2B-Alert App: A mobile application for real-time individualized prediction of alertness.

Knowing how an individual responds to sleep deprivation is a requirement for developing personalized fatigue management strategies. Here we describe and validate the 2B-Alert App, the first mobile application that progressively learns an individual's trait-like response to sleep deprivation in real time, to generate increasingly more accurate individualized predictions of alertness. We incorporated a Bayesian learning algorithm within the validated Unified Model of Performance to automatically and gradually adapt the model parameters to an individual after each psychomotor vigilance test. We implemented the resulting model and the psychomotor vigilance test as a smartphone application (2B-Alert App), and prospectively validated its performance in a 62-hr total sleep deprivation study in which 21 participants used the app to perform psychomotor vigilance tests every 3 hr and obtain real-time individualized predictions after each test. The temporal profiles of reaction times on the app-conducted psychomotor vigilance tests were well correlated with and as sensitive as those obtained with a previously characterized psychomotor vigilance test device. The app progressively learned each individual's trait-like response to sleep deprivation throughout the study, yielding increasingly more accurate predictions of alertness for the last 24 hr of total sleep deprivation as the number of psychomotor vigilance tests increased. After only 12 psychomotor vigilance tests, the accuracy of the model predictions was comparable to the peak accuracy obtained using all psychomotor vigilance tests. With the ability to make real-time individualized predictions of the effects of sleep deprivation on future alertness, the 2B-Alert App can be used to tailor personalized fatigue management strategies, facilitating self-management of alertness and safety in operational and non-operational settings.

PC-PVT: a platform for psychomotor vigilance task testing, analysis, and prediction.

Using a personal computer (PC) for simple visual reaction time testing is advantageous because of the relatively low hardware cost, user familiarity, and the relative ease of software development for specific neurobehavioral testing protocols. However, general-purpose computers are not designed with the millisecond-level accuracy of operation required for such applications. Software that does not control for the various sources of delay may return reaction time values that are substantially different from the true reaction times. We have developed and characterized a freely available system for PC-based simple visual reaction time testing that is analogous to the widely used psychomotor vigilance task (PVT). In addition, we have integrated individualized prediction algorithms for near-real-time neurobehavioral performance prediction. We characterized the precision and accuracy with which the system as a whole measures reaction times on a wide range of computer hardware configurations, comparing its performance with that of the "gold standard" PVT-192 device. We showed that the system is capable of measuring reaction times with an average delay of less than 10 ms, a margin of error that is comparable to that of the gold standard. The most critical aspect of hardware selection is the type of mouse used for response detection, with gaming mice showing a significant advantage over standard ones. The software is free to download from http://bhsai.org/downloads/pc-pvt/ .

PC-PVT 2.0: An updated platform for psychomotor vigilance task testing, analysis, prediction, and visualization.

BACKGROUND: The psychomotor vigilance task (PVT) has been widely used to assess the effects of sleep deprivation on human neurobehavioral performance. To facilitate research in this field, we previously developed the PC-PVT, a freely available software system analogous to the "gold-standard" PVT-192 that, in addition to allowing for simple visual reaction time (RT) tests, also allows for near real-time PVT analysis, prediction, and visualization in a personal computer (PC). NEW METHOD: Here we present the PC-PVT 2.0 for Windows 10 operating system, which has the capability to couple PVT tests of a study protocol with the study's sleep/wake and caffeine schedules, and make real-time individualized predictions of PVT performance for such schedules. We characterized the accuracy and precision of the software in measuring RT, using 44 distinct combinations of PC hardware system configurations. RESULTS: We found that 15 system configurations measured RTs with an average delay of less than 10 ms, an error comparable to that of the PVT-192. To achieve such small delays, the system configuration should always use a gaming mouse as the means to respond to visual stimuli. We recommend using a discrete graphical processing unit for desktop PCs and an external monitor for laptop PCs. COMPARISON WITH EXISTING METHOD: This update integrates a study's sleep/wake and caffeine schedules with the testing software, facilitating testing and outcome visualization, and provides near-real-time individualized PVT predictions for any sleep-loss condition considering caffeine effects. CONCLUSIONS: The software, with its enhanced PVT analysis, visualization, and prediction capabilities, can be freely downloaded from https://pcpvt.bhsai.org.

