A data-driven approach to categorize patients with traumatic spinal cord injury: cluster analysis of a multicentre database

BackgroundConducting clinical trials for traumatic spinal cord injury (tSCI) presents challenges due to patient heterogeneity. Identifying clinically similar subgroups using patient demographics and baseline injury characteristics could lead to better patient-centered care and integrated care delivery.PurposeWe sought to (1) apply an unsupervised machine learning approach of cluster analysis to identify subgroups of tSCI patients using patient demographics and injury characteristics at baseline, (2) to find clinical similarity within subgroups using etiological variables and outcome variables, and (3) to create multi-dimensional labels for categorizing patients.Study designRetrospective analysis using prospectively collected data from a large national multicenter SCI registry.MethodsA method of spectral clustering was used to identify patient subgroups based on the following baseline variables collected since admission until rehabilitation: location of the injury, severity of the injury, Functional Independence Measure (FIM) motor, and demographic data (age, and body mass index). The FIM motor score, the FIM motor score change, and the total length of stay were assessed on the subgroups as outcome variables at discharge to establish the clinical similarity of the patients within derived subgroups. Furthermore, we discussed the relevance of the identified subgroups based on the etiological variables (energy and mechanism of injury) and compared them with the literature. Our study also employed a qualitative approach to systematically describe the identified subgroups, crafting multi-dimensional labels to highlight distinguishing factors and patient-focused insights.ResultsData on 334 tSCI patients from the Rick Hansen Spinal Cord Injury Registry was analyzed. Five significantly different subgroups were identified (p-value ≤0.05) based on baseline variables. Outcome variables at discharge superimposed on these subgroups had statistically different values between them (p-value ≤0.05) and supported the notion of clinical similarity of patients within each subgroup.ConclusionUtilizing cluster analysis, we identified five clinically similar subgroups of tSCI patients at baseline, yielding statistically significant inter-group differences in clinical outcomes. These subgroups offer a novel, data-driven categorization of tSCI patients which aligns with their demographics and injury characteristics. As it also correlates with traditional tSCI classifications, this categorization could lead to improved personalized patient-centered care. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Shahin Basiratzadeh [verfasserIn] Ramtin Hakimjavadi [verfasserIn] Natalie Baddour [verfasserIn] Wojtek Michalowski [verfasserIn] Herna Viktor [verfasserIn] Eugene Wai [verfasserIn] Alexandra Stratton [verfasserIn] Stephen Kingwell [verfasserIn] Jean-Marc Mac-Thiong [verfasserIn] Eve C. Tsai [verfasserIn] Zhi Wang [verfasserIn] Philippe Phan [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	traumatic spinal cord injury patient-centric approach patient categorization data-driven method cluster analysis

Übergeordnetes Werk:	In: Frontiers in Neurology - Frontiers Media S.A., 2010, 14(2023)
Übergeordnetes Werk:	volume:14 ; year:2023

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/fneur.2023.1263291

Katalog-ID:	DOAJ096764694

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520			\|a BackgroundConducting clinical trials for traumatic spinal cord injury (tSCI) presents challenges due to patient heterogeneity. Identifying clinically similar subgroups using patient demographics and baseline injury characteristics could lead to better patient-centered care and integrated care delivery.PurposeWe sought to (1) apply an unsupervised machine learning approach of cluster analysis to identify subgroups of tSCI patients using patient demographics and injury characteristics at baseline, (2) to find clinical similarity within subgroups using etiological variables and outcome variables, and (3) to create multi-dimensional labels for categorizing patients.Study designRetrospective analysis using prospectively collected data from a large national multicenter SCI registry.MethodsA method of spectral clustering was used to identify patient subgroups based on the following baseline variables collected since admission until rehabilitation: location of the injury, severity of the injury, Functional Independence Measure (FIM) motor, and demographic data (age, and body mass index). The FIM motor score, the FIM motor score change, and the total length of stay were assessed on the subgroups as outcome variables at discharge to establish the clinical similarity of the patients within derived subgroups. Furthermore, we discussed the relevance of the identified subgroups based on the etiological variables (energy and mechanism of injury) and compared them with the literature. Our study also employed a qualitative approach to systematically describe the identified subgroups, crafting multi-dimensional labels to highlight distinguishing factors and patient-focused insights.ResultsData on 334 tSCI patients from the Rick Hansen Spinal Cord Injury Registry was analyzed. Five significantly different subgroups were identified (p-value ≤0.05) based on baseline variables. Outcome variables at discharge superimposed on these subgroups had statistically different values between them (p-value ≤0.05) and supported the notion of clinical similarity of patients within each subgroup.ConclusionUtilizing cluster analysis, we identified five clinically similar subgroups of tSCI patients at baseline, yielding statistically significant inter-group differences in clinical outcomes. These subgroups offer a novel, data-driven categorization of tSCI patients which aligns with their demographics and injury characteristics. As it also correlates with traditional tSCI classifications, this categorization could lead to improved personalized patient-centered care.
