ArticleJournal of intensive care2021
Development and validation of an online model to predict critical COVID-19 with immune-inflammatory parameters.
Article in Journal of intensive care, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prognostic models in COVID-19 infection that predict severity: a systematic review.European journal of epidemiology · 2023Pooled it
- Comparative performance of twelve machine learning models in predicting COVID-19 mortality risk in children: a population-based retrospective cohort study in Brazil.PeerJ. Computer science · 2025Article
- Establishment and validation of a prognostic model based on common laboratory indicators for SARS-CoV-2 infection in Chinese population.Annals of medicine · 2024Article
- Article
- Artificial Intelligence-Driven Ensemble Model for Predicting Mortality Due to COVID-19 in East Africa.Diagnostics (Basel, Switzerland) · 2022Article
- Machine Learning Sensors for Diagnosis of COVID-19 Disease Using Routine Blood Values for Internet of Things Application.Sensors (Basel, Switzerland) · 2022Article
- Contemporary narrative review of treatment options for COVID-19.Respirology (Carlton, Vic.) · 2021Review
- An Ensemble Matrix Completion Model for Predicting Potential Drugs Against SARS-CoV-2.Frontiers in microbiology · 2021Article
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17 authors.
Funding
Abstract
backgroundImmune and inflammatory dysfunction was reported to underpin critical COVID-19(coronavirus disease 2019). We aim to develop a machine learning model that enables accurate prediction of critical COVID-19 using immune-inflammatory features at admission.
methodsWe retrospectively collected 2076 consecutive COVID-19 patients with definite outcomes (discharge or death) between January 27, 2020 and March 30, 2020 from two hospitals in China. Critical illness was defined as admission to intensive care unit, receiving invasive ventilation, or death. Least Absolute Shrinkage and Selection Operator (LASSO) was applied for feature selection. Five machine learning algorithms, including Logistic Regression (LR), Support Vector Machine (SVM), Gradient Boosted Decision Tree (GBDT), K-Nearest Neighbor (KNN), and Neural Network (NN) were built in a training dataset, and assessed in an internal validation dataset and an external validation dataset.
resultsSix features (procalcitonin, [T + B + NK cell] count, interleukin 6, C reactive protein, interleukin 2 receptor, T-helper lymphocyte/T-suppressor lymphocyte) were finally used for model development. Five models displayed varying but all promising predictive performance. Notably, the ensemble model, SPMCIIP (severity prediction model for COVID-19 by immune-inflammatory parameters), derived from three contributive algorithms (SVM, GBDT, and NN) achieved the best performance with an area under the curve (AUC) of 0.991 (95% confidence interval [CI] 0.979-1.000) in internal validation cohort and 0.999 (95% CI 0.998-1.000) in external validation cohort to identify patients with critical COVID-19. SPMCIIP could accurately and expeditiously predict the occurrence of critical COVID-19 approximately 20 days in advance.
conclusionsThe developed online prediction model SPMCIIP is hopeful to facilitate intensive monitoring and early intervention of high risk of critical illness in COVID-19 patients.
trial registrationThis study was retrospectively registered in the Chinese Clinical Trial Registry ( ChiCTR2000032161 ). vv.
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