Machine learning approach to stratify complex heterogeneity of chronic heart failure: A report from the CHART‐2 study

Abstract Aims Current approaches to classify chronic heart failure (HF) subpopulations may be limited due to the diversity of pathophysiology and co‐morbidities in chronic HF. We aimed to elucidate the clusters of chronic patients with HF by data‐driven approaches with machine learning in a hospital‐based registry. Methods and results A total of 4649 patients with a broad spectrum of left ventricular ejection fraction (LVEF) in the CHART‐2 (Chronic Heart Failure Analysis and Registry in the Tohoku District‐2) study were enrolled to this study. Chronic HF patients were classified using random forest clustering with 56 multiscale clinical parameters. We assessed the influence of the clusters on cardiovascular death, non‐cardiovascular death, all‐cause death, and free from hospitalization by HF. Latent class analysis using random forest clustering identified 10 clusters with four primary components: cardiac function (LVEF, left atrial and ventricular diameters, diastolic blood pressure, and brain natriuretic peptide), renal function (glomerular filtration rate and blood urea nitrogen), anaemia (red blood cell, haematocrit, haemoglobin, and platelet count), and nutrition (albumin and body mass index). All 11 significant clinical parameters in the four primary components and two disease aetiologies (ischaemic heart disease and valvular heart disease) showed statistically significant differences among the 10 clusters (P < 0.01). Cluster 1 (26.7% of patients), which is characterized by preserved LVEF (<59%, 37% of the total) with lowest brain natriuretic peptide (<111.3 pg/mL, 0.9%) and lowest left atrial diameter (<42 mm, 37.4%), showed the best 5 year survival rate of 98.1% for cardiovascular death, 95.9% for non‐cardiovascular death, 92.9% for all‐cause death, and 91.7% for free from hospitalization by HF. Cluster 10 (6.0% of the total), which is co‐morbid disorders of all four primary components, showed the worst survival rate of 39.1% for cardiovascular death, 68.9% for non‐cardiovascular death, 23.9% for all‐cause death, and 28.1% for free from hospitalization by HF. Conclusions These results suggest the potential applicability of the machine leaning approach, providing useful clinical prognostic information to stratify complex heterogeneity in patients with HF. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Kenji Nakano [verfasserIn] Kotaro Nochioka [verfasserIn] Satoshi Yasuda [verfasserIn] Daito Tamori [verfasserIn] Takashi Shiroto [verfasserIn] Yudai Sato [verfasserIn] Eichi Takaya [verfasserIn] Satoshi Miyata [verfasserIn] Eiryo Kawakami [verfasserIn] Tetsuo Ishikawa [verfasserIn] Takuya Ueda [verfasserIn] Hiroaki Shimokawa [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Heart failure Cohort study Clustering Machine learning Prognosis

Übergeordnetes Werk:	In: ESC Heart Failure - Wiley, 2015, 10(2023), 3, Seite 1597-1604
Übergeordnetes Werk:	volume:10 ; year:2023 ; number:3 ; pages:1597-1604

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1002/ehf2.14288

Katalog-ID:	DOAJ090617932

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520			\|a Abstract Aims Current approaches to classify chronic heart failure (HF) subpopulations may be limited due to the diversity of pathophysiology and co‐morbidities in chronic HF. We aimed to elucidate the clusters of chronic patients with HF by data‐driven approaches with machine learning in a hospital‐based registry. Methods and results A total of 4649 patients with a broad spectrum of left ventricular ejection fraction (LVEF) in the CHART‐2 (Chronic Heart Failure Analysis and Registry in the Tohoku District‐2) study were enrolled to this study. Chronic HF patients were classified using random forest clustering with 56 multiscale clinical parameters. We assessed the influence of the clusters on cardiovascular death, non‐cardiovascular death, all‐cause death, and free from hospitalization by HF. Latent class analysis using random forest clustering identified 10 clusters with four primary components: cardiac function (LVEF, left atrial and ventricular diameters, diastolic blood pressure, and brain natriuretic peptide), renal function (glomerular filtration rate and blood urea nitrogen), anaemia (red blood cell, haematocrit, haemoglobin, and platelet count), and nutrition (albumin and body mass index). All 11 significant clinical parameters in the four primary components and two disease aetiologies (ischaemic heart disease and valvular heart disease) showed statistically significant differences among the 10 clusters (P < 0.01). Cluster 1 (26.7% of patients), which is characterized by preserved LVEF (<59%, 37% of the total) with lowest brain natriuretic peptide (<111.3 pg/mL, 0.9%) and lowest left atrial diameter (<42 mm, 37.4%), showed the best 5 year survival rate of 98.1% for cardiovascular death, 95.9% for non‐cardiovascular death, 92.9% for all‐cause death, and 91.7% for free from hospitalization by HF. Cluster 10 (6.0% of the total), which is co‐morbid disorders of all four primary components, showed the worst survival rate of 39.1% for cardiovascular death, 68.9% for non‐cardiovascular death, 23.9% for all‐cause death, and 28.1% for free from hospitalization by HF. Conclusions These results suggest the potential applicability of the machine leaning approach, providing useful clinical prognostic information to stratify complex heterogeneity in patients with HF.
