Clustering COVID-19 ARDS patients through the first days of ICU admission. An analysis of the CIBERESUCICOVID Cohort.

BACKGROUND: Acute respiratory distress syndrome (ARDS) can be classified into sub-phenotypes according to different inflammatory/clinical status. Prognostic enrichment was achieved by grouping patients into hypoinflammatory or hyperinflammatory sub-phenotypes, even though the time of analysis may change the classification according to treatment response or disease evolution. We aimed to evaluate when patients can be clustered in more than 1 group, and how they may change the clustering of patients using data of baseline or day 3, and the prognosis of patients according to their evolution by changing or not the cluster. METHODS: Multicenter, observational prospective, and retrospective study of patients admitted due to ARDS related to COVID-19 infection in Spain. Patients were grouped according to a clustering mixed-type data algorithm (k-prototypes) using continuous and categorical readily available variables at baseline and day 3. RESULTS: Of 6205 patients, 3743 (60%) were included in the study. According to silhouette analysis, patients were grouped in two clusters. At baseline, 1402 (37%) patients were included in cluster 1 and 2341(63%) in cluster 2. On day 3, 1557(42%) patients were included in cluster 1 and 2086 (57%) in cluster 2. The patients included in cluster 2 were older and more frequently hypertensive and had a higher prevalence of shock, organ dysfunction, inflammatory biomarkers, and worst respiratory indexes at both time points. The 90-day mortality was higher in cluster 2 at both clustering processes (43.8% [n = 1025] versus 27.3% [n = 383] at baseline, and 49% [n = 1023] versus 20.6% [n = 321] on day 3). Four hundred and fifty-eight (33%) patients clustered in the first group were clustered in the second group on day 3. In contrast, 638 (27%) patients clustered in the second group were clustered in the first group on day 3. CONCLUSIONS: During the first days, patients can be clustered into two groups and the process of clustering patients may change as they continue to evolve. This means that despite a vast majority of patients remaining in the same cluster, a minority reaching 33% of patients analyzed may be re-categorized into different clusters based on their progress. Such changes can significantly impact their prognosis.

Effectiveness of prolonged versus standard-course of oseltamivir in critically ill patients with severe influenza infection: A multicentre cohort study.

The aim of this study is to investigate the effectiveness of prolonged versus standard course oseltamivir treatment among critically ill patients with severe influenza. A retrospective study of a prospectively collected database including adults with influenza infection admitted to 184 intensive care units (ICUs) in Spain from 2009 to 2018. Prolonged oseltamivir was defined if patients received the treatment beyond 5 days, whereas the standard-course group received oseltamivir for 5 days. The primary outcome was all-cause ICU mortality. Propensity score matching (PSM) was constructed, and the outcome was investigated through Cox regression and RCSs. Two thousand three hundred and ninety-seven subjects were included, of whom 1943 (81.1%) received prolonged oseltamivir and 454 (18.9%) received standard treatment. An optimal full matching algorithm was performed by matching 2171 patients, 1750 treated in the prolonged oseltamivir group and 421 controls in the standard oseltamivir group. After PSM, 387 (22.1%) patients in the prolonged oseltamivir and 119 (28.3%) patients in the standard group died (p = 0.009). After adjusting confounding factors, prolonged oseltamivir significantly reduced ICU mortality (odds ratio [OR]: 0.53, 95% confidence interval [CI]: 0.40-0.69). Prolonged oseltamivir may have protective effects on survival at Day 10 compared with a standard treatment course. Sensitivity analysis confirmed these findings. Compared with standard treatment, prolonged oseltamivir was associated with reduced ICU mortality in critically ill patients with severe influenza. Clinicians should consider extending the oseltamivir treatment duration to 10 days, particularly in higher-risk groups of prolonged viral shedding. Further randomized controlled trials are warranted to confirm these findings.

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.

Cardiovascular Complications in Coronavirus Disease 2019-Pathogenesis and Management.