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allfields	10.3389/fneur.2023.1263291 doi (DE-627)DOAJ096764694 (DE-599)DOAJd16a1efa1e2c4e1fa8cb179e4b07593d DE-627 ger DE-627 rakwb eng RC346-429 Shahin Basiratzadeh verfasserin aut A data-driven approach to categorize patients with traumatic spinal cord injury: cluster analysis of a multicentre database 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier BackgroundConducting clinical trials for traumatic spinal cord injury (tSCI) presents challenges due to patient heterogeneity. Identifying clinically similar subgroups using patient demographics and baseline injury characteristics could lead to better patient-centered care and integrated care delivery.PurposeWe sought to (1) apply an unsupervised machine learning approach of cluster analysis to identify subgroups of tSCI patients using patient demographics and injury characteristics at baseline, (2) to find clinical similarity within subgroups using etiological variables and outcome variables, and (3) to create multi-dimensional labels for categorizing patients.Study designRetrospective analysis using prospectively collected data from a large national multicenter SCI registry.MethodsA method of spectral clustering was used to identify patient subgroups based on the following baseline variables collected since admission until rehabilitation: location of the injury, severity of the injury, Functional Independence Measure (FIM) motor, and demographic data (age, and body mass index). The FIM motor score, the FIM motor score change, and the total length of stay were assessed on the subgroups as outcome variables at discharge to establish the clinical similarity of the patients within derived subgroups. Furthermore, we discussed the relevance of the identified subgroups based on the etiological variables (energy and mechanism of injury) and compared them with the literature. Our study also employed a qualitative approach to systematically describe the identified subgroups, crafting multi-dimensional labels to highlight distinguishing factors and patient-focused insights.ResultsData on 334 tSCI patients from the Rick Hansen Spinal Cord Injury Registry was analyzed. Five significantly different subgroups were identified (p-value ≤0.05) based on baseline variables. Outcome variables at discharge superimposed on these subgroups had statistically different values between them (p-value ≤0.05) and supported the notion of clinical similarity of patients within each subgroup.ConclusionUtilizing cluster analysis, we identified five clinically similar subgroups of tSCI patients at baseline, yielding statistically significant inter-group differences in clinical outcomes. These subgroups offer a novel, data-driven categorization of tSCI patients which aligns with their demographics and injury characteristics. As it also correlates with traditional tSCI classifications, this categorization could lead to improved personalized patient-centered care. traumatic spinal cord injury patient-centric approach patient categorization data-driven method cluster analysis Neurology. Diseases of the nervous system Ramtin Hakimjavadi verfasserin aut Natalie Baddour verfasserin aut Wojtek Michalowski verfasserin aut Herna Viktor verfasserin aut Eugene Wai verfasserin aut Eugene Wai verfasserin aut Alexandra Stratton verfasserin aut Alexandra Stratton verfasserin aut Stephen Kingwell verfasserin aut Stephen Kingwell verfasserin aut Jean-Marc Mac-Thiong verfasserin aut Jean-Marc Mac-Thiong verfasserin aut Eve C. Tsai verfasserin aut Eve C. Tsai verfasserin aut Zhi Wang verfasserin aut Philippe Phan verfasserin aut Philippe Phan verfasserin aut In Frontiers in Neurology Frontiers Media S.A., 2010 14(2023) (DE-627)631498753 (DE-600)2564214-5 16642295 nnns volume:14 year:2023 https://doi.org/10.3389/fneur.2023.1263291 kostenfrei https://doaj.org/article/d16a1efa1e2c4e1fa8cb179e4b07593d kostenfrei https://www.frontiersin.org/articles/10.3389/fneur.2023.1263291/full kostenfrei https://doaj.org/toc/1664-2295 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2023
