Clinical characteristics and outcomes among hospitalised COVID-19 patients across epidemic waves in Spain: An unCoVer analysis.

BACKGROUND AND OBJECTIVES: The COVID-19 pandemic had a significant impact in population health worldwide, and particularly in people with pre-existing chronic diseases. Early risk identification and stratification is essential to reduce the impact of future outbreaks of pandemic potential. This study aimed to comprehensively examine factors associated with COVID-19 mortality across the pandemic waves in Spain. METHODS: A retrospective study analyzed the characteristics of 13,974 patients admitted to Spanish hospitals due to SARS-CoV-2 infection from 2020-01-28 to 2022-12-31. The demographic and clinical features of patients during hospitalization on each pandemic waves were analyzed. MAIN FINDINGS: The findings highlight the heterogeneity of patient characteristics, comorbidities and outcomes, across the waves. The high prevalence of cardiometabolic diseases (53.9%) among COVID-19 patients emphasizes the importance of controlling these risk factors to prevent severe COVID-19 outcomes. CONCLUSIONS: In summary, the study associate hospital mortality with factors such as advanced age and comorbidities. The decline in mortality after the 4th wave indicates potential influences like vaccination, viral adaptation, or improved treatments. Notably, dementia and cancer metastases emerge as critical factors linked to higher mortality, highlighting the importance of addressing these conditions in COVID-19 management and preparing for future challenges.

Bioavailability and systemic transport of oleanolic acid in humans, formulated as a functional olive oil.

Evidence of the pharmacological activity of oleanolic acid (OA) suggests its potential therapeutic application. However, its use in functional foods, dietary supplements, or nutraceuticals is hindered by limited human bioavailability studies. The BIO-OLTRAD trial is a double-blind, randomized controlled study with 22 participants that received a single dose of 30 mg OA formulated as a functional olive oil. The study revealed that the maximum serum concentration of OA ranged from 500 to 600 ng mL-1, with an AUC0-∞ value of 2862.50 ± 174.50 ng h mL-1. Furthermore, we discovered a physiological association of OA with serum albumin and triglyceride-rich lipoproteins (TRL). UV absorption spectra showed conformational changes in serum albumin due to the formation of an adduct with OA. Additionally, we demonstrated that TRL incorporate OA, reaching a maximum concentration of 140 ng mL-1 after 2-4 hours. We conjecture that both are efficient carriers to reach target tissues and to yield high bioavailability.

Bioactive compounds in pomace olive oil modulate the inflammatory response elicited by postprandial triglyceride-rich lipoproteins in BV-2 cells.

Modulation of microglial response could be a target to reduce neuroinflammation associated with Alzheimer's disease. In this study, we propose that lipophilic bioactive molecules present in pomace olive oil (POO), transported in triglyceride-rich lipoproteins (TRLs), are able to modulate microglial high-oleic sunflower oil (HOSO, points) or pomace olive oil (POO, stripes). In order to prove this hypothesis, a randomized crossover postprandial trial was performed in 18 healthy young women. POO was assayed in opposition to high-oleic sunflower oil (HOSO), a common dietary oil which shares with POO an almost identical fatty acid composition but lacks certain biomolecules with recognized antioxidant and anti-inflammatory activities. TRLs were isolated from blood at the baseline and 2 and 4 hours postprandially and used to treat BV-2 cells to assess their ability to modulate the microglial function. We found that the intake of POO leads to the constitution of postprandial TRLs that are able to modulate the inflammatory response in microglia compared to HOSO. TRL-derived POO reduced the release of pro-inflammatory cytokines (tumor necrosis factor-α, and interleukins 1ß and 6) and nitric oxide and downregulated genes codifying for these cytokines and inducible nitric oxide synthase (iNOS) in BV-2 cells. Moreover, the ingestion of POO by healthy women slightly improved glycemic control and TRL clearance throughout the postprandial phase compared to HOSO. In conclusion, we demonstrated that consuming POO results in postprandial TRLs containing lipophilic bioactive compounds capable of regulating the inflammatory response prompted by microglial activation.

Clinical Benefit of Bempedoic Acid in Randomized Clinical Trials.

