Fundamental & clinical pharmacology · 2026
Linking the NETSARC+ National Sarcoma Database With the SNDS to Evaluate Adjuvant and/or Neoadjuvant Therapy: Report on the Linkage Process and Result (Health Data Hub's DEEPSARC Pilot Project).
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Drezen Erwan, Happe André, Thevenet Vincent, Penel Nicolas, Gouin François, Le Loarer François, Du Bouexic De Pinieux Gonzague, Crochet Hugo, Airiau Claire Chemin, Ducimetiere Françoise, Mathoulin Pelissier Simone, Blay Jean-Yves, Oger Emmanuel
Аннотация
BACKGROUND: DEEPSARC, one of the first projects running on the Health Data Hub, aimed to identify real-life treatment regimens that could improve overall survival. The project is based on matching the national database of the sarcoma reference network with the SNDS.
OBJECTIVES: We aimed to report a transparent description of the linking process and its results.
METHODS: The sarcoma database encompasses 33 548 patients matching the selection criteria divided into three subsets: 13507 patients with a complete dataset gathering clinical and pathological data; 5844 patients with clinical data alone; and 14 197 patients with pathological data alone. As no ICD-10 code reliably identifies patients with sarcoma, the subpopulation extracted from the SNDS was extended to 3 million patients who underwent surgery for their cancer. An indirect record linkage process used a combination (called a signature) of so-called chaining variables to uniquely identify a pair of patients from each of the bases. Two metrics (signature robustness and overall quality) were calculated for ease of interpretation.
RESULTS: The overall matching rate of 73.1% (24 539 pairs out of 33 548 observations), reaching 90.5% in the intersection of the sarcomas databases (with extended data, 12 225 pairs out of 13 507 observations).
CONCLUSION: An optimized and transparent process led to a moderate matching rate but enhanced the confidence in true matching. Representativeness is an issue related to the missing data pattern across the three NETSARC databases. For instance, an individual present only in the RREPS database has a greater probability of not being linked.
OBJECTIVES: We aimed to report a transparent description of the linking process and its results.
METHODS: The sarcoma database encompasses 33 548 patients matching the selection criteria divided into three subsets: 13507 patients with a complete dataset gathering clinical and pathological data; 5844 patients with clinical data alone; and 14 197 patients with pathological data alone. As no ICD-10 code reliably identifies patients with sarcoma, the subpopulation extracted from the SNDS was extended to 3 million patients who underwent surgery for their cancer. An indirect record linkage process used a combination (called a signature) of so-called chaining variables to uniquely identify a pair of patients from each of the bases. Two metrics (signature robustness and overall quality) were calculated for ease of interpretation.
RESULTS: The overall matching rate of 73.1% (24 539 pairs out of 33 548 observations), reaching 90.5% in the intersection of the sarcomas databases (with extended data, 12 225 pairs out of 13 507 observations).
CONCLUSION: An optimized and transparent process led to a moderate matching rate but enhanced the confidence in true matching. Representativeness is an issue related to the missing data pattern across the three NETSARC databases. For instance, an individual present only in the RREPS database has a greater probability of not being linked.