Auto Collision Research Articles

Handling highly imbalanced data for classifying fatality of auto collisions using machine learning techniques

Accurate prediction of fatal events in car accidents has significant health management implications. This research article explores the application of imbalanced data handling techniques in machine learning to enhance prediction performance. By implementing these techniques on car accident data, health organizations can identify and forecast a fatal event, enabling more efficient and effective allocation of limited health resources. Concurrently, enhancing the performance of machine learning models through imbalanced data handling techniques can impact health management decisions. Our findings highlight the significance of imbalanced data handling techniques in predicting fatality in car accidents, ultimately contributing to improved road safety and better management of health resources. Moreover, the effective use of imbalanced data demonstrates a substantial improvement in the specificity of the prediction. Addressing the impact of machine learning techniques on imbalanced car accident data can significantly improve overall health outcomes.

Design and Application of Deep Learning-Based Crash Damage Prediction Model for Self-Driving Cars

Abstract The collision damage of automated cars has grown in importance as self-driving car technology has advanced to the pilot operation stage. To enhance the safety of autonomous vehicles by predicting and preventing potential hazards during autonomous driving, this study presents a model for collision damage prediction in automated driving cars. The model optimizes deep convolutional neural networks using the self-attention mechanism and incorporates a degree convolutional neural network algorithm with the attention mechanism. Its application is key to reduce risks in autonomous driving. The results demonstrated that the accuracy, reliability, specificity, and Mathews correlation coefficient of the improved algorithm were 94.0%, 94.8%, 93.6%, and 0.88, respectively, resulting in a high overall performance. The prediction model's accuracy during training on the training data set and validation data set was 100% and 98%, respectively, demonstrating its efficacy. The prediction model's prediction accuracy in calculating the degree of auto collision damage for 10 working conditions in the validation data set was 83.3%. The prediction results were essentially consistent with the trend of the actual collision damage degree curve, demonstrating both the viability and high prediction accuracy of the prediction model. The aforementioned findings demonstrated the model's strong performance and great application value in the field of self-driving car collision avoidance and warning.

Head injury patients at a tertiary health care center

Background: Head injury is a significant reason for high dreariness and mortality, especially in youthful and useful age bunch patients. As per the World Health Organization report on street security, road traffic accidents (RTAs) would be the fifth driving reason for death worldwide constantly 2030. Larger part of the head wounds are ramifications of street auto collisions (RTAs), falls, attacks, or wounds, happening either in the work environment, during sport, and so forth. Present study was aimed to study patients with head injury coming to our tertiary care center.Methods: Present study was a prospective, observational type, conducted in patients with head injury who were admitted under the department of surgery during study period.Results: After applying inclusion and exclusion criteria, total 230 patients were considered for present study. 57% patients were from 21-50 years age group. Male to female ratio was 2.6:1. RTAs (63%) was most common cause of head injury in present study followed by assaults (20%) and fall from height (10%). 63% patients had mild head injury, while 21% and 16% patients had moderate and severe head injury respectively. Total 17% deaths were noted in present study. Outcome worsens with worsening of severity of head injury.Conclusions: Trauma remains leading cause of head injury in present study. Necessary preventive measures and vigorous training of all medico and para-medico staff for early management of head injury can definitively reduce mortality and morbidity due to head injury.

Road Accident Analysis Using Machine Learning

Abstract: Road accidents are one among the key concerns within the country. The economic impact of traffic accidents cost many billions of dollars for US citizens per annum. Reducing the traffic accidents has always been a challenge. The most important objective of the project is to investigate what are the foundations for mishaps like effect of precipitation or any natural elements and to predict the probability of road accidents supported the present accidents records. By performing data visualization, we are able to identify the car accidents zones, factors effecting accident severity, and locations etc. Identifying the key factors of how these accidents occur can help in implementing well informed actions. Road safety analysts following up on street accidents information have seen outcome in street auto collisions examination through the applying information logical methods, however, little headway was the expectation of street injury. This report says progressed information investigation strategies to anticipate injury seriousness levels and assesses their presentation. The review utilizes prescient demonstrating methods to recognize chance and key factors that adds to mishap seriousness. The review utilizes openly accessible information from US branch of transport that covers the sum from 2005 to 2019. The report presents a methodology which is adequately general so are frequently applied to various informational collections from different nations. The outcomes recognized that tree based methods like Extreme Gradient Boosting beat relapse covers, as Artificial Neural Network (ANN) also to the paper, recognizes fascinating connections were recognized concepts related with nature of information.

