Resolving ambiguities in SUDEP classification.

OBJECTIVE: To examine the consistency of applying the Nashef et al (2012) criteria to classify sudden unexpected death in epilepsy (SUDEP). METHODS: We reviewed cases from the North American SUDEP Registry (n = 250) and Medical Examiner Offices (n = 1301: 698 Maryland, 457 New York City, 146 San Diego). Two epileptologists with expertise in SUDEP and epilepsy-related mortality independently reviewed medical records, scene investigation, autopsy, and toxicology and assigned a SUDEP class. RESULTS: Major areas of disagreement arose between adjudicators concerned differentiating (1) Definite SUDEP Plus Comorbidity from Possible SUDEP and (2) Resuscitated (Near) SUDEP from SUDEP. In many cases, distinguishing between contributing and competing causes of death when trying to classify Definite SUDEP Plus Comorbidity versus Possible SUDEP is ambiguous and relies on judgement. Similarly, determining if an intervention was lifesaving or not (Resuscitated SUDEP or Not SUDEP), or if resuscitation merely delayed SUDEP (Resuscitated SUDEP or SUDEP) is often a judgement call and can differ between experienced adjudicators. Given these persisting ambiguities, we propose more explicit criteria for distinguishing these categories. SIGNIFICANCE: Accurate and consistent classification of cause of death among individuals with epilepsy remains a dire public health concern. SUDEP is likely underestimated in national health statistics. Greater standardization of criteria among epilepsy researchers, medical examiners, and epidemiologists to determine cause and classify death will lead to more accurate tracking of SUDEP and other epilepsy-related mortalities.

Experiences with premorbid SUDEP discussion among participants in the North American SUDEP Registry (NASR).

The North American SUDEP Registry (NASR) is a repository of clinical data and biospecimens in cases of sudden unexpected death in epilepsy (SUDEP), a leading cause of epilepsy-related deaths. We assessed whether bereaved families were aware of SUDEP before their family member's death and their preferences for SUDEP disclosure. At enrollment, next-of-kin of SUDEP cases completed an intake interview, including questions assessing premorbid SUDEP discussions. Only 18.1% of the 138 next-of-kin recalled a previous discussion of SUDEP with a healthcare provider or support resource. Of the 112 who did not recall such a discussion, 72.3% wished it was discussed, 10.7% were satisfied it was not discussed, and 17% were unsure. A history of status epilepticus predicted SUDEP discussion. Rates of SUDEP discussion were not significantly higher among SUDEPs after 2013 (the approximate study midpoint) compared with those before then. Our study suggests that SUDEP remains infrequently discussed with family members of persons with epilepsy. Nearly three-quarters of family members wished they had known of SUDEP before the death. However, some were indifferent or were satisfied that this discussion had not occurred. We must balance more systematic education of patients and families about SUDEP while respecting individual preferences about having this discussion.

A bi-level framework for real-time crash risk forecasting using artificial intelligence-based video analytics.

This study proposes a bi-level framework for real-time crash risk forecasting (RTCF) for signalised intersections, leveraging the temporal dependency among crash risks of contiguous time slices. At the first level of RTCF, a non-stationary generalised extreme value (GEV) model is developed to estimate the rear-end crash risk in real time (i.e., at a signal cycle level). Artificial intelligence techniques, like YOLO and DeepSort were used to extract traffic conflicts and time-varying covariates from traffic movement videos at three signalised intersections in Queensland, Australia. The estimated crash frequency from the non-stationary GEV model is compared against the historical crashes for the study locations (serving as ground truth), and the results indicate a close match between the estimated and observed crashes. Notably, the estimated mean crashes lie within the confidence intervals of observed crashes, further demonstrating the accuracy of the extreme value model. At the second level of RTCF, the estimated signal cycle crash risk is fed to a recurrent neural network to predict the crash risk of the subsequent signal cycles. Results reveal that the model can reasonably estimate crash risk for the next 20-25 min. The RTCF framework provides new pathways for proactive safety management at signalised intersections.

Revisiting the hybrid approach of anomaly detection and extreme value theory for estimating pedestrian crashes using traffic conflicts obtained from artificial intelligence-based video analytics.

Pedestrians represent a group of vulnerable road users who are at a higher risk of sustaining severe injuries than other road users. As such, proactively assessing pedestrian crash risks is of paramount importance. Recently, extreme value theory models have been employed for proactively assessing crash risks from traffic conflicts, whereby the underpinning of these models are two sampling approaches, namely block maxima and peak over threshold. Earlier studies reported poor accuracy and large uncertainty of these models, which has been largely attributed to limited sample size. Another fundamental reason for such poor performance could be the improper selection of traffic conflict extremes due to the lack of an efficient sampling mechanism. To test this hypothesis and demonstrate the effect of sampling technique on extreme value theory models, this study aims to develop hybrid models whereby unconventional sampling techniques were used to select the extreme vehicle-pedestrian conflicts that were then modelled using extreme value distributions to estimate the crash risk. Unconventional sampling techniques refer to unsupervised machine learning-based anomaly detection techniques. In particular, Isolation forest and minimum covariance determinant techniques were used to identify extreme vehicle-pedestrian conflicts characterised by post encroachment time as the traffic conflict measure. Video data was collected for four weekdays (6 am-6 pm) from three four-legged intersections in Brisbane, Australia and processed using artificial intelligence-based video analytics. Results indicate that mean crash estimates of hybrid models were much closer to observed crashes with narrower confidence intervals as compared with traditional extreme value models. The findings of this study demonstrate the suitability of machine learning-based anomaly detection techniques to augment the performance of existing extreme value models for estimating pedestrian crashes from traffic conflicts. These findings are envisaged to further explore the possibility of utilising more advanced machine learning models for traffic conflict techniques.

