Factors related to early readmissions after acute heart failure: a nested case-control study.

AIMS: To describe the main characteristics of patients who were readmitted to hospital within 1 month after an index episode for acute decompensated heart failure (ADHF). METHODS AND RESULTS: This is a nested case-control study in the ReIC cohort, cases being consecutive patients readmitted after hospitalization for an episode of ADHF and matched controls selected from those who were not readmitted. We collected clinical data and also patient-reported outcome measures, including dyspnea, Minnesota Living with Heart Failure Questionnaire (MLHFQ), Tilburg Frailty Indicator (TFI) and Hospital Anxiety and Depression Scale scores, as well as symptoms during a transition period of 1 month after discharge. We created a multivariable conditional logistic regression model. Despite cases consulted more than controls, there were no statistically significant differences in changes in treatment during this first month. Patients with chronic decompensated heart failure were 2.25 [1.25, 4.05] more likely to be readmitted than de novo patients. Previous diagnosis of arrhythmia and time since diagnosis ≥ 3 years, worsening in dyspnea, and changes in MLWHF and TFI scores were significant in the final model. CONCLUSION: We present a model with explanatory variables for readmission in the short term for ADHF. Our study shows that in addition to variables classically related to readmission, there are others related to the presence of residual congestion, quality of life and frailty that are determining factors for readmission for heart failure in the first month after discharge. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT03300791. First registration: 03/10/2017.

On Differential Imaging Using Electromagnetic Simulation for Vehicular Antenna Signature Analysis.

The current trend in vehicles is to integrate a wide number of antennae and sensors operating at a variety of frequencies for sensing and communications. The integration of these antennae and sensors in the vehicle platform is complex because of the way in which the antenna radiation patterns interact with the vehicle structure and other antennae/sensors. Consequently, there is a need to study the radiation pattern of each antenna or, alternatively, the currents induced on the surface of the vehicle to optimize the integration of multiple antennae. The novel concept of differential imaging represents one method by which it is possible to obtain the surface current distribution without introducing any perturbing probe. The aim of this study was to develop and confirm the assumptions that underpin differential imaging by means of full-wave electromagnetic simulation, thereby providing additional verification of the concept. The simulation environment and parameters were selected to replicate the conditions in which real measurements were taken in previous studies. The simulations were performed using Ansys HFSS simulation software. The results confirm that the approximations are valid, and the differential currents are representative of the induced surface currents generated by a monopole positioned on the top of a vehicle.

The Minnesota living with heart failure questionnaire: comparison of different factor structures.

BACKGROUND: The Minnesota Living with Heart Failure Questionnaire (MLHFQ) is one of the most widely used health-related quality of life questionnaires for patients with heart failure (HF). It provides scores for two dimensions, physical and emotional, and a total score. However, there are some concerns about its factor structure and alternatives have been proposed, some including a third factor representing a social dimension. The objectives of the present study were to analyze the internal structure of the MLHFQ and the unidimensionality of the total score, and to compare the different factor structures proposed. METHODS: The MLHFQ was given to 2565 patients with HF. The structural validity of the questionnaire was assessed by confirmatory factor analysis (CFA), and Rasch analysis. These two approaches were also applied to the alternative structures proposed. RESULTS: The CFA results for the hypothesized model of two latent factors and the Rasch analysis confirmed the adequacy of the physical and emotional scales. Rasch analysis for the total score showed only two problematic items. The results of the CFA for other two-factor structures proposed were not better than the results for the original structure. The Rasch analyses applied to the different social factors yielded the best results for Munyombwe's social dimension, composed of six items. CONCLUSIONS: Our results support the validity of using the MLHFQ physical, emotional and total scores in patients with HF, for clinical practice and research. In addition, they confirmed the existence of a third factor, and we recommend the use of Munyombwe's social factor.

Integration of Directional Antennas in an RSS Fingerprinting-Based Indoor Localization System.

In this paper, the integration of directional antennas in a room-level received signal strength (RSS) fingerprinting-based indoor localization system (ILS) is studied. The sensor reader (SR), which is in charge of capturing the RSS to infer the tag position, can be attached to an omnidirectional or directional antenna. Unlike commonly-employed omnidirectional antennas, directional antennas can receive a stronger signal from the direction in which they are pointed, resulting in a different RSS distributions in space and, hence, more distinguishable fingerprints. A simulation tool and a system management software have been also developed to control the system and assist the initial antenna deployment, reducing time-consuming costs. A prototype was mounted in a real scenario, with a number of SRs with omnidirectional and directional antennas properly positioned. Different antenna configurations have been studied, evidencing a promising capability of directional antennas to enhance the performance of RSS fingerprinting-based ILS, reducing the number of required SRs and also increasing the localization success.

