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Two Years with COVID-19: The Electronic Frailty Index Identifies High-Risk Patients in the Stockholm GeroCovid Study.
Mak, Jonathan K L; Eriksdotter, Maria; Annetorp, Martin; Kuja-Halkola, Ralf; Kananen, Laura; Boström, Anne-Marie; Kivipelto, Miia; Metzner, Carina; Bäck Jerlardtz, Viktoria; Engström, Malin; Johnson, Peter; Lundberg, Lars Göran; Åkesson, Elisabet; Sühl Öberg, Carina; Olsson, Maria; Cederholm, Tommy; Hägg, Sara; Religa, Dorota; Jylhävä, Juulia.
  • Mak JKL; Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden, jonathan.mak@ki.se.
  • Eriksdotter M; Division of Clinical Geriatrics, Department of Neurobiology, Care sciences and Society, Karolinska Institutet, Stockholm, Sweden.
  • Annetorp M; Theme Inflammation and Aging, Karolinska University Hospital, Huddinge, Sweden.
  • Kuja-Halkola R; Division of Clinical Geriatrics, Department of Neurobiology, Care sciences and Society, Karolinska Institutet, Stockholm, Sweden.
  • Kananen L; Theme Inflammation and Aging, Karolinska University Hospital, Huddinge, Sweden.
  • Boström AM; Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
  • Kivipelto M; Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
  • Metzner C; Faculty of Social Sciences (Health Sciences) and Gerontology Research Center (GEREC), University of Tampere, Tampere, Finland.
  • Bäck Jerlardtz V; Theme Inflammation and Aging, Karolinska University Hospital, Huddinge, Sweden.
  • Engström M; Division of Nursing, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.
  • Johnson P; Research and Development Unit, Stockholms Sjukhem, Stockholm, Sweden.
  • Lundberg LG; Division of Clinical Geriatrics, Department of Neurobiology, Care sciences and Society, Karolinska Institutet, Stockholm, Sweden.
  • Åkesson E; Theme Inflammation and Aging, Karolinska University Hospital, Huddinge, Sweden.
  • Sühl Öberg C; Division of Clinical Geriatrics, Department of Neurobiology, Care sciences and Society, Karolinska Institutet, Stockholm, Sweden.
  • Olsson M; Theme Inflammation and Aging, Karolinska University Hospital, Huddinge, Sweden.
  • Cederholm T; Department of Geriatric Medicine, Jakobsbergsgeriatriken, Stockholm, Sweden.
  • Hägg S; Department of Geriatric Medicine, Sabbatsbergsgeriatriken, Stockholm, Sweden.
  • Religa D; Department of Geriatric Medicine, Capio Geriatrik Nacka AB, Nacka, Sweden.
  • Jylhävä J; Department of Geriatric Medicine, Dalengeriatriken Aleris Närsjukvård AB, Stockholm, Sweden.
Gerontology ; : 1-10, 2022 Nov 30.
Article in English | MEDLINE | ID: covidwho-2270472
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 (ß-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.
Keywords

Full text: Available Collection: International databases Database: MEDLINE Type of study: Cohort study / Experimental Studies / Observational study / Prognostic study Topics: Vaccines / Variants Language: English Journal: Gerontology Year: 2022 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Cohort study / Experimental Studies / Observational study / Prognostic study Topics: Vaccines / Variants Language: English Journal: Gerontology Year: 2022 Document Type: Article