Visual fixation in disorders of consciousness: Development of predictive models to support differential diagnosis

Abstrast. Clinical, electrophysiological and neuroimaging methods used for differential diagnosis of different forms of epileptic and nonepileptic paroxysms in children in the early stages of the disease are considered. 527 patients aged 1 to 18 years were examined. As a result of a comprehensive survey, taking into account the data of video-electroencephalogram monitoring, 317 patients with epilepsy with a significant predominance of symptomatic forms of epilepsy were revealed, of which 166 (42,37%) patients had focal forms, 36 (11,36%) patients - generalized. A significant number of children were found to have conditionally symptomatic focal epilepsy - 94 (29,65%), only 4 (1,26%) patients - with Landau - Kleffner syndrome and a relatively small group - 17 (5,36%) patients with idiopathic epilepsy. Nonepileptic paroxysms were more common in boys in all age groups except for children from 1 to 3 years, but in this group of children the difference in the frequency of different paroxysms is insignificant (0,48%). In the frequency of nonepileptic paroxysms significantly prevailed in boys aged 3 to 6 years (13,33%) and 6 to 9 years (11,43%). In girls, nonepileptic paroxysms were more common at the age of 3 to 6 years (10,48%) and from 1 to 3 years (9,95%). A comprehensive examination allowed to differentiate children with epileptic and nonepileptic paroxysms and to clarify their diagnoses. With a refined diagnosis of nonepileptic paroxysms, 210 patients were found. The results indicate the need for a thorough comprehensive examination of children suffering from paroxysmal disorders of consciousness to avoid erroneous diagnosis.

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Development and Validation of Diagnostic Models for Hand-Foot-and-Mouth Disease in Children

Disease Markers ◽

10.1155/2021/1923636 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Feng Zhuo ◽

Mengjie Yu ◽

Qiang Chen ◽

Nuoya Li ◽

Li Luo ◽

...

Keyword(s):

Differential Diagnosis ◽

Predictive Model ◽

Predictive Models ◽

Prediction Models ◽

Multivariate Logistic Regression Analysis ◽

Foot And Mouth Disease ◽

Mouth Disease ◽

Risk Markers ◽

Failure Group ◽

Foot And Mouth

Objective. To find risk markers and develop new clinical predictive models for the differential diagnosis of hand-foot-and-mouth disease (HFMD) with varying degrees of disease. Methods. 19766 children with HFMD and 64 clinical indexes were included in this study. The patients included in this study were divided into the mild patients’ group (mild) with 12292 cases, severe patients’ group (severe) with 6508 cases, and severe patients with respiratory failure group (severe-RF) with 966 cases. Single-factor analysis was carried out on 64 indexes collected from patients when they were admitted to the hospital, and the indexes with statistical differences were selected as the prediction factors. Binary multivariate logistic regression analysis was used to construct the prediction models and calculate the adjusted odds ratio (OR). Results. SP, DP, NEUT#, NEUT%, RDW-SD, RDW-CV, GGT, CK/CK-MB, and Glu were risk markers in mild/severe, mild/severe-RF, and severe/severe-RF. Glu was a diagnostic marker for mild/severe-RF ( AUROC = 0.80 , 95% CI: 0.78-0.82); the predictive model constructed by temperature, SP, MOMO%, EO%, RDW-SD, GLB, CRP, Glu, BUN, and Cl could be used for the differential diagnosis of mild/severe ( AUROC > 0.84 ); the predictive model constructed by SP, age, NEUT#, PCT, TBIL, GGT, Mb, β2MG, Glu, and Ca could be used for the differential diagnosis of severe/severe-RF ( AUROC > 0.76 ). Conclusion. By analyzing clinical indicators, we have found the risk markers of HFMD and established suitable predictive models.

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Multiplex and Multilayer Network EEG Analyses: A Novel Strategy in the Differential Diagnosis of Patients with Chronic Disorders of Consciousness

International Journal of Neural Systems ◽

10.1142/s0129065720500525 ◽

2020 ◽

pp. 2050052 ◽

Cited By ~ 1

Author(s):

Antonino Naro ◽

Maria Grazia Maggio ◽

Antonino Leo ◽

Rocco Salvatore Calabrò

Keyword(s):

Differential Diagnosis ◽

Minimally Conscious State ◽

Single Layer ◽

Brain Structures ◽

Conscious State ◽

Disorders Of Consciousness ◽

Multilayer Network ◽

Network Analyses ◽

Minimally Conscious ◽

Network Metrics

The deterioration of specific topological network measures that quantify different features of whole-brain functional network organization can be considered a marker for awareness impairment. Such topological measures reflect the functional interactions of multiple brain structures, which support the integration of different sensorimotor information subtending awareness. However, conventional, single-layer, graph theoretical analysis (GTA)-based approaches cannot always reliably differentiate patients with Disorders of Consciousness (DoC). Using multiplex and multilayer network analyses of frequency-specific and area-specific networks, we investigated functional connectivity during resting-state EEG in 17 patients with Unresponsive Wakefulness Syndrome (UWS) and 15 with Minimally Conscious State (MCS). Multiplex and multilayer network metrics indicated the deterioration and heterogeneity of functional networks and, particularly, the frontal-parietal (FP), as the discriminant between patients with MCS and UWS. These data were not appreciable when considering each individual frequency-specific network. The distinctive properties of multiplex/multilayer network metrics and individual frequency-specific network metrics further suggest the value of integrating the networks as opposed to analyzing frequency-specific network metrics one at a time. The hub vulnerability of these regions was positively correlated with the behavioral responsiveness, thus strengthening the clinically-based differential diagnosis. Therefore, it may be beneficial to adopt both multiplex and multilayer network analyses when expanding the conventional GTA-based analyses in the differential diagnosis of patients with DoC. Multiplex analysis differentiated patients at a group level, whereas the multilayer analysis offered complementary information to differentiate patients with DoC individually. Although further studies are necessary to confirm our preliminary findings, these results contribute to the issue of DoC differential diagnosis and may help in guiding patient-tailored management.

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