R. Godoy Mayoral , M. Benavent Núñez , J. Cruz Ruiz , G. López Yepes , A. Parralejo Jiménez , F.J. Callejas González , J.L. Izquierdo Alonso
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引用次数: 0
Abstract
Introduction
During the COVID pandemic, it was speculated that patients with the virus who were smoking-related might have a lower likelihood of disease exacerbation or death. To assess whether there is an association between smoking and risk of in-hospital mortality, SAVANA's big data and natural language processing (NLP) technology is used.
Method
A retrospective, observational, non-interventional cohort study was conducted based on real-life data extracted from medical records throughout Castilla-La Mancha using natural language processing and artificial intelligence techniques developed by SAVANA. The study covered the entire population of this region with Electronic Medical Records in SESCAM presenting with a diagnosis of COVID from March 1, 2020 to February 28, 2021.
Results
Smokers had a significantly higher percentage of cardiovascular risk factors (hypertension, dyslipidemia and diabetes), COPD, asthma, IDP, IC, CVD, PTE, cancer in general and lung cancer in particular, bronchiectasis, heart failure and a history of pneumonia (P < .0001). Former smokers, current smokers and non-smokers have a significant age difference. As for in-hospital deaths, they were more frequent in the case of ex-smokers, followed by smokers and then non-smokers (P < .0001).
Conclusion
There is an increased risk of dying in hospital in SARS-CoV-2-infected patients who are active smokers or have smoked in the past.
期刊介绍:
Revista Clínica Española published its first issue in 1940 and is the body of expression of the Spanish Society of Internal Medicine (SEMI).
The journal fully endorses the goals of updating knowledge and facilitating the acquisition of key developments in internal medicine applied to clinical practice. Revista Clínica Española is subject to a thorough double blind review of the received articles written in Spanish or English. Nine issues are published each year, including mostly originals, reviews and consensus documents.