Sleep and Circadian Health of Critical Survivors: A 12-Month Follow-Up Study.

OBJECTIVES: To investigate the sleep and circadian health of critical survivors 12 months after hospital discharge and to evaluate a possible effect of the severity of the disease within this context. DESIGN: Observational, prospective study. SETTING: Single-center study. PATIENTS: Two hundred sixty patients admitted to the ICU due to severe acute respiratory syndrome coronavirus 2 infection. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The cohort was composed of 260 patients (69.2% males), with a median (quartile 1-quartile 3) age of 61.5 years (52.0-67.0 yr). The median length of ICU stay was 11.0 days (6.00-21.8 d), where 56.2% of the patients required invasive mechanical ventilation (IMV). The Pittsburgh Sleep Quality Index (PSQI) revealed that 43.1% of the cohort presented poor sleep quality 12 months after hospital discharge. Actigraphy data indicated an influence of the disease severity on the fragmentation of the circadian rest-activity rhythm at the 3- and 6-month follow-ups, which was no longer significant in the long term. Still, the length of the ICU stay and the duration of IMV predicted a higher fragmentation of the rhythm at the 12-month follow-up with effect sizes (95% CI) of 0.248 (0.078-0.418) and 0.182 (0.005-0.359), respectively. Relevant associations between the PSQI and the Hospital Anxiety and Depression Scale (rho = 0.55, anxiety; rho = 0.5, depression) as well as between the fragmentation of the rhythm and the diffusing lung capacity for carbon monoxide (rho = -0.35) were observed at this time point. CONCLUSIONS: Our findings reveal a great prevalence of critical survivors presenting poor sleep quality 12 months after hospital discharge. Actigraphy data indicated the persistence of circadian alterations and a possible impact of the disease severity on the fragmentation of the circadian rest-activity rhythm, which was attenuated at the 12-month follow-up. This altogether highlights the relevance of considering the sleep and circadian health of critical survivors in the long term.

A blood microRNA classifier for the prediction of ICU mortality in COVID-19 patients: a multicenter validation study.

BACKGROUND: The identification of critically ill COVID-19 patients at risk of fatal outcomes remains a challenge. Here, we first validated candidate microRNAs (miRNAs) as biomarkers for clinical decision-making in critically ill patients. Second, we constructed a blood miRNA classifier for the early prediction of adverse outcomes in the ICU. METHODS: This was a multicenter, observational and retrospective/prospective study including 503 critically ill patients admitted to the ICU from 19 hospitals. qPCR assays were performed in plasma samples collected within the first 48 h upon admission. A 16-miRNA panel was designed based on recently published data from our group. RESULTS: Nine miRNAs were validated as biomarkers of all-cause in-ICU mortality in the independent cohort of critically ill patients (FDR < 0.05). Cox regression analysis revealed that low expression levels of eight miRNAs were associated with a higher risk of death (HR from 1.56 to 2.61). LASSO regression for variable selection was used to construct a miRNA classifier. A 4-blood miRNA signature composed of miR-16-5p, miR-192-5p, miR-323a-3p and miR-451a predicts the risk of all-cause in-ICU mortality (HR 2.5). Kaplan‒Meier analysis confirmed these findings. The miRNA signature provides a significant increase in the prognostic capacity of conventional scores, APACHE-II (C-index 0.71, DeLong test p-value 0.055) and SOFA (C-index 0.67, DeLong test p-value 0.001), and a risk model based on clinical predictors (C-index 0.74, DeLong test-p-value 0.035). For 28-day and 90-day mortality, the classifier also improved the prognostic value of APACHE-II, SOFA and the clinical model. The association between the classifier and mortality persisted even after multivariable adjustment. The functional analysis reported biological pathways involved in SARS-CoV infection and inflammatory, fibrotic and transcriptional pathways. CONCLUSIONS: A blood miRNA classifier improves the early prediction of fatal outcomes in critically ill COVID-19 patients.

GATEKEEPER's Strategy for the Multinational Large-Scale Piloting of an eHealth Platform: Tutorial on How to Identify Relevant Settings and Use Cases.