spelling	10.3389/fneur.2023.1263291 doi (DE-627)DOAJ096764694 (DE-599)DOAJd16a1efa1e2c4e1fa8cb179e4b07593d DE-627 ger DE-627 rakwb eng RC346-429 Shahin Basiratzadeh verfasserin aut A data-driven approach to categorize patients with traumatic spinal cord injury: cluster analysis of a multicentre database 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier BackgroundConducting clinical trials for traumatic spinal cord injury (tSCI) presents challenges due to patient heterogeneity. Identifying clinically similar subgroups using patient demographics and baseline injury characteristics could lead to better patient-centered care and integrated care delivery.PurposeWe sought to (1) apply an unsupervised machine learning approach of cluster analysis to identify subgroups of tSCI patients using patient demographics and injury characteristics at baseline, (2) to find clinical similarity within subgroups using etiological variables and outcome variables, and (3) to create multi-dimensional labels for categorizing patients.Study designRetrospective analysis using prospectively collected data from a large national multicenter SCI registry.MethodsA method of spectral clustering was used to identify patient subgroups based on the following baseline variables collected since admission until rehabilitation: location of the injury, severity of the injury, Functional Independence Measure (FIM) motor, and demographic data (age, and body mass index). The FIM motor score, the FIM motor score change, and the total length of stay were assessed on the subgroups as outcome variables at discharge to establish the clinical similarity of the patients within derived subgroups. Furthermore, we discussed the relevance of the identified subgroups based on the etiological variables (energy and mechanism of injury) and compared them with the literature. Our study also employed a qualitative approach to systematically describe the identified subgroups, crafting multi-dimensional labels to highlight distinguishing factors and patient-focused insights.ResultsData on 334 tSCI patients from the Rick Hansen Spinal Cord Injury Registry was analyzed. Five significantly different subgroups were identified (p-value ≤0.05) based on baseline variables. Outcome variables at discharge superimposed on these subgroups had statistically different values between them (p-value ≤0.05) and supported the notion of clinical similarity of patients within each subgroup.ConclusionUtilizing cluster analysis, we identified five clinically similar subgroups of tSCI patients at baseline, yielding statistically significant inter-group differences in clinical outcomes. These subgroups offer a novel, data-driven categorization of tSCI patients which aligns with their demographics and injury characteristics. As it also correlates with traditional tSCI classifications, this categorization could lead to improved personalized patient-centered care. traumatic spinal cord injury patient-centric approach patient categorization data-driven method cluster analysis Neurology. Diseases of the nervous system Ramtin Hakimjavadi verfasserin aut Natalie Baddour verfasserin aut Wojtek Michalowski verfasserin aut Herna Viktor verfasserin aut Eugene Wai verfasserin aut Eugene Wai verfasserin aut Alexandra Stratton verfasserin aut Alexandra Stratton verfasserin aut Stephen Kingwell verfasserin aut Stephen Kingwell verfasserin aut Jean-Marc Mac-Thiong verfasserin aut Jean-Marc Mac-Thiong verfasserin aut Eve C. Tsai verfasserin aut Eve C. Tsai verfasserin aut Zhi Wang verfasserin aut Philippe Phan verfasserin aut Philippe Phan verfasserin aut In Frontiers in Neurology Frontiers Media S.A., 2010 14(2023) (DE-627)631498753 (DE-600)2564214-5 16642295 nnns volume:14 year:2023 https://doi.org/10.3389/fneur.2023.1263291 kostenfrei https://doaj.org/article/d16a1efa1e2c4e1fa8cb179e4b07593d kostenfrei https://www.frontiersin.org/articles/10.3389/fneur.2023.1263291/full kostenfrei https://doaj.org/toc/1664-2295 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2023
allfields_unstemmed	10.3389/fneur.2023.1263291 doi (DE-627)DOAJ096764694 (DE-599)DOAJd16a1efa1e2c4e1fa8cb179e4b07593d DE-627 ger DE-627 rakwb eng RC346-429 Shahin Basiratzadeh verfasserin aut A data-driven approach to categorize patients with traumatic spinal cord injury: cluster analysis of a multicentre database 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier BackgroundConducting clinical trials for traumatic spinal cord injury (tSCI) presents challenges due to patient heterogeneity. Identifying clinically similar subgroups using patient demographics and baseline injury characteristics could lead to better patient-centered care and integrated care delivery.PurposeWe sought to (1) apply an unsupervised machine learning approach of cluster analysis to identify subgroups of tSCI patients using patient demographics and injury characteristics at baseline, (2) to find clinical similarity within subgroups using etiological variables and outcome variables, and (3) to create multi-dimensional labels for categorizing patients.Study designRetrospective analysis using prospectively collected data from a large national multicenter SCI registry.MethodsA method of spectral clustering was