Tracking patient clusters over time enables to extract all the information available in the medico-administrative databases
medRxiv (Cold Spring Harbor Laboratory)(2022)
摘要
Context Identifying clusters (i.e., subgroups) of patients from the analysis of medico-administrative databases is particularly important to better understand disease heterogeneity. However, the complexity of these databases, in particular due to the presence of truncated longitudinal data, requires adaptation of clustering approaches.
Objective We propose here cluster-tracking approaches to identify clusters of patients from longitudinal data contained in medico-administrative databases.
Material and Methods We first cluster patients at each age using either the Markov Cluster algorithm (MCL) from patient networks or Kmeans from raw data. We then track the identified clusters over ages to construct cluster-trajectories. We compared our novel approaches with three longitudinal clustering approaches by calculating the silhouette score. As a use-case, we analyzed antithrombotic drugs prescribed from 2008 to 2018 contained in the Échantillon Généraliste des Bénéficiaires (EGB), a French national cohort.
Results Our cluster-tracking approaches allowed us to identify several cluster-trajectories having clinical significance. Silhouette score comparison between the different approaches reveals that the best score is obtained for the cluster-tracking approaches.
Conclusion The cluster-tracking approaches are a novel and efficient alternative to identify patient clusters from medico-administrative databases by taking into account their specificities.
### Competing Interest Statement
The authors have declared no competing interest.
### Funding Statement
This study did not receive any funding
### Author Declarations
I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.
Yes
The details of the IRB/oversight body that provided approval or exemption for the research described are given below:
This study has been declared to INSERM (Institut National de la Santé et de la Recherche Médicale, https://www.inserm.fr/). The data from this study are extracted from the EGB (échantillon Généraliste des Bénéficiaires), a permanent 1/97 representative sample of the National Health Data System (Système National de Données de Santé, SNDS). The information provided to individuals in EGB on the possible re-use of their data and the procedures for exercising their rights comply with the legislative and regulatory provisions applicable to the processing of personal data in the SNDS. According to French regulation, individuals in SNDS database are informed of the reuse of their data for research and can opposed to this reuse as defined by Articles 92 to 95 of Decree No. 2005-1309 of 20 October 2005 (https://www.legifrance.gouv.fr/loda/article_lc/LEGIARTI000037300884/). As required from French regulation, EGB data can be reuse for research projects from authorized persons once the research project is declared to their institution (INSERM).
I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.
Yes
I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).
Yes
I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.
Yes
All data simulated are available online at https://github.com/JudithLamb/Cluster-tracking
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关键词
patient clusters,databases,tracking,medico-administrative
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