The coronavirus disease 2019 (COVID-19) pandemic has caused a devastating impact on morbidity and mortality around the world. Severe acute respiratory syndrome-coronavirus-2 has a characteristic tropism for the cardiovascular system by entering the host cells and binding to angiotensin-converting enzyme 2 receptors, which are expressed in different cells, particularly endothelial cells. This endothelial injury is linked by a direct intracellular viral invasion leading to inflammation, microthrombosis, and angiogenesis. COVID-19 has been associated with acute myocarditis, cardiac arrhythmias, new onset or worsening heart failure, ischemic heart disease, stroke, and thromboembolic disease. This review summarizes key relevant literature regarding the epidemiology, diagnosis, treatment, and preventive measures related to cardiovascular complications in the setting of COVID-19.

Deploying unsupervised clustering analysis to derive clinical phenotypes and risk factors associated with mortality risk in 2022 critically ill patients with COVID-19 in Spain.

BACKGROUND: The identification of factors associated with Intensive Care Unit (ICU) mortality and derived clinical phenotypes in COVID-19 patients could help for a more tailored approach to clinical decision-making that improves prognostic outcomes. METHODS: Prospective, multicenter, observational study of critically ill patients with confirmed COVID-19 disease and acute respiratory failure admitted from 63 ICUs in Spain. The objective was to utilize an unsupervised clustering analysis to derive clinical COVID-19 phenotypes and to analyze patient's factors associated with mortality risk. Patient features including demographics and clinical data at ICU admission were analyzed. Generalized linear models were used to determine ICU morality risk factors. The prognostic models were validated and their performance was measured using accuracy test, sensitivity, specificity and ROC curves. RESULTS: The database included a total of 2022 patients (mean age 64 [IQR 5-71] years, 1423 (70.4%) male, median APACHE II score (13 [IQR 10-17]) and SOFA score (5 [IQR 3-7]) points. The ICU mortality rate was 32.6%. Of the 3 derived phenotypes, the A (mild) phenotype (537; 26.7%) included older age (< 65 years), fewer abnormal laboratory values and less development of complications, B (moderate) phenotype (623, 30.8%) had similar characteristics of A phenotype but were more likely to present shock. The C (severe) phenotype was the most common (857; 42.5%) and was characterized by the interplay of older age (> 65 years), high severity of illness and a higher likelihood of development shock. Crude ICU mortality was 20.3%, 25% and 45.4% for A, B and C phenotype respectively. The ICU mortality risk factors and model performance differed between whole population and phenotype classifications. CONCLUSION: The presented machine learning model identified three clinical phenotypes that significantly correlated with host-response patterns and ICU mortality. Different risk factors across the whole population and clinical phenotypes were observed which may limit the application of a "one-size-fits-all" model in practice.

The evolution of the ventilatory ratio is a prognostic factor in mechanically ventilated COVID-19 ARDS patients.

BACKGROUND: Mortality due to COVID-19 is high, especially in patients requiring mechanical ventilation. The purpose of the study is to investigate associations between mortality and variables measured during the first three days of mechanical ventilation in patients with COVID-19 intubated at ICU admission. METHODS: Multicenter, observational, cohort study includes consecutive patients with COVID-19 admitted to 44 Spanish ICUs between February 25 and July 31, 2020, who required intubation at ICU admission and mechanical ventilation for more than three days. We collected demographic and clinical data prior to admission; information about clinical evolution at days 1 and 3 of mechanical ventilation; and outcomes. RESULTS: Of the 2,095 patients with COVID-19 admitted to the ICU, 1,118 (53.3%) were intubated at day 1 and remained under mechanical ventilation at day three. From days 1 to 3, PaO2/FiO2 increased from 115.6 [80.0-171.2] to 180.0 [135.4-227.9] mmHg and the ventilatory ratio from 1.73 [1.33-2.25] to 1.96 [1.61-2.40]. In-hospital mortality was 38.7%. A higher increase between ICU admission and day 3 in the ventilatory ratio (OR 1.04 [CI 1.01-1.07], p = 0.030) and creatinine levels (OR 1.05 [CI 1.01-1.09], p = 0.005) and a lower increase in platelet counts (OR 0.96 [CI 0.93-1.00], p = 0.037) were independently associated with a higher risk of death. No association between mortality and the PaO2/FiO2 variation was observed (OR 0.99 [CI 0.95 to 1.02], p = 0.47). CONCLUSIONS: Higher ventilatory ratio and its increase at day 3 is associated with mortality in patients with COVID-19 receiving mechanical ventilation at ICU admission. No association was found in the PaO2/FiO2 variation.

