Encuesta nacional sobre cambios en las políticas de comunicación, visitas y cuidados al final de la vida en las unidades de cuidados intensivos durante las diferentes olas de la pandemia de COVID-19 (estudio COVIFAUCI) / National survey on changes in visitation, communication and end-of-life care policies in intensive care units through the different COVID-19 pandemic's waves (COVIFAUCI study)

Introducción La pandemia derivada de la infección por SARS-CoV-2 propició cambios en los cuidados tanto a familiares como a pacientes de cuidados intensivos durante las diferentes olas de incidencia del virus. La línea de humanización seguida por la mayoría de los hospitales se vio gravemente afectada por las restricciones aplicadas. Como objetivo, planteamos conocer las modificaciones experimentadas durante las diferentes olas de la pandemia por SARS-CoV-2 en España respecto a la política de visitas a los pacientes en UCI, el acompañamiento al final de la vida, y el uso de las nuevas tecnologías de la comunicación entre familiares, pacientes y profesionales. Métodos Estudio descriptivo transversal multicéntrico mediante encuesta a las UCI españolas desde febrero a abril de 2022. Se realizaron métodos de análisis estadísticos a los resultados según lo apropiado. El estudio fue avalado por la Sociedad Española de Enfermería Intensiva y Unidades Coronarias. Resultados Respondieron un 29% de las unidades contactadas. Los minutos de visita diarios de los familiares se redujeron drásticamente de 135 (87,5-255) a 45 (25-60) en el 21,2% de las unidades que permitían su acceso, mejorando levemente con el paso de las olas. En el caso de duelo, la permisividad fue mayor, aumentando el uso de las nuevas tecnologías para la comunicación paciente-familia en el caso del 96,5% de las unidades. Conclusiones Las familias de los pacientes ingresados en UCI durante las diferentes olas de la pandemia por COVID-19 han experimentado restricciones en las visitas y cambio de la presencialidad por técnicas virtuales de comunicación. Los tiempos de acceso se redujeron a niveles mínimos durante la primera ola, recuperándose con el avance de la pandemia pero sin llegar nunca a los niveles iniciales... (AU)

Introduction The pandemic derived from the SARS-CoV-2 infection led to changes in care for both relatives and intensive care patients during the different waves of incidence of the virus. The line of humanization followed by the majority of the hospitals was seriously affected by the restrictions applied. As an objective, we propose to know the modifications suffered during the different waves of the SARS-CoV-2 pandemic in Spain regarding the policy of visits to patients in the ICU, monitoring at the end of life, and the use of new technologies of communication between family members, patients and professionals. Methods Multicenter cross-sectional descriptive study through a survey of Spanish ICUs from February to April 2022. Statistical analysis methods were performed on the results as appropriate. The study was endorsed by the Spanish Society of Intensive Nursing and Coronary Units. Results Twenty-nine percent of the units contacted responded. The daily visiting minutes of relatives dropped drastically from 135 (87.5-255) to 45 (25-60) in the 21.2% of units that allowed their access, improving slightly with the passing of the waves. In the case of bereavement, the permissiveness was greater, increasing the use of new technologies for patient-family communication in the case of 96.5% of the units. Conclusions The family of patients admitted to the ICU during the different waves of the COVID-19 pandemic have suffered restrictions on visits and a change from face-to-face to virtual communication techniques. Access times were reduced to minimum levels during the first wave, recovering with the advance of the pandemic but never reaching initial levels. Despite the implemented solutions and virtual communication, efforts should be directed towards improving the protocols for the humanization of healthcare that allow caring for families and patients whatever the healthcare context. (AU)

Large variation in anti-factor Xa levels with nadroparin as thromboprophylaxis in COVID-19 and non-COVID-19 critically ill patients.

