Identifying definite patterns of unmet needs in patients with multiple sclerosis using unsupervised machine learning.
Cluster
Multiple sclerosis
Quality of life
Unmet needs
Journal
Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
ISSN: 1590-3478
Titre abrégé: Neurol Sci
Pays: Italy
ID NLM: 100959175
Informations de publication
Date de publication:
23 Feb 2024
23 Feb 2024
Historique:
received:
29
11
2023
accepted:
18
02
2024
medline:
23
2
2024
pubmed:
23
2
2024
entrez:
22
2
2024
Statut:
aheadofprint
Résumé
People with multiple sclerosis (PwMS) exhibit a spectrum of needs that extend beyond solely disease-related determinants. Investigating unmet needs from the patient perspective may address daily difficulties and optimize care. Our aim was to identify patterns of unmet needs among PwMS and their determinants. We conducted a cross-sectional multicentre study. Data were collected through an anonymous, self-administered online form. To cluster PwMS according to their main unmet needs, we performed agglomerative hierarchical clustering algorithm. Principal component analysis (PCA) was applied to visualize cluster distribution. Pairwise comparisons were used to evaluate demographics and clinical distribution among clusters. Out of 1764 mailed questionnaires, we received 690 responses. Access to primary care was the main contributor to the overall unmet need burden. Four patterns were identified: cluster C1, 'information-seekers with few unmet needs'; cluster C2, 'high unmet needs'; cluster C3, 'socially and assistance-dependent'; cluster C4, 'self-sufficient with few unmet needs'. PCA identified two main components in determining the patterns: the 'public sphere' (access to information and care) and the 'private sphere' (need for assistance and social life). Older age, lower education, longer disease duration and higher disability characterized clusters with more unmet needs in the private sphere. However, demographic and clinical factors failed in explaining the four identified patterns. Our study identified four unmet need patterns among PwMS, emphasizing the importance of personalized care. While clinical and demographic factors provide some insight, additional variables warrant further investigation to fully understand unmet needs in PwMS.
Identifiants
pubmed: 38388897
doi: 10.1007/s10072-024-07416-9
pii: 10.1007/s10072-024-07416-9
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2024. The Author(s).
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