Metabolic Signatures Associated with Severity in Hospitalized COVID-19 Patients.

The clinical evolution of COVID-19 pneumonia is poorly understood. Identifying the metabolic pathways that are altered early with viral infection and their association with disease severity is crucial to understand COVID-19 pathophysiology, and guide clinical decisions. This study aimed at assessing the critical metabolic pathways altered with disease severity in hospitalized COVID-19 patients. Forty-nine hospitalized patients with COVID-19 pneumonia were enrolled in a prospective, observational, single-center study in Barcelona, Spain. Demographic, clinical, and analytical data at admission were registered. Plasma samples were collected within the first 48 h following hospitalization. Patients were stratified based on the severity of their evolution as moderate (N = 13), severe (N = 10), or critical (N = 26). A panel of 221 biomarkers was measured by targeted metabolomics in order to evaluate metabolic changes associated with subsequent disease severity. Our results show that obesity, respiratory rate, blood pressure, and oxygen saturation, as well as some analytical parameters and radiological findings, were all associated with disease severity. Additionally, ceramide metabolism, tryptophan degradation, and reductions in several metabolic reactions involving nicotinamide adenine nucleotide (NAD) at inclusion were significantly associated with respiratory severity and correlated with inflammation. In summary, assessment of the metabolomic profile of COVID-19 patients could assist in disease severity stratification and even in guiding clinical decisions.

Hypoxemic Patients With Bilateral Infiltrates Treated With High-Flow Nasal Cannula Present a Similar Pattern of Biomarkers of Inflammation and Injury to Acute Respiratory Distress Syndrome Patients.

OBJECTIVE: To examine whether patients with acute hypoxemia and bilateral opacities treated with high-flow nasal cannula and acute respiratory distress syndrome patients who were directly mechanically ventilated are similar in terms of lung epithelial, endothelial, and inflammatory biomarkers. DESIGN: Prospective, multicenter study. SETTING: ICUs at three university tertiary hospitals. PATIENTS: Intubated and nonintubated patients admitted to the ICU with acute hypoxemia (PaO2/FIO2 ≤ 300) and bilateral opacities. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Either high-flow nasal cannula or mechanical ventilation was initiated, at the discretion of the attending physician. We measured plasma biomarkers of lung epithelial injury (receptor for advanced glycation end products and surfactant protein D) and endothelial injury (angiopoietin-2) and inflammation (interleukin-6, interleukin-8, and interleukin-33 and soluble suppression of tumorigenicity-2) within 24 hours of acute respiratory distress syndrome onset. Propensity score matching was performed using six different variables (Acute Physiology and Chronic Health Evaluation II, Sequential Organ Failure Assessment, PaO2/FIO2, origin of acute respiratory distress syndrome, steroids, renal failure and need for vasopressors). Nonhypoxemic mechanically ventilated critically ill patients and healthy volunteers served as controls. Of the 170 patients enrolled, 127 (74.7%) were intubated and 43 (25.3%) were treated with high-flow nasal cannula at acute respiratory distress syndrome onset. After propensity score matching (39 high-flow nasal cannula patients vs 39 mechanical ventilation patients), no significant differences were observed in receptor for advanced glycation end products, surfactant protein D, angiopoietin-2, interleukin-6, interleukin-8, interleukin-33, and soluble suppression of tumorigenicity-2 between matched patients who were treated with high-flow nasal cannula and those who were intubated at acute respiratory distress syndrome onset. After matching, no differences in mortality or length of stay were observed. All biomarkers (with the exception of interleukin-33) were higher in both groups of matched acute respiratory distress syndrome patients than in both control groups. CONCLUSIONS: Acute hypoxemic patients with bilateral infiltrates treated with high-flow nasal cannula presented a similar pattern of biomarkers of inflammation and injury to acute respiratory distress syndrome patients undergoing direct mechanical ventilation. The results suggest that these high-flow nasal cannula patients should be considered as acute respiratory distress syndrome patients.

Therapeutic Approaches in Mitochondrial Dysfunction, Proteolysis, and Structural Alterations of Diaphragm and Gastrocnemius in Rats With Chronic Heart Failure.