Machine learning-based model for prediction of clinical deterioration in hospitalized patients by COVID 19.

Despite the publication of great number of tools to aid decisions in COVID-19 patients, there is a lack of good instruments to predict clinical deterioration. COVID19-Osakidetza is a prospective cohort study recruiting COVID-19 patients. We collected information from baseline to discharge on: sociodemographic characteristics, comorbidities and associated medications, vital signs, treatment received and lab test results. Outcome was need for intensive ventilatory support (with at least standard high-flow oxygen face mask with a reservoir bag for at least 6 h and need for more intensive therapy afterwards or Optiflow high-flow nasal cannula or noninvasive or invasive mechanical ventilation) and/or admission to a critical care unit and/or death during hospitalization. We developed a Catboost model summarizing the findings using Shapley Additive Explanations. Performance of the model was assessed using area under the receiver operating characteristic and prediction recall curves (AUROC and AUPRC respectively) and calibrated using the Hosmer-Lemeshow test. Overall, 1568 patients were included in the derivation cohort and 956 in the (external) validation cohort. The percentages of patients who reached the composite endpoint were 23.3% vs 20% respectively. The strongest predictors of clinical deterioration were arterial blood oxygen pressure, followed by age, levels of several markers of inflammation (procalcitonin, LDH, CRP) and alterations in blood count and coagulation. Some medications, namely, ATC AO2 (antiacids) and N05 (neuroleptics) were also among the group of main predictors, together with C03 (diuretics). In the validation set, the CatBoost AUROC was 0.79, AUPRC 0.21 and Hosmer-Lemeshow test statistic 0.36. We present a machine learning-based prediction model with excellent performance properties to implement in EHRs. Our main goal was to predict progression to a score of 5 or higher on the WHO Clinical Progression Scale before patients required mechanical ventilation. Future steps are to externally validate the model in other settings and in a cohort from a different period and to apply the algorithm in clinical practice.Registration: ClinicalTrials.gov Identifier: NCT04463706.

Incidence of COVID-19 in outpatients with cancer receiving active treatment in the context of a pandemic: An Andalusian cohort study.

BACKGROUND: Leading scientific societies have recommended delaying and/or suspending active cancer treatment during the COVID-19 pandemic. Nevertheless, data on this novel infection in patients with a diagnosis of cancer receiving active treatment are scarce and it is unknown if these recommendations could have repercussions on future progress of the disease. The main objective of this study is to learn the COVID-19 incidence rate in outpatients with cancer receiving active treatment. METHODS: This work is a retrospective cohort study that included all patients with a diagnosis of cancer who received active cancer treatment in two Andalusian hospitals between February 26 and May 13, 2020. Variables regarding the patient, tumor, and development of COVID-19 were collected. A descriptive analysis was performed and the cumulative incidence of COVID-19 in these patients was evaluated. RESULTS: A total of 673 patients were included. The median age was 62 years. There was a low rate of comorbidity and 12.1% had an ECOG >2. Breast cancer was the most common cancer (41%), followed by colorectal and lung cancer. Stage IV cancer was reported in 52.7% of patients. The most common treatment was chemotherapy (53.9%). Treatment was delayed or suspended in 6% of patients. Only three patients developed COVID-19. The cumulative incidence was 0.44% and one person died due to infection. CONCLUSIONS: In the present retrospective cohort study we found a low incidence of COVID-19 infection in patients with cancer receiving active treatment in an outpatient setting. The sociodemographic factors of Andalusia may explain why these results differ from those presented by other colleagues in Spain, but raise questions about whether universal recommendations may put the benefits of antineoplastic therapy at risk.

Short-term mortality risk score for de novo acute heart failure (ESSIC-FEHF).

BACKGROUND: Different variables are playing a role in prognosis of acute heart failure. OBJECTIVES: Our purpose was to create and validate a risk score to predict mortality in patients with a first episode of acute heart failure during the first 2 months after the first hospitalization. DESIGN: This was a prospective cohort study. PARTICIPANTS: We recruited patients diagnosed with a first episode of acute heart failure. MAIN MEASURES: We collected data on sociodemographic characteristics; medical history; symptoms; precipitating factors; signs and symptoms of congestion; echocardiographic parameters; aetiology; vital signs and laboratory findings; and response to initial treatment (yes/no). A Cox proportional hazard regression model was built with mortality during the first 2 months after the index episode as the dependent variable. A risk score is presented. KEY RESULTS: The mortality rate during the first 2 months after a first episode of heart failure was 5%. Age, systolic blood pressure, serum sodium, ejection fraction and blood urea nitrogen were selected in the internal validation, as was right ventricular failure. A risk score was developed. Both the model and the score showed good discrimination and calibration properties when applied to an independent cohort. CONCLUSIONS: Our ESSIC-FEHF risk score showed excellent properties in the derivation cohort and also in a cohort from a different time period. This score is expected to help decision making in patients diagnosed with heart failure for the first time.

