Moving-average processing enables accurate quantification of time delay and compares the trending ability of cardiac output monitors with different response times

REPLY: Several important questions are raised in the recent comparison by Balan and Gottlieb (2009) between the description by Balan et al. (2008) of lateral intraparietal (LIP) neuron activity in monkeys performing visual search with manual responses and our recent report of frontal eye field (FEF) neuron activity in monkeys performing visual search with saccade responses (Cohen et al. 2009). Both studies manipulated the number of distractors in the search display. Balan et al. 2008 reported decreased firing rate with increasing set size, but they reported no delay in the time at which activity for the target exceeded activity for the distractors, referred to as target selection time. Cohen and colleagues (2009) also reported decreased firing rate with increasing set size, but also observed delayed target selection time with increasing set size. How should we interpret the different observations across laboratories, effectors, cortical areas, and task designs? First, we emphasize that this is not a case of one observation being correct and the other incorrect. The results of both studies are valid within their respective contexts. However, the question remains: which difference between studies explains the different outcomes? We argue that task demands are the major factors. In other words, measured under the same task demands, we predict that the same pattern of results would be obtained across effector (manual response vs. saccade) and cortical area (LIP vs. FEF). The first step of the argument is to appreciate that categorical statements about the relationship between target selection time and response time are not possible because these times vary for at least three reasons. First, neuron type matters. Multiple

Background: The national maternal mortality rate is still high at 228 per 100.000 live births. Three late allegedly related to high maternal mortality rate, are 1)too late for decision making, 2) too late for access service and 3) too late for get treatment at referral health facility. Response time for maternal emergency treatment in the hospital plays an important role, since it affects the final result. Objective: To determine the relationship between the time of obstetric emergency management and response time. Method: This study used cross sectional design with the population of study was obstetric emergency patients. One hundred and forty-five subjects were divided into two group: group treated outside of working hours as exposed groups and groups treated during working hours as control groups. Delay of response time were observed. Data was processed by statistical program using computer. Chi square test and logistic regression analysis were used to perform statistical tests. Result and Discussion: From September 1st 2015 to April 30th 2016, there were 145 cases with obstetric emergency handled. A total 82 cases among 145 (56,6%) received emergency management outside working hours. It was found that the 1st response time was delayed in handling outside work hours compared to handling during working hours (RR 2,22; 95% CI 164-3,00). The same was obtained at 2nd response time (RR 1,39; 95% CI 1,04-1,86) and 3rd response time (RR 1,65; 95% CI 1,14-2,31). Multivariate analysis was found that time of the emergency handling was the most dominant variable that affect all response time [1st response time (OR 12,61; 95% CI 4,82-32,03), 2nd response time (OR 2,17; 95% CI 1,05-4,47), 3rd response time (OR 7,70; 95% CI 1,91-31,10)]. PONEK with midwife on duty also influence 1st response time (OR 3,28; 95% CI 1,21-8,93). Conclusion: Obstetric emergency management outside of work hours increased the occurence of response time delay. Keywords: Time of management, Obstetric emergency, 1st, 2nd and 3rd response time.

Dosimetric effect of respiratory motion prediction error on dynamic multileaf collimator based 4D radiation delivery

Influence of delay times and response times on heat release measurements

Introduction: Interfacility transport (IFT) is a complex component of out-of-hospital Emergency Medical Services (EMS) which provides care and transport to patients who need transfer from one facility to another. The aim of IFT is that patients receive the care they need in a time-efficient and safe manner, and it helps in maintaining high practice standards and reduces financial burden. The delay in response time of interfacility transfer for life-saving cases may result in adverse health outcomes. Objective: The study aimed to identify possible causes of delay in ambulance response time for three life-saving categories (CVA, Stroke & RTA) and to reduce the average response time by applying improvement initiatives. Methods: This was a QI study. It was carried out by EMS at Eastern Health Cluster (EHC) Saudi Arabia from August 2022 till June 2023. In first phase retrospective analysis was conducted for the pre-intervention period (Jan 2022 to Aug 2022) to assess average response time for interfacility transfer of life-saving cases and to identify possible causes of delay through root cause analysis (RCA). In the second phase (Sep 2022 to April 2023) IHI improvement approach was applied to improve the efficiency of interfacility transfer for life-saving cases. Results: The retrospective data analysis highlighted the average response time for three lifesaving categories was 17 minutes. Certain interventions were applied and there was a significant reduction in average response time for three life-saving categories from 17 minutes to 9 minutes. The results of the paired-t test indicated that there was a significant difference t (7) = 15.3, p < .001 between before intervention response time (M = 17, SD = 1.7, n=8) and after intervention response time (M = 9.1, SD = 2, n=8). The highest average response was in December 2022 and average number of cases increased from 39 to 42 cases per month. Conclusion: The findings of this showed significant improvement in response time by merely introducing soft interventions e.g., EMS smart solution, and without acquiring additional staff or required ambulances. The rural region in EHC is vast with less concentration of stations, ambulances, and staff. Use of technology and staff resistance were also challenges. A fully functional EMS headquarter with resource control center may further improve EMS functioning. Hiring staff, acquiring ambulances & staff development is necessary to maintain and enhance gains. These findings can be replicated to improve IFT response time in different settings.

