A Tool for Early Prediction of Severe Coronavirus Disease 2019 (COVID-19): A Multicenter Study Using the Risk Nomogram in Wuhan and Guangdong, China

Gong, Jiao; Ou, Jingyi; Qiu, Xiaoguang; Jie, Yusheng; Chen, Yaqiong; Yuan, Lei; Cao, Jing; Tan, Mingkai; Xu, Wenxiong; Zheng, Fang; Shi, Yawei; Hu, Bo

doi:10.1093/cid/ciaa443

Cited by 443 publications

(486 citation statements)

References 20 publications

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“…Most of the listed complications in Fig. 1 and the related biomarkers, especially high C-reactive protein, hs-troponin, brain natriuretic peptide (BNP)/N-terminal prohormone BNT (NT-proBNP), and D-dimer, have been shown to be associated with a more severe, if not fatal, outcome of COVID-19 (1)(2)(3)(4)(5)(10)(11)(12).…”

Section: Risk Management In People Withmentioning

confidence: 99%

Issues of Cardiovascular Risk Management in People With Diabetes in the COVID-19 Era

et al. 2020

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People with diabetes compared with people without exhibit worse prognosis if affected by coronavirus disease 2019 (COVID-19) induced by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), particularly when compromising metabolic control and concomitant cardiovascular disorders are present. This Perspective seeks to explore newly occurring cardio-renal-pulmonary organ damage induced or aggravated by the disease process of COVID-19 and its implications for the cardiovascular risk management of people with diabetes, especially taking into account potential interactions with mechanisms of cellular intrusion of SARS-CoV-2. Severe infection with SARS-CoV-2 can precipitate myocardial infarction, myocarditis, heart failure, and arrhythmias as well as an acute respiratory distress syndrome and renal failure. They may evolve along with multiorgan failure directly due to SARS-CoV-2–infected endothelial cells and resulting endotheliitis. This complex pathology may bear challenges for the use of most diabetes medications in terms of emerging contraindications that need close monitoring of all people with diabetes diagnosed with SARS-CoV-2 infection. Whenever possible, continuous glucose monitoring should be implemented to ensure stable metabolic compensation. Patients in the intensive care unit requiring therapy for glycemic control should be handled solely by intravenous insulin using exact dosing with a perfusion device. Although not only ACE inhibitors and angiotensin 2 receptor blockers but also SGLT2 inhibitors, GLP-1 receptor agonists, pioglitazone, and probably insulin seem to increase the number of ACE2 receptors on the cells utilized by SARS-CoV-2 for penetration, no evidence presently exists that shows this might be harmful in terms of acquiring or worsening COVID-19. In conclusion, COVID-19 and related cardio-renal-pulmonary damage can profoundly affect cardiovascular risk management of people with diabetes.

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Section: Risk Management In People Withmentioning

confidence: 99%

Issues of Cardiovascular Risk Management in People With Diabetes in the COVID-19 Era

et al. 2020

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“…Previous studies have shown several prediction models with different parameters [2,25,31,32]. Compared with other studies, the prediction effect of age, panting, and lymphopenia has been described in previous reports, while the main feature of this study is analysis of the prognostic value of IL-6 in severe COVID-19 patients for the rst time.…”

Section: Discussionmentioning

confidence: 84%

“…Previous studies have indicated that in all COVID-19 patients, the incidence of severe cases is about 15% [2,3]. The mortality rate of severe COVID-19 patients is reported variously from 8% to 61.5% and signi cantly increases among the old patients [4][5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Nomogram for Prediction of fatal outcome in Patients with Severe COVID-19: A Multicenter Study

