scholarly journals The Role of CHA2DS2-VASc Score in Predicting Intensive Care Unit Admission in Patients with COVID-19: Retrospective Study

2021 ◽  
Vol 41 (4) ◽  
pp. 424-430
Author(s):  
Umut KARABULUT ◽  
Dilay KARABULUT
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Retrospective Study ◽  
Intensive Care Unit Admission

scholarly journals Galectin-3 as Prognostic Predictor in Patients with COVID-19 Acute Respiratory Failure

2021 ◽  
Author(s):  
Andrea Portacci ◽  
Fabrizio Diaferia ◽  
Carla Santomasi ◽  
Silvano Dragonieri ◽  
Esterina Boniello ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Respiratory Failure ◽  
Intensive Care Unit Admission ◽  
Acute Respiratory Failure ◽  
Predictive Power ◽  
Distress Syndrome ◽  
Prognostic Predictor ◽  
Galectin 3

Abstract Background. Galectin-3 is β-galactoside-binding lectin with several roles in immune-inflammatory response. To date, there is no evidence of Galectin-3 role as a prognostic predictor in COVID-19 disease. The aim of this study is to clarify the prognostic role of Galectin-3 in patients with COVID 19 acute respiratory failure.Methods. We enrolled 156 consecutive patients with COVID-19 disease. Routine laboratory test, arterial blood gas, chest X-ray or Computed Tomography and Galectin-3 dosage were performed. The primary outcome was to assess Galectin-3 predictive power for 30-day mortality. Secondary outcomes were 30-day Intensive Care Unit admission and Acute Respiratory Distress Syndrome stratification according to Galectin-3 dosage. We performed Mann-Whitney U and Kruskal-Wallis tests for continuous variables comparison. Fisher’s exact test or Chi-square test were used for categorical variables analysis. Relationships between Galectin-3, clinical and laboratory data were identified using Spearman analysis. Receiver Operating Characteristic curves estimated Galectin-3 predictive power for the endpoints. With a fixed cut-off of 35.3 ng/ml, Kaplan-Meier with Log-Rank test and Cox Regression were performed to assess mortality and Intensive Care Unit admission risk. Results. Galectin-3 correlated with many other prognostic predictors tested in our analysis. Moreover, patients with serum levels of Galectin-3 above 35.3 ng/ml had increased risk for mortality, Intensive Care Unit admission and severe Acute Respiratory Distress Syndrome. Conclusions. Our study demonstrates the role of Galectin-3 as a predictor of mortality, Intensive Care Unit access and ARDS stratification in patients with COVID 19 acute respiratory failure.

scholarly journals Increased rates of intensive care unit admission in patients with Mycoplasma pneumoniae : a retrospective study

2016 ◽  
Vol 22 (8) ◽  
pp. 711-714 ◽  
Author(s):  
T. Khoury ◽  
S. Sviri ◽  
A.A. Rmeileh ◽  
A. Nubani ◽  
A. Abutbul ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Retrospective Study ◽  
Intensive Care Unit Admission ◽  
Mycoplasma Pneumoniae

scholarly journals Letter to Editor: Clinical characteristics and prognostic factors for intensive care unit admission of patients with COVID-19: retrospective study using machine learning and natural language processing. (Preprint)

2021 ◽  
Author(s):  
Francisco Martos Pérez ◽  
Ricardo Gomez Huelgas ◽  
María Dolores Martín Escalante ◽  
José Manuel Casas Rojo
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Prognostic Factors ◽  
Retrospective Study ◽  
Intensive Care Unit Admission ◽  
Language Processing ◽  
Clinical Characteristics ◽  
Microbiological Diagnosis

UNSTRUCTURED Letter to Editor. Comment to “Clinical characteristics and prognostic factors for intensive care unit admission of patients with COVID-19: retrospective study using machine learning and natural language processing” publicado por Izquierdo et al en Journal of Medical Internet Research Dear Sir, The article by Izquierdo et al published in the recent issue of Journal of Medical Internet Research (1) employed a combination of conventional and machine-learning tools to describe the clinical characteristics of patients with COVID-19 and the factors that predict intensive care unit (ICU) admission. We would like to make some comments about its design. The authors should have provided the proportion of patients with positive microbiological diagnosis. If the artificial intelligence software’s capacity for retrieving this information is limited in some way, this should be explained. The classification bias introduced by the lack of microbiological confirmation may have been significant, since the study includes patients from 1 January 2020. Although some undiagnosed cases have likely been present prior to the first declared case (1st march 2020)(2) in Castilla-La Mancha, it is improbable that there were many of them. ICU admissions are related to many factors not addressed in the study. The decision not to admit a patient to the ICU because of short life expectancy, low quality of life, or high burden of comorbidities may have had a great impact during the first wave of the COVID-19 pandemic, when a scarcity of ICU beds was manifested in some regions of Spain. The 6,1% ICU admission rate reported by the authors was 36% lower than the 8,3% reported in a national survey of 15111 patients from 150 hospitals in Spain(3). We could hypothesize that the patients included in the study had a milder disease. However, given the absence of microbiological diagnosis in an unknown percentage of patients, inclusion of a significant proportion of patients without a real COVID-19 diagnosis cannot be ruled out. These doubts could have been resolved if a microbiological diagnosis had been a requisite for inclusion. The mortality rate, the most robust and relevant endpoint, should also been reported and the factors related to it analysed. Artificial intelligence is having an increasing impact on the rate of health care information processing. However, minimization of selection and classification biases should be guaranteed in the design of investigations. In this case, this could have been achieved by including only microbiologically confirmed cases and prolonging the period of inclusion, since most of the COVID-19 cases emerged after the end date of the study inclusion period. These changes in the design would have allowed for a better evaluation of the performance of artificial intelligence techniques, making the results obtained in the sample closer to those of real population.   Bibliography 1. Izquierdo JL, Ancochea J; Savana COVID-19 Research Group, Soriano JB. Clinical Characteristics and Prognostic Factors for Intensive Care Unit Admission of Patients With COVID-19: Retrospective Study Using Machine Learning and Natural Language Processing. J Med Internet Res. 2020;22(10):e21801. Published 2020 Oct 28. doi:10.2196/21801. PMID: 33090964 2. Europa Press (2020, march 1st). Un varón de 62 años ingresado en Guadalajara, primer caso positivo por coronavirus en C-LM. Retrieved 2020, January 8th. https://www.europapress.es/castilla-lamancha/noticia-varon-62-anos-ingresado-guadalajara-primer-caso-positivo-coronavirus-lm-20200301103741.html 3. Casas-Rojo JM, Antón-Santos JM, Millán-Núñez-Cortés J, et al. Clinical characteristics of patients hospitalized with COVID-19 in Spain: Results from the SEMI-COVID-19 Registry. Características clínicas de los pacientes hospitalizados con COVID-19 en España: resultados del Registro SEMI-COVID-19. Rev Clin Esp. 2020;220(8):480-494. doi:10.1016/j.rce.2020.07.003. PMID: 32762922

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