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Additional file 4.

Background: White matter (WM) lesions and brain atrophy are present early in multiple sclerosis (MS). However, their spatio-temporal relationship remains unclear. Methods: Yearly magnetic resonance images were analysed in 387 patients with a first clinical demyelinating event (FCDE) from the 5-year REFLEXION study. Patients received early (from baseline; N = 258; ET) or delayed treatment (from month-24; N = 129; DT) with subcutaneous interferon beta-1a. FSL-SIENA/VIENA were used to provide yearly percentage volume change of brain (PBVC) and ventricles (PVVC). Yearly total lesion volume change (TLVC) was determined by a semi-automated method. Using linear mixed models and voxel-wise analyses, we firstly investigated the overall relationship between TLVC and PBVC and between TLVC and PVVC in the same follow-up period. Analyses were then separately performed for: the untreated period of DT patients (first two years), the first year of treatment (year 1 for ET and year 3 for DT), and a period where patients had received at least 1 year of treatment (stable treatment; ET: years 2, 3, 4, and 5; DT: years 4 and 5). Results: Whole brain: across the whole study period, lower TLVC was related to faster atrophy (PBVC: B = 0.046, SE = 0.013, p < 0.001; PVVC: B = −0.466, SE = 0.118, p < 0.001). Within the untreated period of DT patients, lower TLVC was related to faster atrophy (PBVC: B = 0.072, SE = 0.029, p = 0.013; PVVC: B = −0.917, SE = 0.306, p = 0.003). A similar relationship was found within the first year of treatment of ET patients (PBVC: B = 0.081, SE = 0.027, p = 0.003; PVVC: B = −1.08, SE = 0.284, p < 0.001), consistent with resolving oedema and pseudo-atrophy. Voxel-wise: overall, higher TLVC was related to faster ventricular enlargement. Lower TLVC was related to faster widespread atrophy in year 1 in both ET (first year of treatment) and DT (untreated) patients. In the second untreated year of DT patients and within the stable treatment period of ET patients (year 4), faster periventricular and occipital lobe atrophy was associated with higher TLVC. Conclusions: WM lesion changes and atrophy occurred simultaneously in early MS. Spatio-temporal correspondence of these two processes involved mostly the periventricular area. Within the first year of the study, in both treatment groups, faster atrophy was linked to lower lesion volume changes, consistent with higher shrinking and disappearing lesion activity. This might reflect the pseudo-atrophy phenomenon that is probably related to the therapy driven (only in ET patients, as they received treatment from baseline) and “natural” (both ET and DT patients entered the study after a FCDE) resolution of oedema. In an untreated period and later on during stable treatment, (real) atrophy was related to higher lesion volume changes, consistent with increased new and enlarging lesion activity.

Additional file 7. Observer OPTIONMCC scores for geriatricians, patients and informal caregivers (subgroup analysis of eight geriatricians).

Additional file 3. Adapted control preference scale.

Additional file 1. Patient preparatory tool.

Additional file 5. Evaluation of use of the patient preparatory tool in the intervention group (n = 108).

Additional file 3.

Objectives: The researchers aimed to (1) explore the occurrence of psychological resilience in the face of a major life stressor and conflict in older residents of long-term care facilities (LTCFs), and (2) identify factors associated with resilience in this population. Design: Longitudinal cohort study using the Dutch InterRAI-LTCF cohort. Setting and participants: Older residents (≥60 years old) of 21 LTCFs in the Netherlands. Methods: The researchers selected 2 samples of residents who had at least 2 assessments surrounding (1) an incident major life stressor, or (2) incident conflict with other resident or staff. A resilient outcome was operationalized as not having clinically meaningful mood symptoms at the post-stressor assessment and equal or fewer mood symptoms at the post-stressor relative to the pre-stressor assessment. The researchers used 2 resilience outcomes per stressor: 1 based on observer-reported mood symptoms and 1 based on self-reported mood symptoms. The most important factors from among 21 potential resilience factors for each of the 4 operationalizations of resilience were identified using a backward selection procedure with 2-level generalized estimating equations analyses. Results: Forty-eight percent and 50% of residents were resilient in the face of a major life stressor, based on observer-reported (n = 248) and self-reported (n = 211) mood, respectively. In the face of conflict, 26% and 51% of the residents demonstrated resilience, based on the observer-reported (n = 246) and self-reported (n = 183) mood, respectively. Better cognitive functioning, a strong and supportive relationship with family, participation in social activities, and better self-reported health were most strongly associated with resilience in the face of a major life stressor. Better communicative functioning, absence of psychiatric diagnoses, a strong and supportive relationship with family, not being lonely, social engagement, and not reminiscing about life were most strongly associated with resilience in the face of conflict. Conclusions and Implications: Factors with a social aspect appear to be particularly important to psychological resilience in older LTCF residents, and provide a potential target for intervention in the LTCF setting.

Additional file 5.

Additional file 2.

Because missing data are present in almost every study, it is important to handle missing data properly. First of all, the missing data mechanism should be considered. Missing data can be either completely at random (MCAR), at random (MAR), or not at random (MNAR). When missing data are MCAR, a complete case analysis can be valid. Also when missing data are MAR, in some situations a complete case analysis leads to valid results. However, in most situations, missing data imputation should be used. Regarding imputation methods, it is highly advised to use multiple imputations because multiple imputations lead to valid estimates including the uncertainty about the imputed values. When missing data are MNAR, also multiple imputations do not lead to valid results. A complication hereby is that it not possible to distinguish whether missing data are MAR or MNAR. Finally, it should be realized that preventing to have missing data is always better than the treatment of missing data.