Sex differences in delay times in ST-segment elevation myocardial infarction: A cohort study.

BACKGROUND: The present study analyzes a cohort of consecutive patients with ST-segment elevation acute myocardial infarction (STEMI), evaluating the ischemia-reperfusion times from the perspective of gender differences (females versus males), with a long-term follow-up. METHODS: Single-center analytical cohort study of patients with STEMI in a tertiary hospital, between January 2015 and December 2020. RESULTS: A total of 2668 patients were included, 2002 (75%) men and 666 (25%) women. The time elapsed from the onset of symptoms to the opening of the artery was 197min (IQR 140-300) vs 220min (IQR 152-340), p=0.004 in men and women respectively. A delay in health care significantly impacts the occurrence of cardiovascular adverse events at follow-up, HR 1.34 [95%CI 1.06-1.70]; p=0.015. CONCLUSIONS: Women took longer to go to health care services and had a longer delay both in the diagnosis of STEMI and in coronary reperfusion. It is imperative to emphasize the necessity of educating women about the recognition of ischemic heart disease symptoms, empowering them to raise early alarms and seek timely medical attention.

Automatic Assessment of Transcatheter Aortic Valve Implantation Results on Four-Dimensional Computed Tomography Images Using Artificial Intelligence.

Transcatheter aortic valve implantation (TAVI) is a procedure to treat severe aortic stenosis. There are several clinical concerns related to potential complications after the procedure, which demand the analysis of computerized tomography (CT) scans after TAVI to assess the implant's result. This work introduces a novel, fully automatic method for the analysis of post-TAVI 4D-CT scans to characterize the prosthesis and its relationship with the patient's anatomy. The method enables measurement extraction, including prosthesis volume, center of mass, cross-sectional area (CSA) along the prosthesis axis, and CSA difference between the aortic root and prosthesis, all the variables studied throughout the cardiac cycle. The method has been implemented and evaluated with a cohort of 13 patients with five different prosthesis models, successfully extracting all the measurements from each patient in an automatic way. For Allegra patients, the mean of the obtained inner volume values ranged from 10,798.20 mm3 to 18,172.35 mm3, and CSA in the maximum diameter plane varied from 396.35 mm2 to 485.34 mm2. The implantation of this new method could provide information of the important clinical value that would contribute to the improvement of TAVI, significantly reducing the time and effort invested by clinicians in the image interpretation process.

Protein Plasma Levels of the IGF Signalling System Are Altered in Major Depressive Disorder.

The Insulin-like growth factor 2 (IGF-2) has been recently proven to alleviate depressive-like behaviors in both rats and mice models. However, its potential role as a peripheral biomarker has not been evaluated in depression. To do this, we measured plasma IGF-2 and other members of the IGF family such as Binding Proteins (IGFBP-1, IGFBP-3, IGFBP-5 and IGFBP-7) in a depressed group of patients (n = 51) and in a healthy control group (n = 48). In some of these patients (n = 15), we measured these proteins after a period (19 ± 6 days) of treatment with antidepressants. The Hamilton Depressive Rating Scale (HDRS) and the Self-Assessment Anhedonia Scale (SAAS) were used to measure depression severity and anhedonia, respectively. The general cognition state was assessed by the Mini-Mental State Examination (MMSE) test and memory with the Free and Cued Selective Reminding Test (FCSRT). The levels of both IGF-2 and IGFBP-7 were found to be significantly increased in the depressed group; however, only IGF-2 remained significantly elevated after correction by age and sex. On the other hand, the levels of IGF-2, IGFBP-3 and IGFBP-5 were significantly decreased after treatment, whereas only IGFBP-7 was significantly increased. Therefore, peripheral changes in the IGF family and their response to antidepressants might represent alterations at the brain level in depression.

Data-Driven Phenotyping of Alzheimer's Disease under Epigenetic Conditions Using Partial Volume Correction of PET Studies and Manifold Learning.

Alzheimer's disease (AD) is the most common form of dementia. An increasing number of studies have confirmed epigenetic changes in AD. Consequently, a robust phenotyping mechanism must take into consideration the environmental effects on the patient in the generation of phenotypes. Positron Emission Tomography (PET) is employed for the quantification of pathological amyloid deposition in brain tissues. The objective is to develop a new methodology for the hyperparametric analysis of changes in cognitive scores and PET features to test for there being multiple AD phenotypes. We used a computational method to identify phenotypes in a retrospective cohort study (532 subjects), using PET and Magnetic Resonance Imaging (MRI) images and neuropsychological assessments, to develop a novel computational phenotyping method that uses Partial Volume Correction (PVC) and subsets of neuropsychological assessments in a non-biased fashion. Our pipeline is based on a Regional Spread Function (RSF) method for PVC and a t-distributed Stochastic Neighbor Embedding (t-SNE) manifold. The results presented demonstrate that (1) the approach to data-driven phenotyping is valid, (2) the different techniques involved in the pipelines produce different results, and (3) they permit us to identify the best phenotyping pipeline. The method identifies three phenotypes and permits us to analyze them under epigenetic conditions.

