Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, China.
National Clinical Research Center for Geriatric Disorders, Changsha, China.
Front Public Health. 2022 May 24;10:880999. doi: 10.3389/fpubh.2022.880999. eCollection 2022.
Patients with novel coronavirus disease 2019 (COVID-19) worsen into critical illness suddenly is a matter of great concern. Early identification and effective triaging of patients with a high risk of developing critical illness COVID-19 upon admission can aid in improving patient care, increasing the cure rate, and mitigating the burden on the medical care system. This study proposed and extended classical least absolute shrinkage and selection operator (LASSO) logistic regression to objectively identify clinical determination and risk factors for the early identification of patients at high risk of progression to critical illness at the time of hospital admission.
In this retrospective multicenter study, data of 1,929 patients with COVID-19 were assessed. The association between laboratory characteristics measured at admission and critical illness was screened with logistic regression. LASSO logistic regression was utilized to construct predictive models for estimating the risk that a patient with COVID-19 will develop a critical illness.
The development cohort consisted of 1,363 patients with COVID-19 with 133 (9.7%) patients developing the critical illness. Univariate logistic regression analysis revealed 28 variables were prognosis factors for critical illness COVID-19 ( < 0.05). Elevated CK-MB, neutrophils, PCT, α-HBDH, D-dimer, LDH, glucose, PT, APTT, RDW (SD and CV), fibrinogen, and AST were predictors for the early identification of patients at high risk of progression to critical illness. Lymphopenia, a low rate of basophils, eosinophils, thrombopenia, red blood cell, hematocrit, hemoglobin concentration, blood platelet count, and decreased levels of K, Na, albumin, albumin to globulin ratio, and uric acid were clinical determinations associated with the development of critical illness at the time of hospital admission. The risk score accurately predicted critical illness in the development cohort [area under the curve (AUC) = 0.83, 95% CI: 0.78-0.86], also in the external validation cohort ( = 566, AUC = 0.84).
A risk prediction model based on laboratory findings of patients with COVID-19 was developed for the early identification of patients at high risk of progression to critical illness. This cohort study identified 28 indicators associated with critical illness of patients with COVID-19. The risk model might contribute to the treatment of critical illness disease as early as possible and allow for optimized use of medical resources.
患有 2019 年新型冠状病毒病（COVID-19）的患者突然恶化成危重症是一个令人关注的问题。在入院时，对有发展为危重症 COVID-19 高风险的患者进行早期识别和有效分诊，可以帮助改善患者的治疗效果，提高治愈率，并减轻医疗系统的负担。本研究提出并扩展了经典最小绝对收缩和选择算子（LASSO）逻辑回归，以客观地识别入院时患者发展为危重症的高风险的临床判断和危险因素。
在这项回顾性多中心研究中，评估了 1929 例 COVID-19 患者的数据。使用逻辑回归筛选入院时实验室特征与危重症之间的关联。LASSO 逻辑回归用于构建预测模型，以估计 COVID-19 患者发展为危重症的风险。
发展队列包括 1363 例 COVID-19 患者，其中 133 例（9.7%）患者发展为危重症。单因素逻辑回归分析显示，28 个变量是 COVID-19 危重症的预后因素（<0.05）。升高的 CK-MB、中性粒细胞、PCT、α-HBDH、D-二聚体、LDH、血糖、PT、APTT、RDW（SD 和 CV）、纤维蛋白原和 AST 是预测患者向危重症进展高风险的指标。入院时淋巴细胞减少、嗜碱性粒细胞、嗜酸性粒细胞、血小板减少、红细胞、血细胞比容、血红蛋白浓度、血小板计数以及钾、钠、白蛋白、白蛋白与球蛋白比值、尿酸水平降低是与危重症发生相关的临床判断。风险评分准确预测了发展队列中的危重症[曲线下面积（AUC）=0.83，95%置信区间：0.78-0.86]，也预测了外部验证队列（=566，AUC=0.84）中的危重症。
基于 COVID-19 患者实验室结果开发了一种用于早期识别向危重症进展高风险患者的风险预测模型。本队列研究确定了 28 个与 COVID-19 患者危重症相关的指标。该风险模型可能有助于尽早治疗危重症疾病，并优化医疗资源的利用。
