Identification of Gender Differences in Acute Myocardial Infarction Presentation and Management at Aga Khan University Hospital-Pakistan: Natural Language Processing Application in a Dataset of Patients With Cardiovascular Disease.

Autor:	Ngaruiya C; Department of Emergency Medicine, Yale School of Medicine, New Haven, CT, United States.; Department of Emergency Medicine, Stanford School of Medicine, Palo Alto, CA, United States., Samad Z; Department of Medicine, Aga Khan University, Karachi, Pakistan., Tajuddin S; Department of Medicine, Aga Khan University, Karachi, Pakistan.; CITRIC Health Data Science Center, Aga Khan University, Karachi, Pakistan., Nasim Z; CITRIC Health Data Science Center, Aga Khan University, Karachi, Pakistan., Leff R; Department of Emergency Medicine, Mayo Clinic School of Graduate Medical Education, Rochester, MN, United States., Farhad A; Department of Medicine, Aga Khan University, Karachi, Pakistan., Pires K; Department of Emergency Medicine, Yale School of Medicine, New Haven, CT, United States., Khan MA; School of Medicine, Aga Khan University, Karachi, Pakistan., Hartz L; Department of Emergency Medicine, Yale School of Medicine, New Haven, CT, United States., Safdar B; Department of Emergency Medicine, Yale School of Medicine, New Haven, CT, United States.
Jazyk:	angličtina
Zdroj:	JMIR formative research [JMIR Form Res] 2024 Dec 20; Vol. 8, pp. e42774. Date of Electronic Publication: 2024 Dec 20.
DOI:	10.2196/42774
Abstrakt:	Background: Ischemic heart disease is a leading cause of death globally with a disproportionate burden in low- and middle-income countries (LMICs). Natural language processing (NLP) allows for data enrichment in large datasets to facilitate key clinical research. We used NLP to assess gender differences in symptoms and management of patients hospitalized with acute myocardial infarction (AMI) at Aga Khan University Hospital-Pakistan. Objective: The primary objective of this study was to use NLP to assess gender differences in the symptoms and management of patients hospitalized with AMI at a tertiary care hospital in Pakistan. Methods: We developed an NLP-based methodology to extract AMI symptoms and medications from 5358 discharge summaries spanning the years 1988 to 2018. This dataset included patients admitted and discharged between January 1, 1988, and December 31, 2018, who were older than 18 years with a primary discharge diagnosis of AMI (using ICD-9 [International Classification of Diseases, Ninth Revision], diagnostic codes). The methodology used a fuzzy keyword-matching algorithm to extract AMI symptoms from the discharge summaries automatically. It first preprocesses the free text within the discharge summaries to extract passages indicating the presenting symptoms. Then, it applies fuzzy matching techniques to identify relevant keywords or phrases indicative of AMI symptoms, incorporating negation handling to minimize false positives. After manually reviewing the quality of extracted symptoms in a subset of discharge summaries through preliminary experiments, a similarity threshold of 80% was determined. Results: Among 1769 women and 3589 men with AMI, women had higher odds of presenting with shortness of breath (odds ratio [OR] 1.46, 95% CI 1.26-1.70) and lower odds of presenting with chest pain (OR 0.65, 95% CI 0.55-0.75), even after adjustment for diabetes and age. Presentation with abdominal pain, nausea, or vomiting was much less frequent but consistently more common in women (P<.001). "Ghabrahat," a culturally distinct term for a feeling of impending doom was used by 5.09% of women and 3.69% of men as presenting symptom for AMI (P=.06). First-line medication prescription (statin and β-blockers) was lower in women: women had nearly 30% lower odds (OR 0.71, 95% CI 0.57-0.90) of being prescribed statins, and they had 40% lower odds (OR 0.67, 95% CI 0.57-0.78) of being prescribed β-blockers. Conclusions: Gender-based differences in clinical presentation and medication management were demonstrated in patients with AMI at a tertiary care hospital in Pakistan. The use of NLP for the identification of culturally nuanced clinical characteristics and management is feasible in LMICs and could be used as a tool to understand gender disparities and address key clinical priorities in LMICs. (©Christine Ngaruiya, Zainab Samad, Salma Tajuddin, Zarmeen Nasim, Rebecca Leff, Awais Farhad, Kyle Pires, Muhammad Alamgir Khan, Lauren Hartz, Basmah Safdar. Originally published in JMIR Formative Research (https://formative.jmir.org), 20.12.2024.)
Databáze:	MEDLINE
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