BACKGROUND: The World Health Organization's strategy toward healthy aging fosters person-centered integrated care sustained by eHealth systems. However, there is a need for standardized frameworks or platforms accommodating and interconnecting multiple of these systems while ensuring secure, relevant, fair, trust-based data sharing and use. The H2020 project GATEKEEPER aims to implement and test an open-source, European, standard-based, interoperable, and secure framework serving broad populations of aging citizens with heterogeneous health needs. OBJECTIVE: We aim to describe the rationale for the selection of an optimal group of settings for the multinational large-scale piloting of the GATEKEEPER platform. METHODS: The selection of implementation sites and reference use cases (RUCs) was based on the adoption of a double stratification pyramid reflecting the overall health of target populations and the intensity of proposed interventions; the identification of a principles guiding implementation site selection; and the elaboration of guidelines for RUC selection, ensuring clinical relevance and scientific excellence while covering the whole spectrum of citizen complexities and intervention intensities. RESULTS: Seven European countries were selected, covering Europe's geographical and socioeconomic heterogeneity: Cyprus, Germany, Greece, Italy, Poland, Spain, and the United Kingdom. These were complemented by the following 3 Asian pilots: Hong Kong, Singapore, and Taiwan. Implementation sites consisted of local ecosystems, including health care organizations and partners from industry, civil society, academia, and government, prioritizing the highly rated European Innovation Partnership on Active and Healthy Aging reference sites. RUCs covered the whole spectrum of chronic diseases, citizen complexities, and intervention intensities while privileging clinical relevance and scientific rigor. These included lifestyle-related early detection and interventions, using artificial intelligence-based digital coaches to promote healthy lifestyle and delay the onset or worsening of chronic diseases in healthy citizens; chronic obstructive pulmonary disease and heart failure decompensations management, proposing integrated care management based on advanced wearable monitoring and machine learning (ML) to predict decompensations; management of glycemic status in diabetes mellitus, based on beat to beat monitoring and short-term ML-based prediction of glycemic dynamics; treatment decision support systems for Parkinson disease, continuously monitoring motor and nonmotor complications to trigger enhanced treatment strategies; primary and secondary stroke prevention, using a coaching app and educational simulations with virtual and augmented reality; management of multimorbid older patients or patients with cancer, exploring novel chronic care models based on digital coaching, and advanced monitoring and ML; high blood pressure management, with ML-based predictions based on different intensities of monitoring through self-managed apps; and COVID-19 management, with integrated management tools limiting physical contact among actors. CONCLUSIONS: This paper provides a methodology for selecting adequate settings for the large-scale piloting of eHealth frameworks and exemplifies with the decisions taken in GATEKEEPER the current views of the WHO and European Commission while moving forward toward a European Data Space.

Adherence to CPAP Treatment and the Risk of Recurrent Cardiovascular Events: A Meta-Analysis.

Importance: The effect of continuous positive airway pressure (CPAP) on secondary cardiovascular disease prevention is highly debated. Objective: To assess the effect of CPAP treatment for obstructive sleep apnea (OSA) on the risk of adverse cardiovascular events in randomized clinical trials. Data Sources: PubMed (MEDLINE), EMBASE, Current Controlled Trials: metaRegister of Controlled Trials, ISRCTN Registry, European Union clinical trials database, CENTRAL (Cochrane Central Register of Controlled Trials), and ClinicalTrials.gov databases were systematically searched through June 22, 2023. Study Selection: For qualitative and individual participant data (IPD) meta-analysis, randomized clinical trials addressing the therapeutic effect of CPAP on cardiovascular outcomes and mortality in adults with cardiovascular disease and OSA were included. Data Extraction and Synthesis: Two reviewers independently screened records, evaluated potentially eligible primary studies in full text, extracted data, and cross-checked errors. IPD were requested from authors of the selected studies (SAVE [NCT00738179], ISAACC [NCT01335087], and RICCADSA [NCT00519597]). Main Outcomes and Measures: One-stage and 2-stage IPD meta-analyses were completed to estimate the effect of CPAP treatment on risk of recurrent major adverse cardiac and cerebrovascular events (MACCEs) using mixed-effect Cox regression models. Additionally, an on-treatment analysis with marginal structural Cox models using inverse probability of treatment weighting was fitted to assess the effect of good adherence to CPAP (≥4 hours per day). Results: A total of 4186 individual participants were evaluated (82.1% men; mean [SD] body mass index, 28.9 [4.5]; mean [SD] age, 61.2 [8.7] years; mean [SD] apnea-hypopnea index, 31.2 [17] events per hour; 71% with hypertension; 50.1% receiving CPAP [mean {SD} adherence, 3.1 {2.4} hours per day]; 49.9% not receiving CPAP [usual care], mean [SD] follow-up, 3.25 [1.8] years). The main outcome was defined as the first MACCE, which was similar for the CPAP and no CPAP groups (hazard ratio, 1.01 [95% CI, 0.87-1.17]). However, an on-treatment analysis by marginal structural model revealed a reduced risk of MACCEs associated with good adherence to CPAP (hazard ratio, 0.69 [95% CI, 0.52-0.92]). Conclusions and Relevance: Adherence to CPAP was associated with a reduced MACCE recurrence risk, suggesting that treatment adherence is a key factor in secondary cardiovascular prevention in patients with OSA.