used to identify patient subgroups based on the following baseline variables collected since admission until rehabilitation: location of the injury, severity of the injury, Functional Independence Measure (FIM) motor, and demographic data (age, and body mass index). The FIM motor score, the FIM motor score change, and the total length of stay were assessed on the subgroups as outcome variables at discharge to establish the clinical similarity of the patients within derived subgroups. Furthermore, we discussed the relevance of the identified subgroups based on the etiological variables (energy and mechanism of injury) and compared them with the literature. Our study also employed a qualitative approach to systematically describe the identified subgroups, crafting multi-dimensional labels to highlight distinguishing factors and patient-focused insights.ResultsData on 334 tSCI patients from the Rick Hansen Spinal Cord Injury Registry was analyzed. Five significantly different subgroups were identified (p-value ≤0.05) based on baseline variables. Outcome variables at discharge superimposed on these subgroups had statistically different values between them (p-value ≤0.05) and supported the notion of clinical similarity of patients within each subgroup.ConclusionUtilizing cluster analysis, we identified five clinically similar subgroups of tSCI patients at baseline, yielding statistically significant inter-group differences in clinical outcomes. These subgroups offer a novel, data-driven categorization of tSCI patients which aligns with their demographics and injury characteristics. As it also correlates with traditional tSCI classifications, this categorization could lead to improved personalized patient-centered care. traumatic spinal cord injury patient-centric approach patient categorization data-driven method cluster analysis Neurology. Diseases of the nervous system Ramtin Hakimjavadi verfasserin aut Natalie Baddour verfasserin aut Wojtek Michalowski verfasserin aut Herna Viktor verfasserin aut Eugene Wai verfasserin aut Eugene Wai verfasserin aut Alexandra Stratton verfasserin aut Alexandra Stratton verfasserin aut Stephen Kingwell verfasserin aut Stephen Kingwell verfasserin aut Jean-Marc Mac-Thiong verfasserin aut Jean-Marc Mac-Thiong verfasserin aut Eve C. Tsai verfasserin aut Eve C. Tsai verfasserin aut Zhi Wang verfasserin aut Philippe Phan verfasserin aut Philippe Phan verfasserin aut In Frontiers in Neurology Frontiers Media S.A., 2010 14(2023) (DE-627)631498753 (DE-600)2564214-5 16642295 nnns volume:14 year:2023 https://doi.org/10.3389/fneur.2023.1263291 kostenfrei https://doaj.org/article/d16a1efa1e2c4e1fa8cb179e4b07593d kostenfrei https://www.frontiersin.org/articles/10.3389/fneur.2023.1263291/full kostenfrei https://doaj.org/toc/1664-2295 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2023
allfieldsGer	10.3389/fneur.2023.1263291 doi (DE-627)DOAJ096764694 (DE-599)DOAJd16a1efa1e2c4e1fa8cb179e4b07593d DE-627 ger DE-627 rakwb eng RC346-429 Shahin Basiratzadeh verfasserin aut A data-driven approach to categorize patients with traumatic spinal cord injury: cluster analysis of a multicentre database 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier BackgroundConducting clinical trials for traumatic spinal cord injury (tSCI) presents challenges due to patient heterogeneity. Identifying clinically similar subgroups using patient demographics and baseline injury characteristics could lead to better patient-centered care and integrated care delivery.PurposeWe sought to (1) apply an unsupervised machine learning approach of cluster analysis to identify subgroups of tSCI patients using patient demographics and injury characteristics at baseline, (2) to find clinical similarity within subgroups using etiological variables and outcome variables, and (3) to create multi-dimensional labels for categorizing patients.Study designRetrospective analysis using prospectively collected data from a large national multicenter SCI registry.MethodsA method of spectral clustering was used to identify patient subgroups based on the following baseline variables collected since admission until rehabilitation: location of the injury, severity of the injury, Functional Independence Measure (FIM) motor, and demographic data (age, and body mass index). The FIM motor score, the FIM motor score change, and the total length of stay were assessed on the subgroups as outcome variables at discharge to establish the clinical similarity of the patients within derived subgroups. Furthermore, we discussed the relevance of the identified subgroups based on the etiological variables (energy and mechanism of injury) and