ArticleKorean journal of radiology2020
Development and Validation of a Prognostic Nomogram Based on Clinical and CT Features for Adverse Outcome Prediction in Patients with COVID-19.
Article in Korean journal of radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 31 citations in OpenAlex.
- Pooled it
- Enhancing physicians' radiology diagnostics of COVID-19's effects on lung health by leveraging artificial intelligence.Frontiers in bioengineering and biotechnology · 2023Article
- Developing and Validating Multi-Modal Models for Mortality Prediction in COVID-19 Patients: a Multi-center Retrospective Study.Journal of digital imaging · 2022Article
- Developing medical imaging AI for emerging infectious diseases.Nature communications · 2022Article
- How to Clearly and Accurately Report Odds Ratio and Hazard Ratio in Diagnostic Research Studies?Korean journal of radiology · 2022Article
- Quantitative chest computed tomography combined with plasma cytokines predict outcomes in COVID-19 patients.Heliyon · 2022Article
- A Hybrid Feature Selection Approach to Screen a Novel Set of Blood Biomarkers for Early COVID-19 Mortality Prediction.Diagnostics (Basel, Switzerland) · 2022Article
- [Korean Clinical Imaging Guidelines for Justification of Diagnostic Imaging Study for COVID-19].Taehan Yongsang Uihakhoe chi · 2022Review
- Quantitative chest CT combined with plasma cytokines predict outcomes in COVID-19 patients.medRxiv : the preprint server for health sciences · 2021Article
- Mistakes to Avoid for Accurate and Transparent Reporting of Survival Analysis in Imaging Research.Korean journal of radiology · 2021Article
- Machine Learning-Based Prediction of COVID-19 Severity and Progression to Critical Illness Using CT Imaging and Clinical Data.Korean journal of radiology · 2021Article
- Prediction of Patient Management in COVID-19 Using Deep Learning-Based Fully Automated Extraction of Cardiothoracic CT Metrics and Laboratory Findings.Korean journal of radiology · 2021Article
- Automated CT Lung Density Analysis of Viral Pneumonia and Healthy Lungs Using Deep Learning-Based Segmentation, Histograms and HU Thresholds.Diagnostics (Basel, Switzerland) · 2021Article
- Longitudinal Radiological Findings in Patients With COVID-19 With Different Severities: From Onset to Long-Term Follow-Up After Discharge.Frontiers in medicine · 2021Article
- What's New in theKorean journal of radiology · 2021Article
- CANPT Score: A Tool to Predict Severe COVID-19 on Admission.Frontiers in medicine · 2021Article
- Blood Urea Nitrogen to Serum Albumin Ratio (BAR) Predicts Critical Illness in Patients with Coronavirus Disease 2019 (COVID-19).International journal of general medicine · 2021Article
- Cerebral Micro-Structural Changes in COVID-19 Patients - An MRI-based 3-month Follow-up Study.EClinicalMedicine · 2020Article
Corrections and comments
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Authors and funding
11 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThe purpose of our study was to investigate the predictive abilities of clinical and computed tomography (CT) features for outcome prediction in patients with coronavirus disease (COVID-19). MATERIALS AND
methodsThe clinical and CT data of 238 patients with laboratory-confirmed COVID-19 in our two hospitals were retrospectively analyzed. One hundred sixty-six patients (103 males; age 43.8 ± 12.3 years) were allocated in the training cohort and 72 patients (38 males; age 45.1 ± 15.8 years) from another independent hospital were assigned in the validation cohort. The primary composite endpoint was admission to an intensive care unit, use of mechanical ventilation, or death. Univariate and multivariate Cox proportional hazard analyses were performed to identify independent predictors. A nomogram was constructed based on the combination of clinical and CT features, and its prognostic performance was externally tested in the validation group. The predictive value of the combined model was compared with models built on the clinical and radiological attributes alone.
resultsOverall, 35 infected patients (21.1%) in the training cohort and 10 patients (13.9%) in the validation cohort experienced adverse outcomes. Underlying comorbidity (hazard ratio [HR], 3.35; 95% confidence interval [CI], 1.67-6.71;
conclusionUnderlying comorbidity, lymphocyte count and crazy-paving sign were independent predictors of adverse outcomes. The prognostic nomogram based on the combination of clinical and CT features could be a useful tool for predicting adverse outcomes of patients with COVID-19.
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