Congestive Heart Failure Readmission Research Articles

Readmissions related to antihypertensive medications used in chronic hemodialysis

Background: In hemodialysis, hypertension is treated by removing excess fluid and antihypertensive therapy. Commonly, the antihypertensives used to treat hypertension in earlier stages of kidney disease are continued as the patient progresses into end-stage renal disease and begins dialysis, without much evidence for benefit. Methods: This study is a single center, retrospective chart review that included hemodialysis patients admitted for congestive heart failure (CHF), fluid overload, or pulmonary edema as determined by ICD-9 code (428.x, 276.6, 518.4, 506.1). The primary objective was to determine if the number or class of antihypertensives used in the chronic hemodialysis population increased the number of readmissions related to CHF, fluid overload, or pulmonary edema. Patients were separated into two groups based on total number of antihypertensive medications, less than or equal to 2 medications for group 1 and greater than two medications for group 2. The primary endpoint was 30-day readmission for CHF, fluid overload, or pulmonary edema. Results: For the study period, 85 individual patient charts met inclusion criteria. Group 1 (n = 44) experienced seven readmissions (16%) and group 2 (n = 41) experienced eight readmissions (18%) (p = 0.663). The most common antihypertensives at discharge were ACE inhibitors for group 1 (45%) and dihydropyridine calcium channel blockers for group 2 (66%). No difference in systolic blood pressures before, during and after hemodialysis was found between groups. Conclusions: Antihypertensive medications continue to play an important role in the hemodialysis population. This study suggests that drug class and quantity of antihypertensives do not alter readmission rate in the setting of fluid overload.

Mobile Integrated Health Care and Community Paramedicine: An Emerging Emergency Medical Services Concept

Mobile integrated health care and community paramedicine are models of health care delivery that use emergency medical services (EMS) personnel to fill gaps in local health care infrastructure. Community paramedics may perform in an expanded role and require additional training in the management of chronic disease, communication skills, and cultural sensitivity, whereas other models use all levels of EMS personnel without additional training. Currently, there are few studies of the efficacy, safety, and cost-effectiveness of mobile integrated health care and community paramedicine programs. Observations from existing program data suggest that these systems may prevent congestive heart failure readmissions, reduce EMS frequent-user transports, and reduce emergency department visits. Additional studies areneeded to support the clinical and economic benefit of mobile integrated health care and community paramedicine.

Hearing the Veteran's Voice in Congestive Heart Failure Readmissions.

Our purpose was to examine congestive heart failure (CHF) readmissions from the veterans' perspective. The use of health care provider interventions, such as standardized education materials, home telehealth, and a CHF clinic, was able to reduce readmissions rates from 35% to 23%. Our objective was to use input from the veterans to fine-tune our efforts and achieve readmission rates for patients with CHF below the national average of 21%. We wanted to identify factors that result in CHF readmissions, including disease education, self-care management, and barriers to self-care. This study was directed toward answering two questions: 1. What is the veteran’s explanation for readmission? 2. According to the veteran, what are the barriers to following their treatment regimen? It was a rural 84-bed Veterans Health Administration hospital in the Western United States. Before this study, our efforts to reduce CHF readmissions were one-sided, all from the health care professionals' viewpoint. We wanted to hear what the veteran had to say; so, we interviewed 25 veterans. Four veterans were excluded due to issues with their consents. Ninety percent (n = 19/21) responded that they knew their CHF was worse by a change in their breathing (shortness of breath). They identified 48 signs/symptoms that indicated worsening CHF. Weight gain was noted as an indication of worsening CHF symptoms (n = 6/48) in 12.5% of the responses. Twenty-five percent (n = 12/48) of the veterans stated they recognized the early symptoms of worsening CHF. Thirty-eight percent (n = 8/21) of the veterans stated they had early symptoms of worsening CHF, but only two of them contacted their doctor. It is interesting to note that only 29% (n = 6/21) of the veterans recognized weight gain as a sign of worsening CHF and all of these veterans listed other symptoms (such as shortness of breath) along with weight gain. Weighing on a daily basis was practiced by only 30% of the group (n = 7/21); all but two of the veterans had no problems with weighing themselves. More than 71% of the veterans responded that they had no problems following their diet or taking their medications. More than half of the veterans did not need help with meals, transportation, or daily grooming/dressing/toileting. We were concerned about the evident delays in seeking medical care for worsening CHF. All veterans who did need help with the activities of daily living, medications, or diet had their needs met through their support systems. They did not perceive any barriers to seeking care. However, there remain many unanswered questions. Does the patient understand their discharge education and know how to use this information from daily weights or recognition of early symptoms, to indicate their need for urgent and emergency medical interventions? Or is it a problem that the education is not sufficient? Is it a question of the burden of care from multiple comorbid conditions or of taking too many medications? Do social issues drive readmissions? These questions are further explored in a second study, which is in the data analysis stage. There are three key findings from our study. 1. Veterans think in terms of symptoms that increase the impact of CHF on their life. 2. The relationship between daily weight and controlling CHF is not clear to veterans. 3. Hospital discharge instructions should clearly associate symptoms that are associated with worsening CHF.