Objective To assess the relationship between emergency response time and prognosis of patients with out-of-hospital cardiac arrest. Design Systematic review. Methods Relevant observational studies were identified by a search of PubMed and ISI databases to 30 January 2014. Primary outcome was survival to discharge. The weighted mean differences (WMD) for response time were calculated for those survivals to discharge and death in hospital. We also carried out a dose response meta-analysis for assessing summary odds ratio (OR) of survival by response time. Results A total of 13 studies with 804,998 patients included in the meta-analysis. The WMD of response time between survivals to discharge group and death in hospital group was 1.976 (95% confidence interval [CI] = 1.161-2.792; p<0.001). Sensitivity analyses by only included prospective cohorts showed the WMD of response time between two groups was 1.115 (95% CI=0.508-1.723; p<0.001). Dose-response relationship between response time and survival to discharge risk was observed statistically significant (χ2=39.86, p<0.001). In linear model, the summary OR was 0.914 (95% CI=0.889-0.940) for every 1 minutes delay in response time. In spline model, the survival OR decreased along with the response time, especially when response time less than 7 minutes. Conclusions Emergency response time is an important risk factor for prognosis after OHCA in adults. The EMS team must arrive as soon as possible to the site of the event. (Hong Kong j.emerg.med. 2015;22:345-351)

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.

We investigated the relation between the response time of an email-based counselling system and the re-utilization of the service. Activity data from the Kids Help Line (KHL) for a 22 month period were examined. The median response time for subjects who re-used the KHL service (n = 2822) was 140 h (interquartile range, IQR 37–257 h) which was significantly less than for subjects who did not re-use the service (n = 2781, median 183 h, IQR 45–289 h). The re-use rate was highest for a 4 h response time, and decreased as the response time lengthened. Response times less than 32 h did not have a significant difference between the observed and expected re-use rates, whereas response times of 32 h and greater did. The results showed that a greater delay in counsellor response time was associated with a lower re-use rate. Response time is a performance indicator which may be important in the ultimate success or failure of an email based counselling service.

Background: The most important basis of medical care consists of emergency care, especially that of pre-hospital type, and it plays a significant role in reducing deaths rate and trauma-induced disabilities. Objectives: This research addressed the response time index of providing pre-hospital emergency call services to trauma victims in Golestan province in 2020. Methods: This retrospective descriptive-analytical study was conducted on all the missions performed for trauma victims in Golestan pre-hospital emergency in 2020. The sampling was done as the census. The data were collected by referring to the existing systems in the pre-hospital emergency centers using a checklist based on the mission forms of the countrywide pre-hospital emergency. The data were analyzed using descriptive statistics (frequency, mean, and standard deviation) and inferential statistics using parametric tests (such as t-test and Tukey’s test to compare the two groups). Results: Out of 9867 trauma victims transferred by Golestan pre-hospital emergency call service, 67.02% were men, 14.56% were women, and 18.43% were not registered by gender. The maximum mean age was related to the middle-aged group (38.86%) caused by traffic incidents. According to the international standard, the mean delay time was 01:11 m, the EMS response time for the central urban districts and the suburbs was 10:10 m and 12:00 m, the time of transfer to the central urban district and the suburb hospitals was 08:28 m and 17:28 m, the delivery time to the hospital was 7:14 m, the total mission time of the central urban district and the suburb bases was 1:01:25 h 1:30:20 h, and the mean response time for the trauma victims was 65.89%. Conclusions: The time indices of pre-hospital emergency missions in Golestan province are within the normal range of the standard time, and considering the effective role of pre-hospital emergency in reducing the mortality and disability of trauma victims, more attention should be paid to the structural and functional indices and management, especially the response and scene time.

