Development and Validation of a Predictive Nomogram with Age and Laboratory Findings for Severe COVID-19 in Hunan Province, China

Jiang, Junyi; Zhong, Weijun; Huang, Wei‐Hua; Gao, Yongchao; He, Yijing; Li, Xi; Liu, Zhao‐Qian; Zhou, Hong‐Hao; Fu, Yacheng; Liu, Rong; Zhang, Wei

doi:10.2147/tcrm.s361936

Cited by 3 publications

(1 citation statement)

References 65 publications

(77 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Previous studies [24][25][26] concluded that elderly patients were at increased risk to deteriorate during the course of a COVID-19 infection. A model created by age and laboratory findings showed that patients > 65 years of age had a substantially increased probability of developing a severe COVID-19 infection than patients < 65 years of age [27]. Earlier nomograms with clinical characteristics also demonstrated that age was a significant variable in severe cases [1,17].…”

Section: Discussionmentioning

confidence: 99%

A nomogram for predicting the severity of COVID-19 infections among patients in Chengdu, China

Luo¹,

Zhang²,

Zheng³

et al. 2023

Radiology Science

View full text Add to dashboard Cite

Introduction: Although most COVID-19 infections are currently mild, poor prognoses and fatalities continue to occur, which remain a threat to the safety of people in China. The goal of this study was to create an efficient model that combines the clinical characteristics with computed tomography (CT) scores at the time of admission to predict the severity of COVID-19. Methodology: A total of 346 COVID-19 patients in the current study, of whom 46 had severe infections and 300 had non-severe infections according to the clinal outcomes. Clinical, laboratory, CT findings, and CT scores at admission were collected. To identify the independent risk factors, univariable and multivariable logistic regression analyses were performed. A nomogram model was built with the extracted risk factors. The calibration curve and decision curve (DCA) operated to validate model performance. Results: The receiver operating characteristic curve indicated that the severity CT score had an area under the curve of 0.933 (95% CI, 0.901-0.965) and a cut-off value of 6.5 (sensitivity, 95.70%; specificity, 78%). The CT score, age, lactic dehydrogenase and hydroxybutyrate dehydrogenase levels, and hypertension were exacted for the nomogram. The nomogram had good calibration (P = 0.539) and excellent clinical value based on the DCA. Conclusions: The nomogram presented herein could be a valuable model to predict severe COVID-19 among patients in Chengdu, China.

show abstract

Section: Discussionmentioning

confidence: 99%

A nomogram for predicting the severity of COVID-19 infections among patients in Chengdu, China

Luo¹,

Zhang²,

Zheng³

et al. 2023

Radiology Science

View full text Add to dashboard Cite

show abstract

Prognostic models in COVID-19 infection that predict severity: a systematic review

et al. 2023

View full text Add to dashboard Cite

Current evidence on COVID-19 prognostic models is inconsistent and clinical applicability remains controversial. We performed a systematic review to summarize and critically appraise the available studies that have developed, assessed and/or validated prognostic models of COVID-19 predicting health outcomes. We searched six bibliographic databases to identify published articles that investigated univariable and multivariable prognostic models predicting adverse outcomes in adult COVID-19 patients, including intensive care unit (ICU) admission, intubation, high-flow nasal therapy (HFNT), extracorporeal membrane oxygenation (ECMO) and mortality. We identified and assessed 314 eligible articles from more than 40 countries, with 152 of these studies presenting mortality, 66 progression to severe or critical illness, 35 mortality and ICU admission combined, 17 ICU admission only, while the remaining 44 studies reported prediction models for mechanical ventilation (MV) or a combination of multiple outcomes. The sample size of included studies varied from 11 to 7,704,171 participants, with a mean age ranging from 18 to 93 years. There were 353 prognostic models investigated, with area under the curve (AUC) ranging from 0.44 to 0.99. A great proportion of studies (61.5%, 193 out of 314) performed internal or external validation or replication. In 312 (99.4%) studies, prognostic models were reported to be at high risk of bias due to uncertainties and challenges surrounding methodological rigor, sampling, handling of missing data, failure to deal with overfitting and heterogeneous definitions of COVID-19 and severity outcomes. While several clinical prognostic models for COVID-19 have been described in the literature, they are limited in generalizability and/or applicability due to deficiencies in addressing fundamental statistical and methodological concerns. Future large, multi-centric and well-designed prognostic prospective studies are needed to clarify remaining uncertainties.

show abstract

A systematic review and meta-analysis of the association between the neutrophil, lymphocyte, and platelet count, neutrophil-to-lymphocyte ratio, and platelet-to-lymphocyte ratio and COVID-19 progression and mortality

Zinellu

Mangoni

2022

Expert Review of Clinical Immunology

View full text Add to dashboard Cite

Development and Validation of a Predictive Nomogram with Age and Laboratory Findings for Severe COVID-19 in Hunan Province, China

Cited by 3 publications

References 65 publications

A nomogram for predicting the severity of COVID-19 infections among patients in Chengdu, China

A nomogram for predicting the severity of COVID-19 infections among patients in Chengdu, China

Prognostic models in COVID-19 infection that predict severity: a systematic review

A systematic review and meta-analysis of the association between the neutrophil, lymphocyte, and platelet count, neutrophil-to-lymphocyte ratio, and platelet-to-lymphocyte ratio and COVID-19 progression and mortality

Contact Info

Product

Resources

About