Findings and Prognostic Value of Lung Ultrasound in COVID-19 Pneumonia.

OBJECTIVES: The aim of this study was to systematically describe the findings of lung ultrasound (US) in patients with coronavirus 2019 (COVID-19) pneumonia and to analyze its prognostic value. METHODS: Lung US examinations were performed in 63 patients with COVID-19 pneumonia admitted to a university hospital. Lung involvement was evaluated on a 4-point scale with a 12-area pulmonary division, obtaining a lung score (LS). Ultrasound findings and clinical characteristics were recorded. RESULTS: All patients showed US involvement in at least 1 area (mean ± SD, 8 ± 3.5). The total LS was 15.3 ± 8.1, without differences between left and right lungs. Most affected regions were the lower region (95.2%) and the posterior region (73.8%). The total LS showed a strong correlation (r = -0.765) with the oxygen pressure-to-fraction of inspired oxygen ratio; by lung region, those with a higher correlation were the LS of the anterior region (r = -0.823) and the LS of the upper region (r = -0.731). In total, 22.2% of patients required noninvasive respiratory support (NIRS). A multivariate analysis showed that the anterior region LS, adjusted for age and sex, was significant (odds ratio, 2.159; 95% confidence interval, 1.309-3.561) for the risk of requiring NIRS. An anterior region LS of 4 or higher and a total LS of 19 or higher had similar characteristics to predict the need for NIRS. CONCLUSIONS: Ultrasound involvement in COVID-19 pneumonia is bilateral and heterogeneous. Most affected regions are the posterior and the lower regions. The anterior region has prognostic value because its involvement strongly correlates with the risk of requiring NIRS, and an anterior region LS of 4 or higher has high sensitivity and specificity for predicting the need for NIRS.

High sensitivity flow cytometry immunophenotyping increases the diagnostic yield of malignant pleural effusions.

Diagnosing malignant pleural effusions (MPE) is challenging when patients lack a history of cancer and cytopathology does not detect malignant cells in pleural effusions (PE). We investigated whether a systematic analysis of PE by flow cytometry immunophenotyping (FCI) had any impact on the diagnostic yield of MPE. Over 7 years, 570 samples from patients with clinical suspicion of MPE were submitted for the FCI study. To screen for epithelial malignancies, a 3-color FCI high sensitivity assay was used. The FCI results, qualified as "malignant" (FCI+) or "non-malignant" (FCI-), were compared to integrated definitive diagnosis established by clinicians based on all available information. MPE was finally diagnosed in 182 samples and FCI detected 141/182 (77.5%). Morphology further confirmed FCI findings by cytopathology detection of malignant cells in PE (n = 91) or histopathology (n = 29). Imaging tests and clinical history supported the diagnosis in the remaining samples. The median percentage of malignant cells was 6.5% for lymphoma and 0.23% for MPE secondary to epithelial cell malignancies. FCI identified a significantly lower percentage of EpCAM+ cells in cytopathology-negative MPE than in cytopathology-positive cases (0.02% vs. 1%; p < 0.0001). Interestingly, 29/52 MPE (55.8%) where FCI alerted of the presence of malignant cells were new diagnosis of cancer. Overall, FCI correctly diagnosed 456/522 samples (87.4%) suitable for comparison with cytopathology. These findings show that high sensitivity FCI significantly increases the diagnostic yield of MPE. Early detection of FCI + cases accelerates the diagnostic pathway of unsuspected MPE, thus supporting its implementation in clinical diagnostic work-up as a diagnostic tool.

Defining the Heterogeneity of Sleep Apnea Syndrome: A Cluster Analysis With Implications for Patient Management.

INTRODUCTION: Obstructive sleep apnea (OSA) is a complex pathology with heterogeneity that has not been fully characterized to date. Our objective is to identify groups of patients with common clinical characteristics through cluster analysis that could predict patient prognosis, the impact of comorbidities and/or the response to a common treatment. METHODS: Cluster analysis was performed using the hierarchical cluster method in 2025 patients in the apnea-HUGU cohort. The variables used for building the clusters included general data, comorbidity, sleep symptoms, anthropometric data, physical exam and sleep study results. RESULTS: Four clusters were identified: (1) young male without comorbidity with moderate apnea and otorhinolaryngological malformations; (2) middle-aged male with very severe OSA with comorbidity without cardiovascular disease; (3) female with mood disorder; and (4) symptomatic male with established cardiovascular disease and severe OSA. CONCLUSIONS: The characterization of these four clusters in OSA can be decisive when identifying groups of patients who share a special risk or common therapeutic strategies, orienting us toward personalized medicine and facilitating the design of future clinical trials.

Defining the Heterogeneity of Sleep Apnea Syndrome: A Cluster Analysis With Implications for Patient Management / Definiendo la heterogeneidad del Síndrome de Apneas del Sueño: un análisis de clúster con implicaciones en el manejo del paciente

Introduction Obstructive sleep apnea (OSA) is a complex pathology with heterogeneity that has not been fully characterized to date. Our objective is to identify groups of patients with common clinical characteristics through cluster analysis that could predict patient prognosis, the impact of comorbidities and/or the response to a common treatment. Methods Cluster analysis was performed using the hierarchical cluster method in 2025 patients in the apnea-HUGU cohort. The variables used for building the clusters included general data, comorbidity, sleep symptoms, anthropometric data, physical exam and sleep study results. Results Four clusters were identified: (1) young male without comorbidity with moderate apnea and otorhinolaryngological malformations; (2) middle-aged male with very severe OSA with comorbidity without cardiovascular disease; (3) female with mood disorder; and (4) symptomatic male with established cardiovascular disease and severe OSA. Conclusions The characterization of these four clusters in OSA can be decisive when identifying groups of patients who share a special risk or common therapeutic strategies, orienting us toward personalized medicine and facilitating the design of future clinical trials.

Introducción La Apnea Obstructiva del Sueño (AOS) es una patología compleja en la que su heterogeneidad no ha sido completamente caracterizada hasta la fecha. Nuestro objetivo es identificar grupos de pacientes con características clínicas comunes, por medio de análisis de clúster, que pudieran se predictivos de un pronóstico, impacto de comorbilidades y/o respuesta a un tratamiento común. Métodos Se realizó un análisis de clúster por el método de conglomerados jerárquico en 2025 pacientes de la cohorte apnea-HUGU. Las variables utilizadas para la construcción de los clúster incluían datos generales, comorbilidad, síntomas de sueño, datos antropométricos, exploración física y resultados del estudio de sueño. Resultados Se identificaron 4 clúster: 1) varón joven sin comorbilidad con apnea moderada y alteraciones de la esfera otorrinolaringológica (ORL) 2) Varón de edad media con AOS muy grave sintomático con comorbilidad sin enfermedad cardiovascular desarrollada. 3) Mujer con alteraciones en el estado de ánimo 4) Varón sintomático con enfermedad cardiovascular establecida y AOS grave. Conclusiones La caracterización de estos cuatro clúster en la AOS puede ser determinante a la hora de identificar grupos de pacientes que comparten un especial riesgo o estrategias terapéuticas comunes orientándonos hacia la medicina personalizada y facilitando el diseño de futuros ensayos clínicos.