Characterization of Pupillary Light Response Features for the Classification of Patients with Optic Neuritis

Pupillometry is a promising technique for the potential diagnosis of several neurological pathologies. However, its potential is not fully explored yet, especially for prediction purposes and results interpretation. In this work, we analyzed 100 pupillometric curves obtained by 12 subjects, applying both advanced signal processing techniques and physics methods to extract typically collected features and newly proposed ones. We used machine learning techniques for the classification of Optic Neuritis (ON) vs. Healthy subjects, controlling for overfitting and ranking the features by random permutation, following their importance in prediction. All the extracted features, except one, turned out to have significant importance for prediction, with an average accuracy of 76%, showing the complexity of the processes involved in the pupillary light response. Furthermore, we provided a possible neurological interpretation of this new set of pupillometry features in relation to ON vs. Healthy classification

Development and validation of a semi-automated and unsupervised method for femur segmentation from CT

: Quantitative computed tomography (QCT)-based in silico models have demonstrated improved accuracy in predicting hip fractures with respect to the current gold standard, the areal bone mineral density. These models require that the femur bone is segmented as a first step. This task can be challenging, and in fact, it is often almost fully manual, which is time-consuming, operator-dependent, and hard to reproduce. This work proposes a semi-automated procedure for femur bone segmentation from CT images. The proposed procedure is based on the bone and joint enhancement filter and graph-cut algorithms. The semi-automated procedure performances were assessed on 10 subjects through comparison with the standard manual segmentation. Metrics based on the femur geometries and the risk of fracture assessed in silico resulting from the two segmentation procedures were considered. The average Hausdorff distance (0.03 ± 0.01 mm) and the difference union ratio (0.06 ± 0.02) metrics computed between the manual and semi-automated segmentations were significantly higher than those computed within the manual segmentations (0.01 ± 0.01 mm and 0.03 ± 0.02). Besides, a blind qualitative evaluation revealed that the semi-automated procedure was significantly superior (p < 0.001) to the manual one in terms of fidelity to the CT. As for the hip fracture risk assessed in silico starting from both segmentations, no significant difference emerged between the two (R2 = 0.99). The proposed semi-automated segmentation procedure overcomes the manual one, shortening the segmentation time and providing a better segmentation. The method could be employed within CT-based in silico methodologies and to segment large volumes of images to train and test fully automated and supervised segmentation methods

Characterization of Pupillary Light Response Features for the Classification of Patients with Optic Neuritis

Pupillometry is a promising technique for the potential diagnosis of several neurological pathologies. However, its potential is not fully explored yet, especially for prediction purposes and results interpretation. In this work, we analyzed 100 pupillometric curves obtained by 12 subjects, applying both advanced signal processing techniques and physics methods to extract typically collected features and newly proposed ones. We used machine learning techniques for the classification of Optic Neuritis (ON) vs. Healthy subjects, controlling for overfitting and ranking the features by random permutation, following their importance in prediction. All the extracted features, except one, turned out to have significant importance for prediction, with an average accuracy of 76%, showing the complexity of the processes involved in the pupillary light response. Furthermore, we provided a possible neurological interpretation of this new set of pupillometry features in relation to ON vs. Healthy classification

PROSPECTIVE STUDY ON PHOTOPLETYSMOGRAPHIC AND ELECTROENCEPHALOGRAPHIC SIGNALS FOR THE MONITORING OF CANDIDATES TO ELECTRICAL CARDIOVERSION OF ATRIAL ARRHYTHMIAS (PPEEG-AF PILOT STUDY)

