Development and Validation of a Nomogram for Assessing Survival in Patients With COVID-19 Pneumonia

Dong, Ying; Sun, Jia; Li, Yixin; Chen, Qian; Liu, Qingquan; Zhou, Sun; Pang, Ran; Chen, Fei; Xu, Bingyang; Manyande, Anne; Clark, Taane G.; Li, Jinping; Orhan, İlkay Erdoğan; Tian, Ye; Wang, Tao; Wu, Wei; Ye, Dawei

doi:10.1093/cid/ciaa963

Cited by 94 publications

(85 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In COVID-19 patients, elevated NLR on admission was reported to be signi cantly associated with disease severity 13,14 To date, a few prognostic models of COVID-19 have been developed. Dong et al found that hypertension, higher NLR and increased NT-proBNP value were signi cantly poorer prognostic factors in hospitalized COVID-19 patients 21 . A nomogram for predicting 14-day and 21day probability of in-hospital survival based on the three factors was developed, with high c-index and AUC in both training and validation cohorts.…”

Section: Discussionmentioning

confidence: 99%

A Machine Learning Model to Predict Death Outcome in Severe COVID-19 Patients

Qiao

Zhang

et al. 2020

Preprint

View full text Add to dashboard Cite

BackgroundThe novel coronavirus disease 2019 (COVID-19) spreads rapidly among people and causes a global pandemic. It is of great clinical significance to identify COVID-19 patients with high risk of death.MethodsA total of 2,169 adult COVID-19 patients were enrolled from Wuhan, China between February 10th and April 15th, 2020. Difference analyses of medical records were performed between severe and non-severe groups as well as between survivors and non-survivors. In addition, we developed a decision tree classifier to identify risk factors for death outcome.ResultsOf the 2,169 COVID-19 patients, the median age was 61 years and male patients accounted for 48%. A total of 646 patients were diagnosed with severe illness, and 75 patients died. The most common system symptoms were respiratory, systemic and digestive symptoms. Obvious differences in demographics, clinical characteristics and laboratory examinations were found between severe and non-severe groups, as well as between survivors and non-survivors. A machine learning model was developed to predict death outcome in severe patients. The decision tree classifier included three biomarkers, neutrophil-to-lymphocyte ratio, C-reactive protein and lactic dehydrogenase. The area under the curve of the receiver operating characteristic of this model was 0.96. This model performed well both in train dataset and test dataset. The accuracy of this model was 0.98 and 0.98, respectively.ConclusionThe machine learning model was robust and effective in predicting the death outcome in severe COVID-19 patients.

show abstract

Section: Discussionmentioning

confidence: 99%

A Machine Learning Model to Predict Death Outcome in Severe COVID-19 Patients

Qiao

Zhang

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Hence, we could not validate their tree-model in our discovery dataset, but our prediction forest model achieved considerable accuracy (AUC 87%) in Yan's dataset. Notably, Dong et al built a predictive nomogram by using only three indicators: hypertension, neutrophil-to-lymphocyte ratio and NT-proBNP at admission to hospital, which surprisingly achieved 89.2% of C-index in internal validation set [13]. However, NT-proBNP is usually examined in patients with symptoms of cardiac dysfunction rather than regularly measured at admission, which may limit its application.…”

Section: Discussionmentioning

confidence: 99%

“…We identi ed 11 laboratory measures at admission which appear to associate with the poor COVID-19 outcomes. Among the regularly measured laboratory indicators at admission, the high neutrophil count, low lymphocyte count, high neutrophil-to-lymphocyte ratio, high direct bilirubin, and elevated lactate dehydrogenase have previously been identi ed as prognostic predictors for an unfavorable outcome [12,13,22,23]. However, in our discovery set, these predictors were not ranked at the top of VIS list for all candidate factors possibly because all patients in our study had progressed to severe or critical disease and the previous predictors may be less prognostic for severity.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Dynamic Prognosis Model for Predicting Survival in Severe and Critically Ill COVID-19 Patients Using Machine Learning

