Klinik und Poliklinik für Neurologie
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Background:
Post-stroke delirium (PSD) is a modifiable predictor for worse outcome in stroke. Knowledge of its risk factors would facilitate clinical management of affected patients, but recently updated national guidelines consider available evidence insufficient.
Aims:
The study aimed to establish risk factors for PSD incidence and duration using high-frequency screening.
Methods:
We prospectively investigated patients with ischemic stroke admitted within 24 h. Patients were screened twice daily for the presence of PSD throughout the treatment period. Sociodemographic, treatment-related, and neuroimaging characteristics were evaluated as predictors of either PSD incidence (odds ratios (OR)) or duration (PSD days/unit of the predictor, b), using logistic and linear regression models, respectively.
Results:
PSD occurred in 55/141 patients (age = 73.8 ± 10.4 years, 61 female, National Institutes of Health Stroke Scale (NIHSS) = 6.4 ± 6.5). Age (odds ratio (OR) = 1.06 (95% confidence interval (CI): 1.02–1.10), b = 0.08 (95% CI = 0.04–0.13)), and male gender (b = 0.99 (95% CI = 0.05–1.93)) were significant non-modifiable risk factors. In a multivariable model adjusted for age and gender, presence of pain (OR < sub > mvar </sub >= 1.75 (95% CI = 1.12–2.74)), urinary catheter (OR < sub > mvar </sub > = 3.16 (95% CI = 1.10–9.14)) and post-stroke infection (PSI; OR < sub > mvar </sub > = 4.43 (95% CI = 1.09–18.01)) were predictors of PSD incidence. PSD duration was impacted by presence of pain (b < sub > mvar </sub >= 0.49 (95% CI = 0.19–0.81)), urinary catheter (b < sub > mvar </sub > = 1.03 (95% CI = 0.01–2.07)), intravenous line (b < sub > mvar </sub >= 0.36 (95% CI = 0.16–0.57)), and PSI (b < sub > mvar </sub >= 1.60 (95% CI = 0.42–2.78)). PSD (OR = 3.53 (95% CI = 1.48–5.57)) and PSI (OR = 5.29 (95% CI = 2.92–7.66)) independently predicted inferior NIHSS at discharge. Insular and basal ganglia lesions increased the PSD risk about four- to eight-fold.
Discussion/Conclusion:
This study identified modifiable risk factors, the management of which might reduce the negative impact PSD has on outcome.
Polypharmacy in patients with multiple sclerosis and the impact on levels of care and therapy units
(2023)
Background: The aim of this study was to examine the societal costs of polypharmacy in patients with multiple sclerosis (MS). We therefore focused on the association between the number of medications on the level of care (LOC), the German classification of the need for care, and the number of therapy sessions (TTU).
Methods: In addition to demographic information and medication, 101 MS patients performed the Multiple Sclerosis Health Resource Utilization Survey (MS-HRS). Medications were subdivided into a total number of medications (TD), MS-related medication [MSD, i.e., disease-modifying drugs (DMDs) and symptomatic treatment (SD)], and medication for comorbidities (CDs). Multivariate linear regression models were performed to estimate if the amount of each medication type affects LOC or TTU.
Results: Polypharmacy appeared in 54 patients at the time of the survey. The relative risk (RR) of LOC 1 increased significantly by 2.46 (p = 0.001) per TD and by 2.55 (p = 0.004) per MSD, but not per CD (RR 1.44; p = 0.092). The effect of RR on MSD was driven by SD (RR 2.2; p = 0.013) but not DMD (RR 2.6; p = 0.4). RR of MSD remained significant for LOC 2 (1.77; p = 0.009) and LOC 3/4 (1.91; p = 0.015), with a strong trend in RR of SD, but not DMD. TTU increased significantly per MSD (p = 0.012), but not per TD (p = 0.081) and CD (p = 0.724).
Conclusion: The number of MSDs is related to the likelihood of a higher level of care and the number of therapy sessions and is therefore a good indication of the extent of the societal costs.
Background
Fatigue is a common symptom in patients with multiple sclerosis. Several studies suggest that outdoor temperature can impact fatigue severity, but a systematic study of seasonal variations is lacking.
Methods
Fatigue was assessed with the Fatigue Scale for Motor and Cognitive Functions (FSMC) in a temperate climatic zone with an average outdoor temperature of 8.8°C. This study included 258 patients with multiple sclerosis from 572 visits temporally distributed over the year. The data were adjusted for age, sex, cognition, depression, disease severity, and follow-up time. Linear regression models were performed to determine whether the temporal course of fatigue was time-independent, linearly time dependent, or non-linearly time dependent.
Results
Fatigue was lowest during January (mean FSMC: 49.84) and highest during August (mean FSMC: 53.88). The regression analysis showed the best fit with a model that included months + months2, which was a non-linear time dependency. Mean FSMC per month correlated significantly with the average monthly temperature (ρ = 0.972; p < 0.001).
Conclusion
In multiple sclerosis, fatigue showed a natural temporal fluctuation. Fatigue was higher during summer compared to winter, with a significant relationship of fatigue with outdoor temperature. This finding should be carefully taken into account when clinically monitoring patients over time to not interpret higher or lower scores independent of seasonal aspects.