Can predicting COVID-19 mortality in a European cohort using only demographic and comorbidity data surpass age-based prediction: An externally validated study.
Can predicting COVID-19 mortality in a European cohort using only demographic and comorbidity data surpass age-based prediction: An externally validated study.
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DOI：
10.1371/journal.pone.0249920
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发表时间：
2021
期刊：
影响因子：
3.7
通讯作者：
Lambin P
中科院分区：
文献类型：
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作者：
Chatterjee A;Wu G;Primakov S;Oberije C;Woodruff H;Kubben P;Henry R;Aries MJH;Beudel M;Noordzij PG;Dormans T;Gritters van den Oever NC;van den Bergh JP;Wyers CE;Simsek S;Douma R;Reidinga AC;de Kruif MD;Guiot J;Frix AN;Louis R;Moutschen M;Lovinfosse P;Lambin P
确定是否可以仅基于人口统计学和合并症数据建立COVID-19患者死亡率预测模型，其效果优于仅基于年龄。这种模式可能是实施智能封锁和疫苗分发战略的先驱。培训队列包括来自荷兰9家医院的2337名COVID-19住院患者。临床结果为出院后21天内死亡。这些特征来源于住院期间收集的电子健康记录。使用了三种特征选择方法：LASSO，使用新度量的单变量和两两（年龄为每对的一半）。来自比利时的478名患者被用来测试该模型。所有的建模尝试都与只有年龄的模型进行了比较。在训练队列中，死亡组的中位年龄为77岁（四分位数范围= 70-83），高于非死亡组（中位年龄= 65,IQR = 55-75）。前吸烟者/活跃吸烟者、男性、高血压、糖尿病、痴呆、癌症、慢性阻塞性肺病、慢性心脏病、慢性神经系统疾病和慢性肾病的发病率在死亡组中较高。经Bonferroni校正后，所有差异均有统计学意义。LASSO选择了8个特征，novel单变量选择了5个特征，两两选择了0个特征。在外部验证集中，没有模型能够超过仅年龄的模型，其中年龄的AUC为0.85，平衡精度为0.77。当应用于外部验证集时，我们发现使用三种特征选择方法对22个人口统计学和合并症特征进行建模时，仅年龄死亡率模型优于所有建模尝试（在www.covid19risk.ai上整理）。
To establish whether one can build a mortality prediction model for COVID-19 patients based solely on demographics and comorbidity data that outperforms age alone. Such a model could be a precursor to implementing smart lockdowns and vaccine distribution strategies. The training cohort comprised 2337 COVID-19 inpatients from nine hospitals in The Netherlands. The clinical outcome was death within 21 days of being discharged. The features were derived from electronic health records collected during admission. Three feature selection methods were used: LASSO, univariate using a novel metric, and pairwise (age being half of each pair). 478 patients from Belgium were used to test the model. All modeling attempts were compared against an age-only model. In the training cohort, the mortality group’s median age was 77 years (interquartile range = 70–83), higher than the non-mortality group (median = 65, IQR = 55–75). The incidence of former/active smokers, male gender, hypertension, diabetes, dementia, cancer, chronic obstructive pulmonary disease, chronic cardiac disease, chronic neurological disease, and chronic kidney disease was higher in the mortality group. All stated differences were statistically significant after Bonferroni correction. LASSO selected eight features, novel univariate chose five, and pairwise chose none. No model was able to surpass an age-only model in the external validation set, where age had an AUC of 0.85 and a balanced accuracy of 0.77. When applied to an external validation set, we found that an age-only mortality model outperformed all modeling attempts (curated on www.covid19risk.ai) using three feature selection methods on 22 demographic and comorbid features.
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影响因子：
39.2
作者：
Collins, Gary S.;Reitsma, Johannes B.;Moons, Karel G. M.
通讯作者：
Moons, Karel G. M.
影响因子：
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作者：
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通讯作者：
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影响因子：
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作者：
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通讯作者：
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影响因子：
16.6
作者：
Gao Y;Cai GY;Fang W;Li HY;Wang SY;Chen L;Yu Y;Liu D;Xu S;Cui PF;Zeng SQ;Feng XX;Yu RD;Wang Y;Yuan Y;Jiao XF;Chi JH;Liu JH;Li RY;Zheng X;Song CY;Jin N;Gong WJ;Liu XY;Huang L;Tian X;Li L;Xing H;Ma D;Li CR;Ye F;Gao QL
通讯作者：
Gao QL
DOI：
10.1007/s42000-020-00246-2
发表时间：
2021-06
期刊：
Hormones (Athens, Greece)
影响因子：
--
作者：
Palaiodimos L;Chamorro-Pareja N;Karamanis D;Li W;Zavras PD;Chang KM;Mathias P;Kokkinidis DG
通讯作者：
Kokkinidis DG