Multimorbidity Patterns in Patients with Pulmonary Arterial Hypertension Identified Through Hospital Discharge Records: A Network-Based Analysis
Considerable variability exists in the clinical presentations of pulmonary arterial hypertension (PAH). Greater understanding of the comorbidities observed in Chinese patients with PAH is urgently needed. This 10-year retrospective analysis was based on clinical data from hospital discharge records...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Compuscript Ltd
2025-03-01
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| Series: | Cardiovascular Innovations and Applications |
| Online Access: | https://www.scienceopen.com/hosted-document?doi=10.15212/CVIA.2025.0008 |
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| Summary: | Considerable variability exists in the clinical presentations of pulmonary arterial hypertension (PAH). Greater understanding of the comorbidities observed in Chinese patients with PAH is urgently needed. This 10-year retrospective analysis was based on clinical data from hospital discharge records for individuals diagnosed with PAH (n = 2584). We used propensity score matching to match patients with PAH to individuals without a PAH diagnosis in a ratio of 1:1, by age, sex, discharge time, and department, over the same period. We constructed multimorbidity networks based on sex and age, and used the cosine index to measure the co-occurrence of chronic diseases. The mean numbers of comorbidities were 4.7 and 3.8 for patients with PAH and controls, respectively. The main central and hub disorders were renal osteodystrophy, cardiovascular illnesses, background retinopathy, diabetes mellitus, systemic lupus erythematosus, epilepsy, and autoimmune hemolytic anemia. The average neighbor degree and closeness were significantly smaller in the multimorbidity networks of patients with PAH than control participants (Kolmogorov–Smirnov test, all P < 0.05). Our findings may aid in preventing comorbidities among patients with PAH and deepening understanding of the underlying physiopathological mechanisms. |
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| ISSN: | 2009-8618 2009-8782 |