Adapting radiation delivery to respiratory motion is made possible through corrective action based on real-time feedback of target position during respiration. The advantage of this approach lies with its ability to allow tighter margins around the target while simultaneously following its motion. A significant hurdle to the successful implementation of real-time target-tracking-based radiation delivery is the existence of a finite time delay between the acquisition of target position and the mechanical response of the system to the change in position. Target motion during the time delay leads to a resultant lag in the system's response to a change in tumor position. Predicting target position in advance is one approach to ensure accurate delivery. The aim of this manuscript is to estimate the predictive ability of sinusoidal and adaptive filter-based prediction algorithms on multiple sessions of patient respiratory patterns. Respiratory motion information was obtained from recordings of diaphragm motion for five patients over 60 sessions. A prediction algorithm that employed both prediction models-the sinusoidal model and the adaptive filter model-was developed to estimate prediction accuracy over all the sessions. For each session, prediction error was computed for several time instants (response time) in the future (0-1.8 seconds at 0.2-second intervals), based on position data collected over several signal-history lengths (1-7 seconds at 1-second intervals). Based on patient data included in this study, the following observations are made. Qualitative comparison of predicted and actual position indicated a progressive increase in prediction error with an increase in response time. A signal-history length of 5 seconds was found to be the optimal signal history length for prediction using the sinusoidal model for all breathing training modalities. In terms of overall error in predicting respiratory motion, the adaptive filter model performed better than the sinusoidal model. With the adaptive filter, average prediction errors of less than 0.2 cm (1sigma) are possible for response times less than 0.4 seconds. In comparing prediction error with system latency error (no prediction), the adaptive filter model exhibited lesser prediction errors as compared to the sinusoidal model, especially for longer response time values (>0.4 seconds). At smaller response time values (<0.4 seconds), improvements in prediction error reduction are required for both predictive models in order to maximize gains in position accuracy due to prediction. Respiratory motion patterns are inherently complex in nature. While linear prediction-based prediction models perform satisfactorily for shorter response times, their prediction accuracy significantly deteriorates for longer response times. Successful implementation of real-time target-tracking-based radiotherapy requires response times less than 0.4 seconds or improved prediction algorithms.

Previous studies have reported that unloading causes impairment in oxidative enzyme activity and reduction in mitochondrial volume density in the unloaded leg. Exercise training is expected to prevent the deterioration of muscle function. However, there are no studies that investigate the effect of exercise training during unloading on the parameters of near-infrared spectroscopy (NIRS) in the measurement of oxygen supply and utilization. PURPOSE: The purpose of the present study was to determine the effect of intensive interval training during 20 days of unloading on local muscle oxygen kinetics using NIRS. METHODS: Eleven healthy adult men completed 20 days of unloading. Five subjects were assigned to a control group (CON) and six subjects engaged in exercise training sessions during unloading as a training group (TR). All subjects performed isometric knee extension exercise at 50% of their maximum voluntary contraction force (MVC) in the unloaded leg before and after unloading. NIRS [deoxy-Hb/Mb] signal was recorded at the vastus lateralis and fitted to an exponential equation to determine the time constant (τ), time delay (TD), amplitude (AP) and response time (RT). Exercise training was one-legged submaximal cycle exercise in the unloaded leg, and consisted of 40∼80% of VO2peak, 25 min/day, 10 times for 20 days. RESULTS: MVC significantly decreased after unloading in both groups (TR, −14.7 ± 9.5%, p< 0.05; CON,-22.6%, p< 0.01). ? was unchanged in TR (pre, 8.3 ± 1.7; post, 9.3 ± 3.6 sec), while ? significantly increased (p < 0.05) in CON after unloading (pre, 5.0 ± 1.0; post, 7.4 ± 1.0 sec). Similarly, RT in TR was preserved (pre, 13.1 ± 2.4; post, 14.9 ±5.5 sec) although RT in CON showed significantly (p < 0.05) higher value than that before unloading (pre, 9.0 ± 1.4; post, 13.1 ±3.2 sec). Other values of the NIRS kinetics profile were no significant changes: TD, +0.7 ± 0.7 sec (TR), +1.6 ±2.1 sec (CON); AP, +2.1 ± 39.6% (TR), −5.3 ± 20.7% (CON). CONCLUSIONS: In the present study, results showing that 20 days of unloading causes a delay in τ and RT in CON suggested impairment of oxidative capacity and oxygen utilization. However, the fact no change in τ and RT in TR indicated that intensive interval training was effective in maintaining oxygenation capacity in skeletal muscle.

