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Hulot JS, Janiak P, Boutinaud P, Boutouyrie P, Chézalviel-Guilbert F, Christophe JJ, Cohen A, Damy T, Djadi-Prat J, Firat H, Hervé PY, Isnard R, Jondeau G, Mousseaux E, Pernot M, Prot P, Tyl B, Soulat G, Logeart D. 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. Arch Cardiovasc Dis 2024:S1875-2136(24)00057-3. [PMID: 38644067 DOI: 10.1016/j.acvd.2024.02.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Revised: 02/23/2024] [Accepted: 02/26/2024] [Indexed: 04/23/2024]
Abstract
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.
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Affiliation(s)
- Jean-Sébastien Hulot
- Université Paris Cité, INSERM, PARCC, 75015 Paris, France; CIC1418 and DMU CARTE, Hôpital Européen Georges-Pompidou, AP-HP, 75015 Paris, France.
| | | | | | - Pierre Boutouyrie
- Université Paris Cité, INSERM, PARCC, 75015 Paris, France; Pharmacology and DMU CARTE, Hôpital Européen Georges-Pompidou, AP-HP, 75015 Paris, France
| | | | | | - Ariel Cohen
- Cardiology, Hôpital Saint-Antoine, AP-HP, ICAN 1166, Sorbonne Université, 75012 Paris, France
| | - Thibaud Damy
- Cardiology, Hôpital Henri-Mondor, AP-HP, 94000 Créteil, France
| | - Juliette Djadi-Prat
- Clinical Research Unit, Hôpital Européen Georges-Pompidou, AP-HP, 75015 Paris, France
| | | | | | - Richard Isnard
- Cardiology, Hôpital Pitié-Salpêtrière, AP-HP, 75013 Paris, France
| | | | - Elie Mousseaux
- Université Paris Cité, INSERM, PARCC, 75015 Paris, France; Cardiac Imaging Radiology, Hôpital Européen Georges-Pompidou, AP-HP, 75015 Paris, France
| | - Mathieu Pernot
- Physics for Medicine Paris, INSERM U1273, ESPCI Paris, PSL University, CNRS FRE 2031, 75015 Paris, France
| | | | | | - Gilles Soulat
- Université Paris Cité, INSERM, PARCC, 75015 Paris, France; Cardiac Imaging Radiology, Hôpital Européen Georges-Pompidou, AP-HP, 75015 Paris, France
| | - Damien Logeart
- Cardiology, Hôpital Lariboisière, AP-HP, 75018 Paris, France
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Bazoge A, Morin E, Daille B, Gourraud PA. Applying Natural Language Processing to Textual Data From Clinical Data Warehouses: Systematic Review. JMIR Med Inform 2023; 11:e42477. [PMID: 38100200 PMCID: PMC10757232 DOI: 10.2196/42477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 01/16/2023] [Accepted: 09/07/2023] [Indexed: 12/17/2023] Open
Abstract
BACKGROUND In recent years, health data collected during the clinical care process have been often repurposed for secondary use through clinical data warehouses (CDWs), which interconnect disparate data from different sources. A large amount of information of high clinical value is stored in unstructured text format. Natural language processing (NLP), which implements algorithms that can operate on massive unstructured textual data, has the potential to structure the data and make clinical information more accessible. OBJECTIVE The aim of this review was to provide an overview of studies applying NLP to textual data from CDWs. It focuses on identifying the (1) NLP tasks applied to data from CDWs and (2) NLP methods used to tackle these tasks. METHODS This review was performed according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. We searched for relevant articles in 3 bibliographic databases: PubMed, Google Scholar, and ACL Anthology. We reviewed the titles and abstracts and included articles according to the following inclusion criteria: (1) focus on NLP applied to textual data from CDWs, (2) articles published between 1995 and 2021, and (3) written in English. RESULTS We identified 1353 articles, of which 194 (14.34%) met the inclusion criteria. Among all identified NLP tasks in the included papers, information extraction from clinical text (112/194, 57.7%) and the identification of patients (51/194, 26.3%) were the most frequent tasks. To address the various tasks, symbolic methods were the most common NLP methods (124/232, 53.4%), showing that some tasks can be partially achieved with classical NLP techniques, such as regular expressions or pattern matching that exploit specialized lexica, such as drug lists and terminologies. Machine learning (70/232, 30.2%) and deep learning (38/232, 16.4%) have been increasingly used in recent years, including the most recent approaches based on transformers. NLP methods were mostly applied to English language data (153/194, 78.9%). CONCLUSIONS CDWs are central to the secondary use of clinical texts for research purposes. Although the use of NLP on data from CDWs is growing, there remain challenges in this field, especially with regard to languages other than English. Clinical NLP is an effective strategy for accessing, extracting, and transforming data from CDWs. Information retrieved with NLP can assist in clinical research and have an impact on clinical practice.
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Affiliation(s)
- Adrien Bazoge
- Nantes Université, École Centrale Nantes, CNRS, LS2N, UMR 6004, F-44000 Nantes, France
- Nantes Université, CHU de Nantes, Pôle Hospitalo-Universitaire 11: Santé Publique, Clinique des données, INSERM, CIC 1413, F-44000 Nantes, France
| | - Emmanuel Morin
- Nantes Université, École Centrale Nantes, CNRS, LS2N, UMR 6004, F-44000 Nantes, France
| | - Béatrice Daille
- Nantes Université, École Centrale Nantes, CNRS, LS2N, UMR 6004, F-44000 Nantes, France
| | - Pierre-Antoine Gourraud
- Nantes Université, CHU de Nantes, Pôle Hospitalo-Universitaire 11: Santé Publique, Clinique des données, INSERM, CIC 1413, F-44000 Nantes, France
- Nantes Université, INSERM, CHU de Nantes, École Centrale Nantes, Centre de Recherche Translationnelle en Transplantation et Immunologie, CR2TI, F-44000 Nantes, France
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Katz DH, Thompson AD. Proteomics in Heart Failure: From Benchtop to Bedside. J Card Fail 2021; 28:601-603. [PMID: 34933100 DOI: 10.1016/j.cardfail.2021.12.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 12/09/2021] [Indexed: 11/16/2022]
Affiliation(s)
- Daniel H Katz
- Cardiovascular Medicine, Stanford University, Stanford, CA
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