ArticleJournal of anesthesia, analgesia and critical care2022
Machine learning and predictive models: 2 years of Sars-CoV-2 pandemic in a single-center retrospective analysis.
Article in Journal of anesthesia, analgesia and critical care, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Predicting failure of extubation and non-invasive respiratory support in critically ill patients: clinical complexity, limitations of traditional indices, and machine learning perspectives.Frontiers in medicine · 2026Review
- COVID-19 mortality prediction in Hungarian ICU settings implementing random forest algorithm.Scientific reports · 2024Article
- ANCOC Score to Predict Mortality in Different SARS-CoV-2 Variants and Vaccination Status.Journal of clinical medicine · 2023Article
- Evaluation of Mutual Information and Feature Selection for SARS-CoV-2 Respiratory Infection.Bioengineering (Basel, Switzerland) · 2023Article
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Authors and funding
11 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundSince January 2020, coronavirus disease 19 (COVID-19) has rapidly spread all over the world. An early assessment of illness severity is crucial for the stratification of patients in order to address them to the right intensity path of care. We performed an analysis on a large cohort of COVID-19 patients (n=581) hospitalized between March 2020 and May 2021 in our intensive care unit (ICU) at Policlinico Riuniti di Foggia hospital. Through an integration of the scores, demographic data, clinical history, laboratory findings, respiratory parameters, a correlation analysis, and the use of machine learning our study aimed to develop a model to predict the main outcome.
methodsWe deemed eligible for analysis all adult patients (age >18 years old) admitted to our department. We excluded all the patients with an ICU length of stay inferior to 24 h and the ones that declined to participate in our data collection. We collected demographic data, medical history, D-dimers, NEWS2, and MEWS scores on ICU admission and on ED admission, PaO
resultsSARS-CoV-2 mortality was positively correlated to age, length of stay in HDU, MEWS, and NEWS2 on ICU admission, D-dimer value on ICU admission, early orotracheal intubation, and late orotracheal intubation. We found a negative correlation between the PaO
conclusionIn our cohort of COVID patients, the choice of the right ventilatory support at the right time has been crucial, severity scores, and clinical judgment gave support in identifying patients at risk of developing a severe disease, comorbidities showed a lower weight than expected considering the main outcome, and machine learning method integration could be a fundamental statistical tool in the comprehensive evaluation of such complex diseases.
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