Summary
This paper investigates the influence of various non-patient factors on emergency medical service (EMS) response times and outcomes in out-of-hospital cardiac arrest (OHCA) cases, specifically focusing on the likelihood of achieving Return of Spontaneous Circulation (ROSC). Conducted as a retrospective study using EMS data from the Lublin Voivodeship, Poland, spanning 2014 to 2017, it included 4,361 OHCA cases identified from EMS records. The study assessed key variables such as location, time of day, season, and priority level of the incident to understand how these factors affect response times and ROSC outcomes. The results demonstrated a significant association between shorter EMS response times and higher ROSC rates, emphasizing that rapid intervention is crucial for favorable OHCA outcomes. Cases classified with high urgency (K-1) showed improved ROSC rates compared to lower-priority cases, while demographic factors like age and gender also impacted outcomes, with males and individuals aged 60–79 showing higher ROSC rates. However, time of day and season did not significantly alter response efficiency. The findings underscore the importance of optimizing EMS protocols, prioritization, and training to reduce response times and improve patient outcomes. By considering operational and demographic specifics, EMS systems can develop tailored strategies that enhance survival rates in OHCA cases.
Introduction
Across the globe, emergency medical responses to critical health events determine the thin line between life and death. Out-of-hospital cardiac arrests (OHCAs) represent one of the most urgent medical emergencies, where every second counts, and the effectiveness of the response can significantly influence patient outcomes [1, 2]. While patient-related factors such as the initial rhythm of the heart play a crucial role, this study focuses on non-patient factors – including location, time of day, season, and emergency medical service (EMS) response times – that also significantly influence OHCA outcomes [3, 4].
Cardiac arrests occurring outside hospital settings are a major public health challenge worldwide, with survival rates varying significantly across different regions and communities [5]. Studies estimate that hundreds of thousands of OHCAs occur globally each year, many resulting in death or severe brain damage due to a delayed or inadequate response [6]. The critical nature of timely intervention is underscored by the finding that each minute of defibrillation delay reduces the chance of survival by 7–10% [7]. This variability in outcomes highlights the need to understand and optimize local EMS systems to improve cardiac arrest management effectively, making such research universally relevant and urgently needed.
While it is well established that patient-related factors, such as the initial rhythm of the heart and EMS resuscitation efforts, significantly influence outcomes, this study emphasizes non-patient factors, such as the location of the cardiac arrest, season, time of day, and EMS response times, which are critical to determining outcomes such as cardiopulmonary resuscitation (CPR) initiation and return of spontaneous circulation (ROSC). Many studies have already reported that cardiac arrests occurring in non-residential areas and shorter EMS response times are key factors associated with favorable patient outcomes [8–10].
This study examines OHCA cases in the Lublin Voivodeship of Poland, analyzing EMS records from 2014 to 2017, including call-out cards and medical rescue activity cards. Through a retrospective analysis, we explore how socio-demographic factors (e.g., age, gender) and operational variables (e.g., call timing, response urgency) influence EMS response times and resuscitation outcomes such as CPR initiation and ROSC.
Central to this inquiry are several pressing questions: How do non-patient factors such as the location of the arrest, the time of day, and the season affect EMS response times? How do these response times subsequently impact outcomes such as CPR initiation and ROSC? What operational improvements can be made to EMS protocols to ensure faster response times and, in turn, better patient outcomes? The findings aim to inform actionable improvements in EMS protocols and training, ultimately contributing to enhanced emergency response effectiveness and patient survival rates.
In addition to examining these critical non-patient factors, this study also aims to address the broader implications of EMS efficiency for public health outcomes. The variability in EMS response times and the effectiveness of interventions not only reflect the operational capabilities of emergency services but also highlight systemic inequalities in access to life-saving care. By providing a focused analysis on the Lublin Voivodeship, this research contributes to a growing body of evidence that underscores the need for region-specific strategies to optimize EMS performance. The insights gained from this study have the potential to inform policy decisions, enhance training programs, and ultimately reduce the mortality and morbidity associated with out-of-hospital cardiac arrests in diverse communities.
Aim
This study aims to evaluate the influence of operational and situational factors on EMS response times and their association with ROSC rates, providing a unique perspective by analyzing non-patient, operational factors within a regional context in Poland.
