Clustering of prostate cancer healthcare pathways in the French National Healthcare database

作     者：Roméo Baulain Jérémy Jové Dunia Sakr Marine Gross‐Goupil Magali Rouyer Marius Puel Patrick Blin Cécile Droz‐Perroteau Régis Lassalle Nicolas H.Thurin 

作者机构：École nationale de la statistique et de l'administrationéconomique Paris(ENSAE)Institut Polytechnique ParisPalaiseauFrance Univ.BordeauxINSERM CIC‐P 1401Bordeaux PharmacoEpiBordeauxFrance Medical OncologyHôpital Saint AndréCHU de BordeauxBordeauxFrance 

出 版 物：《Cancer Innovation》 (肿瘤创新（英文）)

年 卷 期：2023年第2卷第1期

页      面：52-64页

学科分类：1002[医学-临床医学] 100214[医学-肿瘤学] 10[医学] 

基　　金：CAMERRA Janssen‐Cilag Cilag 

主　　题：prostate cancer healthcare pathway clustering machine learning SNDS 

摘      要：Background:Healthcare pathways of patients with prostate cancer are heterogeneous and complex to apprehend using traditional descriptive statistics.Clustering and visualization methods can enhance their characterization.Methods:Patients with prostate cancer in 2014 were identified in the French National Healthcare database(Système National des Données de Santé—SNDS)and their data were extracted with up to 5 years of history and 4 years of follow‐up.Fifty‐one‐specific encounters constitutive of prostate cancer management were synthesized into four macro‐variables using a clustering approach.Their values over patient follow‐ups constituted healthcare pathways.Optimal matching was applied to calculate distances between pathways.Partitioning around medoids was then used to define consistent groups across four exclusive cohorts of incident prostate cancer patients:Hormone‐sensitive(HSPC),metastatic hormone‐sensitive(mHSPC),castration‐resistant(CRPC),and metastatic castration‐resistant(mCRPC).Index plots were used to represent pathways clusters.Results:The repartition of macro‐variables values—surveillance,local treatment,androgenic deprivation,and advanced treatment—appeared to be consistent with prostate cancer status.Two to five clusters of healthcare pathways were observed in each of the different cohorts,corresponding for most of them to relevant clinical patterns,although some heterogeneity remained.For instance,clustering allowed to distinguish patients undergoing active surveillance,or treated according to cancer progression risk in HSPC,and patients receiving treatment for potentially curative or palliative purposes in mHSPC and mCRPC.Conclusion:Visualization methods combined with a clustering approach enabled the identification of clinically relevant patterns of prostate cancer management.Characterization of these care pathways is an essential element for the comprehension and the robust assessment of healthcare technology effectiveness.