Patients with chronic heart failure (CHF) experience exercise intolerance, fatigue and muscle wasting, which negatively influence their survival. We hypothesized that treatment with either the antioxidant N-acetyl cysteine (NAC) or the proteasome inhibitor bortezomib of rats with monocrotaline-induced CHF may restore inspiratory and limb muscle mass, function, and structure through several molecular mechanisms involved in protein breakdown and metabolism in the diaphragm and gastrocnemius. In these muscles of CHF-cachectic rats with and without treatment with NAC or bortezomib (N = 10/group) and non-cachectic controls, proteolysis (tyrosine release, proteasome activities, ubiquitin-proteasome markers), oxidative stress, inflammation, mitochondrial function, myosin, NF-κB transcriptional activity, muscle structural abnormalities, and fiber morphometry were analyzed together with muscle and cardiac functions. In diaphragm and gastrocnemius of CHF-cachectic rats, tyrosine release, proteasome activity, protein ubiquitination, atrogin-1, MURF-1, NF-κB activity, oxidative stress, inflammation, and structural abnormalities were increased, while muscle and cardiac functions, myosin content, slow- and fast-twitch fiber sizes, and mitochondrial activity were decreased. Concomitant treatment of CHF-cachectic rats with NAC or bortezomib improved protein catabolism, oxidative stress, inflammation, muscle fiber sizes, function and damage, superoxide dismutase and myosin levels, mitochondrial function (complex I, gastrocnemius), cardiac function and decreased NF-κB transcriptional activity in both muscles. Treatment of CHF-cachectic animals with NAC or bortezomib attenuated the functional (heart, muscles), biological, and structural alterations in muscles. Nonetheless, future studies conducted in actual clinical settings are warranted in order to assess the potential beneficial effects and safety concerns of these pharmacological agents on muscle mass loss and wasting in CHF-cachectic patients.

Delay in diagnosis of influenza A (H1N1)pdm09 virus infection in critically ill patients and impact on clinical outcome.

BACKGROUND: Patients infected with influenza A (H1N1)pdm09 virus requiring admission to the ICU remain an important source of mortality during the influenza season. The objective of the study was to assess the impact of a delay in diagnosis of community-acquired influenza A (H1N1)pdm09 virus infection on clinical outcome in critically ill patients admitted to the ICU. METHODS: A prospective multicenter observational cohort study was based on data from the GETGAG/SEMICYUC registry (2009-2015) collected by 148 Spanish ICUs. All patients admitted to the ICU in which diagnosis of influenza A (H1N1)pdm09 virus infection had been established within the first week of hospitalization were included. Patients were classified into two groups according to the time at which the diagnosis was made: early (within the first 2 days of hospital admission) and late (between the 3rd and 7th day of hospital admission). Factors associated with a delay in diagnosis were assessed by logistic regression analysis. RESULTS: In 2059 ICU patients diagnosed with influenza A (H1N1)pdm09 virus infection within the first 7 days of hospitalization, the diagnosis was established early in 1314 (63.8 %) patients and late in the remaining 745 (36.2 %). Independent variables related to a late diagnosis were: age (odds ratio (OR) = 1.02, 95 % confidence interval (CI) 1.01-1.03, P < 0.001); first seasonal period (2009-2012) (OR = 2.08, 95 % CI 1.64-2.63, P < 0.001); days of hospital stay before ICU admission (OR = 1.26, 95 % CI 1.17-1.35, P < 0.001); mechanical ventilation (OR = 1.58, 95 % CI 1.17-2.13, P = 0.002); and continuous venovenous hemofiltration (OR = 1.54, 95 % CI 1.08-2.18, P = 0.016). The intra-ICU mortality was significantly higher among patients with late diagnosis as compared with early diagnosis (26.9 % vs 17.1 %, P < 0.001). Diagnostic delay was one independent risk factor for mortality (OR = 1.36, 95 % CI 1.03-1.81, P < 0.001). CONCLUSIONS: Late diagnosis of community-acquired influenza A (H1N1)pdm09 virus infection is associated with a delay in ICU admission, greater possibilities of respiratory and renal failure, and higher mortality rate. Delay in diagnosis of flu is an independent variable related to death.

Natural language processing to identify and characterize spondyloarthritis in clinical practice.