Materials and methods
Research design and data acquisition
This study is a retrospective analysis of medical documentation from the EMS of Lublin Voivodeship, covering the years from 2014 to 2017. The primary data sources include call-out cards and medical rescue activity cards, which are standardized forms utilized by EMS personnel to systematically document each emergency response. These records provide comprehensive data on patient demographics, clinical assessments, interventions performed, and observed outcomes. Both types of records are digitized and maintained in a centralized EMS database, ensuring data consistency and ease of access for analysis.
These variables include the exact time when the emergency call was received by the EMS dispatch center, which is critical for analyzing response times and temporal patterns; the duration from the receipt of the emergency call to the arrival of the EMS team at the scene, which serves as a key metric in evaluating the efficiency of the EMS response; and the specific location where the cardiac arrest occurred, categorized into residential, public, workplace, and other settings, to assess the impact of the environment on patient outcomes.
Additionally, the study examined patient demographics, including age and gender, allowing for demographic analysis of survival rates and response effectiveness. The initial cardiac rhythm recorded upon EMS arrival, a critical determinant of the likelihood of achieving ROSC, was also included. The analysis of whether CPR was initiated specifically focused on resuscitation efforts started by EMS personnel, excluding CPR performed by bystanders prior to EMS arrival to ensure a clear understanding of EMS-led interventions. ROSC, in this study, is defined as the return of a palpable pulse and effective blood circulation prior to or upon EMS arrival at the hospital, serving as a metric to assess the immediate success of resuscitation efforts.
Geographical focus and demographic coverage
The study was conducted within the Lublin Voivodeship, a region known for its sophisticated EMS system designed to manage cardiac arrests and other medical emergencies efficiently. The regional EMS operates through a tiered response system, which ensures that the appropriate level of care is provided based on the severity of the emergency. This tiered system includes Basic Life Support (BLS) units and Advanced Life Support (ALS) units. BLS units, staffed by emergency medical technicians (EMTs), provide initial emergency care, including CPR, basic airway management, and the use of automated external defibrillators (AEDs). ALS units, staffed by paramedics or physicians equipped with advanced medical equipment and medications, perform more complex interventions such as advanced airway management, intravenous drug administration, and manual defibrillation. In cases of OHCA, ALS units are dispatched alongside BLS units to provide a higher level of care.
The EMS dispatch center uses a priority-based system to triage emergency calls and determine the appropriate response. Calls are categorized based on the perceived severity of the situation, with OHCA cases typically receiving the highest priority (K-1). The K-1 code is assigned to incidents that require an immediate, high-priority response due to life-threatening conditions, including suspected cardiac arrest. The K-2 code is assigned to lower-priority cases where the situation is urgent but less critical than those requiring a K-1 response. The dispatch center employs protocols to quickly recognize cardiac arrest situations, relying on standardized questions and decision-support tools to ensure that K-1 codes are issued promptly when needed. This system aims to minimize response times and enhance the chances of successful resuscitation.
Selection criteria for study participation
Out of the initial 5,111 cases identified using the International Classification of Diseases, Tenth Revision (ICD-10) diagnosis codes and the Ninth Revision (ICD-9) procedure codes, 4,361 cases were selected for detailed analysis. Exclusions, amounting to 750 cases, were due to several specific reasons. A total of 300 records did not have a confirmed diagnosis of sudden cardiac arrest (SCA) based on the medical rescue activity cards, which was a critical criterion for inclusion in the study. Additionally, 173 cases were misclassified by EMS personnel, leading to incorrect ICD-10 or ICD-9 coding that did not accurately represent SCA events. Instances where multiple EMS teams were dispatched to a single incident, potentially distorting data on intervention efficacy, resulted in further exclusions to maintain the clarity and accuracy of the response and outcome data. Furthermore, 131 records with significant gaps in documentation or those that were illegible were excluded to ensure the integrity and reliability of the study data. In some cases, 146 patients had advanced directives or do-not-resuscitate (DNR) orders in place, which precluded the initiation of CPR by EMS personnel, and these cases were not included in the analysis of resuscitation efforts. Importantly, patients who achieved ROSC before the arrival of emergency medical services were also excluded from the analysis to focus on the impact of EMS interventions on out-of-hospital cardiac arrest outcomes.