Sleep and Circadian Health of Critical COVID-19 Survivors 3 Months After Hospital Discharge.

OBJECTIVES: To evaluate the sleep and circadian rest-activity pattern of critical COVID-19 survivors 3 months after hospital discharge. DESIGN: Observational, prospective study. SETTING: Single-center study. PATIENTS: One hundred seventy-two consecutive COVID-19 survivors admitted to the ICU with acute respiratory distress syndrome. INTERVENTIONS: Seven days of actigraphy for sleep and circadian rest-activity pattern assessment; validated questionnaires; respiratory tests at the 3-month follow-up. MEASUREMENTS AND MAIN RESULTS: The cohort included 172 patients, mostly males (67.4%) with a median (25th-75th percentile) age of 61.0 years (52.8-67.0 yr). The median number of days at the ICU was 11.0 (6.00-24.0), and 51.7% of the patients received invasive mechanical ventilation (IMV). According to the Pittsburgh Sleep Quality Index (PSQI), 60.5% presented poor sleep quality 3 months after hospital discharge, which was further confirmed by actigraphy. Female sex was associated with an increased score in the PSQI (p < 0.05) and IMV during ICU stay was able to predict a higher fragmentation of the rest-activity rhythm at the 3-month follow-up (p < 0.001). Furthermore, compromised mental health measured by the Hospital Anxiety and Depression Scale was associated with poor sleep quality (p < 0.001). CONCLUSIONS: Our findings highlight the importance of considering sleep and circadian health after hospital discharge. Within this context, IMV during the ICU stay could aid in predicting an increased fragmentation of the rest-activity rhythm at the 3-month follow-up. Furthermore, compromised mental health could be a marker for sleep disruption at the post-COVID period.

Impact of time to intubation on mortality and pulmonary sequelae in critically ill patients with COVID-19: a prospective cohort study.

QUESTION: We evaluated whether the time between first respiratory support and intubation of patients receiving invasive mechanical ventilation (IMV) due to COVID-19 was associated with mortality or pulmonary sequelae. MATERIALS AND METHODS: Prospective cohort of critical COVID-19 patients on IMV. Patients were classified as early intubation if they were intubated within the first 48 h from the first respiratory support or delayed intubation if they were intubated later. Surviving patients were evaluated after hospital discharge. RESULTS: We included 205 patients (140 with early IMV and 65 with delayed IMV). The median [p25;p75] age was 63 [56.0; 70.0] years, and 74.1% were male. The survival analysis showed a significant increase in the risk of mortality in the delayed group with an adjusted hazard ratio (HR) of 2.45 (95% CI 1.29-4.65). The continuous predictor time to IMV showed a nonlinear association with the risk of in-hospital mortality. A multivariate mortality model showed that delay of IMV was a factor associated with mortality (HR of 2.40; 95% CI 1.42-4.1). During follow-up, patients in the delayed group showed a worse DLCO (mean difference of - 10.77 (95% CI - 18.40 to - 3.15), with a greater number of affected lobes (+ 1.51 [95% CI 0.89-2.13]) and a greater TSS (+ 4.35 [95% CI 2.41-6.27]) in the chest CT scan. CONCLUSIONS: Among critically ill patients with COVID-19 who required IMV, the delay in intubation from the first respiratory support was associated with an increase in hospital mortality and worse pulmonary sequelae during follow-up.

Decrease in sleep quality during COVID-19 outbreak.

PURPOSE: The COVID-19 outbreak witnessed in the first months of 2020 has led to unprecedented changes in society's lifestyles. In the current study, we aimed to investigate the effect of this unexpected context on sleep. METHODS: During the COVID-19 outbreak, we performed an online survey with individuals formerly recruited for validation of the Spanish version of the sleep questionnaire Satisfaction, Alertness, Timing, Efficiency, and Duration (SATED). In the current survey, we asked the participants to complete the previously answered questionnaires including the Pittsburgh Sleep Quality Index (PSQI), a modified version of the Epworth Sleepiness Scale (ESS), and the SATED questionnaire. We also assessed the mood by the Profile of Mood States (POMS) questionnaire. RESULTS: The 71 participants were mostly women (75%) with a mean (± SD) age of 40.7 ± 11.9 years. Comparing the previous PSQI score to that during the COVID-19 outbreak, we observed worsening sleep quality (5.45 ± 3.14 to 6.18 ± 3.03 points, p = 0.035). In parallel, there was an increase in the negative mood (p = 0.002). Accordingly, the decrease in sleep quality was substantially correlated with negative mood (p < 0.001). There were no differences in the ESS or SATED. CONCLUSIONS: The COVID-19 outbreak-associated events correlate with decreased sleep quality in association with an increase in negative mood. Considering the importance of sleep for a healthy life, and in particular for immune function, efforts should be made to improve awareness on this matter and to offer psychological assistance to affected individuals.