Prompt responses by emergency medical services (EMSs) are crucial in delivering efficient prehospital emergency treatment. The prompt arrival of EMS is strongly correlated with improved patient outcomes and increased chances of survival. To reduce the occurrence of long-term impairment or illness, the majority of EMS companies follow globally acknowledged response time benchmarks. Internationally, EMSs strive to achieve a response time of 8 min or less for 90% of life-threatening incidents. This research examines many variables that influence EMS response times and their subsequent consequences on patient outcomes. The measurement of response time starts with receipt of a clinical complaint call and concludes upon the arrival of EMS at the site. Timely and effective reactions are essential for the survival of patients, particularly in urgent medical situations. In contrast, there are data indicating that longer reaction times are associated with increased death rates. Various obstacles might hinder prompt EMS responses, including geographical obstacles, unfavorable weather and traffic conditions, and patient-specific considerations such as the kind of injury or sickness, medical history, age, and gender. In addition, a lack of staff, including insufficient workers, poor training, and the absence of standardized, up-to-date technology that simplify care delivery, may also cause delays in response times. Due to the crucial importance of EMS response times in prehospital emergency treatment, it is essential for EMS systems to make every effort to meet their response time goals.

Background: Research has shown that each minute delay in response time reduces survival from OHCA. Although Utstein variables like public location, witnessed, bystander CPR, and bystander AED shock are known to independently improve survival, how they moderate the effect of response time delays on survival is unknown. Methods: We included OHCAs from the Resuscitation Outcomes Consortium Epistry-Cardiac Arrest database from December 1, 2005 to June 30, 2015. We included all adult, non-traumatic, non-EMS witnessed, and EMS-treated OHCA episodes. We used a logistic regression model to estimate survival to hospital discharge as a function of response time. We adjusted for standard Utstein variables and included interaction terms between response time and public location, bystander witnessed, bystander CPR, and bystander AED shock. With four binary interacted variables, there were a total of sixteen subpopulations, each with a different effect of response time on survival. Results: 83,275 patients were included (15% public, 45% witnessed, 47% CPR, 2% AED shock). Across the 10 subpopulations that comprise 99%+ of the data, a one-minute delay in response time reduced the odds of survival from 1.7% to 10.9%, depending on the arrest characteristics. All interaction tests for effect modification were significant. The reduction in odds of survival was largest for witnessed arrests (OR=0.961; 95% CI: 0.944-0.978), followed by arrests with bystander CPR (OR=0.965; 95% CI: 0.948-0.982) and in public locations (OR=0.978; 95% CI: 0.960-0.996). In contrast, a one-minute delay for arrests with bystander AED shock (OR=1.086; 95% CI: 1.058-1.114) increased the odds of survival. Conclusions: Utstein predictors significantly moderate the effect of response time on survival. Arrests that are witnessed, public location, and/or receive bystander CPR are negatively affected by slower response time. Arrests with a bystander AED shock are not sensitive to response time delays.

The aim of our study is to investigate whether coupling power output (PO) and heart rate (HR) data of semi-professional road cyclists collected in the field is helpful for optimising the training process. Therefore, HR and PO data during all cycling activities were collected from 23 semi-professional road cyclists for 2 years. Weekly cyclist-specific HR response times (recovery, delay and maximal response time) were extracted from models connecting HR and PO. Linear regression was performed between performance, defined as mean PO during a 1- and 10-min indoor time trial (TT) under controlled circumstances, and weekly HR response times. No significant correlations were found between 1-min TT PO and HR response times. In contrast, significant correlations were obtained between 10-min TT PO and recovery time (r = −0.74, p < 0.01), maximal response time (r = -0.70, p < 0.01) and delay time (r = −0.48, p = 0.03). Moreover, linear relationships were found between 10-min TT PO and delay time (r = −0.68, p < 0.01) or maximal response times (r = −0.61, p = 0.02) within 14 days of the performed lab test. This suggests that HR response times are important physiological characteristics related to 10-min TT PO for cyclists.

Asynchronous consultations in telehealth provide the convenience of not requiring an appointment. However, some patients may choose to visit a medical facility instead of waiting for a response, and the time delay could negatively affect user satisfaction. This study investigated the relationship between response time and both hospital visits prior to response confirmation, as well as user satisfaction with the service in asynchronous consultations. We collected data from a telehealth consulting service in Japan (Sanfujin-ka Online) between July 2021 and June 2023, including consultation content, response times, and post-consultation questionnaires. The primary outcome was hospital visits before response confirmation, and the secondary outcome was satisfaction with the service. A chi-square test and multivariable logistic regression analysis were performed to assess the relationship between response time and these outcomes. This study included data from 7394 online consultations, with 99.8% of responses provided within 24 h. The multivariable logistic regression analysis revealed no significant association between response time and hospital visits before response confirmation, after adjusting for user age and consultation content. Furthermore, no significant difference was found between the primary outcome and the intention to reuse the service. After using the service, there were no contacts from users or medical institutions related to unexpected sudden changes or serious illnesses. The response time was not associated with hospital visits before response confirmation in asynchronous online consultation service, which are generally responded to within 24 h, in obstetrics and gynecology.