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Development and validation of frailty risk prediction model for elderly patients with coronary heart disease
To analyze the influential factors of frailty in elderly patients with coronary heart disease (CHD), develop a nomogram-based risk prediction model for this population, and validate its predictive performance. A total of 592 elderly patients with CHD were conveniently selected and enrolled from 3 te...
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| Published in: | BMC geriatrics 2024-09, Vol.24 (1), p.742-11, Article 742 |
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| description | To analyze the influential factors of frailty in elderly patients with coronary heart disease (CHD), develop a nomogram-based risk prediction model for this population, and validate its predictive performance.
A total of 592 elderly patients with CHD were conveniently selected and enrolled from 3 tertiary hospitals, 5 secondary hospitals, and 3 community health service centers in China between October 2022 and January 2023. Data collection involved the use of the general information questionnaire, the Frail scale, and the instrumental ability of daily living assessment scale. And the patients were categorized into two groups based on frailty, and χ
test as well as logistic regression analysis were used to identify and determine the influencing factors of frailty. The nomograph prediction model for elderly patients with CHD was developed using R software (version 4.2.2). The Hosmer-Lemeshow test and the area under the receiver operating characteristic (ROC) curve were employed to assess the predictive performance of the model. Additionally, the Bootstrap resampling method was utilized to validate the model and generate the calibration curve of the prediction model.
The prevalence of frailty in elderly patients with CHD was 30.07%. The multiple factor analysis revealed that poor health status (OR = 28.169)/general health status (OR = 18.120), age (OR = 1.046), social activities (OR = 0.673), impaired instrumental ability of daily living (OR = 2.384) were independent risk factors for frailty (all P |
| doi_str_mv | 10.1186/s12877-024-05320-7 |
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| fullrecord | <record><control><sourceid>gale_doaj_</sourceid><recordid>TN_cdi_doaj_primary_oai_doaj_org_article_1ea95bc35af24dfcaea27cd8e85ee48c</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A808336615</galeid><doaj_id>oai_doaj_org_article_1ea95bc35af24dfcaea27cd8e85ee48c</doaj_id><sourcerecordid>A808336615</sourcerecordid><originalsourceid>FETCH-LOGICAL-c389t-ce62b36cd746c34ec01e472c9950f9cfac65a172a55814d267bb117ab225a9d43</originalsourceid><addsrcrecordid>eNptUstuFDEQHCEQCQs_wAFZ4pLLhPFjbM8xCgQiReICZ6vHbidePOPFng3av8e7G8JDyAdb7arqarua5jXtzinV8l2hTCvVdky0Xc9Z16onzSkViraMU_30j_NJ86KUdddRpZl83pzwgQnRC37axPd4jzFtJpwXArMj9xCDgyWkmSRPfIYQlx3JoXwjm4wu2MPVlBxG4lMmGB3muCObyqkahfwIyx2xKacZ8o7cIeSFuFAQCr5snnmIBV897Kvm69WHL5ef2pvPH68vL25ay_WwtBYlG7m0TglpuUDbURSK2WHoOz9YD1b2QBWDvtdUOCbVOFKqYGSsh8EJvmquj7ouwdpscpiqFZMgmEMh5VtTXQUb0VCEoR8t78Ez4bwFBKas06h7RKFt1To7am1y-r7FspgpFIsxwoxpWwyn9VUHKeoPrJq3_0DXaZvnOukexYTSA5e_UbdQ-4fZpyWD3YuaC91pzqWke63z_6DqcjgFm2b0odb_IrAjweZUSkb_ODftzD4u5hgXU42YQ1yMqqQ3D46344TukfIrH_wn0ue6uQ</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3102478936</pqid></control><display><type>article</type><title>Development and validation of frailty risk prediction model for elderly patients with coronary heart disease</title><source>PubMed</source><source>ProQuest - Publicly Available Content Database</source><creator>Liu, Siqin ; Yuan, Xiaoli ; Liang, Heting ; Jiang, Zhixia ; Yang, Xiaoling ; Gao, Huiming</creator><creatorcontrib>Liu, Siqin ; Yuan, Xiaoli ; Liang, Heting ; Jiang, Zhixia ; Yang, Xiaoling ; Gao, Huiming</creatorcontrib><description>To analyze the influential factors of frailty in elderly patients with coronary heart disease (CHD), develop a nomogram-based risk prediction model for this population, and validate its predictive performance.