Two-Step Approach for Occupancy Estimation in Intensive Care Units Based on Bayesian Optimization Techniques.

Due to the high occupational pressure suffered by intensive care units (ICUs), a correct estimation of the patients' length of stay (LoS) in the ICU is of great interest to predict possible situations of collapse, to help healthcare personnel to select appropriate treatment options and to predict patients' conditions. There has been a high amount of data collected by biomedical sensors during the continuous monitoring process of patients in the ICU, so the use of artificial intelligence techniques in automatic LoS estimation would improve patients' care and facilitate the work of healthcare personnel. In this work, a novel methodology to estimate the LoS using data of the first 24 h in the ICU is presented. To achieve this, XGBoost, one of the most popular and efficient state-of-the-art algorithms, is used as an estimator model, and its performance is optimized both from computational and precision viewpoints using Bayesian techniques. For this optimization, a novel two-step approach is presented. The methodology was carefully designed to execute codes on a high-performance computing system based on graphics processing units, which considerably reduces the execution time. The algorithm scalability is analyzed. With the proposed methodology, the best set of XGBoost hyperparameters are identified, estimating LoS with a MAE of 2.529 days, improving the results reported in the current state of the art and probing the validity and utility of the proposed approach.

Improving Intensive Care Unit Early Readmission Prediction Using Optimized and Explainable Machine Learning.

It is of great interest to develop and introduce new techniques to automatically and efficiently analyze the enormous amount of data generated in today's hospitals, using state-of-the-art artificial intelligence methods. Patients readmitted to the ICU in the same hospital stay have a higher risk of mortality, morbidity, longer length of stay, and increased cost. The methodology proposed to predict ICU readmission could improve the patients' care. The objective of this work is to explore and evaluate the potential improvement of existing models for predicting early ICU patient readmission by using optimized artificial intelligence algorithms and explainability techniques. In this work, XGBoost is used as a predictor model, combined with Bayesian techniques to optimize it. The results obtained predicted early ICU readmission (AUROC of 0.92 ± 0.03) improves state-of-the-art consulted works (whose AUROC oscillate between 0.66 and 0.78). Moreover, we explain the internal functioning of the model by using Shapley Additive Explanation-based techniques, allowing us to understand the model internal performance and to obtain useful information, as patient-specific information, the thresholds from which a feature begins to be critical for a certain group of patients, and the feature importance ranking.

Using Bayesian Optimization and Wavelet Decomposition in GPU for Arterial Blood Pressure Estimation.

Continuous monitoring of arterial blood pressure (ABP) of patients in hospital is currently carried out in an invasive way, which could represent a risk for them. In this paper, a noninvasive methodology to optimize ABP estimators using electrocardiogram and photoplethysmography signals is proposed. For this, the XGBoost machine learning model, optimized with Bayesian techniques, is executed in a Graphics Processing Unit, which drastically reduces execution time. The methodology is evaluated using the MIMIC-III Waveform Database. Systolic and diastolic pressures are estimated with mean absolute error values of 15.85 and 11.59 mmHg, respectively, similar to those of the state of the art. The main advantage of the proposed methodology with respect to others of the current state of the art is that it allows the optimization of the estimator model to be performed automatically and more efficiently at the computational level for the data available. Clinical Relevance- This approach has the advantage of using noninvasive methods to continuously monitor patient's arterial blood pressure, reducing the risk for patients.

Intensive Olfactory Training in Post-COVID-19 Patients: A Multicenter Randomized Clinical Trial.