650		4	\|a Heart failure
650		4	\|a Cohort study
650		4	\|a Clustering
650		4	\|a Machine learning
650		4	\|a Prognosis
653		0	\|a Diseases of the circulatory (Cardiovascular) system
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700	0		\|a Yudai Sato \|e verfasserin \|4 aut
700	0		\|a Eichi Takaya \|e verfasserin \|4 aut
700	0		\|a Satoshi Miyata \|e verfasserin \|4 aut
700	0		\|a Eiryo Kawakami \|e verfasserin \|4 aut
700	0		\|a Tetsuo Ishikawa \|e verfasserin \|4 aut
700	0		\|a Takuya Ueda \|e verfasserin \|4 aut
700	0		\|a Hiroaki Shimokawa \|e verfasserin \|4 aut
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allfields	10.1002/ehf2.14288 doi (DE-627)DOAJ090617932 (DE-599)DOAJdc22020d47644ba383f2dc5e0c81f81c DE-627 ger DE-627 rakwb eng RC666-701 Kenji Nakano verfasserin aut Machine learning approach to stratify complex heterogeneity of chronic heart failure: A report from the CHART‐2 study 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Aims Current approaches to classify chronic heart failure (HF) subpopulations may be limited due to the diversity of pathophysiology and co‐morbidities in chronic HF. We aimed to elucidate the clusters of chronic patients with HF by data‐driven approaches with machine learning in a hospital‐based registry. Methods and results A total of 4649 patients with a broad spectrum of left ventricular ejection fraction (LVEF) in the CHART‐2 (Chronic Heart Failure Analysis and Registry in the Tohoku District‐2) study were enrolled to this study. Chronic HF patients were classified using random forest clustering with 56 multiscale clinical parameters. We assessed the influence of the clusters on cardiovascular death, non‐cardiovascular death, all‐cause death, and free from hospitalization by HF. Latent class analysis using random forest clustering identified 10 clusters with four primary components: cardiac function (LVEF, left atrial and ventricular diameters, diastolic blood pressure, and brain natriuretic peptide), renal function (glomerular filtration rate and blood urea nitrogen), anaemia (red blood cell, haematocrit, haemoglobin, and platelet count), and nutrition (albumin and body mass index). All 11 significant clinical parameters in the four primary components and two disease aetiologies (ischaemic heart disease and valvular heart disease) showed statistically significant differences among the 10 clusters (P < 0.01). Cluster 1 (26.7% of patients), which is characterized by preserved LVEF (<59%, 37% of the total) with lowest brain natriuretic peptide (<111.3 pg/mL, 0.9%) and lowest left atrial diameter (<42 mm, 37.4%), showed the best 5 year survival rate of 98.1% for cardiovascular death, 95.9% for non‐cardiovascular death, 92.9% for all‐cause death, and 91.7% for free from hospitalization by HF. Cluster 10 (6.0% of the total), which is co‐morbid disorders of all four primary components, showed the worst survival rate of 39.1% for cardiovascular death, 68.9% for non‐cardiovascular death, 23.9% for all‐cause death, and 28.1% for free from hospitalization by HF. Conclusions These results suggest the potential applicability of the machine leaning approach, providing useful clinical prognostic information to stratify complex heterogeneity in patients with HF. Heart failure Cohort study Clustering Machine learning Prognosis Diseases of the circulatory (Cardiovascular) system Kotaro Nochioka verfasserin aut Satoshi Yasuda verfasserin aut Daito Tamori verfasserin aut Takashi Shiroto verfasserin aut Yudai Sato verfasserin aut Eichi Takaya verfasserin aut Satoshi Miyata verfasserin aut Eiryo Kawakami verfasserin aut Tetsuo Ishikawa verfasserin aut Takuya Ueda verfasserin aut Hiroaki Shimokawa verfasserin aut In ESC Heart Failure Wiley, 2015 10(2023), 3, Seite 1597-1604 (DE-627)820686506 (DE-600)2814355-3 20555822 nnns volume:10 year:2023 number:3 pages:1597-1604 https://doi.org/10.1002/ehf2.14288 kostenfrei https://doaj.org/article/dc22020d47644ba383f2dc5e0c81f81c kostenfrei https://doi.org/10.1002/ehf2.14288 kostenfrei https://doaj.org/toc/2055-5822 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2023 3 1597-1604
