Development and Validation of an Electronic Frailty Index Using Routine Electronic Health Records: An Observational Study From a General Hospital in China
Background: This study aimed to develop and validate an electronic frailty index (eFI) based on routine electronic health records (EHR) for older adult inpatients and to analyze the correlations between frailty and hospitalized events and costs. Methods: We created an eFI from routine EHR and valida...
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Published in | Frontiers in Medicine Vol. 8; p. 731445 |
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Main Authors | , , , , , |
Format | Journal Article |
Language | English |
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28.09.2021
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Abstract | Background:
This study aimed to develop and validate an electronic frailty index (eFI) based on routine electronic health records (EHR) for older adult inpatients and to analyze the correlations between frailty and hospitalized events and costs.
Methods:
We created an eFI from routine EHR and validated the effectiveness by the consistency of the comprehensive geriatric assessment-frailty index (CGA-FI) with an independent prospective cohort. Then, we analyzed the correlations between frailty and hospitalized events and costs by regressions.
Results:
During the study period, 49,226 inpatients were included in the analysis, 42,821 (87.0%) of which had enough data to calculate an eFI. A strong correlation between the CGA-FI and eFI was shown in the validation cohort of 685 subjects (Pearson's
r
= 0.716,
P
< 0.001). The sensitivity and specificity for an eFI≥0.15, the upper tertile, to identify frailty, defined as a CGA-FI≥0.25, were 64.8 and 88.7%, respectively. After adjusting for age, sex, and operation, an eFI≥0.15 showed an independent association with long hospital stay (odds ratio [OR] = 2.889,
P
< 0.001) and death in hospital (OR = 19.97,
P
< 0.001). Moreover, eFI values (per 0.1) were positively associated with total costs (β = 0.453,
P
< 0.001), examination costs (β = 0.269,
P
< 0.001), treatment costs (β = 0.414,
P
< 0.001), nursing costs (β = 0.381,
P
< 0.001), pharmacy costs (β = 0.524,
P
< 0.001), and material costs (β = 0.578,
P
< 0.001) after adjusting aforementioned factors.
Conclusions:
We successfully developed an effective eFI from routine EHR from a general hospital in China. Frailty is an independent risk factor for long hospital stay and death in hospital. As the degree of frailty increases, the hospitalized costs increase accordingly. |
---|---|
AbstractList | Background: This study aimed to develop and validate an electronic frailty index (eFI) based on routine electronic health records (EHR) for older adult inpatients and to analyze the correlations between frailty and hospitalized events and costs.Methods: We created an eFI from routine EHR and validated the effectiveness by the consistency of the comprehensive geriatric assessment-frailty index (CGA-FI) with an independent prospective cohort. Then, we analyzed the correlations between frailty and hospitalized events and costs by regressions.Results: During the study period, 49,226 inpatients were included in the analysis, 42,821 (87.0%) of which had enough data to calculate an eFI. A strong correlation between the CGA-FI and eFI was shown in the validation cohort of 685 subjects (Pearson's r = 0.716, P < 0.001). The sensitivity and specificity for an eFI≥0.15, the upper tertile, to identify frailty, defined as a CGA-FI≥0.25, were 64.8 and 88.7%, respectively. After adjusting for age, sex, and operation, an eFI≥0.15 showed an independent association with long hospital stay (odds ratio [OR] = 2.889, P < 0.001) and death in hospital (OR = 19.97, P < 0.001). Moreover, eFI values (per 0.1) were positively associated with total costs (β = 0.453, P < 0.001), examination costs (β = 0.269, P < 0.001), treatment costs (β = 0.414, P < 0.001), nursing costs (β = 0.381, P < 0.001), pharmacy costs (β = 0.524, P < 0.001), and material costs (β = 0.578, P < 0.001) after adjusting aforementioned factors.Conclusions: We successfully developed an effective eFI from routine EHR from a general hospital in China. Frailty is an independent risk factor for long hospital stay and death in hospital. As the degree of frailty increases, the hospitalized costs increase accordingly. Background: This study aimed to develop and validate an electronic frailty index (eFI) based on routine electronic health records (EHR) for older adult inpatients and to analyze the correlations between frailty and hospitalized events and costs. Methods: We created an eFI from routine EHR and validated the effectiveness by the consistency of the