Scoring systems for predicting mortality for severe patients with COVID-19

Shang, Yufeng; Liu, Tao; Wei, Yongchang; Li, Jing‐Feng; Shao, Liang; Liu, Minghui; Zhang, Yongxi; Zhao, Zhiyuan; Xu, Haibo; Peng, Zhiyong; Zhou, Fuling; Wang, Xinghuan

doi:10.1016/j.eclinm.2020.100426

Cited by 155 publications

(166 citation statements)

References 49 publications

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“…Interestingly, AKI appeared to be relevant in our analysis, while other scores do not include altered kidney function. However, several studies pointed out the association of kidney injury at admission with pejorative outcomes [2,13,14].…”

Section: Discussionmentioning

confidence: 99%

Predictive Accuracy of COVID-19 World Health Organization (WHO) Severity Classification and Comparison with a Bayesian-Method-Based Severity Score (EPI-SCORE)

Terwangne

Laouni

Jouffe³

et al. 2020

Pathogens

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Assess the predictive accuracy of the WHO COVID-19 severity classification on COVID-19 hospitalized patients. The secondary aim was to compare its predictive power with a new prediction model, named COVID-19 EPI-SCORE, based on a Bayesian network analysis. Methods: We retrospectively analyzed a population of 295 COVID-19 RT-PCR positive patients hospitalized at Epicura Hospital Center, Belgium, admitted between March 1st and April 30th, 2020. Results: Our cohort’s median age was 73 (62–83) years, and the female proportion was 43%. All patients were classified following WHO severity classification at admission. In total, 125 (42.4%) were classified as Moderate, 69 (23.4%) as Severe, and 101 (34.2%) as Critical. Death proportions through these three classes were 11.2%, 33.3%, and 67.3%, respectively, and the proportions of critically ill patients (dead or needed Invasive Mechanical Ventilation) were 11.2%, 34.8%, and 83.2%, respectively. A Bayesian network analysis was used to create a model to analyze predictive accuracy of the WHO severity classification and to create the EPI-SCORE. The six variables that have been automatically selected by our machine learning algorithm were the WHO severity classification, acute kidney injury, age, Lactate Dehydrogenase Levels (LDH), lymphocytes and activated prothrombin time (aPTT). Receiver Operation Characteristic (ROC) curve indexes hereby obtained were 83.8% and 91% for the models based on WHO classification only and our EPI-SCORE, respectively. Conclusions: Our study shows that the WHO severity classification is reliable in predicting a severe outcome among COVID-19 patients. The addition to this classification of a few clinical and laboratory variables as per our COVID-19 EPI-SCORE has demonstrated to significantly increase its accuracy.

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Section: Discussionmentioning

confidence: 99%

Predictive Accuracy of COVID-19 World Health Organization (WHO) Severity Classification and Comparison with a Bayesian-Method-Based Severity Score (EPI-SCORE)

Terwangne

Laouni

Jouffe³

et al. 2020

Pathogens

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“…Each of these features have been identified as predictors of mortality associated with the COVID-19 disease. 9,[11][12][13][14] Age has been identified as an important factor in COVID-19 disease progression and hence it has been included in all the models here. [3][4][5] Patients aged ≥60 years had a higher rate of respiratory failure and needed more prolonged treatment than those aged <60 years, 3 implying that the elderly showed poorer response to treatments than the younger age group.…”

Section: /21mentioning

confidence: 99%

“…The clinical model can prove to be useful given the ease of data collection of all the three features. Shang et al 12 established a scoring system of COVID-19 (CSS) to split patients into low-risk and high-risk groups. Here, high-risk group patients would have significantly higher chances of death than those of the low-risk group.…”

Section: Introductionmentioning

confidence: 99%

Machine learning based clinical decision support system for early COVID-19 mortality prediction

Karthikeyan

Garg

Vinod

2020

Preprint

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The coronavirus disease 2019 (COVID-19) is an acute respiratory disease that has been classified as a pandemic by World Health Organization (WHO). The sudden spike in the number of infections and high mortality rates have put immense pressure on the public medical systems. Hence, it is crucial to identify the key factors of mortality that yield high accuracy and consistency to optimize patient treatment strategy. This study uses machine learning methods to identify a powerful combination of five features that help predict mortality with 96% accuracy: neutrophils, lymphocytes, lactate dehydrogenase (LDH), high-sensitivity C-reactive protein (hs-CRP) and age. Various machine learning algorithms have been compared to achieve a consistent high accuracy across the days that span the disease. Robust testing with three cases confirm the strong predictive performance of the proposed model. The model predicts with an accuracy of 90% as early as 16 days before the outcome. This study would help accelerate the decision making process in healthcare systems for focused medical treatments early and accurately.

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“…As of 01:33:17 AM on Jun 17, 2020, the total numbers of confirmed cases and the death toll worldwide had reached 8,084,396 and 438,399, respectively, and the global data curve of confirmed cases outside China exhibited a sharp upward trend [2] . It was reported that most of the dead were those with severe disease [3] , casting a spotlight on the identification and management of high-risk cases with poor outcomes. Although several pre-existing scoring systems derived from the emergency department possess certain prediction value for critically ill patients with COVID-19, their effectiveness are still far from perfection and satisfaction [4] .…”

mentioning

confidence: 99%

“…The study by Shang et al [3] , for the first time, attempted to generate a specific scoring system to predict in-hospital deaths for severe COVID-19 patients. They found old age, coronary heart disease, percentage of lymphocytes, procalcitonin and d -dimer were independently related to mortality.…”

mentioning

confidence: 99%