spelling	10.1002/ehf2.14288 doi (DE-627)DOAJ090617932 (DE-599)DOAJdc22020d47644ba383f2dc5e0c81f81c DE-627 ger DE-627 rakwb eng RC666-701 Kenji Nakano verfasserin aut Machine learning approach to stratify complex heterogeneity of chronic heart failure: A report from the CHART‐2 study 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Aims Current approaches to classify chronic heart failure (HF) subpopulations may be limited due to the diversity of pathophysiology and co‐morbidities in chronic HF. We aimed to elucidate the clusters of chronic patients with HF by data‐driven approaches with machine learning in a hospital‐based registry. Methods and results A total of 4649 patients with a broad spectrum of left ventricular ejection fraction (LVEF) in the CHART‐2 (Chronic Heart Failure Analysis and Registry in the Tohoku District‐2) study were enrolled to this study. Chronic HF patients were classified using random forest clustering with 56 multiscale clinical parameters. We assessed the influence of the clusters on cardiovascular death, non‐cardiovascular death, all‐cause death, and free from hospitalization by HF. Latent class analysis using random forest clustering identified 10 clusters with four primary components: cardiac function (LVEF, left atrial and ventricular diameters, diastolic blood pressure, and brain natriuretic peptide), renal function (glomerular filtration rate and blood urea nitrogen), anaemia (red blood cell, haematocrit, haemoglobin, and platelet count), and nutrition (albumin and body mass index). All 11 significant clinical parameters in the four primary components and two disease aetiologies (ischaemic heart disease and valvular heart disease) showed statistically significant differences among the 10 clusters (P < 0.01). Cluster 1 (26.7% of patients), which is characterized by preserved LVEF (<59%, 37% of the total) with lowest brain natriuretic peptide (<111.3 pg/mL, 0.9%) and lowest left atrial diameter (<42 mm, 37.4%), showed the best 5 year survival rate of 98.1% for cardiovascular death, 95.9% for non‐cardiovascular death, 92.9% for all‐cause death, and 91.7% for free from hospitalization by HF. Cluster 10 (6.0% of the total), which is co‐morbid disorders of all four primary components, showed the worst survival rate of 39.1% for cardiovascular death, 68.9% for non‐cardiovascular death, 23.9% for all‐cause death, and 28.1% for free from hospitalization by HF. Conclusions These results suggest the potential applicability of the machine leaning approach, providing useful clinical prognostic information to stratify complex heterogeneity in patients with HF. Heart failure Cohort study Clustering Machine learning Prognosis Diseases of the circulatory (Cardiovascular) system Kotaro Nochioka verfasserin aut Satoshi Yasuda verfasserin aut Daito Tamori verfasserin aut Takashi Shiroto verfasserin aut Yudai Sato verfasserin aut Eichi Takaya verfasserin aut Satoshi Miyata verfasserin aut Eiryo Kawakami verfasserin aut Tetsuo Ishikawa verfasserin aut Takuya Ueda verfasserin aut Hiroaki Shimokawa verfasserin aut In ESC Heart Failure Wiley, 2015 10(2023), 3, Seite 1597-1604 (DE-627)820686506 (DE-600)2814355-3 20555822 nnns volume:10 year:2023 number:3 pages:1597-1604 https://doi.org/10.1002/ehf2.14288 kostenfrei https://doaj.org/article/dc22020d47644ba383f2dc5e0c81f81c kostenfrei https://doi.org/10.1002/ehf2.14288 kostenfrei https://doaj.org/toc/2055-5822 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2023 3 1597-1604
allfields_unstemmed	10.1002/ehf2.14288 doi (DE-627)DOAJ090617932 (DE-599)DOAJdc22020d47644ba383f2dc5e0c81f81c DE-627 ger DE-627 rakwb eng RC666-701 Kenji Nakano verfasserin aut Machine learning approach to stratify complex heterogeneity of chronic heart failure: A report from the CHART‐2 study 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Aims Current approaches to classify chronic heart failure (HF) subpopulations may be limited due to the diversity of pathophysiology and co‐morbidities in chronic HF. We aimed to elucidate the clusters of chronic patients with HF by data‐driven approaches with machine learning in a hospital‐based registry. Methods and results A total of 4649 patients with a broad spectrum of left ventricular ejection fraction (LVEF) in the CHART‐2 (Chronic Heart Failure Analysis and Registry in the Tohoku District‐2) study were enrolled to this study. Chronic HF patients were classified using random forest clustering with 56 multiscale clinical parameters. We assessed the influence of the clusters on cardiovascular death, non‐cardiovascular death, all‐cause death, and free from hospitalization by HF. Latent class analysis using random forest clustering identified 10 clusters with four primary components: cardiac function (LVEF, left atrial and ventricular diameters, diastolic blood pressure, and brain natriuretic peptide), renal function (glomerular filtration rate and blood urea nitrogen), anaemia (red blood cell, haematocrit, haemoglobin, and platelet count), and nutrition (albumin and body mass index). All 11 significant clinical parameters in the four primary