ArticleActa neurochirurgica2024
Profiling iNPH features through cluster analysis: an aid for clinical suspicion and diagnosis.
Article in Acta neurochirurgica, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Clinical Subgroups and Treatment Outcomes in Idiopathic Normal Pressure Hydrocephalus: Application of Machine Learning-Based Clustering.Neurosurgery practice · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
purposeIdiopathic Normal Pressure Hydrocephalus (iNPH) is a neurological syndrome defined by gait disturbance, cognitive impairment and urinary incontinence. However, its clinical presentation can vary widely due to overlapping syndromes and common comorbidities in older adults. This study aims to provide practical guidance to aid in the clinical suspicion and support the diagnostic and therapeutic processes for these patients.
methodsSix quantitative variables regarding clinical, functional, and demographic aspects were considered for a large sample of patients with diagnosed iNPH. Principal component analysis (PCA) was adopted to define the main dimensions explaining the variability of the phenomenon. Then, two clusters of iNPH patients were described.
results178 patients were included in the analysis. The PCA produced two dimensions covering 61.8% of the total variability. The first one relied mainly on both clinical (mRS, iNPHGs) and functional (TUG, Tinetti) variables, while the second one was represented mainly on the demographic pattern (age and education). Cluster analysis depicted two main groups of patients. Cluster n.1 is composed of individuals who are older, more disabled, with poor functional performances, and highly symptomatic. Cluster n.2 patients are slightly younger, more educated, fitter, and with more nuanced clinical aspects.
conclusionsProfiling iNPH patients using quantitative variables and cluster analysis can help identify distinct characteristics of these patients, aiding in the guidance of both medical and surgical interventions.
Indexed as
Identifiers
39298012What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.