French administrative health care database (SNDS): The value of its enrichment

Scailteux, Lucie‐Marie; Droitcourt, Catherine; Balusson, Frédéric; Nowak, Emmanuel; Kerbrat, Sandrine; Dupuy, Alain; Drézen, Erwan; Happe, André; Oger, Emmanuel

doi:10.1016/j.therap.2018.09.072

Cited by 63 publications

(50 citation statements)

References 52 publications

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“…This could lead to the initiation of better method practices regarding the performance analysis of clustering that should enable a better statistical comparison between studies. We will also validate the proposed approach in future studies by applying it to more classic chronic diseases contexts with other datasets such as the French Health Data Hub [40].…”

Section: Discussionmentioning

confidence: 95%

Clustering of a Health Dataset Using Diagnosis Co-Occurrences

et al. 2021

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Assessing the health profiles of populations is a crucial task to create a coherent healthcare offer. Emergency Departments (EDs) are at the core of the healthcare system and could benefit from this evaluation via an improved understanding of the healthcare needs of their population. This paper proposes a novel hierarchical agglomerative clustering algorithm based on multimorbidity analysis. The proposed approach constructs the clustering dendrogram by introducing new quality indicators based on the relative risk of co-occurrences of patient diagnoses. This algorithm enables the detection of multimorbidity patterns by merging similar patient profiles according to their common diagnoses. The multimorbidity approach has been applied to the data of the largest ED of the Aube Department (Eastern France) to cluster its patient visits. Among the 120,718 visits identified during a 24-month period, 16 clusters were identified, accounting for 94.8% of the visits, with the five most prevalent clusters representing 63.0% of them. The new quality indicators show a coherent and good clustering solution with a cluster membership of 1.81 based on a cluster compactness of 1.40 and a cluster separation of 0.77. Compared to the literature, the proposed approach is appropriate for the discovery of multimorbidity patterns and could help to develop better clustering algorithms for more diverse healthcare datasets.

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Section: Discussionmentioning

confidence: 95%

Clustering of a Health Dataset Using Diagnosis Co-Occurrences

et al. 2021

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“…This was a retrospective observational cohort study that used data obtained from the SNDS database, which includes claims data for more than 65 million individuals (including 50 million adults), from birth (or immigration) to death, and is highly representative of the French population, covering 99% of the total population [ 26 ]. The SNDS database contains several datasets linked together via a unique patient ID (social security number).…”

Section: Methodsmentioning

confidence: 99%

Survival and treatment patterns of patients with relapsed or refractory multiple myeloma in France — a cohort study using the French National Healthcare database (SNDS)

et al. 2021

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Over the past decade, several drugs have been approved for the treatment of relapsed or refractory multiple myeloma (RRMM). This retrospective study, using the French National Healthcare database (SNDS), describes the treatment patterns and outcomes of patients with RRMM treated in real-world clinical practice in France. Patients were adults, with a diagnosis of multiple myeloma, who initiated second-line (2L) treatment approved for use in France between 2014 and 2018; this included bortezomib, carfilzomib, daratumumab, ixazomib, lenalidomide, or pomalidomide. Data were analyzed overall, by first-line (1L) autologous stem cell transplant (ASCT) status and by lenalidomide treatment status at 2L. In total, 12987 patients with RRMM were included in the study (mean age 69.5 years); 27% received an ASCT at 1L, and 30% received a lenalidomide-sparing regimen at 2L. Overall, and among the ASCT and non-ASCT subgroups, most patients received a bortezomib-based regimen at 1L, whereas lenalidomide-based regimens were most common at 2L. Among patients who received a lenalidomide-sparing regimen at 2L, this was most often a proteasome inhibitor-based regimen. Mortality rate was 26.1/100 person-years, and median (95% confidence interval) survival from 2L initiation was 32.4 (31.2–33.6) months. Survival differed by various factors, shorter survival was reported in the non-ASCT group, those receiving a lenalidomide-sparing regimen at 2L, older patients (≥ 70 years), and those with multiple comorbidities. This analysis provides insight into the real-world use of approved novel MM treatments and highlights an ongoing unmet need to improve outcomes, particularly for selected patient groups.

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“…Based on these data, we made the hypothesis that a history of BS could have a protective effect against the occurrence of the severe form of COVID-19 and its related death. We used the French National Health Insurance Information System [23] to compare the need for invasive mechanical ventilation and death in obese individuals hospitalized for COVID-19 with or without a history of BS.…”

Section: Introductionmentioning

confidence: 99%