compared them with the literature. Our study also employed a qualitative approach to systematically describe the identified subgroups, crafting multi-dimensional labels to highlight distinguishing factors and patient-focused insights.ResultsData on 334 tSCI patients from the Rick Hansen Spinal Cord Injury Registry was analyzed. Five significantly different subgroups were identified (p-value ≤0.05) based on baseline variables. Outcome variables at discharge superimposed on these subgroups had statistically different values between them (p-value ≤0.05) and supported the notion of clinical similarity of patients within each subgroup.ConclusionUtilizing cluster analysis, we identified five clinically similar subgroups of tSCI patients at baseline, yielding statistically significant inter-group differences in clinical outcomes. These subgroups offer a novel, data-driven categorization of tSCI patients which aligns with their demographics and injury characteristics. As it also correlates with traditional tSCI classifications, this categorization could lead to improved personalized patient-centered care. traumatic spinal cord injury patient-centric approach patient categorization data-driven method cluster analysis Neurology. Diseases of the nervous system Ramtin Hakimjavadi verfasserin aut Natalie Baddour verfasserin aut Wojtek Michalowski verfasserin aut Herna Viktor verfasserin aut Eugene Wai verfasserin aut Eugene Wai verfasserin aut Alexandra Stratton verfasserin aut Alexandra Stratton verfasserin aut Stephen Kingwell verfasserin aut Stephen Kingwell verfasserin aut Jean-Marc Mac-Thiong verfasserin aut Jean-Marc Mac-Thiong verfasserin aut Eve C. Tsai verfasserin aut Eve C. Tsai verfasserin aut Zhi Wang verfasserin aut Philippe Phan verfasserin aut Philippe Phan verfasserin aut In Frontiers in Neurology Frontiers Media S.A., 2010 14(2023) (DE-627)631498753 (DE-600)2564214-5 16642295 nnns volume:14 year:2023 https://doi.org/10.3389/fneur.2023.1263291 kostenfrei https://doaj.org/article/d16a1efa1e2c4e1fa8cb179e4b07593d kostenfrei https://www.frontiersin.org/articles/10.3389/fneur.2023.1263291/full kostenfrei https://doaj.org/toc/1664-2295 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2023
allfieldsSound	10.3389/fneur.2023.1263291 doi (DE-627)DOAJ096764694 (DE-599)DOAJd16a1efa1e2c4e1fa8cb179e4b07593d DE-627 ger DE-627 rakwb eng RC346-429 Shahin Basiratzadeh verfasserin aut A data-driven approach to categorize patients with traumatic spinal cord injury: cluster analysis of a multicentre database 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier BackgroundConducting clinical trials for traumatic spinal cord injury (tSCI) presents challenges due to patient heterogeneity. Identifying clinically similar subgroups using patient demographics and baseline injury characteristics could lead to better patient-centered care and integrated care delivery.PurposeWe sought to (1) apply an unsupervised machine learning approach of cluster analysis to identify subgroups of tSCI patients using patient demographics and injury characteristics at baseline, (2) to find clinical similarity within subgroups using etiological variables and outcome variables, and (3) to create multi-dimensional labels for categorizing patients.Study designRetrospective analysis using prospectively collected data from a large national multicenter SCI registry.MethodsA method of spectral clustering was used to identify patient subgroups based on the following baseline variables collected since admission until rehabilitation: location of the injury, severity of the injury, Functional Independence Measure (FIM) motor, and demographic data (age, and body mass index). The FIM motor score, the FIM motor score change, and the total length of stay were assessed on the subgroups as outcome variables at discharge to establish the clinical similarity of the patients within derived subgroups. Furthermore, we discussed the relevance of the identified subgroups based on the etiological variables (energy and mechanism of injury) and compared them with the literature. Our study also employed a qualitative approach to systematically describe the identified subgroups, crafting multi-dimensional labels to highlight distinguishing factors and patient-focused insights.ResultsData on 334 tSCI patients from the Rick Hansen Spinal Cord Injury Registry was analyzed. Five significantly different subgroups were identified (p-value ≤0.05) based on baseline variables. Outcome variables at discharge superimposed on these subgroups had statistically different values between them (p-value ≤0.05) and supported the notion of clinical similarity of patients within