Machine learning based on clinical characteristics and chest CT quantitative measurements for prediction of adverse clinical outcomes in hospitalized patients with COVID-19.
Machine learning based on clinical characteristics and chest CT quantitative measurements for prediction of adverse clinical outcomes in hospitalized patients with COVID-19.
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DOI：
10.1007/s00330-021-07957-z
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发表时间：
2021-10
影响因子：
5.9
通讯作者：
Wang W
中科院分区：
文献类型：
--
作者：
Feng Z;Shen H;Gao K;Su J;Yao S;Liu Q;Yan Z;Duan J;Yi D;Zhao H;Li H;Yu Q;Zhou W;Mao X;Ouyang X;Mei J;Zeng Q;Williams L;Ma X;Rong P;Hu D;Wang W
开发并验证机器学习模型，用于预测住院 COVID-19 患者的不良后果。我们纳入了 2020 年 1 月 17 日至 2020 年 2 月 17 日入院时患有非重症 COVID-19 的 424 名患者，作为这项回顾性多中心研究的主要队列。通过基于深度学习的框架对胸部 CT 图像上的肺部受累程度进行量化。复合终点是住院期间发生严重或危重 COVID-19 或死亡。选择最佳的机器学习分类器和特征子集来构建模型。该性能在由 98 名患者组成的外部验证队列中得到了进一步测试。主要队列和验证队列之间的不良结果发生率没有显着差异（8.7% vs. 8.2%，p = 0.858）。选择机器学习方法极限梯度提升（XGBoost）和最佳特征子集（包括乳酸脱氢酶（LDH）、是否存在合并症、CT病变比率（病变％）和超敏心肌肌钙蛋白I（hs-cTnI））进行模型构建。基于最佳特征子集的 XGBoost 分类器在预测主要队列和验证队列中出现不良结果方面表现良好，AUC 分别为 0.959（95% 置信区间 [CI]：0.936–0.976）和 0.953（95% CI：0.891–0.986）。此外，XGBoost 分类器还显示出临床实用性。我们提出了一种机器学习模型，可以有效地用作 COVID-19 住院患者不良结果的预测因子，为患者分层和治疗分配提供了可能性。 • 开发针对COVID-19 的个体预后模型有可能实现医疗资源的有效分配。 • 我们提出了一个基于深度学习的框架，用于在胸部 CT 图像上准确量化肺部受累情况。 • 基于临床和 CT 变量的机器学习可以促进预测 COVID-19 的不良后果。在线版本包含可在 10.1007/s00330-021-07957-z 获取的补充材料。
To develop and validate a machine learning model for the prediction of adverse outcomes in hospitalized patients with COVID-19. We included 424 patients with non-severe COVID-19 on admission from January 17, 2020, to February 17, 2020, in the primary cohort of this retrospective multicenter study. The extent of lung involvement was quantified on chest CT images by a deep learning–based framework. The composite endpoint was the occurrence of severe or critical COVID-19 or death during hospitalization. The optimal machine learning classifier and feature subset were selected for model construction. The performance was further tested in an external validation cohort consisting of 98 patients. There was no significant difference in the prevalence of adverse outcomes (8.7% vs. 8.2%, p = 0.858) between the primary and validation cohorts. The machine learning method extreme gradient boosting (XGBoost) and optimal feature subset including lactic dehydrogenase (LDH), presence of comorbidity, CT lesion ratio (lesion%), and hypersensitive cardiac troponin I (hs-cTnI) were selected for model construction. The XGBoost classifier based on the optimal feature subset performed well for the prediction of developing adverse outcomes in the primary and validation cohorts, with AUCs of 0.959 (95% confidence interval [CI]: 0.936–0.976) and 0.953 (95% CI: 0.891–0.986), respectively. Furthermore, the XGBoost classifier also showed clinical usefulness. We presented a machine learning model that could be effectively used as a predictor of adverse outcomes in hospitalized patients with COVID-19, opening up the possibility for patient stratification and treatment allocation. • Developing an individually prognostic model for COVID-19 has the potential to allow efficient allocation of medical resources. • We proposed a deep learning–based framework for accurate lung involvement quantification on chest CT images. • Machine learning based on clinical and CT variables can facilitate the prediction of adverse outcomes of COVID-19. The online version contains supplementary material available at 10.1007/s00330-021-07957-z.
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影响因子：
6.5
作者：
Muchtar E;Dispenzieri A;Lacy MQ;Buadi FK;Kapoor P;Hayman SR;Gonsalves W;Warsame R;Kourelis TV;Chakraborty R;Russell S;Lust JA;Lin Y;Go RS;Zeldenrust S;Dingli D;Leung N;Rajkumar SV;Kyle RA;Kumar SK;Gertz MA
通讯作者：
Gertz MA
影响因子：
39.2
作者：
Hammadah M;Kim JH;Tahhan AS;Kindya B;Liu C;Ko YA;Al Mheid I;Wilmot K;Ramadan R;Alkhoder A;Choudhary F;Gafeer MM;Abdelhadi N;Pimple P;Sandesara P;Lima BB;Shah AJ;Ward L;Kutner M;Bremner JD;Sheps DS;Raggi P;Sperling LS;Vaccarino V;Quyyumi AA
通讯作者：
Quyyumi AA
DOI：
10.1164/rccm.202002-0445oc
发表时间：
2020-06-01
影响因子：
24.7
作者：
Feng, Yun;Ling, Yun;Qu, Jieming
通讯作者：
Qu, Jieming
影响因子：
168.9
作者：
Huang, Chaolin;Wang, Yeming;Cao, Bin
通讯作者：
Cao, Bin
影响因子：
158.5
作者：
Grein, J.;Ohmagari, N.;Flanigan, T.
通讯作者：
Flanigan, T.