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- Andersson Hagiwara, Magnus, et al.
(författare)
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The Effects of Integrated IT Support on the Prehospital Stroke Process: Results from a Realistic Experiment
- 2019
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Ingår i: Journal of Healthcare Informatics Research. - : Springer Science and Business Media LLC. - 2509-498X .- 2509-4971. ; 3:3, s. 300-328
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Tidskriftsartikel (refereegranskat)abstract
- Stroke is a serious condition and the stroke chain of care is a complex. The present study aims to explore the impact of a computerised decision support system (CDSS) for the prehospital stroke process, with focus on work processes and performance. The study used an exploratory approach with a randomised controlled crossover design in a realistic contextualised simulation experiment. The study compared clinical performance among 11 emergency medical services (EMS) teams of 22 EMS clinicians using (1) a computerised decision support system (CDSS) and (2) their usual paper-based process support. Data collection consisted of video recordings, postquestionnaires and post-interviews, and data were analysed using a combination of qualitative and quantitative approaches. In this experiment, using a CDSS improved patient assessment, decision making and compliance to process recommendations. Minimal impact of the CDSS was found on EMS clinicians’ self-efficacy, suggesting that even though the system was found to be cumbersome to use it did not have any negative effects on self-efficacy. Negative effects of the CDSS include increased on-scene time and a cognitive burden of using the system, affecting patient interaction and collaboration with team members. The CDSS’s overall process advantage to the prehospital stroke process is assumed to lead to a prehospital care that is both safer and of higher quality. The key to user acceptance of a system such as this CDSS is the relative advantages of improved documentation process and the resulting patient journal. This could improve the overall prehospital stroke process.
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| 4. |
- Bakidou, Anna, 1996, et al.
(författare)
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On Scene Injury Severity Prediction (OSISP) model for trauma developed using the Swedish Trauma Registry
- 2023
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Ingår i: BMC Medical Informatics and Decision Making. - 1472-6947. ; 23:1
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Tidskriftsartikel (refereegranskat)abstract
- Background: Providing optimal care for trauma, the leading cause of death for young adults, remains a challenge e.g., due to field triage limitations in assessing a patient’s condition and deciding on transport destination. Data-driven On Scene Injury Severity Prediction (OSISP) models for motor vehicle crashes have shown potential for providing real-time decision support. The objective of this study is therefore to evaluate if an Artificial Intelligence (AI) based clinical decision support system can identify severely injured trauma patients in the prehospital setting. Methods: The Swedish Trauma Registry was used to train and validate five models – Logistic Regression, Random Forest, XGBoost, Support Vector Machine and Artificial Neural Network – in a stratified 10-fold cross validation setting and hold-out analysis. The models performed binary classification of the New Injury Severity Score and were evaluated using accuracy metrics, area under the receiver operating characteristic curve (AUC) and Precision-Recall curve (AUCPR), and under- and overtriage rates. Results: There were 75,602 registrations between 2013–2020 and 47,357 (62.6%) remained after eligibility criteria were applied. Models were based on 21 predictors, including injury location. From the clinical outcome, about 40% of patientswere undertriaged and 46% were overtriaged. Models demonstrated potential for improved triaging and yielded AUC between 0.80–0.89 and AUCPR between 0.43–0.62. Conclusions: AI based OSISP models have potential to provide support during assessment of injury severity. The findings may be used for developing tools to complement field triage protocols, with potential to improve prehospital trauma care and thereby reduce morbidity and mortality for a large patient population.
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| 5. |
- Buendia, Ruben, 1982, et al.
(författare)
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Assessing mechanisms of injury as predictors of severe injury for adult car and truck occupants
- 2015
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Ingår i: The 24th International Technical Conference on the Enhanced Safety of Vehicles (ESV).
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- This study evaluates Mechanisms of Injury (MOI) that can be rapidly assessed at the scene of accident and may be used as predictors of severe injury for traffic accidents involving occupants in cars or trucks. The objective is to increase the knowledge of how MOI can be used to differentiate whether a patient is severely injured or not. This knowledge can be used to improve trauma triage systems. Furthermore, an objective is to analyze safety differences between cars and light/heavy trucks. The scope is adult occupants of cars, light and heavy trucks injured in accidents registered in the Swedish Traffic Accident Data Acquisition (STRADA) database from 2003 to 2013. Partition between severe and non-severe injury was done according to the Injury Severity Score (ISS) with ISS > 15 as definition of severe injury. The MOIs considered were: belt use, airbag deployment, posted speed limit, elderly occupant (age ≥ 55 years), sex, type of accident (single, intersection, turning, head-on, overtaking, rear end, tram/train, wild animal or other) and location of the accident (urban or rural). The different MOI were evaluated individually using univariate chi-square tests and together using multivariate logistic regression models. Results show that belt use is the most crucial factor determining risk of severe injury for all vehicle types. Age is the second most important factor, with elderly occupants exhibiting a higher risk. Head-on accidents are the most dangerous for cars and light trucks while single accidents are the most dangerous for heavy trucks. Belt use compliance is much lower for truck occupants. This appears to be the main reason for the frequency of severe injury being higher for truck occupants than for car occupants.
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| 6. |
- Buendia, Ruben, 1982, et al.