Research paper on improvement of road infrastructure to implement road safety at an intersection of military hospital road.

Geometrical plan lacks on existing streets would prompt an expected mishap, for example, a mishap occurs at the sharp bends, layered asphalt conditions, and dangerous asphalt surface. Street auto collision has been expanding in the Southern Region of Tigray, of which this region was appeared to have disturbing rates. As indicated by the Southern Tigray Regional Police, the more rate of street car crashes were recorded in Garhi, Brimah pull and jekhani regions. This examination study zeroed in on the investigation of auto collisions identified with mathematical plan boundaries of the current black top street. While the information for the investigation covered course information, auto collision report from the police headquarters containing a few wounds, crashes, and destroyed the properties, just as meetings and poll studies to individuals who are straightforwardly engaged with street voyages, are thought of. The essential information has basically covered the math of the street which was estimated during the site study, street security review utilizing the agenda, meeting, and poll study. Then again, the auxiliary information gathered from the traffic the executive’s office in the area workplaces. On this, the outcomes introduced as line diagrams, pie outlines, figures for street car crash and sketch for the proposed improvement in the street plan issue. In view of the aftereffects of the investigation in the year 2015 to year 2019, it discovered that there were 1866 Road Traffic Accidents have been happening nearby the overview street areas. It uncovered that the essential driver of street car crashes in the investigation region exuded from the street plan components because of some mathematical lacks at the auto collision inclined regions uncovered that the essential driver of street car crashes in the examination region radiated from the street plan components because of some mathematical insufficiencies at the car crash inclined regions. Hence, this investigation reasoned that the recurrence of event of street auto collisions and the figure of setbacks is altogetherexpanding. This street mishap would endure if the concerned offices don’t satisfactorily address the disease.

Impact Analysis of Accident Using AI

There are numerous inventories in vehicle enterprises to plan and fabricate security measures for autos, yet car crashes are unavoidable. There is countless mishaps winning in all metropolitan and country regions. Examples associated with various conditions can be identified by fostering a precise forecast models which will be equipped for programmed partition of different inadvertent situations .These group will be valuable to forestall mishaps and foster security measures. We accept to procure greatest conceivable outcomes of mishap decrease utilizing low spending assets by utilizing some logical measures. There is a gigantic effect on the general public because of auto collisions where there is an extraordinary expenses of fatalities and wounds. Lately, there is an increment in the investigate thoughtfulness regarding decide the fundamentally influence the seriousness of the drivers wounds which is caused because of the street mishaps. Exact and complete mishap records are the premise of mishap investigation .the compelling utilization of mishap records relies upon certain variables, similar to the exactness of the information, record maintenance, and information examination. There is numerous methodologies applied to this situation to examine this issue. In this examination paper Open CV apparatus is utilized to mishap sway investigation. A new report delineated that the private and shopping locales are more perilous than town areas.as may have been anticipated, the frequencies of the setbacks were higher close to the zones of home perhaps in view of the greater openness .An examination uncovered that the loss rates among the neighborhoods are delegated generally denied and altogether higher than those from moderately rich regions.

Application Edge Designs to Study The Actual Causes That Led to The Frequent Traffic Accidents

Background: Hundreds of people die every day as a result of traffic collisions all over the country. The majority of these collisions are caused by cars or pedestrians breaking traffic laws, such as speeding or talking on their phones. Most drivers drive their cars while feeling sleepy or tired, or driving under the influence of medication that causes drowsiness, or lack of concentration, or hallucinations. The latest statistics from Ministry of health of Kingdom of Saudi Arabia indicate that the death rate in road accidents in Saudi Arabia is 17 people every 40 minutes. Therefore, based on this statistic, traffic accidents have become a source of concern for the Saudi Arabian society. They cause great losses in human and material resources, in addition to psychological and social problems. It is necessary to conduct a field study to find out the actual cause of the spread of accidents in Saudi Arabia. Objective: This study sets out to examine the actual reasons that led to the frequent traffic accidents in the Saudi Arabian society. Methods: An online survey provided quantitative data from 1000 participants. Factual importance was investigated utilizing edge plan examination and the relapse investigation utilizing the SPSS program to decide the real makes that drove the spread of auto collisions in the Saudi Arabian culture. Results: The findings indicated that driving vehicles by unqualified persons, drug abuse and grain deprivation are the factors that led to the spread of traffic accidents in the Saudi Arabian society. Conclusion: it is recommended that Saudi Arabian government professionals develop an operational plan to study these causes to take action. In the future, data may be analyzed using supersaturated prototypes, in which a large number of variables are studied for just a few simulation trials.

