Departamento de Epidemiología. Complejo Hospitalario Universitario de Santiago de Compostela. Santiago de Compostela, Spain.
Grupo de Métodos de Investigación, Instituto de Investigaciones Sanitarias de Santiago (IDIS), Santiago de Compostela, Spain.
Int J Epidemiol. 2021 Mar 3;50(1):64-74. doi: 10.1093/ije/dyaa209.
The prognosis of patients with COVID-19 infection is uncertain. We derived and validated a new risk model for predicting progression to disease severity, hospitalization, admission to intensive care unit (ICU) and mortality in patients with COVID-19 infection (Gal-COVID-19 scores).
This is a retrospective cohort study of patients with COVID-19 infection confirmed by reverse transcription polymerase chain reaction (RT-PCR) in Galicia, Spain. Data were extracted from electronic health records of patients, including age, sex and comorbidities according to International Classification of Primary Care codes (ICPC-2). Logistic regression models were used to estimate the probability of disease severity. Calibration and discrimination were evaluated to assess model performance.
The incidence of infection was 0.39% (10 454 patients). A total of 2492 patients (23.8%) required hospitalization, 284 (2.7%) were admitted to the ICU and 544 (5.2%) died. The variables included in the models to predict severity included age, gender and chronic comorbidities such as cardiovascular disease, diabetes, obesity, hypertension, chronic obstructive pulmonary disease, asthma, liver disease, chronic kidney disease and haematological cancer. The models demonstrated a fair-good fit for predicting hospitalization {AUC [area under the receiver operating characteristics (ROC) curve] 0.77 [95% confidence interval (CI) 0.76, 0.78]}, admission to ICU [AUC 0.83 (95%CI 0.81, 0.85)] and death [AUC 0.89 (95%CI 0.88, 0.90)].
The Gal-COVID-19 scores provide risk estimates for predicting severity in COVID-19 patients. The ability to predict disease severity may help clinicians prioritize high-risk patients and facilitate the decision making of health authorities.
COVID-19 感染患者的预后不确定。我们推导并验证了一种新的风险模型，用于预测 COVID-19 感染患者疾病严重程度、住院、入住重症监护病房（ICU）和死亡的进展（Gal-COVID-19 评分）。
这是一项在西班牙加利西亚通过逆转录聚合酶链反应（RT-PCR）确诊的 COVID-19 感染患者的回顾性队列研究。数据从患者的电子健康记录中提取，包括年龄、性别和根据国际初级保健分类（ICPC-2）编码的合并症。使用逻辑回归模型估计疾病严重程度的概率。校准和判别用于评估模型性能。
感染发生率为 0.39%（10454 例）。共有 2492 例（23.8%）患者需要住院治疗，284 例（2.7%）患者入住 ICU，544 例（5.2%）患者死亡。预测严重程度的模型中包括年龄、性别和心血管疾病、糖尿病、肥胖、高血压、慢性阻塞性肺疾病、哮喘、肝病、慢性肾脏病和血液系统癌症等慢性合并症。这些模型在预测住院（AUC [接受者操作特征曲线下面积] 0.77 [95%置信区间（CI）0.76，0.78]）、入住 ICU（AUC 0.83 [95%CI 0.81，0.85]）和死亡（AUC 0.89 [95%CI 0.88，0.90]）方面表现出良好的拟合度。
Gal-COVID-19 评分提供了预测 COVID-19 患者严重程度的风险估计。预测疾病严重程度的能力可能有助于临床医生优先考虑高危患者，并为卫生当局的决策提供便利。
