PDF(Pubmed)
Abstract：
UNASSIGNED: Early prediction of prognosis may help early treatment measures to reduce mortality in critically ill coronavirus disease (COVID-19) patients. The study aimed to develop a mortality prediction model for critically ill COVID-19 patients.
UNASSIGNED: This retrospective study analyzed the clinical data of critically ill COVID-19 patients in an intensive care unit between April and June 2022. Propensity matching scores were used to reduce the effect of confounding factors. A predictive model was built using logistic regression analysis and visualized using a nomogram. Calibration and receiver operating characteristic (ROC) curves were used to estimate the accuracy and predictive value of the model. Decision curve analysis (DCA) was used to examine the value of the model for clinical interventions.
UNASSIGNED: In total, 137 critically ill COVID-19 patients were enrolled; 84 survived, and 53 died. Univariate and multivariate logistic regression analyses revealed that aspartate aminotransferase (AST), creatinine, and myoglobin levels were independent prognostic factors. We constructed logistic regression prediction models using the seven least absolute shrinkage and selection operator regression-selected variables (hematocrit, red blood cell distribution width-standard deviation, procalcitonin, AST, creatinine, potassium, and myoglobin; Model 1) and three independent factor variables (Model 2). The calibration curves suggested that the actual predictions of the two models were similar to the ideal predictions. The ROC curve indicated that both models had good predictive power, and Model 1 had better predictive power than Model 2. The DCA results suggested that the model intervention was beneficial to patients and patients benefited more from Model 1 than from Model 2.
UNASSIGNED: The predictive model constructed using characteristic variables screened using LASSO regression can accurately predict the prognosis of critically ill COVID-19 patients. This model can assist clinicians in implementing early interventions. External validation by prospective large-sample studies is required.
UNASSIGNED: This retrospective study analyzed the clinical data of critically ill COVID-19 patients in an intensive care unit between April and June 2022. Propensity matching scores were used to reduce the effect of confounding factors. A predictive model was built using logistic regression analysis and visualized using a nomogram. Calibration and receiver operating characteristic (ROC) curves were used to estimate the accuracy and predictive value of the model. Decision curve analysis (DCA) was used to examine the value of the model for clinical interventions.
UNASSIGNED: In total, 137 critically ill COVID-19 patients were enrolled; 84 survived, and 53 died. Univariate and multivariate logistic regression analyses revealed that aspartate aminotransferase (AST), creatinine, and myoglobin levels were independent prognostic factors. We constructed logistic regression prediction models using the seven least absolute shrinkage and selection operator regression-selected variables (hematocrit, red blood cell distribution width-standard deviation, procalcitonin, AST, creatinine, potassium, and myoglobin; Model 1) and three independent factor variables (Model 2). The calibration curves suggested that the actual predictions of the two models were similar to the ideal predictions. The ROC curve indicated that both models had good predictive power, and Model 1 had better predictive power than Model 2. The DCA results suggested that the model intervention was beneficial to patients and patients benefited more from Model 1 than from Model 2.
UNASSIGNED: The predictive model constructed using characteristic variables screened using LASSO regression can accurately predict the prognosis of critically ill COVID-19 patients. This model can assist clinicians in implementing early interventions. External validation by prospective large-sample studies is required.
摘要：
■早期预测预后可能有助于早期治疗措施，以降低重症冠状病毒病（COVID-19）患者的死亡率。该研究旨在建立危重COVID-19患者的死亡率预测模型。
■这项回顾性研究分析了2022年4月至6月间重症监护病房中危重COVID-19患者的临床数据。倾向匹配得分用于减少混杂因素的影响。使用逻辑回归分析建立了预测模型，并使用列线图进行了可视化。使用校准和受试者工作特征（ROC）曲线来估计模型的准确性和预测值。使用决策曲线分析（DCA）来检查模型对临床干预的价值。
■总共，纳入137例重症COVID-19患者；84例存活，53人死亡单因素和多因素logistic回归分析显示天冬氨酸转氨酶(AST)、肌酐,和肌红蛋白水平是独立的预后因素。我们使用七个最小绝对收缩和选择算子回归选择变量（血细胞比容，红细胞分布宽度-标准偏差，降钙素原,AST,肌酐,钾，和肌红蛋白；模型1)和三个独立因素变量(模型2)。校准曲线表明两个模型的实际预测与理想预测相似。ROC曲线表明，两种模型都具有良好的预测能力，模型1比模型2具有更好的预测能力。DCA结果表明，模型干预对患者有益，患者从模型1中受益比从模型2中受益更多。
■使用LASSO回归筛选的特征变量构建的预测模型可以准确预测重症COVID-19患者的预后。该模型可以帮助临床医生实施早期干预措施。需要通过前瞻性大样本研究进行外部验证。
■这项回顾性研究分析了2022年4月至6月间重症监护病房中危重COVID-19患者的临床数据。倾向匹配得分用于减少混杂因素的影响。使用逻辑回归分析建立了预测模型，并使用列线图进行了可视化。使用校准和受试者工作特征（ROC）曲线来估计模型的准确性和预测值。使用决策曲线分析（DCA）来检查模型对临床干预的价值。
■总共，纳入137例重症COVID-19患者；84例存活，53人死亡单因素和多因素logistic回归分析显示天冬氨酸转氨酶(AST)、肌酐,和肌红蛋白水平是独立的预后因素。我们使用七个最小绝对收缩和选择算子回归选择变量（血细胞比容，红细胞分布宽度-标准偏差，降钙素原,AST,肌酐,钾，和肌红蛋白；模型1)和三个独立因素变量(模型2)。校准曲线表明两个模型的实际预测与理想预测相似。ROC曲线表明，两种模型都具有良好的预测能力，模型1比模型2具有更好的预测能力。DCA结果表明，模型干预对患者有益，患者从模型1中受益比从模型2中受益更多。
■使用LASSO回归筛选的特征变量构建的预测模型可以准确预测重症COVID-19患者的预后。该模型可以帮助临床医生实施早期干预措施。需要通过前瞻性大样本研究进行外部验证。
