Aims Heart failure (HF) is a clinical syndrome caused by a structural and/or functional cardiac abnormality, resulting in a reduced cardiac output and/or elevated intracardiac pressures at rest or during stress. This disease often causes decompensations, which may lead to hospital admissions, deteriorating patients' quality of life and causing an increment on the healthcare cost. Environmental exposure is an important but underappreciated risk factor contributing to the development and severity of cardiovascular diseases, such as HF. Methods and resultsWe used two different sets of data (January 2012 to August 2017): one related to the number of hospital admissions and the other one related to the environmental factors (weather and air quality). Admissions related data were grouped in weeks, and then two different studies were performed: (i) a univariate regression to determine whether the admissions may influence future hospitalizations prediction and (ii) a multivariate regression to determine the impact of environmental factors on admission rates. A total number of 8338 hospitalizations of 5343 different patients are available in this dataset, with a mean of 4.02 admissions per day. In European warm period (from June to October), there are significant less admissions than that in the cold period (from December to March), with a clear seasonality of admissions, because there is a similar pattern every year. Air temperature is the most significant environmental factor (r = À0.3794, P < 0.001) related to HF hospital admissions, showing an inversed correlation. Some other attributes, such as precipitation (r = 0.0795, P = 0.05), along with SO 2 (precursor of acid rain) (r = 0.2692, P < 0.001) and NOX air (major air pollutant formed by combustion systems and motor vehicles) (r = 0.2196, P < 0.001) quality parameters, are also relevant. Humidity and PM10 parameters do not have significant correlations in this study (r = 0.0469 and r = À0.0485 respectively), neither relevant P-values (P = 0.238 and P = 0.324, respectively). Conclusions Several environmental factors, such as weather temperature and precipitation, and major air pollutants, such as SO 2 and NOX air, have an impact on the HF-related hospital admissions rate and, hence, on HF decompensations and patient's quality of life.

Rapid advances in ICT and collection of large amount of mobile health data are giving room to new ways of treating patients. Studies suggest that telemonitoring systems and predictive models for clinical support and patient empowerment may improve several pathologies, such as heart failure, which admissions rate is high. In the current medical practice, clinicians make use of simple rules that generate large number of false alerts. In order to reduce the false alerts, in this study, the predictive models to prevent decompensations that may lead into admissions are presented. They are based on mobile clinical data of 242 heart failure (HF) patients collected for a period of 44 months in the public health service of Basque Country (Osakidetza). The best predictive model obtained is a combination of alerts based on monitoring data and a questionnaire with a Naive Bayes classifier using Bernoulli distribution. This predictive model performs with an AUC = 67% and reduces the false alerts per patient per year from 28.64 to 7.8. This way, the system predicts the risk of admission of ambulatory patients with higher reliability than current alerts.

Aim Patients with advanced heart failure (AHF) who are not candidates to advanced therapies have poor prognosis. Some trials have shown that intermittent levosimendan can reduce HF hospitalizations in AHF in the short term. In this real‐life registry, we describe the patterns of use, safety and factors related to the response to intermittent levosimendan infusions in AHF patients not candidates to advanced therapies. Methods and results Multicentre retrospective study of patients diagnosed with advanced heart failure, not HT or LVAD candidates. Patients needed to be on the optimal medical therapy according to their treating physician. Patients with de novo heart failure or who underwent any procedure that could improve prognosis were not included in the registry. Four hundred three patients were included; 77.9% needed at least one admission the year before levosimendan was first administered because of heart failure. Death rate at 1 year was 26.8% and median survival was 24.7 [95% CI: 20.4–26.9] months, and 43.7% of patients fulfilled the criteria for being considered a responder lo levosimendan (no death, heart failure admission or unplanned HF visit at 1 year after first levosimendan administration). Compared with the year before there was a significant reduction in HF admissions (38.7% vs. 77.9%; P < 0.0001), unplanned HF visits (22.7% vs. 43.7%; P < 0.0001) or the combined event including deaths (56.3% vs. 81.4%; P < 0.0001) during the year after. We created a score that helps predicting the responder status at 1 year after levosimendan, resulting in a score summatory of five variables: TEER (+2), treatment with beta‐blockers (+1.5), Haemoglobin >12 g/dL (+1.5), amiodarone use (−1.5) HF visit 1 year before levosimendan (−1.5) and heart rate >70 b.p.m. (−2). Patients with a score less than −1 had a very low probability of response (21.5% free of death or HF event at 1 year) meanwhile those with a score over 1.5 had the better chance of response (68.4% free of death or HF event at 1 year). LEVO‐D score performed well in the ROC analysis. Conclusion In this large real‐life series of AHF patients treated with levosimendan as destination therapy, we show a significant decrease of heart failure events during the year after the first administration. The simple LEVO‐D Score could be of help when deciding about futile therapy in this population.

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Cardiovascular diseases are the leading cause of death globally, taking an estimated 17.9 million lives each year. Heart failure (HF) occurs when the heart is not able to pump enough blood to satisfy metabolic needs. People diagnosed with chronic HF may suffer from cardiac decompensation events (CDEs), which cause patients’ worsening. Being able to intervene before decompensation occurs is the major challenge addressed in this study. The aim of this study is to exploit available patient data to develop an artificial intelligence (AI) model capable of predicting the risk of CDEs timely and accurately. Materials and Methods: The vital variables of patients (n = 488) diagnosed with chronic heart failure were monitored between 2014 and 2022. Several supervised classification models were trained with these monitoring data to predict CDEs, using clinicians’ annotations as the gold standard. Feature extraction methods were applied to identify significant variables. Results: The XGBoost classifier achieved an AUC of 0.72 in the cross-validation process and 0.69 in the testing set. The most predictive physiological variables for CAE decompensations are weight gain, oxygen saturation in the final days, and heart rate. Additionally, the answers to questionnaires on wellbeing, orthopnoea, and ankles are strongly significant predictors.

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Introduction: Heart failure (HF) is a major concern in public health. We have used artificial intelligence to analyze information and improve patient outcomes. Method: An Observational, retrospective, and non-randomized study with patients enrolled in our telemonitoring program (May 2014-February 2018. We collected patients' clinical data, telemonitoring transmissions, and HF decompensations. Results: A total of 240 patients were enrolled with a follow-up of 13.44 ± 8.65 months. During this interval, 527 HF decompensations in 148 different patients were detected. Significant weight increases, desaturation below 90% and perception of clinical worsening are good predictors of HF decompensation. We have built a predictive model applying machine learning (ML) techniques, obtaining the best results with the combination of "Weight + Ankle + well-being plus alerts of systolic and diastolic blood pressure, oxygen saturation, and heart rate." Conclusions: ML techniques are useful tools for the analysis of HF datasets and the creation of predictive models that improve the accuracy of the actual remote patient telemonitoring programs.

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