Data gathering and analytical procedures
Data input and validation process
Data from the call-out cards and medical rescue activity cards were meticulously entered into Microsoft Excel by two independent researchers to minimize entry errors. Following this initial data entry, a third researcher, who was not involved in the initial data collection, conducted a cross-validation process to ensure accuracy and objectivity. During this cross-validation process, any mismatches or discrepancies identified between the independently entered datasets were addressed through a systematic approach. The third researcher generated a discrepancy report highlighting all mismatches between the two independently entered datasets. Each discrepancy was reviewed by a panel consisting of the two original data entry researchers and the third researcher. They referred back to the original call-out cards and medical rescue activity cards to verify the correct data. If the original records clarified the discrepancy, the correct value was agreed upon and updated in the final dataset. In cases where the original records were unclear or incomplete, the panel used consensus and professional judgment to determine the most likely accurate entry, ensuring the highest possible data integrity. All changes and resolutions were documented, including the rationale for any decisions made in cases where data could not be unequivocally verified from the original records. This rigorous validation strategy ensured the accuracy and reliability of the data used in our analysis, mitigating the inherent risks associated with manual data entry.
Statistical evaluation methods
Statistical analyses were performed using Statistica version 12.5 (StatSoft Poland). The normality of data distribution was assessed using the Shapiro-Wilk test. For normally distributed data, means and standard deviations (SD) were calculated to describe the central tendency and dispersion, respectively. For data not adhering to a normal distribution, medians and interquartile ranges (IQR) were used as measures of central tendency and variability. This approach ensures the appropriate statistical description based on the underlying data characteristics. The χ2 test was utilized to explore relationships between variables, with the significance threshold set at p < 0.05. Missing data were handled by conducting sensitivity analyses, wherein cases with missing variables were compared to those with complete data to assess the potential impact of missingness on the results. If the missingness was found to be random, those cases were excluded from the final analysis. If patterns of missingness were identified, multiple imputation techniques were applied to estimate the missing values, ensuring that the analyses remained robust and unbiased.
Ethical compliance and oversight
All data handling procedures were strictly compliant with ethical standards. Data were pseudonymized to ensure privacy, in line with the personal data protection act enforced on May 10, 2018.
Variables omitted from analysis
Initial analyses showed that annual differences in data did not significantly affect the outcome measures of resuscitation success. As such, the focus of the research was directed towards more impactful variables, such as response times, location of cardiac arrest, and other operational factors. However, it is important to note that certain key variables – such as the initial cardiac rhythm, whether the arrest was witnessed, and the administration of bystander CPR – were not included in the final analysis due to limitations in the available data. These factors are well established in the literature as significant predictors of ROSC and survival outcomes. The omission of these variables limits the ability to fully adjust for all confounding factors, and therefore the conclusions drawn from this study should be interpreted with caution. Future research should aim to include these critical variables to provide a more comprehensive understanding of the factors influencing OHCA outcomes.
Study endpoints and sample size calculation
The primary endpoint of this study was the achievement of ROSC following EMS intervention for OHCAs. Secondary endpoints included the analysis of EMS response times and the impact of various demographic and operational variables on ROSC outcomes. While this study provides valuable insights into the operational aspects influencing OHCA outcomes, it is important to recognize that the exclusion of key clinical variables, such as initial cardiac rhythm and bystander CPR, means that the results primarily reflect the influence of non-patient factors. The retrospective nature of the study also precluded an initial calculation of required sample sizes, which is typically seen in prospective studies. However, the sample size for this retrospective analysis was determined based on the total number of EMS responses available from the Lublin Voivodeship database for the years 2014 to 2017. With 4,361 cases meeting our inclusion criteria from an initial dataset of 5,111 instances, the sample size was deemed sufficient to ensure robust statistical power. Statistical power calculations performed post hoc confirmed that this sample size could adequately detect significant effects, with a power of 80% and an α level of 0.05, given the observed effect sizes in ROSC outcomes between different response times and patient demographics.
