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

被引:8
|
作者
Mak, Jonathan K. L. [1 ]
Eriksdotter, Maria [2 ,3 ]
Annetorp, Martin [2 ,3 ]
Kuja-Halkola, Ralf [1 ]
Kananen, Laura [1 ,4 ,5 ]
Bostrom, Anne-Marie [3 ,6 ,7 ]
Kivipelto, Miia [2 ,3 ]
Metzner, Carina [2 ,3 ]
Jerlardtz, Viktoria Back [8 ]
Engstrom, Malin [9 ]
Johnson, Peter [10 ]
Lundberg, Lars Goran [11 ]
Akesson, Elisabet [7 ,12 ]
Oberg, Carina Suhl [13 ]
Olsson, Maria [14 ,15 ]
Cederholm, Tommy [2 ,3 ]
Hagg, Sara [1 ]
Religa, Dorota [2 ,3 ]
Jylhava, Juulia [1 ,4 ,5 ]
机构
[1] Karolinska Inst, Dept Med Epidemiol & Biostat, Stockholm, Sweden
[2] Karolinska Inst, Dept Neurobiol Care Sci & Soc, Div Clin Geriatr, Stockholm, Sweden
[3] Karolinska Univ Hosp, Theme Inflammat & Aging, Huddinge, Sweden
[4] Univ Tampere, Fac Social Sci, Hlth Sci, Tampere, Finland
[5] Univ Tampere, Gerontol Res Ctr GEREC, Tampere, Finland
[6] Karolinska Inst, Dept Neurobiol Care Sci & Soc, Div Nursing, Stockholm, Sweden
[7] Stockholms Sjukhem, Res & Dev Unit, Stockholm, Sweden
[8] Jakobsbergsgeriatriken, Dept Geriatr Med, Stockholm, Sweden
[9] Sabbatsbergsgeriatriken, Dept Geriatr Med, Stockholm, Sweden
[10] Capio Geriatr Nacka AB, Dept Geriatr Med, Nacka, Sweden
[11] Dalengeriatriken Aleris Narsjukvard AB, Dept Geriatr Med, Stockholm, Sweden
[12] Karolinska Inst, Dept Neurobiol Care Sci & Soc, Div Neurogeriatr, Stockholm, Sweden
[13] Handengeriatriken, Dept Geriatr Med, Aleris Narsjukvard AB, Stockholm, Sweden
[14] Capio Geriatr Sollentuna, Dept Geriatr Med, Stockholm, Sweden
[15] Uppsala Univ, Dept Publ Hlth & Caring Sci, Uppsala, Sweden
基金
芬兰科学院; 瑞典研究理事会;
关键词
Frailty; COVID-19; Electronic frailty index; Comorbidity; Older adults; CORONAVIRUS DISEASE 2019; OLDER-ADULTS; MORTALITY; METAANALYSIS; COMORBIDITY; PREDICTOR; OUTCOMES; AGE;
D O I
10.1159/000527206
中图分类号
R592 [老年病学]; C [社会科学总论];
学科分类号
03 ; 0303 ; 100203 ;
摘要
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 (beta-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.
引用
收藏
页码:396 / 405
页数:10
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