BACKGROUND: Olfactory dysfunction (OD)-including anosmia and hyposmia-is a common symptom of COVID-19. Previous studies have identified olfactory training (OT) as an important treatment for postinfectious OD; however, little is known about its benefits and optimizations after SARS-CoV-2 infection. OBJECTIVE: This study aimed to assess whether olfactory training performance can be optimized using more fragrances over a shorter period of time in patients with persistent OD after COVID-19. In addition, we determined the presence of other variables related to OD and treatment response in this population. METHODS: This multicenter randomized clinical trial recruited 80 patients with persistent OD and prior COVID-19 infection for less than 3 months. The patients were divided into 2 groups receiving either 4 or 8 essences over 4 weeks. Subjective assessments and the University of Pennsylvania Smell Identification Test (UPSIT) were performed before and after the treatment. RESULTS: Significant olfactory improvement was measured subjectively and using the UPSIT in both groups; however, no significant differences between the groups were observed. Additionally, the presence of olfactory fluctuations was associated with higher UPSIT scores. CONCLUSION: These data suggest that training intensification by increasing the number of essences for 4 weeks does not show superiority over the classical method. Moreover, fluctuant olfaction seems to be related to a higher score on the UPSIT.

Coronary Artery Disease in Very Young Patients: Analysis of Risk Factors and Long-Term Follow-Up.

Coronary artery disease (CAD) is a common chronic condition in the elderly. However, the earlier CAD begins, the stronger its impact on lifestyle and costs of health and social care. The present study analyzes clinical and angiographic features and the outcome of very young patients undergoing coronary angiography due to suspected CAD, including a nested case-control study of ≤40-year-old patients referred for coronary angiography. Patients were divided into two groups: cases with significant angiographic stenosis, and controls with non-significant stenosis. Of the 19,321 coronary angiographies performed in our center in a period of 10 years, 504 (2.6%) were in patients ≤40 years. The most common cardiovascular risk factors for significant CAD were smoking (OR 2.96; 95% CI 1.65-5.37), dyslipidemia (OR 2.18; 95% CI 1.27-3.82), and family history of CAD (OR 1.95; 95% CI 1.05-3.75). The incidence of major adverse cardiovascular events (MACE) at follow-up was significantly higher in the cases compared to controls (HR 2.71; 95% CI 1.44-5.11). Three conventional coronary risk factors were directly related to the early signs of CAD. MACE in the long-term follow-up is associated to dyslipidaemia and hypertriglyceridemia. Focusing efforts for the adequate control of CAD in young patients is a priority given the high socio-medical cost that this disease entails to society.

Automatic Identification of Bioprostheses on X-ray Angiographic Sequences of Transcatheter Aortic Valve Implantation Procedures Using Deep Learning.

Transcatheter aortic valve implantation (TAVI) has become the treatment of choice for patients with severe aortic stenosis and high surgical risk. Angiography has been established as an essential tool in TAVI, as this modality provides real-time images required to support the intervention. The automatic interpretation and parameter extraction on such images can lead to significative improvements and new applications in the procedure that, in most cases, rely on a prior identification of the transcatheter heart valve (THV). In this paper, U-Net architecture is proposed for the automatic segmentation of THV on angiographies, studying the role of its hyperparameters in the quality of the segmentations. Several experiments have been conducted, testing the methodology using multiple configurations and evaluating the results on different types of frames captured during the procedure. The evaluation has been performed in terms of conventional classification metrics, complemented with two new metrics, specifically defined for this problem. Those new metrics provide a more appropriate assessment of the quality of the results, given the class imbalance in the dataset. From an analysis of the evaluation results, it can be concluded that the method provides appropriate segmentation results for this dataset.

Using Machine Learning Techniques to Predict MACE in Very Young Acute Coronary Syndrome Patients.

Coronary artery disease is a chronic disease with an increased expression in the elderly. However, different studies have shown an increased incidence in young subjects over the last decades. The prediction of major adverse cardiac events (MACE) in very young patients has a significant impact on medical decision-making following coronary angiography and the selection of treatment. Different approaches have been developed to identify patients at a higher risk of adverse outcomes after their coronary anatomy is known. This is a prognostic study of combined data from patients ≤40 years old undergoing coronary angiography (n = 492). We evaluated whether different machine learning (ML) approaches could predict MACE more effectively than traditional statistical methods using logistic regression (LR). Our most effective model for long-term follow-up (60 ± 27 months) was random forest (RF), obtaining an area under the curve (AUC) = 0.79 (95%CI 0.69-0.88), in contrast with LR, obtaining AUC = 0.66 (95%CI 0.53-0.78, p = 0.021). At 1-year follow-up, the RF test found AUC 0.80 (95%CI 0.71-0.89) vs. LR 0.50 (95%CI 0.33-0.66, p < 0.001). The results of our study support the hypothesis that ML methods can improve both the identification of MACE risk patients and the prediction vs. traditional statistical techniques even in a small sample size. The application of ML techniques to focus the efforts on the detection of MACE in very young patients after coronary angiography could help tailor upfront follow-up strategies in such young patients according to their risk of MACE and to be used for proper assignment of health resources.

