EMS Response Time for Patients Critically-Injured from Automobile Accidents Using Regression Analysis

Itroduction: Previous research has quantified the impact of EMS response time on the probability of survival from OHCA, but the impact on different subpopulations is currently unknown. Aim: To investigate how response time affects OHCA survival for different patient subpopulations. Methods: We conducted a logistic regression analysis on non-EMS witnessed OHCAs of presumed cardiac etiology from the Toronto Regional RescuNet between January 1, 2007 and December 31, 2016. We predicted survival using age, sex, public location, presenting rhythm, bystander witnessed, bystander resuscitation, and response time, defined as the time interval from 911 call to EMS arrival at the patient. We conducted subgroup analyses to quantify the effect of response time on survival for eight different subpopulations: public, private, bystander resuscitation, no bystander resuscitation, patients ≥65, patients <65, witnessed, and unwitnessed OHCA. We also quantified the effect of response time on survival for pairwise intersections of the subpopulations. We compared our results to Valenzuela et al. (1997), which suggests survival odds decrease by 10% for each minute delay in response time. Results: We identified 22,988 OHCAs. Overall, a one-minute delay in EMS response time was associated with a 13.2% reduction in the odds of survival. The reduction varied by subpopulation, ranging from a 7.2% reduction in survival odds for unwitnessed arrests to a 16.4% reduction in survival odds for arrests with bystander resuscitation. Response time had the largest impact on survival for the subpopulation of OHCAs that were both witnessed and received bystander resuscitation (17.4% reduction in survival odds). Conclusion: The effect of a one-minute delay in EMS response on the odds of survival from OHCA can be as low as a 7.2% reduction and as high as a 17.4% reduction. This variability contrasts with the currently accepted 10% rule that is assumed across the entire population.

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Decision-Making Skills in Youth Basketball Players: Diagnostic and External Validation of a Video-Based Assessment

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18052331 ◽

2021 ◽

Vol 18 (5) ◽

pp. 2331

Author(s):

David Rösch ◽

Florian Schultz ◽

Oliver Höner

Keyword(s):

Decision Making ◽

Regression Analysis ◽

Response Time ◽

External Validity ◽

Elite Athletes ◽

Performance Data ◽

Response Accuracy ◽

Basketball Players ◽

Performance Levels ◽

National Team

Decision-making is a central skill of basketball players intending to excel individually and contribute to their teams’ success. The assessment of such skills is particularly challenging in complex team sports. To address this challenge, this study aimed to conceptualize a reliable and valid video-based decision-making assessment in youth basketball. The study sample comprised youth basketball players of the German U16 national team (n = 17; MAge = 16.01 ± 0.25 years) and students of a sports class (n = 17; MAge = 15.73 ± 0.35 years). Diagnostic validity was tested by determination of the performance levels according to response accuracy as well as response time in the assessment. External validity was examined by investigation of the correlation between the diagnostic results of the elite athletes and their real game performance data associated with passing skills. Logistic regression analysis revealed that the diagnostic results discriminate between performance levels (χ2(2) = 20.39, p < 0.001, Nagelkerke’s R2 = 0.60). Multiple regression analysis demonstrated a positive relationship between the diagnostic results and assists (F(2,10) = 4.82, p < 0.05; R2 = 0.49) as well as turnovers per game (F(2,10) = 5.23, p < 0.05; R2 = 0.51). However, no relationship was detected regarding the assist-turnover ratio. Further, response time discriminated within the elite athletes’ performance data but not between performance levels while for response accuracy the opposite is the case. The results confirm the diagnostic and external validity of the assessment and indicate its applicability to investigate decision-making skills in youth basketball.

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Estimation of simple and complex response time development using regression analysis

Polish Journal of Sport and Tourism ◽

10.2478/pjst-2013-0023 ◽

2013 ◽

Vol 20 (4) ◽

pp. 249-253

Author(s):

Janusz Jaworski ◽

Eligiusz Madejski ◽

Grażyna Kosiba ◽

Sylwia Wiatr

Keyword(s):

Regression Analysis ◽

Reaction Time ◽

Response Time ◽

Simple Reaction Time ◽

Time Development ◽

Complex Reaction ◽

Time Dynamics ◽

Complex Response ◽

Study Materials ◽

Dynamic Growth

Abstract Introduction.The aim of the study was to determine the age, level of achieving maximum results and growth of reaction time dynamics. Materials and methods. The study included 567 males of age between 7 and 22 years. Study materials included the results of simple reaction time and complex reaction time during progressive period. Conclusions. Progressive period of results' development lasts until the age of about 17-17.5, then stabilization of the analyzed results was observe. The most dynamic growth of all types of reaction time was observed in the analyzed 7 and 8-year-old boys.

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A Quantile Regression Analysis of Ambulance Response Time

Prehospital Emergency Care ◽

10.3109/10903127.2012.729127 ◽

2012 ◽

Vol 17 (2) ◽

pp. 170-176 ◽

Cited By ~ 13

Author(s):

Young Kyung Do ◽

Kelvin Foo ◽

Yih Yng Ng ◽

Marcus Eng Hock Ong

Keyword(s):

Regression Analysis ◽

Response Time ◽

Quantile Regression ◽

Quantile Regression Analysis

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Abstract P146: Assessment of Telestroke Sub-Events and Their Contribution to Door-To-Needle Time in a Telestroke Network

Stroke ◽

10.1161/str.52.suppl_1.p146 ◽

2021 ◽

Vol 52 (Suppl_1) ◽

Author(s):

Christopher T Hackett ◽

Konark Malhotra ◽

Russell Cerejo ◽

Nicholas Fuller ◽

David G Wright ◽

...