A retrospective analysis was conducted on medical intervention data from the Lublin region for the years 2014 to 2017. Out of 277,998 medical team interventions, 5,111 cases were identified based on ICD-10 diagnosis codes and ICD-9 procedure codes related to SCA. After applying exclusion criteria, 4,361 cases were analyzed in detail to assess the impact of various factors on the effectiveness of emergency interventions and the rate of ROSC.
Results
A retrospective analysis was conducted on medical intervention data from the Lublin region for the years 2014 to 2017. Out of 277,998 medical team interventions, 5,111 cases were identified based on ICD-10 diagnosis codes and ICD-9 procedure codes related to SCA. After applying exclusion criteria, 4,361 cases were analyzed in detail to assess the impact of various factors on the effectiveness of emergency interventions and the rate of ROSC.
Impact of response time on resuscitation outcomes
The analysis revealed a statistically significant correlation between shorter EMS response times and the likelihood of achieving ROSC (Table I). The mean response time for cases where ROSC was achieved was 7.92 min (SD = 4.86), compared to 8.29 min (SD = 4.19) for cases without ROSC (Z = –2.0233, p = 0.0430). Although the difference in response times between the ROSC and no-ROSC groups was minimal (approximately 20 s on average), it was statistically significant.
Table I
Comparative analysis of EMS response times and ROSC achievement
| ROSC | Response time to patient |
|---|---|
| Yes | Mean: 7.92 min, SD = 4.86 |
| No | Mean: 8.29 min, SD = 4.19 |
| Statistical analysis | Z = –2.0233, p = 0.0430 |
Further analysis indicated that EMS response times varied by the time of day but showed no significant differences based on season or day of the week (p > 0.05). The shortest response times were recorded during the night (00:00 to 05:59) with a mean of 7.29 min (SD = 5.42) and morning (06:00 to 11:59) with a mean of 7.27 min (SD = 6.09). The longest response times occurred in the afternoon (12:00 to 17:59) at 7.71 min (SD = 7.13) and in the evening (18:00 to 23:59) at 7.46 min (SD = 7.13). These findings highlight that while time of day influences EMS response speed, other temporal factors, such as season and day of the week, do not significantly impact response times (Table II).
Table II
Temporal factors influencing EMS response times
[i] Mean ± SD represents the mean response time and its standard deviation. Me refers to the median response time. Q1–Q3 indicates the interquartile range, which spans from the first quartile (Q1) to the third quartile (Q3). The H statistic is derived from the Kruskal-Wallis test, used to assess differences across groups.
Analysis of the impact of call reasons and dispatch times on EMS response times and resuscitation outcomes
The analysis identified significant correlations between the reasons for EMS dispatch, the timing of dispatch, and EMS response times, which subsequently influenced resuscitation outcomes such as CPR initiation and ROSC. Resuscitation was most frequently initiated following EMS calls for chest pain (74.29%), with a mean response time of 6.45 min (SD = 5.14); sudden cardiac arrest (62.31%), with a mean response time of 7.12 min (SD = 5.78); and fainting (53.49%), with a mean response time of 7.54 min (SD = 5.90). Resuscitation was least frequent for incidents of drowning (20.69%) and hanging (23.18%), both of which had longer mean response times of 10.42 min and 8.27 min, respectively.
The likelihood of initiating CPR was highest during the evening hours from 18:00 to 23:59 (35.96%), which coincided with shorter EMS response times (mean of 7.29 min), and lowest during early morning hours from 00:00 to 05:59 (28.97%), with longer response times (mean of 7.92 min). Similarly, ROSC was most commonly achieved during evening hours (15.19%), again reflecting the impact of faster response times, while the lowest success rates were observed during early morning hours (9.87%).
Table III presents the statistical correlations between EMS call reasons, response times, dispatch times, and resuscitation outcomes, emphasizing how non-patient factors such as the nature of the emergency and time of day influence EMS response times. Faster response times were associated with better outcomes, including higher rates of CPR initiation and ROSC. Incorporating response times alongside these resuscitation outcomes clarifies the relationship between operational efficiency and patient outcomes.
