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

• Published in: Cancer innovation (Print) 2023-02, Vol.2 (1), p.52-64
• Main Authors: Baulain, Roméo, Jové, Jérémy, Sakr, Dunia, Gross‐Goupil, Marine, Rouyer, Magali, Puel, Marius, Blin, Patrick, Droz‐Perroteau, Cécile, Lassalle, Régis, Thurin, Nicolas H.
• Format: Article
• Language: English
• Subjects: Algorithms; Antigens; Cancer; Cancer therapies; clustering; Costs; Disease; healthcare pathway; Hospitalization; Hospitals; Human health and pathology; Laboratories; Life Sciences; Machine Learning; Medical equipment; Metastasis; Original; Patients; Pharmaceutical sciences; Pharmacology; Prostate cancer; Radiation therapy; Santé publique et épidémiologie; SNDS; Statistics; Surveillance; Urology and Nephrology; Variables
• ISSN: 2770-9183; 2770-9191; 2770-9183
• DOI: 10.1002/cai2.42

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. Patients with prostate cancer go through multiple heterogeneous healthcare encounters. Clustering methods were used to summarize correlated encounters in macro‐variables and reconstitute patient healthcare pathways. In the second stage, clinically relevant patterns of prostate cancer management (e.g., watchful waiting and active surveillance) were revealed by applying clustering methods to these healthcare pathways according to the patient cancer stage, highlighting the interest of this ap