Longitudinally monitored immune biomarkers predict the timing of COVID-19 outcomes.
Longitudinally monitored immune biomarkers predict the timing of COVID-19 outcomes.
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DOI：
10.1371/journal.pcbi.1009778
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发表时间：
2022-01
影响因子：
4.3
通讯作者：
Vergnolle O
中科院分区：
文献类型：
--
作者：
Lasso G;Khan S;Allen SA;Mariano M;Florez C;Orner EP;Quiroz JA;Quevedo G;Massimi A;Hegde A;Wirchnianski AS;Bortz RH 3rd;Malonis RJ;Georgiev GI;Tong K;Herrera NG;Morano NC;Garforth SJ;Malaviya A;Khokhar A;Laudermilch E;Dieterle ME;Fels JM;Haslwanter D;Jangra RK;Barnhill J;Almo SC;Chandran K;Lai JR;Kelly L;Daily JP;Vergnolle O
感染SARS-CoV-2的临床结果在不同的人之间差别很大。机器学习模型可以通过评估尚未显示出严重新冠肺炎迹象的患者的死亡风险，来支持医疗保健决策。大多数预测模型依赖于静态的人口学特征和住院时获得的临床价值。然而，与新冠肺炎严重程度相关的时间依赖性生物标记物，如抗体滴度，可以大大有助于开发更准确的结果模型。在这里，我们表明，经过免疫生物标记物训练的模型，在整个住院期间进行纵向监测，预测死亡率，并且比基于入院时的人口统计学和临床数据的模型更准确。我们最好的预测模型是基于对抗SARS-CoV-2尖峰抗体滴度、白细胞(WBC)、中性粒细胞和淋巴细胞计数的时间分析。这些生物标志物，连同C反应蛋白和血尿素氮水平，被发现与疾病的严重程度和死亡率以一种时间依赖的方式相关。Shapley对我们的模型的相加解释显示，随着住院进程的进展，症状后发病天数(PSO)的预测价值更高，并表明免疫生物标记物如何有助于预测死亡率。综上所述，我们证明了免疫生物标记物的动力学可以为临床模型提供信息，作为预测住院新冠肺炎患者死亡风险的有力监测工具，强调了根据症状出现后的时间来确定临床参数的重要性。SARS-CoV-2感染的患者具有不同的临床特征，从无症状到严重的呼吸衰竭和死亡。及早发现高危患者是预测疾病进展和防止不良结果的量身定制治疗干预措施的基础。机器学习可以将描述患者健康的多个因素整合到一个能够预测最可能结果的模型中，从而帮助卫生工作者对患者进行分诊。这在必须有效利用临床资源的激增环境中可能特别相关。到目前为止，大多数模型使用入院时获得的患者数据来预测新冠肺炎的结果。然而，住院期间纵向获得的临床数据可以为建立更精确的模型提供丰富的信息。考虑到这一点，我们通过频繁收集临床参数来监测147名新冠肺炎患者在住院期间的疾病进展。我们表明，根据纵向监测的免疫生物标志物训练的模型预测死亡率，并且比基于入院时获得的人口统计学和临床数据的模型更准确。我们的工作鼓励开发更广泛的计算框架，将入院患者的临床数据与在整个住院过程中收集的纵向监控生物标志物相结合，以更好地帮助医护人员对新冠肺炎患者进行日常预测。
The clinical outcome of SARS-CoV-2 infection varies widely between individuals. Machine learning models can support decision making in healthcare by assessing fatality risk in patients that do not yet show severe signs of COVID-19. Most predictive models rely on static demographic features and clinical values obtained upon hospitalization. However, time-dependent biomarkers associated with COVID-19 severity, such as antibody titers, can substantially contribute to the development of more accurate outcome models. Here we show that models trained on immune biomarkers, longitudinally monitored throughout hospitalization, predicted mortality and were more accurate than models based on demographic and clinical data upon hospital admission. Our best-performing predictive models were based on the temporal analysis of anti-SARS-CoV-2 Spike IgG titers, white blood cell (WBC), neutrophil and lymphocyte counts. These biomarkers, together with C-reactive protein and blood urea nitrogen levels, were found to correlate with severity of disease and mortality in a time-dependent manner. Shapley additive explanations of our model revealed the higher predictive value of day post-symptom onset (PSO) as hospitalization progresses and showed how immune biomarkers contribute to predict mortality. In sum, we demonstrate that the kinetics of immune biomarkers can inform clinical models to serve as a powerful monitoring tool for predicting fatality risk in hospitalized COVID-19 patients, underscoring the importance of contextualizing clinical parameters according to their time post-symptom onset. SARS-CoV-2 infected patients present with diverse clinical profiles, ranging from asymptomatic to severe respiratory failure and death. Early detection of high-risk patients is fundamental to tailor therapeutic interventions that anticipate disease progression and prevent poor outcomes. Machine learning can assist health workers in triaging patients by bringing together multiple factors, describing the patient’s health, into a single model capable of predicting the most likely outcome. This can be particularly relevant in surge settings where clinical resources must be efficiently utilized. To date, most models predict COVID-19 outcomes using patient data obtained upon hospital admission. However, clinical data obtained longitudinally during hospitalization can provide a wealth of information to build more precise models. With this in mind, we monitored disease progression in 147 COVID-19 patients during hospitalization by frequently collecting clinical parameters. We show that models trained on longitudinally monitored immune biomarkers predicted mortality and were more accurate than models based on demographic and clinical data obtained upon hospital admission. Our work encourages the development of a broader computational framework that combines patient clinical data from hospital admission with longitudinally monitored biomarkers collected throughout the hospitalization to better assist health care workers with daily prognostication of COVID-19 patients.