Publications by authors named "Jose Manuel Casas Rojo"

Background: This study aimed to validate the role of the D-dimer to lymphocyte ratio (DLR) for mortality prediction in a large national cohort of hospitalized coronavirus disease 2019 (COVID-19) patients.

Methods: A retrospective, multicenter, observational study that included hospitalized patients due to SARS-CoV-2 infection in Spain was conducted from March 2020 to March 2022. All biomarkers and laboratory indices analyzed were measured once at admission.

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  • The study analyzed COVID-19 hospital admissions from 2020 in Spain, focusing on older patients during two pandemic waves, to evaluate mortality differences.
  • Results showed a mortality rate of 20.4% during the first wave and 17.2% during the second wave, particularly among patients aged 70 and older, who had a significant mortality reduction of 7.6%.
  • The lower mortality in the second wave may be due to improved healthcare standards, greater clinical experience, or less strain on the healthcare system, independent of patient or disease severity.
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  • New variants of SARS-CoV-2, changes in public health measures, and decreased immunity in high-risk groups are leading to predictions of increased hospitalizations and intensive care admissions, highlighting a need for effective Early Warning Scores (EWSs) to predict patient complications within 24-48 hours.* -
  • The developed COVID-19 Early Warning Score (COEWS) relies on easily accessible laboratory parameters, distinguishing it from existing models like NEWS2, and assesses risk in both vaccinated and unvaccinated patients.* -
  • The COEWS model incorporates key lab results, transforming predictive coefficients into individual scores that help identify patients at risk of mechanical ventilation or death; its predictive performance shows promising results with a discrimination score of
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  • Researchers developed a machine learning model using Gradient Boosting Decision Trees (GBDT) to predict mortality in COVID-19 hospitalized patients, utilizing data from the Spanish SEMI-COVID-19 registry which included over 24,000 cases.
  • The model employed advanced classifiers like CatBoost and BorutaShap to identify key indicators and risk levels for mortality, achieving a notable AUC performance of 84.76 in a test group likely containing vaccinated individuals.
  • The study highlights the model's high predictive capacity despite needing a significant number of predictors, indicating its potential utility in clinical settings for managing COVID-19 patient care.
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Mortality rates for COVID-19 have declined over time in the general population, but data in patients with hematologic malignancies are contradictory. We identified independent prognostic factors for COVID-19 severity and survival in unvaccinated patients with hematologic malignancies, compared mortality rates over time and versus non-cancer inpatients, and investigated post COVID-19 condition. Data were analyzed from 1166 consecutive, eligible patients with hematologic malignancies from the population-based HEMATO-MADRID registry, Spain, with COVID-19 prior to vaccination roll-out, stratified into early (February-June 2020; = 769 (66%)) and later (July 2020-February 2021; = 397 (34%)) cohorts.

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The significant impact of COVID-19 worldwide has made it necessary to develop tools to identify patients at high risk of severe disease and death. This work aims to validate the RIM Score-COVID in the SEMI-COVID-19 Registry. The RIM Score-COVID is a simple nomogram with high predictive capacity for in-hospital death due to COVID-19 designed using clinical and analytical parameters of patients diagnosed in the first wave of the pandemic.

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  • A study was conducted to see if lung ultrasound (LUS) could improve treatment outcomes for heart failure patients by detecting pulmonary congestion (PC).
  • Patients who had been hospitalized for heart failure were divided into two groups: one received treatment guided by LUS signs of congestion, while the other received standard care.
  • Results indicated no significant difference in 6-month outcomes, such as readmissions or emergency visits, between the LUS group and the standard care group, suggesting LUS-guided therapy may not provide additional benefits.
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  • Heart failure is a common issue among the elderly, often leading to symptoms related to fluid congestion, which can be evaluated using clinical signs, imaging tests, and increasingly, ultrasound methods.
  • This study examines how effective lung ultrasound and inferior vena cava (IVC) measurements can be in predicting mortality risk in elderly heart failure patients who are admitted to the hospital.
  • Findings from the study involving 482 patients indicated that those with more than 6 B-lines on lung ultrasound and IVC collapsibility of less than 50% had significantly higher mortality rates within 30 days, highlighting the utility of these ultrasound techniques in assessing patient prognosis.
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  • New SARS-CoV-2 variants, breakthrough infections, waning immunity, and low vaccination rates are causing increased hospitalizations and deaths, highlighting the need for better resource allocation tools in hospitals, especially in resource-limited areas.
  • The CODOP tool, developed using machine learning, predicts the clinical outcomes of hospitalized COVID-19 patients by analyzing 12 clinical parameters, demonstrating high accuracy levels (AUROC: 0.90-0.96) before clinical resolution.
  • CODOP's effectiveness is consistent across different virus variants and vaccination statuses, and it includes online calculators for efficient patient triage, validated through extensive testing in Latin America.
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Accumulated data show the utility of diagnostic multi-organ point-of-care ultrasound (PoCUS) in the assessment of patients admitted to an internal medicine ward. We assessed whether multi-organ PoCUS (lung, cardiac, and abdomen) provides relevant diagnostic and/or therapeutic information in patients admitted for any reason to an internal medicine ward. We conducted a prospective, observational, and single-center study, at a secondary hospital.

