Two Years with COVID-19: The Electronic Frailty Index Identifies High-Risk Patients in the Stockholm GeroCovid Study

Jonathan K L Mak; Maria Eriksdotter; Martin Annetorp; Ralf Kuja-Halkola; Laura Kananen; Anne-Marie Boström; Miia Kivipelto; Carina Metzner; Viktoria Bäck Jerlardtz; Malin Engström; Peter Johnson; Lars Göran Lundberg; Elisabet Åkesson; Carina Sühl Öberg; Maria Olsson; Tommy Cederholm; Sara Hägg; Dorota Religa; Juulia Jylhävä

doi:10.1159/000527206

Two Years with COVID-19: The Electronic Frailty Index Identifies High-Risk Patients in the Stockholm GeroCovid Study

Jonathan K L Mak, Maria Eriksdotter, Martin Annetorp, Ralf Kuja-Halkola, Laura Kananen, Anne-Marie Boström, Miia Kivipelto, Carina Metzner, Viktoria Bäck Jerlardtz, Malin Engström, Peter Johnson, Lars Göran Lundberg, Elisabet Åkesson, Carina Sühl Öberg, Maria Olsson, Tommy Cederholm, Sara Hägg, Dorota Religa, Juulia Jylhävä

Health Sciences

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Abstract

INTRODUCTION: Frailty, a measure of biological aging, has been linked to worse COVID-19 outcomes. However, as the mortality differs across the COVID-19 waves, it is less clear whether a medical record-based electronic frailty index (eFI) that we have previously developed for older adults could be used for risk stratification in hospitalized COVID-19 patients.

OBJECTIVES: The aim of the study was to examine the association of frailty with mortality, readmission, and length of stay in older COVID-19 patients and to compare the predictive accuracy of the eFI to other frailty and comorbidity measures.

METHODS: This was a retrospective cohort study using electronic health records (EHRs) from nine geriatric clinics in Stockholm, Sweden, comprising 3,980 COVID-19 patients (mean age 81.6 years) admitted between March 2020 and March 2022. Frailty was assessed using a 48-item eFI developed for Swedish geriatric patients, the Clinical Frailty Scale, and the Hospital Frailty Risk Score. Comorbidity was measured using the Charlson Comorbidity Index. We analyzed in-hospital mortality and 30-day readmission using logistic regression, 30-day and 6-month mortality using Cox regression, and the length of stay using linear regression. Predictive accuracy of the logistic regression and Cox models was evaluated by area under the receiver operating characteristic curve (AUC) and Harrell's C-statistic, respectively.

RESULTS: Across the study period, the in-hospital mortality rate decreased from 13.9% in the first wave to 3.6% in the latest (Omicron) wave. Controlling for age and sex, a 10% increment in the eFI was significantly associated with higher risks of in-hospital mortality (odds ratio = 2.95; 95% confidence interval = 2.42-3.62), 30-day mortality (hazard ratio [HR] = 2.39; 2.08-2.74), 6-month mortality (HR = 2.29; 2.04-2.56), and a longer length of stay (β-coefficient = 2.00; 1.65-2.34) but not with 30-day readmission. The association between the eFI and in-hospital mortality remained robust across the waves, even after the vaccination rollout. Among all measures, the eFI had the best discrimination for in-hospital (AUC = 0.780), 30-day (Harrell's C = 0.733), and 6-month mortality (Harrell's C = 0.719).

CONCLUSION: An eFI based on routinely collected EHRs can be applied in identifying high-risk older COVID-19 patients during the continuing pandemic.

Original language	English
Pages (from-to)	396–405
Number of pages	10
Journal	GERONTOLOGY
Volume	69
Issue number	4
Early online date	30 Nov 2022
DOIs	https://doi.org/10.1159/000527206
Publication status	Published - 2023
Publication type	A1 Journal article-refereed

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https://urn.fi/URN:NBN:fi:tuni-202301271792Licence: CC BY-NC

Supplementary Material for: Two Years with COVID-19: The Electronic Frailty Index Identifies High-Risk Patients in the Stockholm GeroCovid Study
Mak, J. K. L. (Contributor), Eriksdotter, M. (Contributor), Annetorp, M. (Contributor), Kuja-Halkola, R. (Contributor), Kananen, L. (Contributor), Boström, A.-M. (Contributor), Kivipelto, M. (Contributor), Metzner, C. (Contributor), Engström, M. (Contributor), Johnson, P. (Contributor), Lundberg, L. G. (Contributor), Åkesson, E. (Contributor), Olsson, M. (Contributor), Cederholm, T. (Contributor), Hägg, S. (Contributor), Religa, D. (Contributor) & Jylhävä, J. (Creator), figshare, 30 Nov 2022
DOI: 10.6084/m9.figshare.21647129.v1
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Mak, J. K. L., Eriksdotter, M., Annetorp, M., Kuja-Halkola, R., Kananen, L., Boström, A.-M., Kivipelto, M., Metzner, C., Bäck Jerlardtz, V., Engström, M., Johnson, P., Lundberg, L. G., Åkesson, E., Sühl Öberg, C., Olsson, M., Cederholm, T., Hägg, S., Religa, D., & Jylhävä, J. (2023). Two Years with COVID-19: The Electronic Frailty Index Identifies High-Risk Patients in the Stockholm GeroCovid Study. GERONTOLOGY, 69(4), 396–405. https://doi.org/10.1159/000527206

