Tonic REM sleep muscle activity is the strongest predictor of phenoconversion risk to neurodegenerative disease in isolated REM sleep behaviour disorder.

Previous studies have shown that rapid eye movement sleep without atonia during polysomnography can predict the risk of phenoconversion to neurodegenerative disease in patients with isolated rapid eye movement sleep behaviour disorder. Discrepancy remains with regards to the morphology of rapid eye movement sleep without atonia that best predicts phenoconversion risk. This study aimed to ascertain the predictive value of tonic, phasic and mixed rapid eye movement sleep without atonia in patients with isolated rapid eye movement sleep behaviour disorder, at time of diagnosis. Sixty-four patients with polysomnography-confirmed isolated rapid eye movement sleep behaviour disorder, including 19 who phenoconverted during follow-up, were identified from an existing database. Tonic, phasic, mixed and "any" rapid eye movement sleep without atonia activity from the mentalis, tibialis anterior and flexor digitorum superficialis muscles was analysed blind to status using the diagnostic polysomnography. Rapid eye movement sleep without atonia variables were compared between converters and non-converters. Rapid eye movement sleep without atonia cut-offs predicting phenoconversion were established using receiver-operating characteristic analysis. The mean follow-up duration was 5.50 ± 4.73 years. Phenoconverters (n = 19) had significantly higher amounts of tonic (22.2 ± 19.1%, p = 0.0014), mixed (18.1 ± 14.1%, p = 0.0074) and "any" (mentalis muscle; 58.7 ± 28.0%, p = 0.0009) and all muscles (68.0 ± 20.8%, p = 0.0049) rapid eye movement sleep without atonia at diagnosis than non-converters. Optimal rapid eye movement sleep without atonia cut-off values predicting phenoconversion were 5.8% for tonic (73.7% sensitivity; 75.6% specificity), 7.3% for mixed (68.4% sensitivity; 73.3% specificity) and 43.6% for "any" (mentalis muscle; 68.4% sensitivity; 80.0% specificity) activity. "Any" (mentalis muscle) rapid eye movement sleep without atonia had the highest area under the curve (0.809) followed by tonic (0.799). The percentage of tonic rapid eye movement sleep without atonia was the strongest biomarker of phenoconversion in this cohort of patients with isolated rapid eye movement sleep behaviour disorder.

Baseline Predictors of Longitudinal Cognitive Outcomes in Persons with Mild Cognitive Impairment.

INTRODUCTION: The study aimed to explore longitudinal cognitive outcomes and to ascertain predictors of conversion to dementia in a hospital-based mild cognitive impairment (MCI) cohort classified according to the neuropsychological phenotype at baseline. MATERIALS AND METHODS: Subjects aged >55 years who had a clinical diagnosis of MCI at initial visit between 2010 and 2018, with at least one formal neuropsychological assessment at baseline and follow-up of a minimum of 2 years were included. The prospective study was completed based on evaluation at last follow-up to gauge conversion to dementia, quantification of performance on activities of daily living and when available, longitudinal neuropsychological test scores. RESULTS: Ninety-five patients with MCI met the inclusion criteria with a mean age of 68.4 ± 6.4 years at baseline and a mean duration of follow-up for 6.4 ± 3.2 years. The cumulative conversion rate to dementia was 22.2% (21/95) and the annualized conversion rate was 3.3% per year of follow-up. The majority of subjects who had converted had multidomain MCI (66%). Only white matter changes on MRI brain revealed correlation with baseline neuropsychology tests. The multivariate logistic regression analysis revealed the utility of lower baseline list recognition (adjusted odds ratio: 0.735 [95% confidence interval: 0.589-0.916]; p 0.006), lower immediate logical memory (0.885 [0.790-0.990]; p 0.03), and high perseverative error scores on set shifting (3.116 [1.425-6.817]; p 0.004) as predictors of conversion. A model score of +2.615 could predict conversion with sensitivity of 72% and specificity of 98% over 6.4 years follow-up. CONCLUSION: There was a higher risk of conversion associated with multidomain MCI. Logistic regression-based estimations of dementia risk utilizing domain-based neuropsychology test scores in MCI have high specificity for diagnosis at baseline.

