Current best evidence for clinical care (more info)
BACKGROUND: Patients with severe Coronavirus Disease 2019 (COVID-19) will progress rapidly to acute respiratory failure or death. We aimed to develop a quantitative tool for early predicting mortality risk of patients with COVID-19.
METHODS: 301 patients with confirmed COVID-19 admitted to Main District and Tumor Center of the Union Hospital of Huazhong University of Science and Technology (Wuhan, China) between January 1, 2020 to February 15, 2020 were enrolled in this retrospective two-centers study. Data on patient demographic characteristics, laboratory findings and clinical outcomes was analyzed. A nomogram was constructed to predict the death probability of COVID-19 patients.
RESULTS: Age, neutrophil-to-lymphocyte ratio, D-dimer and C-reactive protein obtained on admission were identified as predictors of mortality for COVID-19 patients by LASSO. The nomogram demonstrated good calibration and discrimination with the area under the curve (AUC) of 0.921 and 0.975 for the derivation and validation cohort, respectively. An integrated score (named ANDC) with its corresponding death probability was derived. Using ANDC cut-off values of 59 and 101, COVID-19 patients were classified into three subgroups. The death probability of low risk group (ANDC < 59) was less than 5%, moderate risk group (59 = ANDC = 101) was 5% to 50%, and high risk group (ANDC > 101) was more than 50%, respectively.
CONCLUSION: The prognostic nomogram exhibited good discrimination power in early identification of COVID-19 patients with high mortality risk, and ANDC score may help physicians to optimize patient stratification management.
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This is very promising work using retrospective analyses of data. It provides an interesting approach to evaluating sick patients as they are admitted. Efforts to identify risk will help provide earlier treatment in a more pre-emptive fashion.
With the pandemic continuing, any ability to determine the probability of progression to death is helpful, albeit unclear what benefit is provided. In this study, while a "scoring" system was created, there are multiple flaws. First, they already included severe patients. As a result, these patients already receive the max treatment options possible in US hospital systems. Thus, this identification score does NOT change management. Second, the patients were in only one center, and do not reflect the different ethnicities worldwide. Third, this was retrospective, therefore it is unclear whether it would hold up in a prospective setting. Although interesting, this article provides no clinical benefit to the medical community.
This is a study of prognosis in patients hospitalized with COVID-19, finding that a decision rule containing 4 variables (age, neutrophil to lymphocyte ratio, D-dimer and CRP) was accurate for discriminating mortality risk. At face value, it appears superior and better validated that any proposed variables to date. However, it seems odd that sex and comorbidities did not make it into the prediction rule, given the striking differences at baseline. The apparently high predictive accuracy without these and with only 4 variables seems too good to be true. Also, all patients were treated with antivirals that would not be considered standard therapy now and some of which have since been shown to be ineffective for COVID-19. There is no mention of which patients received steroids, which we now know is effective in reducing mortality in COVID-19. Therefore, it is hard to know how these findings can be generalized to our patient populations.