Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Sirds mazspējas somatiskās apzināšanās skala (HFSAS)× | Ņujorkas Sirds asociācijas (NYHA) funkcionālā klasifikācija× | |
|---|---|---|
| Nozare | Kardioloģija | Kardioloģija |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | 2017 | 1994 |
| Autors≠ | Steven R. Steinhubl | New York Heart Association |
| Tips≠ | Self-report questionnaire | Ordinal clinician-assessment classification system |
| Pirmavots≠ | Steinhubl, S. R., Mehta, P. K., & Ebner, G. S. (2017). The digital health revolution and consumer empowerment. Current Cardiology Reports, 19(11), 105. link ↗ | The Criteria Committee of the New York Heart Association. (1994). Nomenclature and Criteria for Diagnosis of Diseases of the Heart and Great Vessels (9th ed.). Little, Brown and Company. link ↗ |
| Citi nosaukumi≠ | HFSAS | NYHA, NYHA Class, Functional Classification |
| Saistītās | 4 | 4 |
| Kopsavilkums≠ | The Heart Failure Somatic Awareness Scale (HFSAS) is a specialized measure that assesses heart failure patients' ability to recognize and accurately perceive early warning signs of disease worsening (somatic awareness), such as subtle changes in dyspnea, edema, weight, fatigue, or palpitations. Early recognition of decompensation signs enables prompt self-management action (diuretic adjustment, physician contact) and prevents costly hospitalizations. The HFSAS is essential in modern HF management, particularly with remote monitoring technologies and self-management support programs. | The New York Heart Association (NYHA) Functional Classification is a four-category ordinal system for grading heart failure severity based on the level of physical activity that precipitates dyspnea or other HF symptoms. Established by the NYHA in 1928 and refined in 1994, the NYHA classification is the oldest and most widely used functional status metric in cardiology, providing a simple, clinically intuitive framework for describing HF symptom burden, guiding treatment intensity, and predicting prognosis. |
| ScholarGateDatu kopa ↗ |
|
|