@article{d7af47fdcf944b67be1c1296d7e06fb8,

title = "Two Years with COVID-19: The Electronic Frailty Index Identifies High-Risk Patients in the Stockholm GeroCovid Study",

abstract = "INTRODUCTION: Frailty, a measure of biological aging, has been linked to worse COVID-19 outcomes. However, as the mortality differs across the COVID-19 waves, it is less clear whether a medical record-based electronic frailty index (eFI) that we have previously developed for older adults could be used for risk stratification in hospitalized COVID-19 patients.OBJECTIVES: The aim of the study was to examine the association of frailty with mortality, readmission, and length of stay in older COVID-19 patients and to compare the predictive accuracy of the eFI to other frailty and comorbidity measures.METHODS: This was a retrospective cohort study using electronic health records (EHRs) from nine geriatric clinics in Stockholm, Sweden, comprising 3,980 COVID-19 patients (mean age 81.6 years) admitted between March 2020 and March 2022. Frailty was assessed using a 48-item eFI developed for Swedish geriatric patients, the Clinical Frailty Scale, and the Hospital Frailty Risk Score. Comorbidity was measured using the Charlson Comorbidity Index. We analyzed in-hospital mortality and 30-day readmission using logistic regression, 30-day and 6-month mortality using Cox regression, and the length of stay using linear regression. Predictive accuracy of the logistic regression and Cox models was evaluated by area under the receiver operating characteristic curve (AUC) and Harrell's C-statistic, respectively.RESULTS: Across the study period, the in-hospital mortality rate decreased from 13.9% in the first wave to 3.6% in the latest (Omicron) wave. Controlling for age and sex, a 10% increment in the eFI was significantly associated with higher risks of in-hospital mortality (odds ratio = 2.95; 95% confidence interval = 2.42-3.62), 30-day mortality (hazard ratio [HR] = 2.39; 2.08-2.74), 6-month mortality (HR = 2.29; 2.04-2.56), and a longer length of stay (β-coefficient = 2.00; 1.65-2.34) but not with 30-day readmission. The association between the eFI and in-hospital mortality remained robust across the waves, even after the vaccination rollout. Among all measures, the eFI had the best discrimination for in-hospital (AUC = 0.780), 30-day (Harrell's C = 0.733), and 6-month mortality (Harrell's C = 0.719).CONCLUSION: An eFI based on routinely collected EHRs can be applied in identifying high-risk older COVID-19 patients during the continuing pandemic.",

author = "Mak, {Jonathan K L} and Maria Eriksdotter and Martin Annetorp and Ralf Kuja-Halkola and Laura Kananen and Anne-Marie Bostr{\"o}m and Miia Kivipelto and Carina Metzner and {B{\"a}ck Jerlardtz}, Viktoria and Malin Engstr{\"o}m and Peter Johnson and Lundberg, {Lars G{\"o}ran} and Elisabet {\AA}kesson and {S{\"u}hl {\"O}berg}, Carina and Maria Olsson and Tommy Cederholm and Sara H{\"a}gg and Dorota Religa and Juulia Jylh{\"a}v{\"a}",

note = "{\textcopyright} 2022 The Author(s). Published by S. Karger AG, Basel.",

year = "2023",

doi = "10.1159/000527206",

language = "English",

volume = "69",

pages = "396–405",

journal = "GERONTOLOGY",

issn = "0304-324X",

publisher = "S. Karger AG",

number = "4",

}

Mak, JKL, Eriksdotter, M, Annetorp, M, Kuja-Halkola, R, Kananen, L, Boström, A-M, Kivipelto, M, Metzner, C, Bäck Jerlardtz, V, Engström, M, Johnson, P, Lundberg, LG, Åkesson, E, Sühl Öberg, C, Olsson, M, Cederholm, T, Hägg, S, Religa, D & Jylhävä, J 2023, 'Two Years with COVID-19: The Electronic Frailty Index Identifies High-Risk Patients in the Stockholm GeroCovid Study', GERONTOLOGY, vol. 69, no. 4, pp. 396–405. https://doi.org/10.1159/000527206

TY - JOUR

T1 - Two Years with COVID-19

T2 - The Electronic Frailty Index Identifies High-Risk Patients in the Stockholm GeroCovid Study