Bempedoic acid is a selective inhibitor of the adenosine triphosphate citrate lyase that reduces low-density lipoprotein cholesterol (LDLc) levels by 17% to 28%. Although the Evaluation of Major Cardiovascular Events in Patients With, or at High Risk for, Cardiovascular Disease Who Are Statin Intolerant Treated With Bempedoic Acid (CLEAR-OUTCOMES) trials demonstrated the efficacy on cardiovascular outcomes there is a controversy related to the possible net clinical benefit. Thereafter, we performed an intention-to-treat meta-analysis in line with recommendations from the Cochrane Collaboration and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. The primary outcome of the metanalysis was the incidence of major adverse cardiovascular events, defined by each study protocol. Secondary outcomes for the analyses were myocardial infarction, stroke, myocardial revascularization, cardiovascular death, and all-cause death. Results of 4 clinical trials evaluated contained a total of 17,324 patients; 9,236 received bempedoic acid for a median of 46.6 months. The mean baseline LDLc was 129.4 (22.8) mg/100 ml and treatment was associated with a mean LDLc reduction of 26.0 (12.6) mg/100 ml. Treatment with bempedoic acid significantly reduced the incidence of major adverse cardiovascular events (hazard ratio [HR] 0.88, 95% confidence interval [CI] 0.81 to 0.96), myocardial infarction (HR 0.76, 95% CI 0.66 to 0.89) and myocardial revascularization (HR 0.82, 95% CI 0.73 to 0.92); the crude incidence of stroke, cardiovascular or all-cause mortality were lower in patients in the bempedoic acid groups although no significant risk reduction was observed. No heterogeneity was observed in any of the end points. In conclusion, the metanalysis of the 4 clinical trials currently available with bempedoic acid provides reliable evidence of its clinical benefit with no signs of heterogeneity or harm.

Deficiency in the production of antibodies to lipids correlates with increased lipid metabolism in severe COVID-19 patients.

Background: Antibodies to lipids are part of the first line of defense against microorganisms and regulate the pro/anti-inflammatory balance. Viruses modulate cellular lipid metabolism to enhance their replication, and some of these metabolites are proinflammatory. We hypothesized that antibodies to lipids would play a main role of in the defense against SARS-CoV-2 and thus, they would also avoid the hyperinflammation, a main problem in severe condition patients. Methods: Serum samples from COVID-19 patients with mild and severe course, and control group were included. IgG and IgM to different glycerophospholipids and sphingolipids were analyzed using a high-sensitive ELISA developed in our laboratory. A lipidomic approach for studying lipid metabolism was performed using ultra-high performance liquid chromatography coupled to electrospray ionization and quadrupole time-of-flight mass spectrometry (UHPLC-ESI-QTOF-MS). Results: Mild and severe COVID-19 patients had higher levels of IgM to glycerophosphocholines than control group. Mild COVID-19 patients showed higher levels of IgM to glycerophosphoinositol, glycerophosphoserine and sulfatides than control group and mild cases. 82.5% of mild COVID-19 patients showed IgM to glycerophosphoinositol or glycerophosphocholines plus sulfatides or glycerophosphoserines. Only 35% of severe cases and 27.5% of control group were positive for IgM to these lipids. Lipidomic analysis identify a total of 196 lipids, including 172 glycerophospholipids and 24 sphingomyelins. Increased levels of lipid subclasses belonging to lysoglycerophospholipids, ether and/or vinyl-ether-linked glycerophospholipids, and sphingomyelins were observed in severe COVID-19 patients, when compared with those of mild cases and control group. Conclusion: Antibodies to lipids are essential for defense against SARS-CoV-2. Patients with low levels of anti-lipid antibodies have an elevated inflammatory response mediated by lysoglycerophospholipids. These findings provide novel prognostic biomarkers and therapeutic targets.

Artificial intelligence cloud platform improves arrhythmia detection from insertable cardiac monitors to 25 cardiac rhythm patterns through multi-label classification.

