An algorithmic approach to TAAD including (1) rapid transport-to-incision-to-cardiopulmonary bypass established centrally, (2) neurocerebral monitoring, (3) liberal use of total arch replacement for clearly defined indications (and hemiarch for all others), and (4) common carotid arterial replacement for concomitant carotid dissections significantly improves outcomes.
The use of a PETTICOAT concept with an addition of a bare metal stent distal to the proximal thoracic endograft offers positive aortic remodeling in the thoracoabdominal aorta at 6 months.
Wearable sensors are becoming increasingly more available in Parkinson’s disease and are used to measure motor function. Whether non-motor symptoms (NMS) can also be measured with these wearable sensors remains unclear. We therefore performed a retrospective, exploratory, analysis of 108 patients with a diagnosis of idiopathic Parkinson’s disease enroled in the Non-motor Longitudinal International Study (UKCRN No. 10084) at King’s College Hospital, London, to determine the association between the range and nature of NMS and an accelerometer-based outcome measure of bradykinesia (BKS) and dyskinesia (DKS). NMS were assessed by the validated NMS Scale, and included, e.g., cognition, mood and sleep, and gastrointestinal, urinary and sexual problems. Multiple linear regression modelling was used to identify NMS associated with BKS and DKS. We found that BKS was associated with domains 6 (gastrointestinal tract; p = 0.006) and 8 (sexual function; p = 0.003) of the NMS scale. DKS was associated with domains 3 (mood/cognition; p = 0.016), 4 (perceptual problems; p = 0.025), 6 (gastrointestinal tract; p = 0.029) and 9 (miscellaneous, p = 0.003). In the separate domains, constipation was significantly associated with BKS. Delusions, dysphagia, hyposmia, weight change and hyperhidrosis were identified as significantly associated with DKS. None of the NMSS domains were associated with disease duration (p ≥ 0.08). In conclusion, measures of BKS and DKS were mainly associated with gastrointestinal problems, independent of disease duration, showing the potential for wearable devices to pick up on these symptoms. These exploratory results deserve further exploration, and more research on this topic in the form of comprehensive large-scale studies is needed.
Introduction: Although in some countries, palliative care (PC) still remains poorly implemented, its importance throughout the course of Parkinson's disease (PD) is increasingly being acknowledged. With an emergence of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) pandemic, growing emphasis has been placed on the palliative needs of people with Parkinson's (PwP), particularly elderly, frail, and with comorbidities. Areas covered: The ongoing COVID-19 pandemic poses an enormous challenge on aspects of daily living in PwP and might interact negatively with a range of motor and non-motor symptoms (NMS), both directly and indirectly -as a consequence of pandemic-related social and health care restrictions.Here, the authors outline some of the motor and NMS relevant to PC, and propose a pragmatic and rapidly deployable, consensus-based PC approach for PwP during the ongoing COVID-19 pandemic, potentially relevant also for future pandemics. Expert opinion: The ongoing COVID-19 pandemic poses a considerable impact on PwP and their caregivers, ranging from mental health issues to worsening of physical symptoms -both in the shortand long-term, (Long-COVID) and calls for specific, personalized PC strategies relevant in a lockdown setting globally. Validated assessment tools should be applied remotely to flag up particular motor or NMS that require special attention, both in short-and long-term.
Fine-motor impairment (FMI) is progressively expressed in early Parkinson’s Disease (PD) patients and is now known to be evident in the immediate prodromal stage of the condition. The clinical techniques for detecting FMI may not be robust enough and here, we show that the subtle FMI of early PD patients can be effectively estimated from the analysis of natural smartphone touchscreen typing via deep learning networks, trained in stages of initialization and fine-tuning. In a validation dataset of 36,000 typing sessions from 39 subjects (17 healthy/22 PD patients with medically validated UPDRS Part III single-item scores), the proposed approach achieved values of area under the receiver operating characteristic curve (AUC) of 0.89 (95% confidence interval: 0.80–0.96) with sensitivity/specificity: 0.90/0.83. The derived estimations result in statistically significant (
) correlation of 0.66/0.73/0.58 with the clinical standard UPDRS Part III items 22/23/31, respectively. Further validation analysis on 9 de novo PD patients vs. 17 healthy controls classification resulted in AUC of 0.97 (0.93–1.00) with 0.93/0.90. For 253 remote study participants, with self-reported health status providing 252.000 typing sessions via a touchscreen typing data acquisition mobile app (iPrognosis), the proposed approach predicted 0.79 AUC (0.66–0.91) with 0.76/0.71. Remote and unobtrusive screening of subtle FMI via natural smartphone usage, may assist in consolidating early and accurate diagnosis of PD.
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