PURPOSE: Critically ill COVID-19 and non-COVID-19 patients receive thromboprophylaxis with the LMWH nadroparin. Whether a standard dosage is adequate in attaining the target anti-FXa levels (0.20-0.50 IU/ml) in these groups is unknown. METHODS: This study was a prospective, observational study in the ICU of a large general teaching hospital in the Netherlands. COVID-19 and non-COVID-19 patients admitted to the ICU who received LMWH in a prophylactic dosage of 2850 IU, 5700 IU or 11400 IU subcutaneously were eligible for the study. Anti-FXa levels were determined 4 h after administration. Relevant laboratory parameters, prespecified co-variates and clinical data were extracted from the electronic health record system. The primary goal was to evaluate anti-FXa levels in critically ill patients on a prophylactic dosage of nadroparin. The second goal was to investigate whether covariates had an influence on anti-FXa levels. RESULTS: A total of 62 patients were included in the analysis. In the COVID-19 group and non-COVID-19 group, 29 (96%) and 12 patients (38%) reached anti-FXa levels above 0.20 IU/ml, respectively. In the non-COVID-19 group, 63% of the patients had anti-FXA levels below the target range. When adjusted for nadroparin dosage a significant relation was found between body weight and the anti-FXa level (p = 0.013). CONCLUSION: A standard nadroparin dosage of 2850 IU sc in the critically ill patient is not sufficient to attain target anti-FXa levels in the majority of the studied patient group. We suggest a standard higher dosage in combination with body-weight dependent dosing as it leads to better exposure to nadroparin. CLINICAL TRIALS REGISTRATION: Retrospectively registered, ClinicalTrials.gov ID NTC 05926518 g, date of registration 06/01/23, unique ID 2020/1725.

Association between ABO blood type and coronavirus disease 2019 severe outcomes across dominant variant strains.

Objectives: Existing evidence suggests a link between ABO blood type and severe outcomes in coronavirus disease 2019 (COVID-19). We aimed to assess the relationship between blood type and severe outcomes across variant strains throughout the pandemic. Methods: This was a multicenter retrospective observational cohort analysis from a large health system in southeastern Michigan using electronic medical records to evaluate emergency encounters, hospitalization, and severe outcomes in COVID-19 based on ABO blood type. Consecutive adult patients presenting to the emergency department with a primary diagnosis of COVID-19 (U07.1) from March 1, 2020 through December 31, 2022 were assessed. Patients who presented during three distinct time intervals that coincided with Alpha, Delta, and Omicron variant predominance were included in the analysis. Exclusions included no record of ABO blood type, positive PCR COVID-19 test within the preceding 28 days, and if transferred from out of the health system. Severe outcomes were inclusive of intensive care unit admission, mechanical ventilation, or death, which, as a composite, represented our primary outcome. Secondary outcomes were hospital admission and length of stay. A logistic regression model was employed to test the association between ABO blood type and severe outcome, adjusting for age, sex, race, vaccination status, Elixhauser comorbidity indices, and the dominant variant time period in which the encounter occurred. Results: Of the 33,796 COVID-19 encounters, 9416 met inclusion criteria; 4071 (43.2%) were type O, 3417 (36.3%) were type A, 459 (4.9%) were type AB, and 1469 (15.6%) were type B blood. Note that 66.4% of the cohort was female (p = 0.18). The proportion of composite severe disease among the four blood types was similar and ranged between 8.6% and 8.9% (p = 0.98). Note that 53.0% of type A blood patients required hospital admission, compared to 51.9%, 50.4%, and 48.1% of type AB, B, and O blood, respectively (p < 0.001). Compared to patients with O blood type (43.2%), non-O blood type (58.8%; composite of A, AB, and B) exhibited no statistically significant difference in the proportion of composite severe disease (8.8% vs. 8.7%; p = 0.81) Multivariable regression analyses exhibited no significant difference regarding the presence of severe outcomes among the four blood types or O versus non-O blood types during T1, T2, and T3. Conclusions: ABO blood type was not associated with COVID-19 severe outcomes across the Delta, Alpha, and Omicron dominant COVID waves across a large health system in southeastern Michigan. Further research is needed to better understand if ABO blood type is a risk factor for severe disease among evolving COVID-19 variants and other viral upper respiratory infections.

Effectiveness of the severe acute respiratory syndrome coronavirus 2 Omicron BA.5 bivalent vaccine on symptoms in healthcare workers with BA.5 infection.