components and two disease aetiologies (ischaemic heart disease and valvular heart disease) showed statistically significant differences among the 10 clusters (P < 0.01). Cluster 1 (26.7% of patients), which is characterized by preserved LVEF (<59%, 37% of the total) with lowest brain natriuretic peptide (<111.3 pg/mL, 0.9%) and lowest left atrial diameter (<42 mm, 37.4%), showed the best 5 year survival rate of 98.1% for cardiovascular death, 95.9% for non‐cardiovascular death, 92.9% for all‐cause death, and 91.7% for free from hospitalization by HF. Cluster 10 (6.0% of the total), which is co‐morbid disorders of all four primary components, showed the worst survival rate of 39.1% for cardiovascular death, 68.9% for non‐cardiovascular death, 23.9% for all‐cause death, and 28.1% for free from hospitalization by HF. Conclusions These results suggest the potential applicability of the machine leaning approach, providing useful clinical prognostic information to stratify complex heterogeneity in patients with HF. Heart failure Cohort study Clustering Machine learning Prognosis Diseases of the circulatory (Cardiovascular) system Kotaro Nochioka verfasserin aut Satoshi Yasuda verfasserin aut Daito Tamori verfasserin aut Takashi Shiroto verfasserin aut Yudai Sato verfasserin aut Eichi Takaya verfasserin aut Satoshi Miyata verfasserin aut Eiryo Kawakami verfasserin aut Tetsuo Ishikawa verfasserin aut Takuya Ueda verfasserin aut Hiroaki Shimokawa verfasserin aut In ESC Heart Failure Wiley, 2015 10(2023), 3, Seite 1597-1604 (DE-627)820686506 (DE-600)2814355-3 20555822 nnns volume:10 year:2023 number:3 pages:1597-1604 https://doi.org/10.1002/ehf2.14288 kostenfrei https://doaj.org/article/dc22020d47644ba383f2dc5e0c81f81c kostenfrei https://doi.org/10.1002/ehf2.14288 kostenfrei https://doaj.org/toc/2055-5822 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2023 3 1597-1604
allfieldsGer	10.1002/ehf2.14288 doi (DE-627)DOAJ090617932 (DE-599)DOAJdc22020d47644ba383f2dc5e0c81f81c DE-627 ger DE-627 rakwb eng RC666-701 Kenji Nakano verfasserin aut Machine learning approach to stratify complex heterogeneity of chronic heart failure: A report from the CHART‐2 study 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Aims Current approaches to classify chronic heart failure (HF) subpopulations may be limited due to the diversity of pathophysiology and co‐morbidities in chronic HF. We aimed to elucidate the clusters of chronic patients with HF by data‐driven approaches with machine learning in a hospital‐based registry. Methods and results A total of 4649 patients with a broad spectrum of left ventricular ejection fraction (LVEF) in the CHART‐2 (Chronic Heart Failure Analysis and Registry in the Tohoku District‐2) study were enrolled to this study. Chronic HF patients were classified using random forest clustering with 56 multiscale clinical parameters. We assessed the influence of the clusters on cardiovascular death, non‐cardiovascular death, all‐cause death, and free from hospitalization by HF. Latent class analysis using random forest clustering identified 10 clusters with four primary components: cardiac function (LVEF, left atrial and ventricular diameters, diastolic blood pressure, and brain natriuretic peptide), renal function (glomerular filtration rate and blood urea nitrogen), anaemia (red blood cell, haematocrit, haemoglobin, and platelet count), and nutrition (albumin and body mass index). All 11 significant clinical parameters in the four primary components and two disease aetiologies (ischaemic heart disease and valvular heart disease) showed statistically significant differences among the 10 clusters (P < 0.01). Cluster 1 (26.7% of patients), which is characterized by preserved LVEF (<59%, 37% of the total) with lowest brain natriuretic peptide (<111.3 pg/mL, 0.9%) and lowest left atrial diameter (<42 mm, 37.4%), showed the best 5 year survival rate of 98.1% for cardiovascular death, 95.9% for non‐cardiovascular death, 92.9% for all‐cause death, and 91.7% for free from hospitalization by HF. Cluster 10 (6.0% of the total), which is co‐morbid disorders of all four primary components, showed the worst survival rate of 39.1% for cardiovascular death, 68.9% for non‐cardiovascular death, 23.9% for all‐cause death, and 28.1% for free from hospitalization by HF. Conclusions These results suggest the potential applicability of the machine leaning approach, providing useful clinical prognostic information to stratify complex heterogeneity in patients with HF. Heart failure Cohort study Clustering Machine learning Prognosis Diseases of the circulatory (Cardiovascular) system Kotaro Nochioka verfasserin aut Satoshi Yasuda verfasserin aut Daito Tamori verfasserin aut Takashi Shiroto verfasserin aut Yudai Sato verfasserin aut Eichi Takaya verfasserin aut Satoshi Miyata verfasserin aut Eiryo Kawakami verfasserin aut Tetsuo Ishikawa verfasserin aut Takuya Ueda verfasserin aut Hiroaki Shimokawa verfasserin aut In ESC Heart Failure Wiley, 2015 10(2023), 3, Seite 1597-1604 (DE-627)820686506 (DE-600)2814355-3 20555822 nnns volume:10 year:2023 number:3 pages:1597-1604 https://doi.org/10.1002/ehf2.14288 kostenfrei https://doaj.org/article/dc22020d47644ba383f2dc5e0c81f81c kostenfrei https://doi.org/10.1002/ehf2.14288 kostenfrei https://doaj.org/toc/2055-5822 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2023 3 1597-1604
allfieldsSound	10.1002/ehf2.14288 doi (DE-627)DOAJ090617932 (DE-599)DOAJdc22020d47644ba383f2dc5e0c81f81c DE-627 ger DE-627 rakwb eng RC666-701 Kenji Nakano verfasserin aut Machine learning approach to stratify complex heterogeneity of chronic heart failure: A report from the CHART‐2 study 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Aims Current approaches to classify chronic heart failure (HF) subpopulations may be limited due to the diversity of pathophysiology and co‐morbidities in chronic HF. We aimed to elucidate the clusters of chronic patients with HF by data‐driven approaches with machine learning in a hospital‐based registry. Methods and results A total of 4649 patients with a broad spectrum of left ventricular ejection fraction (LVEF) in the CHART‐2 (Chronic Heart Failure Analysis and Registry in the Tohoku District‐2) study were enrolled to this study. Chronic HF patients were classified using random forest clustering with 56 multiscale clinical parameters. We assessed the influence of the clusters on cardiovascular death, non‐cardiovascular death, all‐cause death, and free from hospitalization by HF. Latent class analysis using random forest clustering identified 10 clusters with four primary components: cardiac function (LVEF, left atrial and ventricular diameters, diastolic blood pressure, and brain natriuretic peptide), renal function (glomerular filtration rate and blood urea nitrogen), anaemia (red blood cell, haematocrit, haemoglobin, and platelet count), and nutrition (albumin and body mass index). All 11 significant clinical parameters in the four primary components and two disease aetiologies (ischaemic heart disease and valvular heart disease) showed statistically significant differences among the 10 clusters (P < 0.01). Cluster 1 (26.7% of patients), which is characterized by preserved LVEF (<59%, 37% of the total) with lowest brain natriuretic peptide (<111.3 pg/mL, 0.9%) and lowest left atrial diameter (<42 mm, 37.4%), showed the best 5 year survival rate of 98.1% for cardiovascular death, 95.9% for non‐cardiovascular death, 92.9% for all‐cause death, and 91.7% for free from hospitalization by HF. Cluster 10 (6.0% of the total), which is co‐morbid disorders of all four primary components, showed the worst survival rate of 39.1% for cardiovascular death, 68.9% for non‐cardiovascular death, 23.9% for all‐cause death, and 28.1% for free from hospitalization by HF. Conclusions These results suggest the potential applicability of the machine leaning approach, providing useful clinical prognostic information to stratify complex heterogeneity in patients with HF. Heart failure Cohort study Clustering Machine learning Prognosis Diseases of the circulatory (Cardiovascular) system Kotaro Nochioka verfasserin aut Satoshi Yasuda verfasserin aut Daito Tamori verfasserin aut Takashi Shiroto verfasserin aut Yudai Sato verfasserin aut Eichi Takaya verfasserin aut Satoshi Miyata verfasserin aut Eiryo Kawakami verfasserin aut Tetsuo Ishikawa verfasserin aut Takuya Ueda verfasserin aut Hiroaki Shimokawa verfasserin aut In ESC Heart Failure Wiley, 2015 10(2023), 3, Seite 1597-1604 (DE-627)820686506 (DE-600)2814355-3 20555822 nnns volume:10 year:2023 number:3 pages:1597-1604 https://doi.org/10.1002/ehf2.14288 kostenfrei https://doaj.org/article/dc22020d47644ba383f2dc5e0c81f81c kostenfrei https://doi.org/10.1002/ehf2.14288 kostenfrei https://doaj.org/toc/2055-5822 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2023 3 1597-1604
language	English
source	In ESC Heart Failure 10(2023), 3, Seite 1597-1604 volume:10 year:2023 number:3 pages:1597-1604
sourceStr	In ESC Heart Failure 10(2023), 3, Seite 1597-1604 volume:10 year:2023 number:3 pages:1597-1604
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Heart failure Cohort study Clustering Machine learning Prognosis Diseases of the circulatory (Cardiovascular) system