each subgroup.ConclusionUtilizing cluster analysis, we identified five clinically similar subgroups of tSCI patients at baseline, yielding statistically significant inter-group differences in clinical outcomes. These subgroups offer a novel, data-driven categorization of tSCI patients which aligns with their demographics and injury characteristics. As it also correlates with traditional tSCI classifications, this categorization could lead to improved personalized patient-centered care. traumatic spinal cord injury patient-centric approach patient categorization data-driven method cluster analysis Neurology. Diseases of the nervous system Ramtin Hakimjavadi verfasserin aut Natalie Baddour verfasserin aut Wojtek Michalowski verfasserin aut Herna Viktor verfasserin aut Eugene Wai verfasserin aut Eugene Wai verfasserin aut Alexandra Stratton verfasserin aut Alexandra Stratton verfasserin aut Stephen Kingwell verfasserin aut Stephen Kingwell verfasserin aut Jean-Marc Mac-Thiong verfasserin aut Jean-Marc Mac-Thiong verfasserin aut Eve C. Tsai verfasserin aut Eve C. Tsai verfasserin aut Zhi Wang verfasserin aut Philippe Phan verfasserin aut Philippe Phan verfasserin aut In Frontiers in Neurology Frontiers Media S.A., 2010 14(2023) (DE-627)631498753 (DE-600)2564214-5 16642295 nnns volume:14 year:2023 https://doi.org/10.3389/fneur.2023.1263291 kostenfrei https://doaj.org/article/d16a1efa1e2c4e1fa8cb179e4b07593d kostenfrei https://www.frontiersin.org/articles/10.3389/fneur.2023.1263291/full kostenfrei https://doaj.org/toc/1664-2295 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2023
language	English
source	In Frontiers in Neurology 14(2023) volume:14 year:2023
sourceStr	In Frontiers in Neurology 14(2023) volume:14 year:2023
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	traumatic spinal cord injury patient-centric approach patient categorization data-driven method cluster analysis Neurology. Diseases of the nervous system
isfreeaccess_bool	true
container_title	Frontiers in Neurology
authorswithroles_txt_mv	Shahin Basiratzadeh @@aut@@ Ramtin Hakimjavadi @@aut@@ Natalie Baddour @@aut@@ Wojtek Michalowski @@aut@@ Herna Viktor @@aut@@ Eugene Wai @@aut@@ Alexandra Stratton @@aut@@ Stephen Kingwell @@aut@@ Jean-Marc Mac-Thiong @@aut@@ Eve C. Tsai @@aut@@ Zhi Wang @@aut@@ Philippe Phan @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	631498753
id	DOAJ096764694
language_de	englisch
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Identifying clinically similar subgroups using patient demographics and baseline injury characteristics could lead to better patient-centered care and integrated care delivery.PurposeWe sought to (1) apply an unsupervised machine learning approach of cluster analysis to identify subgroups of tSCI patients using patient demographics and injury characteristics at baseline, (2) to find clinical similarity within subgroups using etiological variables and outcome variables, and (3) to create multi-dimensional labels for categorizing patients.Study designRetrospective analysis using prospectively collected data from a large national multicenter SCI registry.MethodsA method of spectral clustering was used to identify patient subgroups based on the following baseline variables collected since admission until rehabilitation: location of the injury, severity of the injury, Functional Independence Measure (FIM) motor, and demographic data (age, and body mass index). The FIM motor score, the FIM motor score change, and the total length of stay were assessed on the subgroups as outcome variables at discharge to establish the clinical similarity of the patients within derived subgroups. Furthermore, we discussed the relevance of the identified subgroups based on the etiological variables (energy and mechanism of injury) and compared them with the literature. Our study also employed a qualitative approach to systematically describe the identified subgroups, crafting multi-dimensional labels to highlight distinguishing factors and patient-focused insights.ResultsData on 334 tSCI patients from the Rick Hansen Spinal Cord Injury Registry was analyzed. Five significantly different subgroups were identified (p-value ≤0.05) based on baseline variables. Outcome variables at discharge superimposed on these subgroups had statistically different values between them (p-value ≤0.05) and supported the notion of clinical similarity of patients within each subgroup.ConclusionUtilizing cluster analysis, we identified five clinically similar subgroups of tSCI patients at baseline, yielding statistically significant inter-group differences in clinical outcomes. These subgroups offer a novel, data-driven categorization of tSCI patients which aligns with their demographics and injury characteristics. 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callnumber-first	R - Medicine
