MOSAIC: An Artificial Intelligence-Based Framework for Multimodal Analysis, Classification, and Personalized Prognostic Assessment in Rare Cancers.

PURPOSE: Rare cancers constitute over 20% of human neoplasms, often affecting patients with unmet medical needs. The development of effective classification and prognostication systems is crucial to improve the decision-making process and drive innovative treatment strategies. We have created and implemented MOSAIC, an artificial intelligence (AI)-based framework designed for multimodal analysis, classification, and personalized prognostic assessment in rare cancers. Clinical validation was performed on myelodysplastic syndrome (MDS), a rare hematologic cancer with clinical and genomic heterogeneities. METHODS: We analyzed 4,427 patients with MDS divided into training and validation cohorts. Deep learning methods were applied to integrate and impute clinical/genomic features. Clustering was performed by combining Uniform Manifold Approximation and Projection for Dimension Reduction + Hierarchical Density-Based Spatial Clustering of Applications with Noise (UMAP + HDBSCAN) methods, compared with the conventional Hierarchical Dirichlet Process (HDP). Linear and AI-based nonlinear approaches were compared for survival prediction. Explainable AI (Shapley Additive Explanations approach [SHAP]) and federated learning were used to improve the interpretation and the performance of the clinical models, integrating them into distributed infrastructure. RESULTS: UMAP + HDBSCAN clustering obtained a more granular patient stratification, achieving a higher average silhouette coefficient (0.16) with respect to HDP (0.01) and higher balanced accuracy in cluster classification by Random Forest (92.7% ± 1.3% and 85.8% ± 0.8%). AI methods for survival prediction outperform conventional statistical techniques and the reference prognostic tool for MDS. Nonlinear Gradient Boosting Survival stands in the internal (Concordance-Index [C-Index], 0.77; SD, 0.01) and external validation (C-Index, 0.74; SD, 0.02). SHAP analysis revealed that similar features drove patients' subgroups and outcomes in both training and validation cohorts. Federated implementation improved the accuracy of developed models. CONCLUSION: MOSAIC provides an explainable and robust framework to optimize classification and prognostic assessment of rare cancers. AI-based approaches demonstrated superior accuracy in capturing genomic similarities and providing individual prognostic information compared with conventional statistical methods. Its federated implementation ensures broad clinical application, guaranteeing high performance and data protection.

The relapsed acute lymphoblastic leukemia network (ReALLNet): a multidisciplinary project from the spanish society of pediatric hematology and oncology (SEHOP).

Acute lymphoblastic leukemia (ALL) is the most common pediatric cancer, with survival rates exceeding 85%. However, 15% of patients will relapse; consequently, their survival rates decrease to below 50%. Therefore, several research and innovation studies are focusing on pediatric relapsed or refractory ALL (R/R ALL). Driven by this context and following the European strategic plan to implement precision medicine equitably, the Relapsed ALL Network (ReALLNet) was launched under the umbrella of SEHOP in 2021, aiming to connect bedside patient care with expert groups in R/R ALL in an interdisciplinary and multicentric network. To achieve this objective, a board consisting of experts in diagnosis, management, preclinical research, and clinical trials has been established. The requirements of treatment centers have been evaluated, and the available oncogenomic and functional study resources have been assessed and organized. A shipping platform has been developed to process samples requiring study derivation, and an integrated diagnostic committee has been established to report results. These biological data, as well as patient outcomes, are collected in a national registry. Additionally, samples from all patients are stored in a biobank. This comprehensive repository of data and samples is expected to foster an environment where preclinical researchers and data scientists can seek to meet the complex needs of this challenging population. This proof of concept aims to demonstrate that a network-based organization, such as that embodied by ReALLNet, provides the ideal niche for the equitable and efficient implementation of "what's next" in the management of children with R/R ALL.

DDQN-based optimal targeted therapy with reversible inhibitors to combat the Warburg effect.