isfreeaccess_bool	true
container_title	ESC Heart Failure
authorswithroles_txt_mv	Kenji Nakano @@aut@@ Kotaro Nochioka @@aut@@ Satoshi Yasuda @@aut@@ Daito Tamori @@aut@@ Takashi Shiroto @@aut@@ Yudai Sato @@aut@@ Eichi Takaya @@aut@@ Satoshi Miyata @@aut@@ Eiryo Kawakami @@aut@@ Tetsuo Ishikawa @@aut@@ Takuya Ueda @@aut@@ Hiroaki Shimokawa @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	820686506
id	DOAJ090617932
language_de	englisch
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We aimed to elucidate the clusters of chronic patients with HF by data‐driven approaches with machine learning in a hospital‐based registry. Methods and results A total of 4649 patients with a broad spectrum of left ventricular ejection fraction (LVEF) in the CHART‐2 (Chronic Heart Failure Analysis and Registry in the Tohoku District‐2) study were enrolled to this study. Chronic HF patients were classified using random forest clustering with 56 multiscale clinical parameters. We assessed the influence of the clusters on cardiovascular death, non‐cardiovascular death, all‐cause death, and free from hospitalization by HF. Latent class analysis using random forest clustering identified 10 clusters with four primary components: cardiac function (LVEF, left atrial and ventricular diameters, diastolic blood pressure, and brain natriuretic peptide), renal function (glomerular filtration rate and blood urea nitrogen), anaemia (red blood cell, haematocrit, haemoglobin, and platelet count), and nutrition (albumin and body mass index). All 11 significant clinical parameters in the four primary components and two disease aetiologies (ischaemic heart disease and valvular heart disease) showed statistically significant differences among the 10 clusters (P < 0.01). Cluster 1 (26.7% of patients), which is characterized by preserved LVEF (<59%, 37% of the total) with lowest brain natriuretic peptide (<111.3 pg/mL, 0.9%) and lowest left atrial diameter (<42 mm, 37.4%), showed the best 5 year survival rate of 98.1% for cardiovascular death, 95.9% for non‐cardiovascular death, 92.9% for all‐cause death, and 91.7% for free from hospitalization by HF. Cluster 10 (6.0% of the total), which is co‐morbid disorders of all four primary components, showed the worst survival rate of 39.1% for cardiovascular death, 68.9% for non‐cardiovascular death, 23.9% for all‐cause death, and 28.1% for free from hospitalization by HF. 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callnumber-first	R - Medicine
author	Kenji Nakano
spellingShingle	Kenji Nakano misc RC666-701 misc Heart failure misc Cohort study misc Clustering misc Machine learning misc Prognosis misc Diseases of the circulatory (Cardiovascular) system Machine learning approach to stratify complex heterogeneity of chronic heart failure: A report from the CHART‐2 study
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topic_title	RC666-701 Machine learning approach to stratify complex heterogeneity of chronic heart failure: A report from the CHART‐2 study Heart failure Cohort study Clustering Machine learning Prognosis
topic	misc RC666-701 misc Heart failure misc Cohort study misc Clustering misc Machine learning misc Prognosis misc Diseases of the circulatory (Cardiovascular) system
topic_unstemmed	misc RC666-701 misc Heart failure misc Cohort study misc Clustering misc Machine learning misc Prognosis misc Diseases of the circulatory (Cardiovascular) system
topic_browse	misc RC666-701 misc Heart failure misc Cohort study misc Clustering misc Machine learning misc Prognosis misc Diseases of the circulatory (Cardiovascular) system
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title	Machine learning approach to stratify complex heterogeneity of chronic heart failure: A report from the CHART‐2 study
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title_full	Machine learning approach to stratify complex heterogeneity of chronic heart failure: A report from the CHART‐2 study
author_sort	Kenji Nakano
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author_browse	Kenji Nakano Kotaro Nochioka Satoshi Yasuda Daito Tamori Takashi Shiroto Yudai Sato Eichi Takaya Satoshi Miyata Eiryo Kawakami Tetsuo Ishikawa Takuya Ueda Hiroaki Shimokawa
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title_sort	machine learning approach to stratify complex heterogeneity of chronic heart failure: a report from the chart‐2 study
callnumber	RC666-701
title_auth	Machine learning approach to stratify complex heterogeneity of chronic heart failure: A report from the CHART‐2 study