Background: The infection status of healthcare workers (HCWs) with coronavirus disease 2019 has become a major concern worldwide. In this study, we investigated the efficacy of the number of vaccine doses on symptoms after BA.5-adapted bivalent vaccination in HCWs. Methods: We analyzed the occupation, route of infection, symptoms, and vaccination history of all HCWs who tested positive for severe acute respiratory syndrome coronavirus 2 and worked in our hospital from November 2020 to March 2023. A logistic regression analysis was performed to examine the association between the presence of BA.5-adapted bivalent vaccination and symptoms. Results: During the observation period, 531 HCWs became infected. Of these, 72 % were women, with a median age of 30 years. Nurses accounted for 57 % of the infected cases, and many of the infection routes were from family members. We examined the relationship between symptoms in 352 HCWs infected with the Omicron BA.5* variant and the number of vaccine doses. As the number of vaccine doses increased, the rate of fever decreased, while symptoms such as a runny nose and sore throat tended to increase. The logistic regression analysis showed that the rate of fever tended to decrease (odds ratio = 0.52, 95 % confidence interval: 0.26-1.01, p = 0.056) and that of a runny nose increased (odds ratio = 3.68, 95 % confidence interval: 1.17-10.6, p = 0.018) after BA.5-adapted bivalent vaccination. Conclusion: This study shows that fever is reduced and mild symptoms are increased after BA.5-adapted bivalent vaccination in BA.5-infected HCWs. This result highlights the potential effectiveness of tailored vaccination strategies in the management of emerging COVID-19 variants.

Machine learning versus multivariate logistic regression for predicting severe COVID-19 in hospitalized children with Omicron variant infection.

With the emergence of the Omicron variant, the number of pediatric Coronavirus Disease 2019 (COVID-19) cases requiring hospitalization and developing severe or critical illness has significantly increased. Machine learning and multivariate logistic regression analysis were used to predict risk factors and develop prognostic models for severe COVID-19 in hospitalized children with the Omicron variant in this study. Of the 544 hospitalized children including 243 and 301 in the mild and severe groups, respectively. Fever (92.3%) was the most common symptom, followed by cough (79.4%), convulsions (36.8%), and vomiting (23.2%). The multivariate logistic regression analysis showed that age (1-3 years old, odds ratio (OR): 3.193, 95% confidence interval (CI): 1.778-5.733], comorbidity (OR: 1.993, 95% CI:1.154-3.443), cough (OR: 0.409, 95% CI:0.236-0.709), and baseline neutrophil-to-lymphocyte ratio (OR: 1.108, 95% CI: 1.023-1.200), lactate dehydrogenase (OR: 1.993, 95% CI: 1.154-3.443), blood urea nitrogen (OR: 1.002, 95% CI: 1.000-1.003) and total bilirubin (OR: 1.178, 95% CI: 1.005-3.381) were independent risk factors for severe COVID-19. The area under the curve (AUC) of the prediction models constructed by multivariate logistic regression analysis and machine learning (RandomForest + TomekLinks) were 0.7770 and 0.8590, respectively. The top 10 most important variables of random forest variables were selected to build a prediction model, with an AUC of 0.8210. Compared with multivariate logistic regression, machine learning models could more accurately predict severe COVID-19 in children with Omicron variant infection.

Forecasting disease trajectories in critical illness: comparison of probabilistic dynamic systems to static models to predict patient status in the intensive care unit.

OBJECTIVE: Conventional prediction models fail to integrate the constantly evolving nature of critical illness. Alternative modelling approaches to study dynamic changes in critical illness progression are needed. We compare static risk prediction models to dynamic probabilistic models in early critical illness. DESIGN: We developed models to simulate disease trajectories of critically ill COVID-19 patients across different disease states. Eighty per cent of cases were randomly assigned to a training and 20% of the cases were used as a validation cohort. Conventional risk prediction models were developed to analyse different disease states for critically ill patients for the first 7 days of intensive care unit (ICU) stay. Daily disease state transitions were modelled using a series of multivariable, multinomial logistic regression models. A probabilistic dynamic systems modelling approach was used to predict disease trajectory over the first 7 days of an ICU admission. Forecast accuracy was assessed and simulated patient clinical trajectories were developed through our algorithm. SETTING AND PARTICIPANTS: We retrospectively studied patients admitted to a Cleveland Clinic Healthcare System in Ohio, for the treatment of COVID-19 from March 2020 to December 2022. RESULTS: 5241 patients were included in the analysis. For ICU days 2-7, the static (conventional) modelling approach, the accuracy of the models steadily decreased as a function of time, with area under the curve (AUC) for each health state below 0.8. But the dynamic forecasting approach improved its ability to predict as a function of time. AUC for the dynamic forecasting approach were all above 0.90 for ICU days 4-7 for all states. CONCLUSION: We demonstrated that modelling critical care outcomes as a dynamic system improved the forecasting accuracy of the disease state. Our model accurately identified different disease conditions and trajectories, with a <10% misclassification rate over the first week of critical illness.