BACKGROUND: Electrocardiogram (ECG) is the gold standard for the diagnosis of cardiac arrhythmias and other heart diseases. Insertable cardiac monitors (ICMs) have been developed to continuously monitor cardiac activity over long periods of time and to detect 4 cardiac patterns (atrial tachyarrhythmias, ventricular tachycardia, bradycardia, and pause). However, interpretation of ECG or ICM subcutaneous ECG (sECG) is time-consuming for clinicians. Artificial intelligence (AI) classifies ECG and sECG with high accuracy in short times. OBJECTIVE: To demonstrate whether an AI algorithm can expand ICM arrhythmia recognition from 4 to many cardiac patterns. METHODS: We performed an exploratory retrospective study with sECG raw data coming from 20 patients wearing a Confirm Rx™ (Abbott, Sylmar, USA) ICM. The sECG data were recorded in standard conditions and then analyzed by AI (Willem™, IDOVEN, Madrid, Spain) and cardiologists, in parallel. RESULTS: In nineteen patients, ICMs recorded 2261 sECGs in an average follow-up of 23 months. Within these 2261 sECG episodes, AI identified 7882 events and classified them according to 25 different cardiac rhythm patterns with a pondered global accuracy of 88%. Global positive predictive value, sensitivity, and F1-score were 86.77%, 83.89%, and 85.52% respectively. AI was especially sensitive for bradycardias, pauses, rS complexes, premature atrial contractions, and inverted T waves, reducing the median time spent to classify each sECG compared to cardiologists. CONCLUSION: AI can process sECG raw data coming from ICMs without previous training, extending the performance of these devices and saving cardiologists' time in reviewing cardiac rhythm patterns detection.

Randomized Trial of Anticoagulation Strategies for Noncritically Ill Patients Hospitalized With COVID-19.

BACKGROUND: Prior studies of therapeutic-dose anticoagulation in patients with COVID-19 have reported conflicting results. OBJECTIVES: We sought to determine the safety and effectiveness of therapeutic-dose anticoagulation in noncritically ill patients with COVID-19. METHODS: Patients hospitalized with COVID-19 not requiring intensive care unit treatment were randomized to prophylactic-dose enoxaparin, therapeutic-dose enoxaparin, or therapeutic-dose apixaban. The primary outcome was the 30-day composite of all-cause mortality, requirement for intensive care unit-level of care, systemic thromboembolism, or ischemic stroke assessed in the combined therapeutic-dose groups compared with the prophylactic-dose group. RESULTS: Between August 26, 2020, and September 19, 2022, 3,398 noncritically ill patients hospitalized with COVID-19 were randomized to prophylactic-dose enoxaparin (n = 1,141), therapeutic-dose enoxaparin (n = 1,136), or therapeutic-dose apixaban (n = 1,121) at 76 centers in 10 countries. The 30-day primary outcome occurred in 13.2% of patients in the prophylactic-dose group and 11.3% of patients in the combined therapeutic-dose groups (HR: 0.85; 95% CI: 0.69-1.04; P = 0.11). All-cause mortality occurred in 7.0% of patients treated with prophylactic-dose enoxaparin and 4.9% of patients treated with therapeutic-dose anticoagulation (HR: 0.70; 95% CI: 0.52-0.93; P = 0.01), and intubation was required in 8.4% vs 6.4% of patients, respectively (HR: 0.75; 95% CI: 0.58-0.98; P = 0.03). Results were similar in the 2 therapeutic-dose groups, and major bleeding in all 3 groups was infrequent. CONCLUSIONS: Among noncritically ill patients hospitalized with COVID-19, the 30-day primary composite outcome was not significantly reduced with therapeutic-dose anticoagulation compared with prophylactic-dose anticoagulation. However, fewer patients who were treated with therapeutic-dose anticoagulation required intubation and fewer died (FREEDOM COVID [FREEDOM COVID Anticoagulation Strategy]; NCT04512079).

Cost-Effectiveness of the CNIC-Polypill Strategy Compared With Separate Monocomponents in Secondary Prevention of Cardiovascular and Cerebrovascular Disease in Portugal: The MERCURY Study.