Table III
Influence of EMS dispatch factors on response times, CPR initiation, and ROSC
Demographic and etiological influences on EMS response times, CPR, and ROSC outcomes
The analysis revealed significant correlations between demographic factors, the etiology of cardiac arrest, and EMS response times, which subsequently influenced outcomes such as CPR initiation and ROSC. CPR initiation rates were highest among patients aged 60–79 (35.98%), with a mean response time of 7.08 min (SD = 4.96), and those under 40 (35.96%), with a mean response time of 7.32 min (SD = 4.85). Males had a higher CPR initiation rate (33.40%) compared to females (29.81%), with corresponding mean response times of 7.11 min (SD = 5.05) and 7.39 min (SD = 5.10), respectively. Incidents at workplaces showed the highest CPR initiation rate (72.00%), with a mean response time of 6.95 min, while incidents with a cardiac etiology (58.66%) were also more likely to result in CPR, with a mean response time of 7.19 min.
ROSC was most frequently achieved in the same age groups (60–79 and < 40 years), in urban settings (13.72%), with a mean response time of 7.05 min, workplaces (32.00%), with a mean response time of 6.78 min, and for cardiac-related incidents (25.84%), with a mean response time of 7.14 min.
This analysis emphasizes how demographic factors (age, gender, incident location) and etiological factors influence EMS response times, which in turn correlate with the likelihood of CPR initiation and ROSC. Faster EMS response times in urban settings and workplaces contributed to higher rates of ROSC compared to rural areas and residential settings.
Table IV presents the statistical analysis of CPR initiation and ROSC achievement across different demographic and etiological factors, highlighting how response times vary based on these factors and their subsequent impact on resuscitation outcomes.
Table IV
Demographic and etiological factors influencing EMS response times, CPR, and ROSC outcomes
Temporal trends in EMS response times, CPR, and ROSC outcomes
This analysis examined the variation in CPR administration across different months, days of the week, and seasons, and how these efforts correlated with the likelihood of achieving ROSC. Resuscitation attempts peaked in April (34.34%), with a mean response time of 7.21 min (SD = 5.02), and in November (34.92%), with a mean response time of 7.34 min (SD = 5.27). The lowest frequencies occurred in May (27.55%) and August (27.82%), with response times of 7.55 min and 7.62 min.
On a weekly basis, Saturdays (34.20%) and Wednesdays (34.08%) had the highest rates of resuscitation, with mean response times of 7.17 and 7.38 min, whereas Fridays (28.34%) and Thursdays (30.05%) had the lowest, with response times of 7.76 and 7.64 min, respectively. Despite these temporal variations, statistical analysis revealed no significant differences in CPR initiation or ROSC outcomes based on the month, day of the week, or season (p > 0.05).
These findings suggest that while there is some variation in CPR attempts and response times across different time periods, these differences do not have a significant impact on ROSC outcomes. The variation in resuscitation attempts appears to be influenced more by operational factors such as call volume and EMS availability than any consistent seasonal or weekly trends affecting response times or outcomes.
Table V presents the temporal distribution of CPR efforts, ROSC achievement, and response times, emphasizing how non-patient factors such as the time of year or day can influence EMS response dynamics, but with no significant impact on the ultimate success of resuscitation efforts.
Table V
Temporal distribution of EMS response times, CPR efforts, and ROSC outcomes
Impact of emergency response priority on outcome effectiveness
The study’s findings highlight the significant impact of emergency priority codes on the success rates of resuscitation efforts. We analyzed the relationship between the emergency priority codes K-1 and K-2 and the outcomes of resuscitation (Table VI). The results show a clear association between higher emergency priority levels and improved ROSC rates. Specifically, cases assigned the K-1 code, which indicates a more urgent situation, demonstrated a higher success rate in achieving ROSC compared to those assigned the K-2 code, which corresponds to less urgent cases.
Table VI
Resuscitation outcomes based on emergency priority codes
This correlation may be attributed to several operational dynamics. Higher priority codes often result in shorter EMS arrival times, as these codes trigger a more urgent response. The probability of achieving ROSC diminishes with each passing minute following cardiac arrest, making a rapid EMS response crucial. Additionally, calls assigned higher priority codes are likely to be attended by more specialized EMS teams equipped with advanced life support capabilities, which are better suited to handle severe cases and thus improve the chances of successful resuscitation.
