ArticleScandinavian journal of trauma, resuscitation and emergency medicine2020
Exploiting an early warning Nomogram for predicting the risk of ICU admission in patients with COVID-19: a multi-center study in China.
Article in Scandinavian journal of trauma, resuscitation and emergency medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- Prognostic Significance of Computed Tomography Severity Score for Machine Learning Prediction of Intensive Care Unit Admission in COVID-19 Patients.Thoracic research and practice · 2026Article
- Machine learning algorithms for predicting COVID-19 mortality in Ethiopia.BMC public health · 2024Article
- Risk Factor Analysis and Nomogram for Early Progression of COVID-19 Pneumonia in Older Adult Patients in the Omicron Era.Clinical interventions in aging · 2024Article
- Comparing machine learning algorithms to predict COVID‑19 mortality using a dataset including chest computed tomography severity score data.Scientific reports · 2023Article
- Heterogeneity and Risk of Bias in Studies Examining Risk Factors for Severe Illness and Death in COVID-19: A Systematic Review and Meta-Analysis.Pathogens (Basel, Switzerland) · 2022Review
- Comparing machine learning algorithms for predicting COVID-19 mortality.BMC medical informatics and decision making · 2022Article
- Using logistic regression to develop a diagnostic model for COVID-19: A single-center study.Journal of education and health promotion · 2022Article
- Predicting the Need for Intubation among COVID-19 Patients Using Machine Learning Algorithms: A Single-Center Study.Medical journal of the Islamic Republic of Iran · 2022Article
- Using decision tree algorithms for estimating ICU admission of COVID-19 patients.Informatics in medicine unlocked · 2022Article
- Factors associated with poor outcomes among hospitalized patients with COVID-19: Experience from a MERS-CoV referral hospital.Journal of infection and public health · 2021Article
- Development and validation of nomogram to predict severe illness requiring intensive care follow up in hospitalized COVID-19 cases.BMC infectious diseases · 2021Article
- Emergency department characteristics and associations with intensive care admission among patients with coronavirus disease 2019.Journal of the American College of Emergency Physicians open · 2021Article
- Clinical Utility of a Nomogram for Predicting 30-Days Poor Outcome in Hospitalized Patients With COVID-19: Multicenter External Validation and Decision Curve Analysis.Frontiers in medicine · 2020Article
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8 authors.
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Abstract
backgroundNovel coronavirus disease 2019 (COVID-19) is a global public health emergency. Here, we developed and validated a practical model based on the data from a multi-center cohort in China for early identification and prediction of which patients will be admitted to the intensive care unit (ICU).
methodsData of 1087 patients with laboratory-confirmed COVID-19 were collected from 49 sites between January 2 and February 28, 2020, in Sichuan and Wuhan. Patients were randomly categorized into the training and validation cohorts (7:3). The least absolute shrinkage and selection operator and logistic regression analyzes were used to develop the nomogram. The performance of the nomogram was evaluated for the C-index, calibration, discrimination, and clinical usefulness. Further, the nomogram was externally validated in a different cohort.
resultsThe individualized prediction nomogram included 6 predictors: age, respiratory rate, systolic blood pressure, smoking status, fever, and chronic kidney disease. The model demonstrated a high discriminative ability in the training cohort (C-index = 0.829), which was confirmed in the external validation cohort (C-index = 0.776). In addition, the calibration plots confirmed good concordance for predicting the risk of ICU admission. Decision curve analysis revealed that the prediction nomogram was clinically useful.
conclusionWe established an early prediction model incorporating clinical characteristics that could be quickly obtained on hospital admission, even in community health centers. This model can be conveniently used to predict the individual risk for ICU admission of patients with COVID-19 and optimize the use of limited resources.
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