OBJECTIVE: This study aims to use a novel technology based on natural language processing (NLP) to extract clinical information from electronic health records (EHRs) to characterise the clinical profile of patients diagnosed with spondyloarthritis (SpA) at a large-scale hospital. METHODS: An observational, retrospective analysis was conducted on EHR data from all patients with SpA (including psoriatic arthritis (PsA)) at Hospital Universitario La Paz, between 2020 and 2022. Data were collected using Savana Manager, an NLP-based system, enabling the extraction of information from unstructured, free-text EHRs. Variables analysed included demographic data, SpA subtypes, comorbidities and treatments. The performance of the technology in detecting SpA clinical entities was evaluated through precision, recall and F-1 score metrics. RESULTS: From a hospital population of 639 474 patients, 4337 (0.7%) patients had a diagnosis of SpA or their subtypes in their EHR. The population predominantly comprised men (55.3%) with a mean age of 50.9 years. Peripheral SpA (including PsA) was reported in 31.6%, axial SpA in 20.9%, both axial and peripheral SpA in 3.7%, while 43.7% of patients did not have the SpA subtype reported. Common comorbidities included hypertension (25.0%), dyslipidaemia (22.2%) and diabetes mellitus (15.5%). The use of conventional disease-modifying antirheumatic drugs (csDMARDs) and biological DMARDs (bDMARDs) was documented, with methotrexate (25.3% of patients) being the most used csDMARDs and adalimumab (10.6% of patients) the most used bDMARD. The NLP technology demonstrated high precision and recall, with all the assessed F-1 score values over 0.80, indicating reliable data extraction. CONCLUSION: The application of NLP technology facilitated the characterisation of the SpA patient profile, including demographics, clinical features, comorbidities and treatments. This study supports the utility of NLP in enhancing the understanding of SpA and suggests its potential for improving patient management by extracting meaningful information from unstructured EHR data.

Switches in non-invasive respiratory support strategies during acute hypoxemic respiratory failure: Need to monitoring from a retrospective observational study.

OBJECTIVE: To explore combined non-invasive-respiratory-support (NIRS) patterns, reasons for NIRS switching, and their potential impact on clinical outcomes in acute-hypoxemic-respiratory-failure (AHRF) patients. DESIGN: Retrospective, single-center observational study. SETTING: Intensive Care Medicine. PATIENTS: AHRF patients (cardiac origin and respiratory acidosis excluded) underwent combined NIRS therapies such as non-invasive-ventilation (NIV) and High-Flow-Nasal-Cannula (HFNC). INTERVENTIONS: Patients were classified based on the first NIRS switch performed (HFNC-to-NIV or NIV-to-HFNC), and further specific NIRS switching strategies (NIV trial-like vs. Non-NIV trial-like and single vs. multiples switches) were independently evaluated. MAIN VARIABLES OF INTEREST: Reasons for switching, NIRS failure and mortality rates. RESULTS: A total of 63 patients with AHRF were included, receiving combined NIRS, 58.7% classified in the HFNC-to-NIV group and 41.3% in the NIV-to-HFNC group. Reason for switching from HFNC to NIV was AHRF worsening (100%), while from NIV to HFNC was respiratory improvement (76.9%). NIRS failure rates were higher in the HFNC-to-NIV than in NIV-to-HFNC group (81% vs. 35%, p < 0.001). Among HFNC-to-NIV patients, there was no difference in the failure rate between the NIV trial-like and non-NIV trial-like groups (86% vs. 78%, p = 0.575) but the mortality rate was significantly lower in NIV trial-like group (14% vs. 52%, p = 0.02). Among NIV to HFNC patients, NIV failure was lower in the single switch group compared to the multiple switches group (15% vs. 53%, p = 0.039), with a shorter length of stay (5 [2-8] vs. 12 [8-30] days, p = 0.001). CONCLUSIONS: NIRS combination is used in real life and both switches' strategies, HFNC to NIV and NIV to HFNC, are common in AHRF management. Transitioning from HFNC to NIV is suggested as a therapeutic escalation and in this context performance of a NIV-trial could be beneficial. Conversely, switching from NIV to HFNC is suggested as a de-escalation strategy that is deemed safe if there is no NIRS failure.

Predictive model for a second hip fracture occurrence using natural language processing and machine learning on electronic health records.