abstract	Abstract Aims Current approaches to classify chronic heart failure (HF) subpopulations may be limited due to the diversity of pathophysiology and co‐morbidities in chronic HF. We aimed to elucidate the clusters of chronic patients with HF by data‐driven approaches with machine learning in a hospital‐based registry. Methods and results A total of 4649 patients with a broad spectrum of left ventricular ejection fraction (LVEF) in the CHART‐2 (Chronic Heart Failure Analysis and Registry in the Tohoku District‐2) study were enrolled to this study. Chronic HF patients were classified using random forest clustering with 56 multiscale clinical parameters. We assessed the influence of the clusters on cardiovascular death, non‐cardiovascular death, all‐cause death, and free from hospitalization by HF. Latent class analysis using random forest clustering identified 10 clusters with four primary components: cardiac function (LVEF, left atrial and ventricular diameters, diastolic blood pressure, and brain natriuretic peptide), renal function (glomerular filtration rate and blood urea nitrogen), anaemia (red blood cell, haematocrit, haemoglobin, and platelet count), and nutrition (albumin and body mass index). All 11 significant clinical parameters in the four primary components and two disease aetiologies (ischaemic heart disease and valvular heart disease) showed statistically significant differences among the 10 clusters (P < 0.01). Cluster 1 (26.7% of patients), which is characterized by preserved LVEF (<59%, 37% of the total) with lowest brain natriuretic peptide (<111.3 pg/mL, 0.9%) and lowest left atrial diameter (<42 mm, 37.4%), showed the best 5 year survival rate of 98.1% for cardiovascular death, 95.9% for non‐cardiovascular death, 92.9% for all‐cause death, and 91.7% for free from hospitalization by HF. Cluster 10 (6.0% of the total), which is co‐morbid disorders of all four primary components, showed the worst survival rate of 39.1% for cardiovascular death, 68.9% for non‐cardiovascular death, 23.9% for all‐cause death, and 28.1% for free from hospitalization by HF. Conclusions These results suggest the potential applicability of the machine leaning approach, providing useful clinical prognostic information to stratify complex heterogeneity in patients with HF.
abstractGer	Abstract Aims Current approaches to classify chronic heart failure (HF) subpopulations may be limited due to the diversity of pathophysiology and co‐morbidities in chronic HF. We aimed to elucidate the clusters of chronic patients with HF by data‐driven approaches with machine learning in a hospital‐based registry. Methods and results A total of 4649 patients with a broad spectrum of left ventricular ejection fraction (LVEF) in the CHART‐2 (Chronic Heart Failure Analysis and Registry in the Tohoku District‐2) study were enrolled to this study. Chronic HF patients were classified using random forest clustering with 56 multiscale clinical parameters. We assessed the influence of the clusters on cardiovascular death, non‐cardiovascular death, all‐cause death, and free from hospitalization by HF. Latent class analysis using random forest clustering identified 10 clusters with four primary components: cardiac function (LVEF, left atrial and ventricular diameters, diastolic blood pressure, and brain natriuretic peptide), renal function (glomerular filtration rate and blood urea nitrogen), anaemia (red blood cell, haematocrit, haemoglobin, and platelet count), and nutrition (albumin and body mass index). All 11 significant clinical parameters in the four primary components and two disease aetiologies (ischaemic heart disease and valvular heart disease) showed statistically significant differences among the 10 clusters (P < 0.01). Cluster 1 (26.7% of patients), which is characterized by preserved LVEF (<59%, 37% of the total) with lowest brain natriuretic peptide (<111.3 pg/mL, 0.9%) and lowest left atrial diameter (<42 mm, 37.4%), showed the best 5 year survival rate of 98.1% for cardiovascular death, 95.9% for non‐cardiovascular death, 92.9% for all‐cause death, and 91.7% for free from hospitalization by HF. Cluster 10 (6.0% of the total), which is co‐morbid disorders of all four primary components, showed the worst survival rate of 39.1% for cardiovascular death, 68.9% for non‐cardiovascular death, 23.9% for all‐cause death, and 28.1% for free from hospitalization by HF. Conclusions These results suggest the potential applicability of the machine leaning approach, providing useful clinical prognostic information to stratify complex heterogeneity in patients with HF.
