ABSTRACT
BACKGROUND: The Consortium to Establish a Registry for Alzheimer's Disease (CERAD) developed a neuropsychological battery (CERAD-NP) to screen patients with Alzheimer's dementia. Mild cognitive impairment (MCI) has received attention as a pre-dementia stage. OBJECTIVES: To delineate the CERAD-NP features of MCI and their clinical utility to externally validate MCI diagnosis. METHODS: The study included 60 patients with MCI, diagnosed using the Clinical Dementia Rating, and 63 normal controls. Data were analysed employing receiver operating characteristic analysis, Linear Support Vector Machine, Random Forest, Adaptive Boosting, Neural Network models, and t-distributed stochastic neighbour embedding (t-SNE). RESULTS: MCI patients were best discriminated from normal controls using a combination of Wordlist Recall, Wordlist Memory, and Verbal Fluency Test. Machine learning showed that the CERAD features learned from MCI patients and controls were not strongly predictive of the diagnosis (maximal cross-validation 77.2%), whilst t-SNE showed that there is a considerable overlap between MCI and controls. CONCLUSIONS: The most important features of the CERAD-NP differentiating MCI from normal controls indicate impairments in episodic and semantic memory and recall. While these features significantly discriminate MCI patients from normal controls, the tests are not predictive of MCI.
Subject(s)
Alzheimer Disease/diagnosis , Cognitive Dysfunction/diagnosis , Machine Learning , Neuropsychological Tests/standards , Aged , Aged, 80 and over , Alzheimer Disease/psychology , Cognitive Dysfunction/psychology , Cross-Sectional Studies , Female , Humans , Male , Mental Recall , Middle Aged , Neural Networks, Computer , Reproducibility of Results , Socioeconomic Factors , Support Vector Machine , Thailand , Translations , Verbal BehaviorABSTRACT
BACKGROUND AND OBJECTIVE: Prognosis of cerebral venous sinus thrombosis (CVST) has never been studied in Thailand. A simple prognostic score to predict poor prognosis of CVST has also never been reported. The authors are aiming to establish a simple and reliable prognostic score for this condition. MATERIAL AND METHOD: The medical records of CVST patients from eight neurological training centers in Thailand who received between April 1993 and September 2005 were reviewed as part of this retrospective study. Clinical features included headache, seizure, stroke risk factors, Glasgow coma scale (GCS), blood pressure on arrival, papilledema, hemiparesis, meningeal irritation sign, location of occluded venous sinuses, hemorrhagic infarction, cerebrospinal fluid opening pressure, treatment options, length of stay, and other complications were analyzed to determine the outcome using modified Rankin scale (mRS). Poor prognosis (defined as mRS of 3-6) was determined on the discharge date. RESULTS: One hundred ninety four patients' records, 127 females (65.5%) and mean age of 36.6 +/- 14.4 years, were analyzed Fifty-one patients (26.3%) were in the poor outcome group (mRS 3-6). Overall mortality was 8.4%. Univariate analysis and then multivariate analysis using SPSS version 11.5 revealed only four statistically significant predictors influencing outcome of CVST They were underlying malignancy, low GCS, presence of hemorrhagic infarction (for poor outcome), and involvement of lateral sinus (for good outcome). Thai venous stroke prognostic score (TV-SPSS) was derived from these four factors using a multiple logistic model. CONCLUSION: A simple and pragmatic prognostic score for CVST outcome has been developed with high sensitivity (93%), yet low specificity (33%). The next study should focus on the validation of this score in other prospective populations.