Analysis of Laboratory Critical Values During COVID-19 Pandemic at Tertiary Hospital in Saudi Arabia.

Purpose: One of the most important aspects of post-analytical laboratory activity is the notification of critical results. Therefore, the aim of this study was to illustrate and analyze the prevalence of critical result values of our clinical laboratory investigations during the pandemic stages of coronavirus-19 (COVID-19) and other research pre-pandemic stages. Methods: The seven-month study was conducted between May 2020 and November 2020. Laboratory data of critical results were collected in this retrospective cohort. Results: In total, 221,384 routine tests and 84,451 STAT tests were performed in our clinical laboratory. Of the 3183 (1.44%) tests result was identified as having Critical values, consisting of 2220 (69.74%) and 963 (30.25%) tests in biochemistry and hematology assays. Among the tests with critical values, 39.6% of which were from emergency department (ED) as STAT testing (1262) and 60.3% (1921) as TAT testing. Testing was found in routine inpatients and outpatients, 58% and 2.3%, respectively, and the most frequent parameter notified was sodium. Conclusion: In our practice, we observed that the higher level of frequency of critical values results is related to inpatients, contradicting several researchers reporting that the higher percentages of critical values were from ED.

Substantial health and economic burden of COVID-19 during the year after acute illness among US adults at high risk of severe COVID-19.

BACKGROUND: Post-COVID conditions encompass a range of long-term symptoms after SARS-CoV-2 infection. The potential clinical and economic burden in the United States is unclear. We evaluated diagnoses, medications, healthcare use, and medical costs before and after acute COVID-19 illness in US patients at high risk of severe COVID-19. METHODS: Eligible adults were diagnosed with COVID-19 from April 1 to May 31, 2020, had ≥ 1 condition placing them at risk of severe COVID-19, and were enrolled in Optum's de-identified Clinformatics® Data Mart Database for ≥ 12 months before and ≥ 13 months after COVID-19 diagnosis. Percentages of diagnoses, medications, resource use, and costs were calculated during baseline (12 months preceding diagnosis) and the post-acute phase (12 months after the 30-day acute phase of COVID-19). Data were stratified by age and COVID-19 severity. RESULTS: The cohort included 19,558 patients (aged 18-64 y, n = 9381; aged ≥ 65 y, n = 10,177). Compared with baseline, patients during the post-acute phase had increased percentages of blood disorders (16.3%), nervous system disorders (11.1%), and mental and behavioral disorders (7.7%), along with increases in related prescriptions. Overall, there were substantial increases in inpatient and outpatient healthcare utilization, along with a 23.0% increase in medical costs. Changes were greatest among older patients and those admitted to the intensive care unit for acute COVID-19 but were also observed in younger patients and those who did not require COVID-19 hospitalization. CONCLUSIONS: There is a significant clinical and economic burden of post-COVID conditions among US individuals at high risk for severe COVID-19.

Substantial health and economic burden of COVID-19 during the year after acute illness among US adults not at high risk of severe COVID-19.

BACKGROUND: Patients recovering from SARS-CoV-2 infection and acute COVID-19 illness can experience a range of long-term post-acute effects. The potential clinical and economic burden of these outcomes in the USA is unclear. We evaluated diagnoses, medications, healthcare utilization, and medical costs before and after acute COVID-19 illness in US patients who were not at high risk of severe COVID-19. METHODS: This study included eligible adults who were diagnosed with COVID-19 from April 1 to May 31, 2020, who were 18 - 64 years of age, and enrolled within Optum's de-identified Clinformatics® Data Mart Database for 12 months before and 13 months after COVID-19 diagnosis. Patients with any condition or risk factor placing them at high risk of progression to severe COVID-19 were excluded. Percentages of diagnoses, medications, healthcare utilization, and costs were calculated during baseline (12 months preceding diagnosis) and the post-acute phase (12 months after the 30-day acute phase of COVID-19). Data were stratified into 3 cohorts according to disposition during acute COVID-19 illness (i.e., not hospitalized, hospitalized without intensive care unit [ICU] admission, or admitted to the ICU). RESULTS: The study included 3792 patients; 56.5% of patients were men, 44% were White, and 94% did not require hospitalization. Compared with baseline, patients during the post-acute phase had percentage increases in the diagnosis of the following disorders: blood (166%), endocrine and metabolic (123%), nervous system (115%), digestive system (76%), and mental and behavioral (75%), along with increases in related prescriptions. Substantial increases in all measures of healthcare utilization were observed among all 3 cohorts. Total medical costs increased by 178% during the post-acute phase. Those who were hospitalized with or without ICU admission during the acute phase had the greatest increases in comorbidities and healthcare resource utilization. However, the burden was apparent across all cohorts. CONCLUSIONS: As evidenced by resource use in the post-acute phase, COVID-19 places a significant long-term clinical and economic burden among US individuals, even among patients whose acute infection did not merit hospitalization.

