Potentially preventable trauma deaths: A retrospective review

Reviewing prehospital trauma deaths provides an opportunity to identify system improvements that may reduce trauma mortality. The objective of this study was to identify the number and rate of potentially preventable trauma deaths through expert panel reviews of prehospital and early in-hospital trauma deaths. We conducted a retrospective review of prehospital and early in-hospital (<24?h) trauma deaths following a traumatic out-of-hospital cardiac arrest that were attended by Ambulance Victoria (AV) in the state of Victoria, Australia, between 2008 and 2014. Expert panels were used to review cases that had resuscitation attempted by paramedics and underwent a full autopsy. Patients with a mechanism of hanging, drowning or those with anatomical injuries deemed to be unsurvivable were excluded. Of the 1183 cases that underwent full autopsies, resuscitation was attempted by paramedics in 336 (28%) cases. Of these, 113 cases (34%) were deemed to have potentially survivable injuries and underwent expert panel review. There were 90 (80%) deaths that were not preventable, 19 (17%) potentially preventable deaths and 4 (3%) preventable deaths. Potentially preventable or preventable deaths represented 20% of those cases that underwent review and 7% of cases that had attempted resuscitation. The number of potentially preventable or preventable trauma deaths in the pre-hospital and early in-hospital resuscitation phase was low. Specific circumstances were identified in which the trauma system could be further improved

Automated video-based detection of nocturnal convulsive seizures in a residential care setting

\u3cp\u3ePeople with epilepsy need assistance and are at risk of sudden death when having convulsive seizures (CS). Automated real-time seizure detection systems can help alert caregivers, but wearable sensors are not always tolerated. We determined algorithm settings and investigated detection performance of a video algorithm to detect CS in a residential care setting. The algorithm calculates power in the 2-6 Hz range relative to 0.5-12.5 Hz range in group velocity signals derived from video-sequence optical flow. A detection threshold was found using a training set consisting of video-electroencephalogaphy (EEG) recordings of 72 CS. A test set consisting of 24 full nights of 12 new subjects in residential care and additional recordings of 50 CS selected randomly was used to estimate performance. All data were analyzed retrospectively. The start and end of CS (generalized clonic and tonic-clonic seizures) and other seizures considered desirable to detect (long generalized tonic, hyperkinetic, and other major seizures) were annotated. The detection threshold was set to the value that obtained 97% sensitivity in the training set. Sensitivity, latency, and false detection rate (FDR) per night were calculated in the test set. A seizure was detected when the algorithm output exceeded the threshold continuously for 2 seconds. With the detection threshold determined in the training set, all CS were detected in the test set (100% sensitivity). Latency was ≤10 seconds in 78% of detections. Three/five hyperkinetic and 6/9 other major seizures were detected. Median FDR was 0.78 per night and no false detections occurred in 9/24 nights. Our algorithm could improve safety unobtrusively by automated real-time detection of CS in video registrations, with an acceptable latency and FDR. The algorithm can also detect some other motor seizures requiring assistance.\u3c/p\u3

Automated video-based detection of nocturnal convulsive seizures in a residential care setting

People with epilepsy need assistance and are at risk of sudden death when having convulsive seizures (CS). Automated real-time seizure detection systems can help alert caregivers, but wearable sensors are not always tolerated. We determined algorithm settings and investigated detection performance of a video algorithm to detect CS in a residential care setting. The algorithm calculates power in the 2-6 Hz range relative to 0.5-12.5 Hz range in group velocity signals derived from video-sequence optical flow. A detection threshold was found using a training set consisting of video-electroencephalogaphy (EEG) recordings of 72 CS. A test set consisting of 24 full nights of 12 new subjects in residential care and additional recordings of 50 CS selected randomly was used to estimate performance. All data were analyzed retrospectively. The start and end of CS (generalized clonic and tonic-clonic seizures) and other seizures considered desirable to detect (long generalized tonic, hyperkinetic, and other major seizures) were annotated. The detection threshold was set to the value that obtained 97% sensitivity in the training set. Sensitivity, latency, and false detection rate (FDR) per night were calculated in the test set. A seizure was detected when the algorithm output exceeded the threshold continuously for 2 seconds. With the detection threshold determined in the training set, all CS were detected in the test set (100% sensitivity). Latency was ≤10 seconds in 78% of detections. Three/five hyperkinetic and 6/9 other major seizures were detected. Median FDR was 0.78 per night and no false detections occurred in 9/24 nights. Our algorithm could improve safety unobtrusively by automated real-time detection of CS in video registrations, with an acceptable latency and FDR. The algorithm can also detect some other motor seizures requiring assistance