We cover the Warburg effect with a three-component evolutionary model, where each component represents a different metabolic strategy. In this context, a scenario involving cells expressing three different phenotypes is presented. One tumour phenotype exhibits glycolytic metabolism through glucose uptake and lactate secretion. Lactate is used by a second malignant phenotype to proliferate. The third phenotype represents healthy cells, which performs oxidative phosphorylation. The purpose of this model is to gain a better understanding of the metabolic alterations associated with the Warburg effect. It is suitable to reproduce some of the clinical trials obtained in colorectal cancer and other even more aggressive tumours. It shows that lactate is an indicator of poor prognosis, since it favours the setting of polymorphic tumour equilibria that complicates its treatment. This model is also used to train a reinforcement learning algorithm, known as Double Deep Q-networks, in order to provide the first optimal targeted therapy based on experimental tumour growth inhibitors as genistein and AR-C155858. Our in silico solution includes the optimal therapy for all the tumour state space and also ensures the best possible quality of life for the patients, by considering the duration of treatment, the use of low-dose medications and the existence of possible contraindications. Optimal therapies obtained with Double Deep Q-networks are validated with the solutions of the Hamilton-Jacobi-Bellman equation.

Deep Learning for Efficient and Optimal Motion Planning for AUVs with Disturbances.

We use the recent advances in Deep Learning to solve an underwater motion planning problem by making use of optimal control tools-namely, we propose using the Deep Galerkin Method (DGM) to approximate the Hamilton-Jacobi-Bellman PDE that can be used to solve continuous time and state optimal control problems. In order to make our approach more realistic, we consider that there are disturbances in the underwater medium that affect the trajectory of the autonomous vehicle. After adapting DGM by making use of a surrogate approach, our results show that our method is able to efficiently solve the proposed problem, providing large improvements over a baseline control in terms of costs, especially in the case in which the disturbances effects are more significant.

Deep Reinforcement Learning for Attacking Wireless Sensor Networks.

Recent advances in Deep Reinforcement Learning allow solving increasingly complex problems. In this work, we show how current defense mechanisms in Wireless Sensor Networks are vulnerable to attacks that use these advances. We use a Deep Reinforcement Learning attacker architecture that allows having one or more attacking agents that can learn to attack using only partial observations. Then, we subject our architecture to a test-bench consisting of two defense mechanisms against a distributed spectrum sensing attack and a backoff attack. Our simulations show that our attacker learns to exploit these systems without having a priori information about the defense mechanism used nor its concrete parameters. Since our attacker requires minimal hyper-parameter tuning, scales with the number of attackers, and learns only by interacting with the defense mechanism, it poses a significant threat to current defense procedures.

Repeated Game Analysis of a CSMA/CA Network under a Backoff Attack.

We study a CSMA/CA (Carrier Sense Medium Access with Collision Avoidance) wireless network where some stations deviate from the defined contention mechanism. By using Bianchi's model, we study how this deviation impacts the network throughput and show that the fairness of the network is seriously affected, as the stations that deviate achieve a larger share of the resources than the rest of stations. Previously, we modeled this situation using a static game and now, we use repeated games, which, by means of the Folk theorem, allow all players to have better outcomes. We provide analytical solutions to this game for the two player case using subgame perfect and correlated equilibria concepts. We also propose a distributed algorithm based on communicating candidate equilibrium points for learning the equilibria of this game for an arbitrary number of players. We validate approach using numerical simulations, which allows comparing the solutions we propose and discussing the advantages of using each of the methods we propose.

Model Free Localization with Deep Neural Architectures by Means of an Underwater WSN.

In recent years, there has been a significant effort towards developing localization systems in the underwater medium, with current methods relying on anchor nodes, explicitly modeling the underwater channel or cooperation from the target. Lately, there has also been some work on using the approximation capabilities of Deep Neural Networks in order to address this problem. In this work, we study how the localization precision of using Deep Neural Networks is affected by the variability of the channel, the noise level at the receiver, the number of neurons of the neural network and the utilization of the power or the covariance of the received acoustic signals. Our study shows that using deep neural networks is a valid approach when the channel variability is low, which opens the door to further research in such localization methods for the underwater environment.

Wireless Networks under a Backoff Attack: A Game Theoretical Perspective.