Management and Treatment of Patients With Obstructive Sleep Apnea Using an Intelligent Monitoring System Based on Machine Learning Aiming to Improve Continuous Positive Airway Pressure Treatment Compliance: Randomized Controlled Trial.

BACKGROUND: Continuous positive airway pressure (CPAP) is an effective treatment for obstructive sleep apnea (OSA), but treatment compliance is often unsatisfactory. OBJECTIVE: The aim of this study was to assess the effectiveness and cost-effectiveness of an intelligent monitoring system for improving CPAP compliance. METHODS: This is a prospective, open label, parallel, randomized controlled trial including 60 newly diagnosed patients with OSA requiring CPAP (Apnea-Hypopnea Index [AHI] >15) from Lleida, Spain. Participants were randomized (1:1) to standard management or the MiSAOS intelligent monitoring system, involving (1) early compliance detection, thus providing measures of patient's CPAP compliance from the very first days of usage; (2) machine learning-based prediction of midterm future CPAP compliance; and (3) rule-based recommendations for the patient (app) and care team. Clinical and anthropometric variables, daytime sleepiness, and quality of life were recorded at baseline and after 6 months, together with patient's compliance, satisfaction, and health care costs. RESULTS: Randomized patients had a mean age of 57 (SD 11) years, mean AHI of 50 (SD 27), and 13% (8/60) were women. Patients in the intervention arm had a mean (95% CI) of 1.14 (0.04-2.23) hours/day higher adjusted CPAP compliance than controls (P=.047). Patients' satisfaction was excellent in both arms, and up to 88% (15/17) of intervention patients reported willingness to keep using the MiSAOS app in the future. No significant differences were found in costs (control: mean €90.2 (SD 53.14) (US $105.76 [SD 62.31]); intervention: mean €96.2 (SD 62.13) (US $112.70 [SD 72.85]); P=.70; €1=US $1.17 was considered throughout). Overall costs combined with results on compliance demonstrated cost-effectiveness in a bootstrap-based simulation analysis. CONCLUSIONS: A machine learning-based intelligent monitoring system increased daily compliance, reported excellent patient satisfaction similar to that reported in usual care, and did not incur in a substantial increase in costs, thus proving cost-effectiveness. This study supports the implementation of intelligent eHealth frameworks for the management of patients with CPAP-treated OSA and confirms the value of patients' empowerment in the management of chronic diseases. TRIAL REGISTRATION: ClinicalTrials.gov NCT03116958; https://clinicaltrials.gov/ct2/show/NCT03116958.

Addressing the unsolved challenges in microRNA-based biomarker development: Suitable endogenous reference microRNAs for SARS-CoV-2 infection severity.

Circulating cell-free microRNAs (miRNAs) are promising biomarkers for medical decision-making. Suitable endogenous controls are essential to ensure reproducibility. We aimed to identify and validate endogenous reference miRNAs for qPCR data normalization in samples from SARS-CoV-2-infected hospitalized patients. We used plasma samples (n = 170) from COVID-19 patients collected at hospital admission (COVID-Ponent project, www.clinicaltrials.gov/NCT04824677). First, 179 miRNAs were profiled using RT-qPCR. After stability assessment, candidates were validated using the same methodology. miRNA stability was analyzed using the geNorm, NormFinder and BestKeeper algorithms. Stability was further evaluated using an RNA-seq dataset derived from COVID-19 hospitalized patients, along with plasma samples from patients with critical COVID-19 profiled using RT-qPCR. In the screening phase, after strict control of expression levels, stability assessment selected eleven candidates (miR-17-5p, miR-20a-5p, miR-30e-5p, miR-106a-5p, miR-151a-5p, miR-185-5p, miR-191-5p, miR-423-3p, miR-425-5p, miR-484 and miR-625-5p). In the validation phase, all algorithms identified miR-106a-5p and miR-484 as top endogenous controls. No association was observed between these miRNAs and clinical or sociodemographic characteristics. Both miRNAs were stably detected and showed low variability in the additional analyses. In conclusion, a 2-miRNA panel composed of miR-106a-5p and miR-484 constitutes a first-line normalizer for miRNA-based biomarker development using qPCR in hospitalized patients infected with SARS-CoV-2.

MicroRNA-guided drug discovery for mitigating persistent pulmonary complications in critical COVID-19 survivors: A longitudinal pilot study.

