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Clustering of prostate cancer healthcare pathways in the French National Healthcare database
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 Na...
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Published in: | Cancer innovation (Print) 2023-02, Vol.2 (1), p.52-64 |
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creator | Baulain, Roméo Jové, Jérémy Sakr, Dunia Gross‐Goupil, Marine Rouyer, Magali Puel, Marius Blin, Patrick Droz‐Perroteau, Cécile Lassalle, Régis Thurin, Nicolas H. |
description | 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 |
doi_str_mv | 10.1002/cai2.42 |
format | article |
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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 approach in real‐world research.</description><identifier>ISSN: 2770-9183</identifier><identifier>ISSN: 2770-9191</identifier><identifier>EISSN: 2770-9183</identifier><identifier>DOI: 10.1002/cai2.42</identifier><identifier>PMID: 38090372</identifier><language>eng</language><publisher>England: John Wiley & Sons, Inc</publisher><subject>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</subject><ispartof>Cancer innovation (Print), 2023-02, Vol.2 (1), p.52-64</ispartof><rights>2022 The Authors. published by John Wiley & Sons Ltd. on behalf of Tsinghua University Press.</rights><rights>2022 The Authors. Cancer Innovation published by John Wiley & Sons Ltd. on behalf of Tsinghua University Press.</rights><rights>2023. This work is published under http://creativecommons.org/licenses/by-nc/4.0/ (the "License"). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>Distributed under a Creative Commons Attribution 4.0 International License</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c5342-40fceafaf4519cab8e323a70e5bbc5cfbe3d36f86c7295d79ca8ae9de9a7e2873</citedby><cites>FETCH-LOGICAL-c5342-40fceafaf4519cab8e323a70e5bbc5cfbe3d36f86c7295d79ca8ae9de9a7e2873</cites><orcidid>0000-0002-2560-4412 ; 0000-0002-7697-1167 ; 0000-0001-6726-6215 ; 0000-0003-4005-7928 ; 0000-0002-3176-4076 ; 0000-0003-3589-0819</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC10686138/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/3090887681?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,11562,25753,27924,27925,37012,37013,44590,46052,46476,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/38090372$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink><backlink>$$Uhttps://hal.science/hal-03988556$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Baulain, Roméo</creatorcontrib><creatorcontrib>Jové, Jérémy</creatorcontrib><creatorcontrib>Sakr, Dunia</creatorcontrib><creatorcontrib>Gross‐Goupil, Marine</creatorcontrib><creatorcontrib>Rouyer, Magali</creatorcontrib><creatorcontrib>Puel, Marius</creatorcontrib><creatorcontrib>Blin, Patrick</creatorcontrib><creatorcontrib>Droz‐Perroteau, Cécile</creatorcontrib><creatorcontrib>Lassalle, Régis</creatorcontrib><creatorcontrib>Thurin, Nicolas H.</creatorcontrib><title>Clustering of prostate cancer healthcare pathways in the French National Healthcare database</title><title>Cancer innovation (Print)</title><addtitle>Cancer Innov</addtitle><description>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 approach in real‐world research.</description><subject>Algorithms</subject><subject>Antigens</subject><subject>Cancer</subject><subject>Cancer therapies</subject><subject>clustering</subject><subject>Costs</subject><subject>Disease</subject><subject>healthcare pathway</subject><subject>Hospitalization</subject><subject>Hospitals</subject><subject>Human health and pathology</subject><subject>Laboratories</subject><subject>Life Sciences</subject><subject>Machine Learning</subject><subject>Medical equipment</subject><subject>Metastasis</subject><subject>Original</subject><subject>Patients</subject><subject>Pharmaceutical sciences</subject><subject>Pharmacology</subject><subject>Prostate cancer</subject><subject>Radiation therapy</subject><subject>Santé publique et épidémiologie</subject><subject>SNDS</subject><subject>Statistics</subject><subject>Surveillance</subject><subject>Urology and Nephrology</subject><subject>Variables</subject><issn>2770-9183</issn><issn>2770-9191</issn><issn>2770-9183</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>24P</sourceid><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNp1kl1v0zAUhiMEYtOY-AfIEheAUIc_kti5mqqKrZUquIE7JOvEOWlcpXGxnU399zjrGNskrmzZjx_7-D1Z9pbRC0Yp_2LA8oucv8hOuZR0VjElXj6an2TnIWxpIivGlJKvsxOhaEWF5KfZr0U_hojeDhviWrL3LkSISAwMBj3pEPrYGfBI9hC7WzgEYgcSOyRXHgfTkW8QrRugJ8t_aAMRagj4JnvVQh_w_H48y35eff2xWM7W369Xi_l6ZgqR81lOW4PQQpsXrDJQKxRcgKRY1LUpTFujaETZqtJIXhWNTIwCrBqsQCJXUpxlq6O3cbDVe2934A_agdV3C85vNPhoTY-aCQot47IwDeaGMZC1AtrUBbSSK4bJdXl07cd6h43BIXron0if7gy20xt3oxktVcmESoZPR0P37NxyvtbTGhWVUkVR3rDEfry_zbvfI4aodzYY7HsY0I1B8yqlVpZMTkW-f4Zu3ejTzwctUpop2FJNwg9HyqQkg8f24QWM6qlb9NQtOueJfPe40Afub28k4PMRuLU9Hv7n0Yv5iifdH02WyKA</recordid><startdate>202302</startdate><enddate>202302</enddate><creator>Baulain, Roméo</creator><creator>Jové, Jérémy</creator><creator>Sakr, Dunia</creator><creator>Gross‐Goupil, Marine</creator><creator>Rouyer, Magali</creator><creator>Puel, Marius</creator><creator>Blin, Patrick</creator><creator>Droz‐Perroteau, Cécile</creator><creator>Lassalle, Régis</creator><creator>Thurin, Nicolas H.