We study a wireless sensor network using CSMA/CA in the MAC layer under a backoff attack: some of the sensors of the network are malicious and deviate from the defined contention mechanism. We use Bianchi's network model to study the impact of the malicious sensors on the total network throughput, showing that it causes the throughput to be unfairly distributed among sensors. We model this conflict using game theory tools, where each sensor is a player. We obtain analytical solutions and propose an algorithm, based on Regret Matching, to learn the equilibrium of the game with an arbitrary number of players. Our approach is validated via simulations, showing that our theoretical predictions adjust to reality.

The Impacts of Exposure to Environmental Risk on Physical and Mental Health in a Small Geographic Community in Houston, TX.

Previous research has shown that communities with low average socioeconomic status (SES) and majority minority populations are more likely to be exposed to industrial buildings, waste facilities, and poor infrastructure compared to white communities with higher average SES. While some studies have demonstrated linkages between exposures to specific environmental contaminates within these communities and negative health outcomes, little research has analyzed the effects of environmental contaminants on the mental and physical health of these populations. A cross-sectional survey collected data from residents of Manchester, a small neighborhood in Houston, TX, that is characterized by industrial sites, unimproved infrastructure, nuisance flooding, and poor air quality. Our study (N = 109) utilized the 12 item Short Form Health Survey version 2 (SF12v2) to assess the general mental and physical health of the community. The community as a whole had reduced physical health scores compared to U.S. national averages. The time residents had lived in the neighborhood was also correlated with a reported reduction in physical health scores (r2 = 0.136; p-value <0.001). The association between time lived in the neighborhood and poorer health scores remained after adjusting for age, race, and gender (coef = -0.27, p-value <0.001). Mental health scores were within national averages and time spent living in the neighborhood did not appear to negatively impact respondent's mental health scores. These findings point to the need for more research to determine the potential for additive physical and mental health impacts in long-term residents in neighborhoods characterized by environmental justice issues.

Evaluating community and campus environmental public health programs.

The National Institute of Environmental Health Sciences' (NIEHS) Partnerships for Environmental Public Health (PEPH) program created the Evaluation Metrics Manual as a tool to help grantees understand how to map out their programs using a logic model, and to identify measures for documenting their achievements in environmental public health research. This article provides an overview of the manual, describing how grantees and community partners contributed to the manual, and how the basic components of a logic model can be used to identify metrics. We illustrate how the approach can be implemented, using a real-world case study from the University of Texas Medical Branch, where researchers worked with community partners to develop a network to address environmental justice issues.

Influencia de las enfermeras gestoras de casos en la calidad de vida de las cuidadoras de pacientes pluripatológicos / Influence of nurses case managers in the quality of life of caregivers of patients with complex chronic diseases

Objetivo: determinar el efecto a medio plazo de las enfermeras gestoras de casos hospitalarias sobre la sobre la calidad de vida de las cuidadoras. Método: se realizó un estudio observacional, analítico de tipo cohortes, (gestoras frente a control), entre los pacientes pluripatológicos ingresados en el Complejo Hospitalario de Jaén. La calidad de vida se midió mediante el cuestionario SF-36 durante el ingreso y tras 90 días del alta. Se realizó también el cálculo de las puntuaciones sumario que permite el cuestionario: componente sumario física (CSF) y mental (CSM), mediante la combinación de las puntuaciones de cada dimensión, presentándose como puntuaciones basadas en la normalidad de la población española. Resultados: la puntuación del SF-36 es superior en la cohorte de control en todas las dimensiones durante todo el estudio. La dimensión del rol físico cuando el paciente está ingresado es la que peor puntuación tiene, aunque mejora notablemente tras el alta. La CSF y CSM de las cuidadoras está por debajo de la media española tanto durante el ingreso como a los 90 días del alta. La CSF del grupo gestoras mejora al alta, mientras que empeora en el grupo control. Al contrario ocurre con la CSM, que empeora en el grupo gestoras y mejora en el control. Conclusiones: las cuidadoras de pacientes pluripatológicos presentan una importante afectación en el rol físico al ingreso, que mejora notablemente a los 90 días del alta. La CSF y CSM están por debajo de la media española