BACKGROUND AND PURPOSE: The post-acute sequelae of SARS-CoV-2 infection pose a significant global challenge, with nearly 50% of critical COVID-19 survivors manifesting persistent lung abnormalities. The lack of understanding about the molecular mechanisms and effective treatments hampers their management. Here, we employed microRNA (miRNA) profiling to decipher the systemic molecular underpinnings of the persistent pulmonary complications. EXPERIMENTAL APPROACH: We conducted a longitudinal investigation including 119 critical COVID-19 survivors. A comprehensive pulmonary evaluation was performed in the short-term (median = 94.0 days after hospital discharge) and long-term (median = 358 days after hospital discharge). Plasma miRNAs were quantified at the short-term evaluation using the gold-standard technique, RT-qPCR. The analyses combined machine learning feature selection techniques with bioinformatic investigations. Two additional datasets were incorporated for validation. KEY RESULTS: In the short-term, 84% of the survivors exhibited impaired lung diffusion (DLCO  < 80% of predicted). One year post-discharge, 54.4% of this patient subgroup still presented abnormal DLCO . Four feature selection methods identified two specific miRNAs, miR-9-5p and miR-486-5p, linked to persistent lung dysfunction. The downstream experimentally validated targetome included 1473 genes, with heterogeneous enriched pathways associated with inflammation, angiogenesis and cell senescence. Validation studies using RNA-sequencing and proteomic datasets emphasized the pivotal roles of cell migration and tissue repair in persistent lung dysfunction. The repositioning potential of the miRNA targets was limited. CONCLUSION AND IMPLICATIONS: Our study reveals early mechanistic pathways contributing to persistent lung dysfunction in critical COVID-19 survivors, offering a promising approach for the development of targeted disease-modifying agents.

Breathing cessation events that compose the apnea-hypopnea index are distinctively associated with the adverse outcomes in Alzheimer's disease.

BACKGROUND: Previous studies challenge the impact of obstructive sleep apnea (OSA) once patients are diagnosed with Alzheimer's disease (AD). Nevertheless, OSA recognizably disrupts sleep, and relevant associations between sleep, AD pathological markers, and cognition have been demonstrated. We aimed to further explore this, evaluating the associations between each breathing cessation event that compose the apnea-hypopnea index (AHI) and the sleep structure to finally investigate whether this was related to increased levels of AD markers and higher cognitive decline. METHODS: Observational, prospective study, including consecutive patients diagnosed with mild-moderate AD. The participants were submitted to overnight polysomnography followed by a cerebrospinal fluid collection for AD pathological markers levels determination. Neuropsychological assessment was performed at baseline and after 12 months of follow-up. RESULTS: The cohort was composed of 116 patients (55.2% females) with a median [p25;p75] age of 76.0 [72.0;80.0] years and an AHI of 25.9 [15.1;48.5], which was mainly defined by the presence of hypopneas and obstructive apneas. These were distinctively associated with the sleep structure, with obstructive apneas being related to arousals and sleep lightening and hypopneas being related to an increased number of arousals only. Despite having a lower frequency, mixed and central apneas also presented associations with the sleep structure, particularly increasing the time spent in the lighter sleep stages. In relation to AD pathological markers, obstructive and mixed apneas were related to an augment in neurofilament light levels while hypopneas were associated with a higher phosphorylated-tau/amyloid-beta protein ratio. Hypopneas were the most important event for an increased cognitive decline at the 12-month follow-up. CONCLUSIONS: Our findings highlight the importance of a patient-centered approach, with a comprehensive and detailed analysis of the AHI to effectively predict the different outcomes and tailor the appropriate therapeutic strategies.

Key Factors Associated With Pulmonary Sequelae in the Follow-Up of Critically Ill COVID-19 Patients.

INTRODUCTION: Critical COVID-19 survivors have a high risk of respiratory sequelae. Therefore, we aimed to identify key factors associated with altered lung function and CT scan abnormalities at a follow-up visit in a cohort of critical COVID-19 survivors. METHODS: Multicenter ambispective observational study in 52 Spanish intensive care units. Up to 1327 PCR-confirmed critical COVID-19 patients had sociodemographic, anthropometric, comorbidity and lifestyle characteristics collected at hospital admission; clinical and biological parameters throughout hospital stay; and, lung function and CT scan at a follow-up visit. RESULTS: The median [p25-p75] time from discharge to follow-up was 3.57 [2.77-4.92] months. Median age was 60 [53-67] years, 27.8% women. The mean (SD) percentage of predicted diffusing lung capacity for carbon monoxide (DLCO) at follow-up was 72.02 (18.33)% predicted, with 66% of patients having DLCO<80% and 24% having DLCO<60%. CT scan showed persistent pulmonary infiltrates, fibrotic lesions, and emphysema in 33%, 25% and 6% of patients, respectively. Key variables associated with DLCO<60% were chronic lung disease (CLD) (OR: 1.86 (1.18-2.92)), duration of invasive mechanical ventilation (IMV) (OR: 1.56 (1.37-1.77)), age (OR [per-1-SD] (95%CI): 1.39 (1.18-1.63)), urea (OR: 1.16 (0.97-1.39)) and estimated glomerular filtration rate at ICU admission (OR: 0.88 (0.73-1.06)). Bacterial pneumonia (1.62 (1.11-2.35)) and duration of ventilation (NIMV (1.23 (1.06-1.42), IMV (1.21 (1.01-1.45)) and prone positioning (1.17 (0.98-1.39)) were associated with fibrotic lesions. CONCLUSION: Age and CLD, reflecting patients' baseline vulnerability, and markers of COVID-19 severity, such as duration of IMV and renal failure, were key factors associated with impaired DLCO and CT abnormalities.