A total of 592 elderly patients with CHD were conveniently selected and enrolled from 3 tertiary hospitals, 5 secondary hospitals, and 3 community health service centers in China between October 2022 and January 2023. Data collection involved the use of the general information questionnaire, the Frail scale, and the instrumental ability of daily living assessment scale. And the patients were categorized into two groups based on frailty, and χ
test as well as logistic regression analysis were used to identify and determine the influencing factors of frailty. The nomograph prediction model for elderly patients with CHD was developed using R software (version 4.2.2). The Hosmer-Lemeshow test and the area under the receiver operating characteristic (ROC) curve were employed to assess the predictive performance of the model. Additionally, the Bootstrap resampling method was utilized to validate the model and generate the calibration curve of the prediction model.
The prevalence of frailty in elderly patients with CHD was 30.07%. The multiple factor analysis revealed that poor health status (OR = 28.169)/general health status (OR = 18.120), age (OR = 1.046), social activities (OR = 0.673), impaired instrumental ability of daily living (OR = 2.384) were independent risk factors for frailty (all P < 0.05). The area under the ROC curve of the nomograph prediction model was 0.847 (95% CI: 0.809 ~ 0.878, P < 0.001), with a sensitivity of 0.801, and specificity of 0.793; the Hosmer- Lemeshow χ
value was 12.646 (P = 0.125). The model validation results indicated that the C value of 0.839(95% CI: 0.802 ~ 0.879) and Brier score of 0.139, demonstrating good consistency between predicted and actual values.
The prevalence of frailty is high among elderly patients with CHD, and it is influenced by various factors such as health status, age, lack of social participation, and impaired ability of daily life. These factors have certain predictive value for identifying frailty early and intervention in elderly patients with CHD.</description><identifier>ISSN: 1471-2318</identifier><identifier>EISSN: 1471-2318</identifier><identifier>DOI: 10.1186/s12877-024-05320-7</identifier><identifier>PMID: 39244543</identifier><language>eng</language><publisher>England: BioMed Central Ltd</publisher><subject>Activities of Daily Living ; Age ; Aged ; Aged, 80 and over ; Cardiovascular disease ; China - epidemiology ; Complications and side effects ; Coronary artery disease ; Coronary Disease - diagnosis ; Coronary Disease - epidemiology ; Coronary heart disease ; Demographic aspects ; Elderly ; Factor analysis ; Female ; Frail Elderly ; Frailty ; Frailty - diagnosis ; Frailty - epidemiology ; Geriatric Assessment - methods ; Heart ; Heart diseases ; Hospitals ; Humans ; Male ; Marital status ; Middle Aged ; Mortality ; Nomograms ; Older people ; Patients ; Prediction models ; Predictive model ; Questionnaires ; Regression analysis ; Risk Assessment - methods ; Risk Factors ; Software ; Variables</subject><ispartof>BMC geriatrics, 2024-09, Vol.24 (1), p.742-11, Article 742</ispartof><rights>2024. The Author(s).</rights><rights>COPYRIGHT 2024 BioMed Central Ltd.</rights><rights>2024. This work is licensed under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c389t-ce62b36cd746c34ec01e472c9950f9cfac65a172a55814d267bb117ab225a9d43</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/3102478936?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,25753,27924,27925,37012,37013,44590</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/39244543$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Liu, Siqin</creatorcontrib><creatorcontrib>Yuan, Xiaoli</creatorcontrib><creatorcontrib>Liang, Heting</creatorcontrib><creatorcontrib>Jiang, Zhixia</creatorcontrib><creatorcontrib>Yang, Xiaoling</creatorcontrib><creatorcontrib>Gao, Huiming</creatorcontrib><title>Development and validation of frailty risk prediction model for elderly patients with coronary heart disease</title><title>BMC geriatrics</title><addtitle>BMC Geriatr</addtitle><description>To analyze the influential factors of frailty in elderly patients with coronary heart disease (CHD), develop a nomogram-based risk prediction model for this population, and validate its predictive performance.