Prevalence and outcome of diuretic resistance in heart failure.

Diuretic resistance (DR) is common in patients with decompensated heart failure (HF), and is associated with adverse outcomes. To determine the prevalence of DR and its impact on survival among patients with decompensated HF, we prospectively evaluated the prevalence and influence on prognosis of DR (defined as persistent congestion despite ≥ 80 mg of furosemide per day) in a cohort of elderly patients from the Spanish HF registry (RICA) admitted for an acute decompensation of HF. Patients with new-onset HF were excluded. From the global cohort of 2067 patients, 435 (21%; 95% CI 19.3%-22.7%) patients met criteria for DR. Patients with DR had more comorbidities (hypercholesterolemia, diabetes mellitus, valvular disease, chronic kidney disease, and cancer) and a worse functional status compared to patients without DR. In addition, patients with DR had a higher proportion of ischemic etiology, more advanced functional class and lower left ventricular ejection fraction values. After 1 year of follow-up, all-cause mortality was higher in patients with DR with an adjusted hazard ratio of 1.37 (95% CI 1.06-1.79; p = 0.018). The prevalence of DR in a cohort of elderly patients admitted for acute HF decompensation is 21%. DR is an independent predictor of 1-year mortality.

A one-year mortality clinical prediction rule for patients with heart failure.

AIMS: To create and validate a clinical prediction rule which is easy to manage, reproducible and that allows classifying patients admitted for heart failure according to their one-year mortality risk. METHODS: A prospective cohort study carried out with 2565 consecutive patients admitted with heart failure in 13 hospitals in Spain. The derivation cohort was made up of 1283 patients and 1282 formed the validation cohort. In the derivation cohort, we carried out a multivariate logistic model to predict one-year mortality. The performance of the derived predictive risk score was externally validated in the validation cohort, and internally validated by K-fold cross-validation. The risk score was categorized into four risk levels. RESULTS: The mean age was 77.2years, 49.7% were female and there were 611 (23.8%) deaths in the follow-up period. The variables included in the predictive model were: age≥75, systolic blood pressure<135, New York Heart Association class III-IV, heart valve disease, dementia, prior hospitalization, haemoglobin<13, sodium<136, urea≥86, length of stay≥14 and Physical dimension of Minnesota Living with Heart Failure questionnaire. The AUC for the risk score were 0.73 and 0.70 in the derivation and validation cohorts, respectively, and 0.73 in the K-fold cross-validation. The percentage of mortality ranged from 8.08% in the low-risk to 58.20% in the high-risk groups (p<0.0001; AUC, 0.72). CONCLUSIONS: This model based on routinely available data, for admitted patients and with a follow-up at one year is a simple and easy-to-use tool for improving management of patients with heart failure.

Impact of prealbumin on mortality and hospital readmission in patients with acute heart failure.

BACKGROUND: Prealbumin is a maker of nutritional status and inflammation of potential prognostic value in acute heart failure (HF). The aim of this study is to evaluate if low prealbumin levels on admission predict mortality and readmissions in patients with acute HF. METHOD: We conducted a prospective observational cohort study including 442 patients hospitalized for acute HF. Patients were classified in two groups according to prealbumin levels: "normal" prealbumin (>15mg) and "low" prealbumin (≤15mg/dL). End-points were mortality and readmissions (all-cause and HF-related) and the combined end-point of mortality/readmission at 180days. RESULTS: Out of 442 patients, 159 (36%) had low and 283 (64%) had normal prealbumin levels Mean age was 79.6 (73.9-84.2, p=0,405) years and 183 (41%, p=0,482) were males. After a median 180days of follow-up, 108 (24%, p=0,021) patients died and 170 (38%, p=0,067) were readmitted. Mortality was higher in the low prealbumin group. The combined end-point was more frequent in the low prealbumin group (57% vs. 50%, p=0.199). In the multivariate analysis the following variables were associated with mortality or readmission: older age, exacerbated chronic HF, higher comorbidity, low systolic blood pressure and hemoglobin values and higher pro brain natriuretic peptide levels. CONCLUSIONS: Low prealbumin is common (36%) in patients with acute heart failure and it is associated with a higher short-term mortality.