Incidence and risk factors of peripheral nerve injuries 3 months after ICU discharge: a retrospective study comparing COVID-19 and non-COVID-19 critically ill survivors.

BACKGROUND: Peripheral nerve injuries (PNI) have been associated with prone positioning (PP) in mechanically ventilated (MV) patients with COVID-19 pneumonia. The aims of this retrospective study were to describe PNI prevalence 3 months (M3) after intensive care unit (ICU) discharge, whether patients survived COVID-19 or another critical illness, and to search for risk factors of PNI. RESULTS: A total of 55 COVID (62 [54-69] years) and 22 non-COVID (61.5 [48-71.5] years) patients were followed at M3, after an ICU stay of respectively 15 [9-26.5] and 13.5 [10-19.8] days. PNI symptoms were reported by 23/55 (42.6%) COVID-19 and 8/22 (36%) non-COVID-19 patients (p = 0.798). As the incidence of PNI was similar in both groups, the entire population was used to determine risk factors. The MV duration predicted PNI occurrence (OR (CI95%) = 1.05 (1.01-1.10), p = 0.028), but not the ICU length of stay, glucocorticoids, or inflammation biomarkers. CONCLUSION: In the present cohort, PNI symptoms were reported in at least one-third of the ICU survivors, in similar proportion whether patients suffered from severe COVID-19 or not.

Relationship between the Pre-ECMO and ECMO Time and Survival of Severe COVID-19 Patients: A Systematic Review and Meta-Analysis.

BACKGROUND: Coronavirus disease 2019 (COVID-19) is the etiology of acute respiratory distress syndrome (ARDS). Extracorporeal membrane oxygenation (ECMO) is used to support gas exchange in patients who have failed conventional mechanical ventilation. However, there is no clear consensus on the timing of ECMO use in severe COVID-19 patients. OBJECTIVE: The aim of this study is to compare the differences in pre-ECMO time and ECMO duration between COVID-19 survivors and non-survivors and to explore the association between them. METHODS: PubMed, the Cochrane Library, Embase, and other sources were searched until 21 October 2022. Studies reporting the relationship between ECMO-related time and COVID-19 survival were included. All available data were pooled using random-effects methods. Linear regression analysis was used to determine the correlation between pre-ECMO time and ECMO duration. The meta-analysis was registered with PROSPERO under registration number CRD42023403236. RESULTS: Out of the initial 2473 citations, we analyzed 318 full-text articles, and 54 studies were included, involving 13,691 patients. There were significant differences between survivors and non-survivors in the time from COVID-19 diagnosis (standardized mean difference (SMD) = -0.41, 95% confidence interval (CI): [-0.53, -0.29], p < 0.00001), hospital (SMD = -0.53, 95% CI: [-0.97, -0.09], p = 0.02) and intensive care unit (ICU) admission (SMD = -0.28, 95% CI: [-0.49, -0.08], p = 0.007), intubation or mechanical ventilation to ECMO (SMD = -0.21, 95% CI: [-0.32, -0.09], p = 0.0003) and ECMO duration (SMD = -0.18, 95% CI: [-0.30, -0.06], p = 0.003). There was no statistical association between a longer time from symptom onset to ECMO (hazard ratio (HR) = 1.05, 95% CI: [0.99, 1.12], p = 0.11) or time from intubation or mechanical ventilation (MV) and the risk of mortality (highest vs. lowest time groups odds ratio (OR) = 1.18, 95% CI: [0.78, 1.78], p = 0.42; per one-day increase OR = 1.14, 95% CI: [0.86, 1.52], p = 0.36; HR = 0.99, 95% CI: [0.95, 1.02], p = 0.39). There was no linear relationship between pre-ECMO time and ECMO duration. CONCLUSION: There are differences in pre-ECMO time between COVID-19 survivors and non-survivors, and there is insufficient evidence to conclude that longer pre-ECMO time is responsible for reduced survival in COVID-19 patients. ECMO duration differed between survivors and non-survivors, and the timing of pre-ECMO does not have an impact on ECMO duration. Further studies are needed to explore the association between pre-ECMO and ECMO time in the survival of COVID-19 patients.

