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Can Latent Class Analysis Be Used to Improve the Diagnostic Process in Pediatric Patients with Chronic Ataxia?
- Source :
- Cerebellum (London, England). 16(2)
- Publication Year :
- 2016
-
Abstract
- Chronic ataxia is a relatively common symptom in children. There are numerous causes of chronic ataxia, making it difficult to derive a diagnosis in a timely manner. We hypothesized that the efficiency of the diagnostic process can be improved with systematic analysis of clinical features in pediatric patients with chronic ataxia. Our aim was to improve the efficiency of the diagnostic process in pediatric patients with chronic ataxia. A cohort of 184 patients, aged 0–16 years with chronic ataxia who received medical care at Winnipeg Children’s Hospital during 1991–2008, was ascertained retrospectively from several hospital databases. Clinical details were extracted from hospital charts. The data were compared among the more common diseases using univariate analysis to identify pertinent clinical features that could potentially improve the efficiency of the diagnostic process. Latent class analysis was then conducted to detect unique patterns of clinical features and to determine whether these patterns could be associated with chronic ataxia diagnoses. Two models each with three classes were chosen based on statistical criteria and clinical knowledge for best fit. Each class represented a specific pattern of presenting symptoms or other clinical features. The three classes corresponded to a plausible and shorter list of possible diagnoses. For example, developmental delay and hypotonia correlated best with Angelman syndrome. Specific patterns of presenting symptoms or other clinical features can potentially aid in the initial assessment and diagnosis of pediatric patients with chronic ataxia. This will likely improve the efficiency of the diagnostic process.
- Subjects :
- medicine.medical_specialty
Pediatrics
Ataxia
Adolescent
Databases, Factual
Diagnosis, Differential
03 medical and health sciences
0302 clinical medicine
medicine
Humans
030212 general & internal medicine
Medical diagnosis
Child
Retrospective Studies
Univariate analysis
Models, Statistical
business.industry
Infant, Newborn
Infant
Retrospective cohort study
Latent class model
Hypotonia
Neurology
Child, Preschool
Cohort
Chronic Disease
Multivariate Analysis
Physical therapy
Neurology (clinical)
medicine.symptom
Differential diagnosis
business
030217 neurology & neurosurgery
Follow-Up Studies
Subjects
Details
- ISSN :
- 14734230
- Volume :
- 16
- Issue :
- 2
- Database :
- OpenAIRE
- Journal :
- Cerebellum (London, England)
- Accession number :
- edsair.doi.dedup.....2b6dd4f6488930da6285ad1f09f3b42b