Tachycardic and non-tachycardic responses in trauma patients with haemorrhagic injuries.

BACKGROUND: Analyses of large databases have demonstrated that the association between heart rate (HR) and blood loss is weaker than what is taught by Advanced Trauma Life Support training. However, those studies had limited ability to generate a more descriptive paradigm, because they only examined a single HR value per patient. METHODS: In a comparative, retrospective analysis, we studied the temporal characteristics of HR through time in adult trauma patients with haemorrhage, based on documented injuries and transfusion of ≥3 units of red blood cells (RBCs). We analysed archived vital-sign data of up to 60 min during either pre-hospital or emergency department care. RESULTS: We identified 133 trauma patients who met the inclusion criteria for major haemorrhage and 1640 control patients without haemorrhage. There were 55 haemorrhage patients with a normal median HR and 78 with tachycardia. Median ΔHR was -0.8 and +0.7 bpm per 10 min, respectively. Median time to documented hypotension was 8 and 5 min, respectively. RBCs were not significantly different; median volumes were 6 (IQR: 4-13) and 10 units (IQR: 5-16), respectively. Time-to-hypotension and mortality were not significantly different. Tachycardic patients were significantly younger (P < 0.05). Only 10 patients with normal HR developed transient/temporary tachycardia, and only 11 tachycardic patients developed a transient/temporary normal HR. CONCLUSIONS: The current analysis suggests that some trauma patients with haemorrhage are continuously tachycardic while others have a normal HR. For both cohorts, hypotension typically develops within 30 min, without any consistent temporal increases or trends in HR.

Automated analysis of vital signs to identify patients with substantial bleeding before hospital arrival: a feasibility study.

Trauma outcomes are improved by protocols for substantial bleeding, typically activated after physician evaluation at a hospital. Previous analysis suggested that prehospital vital signs contained patterns indicating the presence or absence of substantial bleeding. In an observational study of adults (aged ≥18 years) transported to level I trauma centers by helicopter, we investigated the diagnostic performance of the Automated Processing of the Physiological Registry for Assessment of Injury Severity (APPRAISE) system, a computational platform for real-time analysis of vital signs, for identification of substantial bleeding in trauma patients with explicitly hemorrhagic injuries. We studied 209 subjects prospectively and 646 retrospectively. In our multivariate analysis, prospective performance was not significantly different from retrospective. The APPRAISE system was 76% sensitive for 24-h packed red blood cells of 9 or more units (95% confidence interval, 59% - 89%) and significantly more sensitive (P < 0.05) than any prehospital Shock Index of 1.4 or higher; sensitivity, 59%; initial systolic blood pressure (SBP) less than 110 mmHg, 50%; and any prehospital SBP less than 90 mmHg, 50%. The APPRAISE specificity for 24-h packed red blood cells of 0 units was 87% (88% for any Shock Index ≥1.4, 88% for initial SBP <110 mmHg, and 90% for any prehospital SBP <90 mmHg). Median APPRAISE hemorrhage notification time was 20 min before arrival at the trauma center. In conclusion, APPRAISE identified bleeding before trauma center arrival. En route, this capability could allow medics to focus on direct patient care rather than the monitor and, via advance radio notification, could expedite hospital interventions for patients with substantial blood loss.

A platform for testing and comparing of real-time decision-support algorithms in mobile environments.

The unavailability of a flexible system for realtime testing of decision-support algorithms in a pre-hospital clinical setting has limited their use. In this study, we describe a plug-and-play platform for real-time testing of decision-support algorithms during the transport of trauma casualties en route to a hospital. The platform integrates a standard-of-care vital-signs monitor, which collects numeric and waveform physiologic time-series data, with a rugged ultramobile personal computer. The computer time-stamps and stores data received from the monitor, and performs analysis on the collected data in real-time. Prior to field deployment, we assessed the performance of each component of the platform by using an emulator to simulate a number of possible fault scenarios that could be encountered in the field. Initial testing with the emulator allowed us to identify and fix software inconsistencies and showed that the platform can support a quick development cycle for real-time decision-support algorithms.