Hip fractures (HFx) are associated with a higher morbidity and mortality rates, leading to a significant reduction in life quality and in limitation of patient´s mobility. The present study aimed to obtain real-world evidence on the clinical characteristics of patients with an initial and a second hip fracture (HFx) and develop a predictive model for second HFx using artificial intelligence. Electronic health records from one hospital centre in Spain from January 2011 to December 2019 were analysed using EHRead® technology, based on natural language processing and machine learning. A total of 1,960 patients with HFx were finally included during the study period after meeting all inclusion and exclusion criteria. From this total, 1835 (93.6%) patients were included in the HFx subgroup, while 124 (6.4%) were admitted to the second HFx (2HFx) subgroup. The mean age of the participants was 84 years and 75.5% were female. Most of comorbidities were more frequently identified in the HFx group, including hypertension (72.0% vs. 67.2%), cognitive impairment (33.0% vs. 31.2%), diabetes mellitus (28.7% vs. 24.8%), heart failure (27.6% vs. 22.4%) and chronic kidney disease (26.9% vs. 16.0%). Based on clinical criteria, 26 features were selected as potential prediction factors. From there, 16 demographics and clinical characteristics such as comorbidities, medications, measures of disabilities for ambulation and type of refracture were selected for development of a competitive risk model. Specifically, those predictors with different associated risk ratios, sorted from higher to lower risk relevance were visual deficit, malnutrition, walking assistance, hypothyroidism, female sex, osteoporosis treatment, pertrochanteric fracture, dementia, age at index, osteoporosis, renal failure, stroke, COPD, heart disease, anaemia, and asthma. This model showed good performance (dependent AUC: 0.69; apparent performance: 0.75) and could help the identification of patients with higher risk of developing a second HFx, allowing preventive measures. This study expands the current available information of HFx patients in Spain and identifies factors that exhibit potential in predicting a second HFx among older patients.

Association of obesity on the outcome of critically ill patients affected by COVID-19.

OBJECTIVE: To evaluate the impact of obesity on ICU mortality. DESIGN: Observational, retrospective, multicentre study. SETTING: Intensive Care Unit (ICU). PATIENTS: Adults patients admitted with COVID-19 and respiratory failure. INTERVENTIONS: None. PRIMARY VARIABLES OF INTEREST: Collected data included demographic and clinical characteristics, comorbidities, laboratory tests and ICU outcomes. Body mass index (BMI) impact on ICU mortality was studied as (1) a continuous variable, (2) a categorical variable obesity/non-obesity, and (3) as categories defined a priori: underweight, normal, overweight, obesity and Class III obesity. The impact of obesity on mortality was assessed by multiple logistic regression and Smooth Restricted cubic (SRC) splines for Cox hazard regression. RESULTS: 5,206 patients were included, 20 patients (0.4%) as underweight, 887(17.0%) as normal, 2390(46%) as overweight, 1672(32.1) as obese and 237(4.5%) as class III obesity. The obesity group patients (n = 1909) were younger (61 vs. 65 years, p < 0.001) and with lower severity scores APACHE II (13 [9-17] vs. 13[10-17, p < 0.01) than non-obese. Overall ICU mortality was 28.5% and not different for obese (28.9%) or non-obese (28.3%, p = 0.65). Only Class III obesity (OR = 2.19, 95%CI 1.44-3.34) was associated with ICU mortality in the multivariate and SRC analysis. CONCLUSIONS: COVID-19 patients with a BMI > 40 are at high risk of poor outcomes in the ICU. An effective vaccination schedule and prolonged social distancing should be recommended.

Comparing lung aeration and respiratory effort using two different spontaneous breathing trial: T-piece vs pressure support ventilation.