Keyword(s):

Logistic Regression ◽

Regression Analysis ◽

Thrombolytic Therapy ◽

Response Time ◽

Regression Models ◽

Time Intervals ◽

Hub And Spoke ◽

Logistic Regression Models ◽

Video Completion ◽

Physician Response

Introduction: Data remains scarce on which telestroke related sub-events (component parts/time intervals) are associated with delays in door-to-needle (DTN) time and goals for each telestroke sub-event. We aimed to assess the telestroke sub-events that contribute to DTN. After establishing set goals for each sub-event, we further evaluated the odds of DTN within 45 minutes if sub-event goals were achieved. Methods: We retrospectively analyzed prospectively collected data from a hub-and-spoke model telestroke network from January 2017 to September 2019. To determine which sub-events significantly contributed to DTN time, a sequential multiple regression analysis was performed. We entered covariates (age, sex, time of telestroke [day or night], NIHSS, average number of telestroke consults at a given site) in the first block followed by sub-events (door-to-telestroke request, door-to-CT, request-to-page, stroke physician response time, telestroke phone-to-video, video duration prior to needle and video completion-to-needle) in the second block. Logistic regression models were performed to estimate the odds of achieving a DTN within 45 minutes if sub-event goals were achieved. Results: During the study, 3361 telestrokes were completed and 306 (9.1%) patients received IV thrombolytics. After exclusions, 253 patients treated with IV thrombolytics were included. Five sub-events contributed to DTN time above and beyond the nuisance variables: door-to-telestroke request, stroke physician response time, telestroke phone-to-video, video duration prior to needle, and video completion-to-needle; each p <0.001. DTN time within 45 minutes was more likely when door-to-telestroke request <10 minutes (OR=12.30, 95%CI 3.47-43.65), video completion to needle <1 minute (OR=4.21, 95%CI 1.45-12.20) and telestroke phone-to-video <7 minutes (OR=5.24, 95%CI 1.41-19.49). Conclusions: Telestroke sub-events involving door-to-telestroke request, stroke physician response, telestroke phone-to-video, video duration prior to needle, and video completion-to-needle significantly contribute to DTN time. Successful achievement of sub-event goals was related to greater likelihood of administration of thrombolytic therapy within 45 minutes.

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Demographic, Clinical Features and Outcome Determinants of Thoracic Trauma in Sri Lanka: A Multicentre Prospective Cohort Study

Canadian Respiratory Journal ◽

10.1155/2020/1219439 ◽

2020 ◽

Vol 2020 ◽

pp. 1-6

Author(s):

Yasith Mathangasinghe ◽

Iddagoda Hewage Don Saman Pradeep ◽

Dhammike Rasnayake

Keyword(s):

Logistic Regression ◽

Regression Analysis ◽

Cohort Study ◽

Sri Lanka ◽

Hospital Stay ◽

Prospective Cohort ◽

Thoracic Trauma ◽

Automobile Accidents ◽

The Mean ◽

Hospital Morbidity

Prognostic determinants in thoracic trauma are of major public health interest. We intended to describe patterns of thoracic trauma, demographic factors, clinical course, and predictors of outcome in selected tertiary care hospitals in Sri Lanka. A multicentre prospective cohort study was conducted in five leading teaching hospitals from June to September 2017. Patients with thoracic trauma were followed up during the hospital stay. A logistic regression analysis was conducted using in-hospital morbidity as the dichotomous outcome variable. One hundred seventy-one patients were included in the study yielding 1450 (median = 8.5) person-days of observation. Of them, 71.9% (n = 123) were males. The mean age was 45.8 ± 17.9 years. Majority (39.2%, n = 67) were recruited from the National Hospital of Sri Lanka. Automobile accidents were the commonest (62.6%, n = 107), followed by falls (26.9%, n = 46), assaults (8.8%, n = 15), and animal attacks (1.8%, n = 3). The ratio of blunt to penetrating trauma was 5.6 : 1. Injury patterns were rib fractures (80.7%, n = 138), haemothorax (44.4%, n = 76), pneumothorax (44.4%, n = 76), lung contusion (22.8%, n = 39), flail segment (15.8%, n = 27), tracheobronchial trauma (7.0%, n = 12), diaphragmatic injury (2.3%, n = 4), vascular injury (2.3%, n = 4), cardiac contusions (1.1%, n = 2), and oesophageal injury (0.6%, n = 1). Ninety nine (57.9%) had extrathoracic injuries. Majority (63.2%, n = 108) underwent operative management including intercostal tube insertion (60.8%, n = 104), wound exploration (6.4%, n = 11), thoracotomy (4.1%, n = 7), rib reconstruction (4.1%, n = 7), and video-assisted thoracoscopic surgery (2.9%, n = 5). Pneumonia (10.5%, n = 8), bronchopleural fistulae (2.3%, n = 4), tracheaoesophageal fistulae (1.8%, n = 3), empyema (1.2%, n = 2), and myocardial infarction (1.2%, n = 2) were the commonest postoperative complications. The mean hospital stay was 15.6 ± 18.0 days. The in-hospital mortality was 11 (6.4%). The binary logistic regression analysis with five predictors (age, gender, mechanism of injury (automobile/fall/assault), type of trauma (blunt/penetrating), and the presence of extrathoracic injuries) was statistically significant to predict in-hospital morbidity (X2 (6, n = 168) = 13.1; p=0.041), explaining between 7.5% (Cox and Snell R2) and 14.5% (Nagelkerke R2) of variance. The automobile accidents (OR: 2.3, 95% CI = 0.2–26.2) and being males (OR: 2.3, 95% CI = 0.6–9.0) were the strongest predictors of morbidity.

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