Aims: To determine the medium-term impact of Hospital Nurses Case Managers on the quality of life of carers. Methods: An observational study cohort type (case-nurse managers vs. control), among patients with complex chronic disease admitted to the Jaén University Hospital. Quality of life was measured by the SF-36 at admission and after 90 days of discharge. Also performed the calculation of summary scores that allows the questionnaire: physical component summary (PCS) and mental (MCS), by combining the scores for each dimension, appearing as normal scores based on Spanish population. Results: The score of the SF-36 is higher in the control group in all dimensions throughout the study. The dimension of the physical role when the patient is admitted is the worst score has improved significantly after discharge. The PCS and MCS is below the Spanish average both during admission and 90 days after discharge. The case-nurse managing group PCS improves to discharge, while worsening in the control group. Unlike the case with MCS worsening in group case-nurse and improving in the control. Conclusions: The caregivers of patients with multiple comorbidities have a significant affectation in the admission physical role, dramatically improving after 90 days of discharge. The CSF and CSM are below the Spanish average

Effect of hospital case-manager nurses on the level of dependence, satisfaction and caregiver burden in patients with complex chronic disease.

AIMS AND OBJECTIVES: To determine the medium-term effects of nurse case management on the dependence and satisfaction of patients with complex chronic disease and on caregiver burden. BACKGROUND: Caregiver exhaustion increases the readmission rate of highly dependent patients with complex chronic disease and their consumption of primary care resources. DESIGN: An observational and analytical cohort study was undertaken in multimorbid patients. METHODS: Data were gathered on Barthel Index and Caregiver Burden Index scores, primary care resource consumption, readmission and mortality rates, and patient satisfaction with care and care continuity. Results were compared between nurse case-managed (n = 62) and control (n = 193) multimorbid patients using univariate and bivariate analyses. RESULTS: The study included 255 patients with complex chronic disease (24·32% in management cohort vs. 75·68% in control cohort). The nurse case-managed group had significantly lower Barthel Index and higher Caregiver Burden Index scores and a significantly longer hospital stay. At 90 days postdischarge, no significant intergroup differences were observed in Barthel Index or Caregiver Burden Index scores, primary care resource consumption, readmission rate or mortality rate; the case-managed patients showed a significantly higher satisfaction level with their care and its continuity. CONCLUSIONS: Nurse case management prevents a postdischarge increase in the dependence of multimorbid patients and the burden of their caregivers. RELEVANCE TO CLINICAL PRACTICE: Application of nurse case management can reduce the readmission rate and primary care consumption of patients with chronic complex disease after their hospital stay and prevent an exacerbation of caregiver exhaustion.

Osteopenia y osteoporosis en mujeres con factores de riesgo / Osteopenia and osteoporosis in women with risk factors

Objetivo: determinar la prevalencia de osteopenia y osteoporosis en mujeres con factores de riesgo en la provincia de Jaén, identificar la relación entre los factores de riesgo y el lugar de realización de la prueba con el diagnostico Material y método: estudio descriptivo transversal. Se incluyeron mujeres que acudieron a una consulta privada de ginecología. Se les realizo una densitometría ósea en columna y cadera (técnica Dexa). Se efectuó un análisis univariante tanto para las variables cuantitativas como cualitativas. El contraste de hipótesis se desarrollo mediante el coeficiente de correlación de Pearson en las cualitativas y la Ji-cuadrado en las cualitativas. Resultados: se estudiaron 151 mujeres, con una edad media de 56 años, y un IMC promedio de 26,86. El 57% se realizaron la prueba para la prevención de osteopenia y osteoporosis. Existía relación entre edad, el peso y el IMC (factor protector) y el diagnostico de osteopenia u osteporosis. La prevalencia de osteoporosis fue de un 4% cuando la prueba se efectuó en cadera y de un 20% cuando se hizo en columna. Conclusiones: la edad se posiciona como un factor de riesgo en la aparición de osteopenia y osteoporosis. El peso y el aumento del IMC parecen actuar como factor protector en estas mujeres. Existe una importante diferencia diagnostica entre ambas en función del lugar de realización de la densitometría ósea, siendo mayor la prevalencia cuando se efectuaba en cadera (AU)

