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Boonstra MJ, Weissenbacher D, Moore JH, Gonzalez-Hernandez G, Asselbergs FW. Artificial intelligence: revolutionizing cardiology with large language models. Eur Heart J 2024; 45:332-345. [PMID: 38170821 PMCID: PMC10834163 DOI: 10.1093/eurheartj/ehad838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 12/01/2023] [Accepted: 12/05/2023] [Indexed: 01/05/2024] Open
Abstract
Natural language processing techniques are having an increasing impact on clinical care from patient, clinician, administrator, and research perspective. Among others are automated generation of clinical notes and discharge letters, medical term coding for billing, medical chatbots both for patients and clinicians, data enrichment in the identification of disease symptoms or diagnosis, cohort selection for clinical trial, and auditing purposes. In the review, an overview of the history in natural language processing techniques developed with brief technical background is presented. Subsequently, the review will discuss implementation strategies of natural language processing tools, thereby specifically focusing on large language models, and conclude with future opportunities in the application of such techniques in the field of cardiology.
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Affiliation(s)
- Machteld J Boonstra
- Department of Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centre, University of Amsterdam, Amsterdam, Netherlands
| | - Davy Weissenbacher
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Jason H Moore
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | | | - Folkert W Asselbergs
- Department of Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centre, University of Amsterdam, Amsterdam, Netherlands
- Institute of Health Informatics, University College London, London, UK
- The National Institute for Health Research University College London Hospitals Biomedical Research Centre, University College London, London, UK
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Zeleke TK, Kemal LK, Mehari EA, Sema FD, Seid AM, Mekonnen GA, Abebe RB. Nephrotoxic drug burden and predictors of exposure among patients with renal impairment in Ethiopia: A multi-center study. Heliyon 2024; 10:e24618. [PMID: 38298684 PMCID: PMC10828699 DOI: 10.1016/j.heliyon.2024.e24618] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Revised: 12/05/2023] [Accepted: 01/11/2024] [Indexed: 02/02/2024] Open
Abstract
Background Nephrotoxic drugs may hasten the decline in kidney function and worsen the progression of renal impairment as a result; these drugs should be avoided or used with caution in patients with pre-existing renal insufficiency. The purpose of this study was to assess the burden of nephrotoxic medication use and its predictors among patients with underlying renal impairment. Methods A multicenter, institution-based, cross-sectional study was conducted from May 30, 2021 to July 30, 2021, at medical wards. Renal impaired patients admitted during the data collection period who took at least one medication were enrolled in the study. A simple random sampling technique was used to select the study participants. Data was collected through an interview and a medical card review. Both bivariable and multivariable binary logistic regression analyses were fitted to identify factors associated with nephrotoxic drug use. Results Among the 422 participants, more than half of them (53.6 %) were male. The mean patient's age was 47.5 (±16.7) years. A total of 1310 drugs were prescribed for 422 patients with renal impairment, of which 80.15 % were nephrotoxic. Nephrotoxic drugs were prescribed for 66.4 % of patients. The burden of nephrotoxic medication prescription was significantly associated with variables like the presence of comorbidity (AOR = 6.31, 95 % CI: 2.01-19.79), the number of medications prescribed (AOR = 1.43, 95 % CI: 1.05-1.93), and the age of participants (AOR = 1.12, 95 % CI: 1.07-1.17). Conclusion The present study demonstrated that two-third of the patients with renal impairment were exposed to nephrotoxic medications. Furosemide, Enalapril, and vancomycin were the most frequently prescribed nephrotoxic medications. The study suggests that prescribers need to give special attention to older patients who have underlying renal insufficiency, a comorbid condition, and polypharmacy regarding exposure to contraindicated nephrotoxic medication.
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Affiliation(s)
- Tirsit Ketsela Zeleke
- Department of Pharmacy, College of Medicine and Health Sciences, Debre Markos University, Debre Markos, Ethiopia
| | - Leila Kenzu Kemal
- Department of Clinical Pharmacy, School of Pharmacy, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia
| | - Eden Abetu Mehari
- Department of Clinical Pharmacy, School of Pharmacy, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia
| | - Faisel Dula Sema
- Department of Clinical Pharmacy, School of Pharmacy, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia
| | - Abdulwase Mohammed Seid
- Department of Clinical Pharmacy, School of Pharmacy, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia
| | - Gizework Alemnew Mekonnen
- Department of Clinical Pharmacy, School of Pharmacy, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia
| | - Rahel Belete Abebe
- Department of Clinical Pharmacy, School of Pharmacy, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia
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Karimzadeh I, Barreto EF, Kellum JA, Awdishu L, Murray PT, Ostermann M, Bihorac A, Mehta RL, Goldstein SL, Kashani KB, Kane-Gill SL. Moving toward a contemporary classification of drug-induced kidney disease. Crit Care 2023; 27:435. [PMID: 37946280 PMCID: PMC10633929 DOI: 10.1186/s13054-023-04720-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 11/01/2023] [Indexed: 11/12/2023] Open
Abstract
Drug-induced kidney disease (DIKD) accounts for about one-fourth of all cases of acute kidney injury (AKI) in hospitalized patients, especially in critically ill setting. There is no standard definition or classification system of DIKD. To address this, a phenotype definition of DIKD using expert consensus was introduced in 2015. Recently, a novel framework for DIKD classification was proposed that incorporated functional change and tissue damage biomarkers. Medications were stratified into four categories, including "dysfunction without damage," "damage without dysfunction," "both dysfunction and damage," and "neither dysfunction nor damage" using this novel framework along with predominant mechanism(s) of nephrotoxicity for drugs and drug classes. Here, we briefly describe mechanisms and provide examples of drugs/drug classes related to the categories in the proposed framework. In addition, the possible movement of a patient's kidney disease between certain categories in specific conditions is considered. Finally, opportunities and barriers to adoption of this framework for DIKD classification in real clinical practice are discussed. This new classification system allows congruencies for DIKD with the proposed categorization of AKI, offering clarity as well as consistency for clinicians and researchers.
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Affiliation(s)
- Iman Karimzadeh
- Department of Clinical Pharmacy, School of Pharmacy, Shiraz University of Medical Sciences, Shiraz, Iran
| | | | - John A Kellum
- Department of Critical Care Medicine, Center for Critical Care Nephrology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Linda Awdishu
- Division of Clinical Pharmacy, San Diego Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, La Jolla, CA, USA
| | | | - Marlies Ostermann
- Department of Intensive Care, King's College London, Guy's and St Thomas' Hospital, London, UK
| | - Azra Bihorac
- Department of Medicine, University of Florida, Gainesville, FL, USA
- Intelligent Critical Care Center, University of Florida, Gainesville, FL, USA
| | - Ravindra L Mehta
- Department of Medicine, University of California, San Diego, CA, USA
| | - Stuart L Goldstein
- Center for Acute Care Nephrology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Kianoush B Kashani
- Division of Nephrology and Hypertension, Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, Rochester, MN, USA
| | - Sandra L Kane-Gill
- Department of Critical Care Medicine, Center for Critical Care Nephrology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
- Department of Pharmacy and Therapeutics, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA, USA.
- Department of Pharmacy, UPMC, Pittsburgh, PA, USA.
- Department of Critical Care Medicine, Department of Biomedical Informatics, School of Medicine and the Clinical Translational Science Institute, University of Pittsburgh, Pittsburgh, PA, USA.
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