Table_1_Early Prediction Model for Critical Illness of Hospitalized COVID-19 Patients Based on Machine Learning Techniques.DOCX

MotivationPatients 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.
MethodsIn 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.
ResultsThe 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 (p < 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 (n = 566, AUC = 0.84).
ConclusionA 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.

研究背景：新型冠状病毒肺炎（COVID-19）患者突发恶化为重症的情况备受学界关注。入院时早期识别并有效分流存在重症进展高风险的新冠患者，有助于优化患者救治方案、提升临床治愈率，同时减轻医疗系统的运行负担。本研究提出并扩展了经典最小绝对收缩和选择算子（least absolute shrinkage and selection operator, LASSO）逻辑回归模型，以客观识别新冠患者入院时进展为重症的临床特征与危险因素。
研究方法：本项回顾性多中心研究共纳入1929例新冠患者的临床数据。采用逻辑回归筛选入院时检测的实验室指标与重症发生的关联，并利用LASSO逻辑回归构建预测模型，用于评估新冠患者进展为重症的风险。
研究结果：开发队列共纳入1363例新冠患者，其中133例（9.7%）进展为重症。单因素逻辑回归分析显示，28项变量为新冠重症的预后危险因素（p < 0.05）。其中，肌酸激酶同工酶（CK-MB）、中性粒细胞、降钙素原（PCT）、α-羟丁酸脱氢酶（α-HBDH）、D-二聚体、乳酸脱氢酶（LDH）、血糖、凝血酶原时间（PT）、活化部分凝血活酶时间（APTT）、红细胞分布宽度（标准差SD与变异系数CV）、纤维蛋白原以及天冬氨酸氨基转移酶（AST）水平升高，是识别新冠患者早期进展为重症的预测指标；而淋巴细胞减少、嗜碱性粒细胞比例降低、嗜酸性粒细胞比例降低、血小板减少、红细胞计数、血细胞比容、血红蛋白浓度、血小板计数降低，以及钾、钠、白蛋白、白球比、尿酸水平下降，则为入院时与重症发生相关的临床特征。本研究构建的风险评分模型在开发队列中可准确预测重症发生风险[曲线下面积（AUC）= 0.83, 95%置信区间（CI）: 0.78–0.86]，在外部验证队列（n = 566）中同样取得了优异的预测性能（AUC = 0.84）。
研究结论：本研究基于新冠患者的实验室检查结果构建了风险预测模型，用于早期识别进展为重症的高风险患者。本队列研究共确定了28项与新冠重症发生相关的指标。该风险模型有助于尽早开展重症救治，并实现医疗资源的优化配置。