comprehensive geriatric assessment-frailty index (CGA-FI) with an independent prospective cohort. Then, we analyzed the correlations between frailty and hospitalized events and costs by regressions. Results: During the study period, 49,226 inpatients were included in the analysis, 42,821 (87.0%) of which had enough data to calculate an eFI. A strong correlation between the CGA-FI and eFI was shown in the validation cohort of 685 subjects (Pearson's r = 0.716, P < 0.001). The sensitivity and specificity for an eFI≥0.15, the upper tertile, to identify frailty, defined as a CGA-FI≥0.25, were 64.8 and 88.7%, respectively. After adjusting for age, sex, and operation, an eFI≥0.15 showed an independent association with long hospital stay (odds ratio [OR] = 2.889, P < 0.001) and death in hospital (OR = 19.97, P < 0.001). Moreover, eFI values (per 0.1) were positively associated with total costs (β = 0.453, P < 0.001), examination costs (β = 0.269, P < 0.001), treatment costs (β = 0.414, P < 0.001), nursing costs (β = 0.381, P < 0.001), pharmacy costs (β = 0.524, P < 0.001), and material costs (β = 0.578, P < 0.001) after adjusting aforementioned factors. Conclusions: We successfully developed an effective eFI from routine EHR from a general hospital in China. Frailty is an independent risk factor for long hospital stay and death in hospital. As the degree of frailty increases, the hospitalized costs increase accordingly.Background: This study aimed to develop and validate an electronic frailty index (eFI) based on routine electronic health records (EHR) for older adult inpatients and to analyze the correlations between frailty and hospitalized events and costs. Methods: We created an eFI from routine EHR and validated the effectiveness by the consistency of the comprehensive geriatric assessment-frailty index (CGA-FI) with an independent prospective cohort. Then, we analyzed the correlations between frailty and hospitalized events and costs by regressions. Results: During the study period, 49,226 inpatients were included in the analysis, 42,821 (87.0%) of which had enough data to calculate an eFI. A strong correlation between the CGA-FI and eFI was shown in the validation cohort of 685 subjects (Pearson's r = 0.716, P < 0.001). The sensitivity and specificity for an eFI≥0.15, the upper tertile, to identify frailty, defined as a CGA-FI≥0.25, were 64.8 and 88.7%, respectively. After adjusting for age, sex, and operation, an eFI≥0.15 showed an independent association with long hospital stay (odds ratio [OR] = 2.889, P < 0.001) and death in hospital (OR = 19.97, P < 0.001). Moreover, eFI values (per 0.1) were positively associated with total costs (β = 0.453, P < 0.001), examination costs (β = 0.269, P < 0.001), treatment costs (β = 0.414, P < 0.001), nursing costs (β = 0.381, P < 0.001), pharmacy costs (β = 0.524, P < 0.001), and material costs (β = 0.578, P < 0.001) after adjusting aforementioned factors. Conclusions: We successfully developed an effective eFI from routine EHR from a general hospital in China. Frailty is an independent risk factor for long hospital stay and death in hospital. As the degree of frailty increases, the hospitalized costs increase accordingly. Background: This study aimed to develop and validate an electronic frailty index (eFI) based on routine electronic health records (EHR) for older adult inpatients and to analyze the correlations between frailty and hospitalized events and costs. Methods: We created an eFI from routine EHR and validated the effectiveness by the consistency of the comprehensive geriatric assessment-frailty index (CGA-FI) with an independent prospective cohort. Then, we analyzed the correlations between frailty and hospitalized events and costs by regressions. Results: During the study period, 49,226 inpatients were included in the analysis, 42,821 (87.0%) of which had enough data to calculate an eFI. A strong correlation between the CGA-FI and eFI was shown in the validation cohort of 685 subjects (Pearson's r = 0.716, P < 0.001). The sensitivity and specificity for an eFI≥0.15, the upper tertile, to identify frailty, defined as a CGA-FI≥0.25, were 64.8 and 88.7%, respectively. After adjusting for age, sex, and operation, an eFI≥0.15 showed an independent association with long hospital stay (odds ratio [OR] = 2.889, P < 0.001) and death in hospital (OR = 19.97, P < 0.001). Moreover, eFI values (per 0.1) were positively associated with total costs (β = 0.453, P < 0.001), examination costs (β = 0.269, P < 0.001), treatment costs (β = 0.414, P < 0.001), nursing costs (β = 0.381, P < 0.001), pharmacy costs (β = 0.524, P < 0.001), and material costs (β = 0.578, P < 0.001) after adjusting aforementioned factors. Conclusions: We successfully developed an effective eFI from routine EHR from a general hospital in China. Frailty is an independent risk factor for long hospital stay and death in hospital. As the degree of frailty increases, the hospitalized costs increase accordingly. |