author	Shahin Basiratzadeh
spellingShingle	Shahin Basiratzadeh misc RC346-429 misc traumatic spinal cord injury misc patient-centric approach misc patient categorization misc data-driven method misc cluster analysis misc Neurology. Diseases of the nervous system A data-driven approach to categorize patients with traumatic spinal cord injury: cluster analysis of a multicentre database
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topic_title	RC346-429 A data-driven approach to categorize patients with traumatic spinal cord injury: cluster analysis of a multicentre database traumatic spinal cord injury patient-centric approach patient categorization data-driven method cluster analysis
topic	misc RC346-429 misc traumatic spinal cord injury misc patient-centric approach misc patient categorization misc data-driven method misc cluster analysis misc Neurology. Diseases of the nervous system
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title	A data-driven approach to categorize patients with traumatic spinal cord injury: cluster analysis of a multicentre database
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title_full	A data-driven approach to categorize patients with traumatic spinal cord injury: cluster analysis of a multicentre database
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author_browse	Shahin Basiratzadeh Ramtin Hakimjavadi Natalie Baddour Wojtek Michalowski Herna Viktor Eugene Wai Alexandra Stratton Stephen Kingwell Jean-Marc Mac-Thiong Eve C. Tsai Zhi Wang Philippe Phan
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title_sort	data-driven approach to categorize patients with traumatic spinal cord injury: cluster analysis of a multicentre database
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title_auth	A data-driven approach to categorize patients with traumatic spinal cord injury: cluster analysis of a multicentre database
abstract	BackgroundConducting clinical trials for traumatic spinal cord injury (tSCI) presents challenges due to patient heterogeneity. Identifying clinically similar subgroups using patient demographics and baseline injury characteristics could lead to better patient-centered care and integrated care delivery.PurposeWe sought to (1) apply an unsupervised machine learning approach of cluster analysis to identify subgroups of tSCI patients using patient demographics and injury characteristics at baseline, (2) to find clinical similarity within subgroups using etiological variables and outcome variables, and (3) to create multi-dimensional labels for categorizing patients.Study designRetrospective analysis using prospectively collected data from a large national multicenter SCI registry.MethodsA method of spectral clustering was used to identify patient subgroups based on the following baseline variables collected since admission until rehabilitation: location of the injury, severity of the injury, Functional Independence Measure (FIM) motor, and demographic data (age, and body mass index). The FIM motor score, the FIM motor score change, and the total length of stay were assessed on the subgroups as outcome variables at discharge to establish the clinical similarity of the patients within derived subgroups. Furthermore, we discussed the relevance of the identified subgroups based on the etiological variables (energy and mechanism of injury) and compared them with the literature. Our study also employed a qualitative approach to systematically describe the identified subgroups, crafting multi-dimensional labels to highlight distinguishing factors and patient-focused insights.ResultsData on 334 tSCI patients from the Rick Hansen Spinal Cord Injury Registry was analyzed. Five significantly different subgroups were identified (p-value ≤0.05) based on baseline variables. Outcome variables at discharge superimposed on these subgroups had statistically different values between them (p-value ≤0.05) and supported the notion of clinical similarity of patients within each subgroup.ConclusionUtilizing cluster analysis, we identified five clinically similar subgroups of tSCI patients at baseline, yielding statistically significant inter-group differences in clinical outcomes. These subgroups offer a novel, data-driven categorization of tSCI patients which aligns with their demographics and injury characteristics. As it also correlates with traditional tSCI classifications, this categorization could lead to improved personalized patient-centered care.