Hearing the Veteran's Voice in Congestive Heart Failure Readmissions

Hearing the Veteran's Voice in Congestive Heart Failure Readmissions

Abstract 16142: Grad 3-Ischemia (QRS Complex Distortion) as a Predictor of Myocardial Damage as Assessed by Cardiac Magnetic Resonance Imaging and Clinical Prognosis in Patients With ST-Elevation Myocardial Infarction

Introduction: Predicting the extent of myocardial damage on early electrocardiographic (ECG) findings could be helpful for improved risk stratification in patients with acute reperfused ST-elevation myocardial infarction (STEMI). Distortion in the terminal portion of the QRS complex (so called grade 3 ischemia, G3I) has been associated with adverse outcomes in STEMI patients. The correlation of G3I with infarct size and microvascular injury is not well defined. Objective: To studied the relation of G3I with myocardial damage as assessed by CMR and the association of G3I with adverse clinical outcomes in a STEMI population treated by primary percutaneous coronary intervention (PCI). Methods: We analyzed the ECGs of 572 consecutive STEMI patients regarding the presence or absence of G3I. G3I was defined as: 1) complete loss of S waves in 2 adjacent leads with typical Rs configuration (i.e. V1-V3), or 2) ST-J point to R wave amplitude ratio >0.5 in other leads with qR configuration. CMR was performed within 1 week after infarction for comprehensive assessment of myocardial damage using a standardised protocol. The primary clinical end-point was major adverse cardiac events (MACE) defined as death, reinfaction and readmission for congestive heart failure within 12 months after the index event. Results: G3I was present in 186 (32%) patients. The presence of G3I was associated with larger infarct size (18.3%LV [10.4 to 27.6] versus 16.5%LV (8.2 to 23.5), p=0.01), late microvascular obstruction (0.4%LV [0 to 2.7] versus 0%LV [0 to 1.5], p= 0.05, presence of intramyocardial hemorrhage (41 versus 32%, p=0.04) and less myocardial salvage (47 [28 to 64] versus 53 (35 to 68), p=0.01). G3I was associated with a significant higher incidence of MACE (p=0.01) and was identified as an independent predictor of MACE in Cox regression analysis (Hazard ratio 2.19 [1.10 to 4.38], p=0.03). Conclusions: This largest study to date correlating G3I on the admission ECG with CMR markers of myocardial damage demonstrates that G3I is significantly associated with infarct size, myocardial salvage and reperfusion injury in a STEMI population reperfused by primary PCI. Moreover, G3I was independently associated with MACE.

Clinical and psychological telemonitoring and telecare of high risk heart failure patients

We conducted a trial of telemonitoring and telecare for patients with chronic heart failure leaving hospital after being treated for clinical instability. Eighty patients were randomized before hospital discharge to a usual care group (n = 40: follow-up at the outpatient clinic) or to an integrated management group (n = 40: patients learned to use a handheld PDA and kept in touch daily with the monitoring centre). At enrolment, the groups were similar for all clinical variables. At one-year follow-up, integrated management patients showed better adherence, reduced anxiety and depression, and lower NYHA class and plasma levels of BNP with respect to the usual care patients (e.g. NYHA class 2.1 vs 2.4, P < 0.02). Mortality and hospital re-admissions for congestive heart failure were also reduced in integrated management patients (P < 0.05). Integrated management was more expensive than usual care, although the cost of adverse events was 42% lower. In heart failure patients at high risk of relapse, the regular acquisition of simple clinical information and the possibility for the patient to contact the clinical staff improved drug titration, produced better psychological status and quality of life, and reduced hospitalizations for heart failure.