Primary arthrodesis in acute ligamentous lisfranc injuries

Lisfranc wounds influence the tarsometatarsal (TMT), intercuneiform, and the naviculocuneiform joints. It very well may be bony, ligamentous, or a mix of the two. The meaningful step forward somewhat recently has been the accentuation on early stable anatomical decrease and adjustment of these wounds. Late examinations have recommended that essential arthrodesis might be a favored method for basically ligamentous Lisfranc wounds. This investigation expected to assess the momentary consequences of essential arthrodesis in unadulterated ligamentous lisfranc wounds. Twenty patients, 13 guys and 7 females with a mean time of 27.4 ± 6.19 with least age 19 years of age and greatest age 39 years of age were remembered for this investigation. The most widely recognized instrument of injury was street auto collision (55%), trailed by tumble from stature (40%), and followed by hyper plantar flexion foot injury during plunging steps (5%). the mean AOFAS score of the included patients was 81.65 ± 1.60 with least score 80 and greatest score 84. the mean EFAS score of the included patients was 31.60 ± 1.76 with least score 28 and most extreme score 34. the mean Pain VAS score of the included patients was 2.05 ± 0.76 with least score 1 and greatest score 3. the mean Union season of the included patients was 12.55 ± 0.51 weeks with least 12 weeks and most extreme 13 weeks. All in all, Lisfranc wounds are unpredictable and care should be taken in choosing the fitting treatment. Essential arthrodesis in unadulterated ligamentous lisfranc injury has benefits: diminished foot deformation rates, supported biomechanical morphology of the feet, diminished intricacies, more elevated level of capacity recuperation, more limited season of surgeries, less entanglements, higher AOFAS, EFAS, torment VAS scores, decreased plantar torment and decline reoperation rates. Most of the combination patients had great outcomes and bony association.

Heterogeneous Choice Sets and Preferences

We propose a robust method of discrete choice analysis when agents' choice sets are unobserved. Our core model assumes nothing about agents' choice sets apart from their minimum size. Importantly, it leaves unrestricted the dependence, conditional on observables, between choice sets and preferences. We first characterize the sharp identification region of the model's parameters by a finite set of conditional moment inequalities. We then apply our theoretical findings to learn about households' risk preferences and choice sets from data on their deductible choices in auto collision insurance. We find that the data can be explained by expected utility theory with low levels of risk aversion and heterogeneous non‐singleton choice sets, and that more than three in four households require limited choice sets to explain their deductible choices. We also provide simulation evidence on the computational tractability of our method in applications with larger feasible sets or higher‐dimensional unobserved heterogeneity.

Determination of Exponential Congestion Factor of Road Traffic Flow Caused By Irregular Occurrences

The present paper deals exponential congestion model of road traffic flow caused by irregular occurrences. Congestion that is happened by unpredictable events, for example, auto collisions, handicapped vehicles, climate conditions, over burdens and unsafe materials of vehicles. On account of these sorts of sudden occasions, the travel times taken on the roadways are questionable. We established the steady state conditions based on number of vehicles on road links. The large c values of those links, M/M/1 queues model under the batch service interruptions may be used. The formulation and assumptions of the proposed models have been developed. The exponential congestion factor (ECF) models based on M/MSP/C queuing have been presented. Finally, the numerical examples have also been discussed.

Characteristic of Car Crash in North of Iraq

Road Traffic Accidents (RTAs) are a significant predicament in idiom of fatality and morbidness confronting the road clients just as the Vehicles transportation and road specialists. The Iraqi populace has expanded by 45% and numeral of vehicles by 3 times during last 18 years. Subsequently, these enhancements were sadly joined with ascending the car crashes numeral, death and grimness. Besides the compassionate catastrophes, consistently, there are significant financial mislaying in north Iraq because of the pandemic of RTAs. predetermined the essential of comprehension, the synergetic agents identified with RTAs for the usage by congestion vehicles and road specialists to ameliorate the road safety road auto collisions are incidents that abruptly, unintentionally and out of the blue happen under unanticipated conditions that include in any event one mobile car and consequence in at least one road clients being slaughtered or harmed. Road auto collisions are difficult to annihilate. In any case, their commonness can be decreased to the minimum degree by means of intermittent appraisals of auto collision attributes and the most significant viewpoints for road administration to think about when structuring, planning and assessing the implementation of a road to recuperate transportation and road clients' safety. Thus, the essential target of this study is to assess car crashes in north of Iraq utilizing a review investigation of mishaps that happened from 2016–2018 mulling over the accompanying parameters: the reason for the mishap, sort and the number of vehicles engaged with the mishap, the hour of the mishap, the seriousness of the mishap, the kind of mishap and the drivers age.

