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Improving quality of life (QoL) is central to amyotrophic lateral sclerosis (ALS) treatment. This Germany-wide, multicenter cross-sectional study analyses the impact of different symptom-specific treatments and ALS variants on QoL. Health-related QoL (HRQoL) in 325 ALS patients was assessed using the Amyotrophic Lateral Sclerosis Assessment Questionnaire 5 (ALSAQ-5) and EuroQol Five Dimension Five Level Scale (EQ-5D-5L), together with disease severity (captured by the revised ALS Functional Rating Scale (ALSFRS-R)) and the current care and therapies used by our cohort. At inclusion, the mean ALSAQ-5 total score was 56.93 (max. 100, best = 0) with a better QoL associated with a less severe disease status (β = −1.96 per increase of one point in the ALSFRS-R score, p < 0.001). “Limb-onset” ALS (lALS) was associated with a better QoL than “bulbar-onset” ALS (bALS) (mean ALSAQ-5 total score 55.46 versus 60.99, p = 0.040). Moreover, with the ALSFRS-R as a covariate, using a mobility aid (β = −7.60, p = 0.001), being tracheostomized (β = −14.80, p = 0.004) and using non-invasive ventilation (β = −5.71, p = 0.030) were associated with an improved QoL, compared to those at the same disease stage who did not use these aids. In contrast, antidepressant intake (β = 5.95, p = 0.007), and increasing age (β = 0.18, p = 0.023) were predictors of worse QoL. Our results showed that the ALSAQ-5 was better-suited for ALS patients than the EQ-5D-5L. Further, the early and symptom-specific clinical management and supply of assistive devices can significantly improve the individual HRQoL of ALS patients. Appropriate QoL questionnaires are needed to monitor the impact of treatment to provide the best possible and individualized care.
Background
Telemedicine improves the quality of acute stroke care in rural regions with limited access to specialized stroke care. We report the first 2 years' experience of implementing a comprehensive telemedical stroke network comprising all levels of stroke care in a defined region.
Methods
The TRANSIT-Stroke network covers a mainly rural region in north-western Bavaria (Germany). All hospitals providing acute stroke care in this region participate in TRANSIT-Stroke, including four hospitals with a supra-regional certified stroke unit (SU) care (level III), three of those providing teleconsultation to two hospitals with a regional certified SU (level II) and five hospitals without specialized SU care (level I). For a two-year-period (01/2015 to 12/2016), data of eight of these hospitals were available; 13 evidence-based quality indicators (QIs) related to processes during hospitalisation were evaluated quarterly and compared according to predefined target values between level-I- and level-II/III-hospitals.
Results
Overall, 7881 patients were included (mean age 74.6 years +/- 12.8; 48.4% female). In level-II/III-hospitals adherence of all QIs to predefined targets was high ab initio. In level-I-hospitals, three patterns of QI-development were observed: a) high adherence ab initio (31%), mainly in secondary stroke prevention; b) improvement over time (44%), predominantly related to stroke specific diagnosis and in-hospital organization; c) no clear time trends (25%). Overall, 10 out of 13 QIs reached predefined target values of quality of care at the end of the observation period.
Conclusion
The implementation of the comprehensive TRANSIT-Stroke network resulted in an improvement of quality of care in level-I-hospitals.
Background
Eye movement abnormalities are commonplace in neurological disorders. However, unaided eye movement assessments lack granularity. Although videooculography (VOG) improves diagnostic accuracy, resource intensiveness precludes its broad use. To bridge this care gap, we here validate a framework for smartphone video-based nystagmography capitalizing on recent computer vision advances.
Methods
A convolutional neural network was fine-tuned for pupil tracking using > 550 annotated frames: ConVNG. In a cross-sectional approach, slow-phase velocity of optokinetic nystagmus was calculated in 10 subjects using ConVNG and VOG. Equivalence of accuracy and precision was assessed using the “two one-sample t-test” (TOST) and Bayesian interval-null approaches. ConVNG was systematically compared to OpenFace and MediaPipe as computer vision (CV) benchmarks for gaze estimation.
Results
ConVNG tracking accuracy reached 9–15% of an average pupil diameter. In a fully independent clinical video dataset, ConVNG robustly detected pupil keypoints (median prediction confidence 0.85). SPV measurement accuracy was equivalent to VOG (TOST p < 0.017; Bayes factors (BF) > 24). ConVNG, but not MediaPipe, achieved equivalence to VOG in all SPV calculations. Median precision was 0.30°/s for ConVNG, 0.7°/s for MediaPipe and 0.12°/s for VOG. ConVNG precision was significantly higher than MediaPipe in vertical planes, but both algorithms’ precision was inferior to VOG.
Conclusions
ConVNG enables offline smartphone video nystagmography with an accuracy comparable to VOG and significantly higher precision than MediaPipe, a benchmark computer vision application for gaze estimation. This serves as a blueprint for highly accessible tools with potential to accelerate progress toward precise and personalized Medicine.