These findings underscore the importance of a well-structured emergency response system that effectively triages calls, ensuring that the most critical patients receive rapid and advanced care. However, it is important to acknowledge that the observed association between higher emergency priority and improved ROSC rates could be influenced by various confounding factors, such as the nature of the emergency call or the location of the arrest. Future studies should explore these operational factors further to better understand their contribution to variations in ROSC outcomes across different priority levels. Table VI provides a breakdown of CPR initiation and ROSC achievement rates according to the emergency priority codes assigned during EMS dispatch. The table emphasizes the impact of perceived emergency severity on patient outcomes.
Variability in EMS response times: effects of incident location and call nature
The study identified significant disparities in EMS response times influenced by the location of the incident and the nature of the emergency call, with statistical significance observed at p < 0.05. Response times were fastest for emergencies at workplaces, averaging 7.16 min, while calls from nursing homes received the slowest responses, averaging 7.99 min. The nature of the call also had a significant impact on response times: “lying down” emergencies had the quickest response, at a mean of 6.90 min, whereas “drowning” incidents experienced the slowest response, with a mean of 10.42 min. Table VII provides a detailed breakdown of EMS response times by incident location and the nature of the call, highlighting the variability in response effectiveness.
Table VII
EMS response times by incident location and call nature
[i] Mean ± SD represents the mean response time with its standard deviation. Me refers to the median response time. Q1–Q3 indicates the interquartile range, which spans from the first quartile (Q1) to the third quartile (Q3). The H statistic is derived from the Kruskal-Wallis test, used to assess differences across groups.
Discussion
The intersection of emergency response effectiveness and patient outcomes in the context of OHCA presents a complex, multidimensional challenge that our study has sought to dissect through rigorous analysis [11]. By exploring a vast array of socio-demographic and operational variables, this study highlights several key factors that significantly influence EMS response times and, subsequently, the likelihood of achieving a ROSC, which is a critical milestone in the resuscitation of patients experiencing OHCA.
One of the most important findings of our analysis suggests an association between EMS response time and ROSC outcomes. While shorter response times were statistically significantly associated with higher ROSC rates, the absolute difference observed was minimal, averaging around 20 s. This minimal difference may not be clinically significant, particularly when unmeasured confounding variables are taken into account. Additionally, the analysis was not adjusted for potential confounders such as bystander CPR, initial heart rhythm, or post-resuscitation care, which means that the observed association could be influenced by other factors (e.g., cardiac arrests in public locations might have both higher ROSC rates and shorter EMS response times). Given the retrospective nature of the study, our findings illustrate an association rather than causality, which is consistent with existing literature suggesting that every minute of delay in response can reduce the likelihood of survival by as much as 7–10% after cardiac arrest [12, 13].
Our study aligns with previous research emphasizing the importance of a rapid EMS response in improving outcomes for OHCA. Numerous studies have demonstrated that shorter response times are associated with higher survival rates, particularly in non-residential areas where the likelihood of bystander intervention is lower [14–16]. For instance, studies by Rea et al. and others have established that every minute of delay in EMS arrival reduces survival rates by 7–10%, a finding consistent with our observation of a correlation between faster response times and increased ROSC rates, despite the minimal absolute difference observed [5].
Non-patient factors such as location, time of day, and season were shown to influence EMS response times. For example, incidents in urban areas and workplaces were associated with faster response times and better outcomes, including higher rates of CPR initiation and ROSC. Similarly, evening hours were linked with faster response times, which positively impacted the rates of CPR initiation and ROSC.
The practical significance of these findings is clear: EMS systems can achieve better outcomes by prioritizing strategies that reduce response times. Faster response times, particularly in high-risk settings such as urban areas or workplaces, lead to higher rates of CPR initiation and ROSC. To further optimize EMS practices, interventions such as improved resource allocation, real-time response tracking, and enhanced dispatcher training could be implemented. These measures would not only decrease response times but also improve patient outcomes, reinforcing the need for EMS policies that focus on operational efficiency.
These findings suggest that operational factors, rather than patient-related characteristics, significantly affect the EMS’s ability to respond quickly and effectively. Although ROSC and CPR are important, the focus of this study remains on understanding how EMS response times vary across non-patient factors and how they might be related to outcomes such as ROSC and CPR initiation.