Postmortem Minimally Invasive Autopsy in Critically Ill COVID-19 Patients at the Bedside: A Proof-of-Concept Study at the ICU.

BACKGROUND: Economic restrictions and workforce cuts have continually challenged conventional autopsies. Recently, the COVID-19 pandemic has added tissue quality and safety requirements to the investigation of this disease, thereby launching efforts to upgrade autopsy strategies. METHODS: In this proof-of-concept study, we performed bedside ultrasound-guided minimally invasive autopsy (US-MIA) in the ICU of critically ill COVID-19 patients using a structured protocol to obtain non-autolyzed tissue. Biopsies were assessed for their quality (vitality) and length of biopsy (mm) and for diagnosis. The efficiency of the procedure was monitored in five cases by recording the time of each step and safety issues by swabbing personal protective equipment and devices for viral contamination. FINDINGS: Ultrasound examination and tissue procurement required a mean time period of 13 min and 54 min, respectively. A total of 318 multiorgan biopsies were obtained from five patients. Quality and vitality standards were fulfilled, which not only allowed for specific histopathological diagnosis but also the reliable detection of SARS-CoV-2 virions in unexpected organs using electronic microscopy and RNA-expressing techniques. INTERPRETATION: Bedside multidisciplinary US-MIA allows for the fast and efficient acquisition of autolytic-free tissue and offers unappreciated potential to overcome the limitations of research in postmortem studies.

Detection of Severe Lung Infection on Chest Radiographs of COVID-19 Patients: Robustness of AI Models across Multi-Institutional Data.

The diagnosis of severe COVID-19 lung infection is important because it carries a higher risk for the patient and requires prompt treatment with oxygen therapy and hospitalization while those with less severe lung infection often stay on observation. Also, severe infections are more likely to have long-standing residual changes in their lungs and may need follow-up imaging. We have developed deep learning neural network models for classifying severe vs. non-severe lung infections in COVID-19 patients on chest radiographs (CXR). A deep learning U-Net model was developed to segment the lungs. Inception-v1 and Inception-v4 models were trained for the classification of severe vs. non-severe COVID-19 infection. Four CXR datasets from multi-country and multi-institutional sources were used to develop and evaluate the models. The combined dataset consisted of 5748 cases and 6193 CXR images with physicians' severity ratings as reference standard. The area under the receiver operating characteristic curve (AUC) was used to evaluate model performance. We studied the reproducibility of classification performance using the different combinations of training and validation data sets. We also evaluated the generalizability of the trained deep learning models using both independent internal and external test sets. The Inception-v1 based models achieved AUC ranging between 0.81 ± 0.02 and 0.84 ± 0.0, while the Inception-v4 models achieved AUC in the range of 0.85 ± 0.06 and 0.89 ± 0.01, on the independent test sets, respectively. These results demonstrate the promise of using deep learning models in differentiating COVID-19 patients with severe from non-severe lung infection on chest radiographs.

MicroRNA-centered theranostics for pulmoprotection in critical COVID-19.

Elucidating the pathobiological mechanisms underlying post-acute pulmonary sequelae following SARS-CoV-2 infection is essential for early interventions and patient stratification. Here, we investigated the potential of microRNAs (miRNAs) as theranostic agents for pulmoprotection in critical illness survivors. Multicenter study including 172 ICU survivors. Diffusion impairment was defined as a lung-diffusing capacity for carbon monoxide (DLCO) <80% within 12 months postdischarge. A disease-associated 16-miRNA panel was quantified in plasma samples collected at ICU admission. Bioinformatic analyses were conducted using KEGG, Reactome, GTEx, and Drug-Gene Interaction databases. The results were validated using an external RNA-seq dataset. A 3-miRNA signature linked to diffusion impairment (miR-27a-3p, miR-93-5p, and miR-199a-5p) was identified using random forest. Levels of miR-93-5p and miR-199a-5p were independently associated with the outcome, improving patient classification provided by the electronic health record. The experimentally validated targets of these miRNAs exhibited enrichment across diverse pathways, with telomere length quantification in an additional set of samples (n = 83) supporting the role of cell senescence in sequelae. Analysis of an external dataset refined the pathobiological fingerprint of pulmonary sequelae. Gene-drug interaction analysis revealed four FDA-approved drugs. Overall, this study advances our understanding of lung recovery in postacute respiratory infections, highlighting the potential of miRNAs and their targets for pulmoprotection.