Conversion of Atrial Fibrillation after Cardiosurgical Procedures by Vernakalant® as an Atrial Repolarization Delaying Agent (ARDA).

BACKGROUND: Postoperative, new-onset atrial fibrillation (POAF) is one of the most common complications after cardiosurgical procedures. Vernakalant has been reported to be effective in the conversion of POAF. The aim of this study was to evaluate the efficacy and safety of vernakalant for atrial fibrillation after cardiac operations, and to investigate predictors for the success of vernakalant treatment. Patients and Methods: Post-cardiac surgery patients with new-onset of atrial fibrillation (AF) were consecutively enrolled in this study. Demographic data as well as intraoperative and postoperative parameters were analyzed. Vernakalant administration was primarily started 5.5 hours after new-onset POAF: 3 mg/kg intravenously over 10 min, and in case of non-conversion, a second dose of 2 mg/kg intravenously over 10 min. Results: 129 consecutive patients (70.2 ± 9.1 years) were included: 61 patients with coronary artery bypass graft (CABG) surgery, 49 patients with isolated valve procedures, and 19 patients with combined procedures (CABG and valve). Conversion in sinus rhythm was achieved after the first vernakalant dose in 57 patients (44%), and after the second dose in 41 patients (32%). The mean time to conversion was 13.7 ± 14.1 min. The patients receiving valve procedures depicted a significantly lower conversion rate. The following variables lowered conversion rate: no preoperative beta blocker, postoperative troponin levels >500 ng/L, and systolic blood pressure >140 mmHg. At the first follow-up, 92% of the converted patients showed sinus rhythm, while 80% of the non-responders showed sinus rhythm (P < .01). Conclusions: The POAF was effectively converted by vernakalant. The conversion rate of POAF after valve surgery was lower when compared to isolated CABG.

Conversion Predictors of Clinically Isolated Syndrome to Multiple Sclerosis in Mexican Patients: A Prospective Study.

BACKGROUND: Clinically Isolated Syndrome (CIS) is the first clinical episode suggestive of Clinical Definite Multiple Sclerosis (CDMS). There are no reports on possible predictors of conversion to CDMS in Mexican mestizo patients. AIM OF THE STUDY: To investigate immunological markers, clinical and paraclinical findings, and the presence of herpesvirus DNA to predict the transition from CIS to CDMS in Mexican patients. METHODS: A single-center prospective cohort study was conducted with newly diagnosed patients with CIS in Mexico between 2006 and 2010. Clinical information, immunophenotype, serum cytokines, anti-myelin protein immunoglobulins, and herpes viral DNA were determined at the time of diagnosis. RESULTS: 273 patients diagnosed with CIS met the enrolment criteria; after 10 years of follow-up, 46% met the 2010 McDonald criteria for CDMS. Baseline parameters associated with conversion to CDMS were motor symptoms, multifocal syndromes, and alterations of somatosensory evoked potentials. The presence of at least one lesion on magnetic resonance imaging was the main factor associated with an increased risk of conversion to CDMS (RR 15.52, 95% CI 3.96-60.79, p = 0.000). Patients who converted to CDMS showed a significantly lower percentage of circulating regulatory T cells, cytotoxic T cells, and B cells, and the conversion to CDMS was associated with the presence of varicella-zoster virus and herpes simplex virus 1 DNA in cerebrospinal fluid and blood. CONCLUSION: There is scarce evidence in Mexico regarding the demographic and clinical aspects of CIS and CDMS. This study shows several predictors of conversion to CDMS to be considered in Mexican patients with CIS.

Tau-PET and in vivo Braak-staging as prognostic markers of future cognitive decline in cognitively normal to demented individuals.

