ArticleEngineering (Beijing, China)2022
A Predictive Nomogram for Predicting Improved Clinical Outcome Probability in Patients with COVID-19 in Zhejiang Province, China.
Article in Engineering (Beijing, China), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 3 of them syntheses that pooled it.
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Who cites it
12 citing papers in PubMed, 3 syntheses or guidelines pooled it, 30 citations in OpenAlex.
- Serum CK-MB, COVID-19 severity and mortality: An updated systematic review and meta-analysis with meta-regression.Advances in medical sciences · 2021Pooled it
- Gastroenterology manifestations and COVID-19 outcomes: A meta-analysis of 25,252 cohorts among the first and second waves.Journal of medical virology · 2021Pooled it
- Serum Complement C3 and C4 and COVID-19 Severity and Mortality: A Systematic Review and Meta-Analysis With Meta-Regression.Frontiers in immunologyPooled it
- Retrospective cohort analysis on predicting pulmonary fibrosis in elderly SARS-CoV-2-infected patients.Frontiers in cellular and infection microbiology · 2025Article
- A Nomogram for Predicting Survival in Patients with SARS-CoV-2 Omicron Variant Pneumonia Based on Admission Data.Infection and drug resistance · 2025Article
- Development and validation of a nomogram for predicting in-hospital survival rates of patients with COVID-19.Heliyon · 2024Article
- A Nomogram Model for Predicting the Polyphenol Content of Pu-Erh Tea.Foods (Basel, Switzerland) · 2023Article
- Fisher discriminant model based on LASSO logistic regression for computed tomography imaging diagnosis of pelvic rhabdomyosarcoma in children.Scientific reports · 2022Article
- Association of obesity with COVID-19 diseases severity and mortality: A meta-analysis of studies.Obesity medicine · 2022Article
- Development and Validation of a Prognostic Risk Score System for COVID-19 Inpatients: A Multi-Center Retrospective Study in China.Engineering (Beijing, China) · 2022Article
- Cytokine Signature Associated With Disease Severity in COVID-19.Frontiers in immunology · 2021Article
- Characteristic of 523 COVID-19 in Henan Province and a Death Prediction Model.Frontiers in public health · 2020Observational
Corrections and comments
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Authors and funding
13 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The aim of this research was to develop a quantitative method for clinicians to predict the probability of improved prognosis in patients with coronavirus disease 2019 (COVID-19). Data on 104 patients admitted to hospital with laboratory-confirmed COVID-19 infection from 10 January 2020 to 26 February 2020 were collected. Clinical information and laboratory findings were collected and compared between the outcomes of improved patients and non-improved patients. The least absolute shrinkage and selection operator (LASSO) logistics regression model and two-way stepwise strategy in the multivariate logistics regression model were used to select prognostic factors for predicting clinical outcomes in COVID-19 patients. The concordance index (C-index) was used to assess the discrimination of the model, and internal validation was performed through bootstrap resampling. A novel predictive nomogram was constructed by incorporating these features. Of the 104 patients included in the study (median age 55 years), 75 (72.1%) had improved short-term outcomes, while 29 (27.9%) showed no signs of improvement. There were numerous differences in clinical characteristics and laboratory findings between patients with improved outcomes and patients without improved outcomes. After a multi-step screening process, prognostic factors were selected and incorporated into the nomogram construction, including immunoglobulin A (IgA), C-reactive protein (CRP), creatine kinase (CK), acute physiology and chronic health evaluation II (APACHE II), and interaction between CK and APACHE II. The C-index of our model was 0.962 (95% confidence interval (CI), 0.931-0.993) and still reached a high value of 0.948 through bootstrapping validation. A predictive nomogram we further established showed close performance compared with the ideal model on the calibration plot and was clinically practical according to the decision curve and clinical impact curve. The nomogram we constructed is useful for clinicians to predict improved clinical outcome probability for each COVID-19 patient, which may facilitate personalized counselling and treatment.
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