Data-driven decisions for reducing readmissions for heart failure: general methodology and case study.

BackgroundSeveral studies have focused on stratifying patients according to their level of readmission risk, fueled in part by incentive programs in the U.S. that link readmission rates to the annual payment update by Medicare. Patient-specific predictions about readmission have not seen widespread use because of their limited accuracy and questions about the efficacy of using measures of risk to guide clinical decisions. We construct a predictive model for readmissions for congestive heart failure (CHF) and study how its predictions can be used to perform patient-specific interventions. We assess the cost-effectiveness of a methodology that combines prediction and decision making to allocate interventions. The results highlight the importance of combining predictions with decision analysis.MethodsWe construct a statistical classifier from a retrospective database of 793 hospital visits for heart failure that predicts the likelihood that patients will be rehospitalized within 30 days of discharge. We introduce a decision analysis that uses the predictions to guide decisions about post-discharge interventions. We perform a cost-effectiveness analysis of 379 additional hospital visits that were not included in either the formulation of the classifiers or the decision analysis. We report the performance of the methodology and show the overall expected value of employing a real-time decision system.FindingsFor the cohort studied, readmissions are associated with a mean cost of $13,679 with a standard error of $1,214. Given a post-discharge plan that costs $1,300 and that reduces 30-day rehospitalizations by 35%, use of the proposed methods would provide an 18.2% reduction in rehospitalizations and save 3.8% of costs.ConclusionsClassifiers learned automatically from patient data can be joined with decision analysis to guide the allocation of post-discharge support to CHF patients. Such analyses are especially valuable in the common situation where it is not economically feasible to provide programs to all patients.

Resident Physicians Using Modern Practices for Excellent Documentation and Care in Heart Failure (PUMPED CHF)

The most common indication for readmission among Medicare patients is congestive heart failure (CHF). Prior studies underscore the use of residents to bolster hospital-wide programs and reduce CHF readmissions. The authors assessed the effectiveness of a novel online training program designed to improve resident documentation and knowledge related to CHF. The findings suggest that despite a significant increase in knowledge scores following the online educational course, there was only a slight increase in documentation scores. Additional teaching modalities need to be identified to foster resident education and create sustained behavior change.

The effects of data sources, cohort selection, and outcome definition on a predictive model of risk of thirty-day hospital readmissions

BackgroundHospital readmission risk prediction remains a motivated area of investigation and operations in light of the hospital readmissions reduction program through CMS. Multiple models of risk have been reported with variable discriminatory performances, and it remains unclear how design factors affect performance. ObjectivesTo study the effects of varying three factors of model development in the prediction of risk based on health record data: (1) reason for readmission (primary readmission diagnosis); (2) available data and data types (e.g. visit history, laboratory results, etc); (3) cohort selection. MethodsRegularized regression (LASSO) to generate predictions of readmissions risk using prevalence sampling. Support Vector Machine (SVM) used for comparison in cohort selection testing. Calibration by model refitting to outcome prevalence. ResultsPredicting readmission risk across multiple reasons for readmission resulted in ROC areas ranging from 0.92 for readmission for congestive heart failure to 0.71 for syncope and 0.68 for all-cause readmission. Visit history and laboratory tests contributed the most predictive value; contributions varied by readmission diagnosis. Cohort definition affected performance for both parametric and nonparametric algorithms. Compared to all patients, limiting the cohort to patients whose index admission and readmission diagnoses matched resulted in a decrease in average ROC from 0.78 to 0.55 (difference in ROC 0.23, p value 0.01). Calibration plots demonstrate good calibration with low mean squared error. ConclusionTargeting reason for readmission in risk prediction impacted discriminatory performance. In general, laboratory data and visit history data contributed the most to prediction; data source contributions varied by reason for readmission. Cohort selection had a large impact on model performance, and these results demonstrate the difficulty of comparing results across different studies of predictive risk modeling.

Commentary on "A comparison of congestive heart failure readmissions among teaching and nonteaching hospital services".

Commentary on "A comparison of congestive heart failure readmissions among teaching and nonteaching hospital services".

Prognosis after a first hospitalisation for acute myocardial infarction and congestive heart failure by country of birth

ObjectiveTo investigate differences in 28-day and 5-year mortality and 5-year readmission after a first hospitalisation for acute myocardial infarction (AMI) and congestive heart failure (CHF) between first generation ethnic minority...