Background: Cardiovascular (CV) diseases remain a leading and costly cause of death globally. Patients with previous CV events are at high risk of recurrence. Secondary prevention therapies improve CV risk factor control and reduce disease costs. Objectives: To assess the cost-effectiveness of a CV polypill strategy (CNIC-Polypill) compared with the loose combination of monocomponents to improve the control of CV risk factors in patients with previous coronary heart disease or stroke. Methods: A Markov model cost-utility analysis was developed using 4 health states, SMART risk equation, and 3-month cycles for year 1 and annual cycles thereafter, over a lifetime horizon from the perspective of the National Health System in Portugal (base case). The NEPTUNO study, Portuguese registries, mortality tables, official reports, and the literature were consulted to define effectiveness, epidemiological costs, and utility data. Outcomes were costs (estimated in 2020 euros) per life-year (LY) and quality-adjusted LY (QALY) gained. A 4% discount rate was applied. Alternative scenarios and one-way and probabilistic sensitivity analyses tested the consistency and robustness of results. Results: The CNIC-Polypill strategy in secondary prevention provides more LY and QALY, at a higher cost, than monocomponents. The incremental cost-utility ratio is €1557/QALY gained. Assuming a willingness-to-pay threshold of €30 000/QALY gained, there is a 79.7% and a 44.4% probability of the CNIC-Polypill being cost-effective and cost-saving, respectively, compared with the loose combination of monocomponents. Results remain consistent in the alternative scenarios and robust in the sensitivity analyses. Discussion: The model reflects increments in the number of years patients would live and in quality of life with the CNIC-Polypill. The clinical effectiveness of the CNIC-Polypill strategy initially demonstrated in the NEPTUNO study has been recently corroborated in the SECURE trial. The incremental cost of the CNIC-Polypill strategy emerges slightly above the comparator, but willingness-to-pay estimates and sensitivity analyses indicate that the CNIC-Polypill strategy is consistently cost-effective compared with monocomponents and remains within acceptable affordability margins. Conclusion: The CNIC-Polypill is a cost-effective secondary prevention strategy. In patients with histories of coronary heart disease or stroke, the CNIC-Polypill more effectively controls CV risk factors compared with monocomponents.

Artificial intelligence augments detection accuracy of cardiac insertable cardiac monitors: Results from a pilot prospective observational study.

Background: Insertable cardiac monitors (ICMs) are indicated for long-term monitoring of patients with unexplained syncope or who are at risk for cardiac arrhythmias. The volume of ICM-transmitted information may result in long data review times to identify true and clinically relevant arrhythmias. Objective: The purpose of this study was to evaluate whether artificial intelligence (AI) may improve ICM detection accuracy. Methods: We performed a retrospective analysis of consecutive patients implanted with the Confirm RxTM ICM (Abbott) and followed in a prospective observational study. This device continuously monitors subcutaneous electrocardiograms (SECGs) and transmits to clinicians information about detected arrhythmias and patient-activated symptomatic episodes. All SECGs were classified by expert electrophysiologists and by the WillemTM AI algorithm (IDOVEN). Results: During mean follow-up of 23 months, of 20 ICM patients (mean age 68 ± 12 years; 50% women), 19 had 2261 SECGs recordings associated with cardiac arrhythmia detections or patient symptoms. True arrhythmias occurred in 11 patients: asystoles in 2, bradycardias in 3, ventricular tachycardias in 4, and atrial tachyarrhythmias (atrial tachycardia/atrial fibrillation [AT/AF]) in 10; with 6 patients having >1 arrhythmia type. AI algorithm overall accuracy for arrhythmia classification was 95.4%, with 97.19% sensitivity, 94.52% specificity, 89.74% positive predictive value, and 98.55% negative predictive value. Application of AI would have reduced the number of false-positive results by 98.0% overall: 94.0% for AT/AF, 87.5% for ventricular tachycardia, 99.5% for bradycardia, and 98.8% for asystole. Conclusion: Application of AI to ICM-detected episodes is associated with high classification accuracy and may significantly reduce health care staff workload by triaging ICM data.

Developing and Validating Multi-Modal Models for Mortality Prediction in COVID-19 Patients: a Multi-center Retrospective Study.

The unprecedented global crisis brought about by the COVID-19 pandemic has sparked numerous efforts to create predictive models for the detection and prognostication of SARS-CoV-2 infections with the goal of helping health systems allocate resources. Machine learning models, in particular, hold promise for their ability to leverage patient clinical information and medical images for prediction. However, most of the published COVID-19 prediction models thus far have little clinical utility due to methodological flaws and lack of appropriate validation. In this paper, we describe our methodology to develop and validate multi-modal models for COVID-19 mortality prediction using multi-center patient data. The models for COVID-19 mortality prediction were developed using retrospective data from Madrid, Spain (N = 2547) and were externally validated in patient cohorts from a community hospital in New Jersey, USA (N = 242) and an academic center in Seoul, Republic of Korea (N = 336). The models we developed performed differently across various clinical settings, underscoring the need for a guided strategy when employing machine learning for clinical decision-making. We demonstrated that using features from both the structured electronic health records and chest X-ray imaging data resulted in better 30-day mortality prediction performance across all three datasets (areas under the receiver operating characteristic curves: 0.85 (95% confidence interval: 0.83-0.87), 0.76 (0.70-0.82), and 0.95 (0.92-0.98)). We discuss the rationale for the decisions made at every step in developing the models and have made our code available to the research community. We employed the best machine learning practices for clinical model development. Our goal is to create a toolkit that would assist investigators and organizations in building multi-modal models for prediction, classification, and/or optimization.

