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Machine-learning-based COVID-19 mortality prediction model and identification of patients at low and high risk of dying
Critical Care ( IF 8.8 ) Pub Date : 2021-09-08 , DOI: 10.1186/s13054-021-03749-5
Mohammad M Banoei 1, 2 , Roshan Dinparastisaleh 3 , Ali Vaeli Zadeh 4 , Mehdi Mirsaeidi 5
Affiliation  

The coronavirus disease 2019 (COVID-19) pandemic caused by the SARS-Cov2 virus has become the greatest health and controversial issue for worldwide nations. It is associated with different clinical manifestations and a high mortality rate. Predicting mortality and identifying outcome predictors are crucial for COVID patients who are critically ill. Multivariate and machine learning methods may be used for developing prediction models and reduce the complexity of clinical phenotypes. Multivariate predictive analysis was applied to 108 out of 250 clinical features, comorbidities, and blood markers captured at the admission time from a hospitalized cohort of patients (N = 250) with COVID-19. Inspired modification of partial least square (SIMPLS)-based model was developed to predict hospital mortality. Prediction accuracy was randomly assigned to training and validation sets. Predictive partition analysis was performed to obtain cutting value for either continuous or categorical variables. Latent class analysis (LCA) was carried to cluster the patients with COVID-19 to identify low- and high-risk patients. Principal component analysis and LCA were used to find a subgroup of survivors that tends to die. SIMPLS-based model was able to predict hospital mortality in patients with COVID-19 with moderate predictive power (Q2 = 0.24) and high accuracy (AUC > 0.85) through separating non-survivors from survivors developed using training and validation sets. This model was obtained by the 18 clinical and comorbidities predictors and 3 blood biochemical markers. Coronary artery disease, diabetes, Altered Mental Status, age > 65, and dementia were the topmost differentiating mortality predictors. CRP, prothrombin, and lactate were the most differentiating biochemical markers in the mortality prediction model. Clustering analysis identified high- and low-risk patients among COVID-19 survivors. An accurate COVID-19 mortality prediction model among hospitalized patients based on the clinical features and comorbidities may play a beneficial role in the clinical setting to better management of patients with COVID-19. The current study revealed the application of machine-learning-based approaches to predict hospital mortality in patients with COVID-19 and identification of most important predictors from clinical, comorbidities and blood biochemical variables as well as recognizing high- and low-risk COVID-19 survivors.

中文翻译:

基于机器学习的 COVID-19 死亡率预测模型和低和高死亡风险患者的识别

由 SARS-Cov2 病毒引起的 2019 年冠状病毒病 (COVID-19) 大流行已成为全球各国最大的健康和争议问题。它与不同的临床表现和高死亡率有关。预测死亡率和确定结果预测因子对于重症 COVID 患者至关重要。多变量和机器学习方法可用于开发预测模型并降低临床表型的复杂性。多变量预测分析应用于 COVID-19 住院患者 (N = 250) 入院时采集的 250 种临床特征、合并症和血液标志物中的 108 种。开发了基于偏最小二乘 (SIMPLS) 的模型的启发性修改来预测医院死亡率。预测准确性被随机分配给训练和验证集。执行预测分区分析以获得连续或分类变量的切割值。进行潜在类别分析 (LCA) 以对 COVID-19 患者进行聚类,以识别低风险和高风险患者。使用主成分分析和 LCA 来寻找倾向于死亡的幸存者亚组。基于 SIMPLS 的模型能够通过将非幸存者与使用训练和验证集开发的幸存者分开,以中等预测能力 (Q2 = 0.24) 和高精度 (AUC > 0.85) 预测 COVID-19 患者的住院死亡率。该模型由 18 个临床和合并症预测因子和 3 个血液生化标志物获得。冠状动脉疾病、糖尿病、精神状态改变、年龄 > 65、和痴呆是区分死亡率最高的预测因子。CRP、凝血酶原和乳酸是死亡率预测模型中最具区分性的生化标志物。聚类分析确定了 COVID-19 幸存者中的高风险和低风险患者。基于临床特征和合并症的住院患者的准确 COVID-19 死亡率预测模型可能在临床环境中发挥有益作用,以更好地管理 COVID-19 患者。目前的研究揭示了应用基于机器学习的方法来预测 COVID-19 患者的住院死亡率,并从临床、合并症和血液生化变量中识别最重要的预测因子,以及识别高风险和低风险 COVID-19幸存者。凝血酶原和乳酸是死亡率预测模型中最具区分性的生化标志物。聚类分析确定了 COVID-19 幸存者中的高风险和低风险患者。基于临床特征和合并症的住院患者的准确 COVID-19 死亡率预测模型可能在临床环境中发挥有益作用,以更好地管理 COVID-19 患者。目前的研究揭示了应用基于机器学习的方法来预测 COVID-19 患者的住院死亡率,并从临床、合并症和血液生化变量中识别最重要的预测因子,以及识别高风险和低风险 COVID-19幸存者。凝血酶原和乳酸是死亡率预测模型中最具区分性的生化标志物。聚类分析确定了 COVID-19 幸存者中的高风险和低风险患者。基于临床特征和合并症的住院患者的准确 COVID-19 死亡率预测模型可能在临床环境中发挥有益作用,以更好地管理 COVID-19 患者。目前的研究揭示了应用基于机器学习的方法来预测 COVID-19 患者的住院死亡率,并从临床、合并症和血液生化变量中识别最重要的预测因子,以及识别高风险和低风险 COVID-19幸存者。聚类分析确定了 COVID-19 幸存者中的高风险和低风险患者。基于临床特征和合并症的住院患者的准确 COVID-19 死亡率预测模型可能在临床环境中发挥有益作用,以更好地管理 COVID-19 患者。目前的研究揭示了应用基于机器学习的方法来预测 COVID-19 患者的住院死亡率,并从临床、合并症和血液生化变量中识别最重要的预测因子,以及识别高风险和低风险 COVID-19幸存者。聚类分析确定了 COVID-19 幸存者中的高风险和低风险患者。基于临床特征和合并症的住院患者的准确 COVID-19 死亡率预测模型可能在临床环境中发挥有益作用,以更好地管理 COVID-19 患者。目前的研究揭示了应用基于机器学习的方法来预测 COVID-19 患者的住院死亡率,并从临床、合并症和血液生化变量中识别最重要的预测因子,以及识别高风险和低风险 COVID-19幸存者。基于临床特征和合并症的住院患者的准确 COVID-19 死亡率预测模型可能在临床环境中发挥有益作用,以更好地管理 COVID-19 患者。目前的研究揭示了应用基于机器学习的方法来预测 COVID-19 患者的住院死亡率,并从临床、合并症和血液生化变量中识别最重要的预测因子,以及识别高风险和低风险 COVID-19幸存者。基于临床特征和合并症的住院患者的准确 COVID-19 死亡率预测模型可能在临床环境中发挥有益作用,以更好地管理 COVID-19 患者。目前的研究揭示了应用基于机器学习的方法来预测 COVID-19 患者的住院死亡率,并从临床、合并症和血液生化变量中识别最重要的预测因子,以及识别高风险和低风险 COVID-19幸存者。
更新日期:2021-09-08
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