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Heart failure is a global pandemic affecting an estimated 26 million people worldwide and resulting in more than 1 million hospitalizations annually in both the United States and Europe. Although the outcomes for ambulatory HF patients with a reduced ejection fraction (EF) have improved with the discovery of multiple evidence-based drug and device therapies, hospitalized heart failure (HHF) patients continue to experience unacceptably high post-discharge mortality and readmission rates that have not changed in the last 2 decades. In addition, the proportion of HHF patients classified as having a preserved EF continues to grow and may overtake HF with a reduced EF in the near future. However, the prognosis for HF with a preserved EF is similar and there are currently no available disease-modifying therapies. HHF registries have significantly improved our understanding of this clinical entity and remain an important source of data shaping both public policy and research efforts. The authors review global HHF registries to describe the patient characteristics, management, outcomes and their predictors, quality improvement initiatives, regional differences, and limitations of the available data. Moreover, based on the lessons learned, they also propose a roadmap for the design and conduct of future HHF registries.

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The term acute myocardial infarction (MI) should be used when there is evidence of myocardial necrosis in a clinical setting consistent with acute myocardial ischemia. Under these conditions any one of the following criteria meets the diagnosis for MI: ● Detection of a rise and/or fall of cardiac biomarker values [preferably cardiac troponin (cTn)] with at least one value above the 99th percentile upper reference limit (URL) and with at least one of the following: y Symptoms of ischemia. y New or presumed new significant ST-segment–T wave (ST–T) changes or new left bundle branch block (LBBB). y Development of pathological Q waves in the ECG. y Imaging evidence of new loss of viable myocardium or new regional wall motion abnormality. y Identification of an intracoronary thrombus by angiography or autopsy. ● Cardiac death with symptoms suggestive of myocardial ischemia and presumed new ischemic ECG changes or new LBBB, but death occurred before cardiac biomarkers were obtained, or before cardiac biomarker values would be increased. ● Percutaneous coronary intervention (PCI) related MI is arbitrarily defined by elevation of cTn values (5 99th percentile URL) in patients with normal baseline values (99th percentile URL) or a rise of cTn values 20% if the baseline values are elevated and are stable or falling. In addition, either (i) symptoms suggestive of myocardial ischemia or (ii) new ischemic ECG changes or (iii) angiographic findings consistent with a procedural complication or (iv) imaging demonstration of new loss of viable myocardium or new regional wall motion abnormality are required. ● Stent thrombosis associated with MI when detected by coronary angiography or autopsy in the setting of myocardial ischemia and with a rise and/or fall of cardiac biomarker values with at least one value above the 99th percentile URL. ● Coronary artery bypass grafting (CABG) related MI is arbitrarily defined by elevation of cardiac biomarker values (10 99th percentile URL) in patients with normal baseline cTn values (99th percentile URL). In addition, either (i) new pathological Q waves or new LBBB, or (ii) angiographic documented new graft or new native coronary artery occlusion, or (iii) imaging evidence of new loss of viable myocardium or new regional wall motion abnormality. Criteria for prior myocardial infarction Any one of the following criteria meets the diagnosis for prior MI: ● Pathological Q waves with or without symptoms in the absence of non-ischemic causes. ● Imaging evidence of a region of loss of viable myocardium that is thinned and fails to contract, in the absence of a non-ischemic cause. ● Pathological findings of a prior MI

Data from the OPTIMIZE-HF registry reveal a high prevalence of HF with PSF, and these patients have a similar post-discharge mortality risk and equally high rates of rehospitalization as patients with HF and LVSD. Despite the burden to patients and health care systems, data are lacking on effective management strategies for patients with HF and PSF. (Organized Program To Initiate Lifesaving Treatment In Hospitalized Patients With Heart Failure [OPTIMIZE-HF]); http://www.clinicaltrials.gov/ct/show/NCT00344513?order=1; NCT00344513).

jor public health concern because of its prevalence and associated morbidity and mortality. In 2003, 1.1 million patients were discharged from the hospital for heart failure, making this the most common primary discharge diagnosis among patients older than 65 years. 1,2 Until recently, the scientific community's understanding of acute heart failure syndromes (AHFS) had been based on demographic and outcome data obtained from randomized controlled trials. 3 While these results drive therapeutic decision making, relying on clinical trial data to characterize the general acute heart failure population is limited by the fact that randomized controlled trials have tended to focus on a small proportion of AHFS patients. Because of selective enrollment criteria, clinical trials may See also pp 2209 and 2259.

Introduction Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous clinical syndrome in need of improved phenotypic classification. We sought to evaluate whether unbiased clustering analysis using dense phenotypic data (“phenomapping”) could identify phenotypically distinct HFpEF categories. Methods and Results We prospectively studied 397 HFpEF patients and performed detailed clinical, laboratory, electrocardiographic, and echocardiographic phenotyping of the study participants. We used several statistical learning algorithms, including unbiased hierarchical cluster analysis of phenotypic data (67 continuous variables) and penalized model-based clustering to define and characterize mutually exclusive groups comprising a novel classification of HFpEF. All phenomapping analyses were performed blinded to clinical outcomes, and Cox regression was used to demonstrate the clinical validity of phenomapping. The mean age was 65±12 years, 62% were female, 39% were African-American, and comorbidities were common. Although all patients met published criteria for the diagnosis of HFpEF, phenomapping analysis classified study participants into 3 distinct groups that differed markedly in clinical characteristics, cardiac structure/function, invasive hemodynamics, and outcomes (e.g., pheno-group #3 had an increased risk of HF hospitalization [hazard ratio 4.2, 95% CI 2.0–9.1] even after adjustment for traditional risk factors [P<0.001]). The HFpEF pheno-group classification, including its ability to stratify risk, was successfully replicated in a prospective validation cohort (n=107). Conclusions Phenomapping results in novel classification of HFpEF. Statistical learning algorithms, applied to dense phenotypic data, may allow for improved classification of heterogeneous clinical syndromes, with the ultimate goal of defining therapeutically homogeneous patient subclasses.