BACKGROUND: To systematically examine the clinical utility of tau-PET and Braak-staging as prognostic markers of future cognitive decline in older adults with and without cognitive impairment. METHODS: In this longitudinal study, we included 396 cognitively normal to dementia subjects with 18F-Florbetapir/18F-Florbetaben-amyloid-PET, 18F-Flortaucipir-tau-PET and ~ 2-year cognitive follow-up. Annual change rates in global cognition (i.e., MMSE, ADAS13) and episodic memory were calculated via linear-mixed models. We determined global amyloid-PET (Centiloid) plus global and Braak-stage-specific tau-PET SUVRs, which were stratified as positive(+)/negative(-) at pre-established cut-offs, classifying subjects as Braak0/BraakI+/BraakI-IV+/BraakI-VI+/Braakatypical+. In bootstrapped linear regression, we assessed the predictive accuracy of global tau-PET SUVRs vs. Centiloid on subsequent cognitive decline. To test for independent tau vs. amyloid effects, analyses were further controlled for the contrary PET-tracer. Using ANCOVAs, we tested whether more advanced Braak-stage predicted accelerated future cognitive decline. All models were controlled for age, sex, education, diagnosis, and baseline cognition. Lastly, we determined Braak-stage-specific conversion risk to mild cognitive impairment (MCI) or dementia. RESULTS: Baseline global tau-PET SUVRs explained more variance (partial R2) in future cognitive decline than Centiloid across all cognitive tests (Cohen's d ~ 2, all tests p < 0.001) and diagnostic groups. Associations between tau-PET and cognitive decline remained consistent when controlling for Centiloid, while associations between amyloid-PET and cognitive decline were non-significant when controlling for tau-PET. More advanced Braak-stage was associated with gradually worsening future cognitive decline, independent of Centiloid or diagnostic group (p < 0.001), and elevated conversion risk to MCI/dementia. CONCLUSION: Tau-PET and Braak-staging are highly predictive markers of future cognitive decline and may be promising single-modality estimates for prognostication of patient-specific progression risk in clinical settings.

Discrimination and conversion prediction of mild cognitive impairment using convolutional neural networks.

BACKGROUND: Recently, studies have demonstrated that machine learning techniques, particularly cutting-edge deep learning technology, have achieved significant progression on the classification of Alzheimer's disease (AD) and its prodromal phase, mild cognitive impairment (MCI). Moreover, accurate prediction of the progress and the conversion risk from MCI to probable AD has been of great importance in clinical application. METHODS: In this study, the baseline MR images and follow-up information during 3 years of 150 normal controls (NC), 150 patients with stable MCI (sMCI) and 157 converted MCI (cMCI) were collected from the Alzheimer's Disease Neuroimaging Initiative (ADNI). The deep convolutional neural networks (CNNs) were adopted to distinguish different stages of MCI from the NC group, and predict the conversion time from MCI to AD. Two CNN architectures including GoogleNet and CaffeNet were explored and evaluated in multiple classifications and estimations of conversion risk using transfer learning from pre-trained ImageNet (via fine-tuning) and five-fold cross-validation. A novel data augmentation approach using random views aggregation was applied to generate abundant image patches from the original MR scans. RESULTS: The GoogleNet acquired accuracies with 97.58%, 67.33% and 84.71% in three-way discrimination among the NC, sMCI and cMCI groups respectively, whereas the CaffeNet obtained promising accuracies of 98.71%, 72.04% and 92.35% in the NC, sMCI and cMCI classifications. Furthermore, the accuracy measures of conversion risk of patients with cMCI ranged from 71.25% to 83.25% in different time points using GoogleNet, whereas the CaffeNet achieved remarkable accuracy measures from 95.42% to 97.01% in conversion risk prediction. CONCLUSIONS: The experimental results demonstrated that the proposed methods had prominent capability in classification among the 3 groups such as sMCI, cMCI and NC, and exhibited significant ability in conversion risk prediction of patients with MCI.