Frailty, Comorbidity, and Associations With In-Hospital Mortality in Older COVID-19 Patients: Exploratory Study of Administrative Data.
Frailty, Comorbidity, and Associations With In-Hospital Mortality in Older COVID-19 Patients: Exploratory Study of Administrative Data.
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DOI：
10.2196/41520
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发表时间：
2022-12-12
影响因子：
2
通讯作者：
Gray, William K.
中科院分区：
文献类型：
--
作者：
Heyl, Johannes;Hardy, Flavien;Tucker, Katie;Hopper, Adrian;Marcha, Maria J. M.;Navaratnam, Annakan, V;Briggs, Tim W. R.;Yates, Jeremy;Day, Jamie;Wheeler, Andrew;Eve-Jones, Sue;Gray, William K.
关键词：
老年人因COVID-19住院后的预后较差，但在这一群体中存在很大差异。虽然虚弱和合并症是死亡率的关键决定因素，但尚不清楚虚弱和合并症的哪些具体表现与最坏的结果有关。我们的目的是使用为机器学习算法开发的模型，确定与COVID-19老年患者住院死亡率相关的关键合并症和虚弱领域。这是一项回顾性研究，使用了2020年3月1日至2021年2月28日的医院事件统计管理数据集，用于英格兰65岁或以上的住院患者。在模型开发期间，数据集被分成单独的训练（70%）、测试（15%）和验证（15%）数据集。使用医院衰弱风险评分（HFRS）评估全局衰弱，使用全局衰弱量表（GFS）确定特定的衰弱领域。使用Charlson共病指数（CCI）评估合并症。随机森林算法中使用的其他特征包括年龄、性别、剥夺、种族、出院月份和年份、地理区域、医院信任、疾病严重程度和入院期间记录的国际疾病统计分类第10版代码。特征被选择、预处理并输入到一系列随机森林分类算法中，以确定与住院死亡率密切相关的因素。开发了两种模型；第一个模型包括上述人口统计、医院相关和疾病相关项目，以及单个GFS域和CCI项目。第二个模型与第一个模型相似，但用HFRS取代了GFS域和CCI项目，作为脆弱性的全球衡量标准。模型的性能评估使用面积下的接收者工作特征（AUROC）曲线和模型精度的措施。共纳入215,831例患者。使用单个GFS域和CCI项目的模型对住院死亡率的AUROC曲线为90%，预测准确率为83%。使用HFRS的模型具有相似的性能（AUROC曲线90%，预测准确率82%）。GFS中最重要的虚弱项目是痴呆/谵妄、跌倒/骨折和压疮/体重减轻。CCI中最重要的合并症项目是癌症、心力衰竭和肾脏疾病。虚弱和合并症的身体表现，特别是有认知障碍和跌倒史，可能有助于确定COVID-19住院期间需要额外支持的患者。
Older adults have worse outcomes following hospitalization with COVID-19, but within this group there is substantial variation. Although frailty and comorbidity are key determinants of mortality, it is less clear which specific manifestations of frailty and comorbidity are associated with the worst outcomes. We aimed to identify the key comorbidities and domains of frailty that were associated with in-hospital mortality in older patients with COVID-19 using models developed for machine learning algorithms. This was a retrospective study that used the Hospital Episode Statistics administrative data set from March 1, 2020, to February 28, 2021, for hospitalized patients in England aged 65 years or older. The data set was split into separate training (70%), test (15%), and validation (15%) data sets during model development. Global frailty was assessed using the Hospital Frailty Risk Score (HFRS) and specific domains of frailty were identified using the Global Frailty Scale (GFS). Comorbidity was assessed using the Charlson Comorbidity Index (CCI). Additional features employed in the random forest algorithms included age, sex, deprivation, ethnicity, discharge month and year, geographical region, hospital trust, disease severity, and International Statistical Classification of Disease, 10th Edition codes recorded during the admission. Features were selected, preprocessed, and input into a series of random forest classification algorithms developed to identify factors strongly associated with in-hospital mortality. Two models were developed; the first model included the demographic, hospital-related, and disease-related items described above, as well as individual GFS domains and CCI items. The second model was similar to the first but replaced the GFS domains and CCI items with the HFRS as a global measure of frailty. Model performance was assessed using the area under the receiver operating characteristic (AUROC) curve and measures of model accuracy. In total, 215,831 patients were included. The model using the individual GFS domains and CCI items had an AUROC curve for in-hospital mortality of 90% and a predictive accuracy of 83%. The model using the HFRS had similar performance (AUROC curve 90%, predictive accuracy 82%). The most important frailty items in the GFS were dementia/delirium, falls/fractures, and pressure ulcers/weight loss. The most important comorbidity items in the CCI were cancer, heart failure, and renal disease. The physical manifestations of frailty and comorbidity, particularly a history of cognitive impairment and falls, may be useful in identification of patients who need additional support during hospitalization with COVID-19.
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影响因子：
4.6
作者：
Baqui P;Marra V;Alaa AM;Bica I;Ercole A;van der Schaar M
通讯作者：
van der Schaar M
通过电子病历预测COVID-19死亡率。
影响因子：
15.2
作者：
Estiri H;Strasser ZH;Klann JG;Naseri P;Wagholikar KB;Murphy SN
通讯作者：
Murphy SN
影响因子：
6.3
作者：
Frenkel, Wijnanda J.;Jongerius, Erika J.;de Rooij, Sophia E.
通讯作者：
de Rooij, Sophia E.
影响因子：
6.3
作者：
Garcez, Flavia B.;Aliberti, Marlon J. R.;Avelino-Silva, Thiago J.
通讯作者：
Avelino-Silva, Thiago J.
影响因子：
6.7
作者：
Geriatric Medicine Research Collaborative;Covid Collaborative;Welch C
通讯作者：
Welch C