Objective: to determine the prevalence of osteopenia and osteoporosis in women with risk factors in the province of Jaen, to identify the relationship between risk factors and the site on which the diagnosis test is conducted. Materials and method: transversal descriptive study. It included women who visited a Gynaecologist in the private setting. They underwent a bone density test of the hip and spine (DEXA technique). Univariate analysis was conducted both for quantitative and qualitative variables. Hypothesis contrast was developed through Pearson Correlation Coefficient in quantitative variables, and Ji-square in qualitative variables. Results: the study was conducted on 151 women, with 56 years as median age, and an average BMI of 26.86. Of these, 57% underwent the test in order to prevent osteopenia and osteoporosis. There was a connection between age, weight and BMI (protective factor) and the diagnosis of osteopenia or osteoporosis. Prevalence of osteoporosis was 4% when the test was conducted on the hip, and 20% when it was conducted on the spine. Conclusions: age is positioned as a risk factor for the presence of osteopenia and osteoporosis. Weight and increase in BMI seem to act as a protective factor in these women. There is an important diagnostic difference between them, based on the area in the body where the bone density test was performed, with a higher prevalence when performed on the hip (AU)

Effectiveness of the 'sleep enhancement' nursing intervention in hospitalized mental health patients.

AIM: To determine the effectiveness of the 'sleep enhancement' nursing intervention (Nursing Interventions Classification) in patients hospitalized with mental illness and having a disturbed sleep pattern and to identify the possible effect of psycho-active medications on this disturbed sleep pattern. DESIGN: A quasi-experimental pretest-posttest type study without control group. METHOD: The study was conducted in all patients admitted to the mental health inpatient unit of University Hospital of Spain from 1 March 2007-31 May 2008. The effectiveness of the 'sleep enhancement' nursing intervention was measured using the Oviedo Sleep Questionnaire score and Nursing Outcome Classification sleep scores at admission and discharge. Psycho-active medication was considered an intervening variable and data were analysed by multivariate analysis of variance for repeated measures. RESULTS: The study included 291 patients. Consumption of psycho-active medications did not change between admission and discharge and was not statistically significantly different in the multivariate analysis of variance. Oviedo Sleep Questionnaire and Nursing Outcome Classification sleep scores at admission and discharge demonstrated significant sleep improvement after the nursing intervention. CONCLUSION: This nursing intervention could be implemented in patients admitted to a mental health inpatient unit with disturbed sleep pattern, regardless of their consumption of psycho-active medications.

Is the Scale for Measuring Motivational Interviewing Skills a valid and reliable instrument for measuring the primary care professionals motivational skills?: EVEM study protocol.

BACKGROUND: Lifestyle is one of the main determinants of people's health. It is essential to find the most effective prevention strategies to be used to encourage behavioral changes in their patients. Many theories are available that explain change or adherence to specific health behaviors in subjects. In this sense the named Motivational Interviewing has increasingly gained relevance. Few well-validated instruments are available for measuring doctors' communication skills, and more specifically the Motivational Interviewing. METHODS/DESIGN: The hypothesis of this study is that the Scale for Measuring Motivational Interviewing Skills (EVEM questionnaire) is a valid and reliable instrument for measuring the primary care professionals skills to get behavior change in patients. To test the hypothesis we have designed a prospective, observational, multi-center study to validate a measuring instrument. - SCOPE: Thirty-two primary care centers in Spain. -Sampling and Size: a) face and consensual validity: A group composed of 15 experts in Motivational Interviewing. b) Assessment of the psychometric properties of the scale; 50 physician- patient encounters will be videoed; a total of 162 interviews will be conducted with six standardized patients, and another 200 interviews will be conducted with 50 real patients (n=362). Four physicians will be specially trained to assess 30 interviews randomly selected to test the scale reproducibility. -Measurements for to test the hypothesis: a) Face validity: development of a draft questionnaire based on a theoretical model, by using Delphi-type methodology with experts. b) Scale psychometric properties: intraobservers will evaluate video recorded interviews: content-scalability validity (Exploratory Factor Analysis), internal consistency (Cronbach alpha), intra-/inter-observer reliability (Kappa index, intraclass correlation coefficient, Bland & Altman methodology), generalizability, construct validity and sensitivity to change (Pearson product-moment correlation coefficient). DISCUSSION: The verification of the hypothesis that EVEM is a valid and reliable tool for assessing motivational interviewing would be a major breakthrough in the current theoretical and practical knowledge, as it could be used to assess if the providers put into practice a patient centered communication style and can be used both for training or researching purposes. TRIALS REGISTRATION Dislip-EM study: NCT01282190 (ClinicalTrials.gov).