Predicting Severity Level of Road Traffic Accidents in Oromiya East Shewa Zone using Iterative Dichotomiser3

Abstract: - Highway traffic accidents are a main community health problem unease ensuing millions fatalities and million serious injuries in the world each year. In the developing country like Ethiopia, is also the victim of road traffic accident or crush causing deaths, property damage and serious injuries. In order to analyses severity level of road traffic accidents, data is important to find out factors that are related to fatal, grievous, minor and non- injuries to gauge a fixed variables that contributes towards forecast the severity level of road traffic crashes. A lane traffic stream pound or impact happens when a vehicle slams into another vehicle, passerby, creature, or geological or building obstruction and result in injury, property harm, and lethal/demise. Path traffic control framework is, where basic information about the squash is recorded and saved for looming use. Expending that information the proposed examination have been extricated the contributing elements of street auto collision and create prescient model to foresee seriousness level for street car crash, wounds and fatalities utilizing information mining methods.. The main task of research is to make known the applicability of data mining techniques in emerging a model to support road traffic accident brutality analysis in preventing and extracting patterns that are corresponding with road accident in different ways of presentation methods. The road traffic accident historical data ,obtained from traffic Oromia police commission of East Shewa Zone, Oromia and police commission of Federal government of Ethiopia.

Cybernetic Organism for Locomotion towards Paradigm

Auto collisions have been taking a great many lives every year, dwarfing any savage ailments or cataclysmic events. Studies demonstrate that about 60% roadway crashes could be kept away from if the vehicle’s driver had been given cautioning at any rate one-half second before an impact. Human drivers experience the ill effects of recognition confinements on roadway crisis occasions, bringing about enormous postponement in spreading crisis alerts. Safety on the road is one of the key main impetuses behind the advancement, improvement, institutionalization and execution of ITS frameworks. Proposed system uses VANET Standards (VANET is a Mobile Ad-hoc Network (MANET)’s sibling which can organize its own communication model without any other infrastructure dependence). In this project, considering the different features and the cost, on a small scale a four- wheel vehicular robotic prototype has been designed that will follow the lane and avoid obstacles.

Accident Prevention System using IR Sensors

As street security innovation advances so does the likelihood that we may one day see normal vehicle collides with be a terrible part of the past. It is not necessarily the case that fender benders will be cleared out all together, rather that their recurrence and reality will be limited to such a degree as to make them a factual irregularity. There have as of late been various promising mechanical advancements which guide the path toward the auto collision avoidance proportions of things to come. Overviews on different innovations have been viewed as, for example, VANETS, Wireless systems. IOT arrangement is given to stay away from street mishaps. This study paper could be utilized as a kind of perspective in the proposed framework. Remote gadgets have just been created to convey among vehicles and adjoining foundation in the dynamic driving situations.

Road Accident Analysis and Prediction using Machine Learning Algorithmic Approaches

Ongoing studies have anticipated that in 2030, car crashes will be the fifth driving reason for death around the world. The main cause of car crashes is difficult to decide these days because of a complex mix of qualities like the mental condition of the driver, road conditions, climate conditions, traffic, and infringement of traffic rules to give some examples. The expenses of fatalities and driver wounds because of car crashes incredibly influence the general public. The use of machine learning methods in the field of road accidents is picking up speed nowadays. The organization of machine learning classifiers has swapped conventional data mining methods for creating higher outcomes and exactness. This work presents a review of different existing businesses related to accident prediction utilizing the machine learning area. Wounds because of road accidents are one of the most pervasive reasons for death separated from health-related issues. The investigation of road accident seriousness was finished by running an accident dataset through a few machine learning arrangement calculations to see which model played out the best in characterizing the accidents into severity classes, for example, slight, extreme, and fatal. It was seen that calculated relapse to perform multilevel order gave the most noteworthy exactness score. It was additionally seen that variables, for example, the number of vehicles, lighting conditions, and road highlights assumed a part in deciding the seriousness of the accident. Engineers and analysts in the car business have attempted to plan and manufacture more secure vehicles, yet auto collisions are unavoidable. Examples associated with hazardous accidents could be identified by building up a prediction model that naturally orders the sort of injury severity of different traffic accidents. These social and roadway designs are valuable in the improvement of traffic security control strategies. Significantly, estimates be founded on logical and target reviews of the reasons for accidents and the seriousness of injuries. This paper presents a few models to predict the seriousness of the injury that happened during traffic accidents utilizing machine-learning paradigms. We considered networks prepared to utilize machine learning methods. Analysis results uncover that among the machine learning ideal models considered different standards paradigm approaches.