A total of 592 elderly patients with CHD were conveniently selected and enrolled from 3 tertiary hospitals, 5 secondary hospitals, and 3 community health service centers in China between October 2022 and January 2023. Data collection involved the use of the general information questionnaire, the Frail scale, and the instrumental ability of daily living assessment scale. And the patients were categorized into two groups based on frailty, and χ
test as well as logistic regression analysis were used to identify and determine the influencing factors of frailty. The nomograph prediction model for elderly patients with CHD was developed using R software (version 4.2.2). The Hosmer-Lemeshow test and the area under the receiver operating characteristic (ROC) curve were employed to assess the predictive performance of the model. Additionally, the Bootstrap resampling method was utilized to validate the model and generate the calibration curve of the prediction model.
The prevalence of frailty in elderly patients with CHD was 30.07%. The multiple factor analysis revealed that poor health status (OR = 28.169)/general health status (OR = 18.120), age (OR = 1.046), social activities (OR = 0.673), impaired instrumental ability of daily living (OR = 2.384) were independent risk factors for frailty (all P < 0.05). The area under the ROC curve of the nomograph prediction model was 0.847 (95% CI: 0.809 ~ 0.878, P < 0.001), with a sensitivity of 0.801, and specificity of 0.793; the Hosmer- Lemeshow χ
value was 12.646 (P = 0.125). The model validation results indicated that the C value of 0.839(95% CI: 0.802 ~ 0.879) and Brier score of 0.139, demonstrating good consistency between predicted and actual values.
The prevalence of frailty is high among elderly patients with CHD, and it is influenced by various factors such as health status, age, lack of social participation, and impaired ability of daily life. These factors have certain predictive value for identifying frailty early and intervention in elderly patients with CHD.</description><subject>Activities of Daily Living</subject><subject>Age</subject><subject>Aged</subject><subject>Aged, 80 and over</subject><subject>Cardiovascular disease</subject><subject>China - epidemiology</subject><subject>Complications and side effects</subject><subject>Coronary artery disease</subject><subject>Coronary Disease - diagnosis</subject><subject>Coronary Disease - epidemiology</subject><subject>Coronary heart disease</subject><subject>Demographic aspects</subject><subject>Elderly</subject><subject>Factor analysis</subject><subject>Female</subject><subject>Frail Elderly</subject><subject>Frailty</subject><subject>Frailty - diagnosis</subject><subject>Frailty - epidemiology</subject><subject>Geriatric Assessment - methods</subject><subject>Heart</subject><subject>Heart diseases</subject><subject>Hospitals</subject><subject>Humans</subject><subject>Male</subject><subject>Marital status</subject><subject>Middle Aged</subject><subject>Mortality</subject><subject>Nomograms</subject><subject>Older people</subject><subject>Patients</subject><subject>Prediction models</subject><subject>Predictive model</subject><subject>Questionnaires</subject><subject>Regression analysis</subject><subject>Risk Assessment - methods</subject><subject>Risk Factors</subject><subject>Software</subject><subject>Variables</subject><issn>1471-2318</issn><issn>1471-2318</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNptUstuFDEQHCEQCQs_wAFZ4pLLhPFjbM8xCgQiReICZ6vHbidePOPFng3av8e7G8JDyAdb7arqarua5jXtzinV8l2hTCvVdky0Xc9Z16onzSkViraMU_30j_NJ86KUdddRpZl83pzwgQnRC37axPd4jzFtJpwXArMj9xCDgyWkmSRPfIYQlx3JoXwjm4wu2MPVlBxG4lMmGB3muCObyqkahfwIyx2xKacZ8o7cIeSFuFAQCr5snnmIBV897Kvm69WHL5ef2pvPH68vL25ay_WwtBYlG7m0TglpuUDbURSK2WHoOz9YD1b2QBWDvtdUOCbVOFKqYGSsh8EJvmquj7ouwdpscpiqFZMgmEMh5VtTXQUb0VCEoR8t78Ez4bwFBKas06h7RKFt1To7am1y-r7FspgpFIsxwoxpWwyn9VUHKeoPrJq3_0DXaZvnOukexYTSA5e_UbdQ-4fZpyWD3YuaC91pzqWke63z_6DqcjgFm2b0odb_IrAjweZUSkb_ODftzD4u5hgXU42YQ1yMqqQ3D46344TukfIrH_wn0ue6uQ</recordid><startdate>20240907</startdate><enddate>20240907</enddate><creator>Liu, Siqin</creator><creator>Yuan, Xiaoli</creator><creator>Liang, Heting</creator><creator>Jiang, Zhixia</creator><creator>Yang, Xiaoling</creator><creator>Gao, Huiming</creator><general>BioMed