</creator><general>John Wiley & Sons, Inc</general><general>John Wiley and Sons Inc</general><general>Wiley</general><scope>24P</scope><scope>WIN</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>K9.</scope><scope>M0S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>1XC</scope><scope>VOOES</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-2560-4412</orcidid><orcidid>https://orcid.org/0000-0002-7697-1167</orcidid><orcidid>https://orcid.org/0000-0001-6726-6215</orcidid><orcidid>https://orcid.org/0000-0003-4005-7928</orcidid><orcidid>https://orcid.org/0000-0002-3176-4076</orcidid><orcidid>https://orcid.org/0000-0003-3589-0819</orcidid></search><sort><creationdate>202302</creationdate><title>Clustering of prostate cancer healthcare pathways in the French National Healthcare database</title><author>Baulain, Roméo ; Jové, Jérémy ; Sakr, Dunia ; Gross‐Goupil, Marine ; Rouyer, Magali ; Puel, Marius ; Blin, Patrick ; Droz‐Perroteau, Cécile ; Lassalle, Régis ; Thurin, Nicolas H.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c5342-40fceafaf4519cab8e323a70e5bbc5cfbe3d36f86c7295d79ca8ae9de9a7e2873</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Algorithms</topic><topic>Antigens</topic><topic>Cancer</topic><topic>Cancer therapies</topic><topic>clustering</topic><topic>Costs</topic><topic>Disease</topic><topic>healthcare pathway</topic><topic>Hospitalization</topic><topic>Hospitals</topic><topic>Human health and pathology</topic><topic>Laboratories</topic><topic>Life Sciences</topic><topic>Machine Learning</topic><topic>Medical equipment</topic><topic>Metastasis</topic><topic>Original</topic><topic>Patients</topic><topic>Pharmaceutical sciences</topic><topic>Pharmacology</topic><topic>Prostate cancer</topic><topic>Radiation therapy</topic><topic>Santé publique et épidémiologie</topic><topic>SNDS</topic><topic>Statistics</topic><topic>Surveillance</topic><topic>Urology and Nephrology</topic><topic>Variables</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Baulain, Roméo</creatorcontrib><creatorcontrib>Jové, Jérémy</creatorcontrib><creatorcontrib>Sakr, Dunia</creatorcontrib><creatorcontrib>Gross‐Goupil, Marine</creatorcontrib><creatorcontrib>Rouyer, Magali</creatorcontrib><creatorcontrib>Puel, Marius</creatorcontrib><creatorcontrib>Blin, Patrick</creatorcontrib><creatorcontrib>Droz‐Perroteau, Cécile</creatorcontrib><creatorcontrib>Lassalle, Régis</creatorcontrib><creatorcontrib>Thurin, Nicolas H.</creatorcontrib><collection>Wiley_OA刊</collection><collection>Wiley Online Library Journals</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Health & Medical Collection (Proquest)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>ProQuest Publicly Available Content database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><collection>Hyper Article en Ligne (HAL)</collection><collection>Hyper Article en Ligne (HAL) (Open Access)</collection><collection>PubMed Central (Full Participant titles)</collection><collection>Directory of Open Access Journals</collection><jtitle>Cancer innovation (Print)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Baulain, Roméo</au><au>Jové, Jérémy</au><au>Sakr, Dunia</au><au>Gross‐Goupil, Marine</au><au>Rouyer, Magali</au><au>Puel, Marius</au><au>Blin, Patrick</au><au>Droz‐Perroteau, Cécile</au><au>Lassalle, Régis</au><au>Thurin, Nicolas H.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Clustering of prostate cancer healthcare pathways in the French National Healthcare database</atitle><jtitle>Cancer innovation (Print)</jtitle><addtitle>Cancer Innov</addtitle><date>2023-02</date><risdate>2023</risdate><volume>2</volume><issue>1</issue><spage>52</spage><epage>64</epage><pages>52-64</pages><issn>2770-9183</issn><issn>2770-9191</issn><eissn>2770-9183</eissn><abstract>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 approach in real‐world research.</abstract><cop>England</cop><pub>John Wiley & Sons, Inc</pub><pmid>38090372</pmid><doi>10.1002/cai2.42</doi><tpages>13</tpages><orcidid>https://orcid.org/0000-0002-2560-4412</orcidid><orcidid>https://orcid.org/0000-0002-7697-1167</orcidid><orcidid>https://orcid.org/0000-0001-6726-6215</orcidid><orcidid>https://orcid.org/0000-0003-4005-7928</orcidid><orcidid>https://orcid.org/0000-0002-3176-4076</orcidid><orcidid>https://orcid.org/0000-0003-3589-0819</orcidid><oa>free_for_read</oa></addata></record> |
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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 |
title | Clustering of prostate cancer healthcare pathways in the French National Healthcare database |
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