Hellish Traffics Congestion Caused by Vagabond Diversity, Ignorance Simply to Applying a Tunnel in Urban Space to Exhaust

The urban dodge is an empiric then politics method concerned including the development or plan about land-use, or the timbered environment, permanency, certain objects such as street furniture In some countries called urban fixed or mobile elements (components). On a global scale do not follow a certain installation rule. Despite urban founder testimonial, their settlement in urban space is irrational, either on the urban designer, local municipal, or others. According to the fact we encounter disorder, not only disastrous accidents, or air pollution, or traffic blockades. But also how much the automobiles themselves space occupy and even worse global ecological issues. In this article, we discuss underground tunnels. Despite using, traffic lights at a 4-way junction presented under the intelligent inspection mechanism. The vehicle density waiting behind the red-light duration is causing traffic blockages and thus wastes of time. There are serious demands for Tunnel which is a fixed urban component on intersections to lubricate an end to traffic jams. Despite using, traffic lights at a 4-way junction presented under the intelligent inspection mechanism. The vehicle density waiting behind the red-light duration is causing traffic blockages and consequently wastes of time. Creation of air pollution and etc. There are serious demands for Tunnel which is a fixed urban component on intersections in order to lubricate an end to traffic jams. Formidably traffic blockage frequently emerges because of the traffic light, auto collisions, the resilience of the community will keep appreciably improved by new tunnel furnishes an alternative crossing. This article intends to propose a scientific natural draft method of ventilation never used before.

Role of ultrasonography in blunt abdominal trauma

Objective The aim of the study was to assess the role of ultrasonography (US) focused assessment with sonography in trauma (FAST) at the time of presentation and ultrasound repeated after 12–24 h in early diagnosis of intra-abdominal injury (IAI) following blunt abdominal trauma (BAT) and for follow-up. Background US was used in patients with IAI. Trauma is the most common cause of mortality. The most common mechanisms resulting in BAT among patients were motor vehicle collisions (73%), motorcycle collisions (7%), auto–pedestrian collisions (6%), and falls (6%). Patients and methods This is a prospective study of 50 patients presented to the Emergency Room of Menoufia University Hospital with BAT who underwent US and FAST examination at the time of presentation and were followed up with US after 12–24 h. Of them, 12 patients were hemodynamically unstable, six patients had severe intra-abdominal hemorrhage and entered the operation theater urgently after FAST examination, and the other six patients entered the ICU for urgent care, FAST findings, and follow-up. US findings were correlated with patients' clinical data. Results US is very useful in the follow-up of patients with minor IAI and it decreases the use of computed tomography. However, ultrasound is operator dependent and could overlook lacerations of solid organs and gastrointestinal injuries. Therefore, contrast-enhanced computed tomography is still considered the gold standard in the evaluation of the exact site and the degree of IAI. Conclusion We concluded that ultrasound is considered the best modality in the initial evaluation of patients with BAT. However, repeated ultrasound in patients of BAT increases the sensitivity of ultrasound for causing intra-abdominal bleeding to 100%.

Two-year follow-up of the Collision Auto Repair Safety Study (CARSS).

This paper presents an evaluation of the sustainability of health and safety improvements in small auto collision shops 1 year after the implementation of a year-long targeted intervention. During the first year (active phase), owners received quarterly phone calls, written reminders, safety newsletters, and access to online services and in-person assistance with creating safety programs and respirator fit testing. During the second year (passive phase), owners received up to three postcard reminders regarding the availability of free health and safety resources. Forty-five shops received an evaluation at baseline and at the end of the first year (Y1). Of these, 33 were evaluated at the end of the second year (Y2), using the same 92-item assessment tool. At Y1, investigators found that between 70 and 81% of the evaluated items were adequate in each business (mean = 73% items, SD = 11%). At Y2, between 63 and 89% of items were deemed adequate (mean = 73% items, SD = 9.5%). Three safety areas demonstrated statistically significant (P < 0.05) changes: compressed gasses (8% improvement), personal protective equipment (7% improvement), and respiratory protection (6% decline). The number of postcard reminders sent to each business did not affect the degree to which shops maintained safety improvements made during the first year of the intervention. However, businesses that received more postcards were more likely to request assistance services than those receiving fewer.