Central Ltd</general><general>BioMed Central</general><general>BMC</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QP</scope><scope>7TK</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</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>M1P</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>DOA</scope></search><sort><creationdate>20240907</creationdate><title>Development and validation of frailty risk prediction model for elderly patients with coronary heart disease</title><author>Liu, Siqin ; 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A total of 592 elderly patients with CHD were conveniently selected and enrolled from 3 tertiary hospitals, 5 secondary hospitals, and 3 community health service centers in China between October 2022 and January 2023. Data collection involved the use of the general information questionnaire, the Frail scale, and the instrumental ability of daily living assessment scale. And the patients were categorized into two groups based on frailty, and χ
test as well as logistic regression analysis were used to identify and determine the influencing factors of frailty. The nomograph prediction model for elderly patients with CHD was developed using R software (version 4.2.2). The Hosmer-Lemeshow test and the area under the receiver operating characteristic (ROC) curve were employed to assess the predictive performance of the model. Additionally, the Bootstrap resampling method was utilized to validate the model and generate the calibration curve of the prediction model.
The prevalence of frailty in elderly patients with CHD was 30.07%. The multiple factor analysis revealed that poor health status (OR = 28.169)/general health status (OR = 18.120), age (OR = 1.046), social activities (OR = 0.673), impaired instrumental ability of daily living (OR = 2.384) were independent risk factors for frailty (all P < 0.05). The area under the ROC curve of the nomograph prediction model was 0.847 (95% CI: 0.809 ~ 0.878, P < 0.001), with a sensitivity of 0.801, and specificity of 0.793; the Hosmer- Lemeshow χ
value was 12.646 (P = 0.125). The model validation results indicated that the C value of 0.839(95% CI: 0.802 ~ 0.879) and Brier score of 0.139, demonstrating good consistency between predicted and actual values.
The prevalence of frailty is high among elderly patients with CHD, and it is influenced by various factors such as health status, age, lack of social participation, and impaired ability of daily life. These factors have certain predictive value for identifying frailty early and intervention in elderly patients with CHD.</abstract><cop>England</cop><pub>BioMed Central Ltd</pub><pmid>39244543</pmid><doi>10.1186/s12877-024-05320-7</doi><tpages>11</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Activities of Daily Living Age Aged Aged, 80 and over Cardiovascular disease China - epidemiology Complications and side effects Coronary artery disease Coronary Disease - diagnosis Coronary Disease - epidemiology Coronary heart disease Demographic aspects Elderly Factor analysis Female Frail Elderly Frailty Frailty - diagnosis Frailty - epidemiology Geriatric Assessment - methods Heart Heart diseases Hospitals Humans Male Marital status Middle Aged Mortality Nomograms Older people Patients Prediction models Predictive model Questionnaires Regression analysis Risk Assessment - methods Risk Factors Software Variables |
| title | Development and validation of frailty risk prediction model for elderly patients with coronary heart disease |
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