (weight, diastolic blood pressure and urinary sodium) to more than 30% (serum iron and ferritin). Quality control led mainly to the validation of the identified data for 80.4%, a direct correction was realized for 12.9%, 5.6% by the lab and only 1.2% were set to missing. Average proteinuria was modified with the quality control process (2.09 g per 24 hours vs. 0.82 g per 24 hours); however, the median remained stable (0.21 g per 24 hours). Conclusion Specialty databases such as TIRCEL could not be used for epidemiological research or for the extraction of indicators for professional practice evaluation without strict quality control or the set-up of data-entering limits and alarms.