Incidencia de caídas en un hospital de nivel 1: factores relacionados / Incidence of falls in a University Hospital: factors related

Introducción: las caídas son un riesgo real que acontece en el medio hospitalario, y constituyen un indicador de calidad asistencial. La Organización Mundial de la Salud(OMS) define el término caída como “la consecuencia de cualquier acontecimiento que precipita al individuo hacia el suelo en contra de su voluntad”. Objetivo: analizar la incidencia de caídas, el perfil de los pacientes que sufren caídas en el hospital e identificar las posibles causas y efectos de la misma. Metodología: estudio descriptivo sobre las caídas registradas en el Complejo Hospitalario de Jaén durante la estancia hospitalaria. La obtención de datos se hizo directamente por las supervisoras. Se elaboró una hoja de recogida de datos cuyas anotaciones se transmitieron a una base de datos electrónica. Las variables que se recogieron fueron datos de filiación del paciente, fecha y hora de la caída, unidad donde se produce la caída, puntuación escala Morse, caídas previas, circunstancias de la caída, factores relacionados como medicación, trastornos (..) (AU)

Introduction: falls are a real risk that occurs in hospitals; falls are health a care quality indicator. The World Health Organization (WHO) defines the term falls as the result of any event which precipitate someone to the ground against their will. Aims: to analyze the incidence of falls, the profile of patients who fall in the hospital and to identify causes and effects. Methodology: design: Descriptive study on falls reported in Jaen’s Hospital, which includes any fall(36) that has taken place at some time of hospital stay. Data collection was performed by each unit supervisors developed a data collection sheet. Data were transcribed to a database. Variables collected were: demographic data, date and time of the fall event, unit which produces the fall, Morse scale score, previous falls, fall circumstances, factors such as medication, cognitive, functional factors environment, care after the fall and need for assistive devices. Results: during the period under review there were a total of 36 falls, representing an incidence of (..) (AU)

Efectividad de las intervenciones para la prevención de caídas en ancianos: revisión sistemática / Effectiveness of interventions for prevention falls in the elderly: systematic review

Objetivos: identificar las principales causas por las que se producen las caídas y determinar qué medidas son efectivas y cuáles no en la prevención de caídas accidentales. Métodos: revisión sistemática sobre los diferentes estudios de caídas en el hospital. Esta revisión incluye ensayos clínicos, estudios de casos/controles, estudios de cohortes y estudios observacionales publicados desde la indexación de cada base de datos, hasta diciembre de 2009. Resultados: se revisó un total de 60 estudios, de los cuales 37 finalmente entraron en la revisión, previo examen de su calidad metodológica mediante el CASP. Las principales causas por las que se producen las caídas están relacionadas con la edad, patologías (Parkinson,demencias, Alzheimer), medicación y el pre/postope -ratorio. De todos los estudios, hay diversas intervenciones que han demostrado ser efectivas y reducir el número de caídas. Conclusiones: todos los estudios, en general, han aplicado medidas de prevención en función del riesgo y/o enfermedad identificados por grupos de pacientes. Las medidas preventivas que han demostrado eficacia han sido el uso correcto de la colocación de medidas de protección, el establecimiento de unos parámetros para la identificación de los posibles factores de riesgo que puedan ocasionar una caída y evaluar la (..) (AU)

