ArticleInternal medicine journal2021
Classification and analysis of outcome predictors in non-critically ill COVID-19 patients.
Article in Internal medicine journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application of machine learning approaches in predicting clinical outcomes in older adults - a systematic review and meta-analysis.BMC geriatrics · 2023Pooled it
- Article
- At-admission prediction of mortality and pulmonary embolism in an international cohort of hospitalised patients with COVID-19 using statistical and machine learning methods.Scientific reports · 2024Article
- Challenges in the Differential Diagnosis of COVID-19 Pneumonia: A Pictorial Review.Diagnostics (Basel, Switzerland) · 2022Review
- 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
- Clinical characteristics and analysis of risk factors for disease progression of patients with SARS-CoV-2 Omicron variant infection: A retrospective study of 25207 cases in a Fangcang hospital.Frontiers in cellular and infection microbiology · 2022Article
- Chromogranin A plasma levels predict mortality in COVID-19.PloS one · 2022Article
- Home management of COVID-19 symptomatic patients: a safety study on COVID committed home medical teams.Le infezioni in medicina · 2022Article
- Research on SARS-COV-2 pandemic: a narrative review focused on the Italian contribution.Journal of anesthesia, analgesia and critical care · 2021Review
- Mental and neurological disorders and risk of COVID-19 susceptibility, illness severity and mortality: A systematic review, meta-analysis and call for action.EClinicalMedicine · 2021Article
- A simple prognostic score based on troponin and presepsin for COVID-19 patients admitted to the emergency department: a single-center pilot study.Acta bio-medica : Atenei Parmensis · 2021Article
- Role of MR-proADM and Monocyte CD169 in Predicting In-Hospital and 60-Day Mortality in COVID-19 Patients.Clinical pathology (Thousand Oaks, Ventura County, Calif.)Article
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Authors and funding
14 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundEarly detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-infected patients who could develop a severe form of COVID-19 must be considered of great importance to carry out adequate care and optimise the use of limited resources.
aimsTo use several machine learning classification models to analyse a series of non-critically ill COVID-19 patients admitted to a general medicine ward to verify if any clinical variables recorded could predict the clinical outcome.
methodsWe retrospectively analysed non-critically ill patients with COVID-19 admitted to the general ward of the hospital in Pordenone from 1 March 2020 to 30 April 2020. Patients' characteristics were compared based on clinical outcomes. Through several machine learning classification models, some predictors for clinical outcome were detected.
resultsIn the considered period, we analysed 176 consecutive patients admitted: 119 (67.6%) were discharged, 35 (19.9%) dead and 22 (12.5%) were transferred to intensive care unit. The most accurate models were a random forest model (M2) and a conditional inference tree model (M5) (accuracy = 0.79; 95% confidence interval 0.64-0.90, for both). For M2, glomerular filtration rate and creatinine were the most accurate predictors for the outcome, followed by age and fraction-inspired oxygen. For M5, serum sodium, body temperature and arterial pressure of oxygen and inspiratory fraction of oxygen ratio were the most reliable predictors.
conclusionsIn non-critically ill COVID-19 patients admitted to a medical ward, glomerular filtration rate, creatinine and serum sodium were promising predictors for the clinical outcome. Some factors not determined by COVID-19, such as age or dementia, influence clinical outcomes.
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