ArticleBMC health services research2022
Reliability of predictive models to support early decision making in the emergency department for patients with confirmed diagnosis of COVID-19: the Pescara Covid Hospital score.
Article in BMC health services research, 2022. 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.
- Artificial intelligence-driven prediction of COVID-19-related hospitalization and death: a systematic review.Frontiers in public health · 2023Pooled it
- Development and External Validation of a Prognostic Prediction Model for Hospitalization in SARS-CoV-2-Infected Ambulatory Patients.Open forum infectious diseases · 2026Article
- Article
- Safety and Efficacy of Dalbavancin in Real Life: Retrospective Analysis of a Large Monocentric Case Series of Patients Treated for Skin/Soft Tissue and Other Difficult-to-Treat Infections.Antibiotics (Basel, Switzerland) · 2024Article
- Efficient clinical decision-making process via AI-based multimodal data fusion: A COVID-19 case study.Heliyon · 2024Article
- Comprehensive analysis of clinical data for COVID-19 outcome estimation with machine learning models.Biomedical signal processing and control · 2023Article
- Prognostic value of cellular population data in patients with COVID-19.Informatics in medicine unlocked · 2023Article
- Monocyte Distribution Width as a predictor of community acquired sepsis in patients prospectively enrolled at the Emergency Department.BMC infectious diseases · 2022Observational
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Authors and funding
10 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundThe hospital management of patients diagnosed with COVID-19 can be hampered by heterogeneous characteristics at entry into the emergency department. We aimed to identify demographic, clinical and laboratory parameters associated with higher risks of hospitalisation, oxygen support, admission to intensive care and death, to build a risk score for clinical decision making at presentation to the emergency department.
methodsWe carried out a retrospective study using linked administrative data and laboratory parameters available in the initial phase of the pandemic at the emergency department of the regional reference hospital of Pescara, Abruzzo, Italy, March-June 2020. Logistic regression and Cox modelling were used to identify independent predictors for risk stratification. Validation was carried out collecting data from an extended timeframe covering other variants of concern, including Alpha (December 2020-January 2021) and Delta/Omicron (January-March 2022).
resultsSeveral clinical and laboratory parameters were significantly associated to the outcomes of interest, independently from age and gender. The strongest predictors were: for hospitalisation, monocyte distribution width ≥ 22 (4.09; 2.21-7.72) and diabetes (OR = 3.04; 1.09-9.84); for oxygen support: saturation < 95% (OR = 11.01; 3.75-41.14), lactate dehydrogenase≥237 U/L (OR = 5.93; 2.40-15.39) and lymphocytes< 1.2 × 10
conclusionsA set of parameters that are normally available at emergency departments of any hospital can be used to stratify patients with COVID-19 at risk of severe conditions. The method shall be calibrated to support timely clinical decision during the first hours of admission with different variants of concern.
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