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(1) Background: This work aims to analyze clinical outcomes according to ethnic groups in patients hospitalized for COVID-19 in Spain. (2) Methods: This nationwide, retrospective, multicenter, observational study analyzed hospitalized patients with confirmed COVID-19 in 150 Spanish hospitals (SEMI-COVID-19 Registry) from 1 March 2020 to 31 December 2021. Clinical outcomes were assessed according to ethnicity (Latin Americans, Sub-Saharan Africans, Asians, North Africans, Europeans).

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Background: The WHO ordinal severity scale has been used to predict mortality and guide trials in COVID-19. However, it has its limitations.

Objective: The present study aims to compare three classificatory and predictive models: the WHO ordinal severity scale, the model based on inflammation grades, and the hybrid model.

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Objective: To compare coronavirus disease 2019 (COVID-19) hospitalization outcomes between persons with and without HIV.

Design: Retrospective observational cohort study in 150 hospitals in Spain.

Methods: Patients admitted from 1 March to 8 October 2020 with COVID-19 diagnosis confirmed by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2 positive) PCR test in respiratory tract samples.

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Objective: To describe the impact of different doses of corticosteroids on the evolution of patients with COVID-19 pneumonia, based on the potential benefit of the non-genomic mechanism of these drugs at higher doses.

Methods: Observational study using data collected from the SEMI-COVID-19 Registry. We evaluated the epidemiological, radiological and analytical scenario between patients treated with megadoses therapy of corticosteroids vs low-dose of corticosteroids and the development of complications.

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Background: The individual influence of a variety of comorbidities on COVID-19 patient outcomes has already been analyzed in previous works in an isolated way. We aim to determine if different associations of diseases influence the outcomes of inpatients with COVID-19.

Methods: Retrospective cohort multicenter study based on clinical practice.

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The prevalence and incidence of heart failure (HF) have been increasing in recent years as the population ages. These patients show a distinct profile of comorbidity, which makes their care more complex. In recent years, the PROFUND index, a specific tool for estimating the mortality rate at one year in pluripathology patients, has been developed.

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Background: Since December 2019, the COVID-19 pandemic has changed the concept of medicine. This work aims to analyze the use of antibiotics in patients admitted to the hospital due to SARS-CoV-2 infection.

Methods: This work analyzes the use and effectiveness of antibiotics in hospitalized patients with COVID-19 based on data from the SEMI-COVID-19 registry, an initiative to generate knowledge about this disease using data from electronic medical records.

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Background: The inflammatory cascade is the main cause of death in COVID-19 patients. Corticosteroids (CS) and tocilizumab (TCZ) are available to treat this escalation but which patients to administer it remains undefined.

Objective: We aimed to evaluate the efficacy of immunosuppressive/anti-inflammatory therapy in COVID-19, based on the degree of inflammation.

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Objectives: Since the results of the RECOVERY trial, WHO recommendations about the use of corticosteroids (CTs) in COVID-19 have changed. The aim of the study is to analyse the evolutive use of CTs in Spain during the pandemic to assess the potential influence of new recommendations.

Material And Methods: A retrospective, descriptive, and observational study was conducted on adults hospitalised due to COVID-19 in Spain who were included in the SEMI-COVID-19 Registry from March to November 2020.

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Aim: To assess the efficacy of sodium-glucose cotransporter-2 inhibitor (SGLT2i) and glucagon-like peptide-1 receptors agonist (GLP-1RA) therapy on liver steatosis measured by fatty liver index (FLI) and hepatic steatosis index (HSI) at 26 weeks in outpatients with diabetes and obesity.

Methods: Observational, prospective, multicenter study. Patients with steatosis determined by FLI (values <30 rule out and >60 indicate steatosis) and HIS (values <30 rule out and >36 indicate steatosis) who received combination therapy were included.

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Objectives: We aimed to develop and validate a prediction model, based on clinical history and examination findings on initial diagnosis of coronavirus disease 2019 (COVID-19), to identify patients at risk of critical outcomes.

Methods: We used data from the SEMI-COVID-19 Registry, a cohort of consecutive patients hospitalized for COVID-19 from 132 centres in Spain (23rd March to 21st May 2020). For the development cohort, tertiary referral hospitals were selected, while the validation cohort included smaller hospitals.

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Our main aim was to describe the effect on the severity of ACEI (angiotensin-converting enzyme inhibitor) and ARB (angiotensin II receptor blocker) during COVID-19 hospitalization. A retrospective, observational, multicenter study evaluating hospitalized patients with COVID-19 treated with ACEI/ARB. The primary endpoint was the incidence of the composite outcome of prognosis (IMV (invasive mechanical ventilation), NIMV (non-invasive mechanical ventilation), ICU admission (intensive care unit), and/or all-cause mortality).

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(1) Background: The inflammation or cytokine storm that accompanies COVID-19 marks the prognosis. This study aimed to identify three risk categories based on inflammatory parameters on admission. (2) Methods: Retrospective cohort study of patients diagnosed with COVID-19, collected and followed-up from 1 March to 31 July 2020, from the nationwide Spanish SEMI-COVID-19 Registry.

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Background: Most patients with COVID-19 receive antibiotics despite the fact that bacterial co-infections are rare. This can lead to increased complications, including antibacterial resistance. We aim to analyze risk factors for inappropriate antibiotic prescription in these patients and describe possible complications arising from their use.

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