Development and validation of algorithms to predict left ventricular ejection fraction class from healthcare claims data.

AIMS: The use of large medical or healthcare claims databases is very useful for population-based studies on the burden of heart failure (HF). Clinical characteristics and management of HF patients differ according to categories of left ventricular ejection fraction (LVEF), but this information is often missing in such databases. We aimed to develop and validate algorithms to identify LVEF in healthcare databases where the information is lacking. METHODS AND RESULTS: Algorithms were built by machine learning with a random forest approach. Algorithms were trained and reinforced using the French national claims database [Système National des Données de Santé (SNDS)] and a French HF registry. Variables were age, gender, and comorbidities, which could be identified by medico-administrative code-based proxies, Anatomical Therapeutic Chemical codes for drug delivery, International Classification of Diseases (Tenth Revision) coding for hospitalizations, and administrative codes for any other type of reimbursed care. The algorithms were validated by cross-validation and against a subset of the SNDS that includes LVEF information. The areas under the receiver operating characteristic curve were 0.84 for the algorithm identifying LVEF ≤ 40% and 0.79 for the algorithms identifying LVEF < 50% and ≥50%. For LVEF ≤ 40%, the reinforced algorithm identified 50% of patients in the validation dataset with a positive predictive value of 0.88 and a specificity of 0.96. The most important predictive variables were delivery of HF medication, sex, age, hospitalization, and testing for natriuretic peptides with different orders of positive or negative importance according to the LVEF category. CONCLUSIONS: The algorithms identify reduced or preserved LVEF in HF patients within a nationwide healthcare claims database with high positive predictive value and low rates of false positives.

Pregnancy and cardiac maternal outcomes in women with inherited cardiomyopathy: interest of the CARPREG II risk score.

AIMS: Inherited cardiomyopathies are relatively rare but carry a high risk of cardiac maternal morbidity and mortality during pregnancy and postpartum. However, data for risk stratification are scarce. The new CARPREG II score improves prediction of prognosis in pregnancies associated with heart disease, though its role in inherited cardiomyopathies is unclear. We aim to describe characteristics and cardiac maternal outcomes in patients with inherited cardiomyopathy during pregnancy, and to evaluate the interest of the CARPREG II risk score in this population. METHODS AND RESULTS: In this retrospective single-centre study, 90 consecutive pregnancies in 74 patients were included (mean age 32 ± 5 years), including 28 cases of dilated cardiomyopathy (DCM), 46 of hypertrophic cardiomyopathy, 11 of arrhythmogenic right ventricular cardiomyopathy and 5 of left ventricular noncompaction, excluding peripartum cardiomyopathy. The discriminatory power of several risk scores was assessed by the area under the receiver-operating characteristic curve (AUC). Median CARPREG II score was 2 [0;3] and was higher in the DCM subgroup. A severe cardiac maternal complication was observed in 18 (20%) pregnancies, mainly driven by arrhythmia and heart failure (each event in 10 pregnancies), with 3 cardiovascular deaths. Forty-three pregnancies (48%) presented foetal/neonatal complications (18 premature delivery, 3 foetal/neonatal death). CARPREG II was significantly associated with cardiac maternal complications (P < 0.05 for all) and showed a higher AUC (0.782) than CARPREG (0.755), mWHO (0.697) and ZAHARA (0.604). CONCLUSIONS: Pregnancy in women with inherited cardiomyopathy carries a high risk of maternal cardiovascular complications. CARPREG II is the most efficient predictor of cardiovascular complications in this population.

Rationale and design of the PACIFIC-PRESERVED (PhenomApping, ClassIFication and Innovation for Cardiac dysfunction in patients with heart failure and PRESERVED left ventricular ejection fraction) study.

BACKGROUND: Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous syndrome that is poorly defined, reflecting an incomplete understanding of its pathophysiology. AIM: To redefine the phenotypic spectrum of HFpEF. METHODS: The PACIFIC-PRESERVED study is a prospective multicentre cohort study designed to perform multidimensional deep phenotyping of patients diagnosed with HFpEF (left ventricular ejection fraction≥50%), patients with heart failure with reduced ejection fraction (left ventricular ejection fraction≤40%) and subjects without overt heart failure (3:2:1 ratio). The study proposes prospective investigations in patients during a 1-day hospital stay: physical examination; electrocardiogram; performance-based tests; blood samples; cardiac magnetic resonance imaging; transthoracic echocardiography (rest and low-level exercise); myocardial shear wave elastography; chest computed tomography; and non-invasive measurement of arterial stiffness. Dyspnoea, depression, general health and quality of life will be assessed by dedicated questionnaires. A biobank will be established. After the hospital stay, patients are asked to wear a connected garment (with digital sensors) to collect electrocardiography, pulmonary and activity variables in real-life conditions (for up to 14 days). Data will be centralized for machine-learning-based analyses, with the aim of reclassifying HFpEF into more distinct subgroups, improving understanding of the disease mechanisms and identifying new biological pathways and molecular targets. The study will also serve as a platform to enable the development of innovative technologies and strategies for the diagnosis and stratification of patients with HFpEF. CONCLUSIONS: PACIFIC-PRESERVED is a prospective multicentre phenomapping study, using novel analytical techniques, which will provide a unique data resource to better define HFpEF and identify new clinically meaningful subgroups of patients.