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Few studies have focused on elderly (>80 years) amyotrophic lateral sclerosis (ALS) patients, who represent a fragile subgroup generally not included in clinical trials and often neglected because they are more difficult to diagnose and manage. We analyzed the clinical and genetic features of very late-onset ALS patients through a prospective, population-based study in the Emilia Romagna Region of Italy. From 2009 to 2019, 222 (13.76%) out of 1613 patients in incident cases were over 80 years old at diagnosis, with a female predominance (F:M = 1.18). Elderly ALS patients represented 12.02% of patients before 2015 and 15.91% from 2015 onwards (p = 0.024). This group presented with bulbar onset in 38.29% of cases and had worse clinical conditions at diagnosis compared to younger patients, with a lower average BMI (23.12 vs. 24.57 Kg/m2), a higher progression rate (1.43 vs. 0.95 points/month), and a shorter length of survival (a median of 20.77 vs. 36 months). For this subgroup, genetic analyses have seldom been carried out (25% vs. 39.11%) and are generally negative. Finally, elderly patients underwent less frequent nutritional- and respiratory-supporting procedures, and multidisciplinary teams were less involved at follow-up, except for specialist palliative care. The genotypic and phenotypic features of elderly ALS patients could help identify the different environmental and genetic risk factors that determine the age at which disease onset occurs. Since multidisciplinary management can improve a patient’s prognosis, it should be more extensively applied to this fragile group of patients.

Amyotrophic lateral sclerosis (ALS) is known to represent a collection of overlapping syndromes. Various classification systems based on empirical observations have been proposed, but it is unclear to what extent they reflect ALS population substructures. We aimed to use machine-learning techniques to identify the number and nature of ALS subtypes to obtain a better understanding of this heterogeneity, enhance our understanding of the disease, and improve clinical care.In this retrospective study, we applied unsupervised Uniform Manifold Approximation and Projection [UMAP]) modelling, semi-supervised (neural network UMAP) modelling, and supervised (ensemble learning based on LightGBM) modelling to a population-based discovery cohort of patients who were diagnosed with ALS while living in the Piedmont and Valle d'Aosta regions of Italy, for whom detailed clinical data, such as age at symptom onset, were available. We excluded patients with missing Revised ALS Functional Rating Scale (ALSFRS-R) feature values from the unsupervised and semi-supervised steps. We replicated our findings in an independent population-based cohort of patients who were diagnosed with ALS while living in the Emilia Romagna region of Italy.Between Jan 1, 1995, and Dec 31, 2015, 2858 patients were entered in the discovery cohort. After excluding 497 (17%) patients with missing ALSFRS-R feature values, data for 42 clinical features across 2361 (83%) patients were available for the unsupervised and semi-supervised analysis. We found that semi-supervised machine learning produced the optimum clustering of the patients with ALS. These clusters roughly corresponded to the six clinical subtypes defined by the Chiò classification system (ie, bulbar, respiratory, flail arm, classical, pyramidal, and flail leg ALS). Between Jan 1, 2009, and March 1, 2018, 1097 patients were entered in the replication cohort. After excluding 108 (10%) patients with missing ALSFRS-R feature values, data for 42 clinical features across 989 patients were available for the unsupervised and semi-supervised analysis. All 1097 patients were included in the supervised analysis. The same clusters were identified in the replication cohort. By contrast, other ALS classification schemes, such as the El Escorial categories, Milano-Torino clinical staging, and King's clinical stages, did not adequately label the clusters. Supervised learning identified 11 clinical parameters that predicted ALS clinical subtypes with high accuracy (area under the curve 0·982 [95% CI 0·980-0·983]).Our data-driven study provides insight into the ALS population substructure and confirms that the Chiò classification system successfully identifies ALS subtypes. Additional validation is required to determine the accuracy and clinical use of these algorithms in assigning clinical subtypes. Nevertheless, our algorithms offer a broad insight into the clinical heterogeneity of ALS and help to determine the actual subtypes of disease that exist within this fatal neurodegenerative syndrome. The systematic identification of ALS subtypes will improve clinical care and clinical trial design.US National Institute on Aging, US National Institutes of Health, Italian Ministry of Health, European Commission, University of Torino Rita Levi Montalcini Department of Neurosciences, Emilia Romagna Regional Health Authority, and Italian Ministry of Education, University, and Research.For the Italian and German translations of the abstract see Supplementary Materials section.

Background During the coronavirus disease 2019 (COVID-19) outbreak, a decrease of stroke’s hospital admissions and reperfusion therapy has been reported worldwide. This retrospective observational study assessed the volume of stroke cases managed in the Emergency Department (ED) and reperfusion therapies in an Italian stroke network with a high incidence of COVID-19, particularly to evaluate if the in-hospital rerouting and the switch from a drip-and-ship to a mothership model could assure an adequate volume of acute treatments. Methods We compared data from March 2020 with those from previous years and formulated five PICO questions regarding (1) incidence of stroke cases in the ED; (2) relation between stroke cases and COVID-19; (3) differences in the number of reperfusion therapies, (4) in the call-to-needle and door-to-needle times for intravenous thrombolysis, and (5) in the call-to-groin and door-to-groin times for thrombectomy. Results We found (1) a 28% decreased of confirmed stroke cases managed in the ED, (2) a negative correlation between stroke cases in ED and COVID-19 progression (rs = − .390, p = .030), and (3) a similar number of treatments in March 2020 and March 2019. The adoption of the mothership model (4) did not delay alteplase infusion (median call-to-needle p = .126, median door-to-needle p = .142) but led to (5) a significant reduction in median call-to-groin (p = .018) and door-to-groin times (p = .010). Conclusion The “hospital avoidance” of stroke patients during the “stay-at-home” appeals needs to be considered for future public health campaigns. A prompt reorganization of the stroke network can guarantee optimal performances at times of crisis. Supplementary Information The online version contains supplementary material available at 10.1007/s10072-020-04914-4.

Early postoperative seizures are defined as those that appear within the first week after surgery and are a well-known and feared complication in patients with supratentorial brain tumors. Few studies have investigated the value of pharmacological prophylaxis in the prevention of postoperative seizures in these patients and their outcome has not been consistent. Furthermore, the efficacy of the new generation of antiepileptic agents in the prophylaxis of perioperative seizures has not been assessed so far. We analyzed the data related to 150 patients harboring supratentorial brain gliomas with the aim to assess the efficacy of oxcarbazepine in preventing the occurrence or the recurrence of early postoperative seizures and its tolerability when it is rapidly titrated. Only four patients (2.7%) experienced seizures within the first week after surgery. Patients did not report disturbances during the titration phase. Regarding adverse events in the first week, six patients (4%) showed minor skin rash. Persistent symptomatic hyponatremia never occurred. Our data showed that oxcarbazepine can be a good alternative to traditional antiepileptic agents in the prevention of perioperative seizures being efficacy, ease of use (rapid titration in 3 days, not requiring close plasma concentration monitoring) and good tolerability (no major side effects during titration and during the first postoperative week) the key factors. Moreover, oxcarbazepine can be a valid choice when long-term therapy is required because of the low interaction with other drugs and the low hematological side effects.