A decision tree algorithm to identify predictors of post-stroke complex regional pain syndrome.
Sci Rep
; 14(1): 9893, 2024 04 30.
Article
em En
| MEDLINE
| ID: mdl-38689114
ABSTRACT
This prospective cohort study aimed to identify the risk factors for post-stroke complex regional pain syndrome (CRPS) using a decision tree algorithm while comprehensively assessing upper limb and lower limb disuse and physical inactivity. Upper limb disuse (Fugl-Meyer assessment of upper extremity [FMA-UE], Action Research Arm Test, Motor Activity Log), lower limb disuse (Fugl-Meyer Assessment of lower extremity [FMA-LE]), balance performance (Berg balance scale), and physical inactivity time (International Physical Activity Questionnaire-Short Form [IPAQ-SF]) of 195 stroke patients who visited the Kishiwada Rehabilitation Hospital were assessed at admission. The incidence of post-stroke CRPS was 15.4% in all stroke patients 3 months after admission. The IPAQ, FMA-UE, and FMA-LE were extracted as risk factors for post-stroke CRPS. According to the decision tree algorithm, the incidence of post-stroke CRPS was 1.5% in patients with a short physical inactivity time (IPAQ-SF < 635), while it increased to 84.6% in patients with a long inactivity time (IPAQ-SF ≥ 635) and severe disuse of upper and lower limbs (FMA-UE score < 19.5; FMA-LE score < 16.5). The incidence of post-stroke CRPS may increase with lower-limb disuse and physical inactivity, in addition to upper-limb disuse. Increasing physical activity and addressing lower- and upper-limb motor paralysis may reduce post-stroke CRPS.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Algoritmos
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Árvores de Decisões
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Síndromes da Dor Regional Complexa
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Acidente Vascular Cerebral
Limite:
Aged
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Aged80
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Female
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Humans
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Male
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Middle aged
Idioma:
En
Revista:
Sci Rep
/
Sci. rep. (Nat. Publ. Group)
/
Scientific reports (Nature Publishing Group)
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Japão