Plasma profiling reveals a blood-based metabolic fingerprint of obstructive sleep apnea.

INTRODUCTION: Obstructive sleep apnea (OSA) is a chronic, heterogeneous and multicomponent disorder with associated cardiovascular and metabolic alterations. Despite being the most common sleep-disordered breathing, it remains a significantly undiagnosed condition. OBJECTIVE: We examined the plasma metabolome and lipidome of patients with suspected OSA, aiming to identify potential diagnosis biomarkers and to provide insights into the pathophysiological mechanisms underlying the disease. Additionally, we evaluated the impact of continuous positive airway pressure (CPAP) treatment on the circulating metabolomic and lipidomic profile. MATERIAL AND METHODS: Observational-prospective-longitudinal study including 206 consecutive subjects referred to the sleep unit. OSA was defined as an apnea-hypopnoea index ≥ 15 events/h after polysomnography (PSG). Patients treated with CPAP were followed-up for 6 months. Untargeted plasma metabolomic and lipidomic profiling was performed using liquid chromatography coulpled to massspectrometry. RESULTS: A plasma profile composed of 33 metabolites (mainly glycerophospholipids and bile acids) was identified in OSA vs. non-OSA patients. This profile correlated with specific PSG measures of OSA severity related to sleep fragmentation and hypoxemia. Machine learning analyses disclosed a 4-metabolites-signature that provided an accuracy (95% CI) of 0.98 (0.95-0.99) for OSA detection. CPAP treatment was associated with changes in 5 plasma metabolites previously altered by OSA. CONCLUSIONS: This analysis of the circulating metabolome and lipidome reveals a molecular fingerprint of OSA, which was modulated after effective CPAP treatment. Our results suggest blood-based biomarker candidates with potential application in the personalized management of OSA and suggest the activation of adaptive mechanisms in response to OSA-derived hypoxia.

Identification of circulating microRNA profiles associated with pulmonary function and radiologic features in survivors of SARS-CoV-2-induced ARDS.

There is a limited understanding of the pathophysiology of postacute pulmonary sequelae in severe COVID-19. The aim of current study was to define the circulating microRNA (miRNA) profiles associated with pulmonary function and radiologic features in survivors of SARS-CoV-2-induced ARDS. The study included patients who developed ARDS secondary to SARS-CoV-2 infection (n = 167) and a group of infected patients who did not develop ARDS (n = 33). Patients were evaluated 3 months after hospital discharge. The follow-up included a complete pulmonary evaluation and chest computed tomography. Plasma miRNA profiling was performed using RT-qPCR. Random forest was used to construct miRNA signatures associated with lung diffusing capacity for carbon monoxide (DLCO) and total severity score (TSS). Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses were conducted. DLCO < 80% predicted was observed in 81.8% of the patients. TSS showed a median [P25;P75] of 5 [2;8]. The miRNA model associated with DLCO comprised miR-17-5p, miR-27a-3p, miR-126-3p, miR-146a-5p and miR-495-3p. Concerning radiologic features, a miRNA signature composed by miR-9-5p, miR-21-5p, miR-24-3p and miR-221-3p correlated with TSS values. These associations were not observed in the non-ARDS group. KEGG pathway and GO enrichment analyses provided evidence of molecular mechanisms related not only to profibrotic or anti-inflammatory states but also to cell death, immune response, hypoxia, vascularization, coagulation and viral infection. In conclusion, diffusing capacity and radiological features in survivors from SARS-CoV-2-induced ARDS are associated with specific miRNA profiles. These findings provide novel insights into the possible molecular pathways underlying the pathogenesis of pulmonary sequelae.Trial registration: ClinicalTrials.gov identifier: NCT04457505..Trial registration: ISRCTN.org identifier: ISRCTN16865246..

One Year Overview and Follow-Up in a Post-COVID Consultation of Critically Ill Patients.