Impact of socioeconomic status measures on hospital profiling in New York City.

Current 30-day readmission models used by the Center for Medicare and Medicaid Services for the purpose of hospital-level comparisons lack measures of socioeconomic status (SES). We examined whether the inclusion of an SES measure in 30-day congestive heart failure readmission models changed hospital risk-standardized readmission rates in New York City (NYC) hospitals. Using a Centers for Medicare & Medicaid Services (CMS)-like model, we estimated 30-day hospital-level risk-standardized readmission rates by adjusting for age, sex, and comorbid conditions. Next, we examined how hospital risk-standardized readmission rates changed relative to the NYC mean with inclusion of the Agency for Healthcare Research and Quality (AHRQ)-validated SES index score. In a secondary analysis, we examined whether inclusion of the AHRQ SES index score in 30-day readmission models disproportionately impacted the risk-standardized readmission rates of minority-serving hospitals. Higher AHRQ SES scores, indicators of higher SES, were associated with lower odds (0.99) of 30-day readmission (P<0.019). The addition of the AHRQ SES index did not change the model's C statistic (0.63). After adjustment for the AHRQ SES index, 1 hospital changed status from worse than the NYC average to no different than the NYC average. After adjustment for the AHRQ SES index, 1 NYC minority-serving hospital was reclassified from worse to no different than average. Although patients with higher SES were less likely to be admitted, the impact of SES on readmission was small. In NYC, inclusion of the AHRQ SES score in a CMS-based model did not impact hospital-level profiling based on 30-day readmission.

Safe Discharges Aren't Simple

Safe Discharges Aren't Simple

Big data analysis in healthcare.

The rise of big data is promising new opportunities to understand and forecast contexts or problems in the healthcare area. Big data related to healthcare has explosively expanded with the development of the Internet. It is not only accumulated in purely healthcare service settings but also in cyber-digital space, such as traditional and mobile Internet, smartphones, smart TVs, sensor and RFID-based ubiquitous networks, and social media [1]. Information and communication technology is the main driver of voluminous and varying big data to be managed and analyzed. These big data, including text, audio, video, or any other digital medium can be analyzed to provide new and useful information. Big data analytics has opened a new era to improve services and solve problems in the healthcare area. Big data analytics has recently attracted interest because it couples social data analytics to traditional analytics, though data analytics has long been important in science and healthcare practice. Forecasting using Google Flu Trends (GFT), beginning in February 2013, has attracted interest beyond the healthcare area, and it illustrates the new usefulness and value of social data and big data analytics. This innovative estimation of flu prevalence of GFT has opened up and motivated new possibilities of handling long-lasting problems in the healthcare area by using social data in cyber space. Many stakeholders recognize that big data analytics may provide opportunities for forecasting, discovering tentative needs, and reducing risks, as well as providing personalized services, real-time sensing and counter-measures more appropriately. New applications of big data analytics in the healthcare area are being developed and introduced all over the world. Seton Healthcare, working with predictive analytics, sought to address the need to reduce the occurrence of high-cost congestive heart failure readmissions [2]. The European Commission has developed 'MediSys', an information scanning tool, to reinforce the network for the surveillance of communicable diseases and the early detection of bioterrorist activities. Breaking news can be detected by algorithm of MediSys using over 20,000 Internet articles per day produced by the Europe Media Monitor and can be sent to key persons by email and SMS. Many successful big data applications are being introduced [3]. Many healthcare institutions and countries have done many trials and made many successful cases of big data analysis to solve traditional problems in healthcare, such as reducing readmissions, increasing the effectiveness and efficiency of healthcare, improving the quality of care, and forecasting demands for healthcare services. There are several factors that must be taken into consideration in using big data analytics for healthcare, although it has the potential to provide insights to enhance our understand of changing phenomena. Big data must be prepared systematically and must be of good quality. Regarding big data analytics, we should remember the popular saying 'garbage in, garbage out'. Proper big data analytics using highly qualified big data would produce useful and valuable results for understanding contexts and forecasting the future of healthcare. Next, we have not to think and use the results of big data analytics as being one and powerful information to understand and forecast something interested. The interpretation of results must be done cautiously by experts who come from diverse fields of expertise and have a wide variety of perspectives. We have to use big data analytics cautiously even after strategic consideration, though it has much potential to improve our understanding of phenomena and strengthen our ability to accurately forecast the future. Finally, we think that academic study of big data analysis could contribute to and expand the practical implementation of big data analytics. Such study should focus on any meaningful and objective criteria, such as clinical and social effectiveness, efficiency, usefulness, and adoption of big data analysis.