abstractGer	BackgroundConducting clinical trials for traumatic spinal cord injury (tSCI) presents challenges due to patient heterogeneity. Identifying clinically similar subgroups using patient demographics and baseline injury characteristics could lead to better patient-centered care and integrated care delivery.PurposeWe sought to (1) apply an unsupervised machine learning approach of cluster analysis to identify subgroups of tSCI patients using patient demographics and injury characteristics at baseline, (2) to find clinical similarity within subgroups using etiological variables and outcome variables, and (3) to create multi-dimensional labels for categorizing patients.Study designRetrospective analysis using prospectively collected data from a large national multicenter SCI registry.MethodsA method of spectral clustering was used to identify patient subgroups based on the following baseline variables collected since admission until rehabilitation: location of the injury, severity of the injury, Functional Independence Measure (FIM) motor, and demographic data (age, and body mass index). The FIM motor score, the FIM motor score change, and the total length of stay were assessed on the subgroups as outcome variables at discharge to establish the clinical similarity of the patients within derived subgroups. Furthermore, we discussed the relevance of the identified subgroups based on the etiological variables (energy and mechanism of injury) and compared them with the literature. Our study also employed a qualitative approach to systematically describe the identified subgroups, crafting multi-dimensional labels to highlight distinguishing factors and patient-focused insights.ResultsData on 334 tSCI patients from the Rick Hansen Spinal Cord Injury Registry was analyzed. Five significantly different subgroups were identified (p-value ≤0.05) based on baseline variables. Outcome variables at discharge superimposed on these subgroups had statistically different values between them (p-value ≤0.05) and supported the notion of clinical similarity of patients within each subgroup.ConclusionUtilizing cluster analysis, we identified five clinically similar subgroups of tSCI patients at baseline, yielding statistically significant inter-group differences in clinical outcomes. These subgroups offer a novel, data-driven categorization of tSCI patients which aligns with their demographics and injury characteristics. As it also correlates with traditional tSCI classifications, this categorization could lead to improved personalized patient-centered care.
abstract_unstemmed	BackgroundConducting clinical trials for traumatic spinal cord injury (tSCI) presents challenges due to patient heterogeneity. Identifying clinically similar subgroups using patient demographics and baseline injury characteristics could lead to better patient-centered care and integrated care delivery.PurposeWe sought to (1) apply an unsupervised machine learning approach of cluster analysis to identify subgroups of tSCI patients using patient demographics and injury characteristics at baseline, (2) to find clinical similarity within subgroups using etiological variables and outcome variables, and (3) to create multi-dimensional labels for categorizing patients.Study designRetrospective analysis using prospectively collected data from a large national multicenter SCI registry.MethodsA method of spectral clustering was used to identify patient subgroups based on the following baseline variables collected since admission until rehabilitation: location of the injury, severity of the injury, Functional Independence Measure (FIM) motor, and demographic data (age, and body mass index). The FIM motor score, the FIM motor score change, and the total length of stay were assessed on the subgroups as outcome variables at discharge to establish the clinical similarity of the patients within derived subgroups. Furthermore, we discussed the relevance of the identified subgroups based on the etiological variables (energy and mechanism of injury) and compared them with the literature. Our study also employed a qualitative approach to systematically describe the identified subgroups, crafting multi-dimensional labels to highlight distinguishing factors and patient-focused insights.ResultsData on 334 tSCI patients from the Rick Hansen Spinal Cord Injury Registry was analyzed. Five significantly different subgroups were identified (p-value ≤0.05) based on baseline variables. Outcome variables at discharge superimposed on these subgroups had statistically different values between them (p-value ≤0.05) and supported the notion of clinical similarity of patients within each subgroup.ConclusionUtilizing cluster analysis, we identified five clinically similar subgroups of tSCI patients at baseline, yielding statistically significant inter-group differences in clinical outcomes. These subgroups offer a novel, data-driven categorization of tSCI patients which aligns with their demographics and injury characteristics. As it also correlates with traditional tSCI classifications, this categorization could lead to improved personalized patient-centered care.
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title_short	A data-driven approach to categorize patients with traumatic spinal cord injury: cluster analysis of a multicentre database
url	https://doi.org/10.3389/fneur.2023.1263291 https://doaj.org/article/d16a1efa1e2c4e1fa8cb179e4b07593d https://www.frontiersin.org/articles/10.3389/fneur.2023.1263291/full https://doaj.org/toc/1664-2295
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author2	Ramtin Hakimjavadi Natalie Baddour Wojtek Michalowski Herna Viktor Eugene Wai Alexandra Stratton Stephen Kingwell Jean-Marc Mac-Thiong Eve C. Tsai Zhi Wang Philippe Phan
author2Str	Ramtin Hakimjavadi Natalie Baddour Wojtek Michalowski Herna Viktor Eugene Wai Alexandra Stratton Stephen Kingwell Jean-Marc Mac-Thiong Eve C. Tsai Zhi Wang Philippe Phan
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A data-driven approach to categorize patients with traumatic spinal cord injury: cluster analysis of a multicentre database

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