RESUMO
BACKGROUND: In addition to other stroke-related deficits, the risk of seizures may impact driving ability after stroke. METHODS: We analysed data from a multicentre international cohort, including 4452 adults with acute ischaemic stroke and no prior seizures. We calculated the Chance of Occurrence of Seizure in the next Year (COSY) according to the SeLECT2.0 prognostic model. We considered COSY<20% safe for private and <2% for professional driving, aligning with commonly used cut-offs. RESULTS: Seizure risks in the next year were mainly influenced by the baseline risk-stratified according to the SeLECT2.0 score and, to a lesser extent, by the poststroke seizure-free interval (SFI). Those without acute symptomatic seizures (SeLECT2.0 0-6 points) had low COSY (0.7%-11%) immediately after stroke, not requiring an SFI. In stroke survivors with acute symptomatic seizures (SeLECT2.0 3-13 points), COSY after a 3-month SFI ranged from 2% to 92%, showing substantial interindividual variability. Stroke survivors with acute symptomatic status epilepticus (SeLECT2.0 7-13 points) had the highest risk (14%-92%). CONCLUSIONS: Personalised prognostic models, such as SeLECT2.0, may offer better guidance for poststroke driving decisions than generic SFIs. Our findings provide practical tools, including a smartphone-based or web-based application, to assess seizure risks and determine appropriate SFIs for safe driving.
Assuntos
Condução de Veículo , AVC Isquêmico , Convulsões , Humanos , Convulsões/etiologia , Convulsões/complicações , AVC Isquêmico/complicações , Masculino , Feminino , Idoso , Pessoa de Meia-Idade , Fatores de Risco , Idoso de 80 Anos ou mais , Prognóstico , Estudos de Coortes , AdultoRESUMO
OBJECTIVE: To assess the possible factors that influence sleep quality in adolescents with and without chronic immunosuppressive conditions quarantined during the coronavirus disease 2019 (COVID-19) pandemic. METHODS: This cross-sectional study included 305 adolescents with chronic immunocompromised conditions and 82 healthy adolescents. Online surveys were completed, which included questions on socio-demographic data and self-rated healthcare routine during COVID-19 quarantine and the following validated questionnaires: the Pittsburgh Sleep Quality Index (PSQI), Pediatric Quality of Life Inventory 4.0 (PedsQL4.0), and Pediatric Outcome Data Collection Instrument (PODCI). RESULTS: The median current age [14 (10-18) vs. 15 (10-18) years, p=0.847] and frequency of female sex (62% vs. 58%, p=0.571) were similar in adolescents with chronic conditions compared with healthy adolescents. The frequency of poor sleep quality was similar in both groups (38% vs. 48%, p=0.118). Logistic regression analysis, including both healthy adolescents and adolescents with chronic conditions (n=387), demonstrated that self-reported increase in screen time (odds ratio [OR] 3.0; 95% confidence interval [CI] 1.3-6.8; p=0.008) and intrafamilial violence report (OR 2.1; 95% CI 1.2-3.5; p=0.008) were independently associated with poor sleep quality in these adolescents. However, the PODCI global function score was associated with a lower OR for poor sleep quality (OR 0.97; 95% CI 0.94-0.99; p=0.001). Further logistic regression, including only adolescents with chronic conditions (n=305), demonstrated that self-reported increase in screen time (OR 3.1; 95% CI 1.4-6.8; p=0.006) and intrafamilial violence report (OR 2.0; 95% CI 1.2-3.4; p=0.011) remained independently associated with poor quality of sleep, whereas a lower PODCI global function score was associated with a lower OR for sleep quality (OR 0.96; 95% CI 0.94-0.98; p<0.001). CONCLUSION: Self-reported increases in screen time and intrafamilial violence report impacted sleep quality in both healthy adolescents and those with chronic conditions. Decreased health-related quality of life was observed in adolescents with poor sleep quality.