A novel method leveraging time series data to improve subphenotyping and application in critically ill patients with COVID-19.

Computational subphenotyping, a data-driven approach to understanding disease subtypes, is a prominent topic in medical research. Numerous ongoing studies are dedicated to developing advanced computational subphenotyping methods for cross-sectional data. However, the potential of time-series data has been underexplored until now. Here, we propose a Multivariate Levenshtein Distance (MLD) that can account for address correlation in multiple discrete features over time-series data. Our algorithm has two distinct components: it integrates an optimal threshold score to enhance the sensitivity in discriminating between pairs of instances, and the MLD itself. We have applied the proposed distance metrics on the k-means clustering algorithm to derive temporal subphenotypes from time-series data of biomarkers and treatment administrations from 1039 critically ill patients with COVID-19 and compare its effectiveness to standard methods. In conclusion, the Multivariate Levenshtein Distance metric is a novel method to quantify the distance from multiple discrete features over time-series data and demonstrates superior clustering performance among competing time-series distance metrics.

Antimicrobial peptides and other potential biomarkers of critical illness in SARS-CoV-2 patients with acute kidney injury. AMPAKI-CoV study.

Antimicrobial peptides (AMPs) constitute a complex network of 10-100 amino acid sequence molecules widely distributed in nature. While over 300 AMPs have been described in mammals, cathelicidins and defensins remain the most extensively studied. Some publications have explored the role of AMPs in COVID-19, but these findings are preliminary, and in vivo studies are still lacking. In this study, we report the plasma levels of five AMPs (LL-37, α-defensin 1, α-defensin 3, ß-defensin 1, and ß-defensin 3), using the ELISA technique (MyBioSource, San Diego, CA, United States, kits MBS2601339 (beta-defensin 1), MBS2602513 (beta-defensin 3), MBS703879 (alpha-defensin 1), MBS706289 (alpha-defensin 3), MBS7234921 (LL37)), and the measurement of six cytokines (tumor necrosis factor-α, interleukin-1ß, interleukin-6, interleukin-10, interferon-γ, and monocyte chemoattractant protein-1), through the magnetic bead immunoassay Milliplex® and the MAGPIX® System (MilliporeSigma, Darmstadt, Germany, kit HCYTOMAG-60 K (cytokines)), in 15 healthy volunteers, 36 COVID-19 patients without Acute Kidney Injury (AKI) and 17 COVID-19 patients with AKI. We found increased levels of α-defensin 1, α-defensin 3 and ß-defensin 3, in our COVID-19 population, when compared to healthy controls, along with higher levels of interleukin-6, interleukin-10, interferon-γ, and monocyte chemoattractant protein-1. These findings suggest that these AMPs and cytokines may play a crucial role in the systemic inflammatory response and tissue damage characterizing severe COVID-19. The levels of α-defensin 1 and α-defensin 3 were significantly higher in COVID-19 AKI group in comparison to the non-AKI group. Furthermore, IL-10 and the product IL-10 × IL-1B showed excellent performance in discriminating AKI, with AUCs of 0.86 and 0.88, respectively. Among patients with COVID-19, AMPs may play a key role in the inflammation process and disease progression. Additionally, α-defensin 1 and α-defensin 3 may mediate the AKI process in these patients, representing an opportunity for further research and potential therapeutic alternatives in the future.

A Systematic Review and Clinical Presentation of Central Nervous System Complications of Severe Acute Respiratory Syndrome Coronavirus 2 in Hospitalized Pediatric Patients During the Coronavirus Disease 2019 Pandemic in Israel.