Wei

Chen

et al. 2020

Preprint

View full text Add to dashboard Cite

Background Novel coronavirus disease (COVID-19) is an emerging, rapidly evolving situation. At present, the prognosis of severe and critically ill patients has become an important focus of attention. We strived to develop a prognostic prediction model for severe and critically ill COVID-19 patients.MethodsTo assess the factors associated with the prognosis of those patients, we retrospectively investigated the clinical, laboratory characteristics of confirmed 112 cases of COVID-19 admitted between 21 January to 6 March 2020 from Huangshi Central Hospital, Huangshi Hospital of Traditional Chinese Medicine, and Daye People’s Hospital. We applied machine learning method (survival random forest) to select predictors for 28-day survival and taken into account the dynamic trajectory of laboratory indicators. Results Fifteen candidate prognostic features, including 11 baseline measures (including platelet count (PLT), urea, creatine kinase (CK), fibrinogen, creatine kinase isoenzyme activity, aspartate aminotransferase (AST), activation of partial thromboplastin time (APTT), albumin, standard deviation of erythrocyte distribution width (RBC-SD), neutrophils (%) and red blood cell count (RBC)) and 4 trajectory clusters (changes during hospitalization in the white blood cell (WBC), PLT large cell ratio (P-LCR), PLT distribution width (PDW) and AST), combined with covariates achieved 100% (95%CI: 99%-100%) AUC and reached 87% (95%CI: 84%-91%) AUC in an external validation set. Conclusions Taking advantage of random forest technique and laboratory dynamic measures, we developed a forest model to predict survival outcome of COVID-19 patients, which achieved 87% AUC in the external validation set. Our online tool will help to facilitate the early recognition of patients with high risk.

show abstract

“…Imaging parameters including severe chest X-ray radiographic abnormalities and diffuse pulmonary infiltration on CT that have also been linked to severe disease. One multiparametric model aimed at predicting the risk of hospitalisation with an area under the curve (AUC) of 0.9, 66 while three studies aimed at predicting survival with AUC between 0.879 and 0.955, [67][68][69] and two other aimed at predicting disease mortality with AUC between 0.871 and 0.975. 70 71 Other algorithms were developed, aiming at predicting disease progression towards a severe phenotype with AUC from 0.77 to 0.9.…”

Section: Platelets Endothelial Dysfunction and Thrombosis And Sars-cmentioning

confidence: 99%

Pathophysiology of acute respiratory syndrome coronavirus 2 infection: a systematic literature review to inform EULAR points to consider

et al. 2021

View full text Add to dashboard Cite

BackgroundThe SARS-CoV-2 pandemic is a global health problem. Beside the specific pathogenic effect of SARS-CoV-2, incompletely understood deleterious and aberrant host immune responses play critical roles in severe disease. Our objective was to summarise the available information on the pathophysiology of COVID-19.MethodsTwo reviewers independently identified eligible studies according to the following PICO framework: P (population): patients with SARS-CoV-2 infection; I (intervention): any intervention/no intervention; C (comparator): any comparator; O (outcome) any clinical or serological outcome including but not limited to immune cell phenotype and function and serum cytokine concentration.ResultsOf the 55 496 records yielded, 84 articles were eligible for inclusion according to question-specific research criteria. Proinflammatory cytokine expression, including interleukin-6 (IL-6), was increased, especially in severe COVID-19, although not as high as other states with severe systemic inflammation. The myeloid and lymphoid compartments were differentially affected by SARS-CoV-2 infection depending on disease phenotype. Failure to maintain high interferon (IFN) levels was characteristic of severe forms of COVID-19 and could be related to loss-of-function mutations in the IFN pathway and/or the presence of anti-IFN antibodies. Antibody response to SARS-CoV-2 infection showed a high variability across individuals and disease spectrum. Multiparametric algorithms showed variable diagnostic performances in predicting survival, hospitalisation, disease progression or severity, and mortality.ConclusionsSARS-CoV-2 infection affects both humoral and cellular immunity depending on both disease severity and individual parameters. This systematic literature review informed the EULAR ‘points to consider’ on COVID-19 pathophysiology and immunomodulatory therapies.

show abstract

Development and Validation of a Nomogram for Assessing Survival in Patients With COVID-19 Pneumonia

Cited by 94 publications

References 33 publications

A Machine Learning Model to Predict Death Outcome in Severe COVID-19 Patients

A Machine Learning Model to Predict Death Outcome in Severe COVID-19 Patients

Dynamic Prognosis Model for Predicting Survival in Severe and Critically Ill COVID-19 Patients Using Machine Learning

Pathophysiology of acute respiratory syndrome coronavirus 2 infection: a systematic literature review to inform EULAR points to consider

Contact Info

Product

Resources

About