The long-term clinical management and evolution of a cohort of critical COVID-19 survivors have not been described in detail. We report a prospective observational study of COVID-19 patients admitted to the ICU between March and August 2020. The follow-up in a post-COVID consultation comprised symptoms, pulmonary function tests, the 6-minute walking test (6MWT), and chest computed tomography (CT). Additionally, questionnaires to evaluate the prevalence of post-COVID-19 syndrome were administered at 1 year. A total of 181 patients were admitted to the ICU during the study period. They were middle-aged (median [IQR] of 61 [52;67]) and male (66.9%), with a median ICU stay of 9 (5-24.2) days. 20% died in the hospital, and 39 were not able to be included. A cohort of 105 patients initiated the follow-up. At 1 year, 32.2% persisted with respiratory alterations and needed to continue the follow-up. Ten percent still had moderate/severe lung diffusion (DLCO) involvement (<60%), and 53.7% had a fibrotic pattern on CT. Moreover, patients had a mean (SD) number of symptoms of 5.7 ± 4.6, and 61.3% met the criteria for post-COVID syndrome at 1 year. During the follow-up, 46 patients were discharged, and 16 were transferred to other consultations. Other conditions, such as emphysema (21.6%), COPD (8.2%), severe neurocognitive disorders (4.1%), and lung cancer (1%) were identified. A high use of health care resources is observed in the first year. In conclusion, one-third of critically ill COVID-19 patients need to continue follow-up beyond 1 year, due to abnormalities on DLCO, chest CT, or persistent symptoms.

Multiplex protein profiling of bronchial aspirates reveals disease-, mortality- and respiratory sequelae-associated signatures in critically ill patients with ARDS secondary to SARS-CoV-2 infection.

Introduction: Bronchial aspirates (BAS) obtained during invasive mechanical ventilation (IMV) constitutes a useful tool for molecular phenotyping and decision making. Aim: To identify the proteomic determinants associated with disease pathogenesis, all-cause mortality and respiratory sequelae in BAS samples from critically ill patients with SARS-CoV-2-induced ARDS. Methods: Multicenter study including 74 critically ill patients with COVID-19 and non-COVID-19 ARDS. BAS were obtained by bronchoaspiration after IMV initiation. Three hundred sixty-four proteins were quantified using proximity extension assay (PEA) technology. Random forest models were used to assess predictor importance. Results: After adjusting for confounding factors, CST5, NADK, SRPK2 and TGF-α were differentially detected in COVID-19 and non-COVID-19 patients. In random forest models for COVID-19, CST5, DPP7, NADK, KYAT1 and TYMP showed the highest variable importance. In COVID-19 patients, reduced levels of ENTPD2 and PTN were observed in nonsurvivors of ICU stay, even after adjustment. AGR2, NQO2, IL-1α, OSM and TRAIL showed the strongest associations with in-ICU mortality and were used to construct a protein-based prediction model. Kaplan-Meier curves revealed a clear separation in mortality risk between subgroups of PTN, ENTPD2 and the prediction model. Cox regression models supported these findings. In survivors, the levels of FCRL1, NTF4 and THOP1 in BAS samples obtained during the ICU stay correlated with lung function (i.e., DLCO levels) 3 months after hospital discharge. Similarly, Flt3L and THOP1 levels were correlated with radiological features (i.e., TSS). These proteins are expressed in immune and nonimmune lung cells. Poor host response to viral infectivity and an inappropriate reparative mechanism seem to be linked with the pathogenesis of the disease and fatal outcomes, respectively. Conclusion: BAS proteomics identified novel factors associated with the pathology of SARS-CoV-2-induced ARDS and its adverse outcomes. BAS-based protein testing emerges as a novel tool for risk assessment in the ICU.

Genome-wide transcriptional profiling of pulmonary functional sequelae in ARDS- secondary to SARS-CoV-2 infection.

BACKGROUND: Up to 80% of patients surviving acute respiratory distress syndrome (ARDS) secondary to SARS-CoV-2 infection present persistent anomalies in pulmonary function after hospital discharge. There is a limited understanding of the mechanistic pathways linked to post-acute pulmonary sequelae. AIM: To identify the molecular underpinnings associated with severe lung diffusion involvement in survivors of SARS-CoV-2-induced ARDS. METHODS: Survivors attended to a complete pulmonary evaluation 3 months after hospital discharge. RNA sequencing (RNA-seq) was performed using Illumina technology in whole-blood samples from 50 patients with moderate to severe diffusion impairment (DLCO<60%) and age- and sex-matched individuals with mild-normal lung function (DLCO≥60%). A transcriptomic signature for optimal classification was constructed using random forest. Transcriptomic data were analyzed for biological pathway enrichment, cellular deconvolution, cell/tissue-specific gene expression and candidate drugs. RESULTS: RNA-seq identified 1357 differentially expressed transcripts. A model composed of 14 mRNAs allowed the optimal discrimination of survivors with severe diffusion impairment (AUC=0.979). Hallmarks of lung sequelae involved cell death signaling, cytoskeleton reorganization, cell growth and differentiation and the immune response. Resting natural killer (NK) cells were the most important immune cell subtype for the prediction of severe diffusion impairment. Components of the signature correlated with neutrophil, lymphocyte and monocyte counts. A variable expression profile of the transcripts was observed in lung cell subtypes and bodily tissues. One upregulated gene, TUBB4A, constitutes a target for FDA-approved drugs. CONCLUSIONS: This work defines the transcriptional programme associated with post-acute pulmonary sequelae and provides novel insights for targeted interventions and biomarker development.