(författare)
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Bioimpedance technology for detection of thoracic injury
- 2017
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Ingår i: Physiological Measurement. - : IOP Publishing. - 0967-3334 .- 1361-6579. ; 38:11, s. 2000-2014
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Tidskriftsartikel (refereegranskat)abstract
- Objective: Thoracic trauma is one of the most common and lethal types of injury, causing over a quarter of traumatic deaths. Severe thoracic injuries are often occult and difficult to diagnose in the field. There is a need for a point-of-care diagnostic device for severe thoracic injuries in the prehospital setting. Electrical bioimpedance (EBI) is non-invasive, portable, rapid and easy to use technology that can provide objective and quantitative diagnostic information for the prehospital environment. Here, we evaluated the performance of EBI to detect thoracic injuries. Approach: In this open study, EBI resistance (R), reactance (X) and phase angle (PA) of both sides of the thorax were measured at 50 kHz on patients suffering from thoracic injuries (n = 20). In parallel, a control group consisting of healthy subjects (n = 20) was recruited. A diagnostic mathematical algorithm, fed with input parameters derived from EBI data, was designed to differentiate patients from healthy controls. Main results: Ratios between the X and PA measurements of both sides of the thorax were significantly different (p < 0.05) between healthy volunteers and patients with left-and right-sided injuries. The diagnostic algorithm achieved a performance evaluated by leave-one-out cross-validation analysis and derived area under the receiver operating characteristic curve of 0.88. Significance: A diagnostic algorithm that accurately discriminates between patients suffering thoracic injuries and healthy subjects was designed using EBI technology. A larger, prospective and blinded study is thus warranted to validate the feasibility of EBI technology as a prehospital tool.
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| 7. |
- Buendia, Ruben, 1982, et al.
(författare)
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Deriving heart rate variability indices from cardiac monitoring : An indicator of driver sleepiness
- 2019
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Ingår i: Traffic Injury Prevention. - : Taylor and Francis Inc.. - 1538-9588 .- 1538-957X.
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Tidskriftsartikel (refereegranskat)abstract
- Objective: Driver fatigue is considered to be a major contributor to road traffic crashes. Cardiac monitoring and heart rate variability (HRV) analysis is a candidate method for early and accurate detection of driver sleepiness. This study has 2 objectives: to evaluate the (1) suitability of different preprocessing strategies for detecting and removing outlier heartbeats and spectral transformation of HRV signals and their impact of driver sleepiness assessment and (2) relation between common HRV indices and subjective sleepiness reported by a large number of drivers in real driving situations, for the first time.Methods: The study analyzed >3,500 5-min driving epochs from 76 drivers on a public motorway in Sweden. The electrocardiograph (ECG) data were recorded in 3 studies designed to evaluate the physiological differences between awake and sleepy drivers. The drivers reported their perceived level of sleepiness according to the Karolinska Sleepiness Scale (KSS) every 5 min. Two standard methods were used for identifying outlier heartbeats: (1) percentage change (PC), where outliers were defined as interbeat intervals deviating >30% from the mean of the four previous intervals and (2) standard deviation (SD), where outliers were defined as interbeat interval deviating >4 SD from the mean interval duration in the current epoch. Three standard methods were used for spectral transformation, which is needed for deriving HRV indices in the frequency domain: (1) Fourier transform; (2) autoregressive model; and (3) Lomb-Scargle periodogram. Different preprocessing strategies were compared regarding their impact on derivation of common HRV indices and their relation to KSS data distribution, using box plots and statistical tests such as analysis of variance (ANOVA) and Student’s t test.Results: The ability of HRV indices to discriminate between alert and sleepy drivers does not differ significantly depending on which outlier detection and spectral transformation methods are used. As expected, with increasing sleepiness, the heart rate decreased, whereas heart rate variability overall increased. Furthermore, HRV parameters representing the parasympathetic branch of the autonomous nervous system increased. An unexpected finding was that parameters representing the sympathetic branch of the autonomous nervous system also increased with increasing KSS level. We hypothesize that this increment was due to stress induced by trying to avoid an incident, because the drivers were in real driving situations.Conclusions: The association of HRV indices to KSS did not depend on the preprocessing strategy. No preprocessing method showed superiority for HRV association to driver sleepiness. This was also true for combinations of methods for frequency domain HRV indices. The results prove clear relationships between HRV indices and perceived sleepiness. Thus, HRV analysis shows promise for driver sleepiness detection.
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| 9. |
- Buendia, Ruben, 1982, et al.
(författare)
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On scene injury prediction (OSISP) algorithm for car occupants
- 2015
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Ingår i: Accident Analysis and Prevention. - : Elsevier BV. - 0001-4575. ; 81, s. 211-217
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Tidskriftsartikel (refereegranskat)abstract
- Many victims in traffic accidents do not receive optimal care due to the fact that the severity of their injuries is not realized early on. Triage protocols are based on physiological and anatomical criteria and subsequently on mechanisms of injury in order to reduce undertriage. In this study the value of accident characteristics for field triage is evaluated by developing an on scene injury severity prediction (OSISP) algorithm using only accident characteristics that are feasible to assess at the scene of accident. A multi-variate logistic regression model is constructed to assess the probability of a car occupant being severely injured following a crash, based on the Swedish Traffic Accident Data Acquisition (STRADA) database. Accidents involving adult occupants for calendar years 2003–2013 included in both police and hospital records, with no missing data for any of the model variables, were included. The total number of subjects was 29 128, who were involved in 22 607 accidents. Partition between severe and non-severe injury was done using the Injury Severity Score (ISS) with two thresholds: ISS > 8 and ISS > 15. The model variables are: belt use, airbag deployment, posted speed limit, type of accident, location of accident, elderly occupant (>55 years old), sex and occupant seat position. The area under the receiver operator characteristic curve (AUC) is 0.78 and 0.83 for ISS > 8 and ISS > 15, respectively, as estimated by 10-fold cross-validation. Belt use is the strongest predictor followed by type of accident. Posted speed limit, age and accident location contribute substantially to increase model accuracy, whereas sex and airbag deployment contribute to a smaller extent and seat position is of limited value. These findings can be used to refine triage protocols used in Sweden and possibly other countries with similar traffic environments.
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