Atrial fibrillation (AF) is the most common arrhythmia, and its incidence is constantly increasing. It is associated with higher stroke risk and the presence of sleep disorders and dementia. The choice between rhythm and rate control in AF patients remains a debated topic, and it should be tailored on specific patient characteristics. In specific situations, electrical cardioversion (ECV) for rhythm control represents the preferred choice; in particular, in patients affected by cardiopathy and/or heart failure. Because of relevant AF social costs, there is a growing interest in developing new devices for large-scale screening and monitoring programs in patients affected or at risk of AF, to reduce the incidence of disabling events.The aim of this study was to evaluate the feasibility of the use of a set-up for multi-parametric monitoring of candidates to AF ECV. In particular, new technologies were exploited for photoplethysmographic (PPG) and electroencephalographic (EEG) signal registration, integrated with clinical and instrumental data. We analyzed the effect of AF ECV on heart rate variability (HRV) and vascular age parameters derived from PPG signals registered with Empatica (CE 1876/MDD 93/42/EEC; Empatica S.r.l, Milan, Italy), and on EEG sleep pattern registered with Neurosteer (IEC 60601-1-2; Neurosteer Inc., Herzliya, Israel).24 patients were enrolled, 75% males, mean age 65.6 +/- 8.5 years. HRV analyses considering time frames registered before and after ECV showed a significant reduction of most variables (p<0.001), only LF/HF ratio did not differ significantly. Considering HRV parameters, comparisons between PPG signals registered during day or night before and after ECV showed a significant difference in SD1/SD2 ratio (p=0.035) and HF (p=0.002). Regarding vascular age parameters, a significant reduction was observed in both turning point ratio (TPR) and a wave after ECV (p < 0.001). Moreover, we observed that patients with Mini-Mental State Examination (MMSE) <= 28 presented higher values of TPR (65.9 +/- 1.6 versus 64.2 +/- 1.4, p=0.035) and CHA(2)DS(2)-VASc score (congestive heart failure, hypertension, age, diabetes mellitus, prior stroke or transient ischemic attack or thromboembolism, vascular disease, age, sex category) (2.9 +/- 0.9 versus 1.7 +/- 1.2, p=0.022). Considering sleep patterns, a tendency to higher coherence was observed in registrations acquired during AF than in presence of sinus rhythm, or considering signals registered before and after ECV for each patient.In conclusion, the use of this new setup of multiparametric monitoring of candidates to ECV showed significant modifications on vascular age parameters derived from PPG signals measured before and after ECV. Moreover, a possible AF effect on sleep pattern registered with Neurosteer was noticed, but more data are necessary to confirm these preliminary results

European Multicenter Study of ET-COVID-19

International audienceBackground and Purpose: Acute ischemic stroke and large vessel occlusion can be concurrent with the coronavirus disease 2019 (COVID-19) infection. Outcomes after mechanical thrombectomy (MT) for large vessel occlusion in patients with COVID-19 are substantially unknown. Our aim was to study early outcomes after MT in patients with COVID-19. Methods: Multicenter, European, cohort study involving 34 stroke centers in France, Italy, Spain, and Belgium. Data were collected between March 1, 2020 and May 5, 2020. Consecutive laboratory-confirmed COVID-19 cases with large vessel occlusion, who were treated with MT, were included. Primary investigated outcome: 30-day mortality. Secondary outcomes: early neurological improvement (National Institutes of Health Stroke Scale improvement ≥8 points or 24 hours National Institutes of Health Stroke Scale 0–1), successful reperfusion (modified Thrombolysis in Cerebral Infarction grade ≥2b), and symptomatic intracranial hemorrhage. Results: We evaluated 93 patients with COVID-19 with large vessel occlusion who underwent MT (median age, 71 years [interquartile range, 59–79]; 63 men [67.7%]). Median pretreatment National Institutes of Health Stroke Scale and Alberta Stroke Program Early CT Score were 17 (interquartile range, 11–21) and 8 (interquartile range, 7–9), respectively. Anterior circulation acute ischemic stroke represented 93.5% of cases. The rate modified Thrombolysis in Cerebral Infarction 2b to 3 was 79.6% (74 patients [95% CI, 71.3–87.8]). Thirty-day mortality was 29% (27 patients [95% CI, 20–39.4]). Early neurological improvement was 19.5% (17 patients [95% CI, 11.8–29.5]), and symptomatic intracranial hemorrhage was 5.4% (5 patients [95% CI, 1.7–12.1]). Patients who died at 30 days exhibited significantly lower lymphocyte count, higher levels of aspartate, and LDH (lactate dehydrogenase). After adjustment for age, initial National Institutes of Health Stroke Scale, Alberta Stroke Program Early CT Score, and successful reperfusion, these biological markers remained associated with increased odds of 30-day mortality (adjusted odds ratio of 2.70 [95% CI, 1.21–5.98] per SD-log decrease in lymphocyte count, 2.66 [95% CI, 1.22–5.77] per SD-log increase in aspartate, and 4.30 [95% CI, 1.43–12.91] per SD-log increase in LDH). Conclusions: The 29% rate of 30-day mortality after MT among patients with COVID-19 is not negligible. Abnormalities of lymphocyte count, LDH and aspartate may depict a patient’s profiles with poorer outcomes after MT. Registration: URL: https://www.clinicaltrials.gov . Unique identifier: NCT04406090