Multimodal, automated detection of nocturnal motor seizures at home : Is a reliable seizure detector feasible?

Objective: Automated seizure detection and alarming could improve quality of life and potentially prevent sudden, unexpected death in patients with severe epilepsy. As currently available systems focus on tonic-clonic seizures, we want to detect a broader range of seizure types, including tonic, hypermotor, and clusters of seizures. Methods: In this multicenter, prospective cohort study, the nonelectroencephalographic (non-EEG) signals heart rate and accelerometry were measured during the night in patients undergoing a diagnostic video-EEG examination. Based on clinical video-EEG data, seizures were classified and categorized as clinically urgent or not. Seizures included for analysis were tonic, tonic-clonic, hypermotor, and clusters of short myoclonic/tonic seizures. Features reflecting physiological changes in heart rate and movement were extracted. Detection algorithms were developed based on stepwise fulfillment of conditions during increases in either feature. A training set was used for development of algorithms, and an independent test set was used for assessing performance. Results: Ninety-five patients were included, but due to sensor failures, data from only 43 (of whom 23 patients had 86 seizures, representing 402 h of data) could be used for analysis. The algorithms yield acceptable sensitivities, especially for clinically urgent seizures (sensitivity = 71-87%), but produce high false alarm rates (2.3-5.7 per night, positive predictive value = 25-43%). There was a large variation in the number of false alarms per patient. Significance: It seems feasible to develop a detector with high sensitivity, but false alarm rates are too high for use in clinical practice. For further optimization, personalization of algorithms may be necessary

Multimodal nocturnal seizure detection in a residential care setting:a long-term prospective trial

\u3cp\u3eObjective To develop and prospectively evaluate a method of epileptic seizure detection combining heart rate and movement. Methods In this multicenter, in-home, prospective, video-controlled cohort study, nocturnal seizures were detected by heart rate (photoplethysmography) or movement (3-D accelerometry) in persons with epilepsy and intellectual disability. Participants with &gt;1 monthly major seizure wore a bracelet (Nightwatch) on the upper arm at night for 2 to 3 months. Major seizures were tonic-clonic, generalized tonic &gt;30 seconds, hyperkinetic, or others, including clusters (&gt;30 minutes) of short myoclonic/tonic seizures. The video of all events (alarms, nurse diaries) and 10% completely screened nights were reviewed to classify major (needing an alarm), minor (needing no alarm), or no seizure. Reliability was tested by interobserver agreement. We determined device performance, compared it to a bed sensor (Emfit), and evaluated the caregivers' user experience. Results Twenty-eight of 34 admitted participants (1,826 nights, 809 major seizures) completed the study. Interobserver agreement (major/no major seizures) was 0.77 (95% confidence interval [CI] 0.65-0.89). Median sensitivity per participant amounted to 86% (95% CI 77%-93%); the false-negative alarm rate was 0.03 per night (95% CI 0.01-0.05); and the positive predictive value was 49% (95% CI 33%-64%). The multimodal sensor showed a better sensitivity than the bed sensor (n = 14, median difference 58%, 95% CI 39%-80%, p &lt; 0.001). The caregivers' questionnaire (n = 33) indicated good sensor acceptance and usability according to 28 and 27 participants, respectively. Conclusion Combining heart rate and movement resulted in reliable detection of a broad range of nocturnal seizures.\u3c/p\u3