Aims: to identify the main causes of falls and determine which interventions are effective to prevent accidental falls. Methods: Systematic review over studies of falls in the hospital. This review includes clinical trials, case/control, cohort and observational studies published since each database indexing until December 2009.Results: we reviewed a total of 60 studies; of these, 37 were eventually included in the review after conductingan analysis of their methodological quality using theCASP. The main causes of falls are related to age, chronicdisease (Parkinson’s, dementia, Alzheimer’s), medication and pre/post operative. There are several interventions thathave proven to be effective and reduce the number of falls.Conclusions: all studies have generally applied preventive interventions based on identified risk and /or disease ingroups of patients. Preventive interventions that have proven efficacy are: the correct use of the placement of protective elements, establishing parameters for the identification of possible risk factors that could cause a fall; evaluate the frequency of falls; to establish a scale of care dependence and compare it with the model of risk factors; analyze extrinsic risk factors favoring falls; use a score to identify patients at high risk, to establish a falls intervention program and develop strategies for prevention (..) (AU)

Influencia de las Enfermeras Gestoras de Casos hospitalarias sobre la preparación de las cuidadoras para asumir el cuidado domiciliario / Nursing Case Management of caregiver emotional heatlh at hospital setting and it´s influence on caregivers decission to take care at home

Objetivos: a) Determinar la influencia de las intervencionesde las Enfermas Gestoras de Casos (EGC)sobre el cansancio en el rol de cuidador y en la decisiónde éstos de asumir la atención del paciente en sudomicilio. b) Cuantificar el ahorro de estancias y decostes. Metodología: Estudio observacional restrospectivosobre pacientes con cansancio en el rol delcuidador gestionados por las EGC entre abril y septiembrede 2008. Se analiza la efectividad de las intervencionesmidiendo la puntuación al ingreso y al altadel CRE Salud Emocional del Cuidador Principal. Elahorro de estancias y costes se ha calculado mediantela fórmula: total de pacientes con ingreso/alta precozconseguida x estancia media x coste diario. Resultados:Sesenta y cuatro pacientes presentaban el diagnóstico.En todos los casos se activó el criterio de saludemocional. Las intervenciones fueron efectivasmejorando el CRE de 2,41 al ingreso a 3,81 al alta (p< 0,0001). Treinta y uno de estos pacientes que teníanprevisto el traslado al hospital media estancia han sidodados de alta a domicilio tras su intervención. Existendiferencias en la puntuación al alta del CRE (> ó < 4)en este grupo comparado con el que continúa cursonormal (p > 0,006). La intervención de las EGC haahorrado un total de 798 días de estancia tasados en360.270 euros en 6 meses. Conclusiones: Las intervencionesde las EGC mejoran la salud emocional delas cuidadoras, sitúa a la cuidadora en disposición de (..) (AU)

Aims: a) To determine the influence of the interventions ofNurse Case-Manager (NCM) at Hospital on “CaregiverRole Strain” and on the decision to take care of the patientat home. b) To quantify saving hospital stays and economiccost. Methodology: A retrospective observational study oncaregivers with “Role Strain” managed by the NCM betweenApril and September 2008. We analysed effectiveness ofinterventions by measuring the score at admission and atdischarge on Nursing Outcome (NOC) “Caregiver emotionalhealth” . Saving hospital stays and economic cost havebeen calculated using the formula: Total number of patientswith income / early Hospital discharge x Averagedaily cost. Results: 64 patients had the diagnosis. In all cases,NOC “Caregiver emotional health” was activated. Interventionswere effective in improving the NOC from2.41 on admission to 3.81 at discharge (p <0.0001). 31 ofthese patients, who were planned to be transfered to a medium-stay hospital, have been discharged home after interventionby the NCM. There are differences in the scores atdischarge from the NOC (> or <4) in this group comparedwith the group that continues normal course (p> 0006).The interventions of the 2 NCM have saved a total of 798days of stay valued at € 360,270 in 6 months. Conclusions:The interventions of the NCM improve the emotionalhealth of caregivers, and succeed in improving the readinessof the caregiver to take care of the patient at home,which has direct impact on the average length of stay andassociated cost (..) (AU)