Analysis of Trajectories of Care After Bariatric Surgery Using Data Mining Method and Health Administrative Information
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ORIGINAL CONTRIBUTIONS
Analysis of Trajectories of Care After Bariatric Surgery Using Data Mining Method and Health Administrative Information Systems Anaïs Charles-Nelson 1,2,3,4
&
Andrea Lazzati 3,5 & Sandrine Katsahian 2,3,4
# Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Context The 30-day readmission rate after bariatric surgery is considered an important metric of the quality of hospital care. However, readmission rate beyond 30 days is rarely reported and does not provide any information about trajectories of care which would be of great interest for healthcare planning. The aim of this study was to analyze trajectories of care during the first year after bariatric surgery on a nationwide basis using data mining methods. Method This was a retrospective descriptive study on the trajectories of care within the first year after bariatric surgery. Data were extracted from a national administrative claims database (the PMSI database) and trajectories were defined as principal diagnosis of successive readmissions. Formal Concept Analysis was performed to find common concepts of trajectories of care. Results We included for analysis 198,389 bariatric procedures performed on 196,323 patients. Twelve main concepts were selected. About one third of patients (32.4%) were readmitted in the first year after surgery. Most common trajectories were as follows: regular follow-up (14.9%), cholelithiasis (2.2%), abdominal pain (1.9%), and abdominal sepsis (1.3%). Important differences were found in trajectories among different bariatric procedures: 1.8% of gastric banding patients had pregnancyrelated events (delivery or medical abortion), while we observed a readmission rate for abdominal sepsis in 2.7% and 5.1% of patients operated of gastric bypass and sleeve gastrectomy respectively. Conclusion Administrative claim data can be analyzed through Formal Concept Analysis in order to classify trajectories of care. This approach permits to quantify expected postoperative complications and to identify unexpected events. Keywords Data mining . Formal Concept Analysis . Claim data . Trajectory of care . Bariatric surgery
* Anaïs Charles-Nelson [email protected] Andrea Lazzati [email protected] Sandrine Katsahian [email protected] 1
Sorbonne Universités, UPMC Univ Paris 06, UMRS 1138, Centre de Recherche des Cordeliers, Paris, France
2
INSERM, UMRS 1138, Centre de Recherche des Cordeliers, Paris, France
3
Université Paris Descartes, Sorbonne Paris Cité, UMRS 1138, Centre de Recherche des Cordeliers, F75006 Paris, France
4
Assistance Publique Hôpitaux de Paris, Hôpital européen Georges Pompidou, Unité d’Épidémiologie et de Recherche Clinique, INSERM, Centre d’Investigation Clinique1418, module Épidémiologie Clinique, HEGP, Paris, France
5
Department of General Surgery, Centre Hospitalier Intercommunal de Créteil, 40 avenue de Verdun, 94000 Créteil, France
Introduction Obesity is a major public health problem whose prevalence has dramatically increased in t
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