OBJECTIVE: To assess the changes in lung aeration and respiratory effort generated by two different spontaneous breathing trial (SBT): T-piece (T-T) vs pressure support ventilation (PSV). DESIGN: Prospective, interventionist and randomized study. SETTING: Intensive Care Unit (ICU) of Hospital del Mar. PARTICIPANTS: Forty-three ventilated patients for at least 24 h and considered eligible for an SBT were included in the study between October 2017 and March 2020. INTERVENTIONS: 30-min SBT with T-piece (T-T group, 20 patients) or 8-cmH2O PSV and 5-cmH2O positive end expiratory pressure (PSV group, 23 patients). MAIN VARIABLES OF INTEREST: Demographics, clinical data, physiological variables, lung aeration evaluated with electrical impedance tomography (EIT) and lung ultrasound (LUS), and respiratory effort using diaphragmatic ultrasonography (DU) were collected at different timepoints: basal (BSL), end of SBT (EoSBT) and one hour after extubation (OTE). RESULTS: There were a loss of aeration measured with EIT and LUS in the different study timepoints, without statistical differences from BSL to OTE, between T-T and PSV [LUS: 3 (1, 5.5) AU vs 2 (1, 3) AU; p = 0.088; EELI: -2516.41 (-5871.88, 1090.46) AU vs -1992.4 (-3458.76, -5.07) AU; p = 0.918]. Percentage of variation between BSL and OTE, was greater when LUS was used compared to EIT (68.1% vs 4.9%, p ≤ 0.001). Diaphragmatic excursion trend to decrease coinciding with a loss of aeration during extubation. CONCLUSION: T-T and PSV as different SBT strategies in ventilated patients do not show differences in aeration loss, nor estimated respiratory effort or tidal volume measured by EIT, LUS and DU.

A Multicentric Observational Study to Determine Myocardial Injury in Severe Community-Acquired Pneumonia (sCAP).

Background: Severe community-acquired pneumonia (sCAP) is the most frequent admission for acute respiratory failure in intensive care medicine. Observational studies have found a correlation between patients who were admitted with CAP and the development of cardiovascular events. The risk of acute myocardial damage in patients with CAP is particularly high within the first 30 days of hospitalization. Research design and methods: Multicenter prospective cohort analysis conducted in consecutive patients admitted to an ICU with microbiologically confirmed diagnoses of sCAP. The aim was to determine any structural cardiac damage detected by advanced imagining techniques (cardiac MRI) and cardiac biomarkers in patients with sCAP. The patients were stratified, according to their etiology, into pneumococcal or not-pneumococcal sCAP. The primary outcome was cardiac damage at day 5 and 7 of clinical presentation. Results: A total of 23 patients were consecutively and prospectively enrolled for two winter periods. No significant differences were observed between the median troponin when comparing the pneumococcal vs. non-pneumococcal. The incidence of myocardial damage was numerically higher in the pneumococcal subgroup (70% vs. 50%, p = 0.61) on day 5 and on day 7 (53% vs. 40%, p = 0.81) but did not achieve significance. Confirming a correlation between the biomarkers of cell damage and the biomarkers of myocardial damage, only a positive and significant correlation was observed between h-FABP and DNA on day 1 (r = 0.74; p < 0.01) and day 3 (r = 0.83; p < 0.010). Twenty cardiac MRIs were performed on the 23 patients (87%). No presence of fibrosis was observed in any of the studies carried out within the first 15 days of admission. Conclusions: No significant myocardial damage was found in patients with sCAP independent of the bacterial etiology in accordance with biomarker alterations (Troponin and/or h-FABP) or cardiac MRI. Using cardiac MRI, we could not find any presence of myocardial fibrosis within the first 15 days of admission.

Using natural language processing to explore characteristics and management of patients with axial spondyloarthritis and psoriatic arthritis treated under real-world conditions in Spain: SpAINET study.