When comparing the EMS system in the Lublin region to those in other countries, it is evident that there are areas for potential improvement. For example, the relatively low rates of CPR initiation observed in our study suggest the need for more targeted public education campaigns and dispatcher training programs to increase bystander CPR rates and improve the early recognition of cardiac arrest. Additionally, the process of issuing K-1 codes could be optimized by adopting more sophisticated decision-support tools and algorithms, which have been successfully implemented in other EMS systems globally. Collecting Utstein variables systematically and using them for continuous feedback and training could also enhance the quality of care provided by EMS teams.
Furthermore, our study’s retrospective nature and the lack of access to key Utstein variables, such as initial rhythm, witnessed status, and bystander CPR, limit the robustness of our conclusions. The observed associations between EMS response times and ROSC rates, as well as between emergency priority codes and resuscitation outcomes, may be influenced by unmeasured confounding factors. For instance, cases occurring in public locations may have shorter response times and higher ROSC rates due to the higher likelihood of bystander intervention and prompt recognition of cardiac arrest. Therefore, while our findings suggest important trends, they should be interpreted with caution and viewed as associations rather than definitive evidence of causality.
The implications of rapid EMS response extend beyond the immediate outcomes of cardiac arrest cases. Integrating EMS optimizations with broader public health initiatives could enhance overall community health resilience. For example, public education campaigns on recognizing cardiac arrest symptoms and performing CPR could complement faster EMS response times, creating a more responsive community environment [17]. This holistic approach would bridge the gap between individual emergency responses and community-wide health outcomes, aligning with global health objectives to improve emergency care efficiency.
Moreover, our study evaluated the influence of the urgency code assigned at the time of dispatch. The findings revealed that higher emergency priority codes (K-1) are associated with better resuscitation outcomes compared to lower priority (K-2), which statistically improved ROSC. However, it is essential to bear in mind that the failure of dispatchers to correctly identify a cardiac arrest might result from several factors, such as non-cardiac causes of arrest, which are known to be associated with poorer outcomes after OHCA. These factors could lead to an underestimation of urgency and therefore a lower priority code (K-2). Consequently, the observed association between higher emergency priority codes and improved ROSC rates might be influenced by these confounding variables. This underscores the need for enhanced dispatcher training and better algorithms to accurately identify cardiac arrests and assign appropriate priority levels. Further research should aim to clarify the impact of these factors by analyzing the nature of emergency calls and the location of arrests in relation to the assigned priority codes.
To further contextualize our findings, it is pertinent to reference the study by Rea et al., which examined the influence of various prognostic factors on survival after OHCA [18]. Their research demonstrated that the Utstein elements – such as initial rhythm, witnessed status, and bystander CPR – are pivotal in predicting survival, yet they account for only a fraction of the outcome variability. Specifically, the Utstein elements explained 72% of the survival variability among all cardiac arrests and 40% among bystander-witnessed ventricular fibrillation cases. This underscores that multiple factors contribute to survival outcomes and that no single element, including EMS response time, fully elucidates this variability. Our observation that shorter EMS response times correlate with higher ROSC rates must be understood within this broader framework. It underscores the necessity for a comprehensive approach to improving OHCA outcomes, which includes rapid EMS response, enhanced public education on emergency response, improved dispatcher training, and the integration of advanced technological solutions. These combined efforts are critical in addressing the diverse determinants of survival and achieving more consistent and favorable outcomes across various settings.
Advancements in technology also offer promising avenues to enhance EMS effectiveness [19–21]. The deployment of real-time data analytics and GPS-based dispatch systems can potentially reduce response times and improve the allocation of resources [22–25]. Moreover, incorporating machine learning algorithms could help predict high-risk zones and times for cardiac arrests, allowing preemptive resource positioning. Such technological integrations could revolutionize the operational frameworks of EMS, making the response to cardiac emergencies more proactive rather than reactive.
Additionally, comparing the EMS practices and innovations in the Lublin Voivodeship with those from other regions or countries can offer valuable insights for further improvements. For example, some countries have successfully implemented advanced pre-hospital care protocols and integrated technology-driven solutions that could be adapted to the local context [26, 27]. In the United States, the implementation of mobile integrated healthcare and community paramedicine (MIH-CP) has significantly reduced response times and improved patient outcomes in rural and underserved areas [28]. Similarly, the United Kingdom’s National Health Service has employed real-time data analytics to predict demand patterns and allocate resources more efficiently, ensuring that the most critical patients receive timely care [29].