Barriers and Solutions to Improve Therapeutic Adherence from the Perspective of Primary Care and Hospital-Based Physicians.

Purpose: To identify the barriers affecting treatment adherence in patients with chronic disease and to determine solutions through the physician's opinion of primary care and hospital settings. Methods: An observational study using the nominal group technique was performed to reach a consensus from experts. A structured face-to-face group discussion was carried out with physicians with more than 10 years of experience in the subject of treatment adherence/compliance in either the primary care setting or the hospital setting. The experts individually rated a list of questions using the Likert scale and prioritized the top 10 questions to identify barriers and seek solutions afterward. The top 10 questions that obtained the maximum score for both groups of experts were prioritized. During the final discussion group, participating experts analyzed the prioritized items and debated on each problem to reach consensual solutions for improvement. Results: A total of 17 professionals experts participated in the study, nine of them were from a primary care setting. In the expert group from the primary care setting, the proposed solution for the barrier identified as the highest priority was to simplify treatments, measure adherence and review medication. In the expert group from the hospital setting, the proposed solution for the barrier identified as the highest priority was training on motivational clinical interviews for healthcare workers undergraduate and postgraduate education. Finally, the expert participants proposed implementing an improvement plan with eight key ideas. Conclusion: A consensual improvement plan to facilitate the control of therapeutic adherence in patients with chronic disease was developed, taking into account expert physicians' opinions from primary care and hospital settings about barriers and solutions to address therapeutic adherence in patients with chronic disease.

Early detection of anthracycline- and trastuzumab-induced cardiotoxicity: value and optimal timing of serum biomarkers and echocardiographic parameters.

AIMS: To evaluate echocardiographic and biomarker changes during chemotherapy, assess their ability to early detect and predict cardiotoxicity and to define the best time for their evaluation. METHODS AND RESULTS: Seventy-two women with breast cancer (52 ± 9.8 years) treated with anthracyclines (26 also with trastuzumab), were evaluated for 14 months (6 echocardiograms/12 laboratory tests). We analysed: high-sensitivity cardiac troponin T, NT-proBNP, global longitudinal strain (GLS), left ventricle end-systolic volume (LVESV), left ventricle end-diastolic volume (LVEDV), and left ventricular ejection fraction (LVEF). Cardiotoxicity was defined as a reduction in LVEF>10% compared with baseline with LVEF<53%. High-sensitivity troponin T levels rose gradually reaching a maximum peak at 96 ± 13 days after starting chemotherapy (P < 0.001) and 62.5% of patients presented increased values during treatment. NT-proBNP augmented after each anthracycline cycle (mean pre-cycle levels of 72 ± 68 pg/mL and post-cycle levels of 260 ± 187 pg/mL; P < 0.0001). Cardiotoxicity was detected in 9.7% of patients (mean onset at 5.2 months). In the group with cardiotoxicity, the LVESV was higher compared with those without cardiotoxicity (40 mL vs. 29.5 mL; P = 0.045) at 1 month post-anthracycline treatment and the decline in GLS was more pronounced (-17.6% vs. -21.4%; P = 0.03). Trastuzumab did not alter serum biomarkers, but it was associated with an increase in LVESV and LVEDV (P < 0.05). While baseline LVEF was an independent predictor of later cardiotoxicity (P = 0.039), LVESV and GLS resulted to be early detectors of cardiotoxicity [odds ratio = 1.12 (1.02-1.24), odds ratio = 0.66 (0.44-0.92), P < 0.05] at 1 month post-anthracycline treatment. Neither high-sensitivity troponin T nor NT-proBNP was capable of predicting subsequent cardiotoxicity. CONCLUSIONS: One month after completion of anthracycline treatment is the optimal time to detect cardiotoxicity by means of imaging parameters (LVESV and GSL) and to determine maximal troponin rise. Baseline LVEF was a predictor of later cardiotoxicity. Trastuzumab therapy does not affect troponin values hence imaging techniques are recommended to detect trastuzumab-induced cardiotoxicity.