AU - Mak, Jonathan K L

AU - Eriksdotter, Maria

AU - Annetorp, Martin

AU - Kuja-Halkola, Ralf

AU - Kananen, Laura

AU - Boström, Anne-Marie

AU - Kivipelto, Miia

AU - Metzner, Carina

AU - Bäck Jerlardtz, Viktoria

AU - Engström, Malin

AU - Johnson, Peter

AU - Lundberg, Lars Göran

AU - Åkesson, Elisabet

AU - Sühl Öberg, Carina

AU - Olsson, Maria

AU - Cederholm, Tommy

AU - Hägg, Sara

AU - Religa, Dorota

AU - Jylhävä, Juulia

PY - 2023

Y1 - 2023

N2 - INTRODUCTION: Frailty, a measure of biological aging, has been linked to worse COVID-19 outcomes. However, as the mortality differs across the COVID-19 waves, it is less clear whether a medical record-based electronic frailty index (eFI) that we have previously developed for older adults could be used for risk stratification in hospitalized COVID-19 patients.OBJECTIVES: The aim of the study was to examine the association of frailty with mortality, readmission, and length of stay in older COVID-19 patients and to compare the predictive accuracy of the eFI to other frailty and comorbidity measures.METHODS: This was a retrospective cohort study using electronic health records (EHRs) from nine geriatric clinics in Stockholm, Sweden, comprising 3,980 COVID-19 patients (mean age 81.6 years) admitted between March 2020 and March 2022. Frailty was assessed using a 48-item eFI developed for Swedish geriatric patients, the Clinical Frailty Scale, and the Hospital Frailty Risk Score. Comorbidity was measured using the Charlson Comorbidity Index. We analyzed in-hospital mortality and 30-day readmission using logistic regression, 30-day and 6-month mortality using Cox regression, and the length of stay using linear regression. Predictive accuracy of the logistic regression and Cox models was evaluated by area under the receiver operating characteristic curve (AUC) and Harrell's C-statistic, respectively.RESULTS: Across the study period, the in-hospital mortality rate decreased from 13.9% in the first wave to 3.6% in the latest (Omicron) wave. Controlling for age and sex, a 10% increment in the eFI was significantly associated with higher risks of in-hospital mortality (odds ratio = 2.95; 95% confidence interval = 2.42-3.62), 30-day mortality (hazard ratio [HR] = 2.39; 2.08-2.74), 6-month mortality (HR = 2.29; 2.04-2.56), and a longer length of stay (β-coefficient = 2.00; 1.65-2.34) but not with 30-day readmission. The association between the eFI and in-hospital mortality remained robust across the waves, even after the vaccination rollout. Among all measures, the eFI had the best discrimination for in-hospital (AUC = 0.780), 30-day (Harrell's C = 0.733), and 6-month mortality (Harrell's C = 0.719).CONCLUSION: An eFI based on routinely collected EHRs can be applied in identifying high-risk older COVID-19 patients during the continuing pandemic.

AB - INTRODUCTION: Frailty, a measure of biological aging, has been linked to worse COVID-19 outcomes. However, as the mortality differs across the COVID-19 waves, it is less clear whether a medical record-based electronic frailty index (eFI) that we have previously developed for older adults could be used for risk stratification in hospitalized COVID-19 patients.OBJECTIVES: The aim of the study was to examine the association of frailty with mortality, readmission, and length of stay in older COVID-19 patients and to compare the predictive accuracy of the eFI to other frailty and comorbidity measures.METHODS: This was a retrospective cohort study using electronic health records (EHRs) from nine geriatric clinics in Stockholm, Sweden, comprising 3,980 COVID-19 patients (mean age 81.6 years) admitted between March 2020 and March 2022. Frailty was assessed using a 48-item eFI developed for Swedish geriatric patients, the Clinical Frailty Scale, and the Hospital Frailty Risk Score. Comorbidity was measured using the Charlson Comorbidity Index. We analyzed in-hospital mortality and 30-day readmission using logistic regression, 30-day and 6-month mortality using Cox regression, and the length of stay using linear regression. Predictive accuracy of the logistic regression and Cox models was evaluated by area under the receiver operating characteristic curve (AUC) and Harrell's C-statistic, respectively.RESULTS: Across the study period, the in-hospital mortality rate decreased from 13.9% in the first wave to 3.6% in the latest (Omicron) wave. Controlling for age and sex, a 10% increment in the eFI was significantly associated with higher risks of in-hospital mortality (odds ratio = 2.95; 95% confidence interval = 2.42-3.62), 30-day mortality (hazard ratio [HR] = 2.39; 2.08-2.74), 6-month mortality (HR = 2.29; 2.04-2.56), and a longer length of stay (β-coefficient = 2.00; 1.65-2.34) but not with 30-day readmission. The association between the eFI and in-hospital mortality remained robust across the waves, even after the vaccination rollout. Among all measures, the eFI had the best discrimination for in-hospital (AUC = 0.780), 30-day (Harrell's C = 0.733), and 6-month mortality (Harrell's C = 0.719).CONCLUSION: An eFI based on routinely collected EHRs can be applied in identifying high-risk older COVID-19 patients during the continuing pandemic.

U2 - 10.1159/000527206

DO - 10.1159/000527206

M3 - Article

C2 - 36450240

SN - 0304-324X

VL - 69

SP - 396

EP - 405

JO - GERONTOLOGY

JF - GERONTOLOGY

IS - 4

ER -

Two Years with COVID-19: The Electronic Frailty Index Identifies High-Risk Patients in the Stockholm GeroCovid Study

Abstract

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Supplementary Material for: Two Years with COVID-19: The Electronic Frailty Index Identifies High-Risk Patients in the Stockholm GeroCovid Study

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