PDF(Pubmed)
Abstract：
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.
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.
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.
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).
An eFI based on routinely collected EHRs can be applied in identifying high-risk older COVID-19 patients during the continuing pandemic.
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.
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.
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).
An eFI based on routinely collected EHRs can be applied in identifying high-risk older COVID-19 patients during the continuing pandemic.
摘要：
背景：脆弱，衡量生物老化，与更糟糕的COVID-19结果有关。然而,由于COVID-19波的死亡率不同，尚不清楚我们先前为老年人开发的基于病历的电子虚弱指数(eFI)是否可用于住院COVID-19患者的风险分层.
目的：本研究的目的是检查虚弱与死亡率的关系，重新接纳,和老年COVID-19患者的住院时间，并比较eFI与其他虚弱和合并症指标的预测准确性。
方法：这是一项回顾性队列研究，使用斯德哥尔摩9个老年诊所的电子健康记录（EHR），瑞典,包括2020年3月至2022年3月期间收治的3,980名COVID-19患者（平均年龄81.6岁）。使用为瑞典老年患者开发的48项eFI评估虚弱，临床虚弱量表，和医院衰弱风险评分。使用Charlson合并症指数测量合并症。我们使用逻辑回归分析了住院死亡率和30天再入院，使用Cox回归的30天和6个月死亡率，和使用线性回归的停留时间。逻辑回归和Cox模型的预测准确性通过受试者工作特征曲线下面积(AUC)和Harrell'sC统计量来评估,分别。
结果：整个研究期间，住院死亡率从第一波的13.9％下降到最新（Omicron）波的3.6％。控制年龄和性别，eFI增加10％与住院死亡率的高风险显着相关（比值比=2.95;95％置信区间=2.42-3.62），30天死亡率（危险比[HR]=2.39；2.08-2.74），6个月死亡率（HR=2.29;2.04-2.56），住院时间较长(β系数=2.00；1.65-2.34)，但不符合30天的再入院。eFI和院内死亡率之间的关联在整个浪潮中仍然强劲，即使在疫苗接种后。在所有措施中，eFI对住院患者的歧视最好(AUC=0.780)，30天（哈雷尔的C=0.733），和6个月死亡率（哈雷尔C=0.719）。
结论：基于常规收集的EHR的eFI可用于识别持续大流行期间的高危老年COVID-19患者。
目的：本研究的目的是检查虚弱与死亡率的关系，重新接纳,和老年COVID-19患者的住院时间，并比较eFI与其他虚弱和合并症指标的预测准确性。
方法：这是一项回顾性队列研究，使用斯德哥尔摩9个老年诊所的电子健康记录（EHR），瑞典,包括2020年3月至2022年3月期间收治的3,980名COVID-19患者（平均年龄81.6岁）。使用为瑞典老年患者开发的48项eFI评估虚弱，临床虚弱量表，和医院衰弱风险评分。使用Charlson合并症指数测量合并症。我们使用逻辑回归分析了住院死亡率和30天再入院，使用Cox回归的30天和6个月死亡率，和使用线性回归的停留时间。逻辑回归和Cox模型的预测准确性通过受试者工作特征曲线下面积(AUC)和Harrell'sC统计量来评估,分别。
结果：整个研究期间，住院死亡率从第一波的13.9％下降到最新（Omicron）波的3.6％。控制年龄和性别，eFI增加10％与住院死亡率的高风险显着相关（比值比=2.95;95％置信区间=2.42-3.62），30天死亡率（危险比[HR]=2.39；2.08-2.74），6个月死亡率（HR=2.29;2.04-2.56），住院时间较长(β系数=2.00；1.65-2.34)，但不符合30天的再入院。eFI和院内死亡率之间的关联在整个浪潮中仍然强劲，即使在疫苗接种后。在所有措施中，eFI对住院患者的歧视最好(AUC=0.780)，30天（哈雷尔的C=0.733），和6个月死亡率（哈雷尔C=0.719）。
结论：基于常规收集的EHR的eFI可用于识别持续大流行期间的高危老年COVID-19患者。