Yang

Zhu

Huang

et al. 2020

Preprint

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Background & Aims: To develop an effective model of predicting fatalOutcome in the severe coronavirus disease 2019 (COVID-19) patients.Methods: Between February 20, 2020 and April 4, 2020, consecutive COVID-19 patients from three designated hospitals were enrolled in this study. Independent high- risk factors associated with death were analyzed using Cox proportional hazard model. A prognostic nomogram was constructed to predict the survival of severe COVID-19 patients.Results: There were 124 severe patients in the training cohort, and there were 71 and 76 severe patients in the two independent validation cohorts, respectively. Multivariate Cox analysis indicated that age ≥ 70 years (HR 1.184, 95% CI 1.061-1.321), Panting(breathing rate ≥ 30/min) (HR 3.300, 95% CI 2.509-6.286), lymphocyte count < 1.0 × 109/L (HR 2.283, 95% CI 1.779-3.267), and IL-6 >10pg/mL (HR 3.029, 95% CI 1.567-7.116) were independent high-risk factors associated with fatal outcome. We developed the nomogram for identifying survival of severe COVID-19 patients in the training cohort (AUC 0.900, [95% CI 0.841-0.960], sensitivity 95.5%, specificity 77.5%); in validation cohort 1 (AUC 0.811, [95% CI 0.698-0.924], sensitivity 77.3%, specificity 73.5); in validation cohort 2 (AUC 0.862, [95% CI 0.763-0.961], sensitivity 92.9%, specificity 64.5%). The calibration curve for probability of death indicated a good consistence between prediction by the nomogram and the actual observation. The prognosis of severe COVID-19 patients with high levels of interleukin-6 (IL-6) receiving tocilizumab was better than that of those patients without tocilizumab both in the training and validation cohorts, but without difference (p = 0.105 for training cohort, p = 0.133 for validation cohort 1, and p = 0.210 for validation cohort 2).Conclusions: This nomogram could help clinicians to identify severe patients who have high risk of death, and to develop more appropriate treatment strategies to reduce the mortality of severe patients. Tocilizumab may improve the prognosis of severe COVID-19 patients with high levels of IL-6.

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“…However, only one veri cation of this model has been made, the e cacy of it is doubted, and the number of patients is insu cient. Another model to early predict severe type of COVID-19 showed older age, higher LDH, CRP, RDW, DBIL, BUN, and lower ALB on admission correlated with higher odds of severe COVID-19, with the AUC reached 0.912 (95% CI 0.846-0.978) in the training set, and 0.853 (95% CI 0.790-0.916) in the validation set [5]. Nonetheless, the small sample size could be the de ciency of this model.…”

Section: Discussionmentioning

confidence: 99%

“…Gong et al, provided a nomogram to help clinicians to early identify patients who will exacerbate to severe COVID-19 but they didn't take clinical factors like underlying comorbidities in consideration which was a universally acknowledged risk factor [5]. Dong et al, used CALL score model to estimate the progressive risk of COVID-19 patients but the sample size was limited, which may cause the volatility of the result, for example the hazard ratio of LDH > 500 is 9.8 (2.8-33.8) [6].…”

Section: Introductionmentioning

confidence: 99%

Construction and Validation of a Machine Learning-based Nomogram: A TOOL to Predict the Risk of Getting Severe Corona Virus Disease 2019 (COVID-19)

Zheng

Yao

Zheng

et al. 2020

Preprint

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Background Identifying patients who may develop severe coronavirus disease 2019 (COVID-19) will facilitate personalized treatment and optimize the distribution of medical resources. Methods In this study, 590 COVID-19 patients during hospitalization were enrolled (Training set: n = 285; Internal validation set: n = 127; Prospective set: n = 178). After filtered by 2 machine learning methods in the training set, 5 out of 31 clinical features were selected into model building to predict the risk of developing severe COVID-19 disease. Multivariate logistic regression was applied to build the prediction nomogram and validated in 2 different sets. Receiver operating characteristic (ROC) analysis and decision curve analysis (DCA) were used to evaluate its performance. Results From 31 potential predictors in the training set, 5 independent predictive factors were identified and included in the risk score: C-reactive protein (CRP), Lactate dehydrogenase (LDH), Age, Charlson/Deyo comorbidity score (CDCS) and Erythrocyte sedimentation rate (ESR). Subsequently, we generated the nomogram based on the above features for predicting severe COVID-19. In the training cohort, the Area under curves (AUCs) were 0.822 (95% CI 0.765–0.875) and the internal validation cohort was 0.762 (95% CI 0.768–0.844). Further, we validated it in a prospective cohort with the AUCs of 0.705 (95% CI 0.627–0.778). The internally bootstrapped calibration curve showed favorable consistency between prediction by nomogram and actual situation. And DCA analysis also conferred high clinical net benefit. Conclusion In this study, our predicting model based on 5 clinical characteristics of COVID-19 patients will enable clinicians to predict the potential risk of developing critical illness and thus optimize medical management.

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A Tool for Early Prediction of Severe Coronavirus Disease 2019 (COVID-19): A Multicenter Study Using the Risk Nomogram in Wuhan and Guangdong, China

Cited by 443 publications

References 20 publications

Issues of Cardiovascular Risk Management in People With Diabetes in the COVID-19 Era

Issues of Cardiovascular Risk Management in People With Diabetes in the COVID-19 Era

Nomogram for Prediction of fatal outcome in Patients with Severe COVID-19: A Multicenter Study

Construction and Validation of a Machine Learning-based Nomogram: A TOOL to Predict the Risk of Getting Severe Corona Virus Disease 2019 (COVID-19)

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