abstract_unstemmed	Abstract Aims Current approaches to classify chronic heart failure (HF) subpopulations may be limited due to the diversity of pathophysiology and co‐morbidities in chronic HF. We aimed to elucidate the clusters of chronic patients with HF by data‐driven approaches with machine learning in a hospital‐based registry. Methods and results A total of 4649 patients with a broad spectrum of left ventricular ejection fraction (LVEF) in the CHART‐2 (Chronic Heart Failure Analysis and Registry in the Tohoku District‐2) study were enrolled to this study. Chronic HF patients were classified using random forest clustering with 56 multiscale clinical parameters. We assessed the influence of the clusters on cardiovascular death, non‐cardiovascular death, all‐cause death, and free from hospitalization by HF. Latent class analysis using random forest clustering identified 10 clusters with four primary components: cardiac function (LVEF, left atrial and ventricular diameters, diastolic blood pressure, and brain natriuretic peptide), renal function (glomerular filtration rate and blood urea nitrogen), anaemia (red blood cell, haematocrit, haemoglobin, and platelet count), and nutrition (albumin and body mass index). All 11 significant clinical parameters in the four primary components and two disease aetiologies (ischaemic heart disease and valvular heart disease) showed statistically significant differences among the 10 clusters (P < 0.01). Cluster 1 (26.7% of patients), which is characterized by preserved LVEF (<59%, 37% of the total) with lowest brain natriuretic peptide (<111.3 pg/mL, 0.9%) and lowest left atrial diameter (<42 mm, 37.4%), showed the best 5 year survival rate of 98.1% for cardiovascular death, 95.9% for non‐cardiovascular death, 92.9% for all‐cause death, and 91.7% for free from hospitalization by HF. Cluster 10 (6.0% of the total), which is co‐morbid disorders of all four primary components, showed the worst survival rate of 39.1% for cardiovascular death, 68.9% for non‐cardiovascular death, 23.9% for all‐cause death, and 28.1% for free from hospitalization by HF. Conclusions These results suggest the potential applicability of the machine leaning approach, providing useful clinical prognostic information to stratify complex heterogeneity in patients with HF.
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title_short	Machine learning approach to stratify complex heterogeneity of chronic heart failure: A report from the CHART‐2 study
url	https://doi.org/10.1002/ehf2.14288 https://doaj.org/article/dc22020d47644ba383f2dc5e0c81f81c https://doaj.org/toc/2055-5822
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up_date	2024-07-03T15:49:42.098Z
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">DOAJ090617932</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230526112734.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230526s2023 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1002/ehf2.14288</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ090617932</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJdc22020d47644ba383f2dc5e0c81f81c</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">RC666-701</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Kenji Nakano</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Machine learning approach to stratify complex heterogeneity of chronic heart failure: A report from the CHART‐2 study</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Aims Current approaches to classify chronic heart failure (HF) subpopulations may be limited due to the diversity of pathophysiology and co‐morbidities in chronic HF. We aimed to elucidate the clusters of chronic patients with HF by data‐driven approaches with machine learning in a hospital‐based registry. Methods and results A total of 4649 patients with a broad spectrum of left ventricular ejection fraction (LVEF) in the CHART‐2 (Chronic Heart Failure Analysis and Registry in the Tohoku District‐2) study were enrolled to this study. Chronic HF patients were classified using random forest clustering with 56 multiscale clinical parameters. We assessed the influence of the clusters on cardiovascular death, non‐cardiovascular death, all‐cause death, and free from hospitalization by HF. Latent class analysis using random forest clustering identified 10 clusters with four primary components: cardiac function (LVEF, left atrial and ventricular diameters, diastolic blood pressure, and brain natriuretic peptide), renal function (glomerular filtration rate and blood urea nitrogen), anaemia (red blood cell, haematocrit, haemoglobin, and platelet count), and nutrition (albumin and body mass index). All 11 significant clinical parameters in the four primary components and two disease aetiologies (ischaemic heart disease and valvular heart disease) showed statistically significant differences among the 10 clusters (P < 0.01). Cluster 1 (26.7% of patients), which is characterized by preserved LVEF (<59%, 37% of the total) with lowest brain natriuretic peptide (<111.3 pg/mL, 0.9%) and lowest left atrial diameter (<42 mm, 37.4%), showed the best 5 year survival rate of 98.1% for cardiovascular death, 95.9% for non‐cardiovascular death, 92.9% for all‐cause death, and 91.7% for free from hospitalization by HF. Cluster 10 (6.0% of the total), which is co‐morbid disorders of all four primary components, showed the worst survival rate of 39.1% for cardiovascular death, 68.9% for non‐cardiovascular death, 23.9% for all‐cause death, and 28.1% for free from hospitalization by HF. Conclusions These results suggest the potential applicability of the machine leaning approach, providing useful clinical prognostic information to stratify complex heterogeneity in patients with HF.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Heart failure</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Cohort study</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Clustering</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Machine learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Prognosis</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Diseases of the circulatory (Cardiovascular) system</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Kotaro Nochioka</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield 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Machine learning approach to stratify complex heterogeneity of chronic heart failure: A report from the CHART‐2 study

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