Utilization of Trained Volunteers Decreases 30-Day Readmissions for Heart Failure

BackgroundThis study evaluated the effectiveness of using trained volunteer staff in reducing 30-day readmissions of congestive heart failure (CHF) patients. MethodsFrom June 2010 to December 2010, 137 patients (mean age 73 years) hospitalized for CHF were randomly assigned to either: an interventional arm (arm A) receiving dietary and pharmacologic education by a trained volunteer, follow-up telephone calls within 48 hours, and a month of weekly calls; or a control arm (arm B) receiving standard care. Primary outcomes were 30-day readmission rates for CHF and worsening New York Heart Association (NYHA) functional classification; composite and all-cause mortality were secondary outcomes. ResultsArm A patients had decreased 30-day readmissions (7% vs 19%; P < .05) with a relative risk reduction (RRR) of 63% and an absolute risk reduction (ARR) of 12%. The composite outcome of 30-day readmission, worsening NYHA functional class, and death was decreased in the arm A (24% vs 49%; P < .05; RRR 51%, ARR 25%). Standard-care treatment and hypertension, age ≥65 years and hypertension, and cigarette smoking were predictors of increased risk for readmissions, worsening NYHA functional class, and all-cause mortality, respectively, in the multivariable analysis. ConclusionsUtilizing trained volunteer staff to improve patient education and engagement might be an efficient and low-cost intervention to reduce CHF readmissions.

GW24-e3575 The clinical observation of Atrial Fibrillation on systolic dysfunction of Congestive Heart Failure Patients

ObjectivesTo explore the prevalence, distribution and prognosis of Atrial fibrillation on systolic dysfunction of congestive heart failure (CHF) in hospitalised patients.MethodsWe reviewed the medical records of 462 unselected consecutive patients...

Comparison of Systolic and Diastolic Criteria for Isolated LV Noncompaction in CMR

This study used cardiac magnetic resonance (CMR) to compare standard criteria for left ventricular noncompaction (LVNC). LVNC as a distinct cardiomyopathy is supported by a growing number of publications. Echocardiographic and CMR criteria have been established to diagnosis LVNC but have led to concerns of diagnostic accuracy. Trabeculation/possible LVNC by CMR was retrospectively observed in 122 consecutive cases. We compared the standard end-systolic noncompacted-to-compacted ratio (ESNCCR), end-diastolic noncompacted:compacted ratio (EDNCCR), and trabecular mass-to-total mass ratio (TMTMR) along with deaths, embolic events, congestive heart failure (CHF) readmissions, ventricular arrhythmias, myocardial thickening (MT), left ventricular ejection fraction (LVEF), 3-dimensional sphericity index (3DSi), and left ventricular end-diastolic volume index. Adjusting for age, race, sex, body surface area, diabetes mellitus, hypertension, hyperlipidemia, coronary artery disease, and CHF, logistic regression was used to compare combined events (death, CHF readmission, embolism, ventricular arrhythmia) between ESNCCR, EDNCCR, and TMTMR. Adjusting for same covariates except CHF, logistic regression was used to compare the odds of CHF for those who met criteria and those who did not. Using analysis of covariance, adjusted means for LVEF, MT, 3DSi, and left ventricular end-diastolic volume index were generated. ES criteria had a higher odds ratio (8.6; 95% confidence interval [CI]: 2.5 to 33) for combined events than ED criteria (1.8; 95% CI: 0.6 to 5.8) or TMTMR criteria (3.14; 95% CI: 1.09 to 10.2). The odds ratio of CHF for those who met ESNCCR criteria was 29.4 (95% CI: 6.6 to 125), but the odds ratio of CHF for those who met EDNCCR criteria was 3.3 (95% CI: 1.1 to 9.2). After adjustment, those who met criteria for noncompaction by ESNCCR had a lower LVEF and less MT than those who did not (p = 0.01 and p = 0.003, respectively), but there was no difference between those who met criteria for EDNCCR or the TMTMR criteria and those who did not. ES measures of LVNC have stronger associations with events, CHF, and systolic dysfunction than other measures.