Intensive Olfactory Training in Post-COVID Patients: A Randomized Multicenter Clinical Trial

Introduction: Olfactory dysfunction (OD) is one of the most reported symptoms of COVID -19. Previous studies have identified olfactory training (OT) as an important treatment for postinfectious OD, but little is known about its effect after SARS-CoV-2 infection and how it can be optimized. Objective: To assess whether OT can be optimized if performed intensively, with more fragrances over a shorter period in patients with persistent OD after COVID -19. Also, to determine the presence of other variables related to OD and treatment response in this population. Method: This multicenter randomized clinical trial recruited 80 patients with persistent OD with previous COVID-19 for less than three months. The patients were divided into two groups, who received treatment with 4 and 8 essences over four weeks. Subjective assessments and the University of Pennsylvania Smell Identification Test (UPSIT) were performed before and after treatment. Results: A significant improvement in olfaction was measured subjectively and on UPSIT in both groups, but without significant differences between groups. In addition, the presence of olfactory fluctuation was associated with higher UPSIT scores. Conclusion: These data suggest that intensifying the training by increasing the number of essences for 4 weeks does not show superiority over the classical method. Moreover, a fluctuating olfactory ability seems to be related to a better score in the UPSIT.

Using Explainable Machine Learning to Improve Intensive Care Unit Alarm Systems.

Due to the continuous monitoring process of critical patients, Intensive Care Units (ICU) generate large amounts of data, which are difficult for healthcare personnel to analyze manually, especially in overloaded situations such as those present during the COVID-19 pandemic. Therefore, the automatic analysis of these data has many practical applications in patient monitoring, including the optimization of alarm systems for alerting healthcare personnel. In this paper, explainable machine learning techniques are used for this purpose, with a methodology based on age-stratification, boosting classifiers, and Shapley Additive Explanations (SHAP) proposed. The methodology is evaluated using MIMIC-III, an ICU patient research database. The results show that the proposed model can predict mortality within the ICU with AUROC values of 0.961, 0.936, 0.898, and 0.883 for age groups 18-45, 45-65, 65-85 and 85+, respectively. By using SHAP, the features with the highest impact in predicting mortality for different age groups and the threshold from which the value of a clinical feature has a negative impact on the patient's health can be identified. This allows ICU alarms to be improved by identifying the most important variables to be sensed and the threshold values at which the health personnel must be warned.

Predicting TAVI paravalvular regurgitation outcomes based on numerical simulation of the aortic annulus eccentricity and perivalvular areas.

Trans-catheter aortic valve implantation (TAVI) is an increasingly adopted technique which provides a minimal invasive solution for patients who suffer from severe aortic stenosis. Some complications of the procedure could be annular rupture or paravalvular leakage, both related with adverse outcome. In TAVI with balloon expandable devices, a mismatch between those two factors leads to a conflict situation, where improving one worsens the other. The presented research proposes a methodology that uses numerical simulation to obtain certain TAVI outcomes related with aortic regurgitation due to paravalvular leakage, such as perivalvular area, aortic eccentricity or annular pressure. The application of the methodology for two patients shows the possibility of predicting those quantities. The highest stress values are distributed along the contact area. Results also show that a great deformation on the aortic annulus does not necessarily imply a higher stress; pressure can either be converted into root reshape or into root stretching. Validation of the results was done using scientific publications, clinical guidelines and clinical reports. Numerical simulation provides a suitable tool that could possibly contribute to optimize the planification procedure adjusting the mismatch between size and pressure.

A biomechanical model of the pathological aortic valve: simulation of aortic stenosis.

Aortic stenosis (AS) disease is a narrowing of the aortic valve (AV) opening which reduces blood flow from the heart causing several health complications. Although a lot of work has been done in AV simulations, most of the efforts have been conducted regarding healthy valves. In this article, a new three-dimensional patient-specific biomechanical model of the valve, based on a parametric formulation of the stenosis that permits the simulation of different degrees of pathology, is presented. The formulation is based on a double approach: the first one is done from the geometric point of view, reducing the effective ejection area of the AV by joining leaflets using a zipper effect to sew them; the second one, in terms of functionality, is based on the modification of AV tissue properties due to the effect of calcifications. Both healthy and stenotic valves were created using patient-specific data and results of the numerical simulation of the valve function are provided. Analysis of the results shows a variation in the first principal stress, geometric orifice area, and blood velocity which were validated against clinical data. Thus, the possibility to create a pipeline which allows the integration of patient-specific data from echocardiographic images and iFR studies to perform finite elements analysis is proved.