Background: Spondyloarthritis (SpA) is a group of related but phenotypically distinct inflammatory disorders that include axial SpA (axSpA) and psoriatic arthritis (PsA). Information on the characteristics and management of these patients in the real world remains scarce. Objectives: To explore the characteristics and management [disease activity assessment and treatment with secukinumab (SEC) or other biologic disease-modifying antirheumatic drugs (bDMARDs)] of axSpA and PsA patients using natural language processing (NLP) in Electronic Health Records (EHRs). Design: National, multicenter, observational, and retrospective study. Methods: We analyzed free-text and structured clinical information from EHR at three hospitals. All adult patients with axSpA, PsA or non-classified SpA from 2018 to 2021 with minimum follow-up of three months were included when starting SEC or other bDMARDs. Clinical variables were extracted using EHRead® technology based on Systemized Nomenclature of Medicine-Clinical Terms (SNOMED CT) terminology. Results: Out of 887,735 patients, 758 were included, of which 328 had axSpA [58.5% male; mean (SD) age of 50.7 (12.7) years], 365 PsA [54.8% female, 53.9 (12.4) years], and 65 non-classified SpA. Mean (SD) time since diagnosis was 36.8 (61.0) and 24.1 (35.2) months for axSpA and PsA, respectively. Only 116 axSpA patients (35.3%) had available Ankylosing Spondylitis Disease Activity Score (ASDAS) or Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) at bDMARD onset, of which 61 presented active disease. Disease Activity in PSoriatic Arthritis (DAPSA) or Disease Assessment Score - 28 joints (DAS-28) values at bDMARD onset were available for only 61 PsA (16.7%) patients, with 23 of them having active disease. The number of patients with available tender joint count or swollen joint count assessment was 68 (20.7%) and 59 (18%) for axSpA, and 115 (31.5%) and 119 (32.6%) for PsA, respectively. SEC was used in 63 (19.2%) axSpA patients and in 63 (17.3%) PsA patients. Conclusion: Using NLP, the study showed that around one-third of axSpA and one-sixth of PsA patients have disease activity assessments with ASDAS/BASDAI or DAPSA/DAS-28, respectively, highlighting an area of improvement in these patients' management.

Investigating axial spondyloarthritis and psoriatic arthritis patients using natural language processing We conducted a study in Spain to better understand patients with specific rheumatic conditions known as axial spondyloarthritis (axSpA) and psoriatic arthritis (PsA). To analyze their characteristics, we used a computer technology called EHRead, which uses Natural Language Processing (NLP) to analyze free text from electronic health records. Out of a large group of patients, we focused on 758 individuals who had axSpA or PsA. Most of the axSpA patients were men, and they were around 51 years old on average. For the PsA patients, most were women, and their average age was about 54 years. We analyzed outcomes and treatments of these patients. Our findings showed that we can describe and assess a cohort of patients from real world using NLP. Besides, only about one-third of axSpA patients and one-sixth of PsA patients had their respective outcomes completely assessed, which indicates that there is potential room for improvement in the management of axSpA and PsA. The most promising feature in our study is the use of NLP, an artificial intelligence technology that helps us understand information in medical records written in free text. This can help us explore the characteristics of patients and their management in the real world, bringing an opportunity to enhance the care of patients with axSpA and PsA.

Bacterial Patterns and Empiric Antibiotic Use in COPD Patients With Community-Acquired Pneumonia.

INTRODUCTION: Chronic obstructive pulmonary disease (COPD) is strongly associated with the development of community-acquired pneumonia (CAP). Limited data are available on risk factors for difficult to manage bacteria such as Pseudomonas aeruginosa in COPD patients with CAP. Our objective was to assess the microbiological patterns associated with risk factors that determine empiric antibiotic therapy in hospitalized COPD patients with CAP. METHODS: We performed a secondary data analysis of an international, multicenter, observational, point-prevalence study involving hospitalized COPD patients with CAP from March to June 2015. After identifying the risk factors associated with different microorganisms, we developed a scoring system to guide decision-making about empiric anti-pseudomonal antibiotic therapy in this population. RESULTS: We enrolled 689 hospitalized COPD patients with CAP with documented microbiological testing. The most frequent microorganisms isolated were Streptococcus pneumoniae (8%) and Gram-negative bacteria (8%), P. aeruginosa (7%) and Haemophilus influenzae (3%). We developed a scoring system incorporating the variables independently associated with P. aeruginosa that include a previous P. aeruginosa isolation or infection (OR 14.2 [95%CI 5.7-35.2]), hospitalization in the past 12 months (OR 3.7 [1.5-9.2]), and bronchiectasis (OR 3.2 [1.4-7.2]). Empiric anti-pseudomonal antibiotics were overutilized in COPD patients with CAP. The new scoring system has the potential to reduce empiric anti-pseudomonal antibiotic use from 54.1% to 6.2%. CONCLUSIONS: COPD patients with CAP present different microbiological profiles associated with unique risk factors. Anti-pseudomonal treatment is a critical decision when selecting empiric antibiotic therapy. We developed a COPD scoring system to guide decision-making about empiric anti-pseudomonal antibiotic therapy.