Impact of chronic obstructive pulmonary disease on patient with acute myocardial infarction undergoing primary percutaneous coronary intervention

This study reported the incidence and prognostic outcome of chronic obstructive lung disease (COPD) patients with acute ST-segment elevation myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention (PCI). Between January 2002 and May 2011, totally 1554 consecutive patients who experienced STEMI undergoing primary PCI were enrolled into the study. Of the 1554 patients, 124 (9.7%) with diagnosis of COPD and 1430 (90.3%) without COPD were categorized into group 1 and group 2. Although no difference in in-hospital mortality was noted between the two groups (p = 0.726). However, the hospitalization duration was notably longer (p = 0.003), the incidences of recurrent MI and re-hospitalization for congestive heart failure were significantly higher in group 1 than in group 2 (all p < 0.02). Although Kaplan-Meier analysis demonstrated that the incidence of freedom from one-year major adverse clinical outcome (MACO) (defined as recurrent MI, re-admission for congestive heart failure was significantly lower in group 1 than group 2 (p = 0.012), multivariate Cox regression analysis showed COPD was not an independent predictor of MACO-free time after adjusting traditional risk factors. COPD was not an independent predictor of short-term and medium-term MACO in patients with STEMI undergoing primary PCI.

Fate of Functional Tricuspid Regurgitation After Mitral Valve Repair for Degenerative Mitral Regurgitation

The issue of whether functional tricuspid regurgitation (TR) should be repaired at the time of mitral valve surgery is controversial, and the long-term durability of tricuspid valve (TV) annuloplasty remains unknown. We retrospectively reviewed 654 patients who underwent mitral valve repair for degenerative mitral regurgitation between 1991 and 2010. Preoperative TR was classed as mild, trivial or absent in 479 (73.2%) patients, moderate in 125 (19.1%) patients and severe in 50 (7.7%) patients. Concomitant TV annuloplasty was performed in 162 patients (24.8%). The mean follow up duration was 7.5±4.9 years. Postoperative transthoracic echocardiography was performed according to a fixed schedule. The long-term survival rate and freedom from re-admission for congestive heart failure were affected by the severity of TR. Although the durability of ring annuloplasty was excellent up to 10 years after surgery, the mean TR grade started to increase after 10 years. Sixteen out of 492 patients who did not undergo TV annuloplasty (3.2%) revealed progression to severe TR. Preoperative atrial fibrillation (odds ratio (OR), 4.85; 95% confidence interval (CI), 1.38-17.1; P=0.014) and preoperative TR grade (OR, 5.16; 95% CI, 1.78-14.9; P=0.003) were predictors for progression to severe TR. Aggressive treatment with concomitant TV annuloplasty should be advocated in cases with atrial fibrillation and more than moderate TR.

Clinical and Psycological Telemonitoring and Telecare of High Risk Patientswith Chronic Heart Failure through Wireless Technologies: The Icaros Project

Background: Disease management is mandatory for the home care of most patients with chronic heart failure. We report the results of ICAROS (Integrated care vs Conventional intervention in cArdiac failure patients: Randomized Open label Study), an Italian trial of telemonitoring and telecare combining wireless and mobile technologies to obtain optimal therapeutic control, fast and effective interpretation of clinical data and improved patients’ adherence.Methods: Eighty patients were randomized before hospital discharge after an episode of acute heart failure, to a usual care group (UC) (n=40: follow-up at our Outpatients’ clinic) or to an integrated management group (IM) (n=40: patients learned to use a PDA computer and kept in touch daily with our Centre to receive instructions, encouragement, clinical and psychological assistance. Results: At enrolment, groups were similar for age (UC 73 ± 3 years; IM 71 ± 2 years); NYHA class (UC 2.90 ± 0.69; IM 3.08 ± 0.57), left ventricular function (EF%) (UC 32 ± 8%; IM 32 ± 7%), plasma levels of BNP (UC 361 ± 72 pg/ml; IM 314 ± 94 pg/ml). At one-year follow-up, IM patients showed better adherence, reduction of anxiety and depression and lower NYHA class and plasma levels of BNP with respect to UC patients (NYHA: 2.08 ± 0.38; BNP: 202 ± 127 pg/ml, p<0.05 vs UC patients). Mortality and hospital re-admissions for congestive heart failure were also reduced in IM patients (p<0.05).Conclusions: In ICAROS, regular acquisition by wireless technologies of clinical and psychological data provided a good model for clinical decision-making, determining better quality of life and reducing mortality and hospitalizations.