N-antigenemia detection by a rapid lateral flow test predicts 90-day mortality in COVID-19: A prospective cohort study.

OBJECTIVES: To evaluate if the detection of N antigen of SARS-CoV-2 in plasma by a rapid lateral flow test predicts 90-day mortality in COVID-19 patients hospitalized at the wards. METHODS: The presence of N-antigenemia was evaluated in the first 36 hours after hospitalization in 600 unvaccinated COVID-19 patients, by using the Panbio COVID-19 Ag Rapid Test Device from Abbott (Abbott Laboratories Inc., Chicago, IL, USA). The impact of N-antigenemia on 90-day mortality was assessed by multivariable Cox regression analysis. RESULTS: Prevalence of N-antigenemia at hospitalization was higher in nonsurvivors (69% (82/118) vs. 52% (250/482); p < 0.001). The patients with N-antigenemia showed more frequently RNAemia (45.7% (148/324) vs. 19.8% (51/257); p < 0.001), absence of anti-SARS-CoV-2 N antibodies (80.7% (264/327) vs. 26.6% (69/259); p < 0.001) and absence of S1 antibodies (73.4% (240/327) vs. 23.6% (61/259); p < 0.001). The patients with antigenemia showed more frequently acute respiratory distress syndrome (30.1% (100/332) vs. 18.7% (50/268); p = 0.001) and nosocomial infections (13.6% (45/331) vs. 7.9% (21/267); p = 0.026). N-antigenemia was a risk factor for increased 90-day mortality in the multivariable analysis (HR, 1.99 (95% CI,1.09-3.61), whereas the presence of anti-SARS-CoV-2 N-antibodies represented a protective factor (HR, 0.47 (95% CI, 0.26-0.85). DISCUSSION: The presence of N-antigenemia or the absence of anti-SARS-CoV-2 N-antibodies after hospitalization is associated to increased 90-day mortality in unvaccinated COVID-19 patients. Detection of N-antigenemia by using lateral flow tests is a quick, widely available tool that could contribute to early identify those COVID-19 patients at risk of deterioration.

Prognostic implications of comorbidity patterns in critically ill COVID-19 patients: A multicenter, observational study.

Background: The clinical heterogeneity of COVID-19 suggests the existence of different phenotypes with prognostic implications. We aimed to analyze comorbidity patterns in critically ill COVID-19 patients and assess their impact on in-hospital outcomes, response to treatment and sequelae. Methods: Multicenter prospective/retrospective observational study in intensive care units of 55 Spanish hospitals. 5866 PCR-confirmed COVID-19 patients had comorbidities recorded at hospital admission; clinical and biological parameters, in-hospital procedures and complications throughout the stay; and, clinical complications, persistent symptoms and sequelae at 3 and 6 months. Findings: Latent class analysis identified 3 phenotypes using training and test subcohorts: low-morbidity (n=3385; 58%), younger and with few comorbidities; high-morbidity (n=2074; 35%), with high comorbid burden; and renal-morbidity (n=407; 7%), with chronic kidney disease (CKD), high comorbidity burden and the worst oxygenation profile. Renal-morbidity and high-morbidity had more in-hospital complications and higher mortality risk than low-morbidity (adjusted HR (95% CI): 1.57 (1.34-1.84) and 1.16 (1.05-1.28), respectively). Corticosteroids, but not tocilizumab, were associated with lower mortality risk (HR (95% CI) 0.76 (0.63-0.93)), especially in renal-morbidity and high-morbidity. Renal-morbidity and high-morbidity showed the worst lung function throughout the follow-up, with renal-morbidity having the highest risk of infectious complications (6%), emergency visits (29%) or hospital readmissions (14%) at 6 months (p<0.01). Interpretation: Comorbidity-based phenotypes were identified and associated with different expression of in-hospital complications, mortality, treatment response, and sequelae, with CKD playing a major role. This could help clinicians in day-to-day decision making including the management of post-discharge COVID-19 sequelae. Funding: ISCIII, UNESPA, CIBERES, FEDER, ESF.