Rational and design of ST-segment elevation not associated with acute cardiac necrosis (LESTONNAC). A prospective registry for validation of a deep learning system assisted by artificial intelligence.

BACKGROUND: Patients with chest pain and persistent ST segment elevation (STE) may not have acute coronary occlusions or serum troponin curves suggestive of acute necrosis. Our objective is the validation and cost-effectiveness analysis of a diagnostic model assisted by artificial intelligence (AI). METHODS: Prospective multicenter registry in two groups of patients with STE: I) coronary arteries without significant lesions and without serum troponin curve suggestive of acute necrosis, II) myocardial infarction with acute coronary occlusion. The inclusion criteria are the following: 1) age ≥ 18 years, 2) chest pain or symptoms suggestive of myocardial ischemia, 3) STE at point J in two contiguous leads ≥0.1 mV, in V2 and V3 ≥ 0,2 mV and 4) signature of informed consent. The exclusion criteria are the following: 1) left bundle branch block, 2) acute cardiac necrosis in the absence of significant epicardial coronary artery stenosis, 3) STE ≤ 0.1 mV with pathologic Q wave, 4) severe anemia (hemoglobin <8.0 g/dl). For each patient without acute cardiac necrosis, the next patient from that center of the same sex and similar age (± 5 years) with myocardial infarction and acute coronary occlusion will be included. A manual centralized electrocardiographic analysis and another by deep learning AI will be performed. CONCLUSIONS: The results of the study will provide new information for the stratification of patients with STE. Our hypothesis is that an AI analysis of the surface electrocardiogram allows a better distinction of patients with STE due to acute myocardial ischemia, from those with another etiology.

Fixed-dose combination therapies with and without aspirin for primary prevention of cardiovascular disease: an individual participant data meta-analysis.

BACKGROUND: In randomised controlled trials, fixed-dose combination treatments (or polypills) have been shown to reduce a composite of cardiovascular disease outcomes in primary prevention. However, whether or not aspirin should be included, effects on specific outcomes, and effects in key subgroups are unknown. METHODS: We did an individual participant data meta-analysis of large randomised controlled trials (each with ≥1000 participants and ≥2 years of follow-up) of a fixed-dose combination treatment strategy versus control in a primary cardiovascular disease prevention population. We included trials that evaluated a fixed-dose combination strategy of at least two blood pressure lowering agents plus a statin (with or without aspirin), compared with a control strategy (either placebo or usual care). The primary outcome was time to first occurrence of a composite of cardiovascular death, myocardial infarction, stroke, or arterial revascularisation. Additional outcomes included individual cardiovascular outcomes and death from any cause. Outcomes were also evaluated in groups stratified by the inclusion of aspirin in the fixed-dose treatment strategy, and effect sizes were estimated in prespecified subgroups based on risk factors. Kaplan-Meier survival curves and Cox proportional hazard regression models were used to compare strategies. FINDINGS: Three large randomised trials were included in the analysis (TIPS-3, HOPE-3, and PolyIran), with a total of 18 162 participants. Mean age was 63·0 years (SD 7·1), and 9038 (49·8%) participants were female. Estimated 10-year cardiovascular disease risk for the population was 17·7% (8·7). During a median follow-up of 5 years, the primary outcome occurred in 276 (3·0%) participants in the fixed-dose combination strategy group compared with 445 (4·9%) in the control group (hazard ratio 0·62, 95% CI 0·53-0·73, p<0·0001). Reductions were also observed for the separate components of the primary outcome: myocardial infarction (0·52, 0·38-0·70), revascularisation (0·54, 0·36-0·80), stroke (0·59, 0·45-0·78), and cardiovascular death (0·65, 0·52-0·81). Significant reductions in the primary outcome and its components were observed in the analyses of fixed-dose combination strategies with and without aspirin, with greater reductions for strategies including aspirin. Treatment effects were similar at different lipid and blood pressure levels, and in the presence or absence of diabetes, smoking, or obesity. Gastrointestinal bleeding was uncommon but slightly more frequent in the fixed-dose combination strategy with aspirin group versus control (19 [0·4%] vs 11 [0·2%], p=0·15). The frequencies of haemorrhagic stroke (10 [0·2%] vs 15 [0·3%]), fatal bleeding (two [<0·1%] vs four [0·1%]), and peptic ulcer disease (32 [0·7%] vs 34 [0·8%]) were low and did not differ significantly between groups. Dizziness was more common with fixed-dose combination treatment (1060 [11·7%] vs 834 [9·2%], p<0·0001). INTERPRETATION: Fixed-dose combination treatment strategies substantially reduce cardiovascular disease, myocardial infarction, stroke, revascularisation, and cardiovascular death in primary cardiovascular disease prevention. These benefits are consistent irrespective of cardiometabolic risk factors. FUNDING: Population Health Research Institute.