Adopting these innovations, particularly the integration of data analytics and predictive modeling into EMS operations, could provide substantial benefits in the Lublin region. Such technologies allow for better anticipation of high-risk situations and more effective resource deployment, ultimately improving response times and outcomes [30]. Furthermore, global health initiatives such as the World Health Organization’s Global Hearts Initiative [31], which emphasizes community-based interventions and the strengthening of emergency care systems, could serve as a model for enhancing EMS protocols and training in the Lublin Voivodeship.
In addition to technological advancements, global efforts to improve bystander CPR rates through widespread public education campaigns have shown promising results [32–34]. For instance, countries such as Norway and Japan have significantly increased bystander CPR rates through mandatory CPR training in schools and public awareness programs [32, 35]. Implementing similar initiatives in the Lublin region could complement existing EMS strategies, ensuring a more comprehensive approach to improving OHCA outcomes.
The role of bystander interventions before EMS arrival is another critical aspect worth considering. Immediate actions such as bystander CPR and the use of automated external defibrillators have been shown to significantly improve survival rates. Investigating the correlation between bystander intervention rates and the effectiveness of EMS could provide insights into potential areas for public education and community-based training programs.
However, variables such as the time of year, day of the week, and time of day did not show a significant statistical impact on the EMS response times, suggesting that operational consistency is maintained across various temporal dimensions. This finding is somewhat reassuring, indicating that EMS services provide a stable response irrespective of these factors.
The demographic and etiological analyses further enriched our understanding of the dynamics at play. Specifically, male patients and those in older age brackets, particularly those aged between 60 and 79 years, showed a higher likelihood of receiving CPR and achieving ROSC. This could be indicative of both the higher incidence of cardiovascular events in these demographics and possibly a more pronounced focus on these groups by responding teams. Similarly, incidents occurring in urban areas and workplaces were associated with better outcomes, likely due to faster response times and perhaps more immediate access to advanced medical care.
Limitations. This study, while comprehensive in its approach, has several inherent limitations that must be considered when interpreting the findings. The retrospective nature of the analysis may introduce biases related to data collection and reporting. Although extensive efforts were made to ensure data accuracy and validity through systematic entry and cross-verification, retrospective data can sometimes miss nuanced clinical details that are not recorded in standard documentation.
Additionally, the dataset used in this study, spanning from 2014 to 2017, might be somewhat outdated. Advancements in EMS protocols and technology that have occurred since this period may not be reflected in our analysis, potentially limiting the relevance of the findings to current practices.
The generalizability of our findings may be limited due to the geographical focus on the Lublin Voivodeship. The EMS operational protocols and demographic specifics of this region may not represent other settings, particularly those with different healthcare infrastructures or cultural contexts. Thus, while the findings provide significant insights into EMS responses within this region, they might require adaptation when applied to different contexts.
A significant limitation of this study is the lack of key prognostic variables, such as initial rhythm, witnessed status, and bystander CPR, in our dataset. The absence of these Utstein variables limits our ability to perform adequately adjusted analyses to control for potential confounders. This makes it difficult to fully account for factors that are known to significantly influence ROSC outcomes, thereby emphasizing associations rather than definitive causal relationships.
Furthermore, we did not perform ROC curve analysis, which could have provided more precise cut-off values associated with the return of spontaneous circulation. Future studies should consider including such analysis to enhance the precision and applicability of the findings.
Therefore, any inferences between the studied variables and clinical outcomes should be interpreted with caution. The limitations mentioned underscore the need for future research that includes more comprehensive data and advanced analytical techniques to provide a more definitive understanding of the factors influencing OHCA outcomes.
Conclusions
This study demonstrates that non-patient factors such as location, time of day, and incident characteristics significantly impact EMS response times and OHCA outcomes, particularly CPR initiation and ROSC. Shorter response times were associated with better outcomes, underscoring the importance of optimizing EMS protocols and prioritization strategies. Our findings suggest that targeted adjustments to EMS protocols and dispatcher training could enhance response effectiveness. Future research should address the influence of unmeasured variables, such as initial rhythm and bystander CPR, to strengthen these findings and further improve OHCA management.