In-hospital and Readmission Outcomes With Percutaneous Balloon Mitral Valvuloplasty.

Percutaneous balloon mitral valvuloplasty (PBMV) is primarily performed for rheumatic mitral stenosis (MS). Therefore, limited data exist on PBMV in countries with a low incidence of rheumatic disease. Using the Nationwide Readmission Database, we examined trends in in-hospital mortality and 30-day readmission among patients who received PBMV for rheumatic and non-rheumatic MS. We also examined the change in 90-day hospitalization rate before vs after PBMV. Between 2016 and 2019, there were 1109 hospitalizations in which patients received PBMV for rheumatic (n = 955, 86.1%) vs non-rheumatic MS (n = 154, 13.9%). The all-cause in-hospital mortality for rheumatic and non-rheumatic MS did not change over time (0.9% → 2.0%, P = 0.94, and 5.9% → 9.5%, P = 0.09 respectively). Similarly, the 30-day readmission for patients with rheumatic and non-rheumatic MS did not change over time (12.4% → 9.9%, P = 0.26, and 4.4% → 10.5%, P = 0.30, respectively). The 90-day all-cause hospitalization rate remained the same before vs after PBMV for rheumatic and non-rheumatic MS (25.5% → 21.8%; P = 0.14, and 24.0% → 33.7%; P = 0.19, respectively). Although no statistically significant change was noted over time for trends in in-hospital mortality, 30-day readmission, or even in the change in 90-day all-cause hospitalizations before and after PBMV for both types of MS, among those with non-rheumatic MS, there was a signal of an increase in the in-hospital mortality, and 30-day readmission, even more, there was 29% relative increase in 90-day hospitalizations after PBMV. Future studies are needed to examine the role of PBMV in patients with non-rheumatic MS.

SUDEP in the North American SUDEP Registry: The full spectrum of epilepsies.

OBJECTIVE: To obtain medical records, family interviews, and death-related reports of sudden unexpected death in epilepsy (SUDEP) cases to better understand SUDEP. METHODS: All cases referred to the North American SUDEP Registry (NASR) between October 2011 and June 2018 were reviewed; cause of death was determined by consensus review. Available medical records, death scene investigation reports, autopsy reports, and next-of-kin interviews were reviewed for all cases of SUDEP. Seizure type, EEG, MRI, and SUDEP classification were adjudicated by 2 epileptologists. RESULTS: There were 237 definite and probable cases of SUDEP among 530 NASR participants. SUDEP decedents had a median age of 26 (range 1-70) years at death, and 38% were female. In 143 with sufficient information, 40% had generalized and 60% had focal epilepsy. SUDEP affected the full spectrum of epilepsies, from benign epilepsy with centrotemporal spikes (n = 3, 1%) to intractable epileptic encephalopathies (n = 27, 11%). Most (93%) SUDEPs were unwitnessed; 70% occurred during apparent sleep; and 69% of patients were prone. Only 37% of cases of SUDEP took their last dose of antiseizure medications (ASMs). Reported lifetime generalized tonic-clonic seizures (GTCS) were <10 in 33% and 0 in 4%. CONCLUSIONS: NASR participants commonly have clinical features that have been previously been associated with SUDEP risk such as young adult age, ASM nonadherence, and frequent GTCS. However, a sizeable minority of SUDEP occurred in patients thought to be treatment responsive or to have benign epilepsies. These results emphasize the importance of SUDEP education across the spectrum of epilepsy severities. We aim to make NASR data and biospecimens available for researchers to advance SUDEP understanding and prevention.

Radial Versus Femoral Approach in Women Undergoing Coronary Angiography: A Meta-Analysis of Randomized Controlled Trials.

OBJECTIVES: We sought to compare outcomes with radial vs femoral approach in female patients undergoing coronary angiography. BACKGROUND: Women undergoing cardiac procedures have increased risk of bleeding and vascular complications, but are under-represented in randomized clinical trials (RCTs) involving coronary angiography. METHODS: We performed a meta-analysis of RCTs comparing outcomes in women undergoing angiography with radial vs femoral approaches. The primary outcome was non-coronary artery bypass graft (CABG) related bleeding at 30 days. Secondary outcomes included major adverse cardiovascular or cerebrovascular events (MACCE; a composite of death, stroke or myocardial infarction), vascular complications, procedure duration, and access-site crossover. RESULTS: Four studies (n = 6041 female patients) met the inclusion criteria. In female patients undergoing coronary angiography, radial access decreased non-CABG related bleeding (odds ratio [OR], 0.56; 95% confidence interval [CI], 0.44-0.72; P<.001), MACCE (OR, 0.73; 95% CI, 0.58-0.93; P=.01), vascular complications (OR, 0.49; 95% CI, 0.32-0.75; P<.001) with no significant difference in procedure time (mean difference, 0.04; 95% CI, -0.97 to 0.89; P=.93). There was an increase in access-site crossover using the radial approach (OR, 2.86; 95% CI, 2.24-3.63; P<.001). Patients undergoing radial approach were more likely to prefer radial access for the next procedure (OR, 6.96; 95% CI, 5.70-8.50; P<.001). CONCLUSIONS: In female patients undergoing coronary angiography or intervention, the radial approach is associated with decreased bleeding, MACCE, and vascular complications. These data suggest that radial access should be the preferred approach for women.