Hemogram-derived ratios as prognostic markers of ICU admission in COVID-19.

BACKGROUND: The vast impact of COVID-19 call for the identification of clinical parameter that can help predict a torpid evolution. Among these, endothelial injury has been proposed as one of the main pathophysiological mechanisms underlying the disease, promoting a hyperinflammatory and prothrombotic state leading to worse clinical outcomes. Leukocytes and platelets play a key role in inflammation and thrombogenesis, hence the objective of the current study was to study whether neutrophil-to-lymphocyte ratio (NLR), platelets-to-lymphocyte ratio (PLR), the systemic immune-inflammation index (SII) as well as the new parameter neutrophil-to-platelet ratio (NPR), could help identify patients who at risk of admission at Intensive Care Units. METHODS: A retrospective observational study was performed at HM Hospitales including electronic health records from 2245 patients admitted due to COVID-19 from March 1 to June 10, 2020. Patients were divided into two groups, admitted at ICU or not. RESULTS: Patients who were admitted at the ICU had significantly higher values in all hemogram-derived ratios at the moment of hospital admission compared to those who did not need ICU admission. Specifically, we found significant differences in NLR (6.9 [4-11.7] vs 4.1 [2.6-7.6], p <  0.0001), PLR (2 [1.4-3.3] vs 1.9 [1.3-2.9], p = 0.023), NPR (3 [2.1-4.2] vs 2.3 [1.6-3.2], p <  0.0001) and SII (13 [6.5-25.7] vs 9 [4.9-17.5], p <  0.0001) compared to those who did not require ICU admission. After multivariable logistic regression models, NPR was the hemogram-derived ratio with the highest predictive value of ICU admission, (OR 1.11 (95% CI: 0.98-1.22, p = 0.055). CONCLUSIONS: Simple, hemogram-derived ratios obtained from early hemogram at hospital admission, especially the novelty NPR, have shown to be useful predictors of risk of ICU admission in patients hospitalized due to COVID-19.

Hemogram as marker of in-hospital mortality in COVID-19.

The clinical impact of COVID-19 disease calls for the identification of routine variables to identify patients at increased risk of death. Current understanding of moderate-to-severe COVID-19 pathophysiology points toward an underlying cytokine release driving a hyperinflammatory and procoagulant state. In this scenario, white blood cells and platelets play a direct role as effectors of such inflammation and thrombotic response. We investigate whether hemogram-derived ratios such as neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio and the systemic immune-inflammation index may help to identify patients at risk of fatal outcomes. Activated platelets and neutrophils may be playing a decisive role during the thromboinflammatory phase of COVID-19 so, in addition, we introduce and validate a novel marker, the neutrophil-to-platelet ratio (NPR).Two thousand and eighty-eight hospitalized patients with COVID-19 admitted at any of the hospitals of HM Hospitales group in Spain, from March 1 to June 10, 2020, were categorized according to the primary outcome of in-hospital death.Baseline values, as well as the rate of increase of the four ratios analyzed were significantly higher at hospital admission in patients who died than in those who were discharged (p<0.0001). In multivariable logistic regression models, NLR (OR 1.05; 95% CI 1.02 to 1.08, p=0.00035) and NPR (OR 1.23; 95% CI 1.12 to 1.36, p<0.0001) were significantly and independently associated with in-hospital mortality.According to our results, hemogram-derived ratios obtained at hospital admission, as well as the rate of change during hospitalization, may easily detect, primarily using NLR and the novel NPR, patients with COVID-19 at high risk of in-hospital mortality.

Risk Score for Predicting In-Hospital Mortality in COVID-19 (RIM Score).