BACKGROUND: Coronavirus disease-associated central nervous system complications (CNS-C) in hospitalized children, especially during the Omicron wave, and in comparison with influenza associated CNS-C, are not well understood. METHODS: The study population included 755 children aged <18 years hospitalized with laboratory-confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) at Sheba Medical Center, during March 2020 to July 2022. A comparative cohort consisted of 314 pediatric patients with influenza during the 2018-2019 and 2019-2020 influenza seasons. RESULTS: Overall, 5.8% (n = 44) of patients exhibited CNS-C. Seizures at presentation occurred in 33 patients with COVID-19 (4.4%), with 2.6% (n = 20) experiencing nonfebrile seizures, 1.1% (n = 8) febrile seizures, and 0.7% (n = 5) status epilepticus. More patients with CNS-C experienced seizures during the Omicron wave versus the pre-Omicron period (77.8% vs 41.2%, P = 0.03). Fewer patients were admitted to the intensive care unit in the Omicron wave (7.4%) versus prior waves (7.4% vs 41.2%, P = 0.02). Fewer patients with SARS-CoV-2 experienced CNS-C (5.8%) versus patients with influenza (9.9%), P = 0.03. More patients with SARS-CoV-2 experienced nonfebrile seizures (2.6% vs 0.6%, P = 0.06), whereas more patients with influenza experienced febrile seizures (7.3% vs 1.1%, P < 0.01). CONCLUSIONS: The Omicron wave was characterized by more seizures and fewer intensive-care-unit admissions than previous waves. Pediatric patients with SARS-CoV-2 experienced fewer CNS-C and more nonfebrile seizures compared with patients with influenza.

The pandemic is gone but its consequences are here to stay: avascular necrosis following corticosteroids administration for severe COVID-19.

BACKGROUND: In patients with COVID-19 infection and respiratory insufficiency, corticosteroid (CCS) administration is recommended. Among the wide range of complications and interactions, time-limited high-dose CCS administration might promote avascular necrosis (AVN) in a cumulative dose. This systematic review updated the current evidence and characterises the trend of AVN following time-limited high-dose CCS administration in patients who had severe COVID-19, discussing management strategies and outcomes. METHODS: This systematic review was conducted according to the 2020 PRISMA statement. In October 2023, the following databases were accessed: PubMed, Web of Science, Google Scholar, and Scopus restricting the search to the years 2019 to 2023. All the clinical studies which investigated the association between time-limited high-dose CCS administration in patients with severe COVID-19 infection and AVN were accessed. RESULTS: A total of 245 patients (9 studies) who experienced AVN following COVID-19 were included in the present investigation. 26% (63 of 245 included patients) were women. The mean age of the patients was 42.9 ± 17.7 years. Four studies focused on AVN of the hip and two on the knee, and the other studies included patients with AVN from mixed areas of the body (spine, pelvis, and shoulder). The mean time elapsed from COVID-19 infection to the development of symptomatic AVN was 79.4 ± 59.2 days (range, 14 to 166 days). CONCLUSION: It is possible that even time-limited high-dose CCS administration in patients with severe COVID-19 infection increased the incidence of AVN. The mean time elapsed from COVID-19 infection to the development of symptomatic AVN was approximately 80 days. Given the high risk of bias in all the included studies, the quality of recommendations of the present investigation is low, and no reliable conclusion can be inferred.

The importance of falsification endpoints in observational studies of vaccination to prevent severe disease: A critique of a harm-benefit analysis of BNT162b2 vaccination of 5- to 11-year-olds.

We explore one systematic review and meta-analysis of both observational and randomized studies examining COVID-19 vaccines in 5- to 11-year-olds, which reported substantial benefits associated with vaccinating this age group. We discuss the limitations of the individual studies that were used to estimate vaccination benefits. The review included five observational studies that evaluated vaccine effectiveness (VE) against COVID-19 severe disease or hospitalization. All five studies failed to adequately assess differences in underlying health between vaccination groups. In terms of vaccination harms, looking only at the randomized studies, a significantly higher odds of adverse events was identified among the vaccinated compared with the unvaccinated. Observational studies are at risk of overestimating the effectiveness of vaccines against severe disease if healthy vaccinee bias is present. Falsification endpoints can provide valuable information about underlying healthy vaccinee bias. Studies that have not adequately ruled out bias due to better health among the vaccinated or more vaccinated should be viewed as unreliable for estimating the VE of COVID-19 vaccination against severe disease and mortality. Existing systematic reviews that include observational studies of the COVID-19 vaccine in children may have overstated or falsely inferred vaccine benefits due to unidentified or undisclosed healthy vaccinee bias.