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.

Preventive care and telemedicine are expected to play an important role in reducing the impact of an increasingly aging global population while increasing the number of healthy years. Virtual coaching is a promising research area to support this process. This paper presents a user-centered virtual coach for older adults at home to promote active and healthy aging and independent living. It supports behavior change processes for improving on cognitive, physical, social interaction and nutrition areas using specific, measurable, achievable, relevant, and time-limited (SMART) goal plans, following the I-Change behavioral change model. Older adults select and personalize which goal plans to join from a catalog designed by domain experts. Intervention delivery adapts to user preferences and minimizes intrusiveness in the user’s daily living using a combination of a deterministic algorithm and incremental machine learning model. The home becomes an augmented reality environment, using a combination of projectors, cameras, microphones and support sensors, where common objects are used for projection and sensed. Older adults interact with this virtual coach in their home in a natural way using speech and body gestures on projected user interfaces with common objects at home. This paper presents the concept from the older adult and the caregiver perspectives. Then, it focuses on the older adult view, describing the tools and processes available to foster a positive behavior change process, including a discussion about the limitations of the current implementation.

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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.

The numerous consequences caused by malnutrition in hospitalized patients can worsen their quality of life. The aim of this study was to evaluate the prevalence of malnutrition on the elderly population, especially focusing on women, identify key factors and develop a malnutrition risk predictive model. The study group consisted of 493 older women admitted to the Asunción Klinika Hospital in the Basque Region (Spain). For this purpose, demographic, clinical, laboratory, and admission information was gathered. Correlations and multivariate analyses and the MNA-SF screening test-based risk of malnutrition were performed. Additionally, different predictive models designed using this information were compared. The estimated frequency of malnutrition among this population in the Basque Region (Spain) is 13.8%, while 41.8% is considered at risk of malnutrition, which is increased in women, with up to 16.4% with malnutrition and 47.5% at risk of malnutrition. Sixteen variables were used to develop a predictive model obtaining Area Under the Curve (AUC) values of 0.76. Elderly women assisted at home and with high scores of dependency were identified as a risk group, as well as patients admitted in internal medicine units, and in admissions with high severity.

Shock is described as an inadequate oxygen supply to the tissues and can be classified in multiple ways. In clinical practice still, old methods are used to discriminate these shock types. This article proposes the application of unsupervised classification methods for the stratification of these patients in order to treat them more appropriately. With a cohort of 90 patients admitted in pediatric intensive care units (PICU), the k-means algorithm was applied in the first 24 h data since admission (physiological and analytical variables and the need for devices), obtaining three main groups. Significant differences were found in variables used (e.g., mean diastolic arterial pressure p < 0.001, age p < 0.001) and not used for training (e.g., EtCO2 min p < 0.001, Troponin max p < 0.01), discharge diagnosis (p < 0.001) and outcomes (p < 0.05). Clustering classification equaled classical classification in its association with LOS (p = 0.01) and surpassed it in its association with mortality (p < 0.04 vs. p = 0.16). We have been able to classify shocked pediatric patients with higher outcome correlation than the clinical traditional method. These results support the utility of unsupervised learning algorithms for patient classification in PICU.

The population in the world is aging dramatically, and therefore, the economic and social effort required to maintain the quality of life is being increased. Assistive technologies are progressively expanding and present great opportunities; however, given the sensitivity of health issues and the vulnerability of older adults, some considerations need to be considered. This paper presents DigiHEALTH, a suite of digital solutions for long-term healthy and active aging. It is the result of a fruitful trajectory of research in healthy aging where we have understood stakeholders’ needs, defined the main suite properties (that would allow scalability and interoperability with health services), and codesigned a set of digital solutions by applying a continuous reflexive cycle. At the current stage of development, the digital suite presents eight digital solutions to carry out the following: (a) minimize digital barriers for older adults (authentication system based on face recognition and digital voice assistant), (b) facilitate active and healthy living (well-being assessment module, recommendation system, and personalized nutritional system), and (c) mitigate specific impairments (heart failure decompensation, mobility assessment and correction, and orofacial gesture trainer). The suite is available online and it includes specific details in terms of technology readiness level and specific conditions for usage and acquisition. This live website will be continually updated and enriched with more digital solutions and further experiences of collaboration.