Infection by SARS-CoV2 has devastating consequences on health care systems. It is a global health priority to identify patients at risk of fatal outcomes. 1955 patients admitted to HM-Hospitales from 1 March to 10 June 2020 due to COVID-19, were were divided into two groups, 1310 belonged to the training cohort and 645 to validation cohort. Four different models were generated to predict in-hospital mortality. Following variables were included: age, sex, oxygen saturation, level of C-reactive-protein, neutrophil-to-platelet-ratio (NPR), neutrophil-to-lymphocyte-ratio (NLR) and the rate of changes of both hemogram ratios (VNLR and VNPR) during the first week after admission. The accuracy of the models in predicting in-hospital mortality were evaluated using the area under the receiver-operator-characteristic curve (AUC). AUC for models including NLR and NPR performed similarly in both cohorts: NLR 0.873 (95% CI: 0.849-0.898), NPR 0.875 (95% CI: 0.851-0.899) in training cohort and NLR 0.856 (95% CI: 0.818-0.895), NPR 0.863 (95% CI: 0.826-0.901) in validation cohort. AUC was 0.885 (95% CI: 0.885-0.919) for VNLR and 0.891 (95% CI: 0.861-0.922) for VNPR in the validation cohort. According to our results, models are useful in predicting in-hospital mortality risk due to COVID-19. The RIM Score proposed is a simple, widely available tool that can help identify patients at risk of fatal outcomes.

Carotid Plaque Burden by 3-Dimensional Vascular Ultrasound as a Risk Marker for Patients with Metabolic Syndrome.

Our aim was to analyse the associations between carotid plaque burden (CPB), cardiovascular risk factors (CVRF), and surrogate markers of CV risk in subjects with metabolic syndrome (MetS). We consecutively included 75 asymptomatic outpatients with MetS components, <60 years old and non-smokers. We determined the presence of CVRF, left ventricular hypertrophy (LVH), carotid intima-media thickness (cIMT), albumin-creatinine ratio (ACR), coronary artery calcium score (CACS) and CPB by 3-dimensional vascular ultrasound (3DVUS) for comparison. A total of 50 (67%) subjects had MetS defined by harmonized criteria. A CPB >0 mm3 and a CACS >0 AU were the risk biomarkers most frequently observed (72% and 77%, respectively), followed by LVH (40%). CPB and CACS revealed association with cardiovascular risk (r = 0.308; p = 0.032 and r = 0.601 p < 0.01, respectively), and CPB also showed association with the burden of CVRF (r = 0.349; p = 0.014). CPB by 3DVUS was a prevalent CV risk marker, directly associated with CVRF and cardiovascular risk in MetS subjects.

Personalized monitoring of electrical remodelling during atrial fibrillation progression via remote transmissions from implantable devices.

AIMS: Atrial electrical remodelling (AER) is a transitional period associated with the progression and long-term maintenance of atrial fibrillation (AF). We aimed to study the progression of AER in individual patients with implantable devices and AF episodes. METHODS AND RESULTS: Observational multicentre study (51 centres) including 4618 patients with implantable cardioverter-defibrillator +/-resynchronization therapy (ICD/CRT-D) and 352 patients (2 centres) with pacemakers (median follow-up: 3.4 years). Atrial activation rate (AAR) was quantified as the frequency of the dominant peak in the signal spectrum of AF episodes with atrial bipolar electrograms. Patients with complete progression of AER, from paroxysmal AF episodes to electrically remodelled persistent AF, were used to depict patient-specific AER slopes. A total of 34 712 AF tracings from 830 patients (87 with pacemakers) were suitable for the study. Complete progression of AER was documented in 216 patients (16 with pacemakers). Patients with persistent AF after completion of AER showed ∼30% faster AAR than patients with paroxysmal AF. The slope of AAR changes during AF progression revealed patient-specific patterns that correlated with the time-to-completion of AER (R2 = 0.85). Pacemaker patients were older than patients with ICD/CRT-Ds (78.3 vs. 67.2 year olds, respectively, P < 0.001) and had a shorter median time-to-completion of AER (24.9 vs. 93.5 days, respectively, P = 0.016). Remote transmissions in patients with ICD/CRT-D devices enabled the estimation of the time-to-completion of AER using the predicted slope of AAR changes from initiation to completion of electrical remodelling (R2 = 0.45). CONCLUSION: The AF progression shows patient-specific patterns of AER, which can be estimated using available remote-monitoring technology.