Author | Hua Wang Jing Shi Yi-Bo Xie Jie-Fu Yang Ming-Hui Du Yao-Dan Liang |
AuthorAffiliation | 4 Beijing Institute of Geriatrics, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences , Beijing , China 3 Information Center, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences , Beijing , China 2 Department of Pulmonary and Critical Care Medicine, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences , Beijing , China 1 Department of Cardiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences , Beijing , China |
AuthorAffiliation_xml | – name: 4 Beijing Institute of Geriatrics, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences , Beijing , China – name: 2 Department of Pulmonary and Critical Care Medicine, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences , Beijing , China – name: 3 Information Center, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences , Beijing , China – name: 1 Department of Cardiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences , Beijing , China |
Author_xml | – sequence: 1 givenname: Yao-Dan surname: Liang fullname: Liang, Yao-Dan – sequence: 2 givenname: Yi-Bo surname: Xie fullname: Xie, Yi-Bo – sequence: 3 givenname: Ming-Hui surname: Du fullname: Du, Ming-Hui – sequence: 4 givenname: Jing surname: Shi fullname: Shi, Jing – sequence: 5 givenname: Jie-Fu surname: Yang fullname: Yang, Jie-Fu – sequence: 6 givenname: Hua surname: Wang fullname: Wang, Hua |
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CitedBy_id | crossref_primary_10_1016_j_archger_2021_104612 crossref_primary_10_2196_54839 crossref_primary_10_3389_fpubh_2022_901068 crossref_primary_10_7586_jkbns_23_0015 crossref_primary_10_1111_jgs_19438 crossref_primary_10_1007_s11357_023_01055_2 crossref_primary_10_1093_ageing_afae025 crossref_primary_10_18632_aging_206141 |
Cites_doi | 10.1016/j.arr.2020.101076 10.1111/j.1532-5415.2004.52521.x 10.2147/CIA.S225149 10.1186/s12877-021-02377-6 10.1093/ageing/afx157 10.1038/s41598-021-87444-z 10.1016/S0140-6736(12)62167-9 10.1093/ageing/afw039 10.1016/j.amjcard.2019.10.021 10.1093/jamia/ocw042 10.1093/gerona/glz017 10.1016/S0140-6736(15)00516-4 10.1186/s12916-015-0400-x 10.1016/S0140-6736(19)31786-6 10.1186/s12916-018-1223-3 10.1007/s12603-016-0727-9 10.1016/j.amjsurg.2011.06.017 10.1016/j.jhealeco.2004.09.011 10.1002/jcsm.12577 10.1186/s12877-020-1496-1 10.1016/j.jpsychores.2015.09.015 10.1037/t02366-000 10.1186/s12916-014-0171-9 10.1111/jgs.15381 10.1093/ageing/afy162 10.1016/j.jamda.2020.03.008 10.1002/jcsm.12509 10.1016/S0140-6736(18)30668-8 10.1186/s12877-019-1119-x 10.2217/cer-2018-0029 10.1007/s12603-021-1612-8 10.1093/gerona/56.3.M146 10.1016/j.cjca.2017.03.019 10.1186/1471-2318-8-24 10.1016/j.jclinane.2020.109820 10.1007/s10198-016-0772-7 |
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Copyright | Copyright © 2021 Liang, Xie, Du, Shi, Yang and Wang. Copyright © 2021 Liang, Xie, Du, Shi, Yang and Wang. 2021 Liang, Xie, Du, Shi, Yang and Wang |
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Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Edited by: Ming Yang, Sichuan University, China These authors have contributed equally to this work and share first authorship Reviewed by: Lina Ma, Capital Medical University, China; Siti Setiati, University of Indonesia, Indonesia; Mario Ulises Pérez-Zepeda, Dalhousie University, Canada This article was submitted to Geriatric Medicine, a section of the journal Frontiers in Medicine |
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Snippet | Background:
This study aimed to develop and validate an electronic frailty index (eFI) based on routine electronic health records (EHR) for older adult... Background: This study aimed to develop and validate an electronic frailty index (eFI) based on routine electronic health records (EHR) for older adult... |
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StartPage | 731445 |
SubjectTerms | electronic health records-EHR frailty hospitalized costs long hospital stay Medicine Medicine (General) mortality R5-920 |
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Title | Development and Validation of an Electronic Frailty Index Using Routine Electronic Health Records: An Observational Study From a General Hospital in China |
URI | https://cir.nii.ac.jp/crid/1873116917477818624 https://www.proquest.com/docview/2582805212 https://pubmed.ncbi.nlm.nih.gov/PMC8505669 https://doaj.org/article/969d9928260d45ddb85bf9870212224c |
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