Workplace Safety and Health Programs, Practices, and Conditions in Auto Collision Repair Businesses

This article describes the results of a pre-intervention safety assessment conducted in 49 auto collision repair businesses and owners’ commitments to specific improvements. A 92-item standardized audit tool employed interviews, record reviews, and observations to assess safety and health programs, training, and workplace conditions. Owners were asked to improve at least one-third of incorrect, deficient, or missing (not in compliance with regulations or not meeting best practice) items, of which a majority were critical or highly important for ensuring workplace safety. Two-thirds of all items were present, with the highest fraction related to electrical safety, machine safety, and lockout/tagout. One-half of shops did not have written safety programs and had not conducted recent training. Many had deficiencies in respiratory protection programs and practices. Thirteen businesses with a current or past relationship with a safety consultant had a significantly higher fraction of correct items, in particular related to safety programs, up-to-date training, paint booth and mixing room conditions, electrical safety, and respiratory protection. Owners selected an average of 58% of recommended improvements; they were most likely to select items related to employee Right-to-Know training, emergency exits, fire extinguishers, and respiratory protection. They were least likely to say they would improve written safety programs, stop routine spraying outside the booth, or provide adequate fire protection for spray areas outside the booth. These baseline results suggest that it may be possible to bring about workplace improvements using targeted assistance from occupational health and safety professionals.

Addition of Impedance Threshold Device Therapy Improves Blood Pressure in the Out-of-Hospital Spontaneously Breathing Hypotensive Patient

An impedance threshold device (ITD) intended for use in spontaneously breathing patients exhibiting low blood pressure. The ITD is reported to provide therapeutic benefit through inspiratory resistance of 7cmH2O, and clinical improvement has been observed as soon as a patient begins to breathe through it. Pre-clinical and clinical studies have shown that the ResQGARD (Advanced Circulatory, Roseville, MN) increases blood pressure during hypotension from a variety of causes. This device has yet to be tested in a large urban out-of-hospital system. This pilot study sought to identify a change in vital signs following paramedic administration of the ITD. Secondary outcomes included patient tolerance and paramedic perception of patient tolerance. A six-month prospective nonrandomized observational cohort study was conducted. All paramedics received 30-45 minutes of training on patient selection and application of the ITD. This initial training was reinforced utilizing a Web-based training video, and a visual reminder (8.5 x 11 laminated sheet) of the study protocol posted inside the ambulance. The cohort was spontaneously breathing adult patients with hypotension due to dehydration, sepsis, anaphylaxis, or hypovolemia. Exclusion criteria included: unable to maintain airway, pulmonary edema, or significant respiratory distress. Upon determination of hypotension (SBP<90mmHG), standard therapy (positioning, reassurance, and intravascular fluids) was initiated and the ITD was applied in accordance with manufacturer's recommendation. The subject was coached to breathe slow and deep utilizing the mask-type device. Supplemental oxygen was supplied if deemed necessary by the paramedic. Vital signs were documented every 2-5 minutes post intervention. Patient tolerance (observed), and patient comfort (reported) were documented. Two hundred subjects were recruited with a mean initial blood pressure 78/50 (± 14/13) heart rate 87 (± 25) beats per minute and ventilatory rate of 19 (± 7) breaths per minute. After paramedic treatment, including ITD mean blood pressure 97/63 (±19/15; p=<.0001) heart rate 87 (± 25; p=.47) beats per minute and ventilatory rate of 19 (± 7; p=.002) breaths per minute, with 66% of blood pressures increased after. Of the 200 hypotensive subjects, 29 were as a result of trauma (fall, auto collisions, shootings/stabbings). The trauma population also demonstrated a significant increase in mean blood pressure from 80 to 103 systolic, and 56 to 71 diastolic (p=<.0001 and .001, respectively), and non-significant change in mean heart rate from 89 to 80 (p=.17). In all groups, 75% of patients reported moderate to easy tolerance, and only 15% of patients reporting difficult tolerance. The ITD was easily assimilated into the standard bundle of care for the treatment of hypotensive patients. A significant increase in blood pressure was observed in the overall cohort. Importantly for the trauma cohort, the ITD did not restore “normal” blood pressure, but continued to allow for permissive hypotension, and continued to be generally well tolerated.