Dataset Information

Retrospective Study on the Features and Outcomes of a Tuscany COVID-19 Hospitalized Patients Cohort: Preliminary Results.

ABSTRACT: Background: A few months after the COVID-19 pandemic onset, knowledge of SARS-CoV-2 infection and outcomes and treatments blew up. This paper aimed to evaluate the features of a Tuscany COVID-19 hospitalized cohort and to identify risk factors for COVID-19 severity. Methods: This retrospective observational COVID-19 cohort study (1 March 2020-1 March 2021) was conducted on patients ≥ 18 years old, admitted to Tuscany Hospital, and subjected to follow-up within 12 months after discharge. Patients were enrolled at Pisana, Senese and Careggi University Hospitals, and South East, North West, and Center Local Hospitals. Results: 2888 patients (M = 58.5%, mean age = 66.2 years) were enrolled, of whom 14.3% (N = 413) were admitted to an intensive care unit. Smokers were 25%, and overweight and obese 65%. The most used drugs were corticosteroids, antacids, antibiotics, and antithrombotics, all antiviral drugs, with slight differences between 2020 and 2021. A strong association was found between outcomes of evolution towards critical COVID-19 (non-invasive mechanical ventilation (NIV) and/or admission to intensive care) and smoking (RR = 4.91), ex-smoking (RR = 3.48), overweight (RR = 1.30), obese subjects (RR = 1.62), comorbidities (aRR = 1.38). The alteration of liver enzymes (aspartate aminotransferase, alanine aminotransferase, or gamma-glutamyl transpeptidase) was associated with NIV (aOR = 2.28). Conclusions: Our cohort, characterized by patients with a mean age of 66.2 years, showed 65% of patients were overweight and obese. Smoking/ex-smoking, overweight/obesity, and other comorbidities were associated with COVID-19 adverse outcomes. The findings also demonstrated that alterations in liver enzymes were associated with worse outcomes.

SUBMITTER: Silvestri C

PROVIDER: S-EPMC11354555 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Retrospective Study on the Features and Outcomes of a Tuscany COVID-19 Hospitalized Patients Cohort: Preliminary Results.

Silvestri Caterina C Stasi Cristina C Profili Francesco F Bartolacci Simone S Sessa Emiliano E Tacconi Danilo D Villari Liliana L Carrozzi Laura L Dotta Francesco F Bargagli Elena E Donnini Sandra S Masotti Luca L Rasero Laura L Lavorini Federico F Pistelli Francesco F Chimera Davide D Sorano Alessandra A Pacifici Martina M Milli Caterina C Voller Fabio F Sprint Study Group

Journal of clinical medicine 20240807 16

<b>Background:</b> A few months after the COVID-19 pandemic onset, knowledge of SARS-CoV-2 infection and outcomes and treatments blew up. This paper aimed to evaluate the features of a Tuscany COVID-19 hospitalized cohort and to identify risk factors for COVID-19 severity. <b>Methods:</b> This retrospective observational COVID-19 cohort study (1 March 2020-1 March 2021) was conducted on patients ≥ 18 years old, admitted to Tuscany Hospital, and subjected to follow-up within 12 months after disch ...[more]

PMID: 39200770

Similar Datasets

Project description:BackgroundPatients appear to maintain sequelae post-coronavirus disease 2019 (COVID-19) affecting daily life and physical health. We investigated the changes in and the effects of pulmonary rehabilitation (PR) on exercise capacity and immunology six months after COVID-19 hospitalization.MethodsThis retrospective cohort reviewed 233 COVID-19 patients admitted from 17 January 2020 to 29 February 2020. Ninety-eight patients who completed 2-week and 6-month follow-ups and tests were included. Among 98 patients, 27 completed at least five sessions of PR at the First Hospital of Changsha, China, during the 6-month convalescence were allocated to the PR group; the reminder who had not performed any PR were assigned to the control group. The primary outcome was the change in six-minute walk distance (6-MWD) between the 2-week and 6-month follow-ups, which was assessed via analysis of covariance with a covariate of propensity score that adjusted for the potential confounders. Secondary outcomes were the changes in 6-MWD, SARS-CoV-2 immunoglobulins, T-lymphocytes and blood chemistry, which were evaluated via paired tests.ResultsParticipants' ages ranged from 19 to 84 years (M = 47, standard deviation (SD)=15) 45.9% identified as male. During the 6-month convalescence, 6-MWD increased 27.0%, with a mean [95% CI] of 113 [92-134] m (p < .001). SARS-CoV-2 IgG and IgM decreased 33.3% (p = .002) and 43.8% (p = .009), CD4+ T cells increased 7.9% (p = .04), and the majority of blood chemistry significantly changed. The patients in the PR group acquired a greater increase in 6-MWD than those in control (unadjusted, 194 [167-221] m, p < .001; adjusted, 123 [68-181] m, p < .001), dose-responsiveness of PR on 6-MWD was observed (p < .001). No differences in immunity variables and blood chemistry were observed between groups.ConclusionsThese findings suggest PR may be a strategy to promote the improvement of exercise capacity after COVID-19.

Project description:BackgroundThe novel coronavirus SARS-CoV-2 and its associated disease, COVID-19, have caused worldwide disruption, leading countries to take drastic measures to address the progression of the disease. As SARS-CoV-2 continues to spread, hospitals are struggling to allocate resources to patients who are most at risk. In this context, it has become important to develop models that can accurately predict the severity of infection of hospitalized patients to help guide triage, planning, and resource allocation.ObjectiveThe aim of this study was to develop accurate models to predict the mortality of hospitalized patients with COVID-19 using basic demographics and easily obtainable laboratory data.MethodsWe performed a retrospective study of 375 hospitalized patients with COVID-19 in Wuhan, China. The patients were randomly split into derivation and validation cohorts. Regularized logistic regression and support vector machine classifiers were trained on the derivation cohort, and accuracy metrics (F1 scores) were computed on the validation cohort. Two types of models were developed: the first type used laboratory findings from the entire length of the patient's hospital stay, and the second type used laboratory findings that were obtained no later than 12 hours after admission. The models were further validated on a multicenter external cohort of 542 patients.ResultsOf the 375 patients with COVID-19, 174 (46.4%) died of the infection. The study cohort was composed of 224/375 men (59.7%) and 151/375 women (40.3%), with a mean age of 58.83 years (SD 16.46). The models developed using data from throughout the patients' length of stay demonstrated accuracies as high as 97%, whereas the models with admission laboratory variables possessed accuracies of up to 93%. The latter models predicted patient outcomes an average of 11.5 days in advance. Key variables such as lactate dehydrogenase, high-sensitivity C-reactive protein, and percentage of lymphocytes in the blood were indicated by the models. In line with previous studies, age was also found to be an important variable in predicting mortality. In particular, the mean age of patients who survived COVID-19 infection (50.23 years, SD 15.02) was significantly lower than the mean age of patients who died of the infection (68.75 years, SD 11.83; P<.001).ConclusionsMachine learning models can be successfully employed to accurately predict outcomes of patients with COVID-19. Our models achieved high accuracies and could predict outcomes more than one week in advance; this promising result suggests that these models can be highly useful for resource allocation in hospitals.

Dataset Information

Retrospective Study on the Features and Outcomes of a Tuscany COVID-19 Hospitalized Patients Cohort: Preliminary Results.

Publications

Retrospective Study on the Features and Outcomes of a Tuscany COVID-19 Hospitalized Patients Cohort: Preliminary Results.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets