Factors affecting the first 24-hour mortality of patients receiving emergency medical service (EMS) in a sub-urban area: a retrospective cohort study

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A hospital-based model drives the Emergency Medical Service (EMS) system in Saraburi’s vicinity. We studied factors related to death within 24 hours of EMS patients. Methods: Patients who received EMS from the high-level operation team of Saraburi Hospital from 1 May 2017 - 7 July 2019 were enrolled in the study. Various factors that may affect death within 24 hours were collected. Data were analyzed by flexible parametric survival analysis using an exploratory model. Result: Out of 2,321 patients, 118 died within 24 hours. The factors associated with a statistically significant increase in the hazard ratio of death within 24 hours were male sex (aHR, 1.69; 95% CI, 1.05-2.71; p = 0.031), time from symptom onset to calling for medical assistance within two hours (aHR, 3.04; 95% CI, 1.12-8.24; p = 0.029), diastolic blood pressure < 60 mmHg (aHR, 3.21; 95% CI, 1.01-10.21; p = 0.049), pulse < 50 or ≥120 beats/min (aHR, 2.17; 95% CI, 1.00-4.71; p = 0.050), Glasgow Coma Scale ≤ 8 (aHR, 16.16; 95% CI, 6.68-39.11; p 15 min (aHR, 2.02; 95% CI, 1.01-4.03; p = 0.046) and present prehospital life-saving intervention (aHR, 3.52; 95% CI, 1.30-9.51, p = 0.013). Factors associated with a statistically significant decrease in this ratio were the distance from the scene to the hospital >10 km (aHR, 0.35; 95% CI, 0.18-0.71; p = 0.004), and the synchronized operation with the Basic Life Support (BLS) team (aHR, 0.40; 95%CI, 0.20-0.81; p=0.010). Conclusions: The study emphasizes the importance of early intervention, synchronized operations, and access to appropriate levels of care in improving patient outcomes and reducing mortality in patients receiving EMS. Further prospective studies are required to confirm these results." } { "@context": "http://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": "1", "item": { "@id": "https://f1000research.com/", "name": "Home" } }, { "@type": "ListItem", "position": "2", "item": { "@id": "https://f1000research.com/browse/articles", "name": "Browse" } }, { "@type": "ListItem", "position": "3", "item": { "@id": "https://f1000research.com/articles/12-899/v1/iparadigms", "name": "Factors affecting the first 24-hour mortality of patients receiving..." } } ] } Home Browse Factors affecting the first 24-hour mortality of patients receiving... ALL Metrics - Views Downloads Get PDF Get XML Cite How to cite this article Duongthong P, Muengtaweepongsa S, Lokeskrawee T et al. Factors affecting the first 24-hour mortality of patients receiving emergency medical service (EMS) in a sub-urban area: a retrospective cohort study [version 1; peer review: 1 approved with reservations] . F1000Research 2023, 12 :899 ( https://doi.org/10.12688/f1000research.137744.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Research Article Factors affecting the first 24-hour mortality of patients receiving emergency medical service (EMS) in a sub-urban area: a retrospective cohort study [version 1; peer review: 1 approved with reservations] Patcharee Duongthong 1 , Sombat Muengtaweepongsa https://orcid.org/0000-0003-3715-4428 2 , Thanin Lokeskrawee 3 , Suppachai Lawanaskol 4 , Natee Sakornyutthadej https://orcid.org/0000-0002-9459-7367 5 , Patraporn Thongyong 1 Patcharee Duongthong 1 , Sombat Muengtaweepongsa https://orcid.org/0000-0003-3715-4428 2 , [...] Thanin Lokeskrawee 3 , Suppachai Lawanaskol 4 , Natee Sakornyutthadej https://orcid.org/0000-0002-9459-7367 5 , Patraporn Thongyong 1 PUBLISHED 28 Jul 2023 Author details Author details 1 Department of Emergency Medicine, Saraburi Hospital, Mueang Saraburi District, Saraburi, Thailand 2 Center of Excellence in Stroke, Faculty of Medicine, Thammasat University, Pathum Thani, 12120, Thailand 3 Department of Emergency Medicine, Lampang Hospital, Lampang, Thailand 4 Chaiprakarn Hospital, Chaing Mai, Thailand 5 Chaki Naruebodindra Medical Institute, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Samut Prakan, Thailand Patcharee Duongthong Roles: Conceptualization, Data Curation, Formal Analysis, Methodology, Resources, Writing – Original Draft Preparation, Writing – Review & Editing Sombat Muengtaweepongsa Roles: Conceptualization, Funding Acquisition, Investigation, Methodology, Project Administration, Supervision, Writing – Original Draft Preparation, Writing – Review & Editing Thanin Lokeskrawee Roles: Data Curation, Supervision, Validation, Visualization Suppachai Lawanaskol Roles: Data Curation, Resources, Validation, Visualization Natee Sakornyutthadej Roles: Data Curation, Supervision, Validation, Visualization Patraporn Thongyong Roles: Data Curation, Validation, Visualization OPEN PEER REVIEW DETAILS REVIEWER STATUS Abstract Background : Saraburi province in central Thailand connects the capital to the north and northeast. A hospital-based model drives the Emergency Medical Service (EMS) system in Saraburi’s vicinity. We studied factors related to death within 24 hours of EMS patients. Methods: Patients who received EMS from the high-level operation team of Saraburi Hospital from 1 May 2017 - 7 July 2019 were enrolled in the study. Various factors that may affect death within 24 hours were collected. Data were analyzed by flexible parametric survival analysis using an exploratory model. Result: Out of 2,321 patients, 118 died within 24 hours. The factors associated with a statistically significant increase in the hazard ratio of death within 24 hours were male sex (aHR, 1.69; 95% CI, 1.05-2.71; p = 0.031), time from symptom onset to calling for medical assistance within two hours (aHR, 3.04; 95% CI, 1.12-8.24; p = 0.029), diastolic blood pressure < 60 mmHg (aHR, 3.21; 95% CI, 1.01-10.21; p = 0.049), pulse < 50 or ≥120 beats/min (aHR, 2.17; 95% CI, 1.00-4.71; p = 0.050), Glasgow Coma Scale ≤ 8 (aHR, 16.16; 95% CI, 6.68-39.11; p 15 min (aHR, 2.02; 95% CI, 1.01-4.03; p = 0.046) and present prehospital life-saving intervention (aHR, 3.52; 95% CI, 1.30-9.51, p = 0.013). Factors associated with a statistically significant decrease in this ratio were the distance from the scene to the hospital >10 km (aHR, 0.35; 95% CI, 0.18-0.71; p = 0.004), and the synchronized operation with the Basic Life Support (BLS) team (aHR, 0.40; 95%CI, 0.20-0.81; p =0.010). Conclusions : The study emphasizes the importance of early intervention, synchronized operations, and access to appropriate levels of care in improving patient outcomes and reducing mortality in patients receiving EMS. Further prospective studies are required to confirm these results. READ ALL READ LESS Keywords Emergency Medical Service, 24-hour mortality, Prehospital factor, Advance life support team, Sub-urban Corresponding Author(s) Sombat Muengtaweepongsa ( [email protected] ) Close Corresponding author: Sombat Muengtaweepongsa Competing interests: No competing interests were disclosed. Grant information: This study was supported by the Center of Excellence in Stroke, Thammasat University, Thailand. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Copyright: © 2023 Duongthong P et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Duongthong P, Muengtaweepongsa S, Lokeskrawee T et al. Factors affecting the first 24-hour mortality of patients receiving emergency medical service (EMS) in a sub-urban area: a retrospective cohort study [version 1; peer review: 1 approved with reservations] . F1000Research 2023, 12 :899 ( https://doi.org/10.12688/f1000research.137744.1 ) First published: 28 Jul 2023, 12 :899 ( https://doi.org/10.12688/f1000research.137744.1 ) Latest published: 28 Jul 2023, 12 :899 ( https://doi.org/10.12688/f1000research.137744.1 ) Introduction Emergency medical services (EMS) in Thailand refer to the system and process of recognizing emergency medical conditions and providing timely and appropriate care to patients until they can be transported to a medical facility for further treatment. The primary goal of EMS is to provide immediate medical attention and stabilize the patient’s condition to prevent further harm or deterioration before reaching a hospital. This process includes the evaluation, management, coordination, monitoring, communication, transportation, diagnosis, and treatment for both inpatients and outpatients. 1 Saraburi, a province in the Central region of Thailand, has a population of about 650,000 and is the gateway to Thailand’s Northern and Northeastern regions. EMS has been developed in Saraburi since 1997 as a hospital-based administration. When receiving the request for medical attention via the number 1669, the officer dispatcher asks for the symptoms and determines the level of assistance. If the patient needs aid from the advanced-level response team, the center will dispatch the hospital nearest to the place of the event to provide first aid and take the patient to the nearest urban hospital ( Figure 1 ). The urban area of Saraburi has an advanced-level response team from Saraburi Hospital to provide EMS. With a population of 118,375, having a dedicated advanced-level response team can significantly enhance emergency care provision in Saraburi’s urban area. Saraburi Hospital, with 700 beds, can handle various medical conditions and emergencies with specialized departments, advanced diagnostic and treatment facilities, and a multidisciplinary team of healthcare professionals, including specialists and experienced medical staff. Figure 1. The map of Thailand and Saraburi Province (Adapted from NordNordWest on Wikimedia Commons, available under GNU Free Documentation License ). In the Thailand EMS process, the following steps are involved: 1) Emergency call: When an incident occurs and requires medical assistance, people in Thailand can call the emergency hotline number 1669 to report the emergency. 2) Phone triage: The Emergency Dispatch and Control Center receives the call and conducts a phone triage. The call taker assesses the situation, gathers information about the incident, and determines the severity of the medical emergency. 3) Operation team selection: Based on the information obtained during the phone triage, the call taker selects the appropriate type of operation team to assist the patient. The operation teams are categorized as Advance Life Support (ALS), Intermediate Life Support (ILS), and Basic Life Support (BLS). 4) Emergency medical triage protocol: The selection of the operation team is based on the principles outlined in the Emergency Medical Triage Protocol and Criteria-Based Dispatch, developed by the Thai National Institute for Emergency Medicine. The protocol considers the life-threatening data of patients to ensure the qualified team is dispatched. 5) Preliminary treatment and transport: Once the appropriate operation team is selected, the Emergency Dispatch and Control Center directs the team closest to the scene to provide preliminary treatment and transport the patient to the nearest hospital. If the scene is far from the base of the ALS team, local BLS teams closer to the area may be requested to assess the patient and initiate basic life support procedures until the ALS team arrives. This step is defined as a synchronized operation with the BLS team. 6) Transfer to ALS Team: The Emergency Dispatch and Control Center determines the meeting points where the ALS team will take over the treatment from the BLS team. The ALS team then assumes responsibility for the patient’s care and continues the treatment. 7) Transfer to hospital: The ALS team transports the patient to the appropriate hospital for further medical care based on the patient’s condition and the hospital’s capabilities. 8) Data recording: Throughout the entire process, including the emergency call, triage, team selection, treatment, and transport, various data points are recorded. This includes the time in minutes, the distance in kilometers at each point, and clinical data related to the patient’s condition. The synchronized operation between the ALS and BLS teams ensures that patients receive the necessary medical attention promptly, even in cases where the ALS team may have a longer response time due to distance. This approach maximizes the efficiency of the emergency response system and improves patient outcomes, as shown in Figure 2 . 2 , 3 Figure 2. Emergency medical service time in Thailand. A previous study reported that out of every 100 patients, approximately 11 of them would decease within one day of receiving EMS. 4 The factors associated with increased mortality within 24 hours of patients receiving EMS included older patients, traffic, injury etiology, prolonged prehospital time, unstable vital signs, long physical distance, occurrence of pre-hospital resuscitation, and lack of assistance from the bystanders. 5 – 10 However, it’s important to note that these factors may not be universally applicable in all EMS settings and can vary depending on the specific circumstances and healthcare system in different regions. We aimed to evaluate the variables that may affect the outcomes under our circumstances in terms of the relative weight of each variable and present the actual affecting factors. Methods A retrospective exploratory cohort study was conducted. The objective was to explore the prehospital factors, including patient age, gender, vital signs at the scene, level of consciousness present as Glasgow Coma Scale (GCS; severe 3-8, moderate 9-12 and mild 13-15), underlying disease, types of patients (medical, trauma, pediatric), response time, on-scene time, transport time, distance, pre-hospital lifesaving intervention, previous transfer by the basic level team, that affect the first 24-hour mortality of patients receiving the EMS. The primary outcome was the mortality rate within the first 24 hours. The sample size, calculated using STATA version 16.0 with a power of 80%, one-sided alpha of 0.05, and a mortality rate in the first 24 hours of 0.05, was 2,213. We categorized and demonstrated the exploratory factor based on the suitability of evidence-based reference and hierarchical data. 11 , 12 Comparisons of baseline characteristics and prognostic factors between patients who died and survived within the first 24 hours were performed using the two-way Fisher Exact Probability Test. The analysis of the effect of prognostic factors on the first 24-hour mortality rate was represented as the hazard ratio (HR), with both univariate and multivariate analyses by flexible parametric analysis. The Human Research Ethics Committee of Thammasat University (Medicine) approved this study and waived the need for informed consent for the study (approval number: 072/2563). Saraburi Hospital (the owner of the electronic medical records) approved the data accession. All methods were performed under the relevant guidelines and regulations. Study setting and participants The study analyzed the medical records of 2,500 individuals who sought emergency assistance between May 2017 and July 2019. These patients contacted the EMS center of Saraburi Hospital through various means, including the emergency number 1669. Specifically, the study focused on both regular emergency cases and severe emergencies. To ensure accurate results, the collected data excluded patients whose ambulance dispatch was canceled, those who passed away before the ambulance’s arrival, and individuals who refused service or requested transportation to alternative healthcare facilities. The chosen timeframe predates the onset of the COVID-19 pandemic, allowing for the elimination of potential confounding variables such as the use of personal protective equipment, which could extend response times. The study flow is shown in Figure 3 . Figure 3. Study flow. Results Out of 2,500 enrolled patients, 179 were excluded. The clinical characteristics of all patients are shown in Table 1 . The number of patients who died within the first 24 hours was 118 (5.1%). The number of male patients who died within 24 hours was significantly greater than that of female patients (64.4% versus 35.6%, p = 0.029). Patients in the death group had a time from symptom onset to calling for medical assistance ranging 0-2 hours, and the on-scene time of more than 20 minutes was higher than for the survival group (85% and 11%, p < 0.001 and = 0.003, respectively). Table 1. Clinical characteristics of all patients. Parameter, n (%) Dead in 24 h N = 118 Survive in 24 h N = 2,203 p -value Gender Male 76 (64.4%) 1,188 (53.9%) 0.029 Female 42(35.6%) 1,015 (46.1%) Age, years >25-80 98 (83.0%) 1,685 (76.5%) 0.228 0-25 8 (6.8%) 250 (11.3%) >80 12 (10.2%) 268 (12.2%) Response time >8 minutes 63 (53.4%) 1,342 (60.9%) 0.122 On-scene time >20 minutes 13 (11.0%) 96 (4.4%) 0.003 Transport time >15 minutes 23 (19.5%) 485 (22.0%) 0.569 Symptom onset to call time 2-8 h 8 (6.8%) 436 (19.8%) 8 h 9 (7.6%) 443 (20.1%) Distance >10 km 28 (23.7%) 566 (25.7%) 0.745 SBP<90 mmHg 82 (69.5%) 36 (30.5%) <0.001 DBP<60 mmHg 87 (73.7%) 319 (14.5%) <0.001 PR<50 or ≥120 beats/minute 89 (75.4%) 432 (19.6%) <0.001 RR≤8 or ≥25/minute 91 (77.1%) 602 (27.3%) <0.001 Oxygen saturation ≤60% 68 (57.6%) 39 (1.8%) <0.001 Level of consciousness, Glasgow coma scale 13-15 21 (17.8%) 1,740 (79.0%) <0.001 9-12 5 (4.2%) 257 (11.7%) ≤8 92 (77.0%) 206 (9.4%) Trauma patients 39 (33.1%) 302 (13.7%) <0.001 Pediatric patients 1 (0.9%) 52 (2.4%) 0.520 Underlying diseases Hypertension 26 (22.0%) 753 (34.2%) 0.007 Acute coronary syndrome 9 (7.6%) 230 (10.4%) 0.436 Diabetes 20 (16.6%) 517 (23.5%) 0.116 Cerebrovascular accident 3 (2.5%) 159 (7.2%) 0.061 Cirrhosis 1 (0.9%) 48 (2.2%) 0.514 Chronic kidney disease 3 (2.5%) 127 (5.8%) 0.212 Chronic obstructive pulmonary disease 2 (1.7%) 81 (3.7%) 0.439 Previous transfer by basic-level team 15 (12.7%) 101 (4.6%) 0.001 Prehospital life-saving intervention 70 (59.3%) 18 (0.8%) <0.001 All vital signs, including systolic blood pressure (SBP) lower than 90 mmHg, diastolic blood pressure (DBP) lower than 60 mmHg, pulse rate (PR) < 50 or ≥ 120 beats per minute, respiratory rate ≤ 8 per minute or ≥ 25 per minute, and oxygen saturation ≤ 60% and level of consciousness with a Glasgow Coma Scale (GCS) ≤ 8 of patients who died within the first 24 hours were significantly lower than those who survived (69.5%, 73.7%, 75.4%, 73.1%, 57.6%, and 77.0%; p < 0.001, respectively). It was found that the proportion of patients with underlying hypertension among those who survived within the first 24 hours was significantly higher than that among the death group (22.0% versus 34.2%, p = 0.007). Moreover, there were significantly higher proportions of traumatic patients (33.1%; p < 0.001), patients with synchronized operation with the BLS team (12.7%; p = 0.001), and receiving a prehospital lifesaving intervention (59.3%; p < 0.001) among those who died within the first 24 hours compared with patients who survived within the first 24 hours. Based on the adjustments made for various factors such as age, on-scene time, transport time, time from symptom onset to calling for medical assistance, physical distance, vital signs, GCS (Glasgow Coma Scale), traumatic etiology, pediatric patient status, underlying diseases, previous transfer from a basic level operation team, and prehospital life-saving interventions, statistical analyses revealed that male patients had a significantly higher risk of death with each passing minute (aHR, 1.69; 95% CI, 1.05-2.71; p = 0.031). When considering the time elapsed from symptom onset to seeking medical assistance, patients who received intervention within two hours had a mortality rate 3.04 times higher (aHR 3.04; 95%CI, 1.12-8.24; p = 0.29) than those whose intervention occurred between two and eight hours. Furthermore, for each delivery time interval, only transportation exceeding 15 minutes was associated with a twofold increase in mortality within 24 hours (aHR, 2.02; 95% CI, 1.01-4.03; p = 0.046). Figure 4 displays the Kaplan-Meier survival curve, which presents the estimated survival rates of overall EMS patients for each minute within a 24-hour period. Figure 4. Kaplan-Meier survival curve estimates each minute in 24 hour of overall EMS patients in this study. The vital signs at the scene correlated with the mortality rate within the first 24 hours were DBP < 60 mmHg, PR < 50, or ≥120 beats per minute with 3.21 times and 2.17 times more (aHR, 3.21 and 2.71; 95% CI, 1.01-10.21 and 1.00-4.71; p = 0.049, and 0.050, respectively). Patients with GCS 3-8 had a 16.16 times greater mortality rate than those with GCS 13-15 (aHR, 16.16; 95%CI, 6.68-39.11; p < 0.001). A physical distance from the scene to the hospital greater than 10 kilometers and the previous transfer from the basic level operation time lowered the first 24-hour mortality rate by 0.35 times (aHR, 0.35; 95% CI, 0.18-0.71; p = 0.004) and 0.40 (aHR, 0.40; 95% CI, 0.20-0.81; p = 0.010), compared with those who survived. Regarding prehospital life-saving intervention, it was found that patients receiving life-saving procedures died within the first 24 hours 3.52 times more often than patients who did not receive the life-saving intervention after the adjustment of gender, age, duration of medical emergency services, vital signs, underlying diseases, distance from the scene to the hospital, trauma patients and pediatric patients (aHR, 3.52; 95% CI, 1.30-9.51; p = 0.013). The crude and adjusted hazard ratio of 24 hours death in all prehospital parameters are shown in Table 2 . Table 2. The crude and adjusted hazard ratio of 24 hours death in all prehospital parameters. Parameter Crude HR 95% CI p -value Adjusted HR 95% CI p -value Male 1.56 1.05-2.33 0.028 1.69 1.05-2.71 0.031 Age, years >25-80 (Reference) 0-25 0.83 0.40-1.70 0.604 0.89 0.37-2.13 0.796 >80 0.82 0.41-1.62 0.563 0.331 0.67-3.24 0.331 Response time >8 minutes 0.76 0.52-1.11 0.157 1.29 0.77-2.15 0.337 On-scene time >20 minutes 2.72 1.52-4.86 0.001 0.94 0.45-1.95 0.864 Transport time >15 minutes 0.85 0.52-1.39 0.523 2.02 1.01-4.03 0.046 Symptom onset to call time 2-8 h (Reference) 0-2 h 4.71 2.06-10.75 8 h 1.40 0.47-4.17 0.543 2.48 0.68-9.04 0.168 Distance >10 km 1.13 0.72-1.76 0.596 0.35 0.18-0.71 0.004 SBP<90 mmHg 34.82 22.57-53.72 <0.001 1.25 0.30-5.32 0.755 DBP<60 mmHg 22.77 14.43-35.93 <0.001 3.21 1.01-10.21 0.049 PR<50 or ≥120 beats/minute 19.94 12.55-31.68 <0.001 2.17 1.00-4.71 0.050 RR≤8 or ≥25/minute 13.06 8.33-20.47 <0.001 1.47 0.74-2.91 0.272 Oxygen saturation ≤60% 43.66 29.37-64.90 <0.001 0.55 0.22-1.39 0.204 Level of consciousness, Glasgow coma scale 13-15 (Reference) 9-12 3.18 1.12-9.04 0.030 2.27 0.76-6.75 0.141 ≤8 61.13 33.41-111.84 <0.001 16.16 6.68-39.11 <0.001 Trauma patients 2.97 1.99-4.43 <0.001 0.77 0.49-1.23 0.273 Pediatric patients 0.89 0.12-6.36 0.906 0.924 0.12-10.30 0.924 Underlying diseases Hypertension 0.48 0.30-0.78 0.003 0.57 0.30-1.10 0.090 Acute coronary syndrome 0.47 0.19-1.16 0.102 2.24 0.81-6.23 0.119 Diabetes 0.54 0.32-0.92 0.024 0.97 0.45-2.29 0.974 Cerebrovascular accident 0.54 0.17-1.69 0.287 0.93 0.24-3.62 0.913 Cirrhosis 0.53 0.07-3.81 0.529 1.24 0.14-10.99 0.848 Chronic kidney disease 0.56 0.18-1.77 0.325 0.44 0.20-2.96 0.704 Chronic obstructive pulmonary disease 0.32 0.05-2.29 0.256 0.60 0.07-5.12 0.637 Previous transfer by basic-level team 3.30 1.84-5.90 <0.001 0.40 0.20-0.81 0.010 Prehospital life-saving intervention 60.96 40.69-91.33 <0.001 3.52 1.30-9.51 0.013 Discussion Male sex The study results indicate that male patients had a higher risk of death within 24 hours than female patients. The subgroup analysis revealed that males had a higher ratio of trauma cases, accounting for 63% of trauma patients in the study. This subgroup of patients also had significantly higher mortality rates. The findings align with a previous study, 11 which demonstrated that males faced an increased mortality risk in traumatic cases with on-scene hypotension. Furthermore, several other studies 12 – 32 have reported similar results, indicating that males are more likely to develop severe conditions such as traumatic brain injury, myocardial infarction, or out-of-hospital cardiac arrest. These collective findings suggest that there may be gender-based differences in the susceptibility and severity of certain diseases and injuries, with males exhibiting a higher risk of developing severe conditions and experiencing worse outcomes. However, it’s important to note that individual factors, genetic predisposition, lifestyle choices, and other variables could also contribute to these differences. Further research is needed to explore the underlying reasons behind these gender-based disparities in disease severity and mortality rates. The time from symptom onset to calling for medical assistance The time from symptom onset to calling for medical assistance can vary depending on several factors, including the individual’s awareness of symptoms, severity, and decision-making process. It is crucial to seek immediate medical attention by calling emergency services (1669) to ensure timely and appropriate care. Ideally, supposing someone experiences severe or life-threatening symptoms, such as chest pain, difficulty breathing, sudden loss of consciousness, or signs of a stroke, they should call for emergency medical assistance immediately. These situations require urgent attention, and delays in seeking help can have serious consequences. However, the time from symptom onset to calling for assistance can be longer for less severe or non-emergency situations. Some individuals may initially try to manage their symptoms at home or seek advice from non-emergency medical helplines or healthcare professionals. Others may delay seeking medical assistance due to fear, uncertainty, or underestimating the severity of their symptoms. It is important to note that early medical intervention is often crucial in many medical conditions to achieve the best possible outcomes. Based on study findings, there was an increased hazard ratio for patients who delayed calling for medical assistance beyond two hours compared to those who sought help within two hours. This suggests that earlier medical intervention within the first two hours of symptom onset was associated with a higher severity of the diseases. On the other hand, patients who waited for more than eight hours since the onset of symptoms were found to have less severe symptoms. This finding could be attributed to patients with milder symptoms who can wait longer before seeking medical attention. It is important to note that these findings may not apply universally to all medical conditions and situations. The response time and urgency for seeking medical assistance can vary depending on the specific condition, individual factors, and clinical guidelines. It is always recommended to promptly seek medical advice in cases of severe or life-threatening symptoms, regardless of the elapsed time since symptom onset. Further research and consideration of various factors are necessary to fully understand the implications of delayed or early medical intervention in different medical conditions and to develop appropriate guidelines for prompt and effective care. Vital signs Based on the study findings, DBP below 60 mmHg was associated with an increased risk of death within 24 hours, even when accounting for other factors that could affect the outcome, such as pre-hospital life-saving interventions. The administration of intravenous fluids during transportation, a standard resuscitation measure, may not be sufficient to mitigate this effect. The decrease in DBP suggests a decrease in left ventricular diastole, which can occur in cardiogenic shock. Additionally, it may be associated with low vascular resistance resulting from the systemic inflammatory response seen in prolonged shock or distributive shock, such as that caused by sepsis. 33 , 34 The prehospital team cannot effectively treat both conditions with adequate fluid administration alone. Patients needed the appropriate vasopressor drug, intravenous thrombolysis or primary percutaneous transluminal coronary angioplasty, intra-aortic balloon pump, or extracorporeal membrane oxygenation, which required specific treatment and referral to the high-level critical care unit. Low SBP (Systolic Blood Pressure) seems to be associated with high mortality in our study. However, the study results were not statistically significant when the SBP was adjusted for another factor. This suggests that the relationship between SBP and mortality may not be significant when considering the influence of this confounding factor. One possible reason for the lack of significance could be the high confounding factor, which means that there is another variable influencing the relationship between SBP and mortality. Additionally, it is mentioned that most patients with a SBP lower than 90 mmHg received early-volume resuscitation as a lifesaving intervention. This intervention might have affected the association between SBP and mortality, potentially reducing the magnitude of the association. The relationship with respiratory rate is also mentioned. Patients with a respiratory rate of ≤8 breaths per minute or ≥25 breaths per minute showed a similar pattern in the study result. It was further stated that endotracheal intubation or oxygen therapy with ventilatory support was performed for these patients when they were detected at the trauma scene, following an offline protocol. Overall, the study found that confounding factors and interventions such as early-volume resuscitation and ventilatory support influenced the association between SBP and mortality. These factors may have affected the significance of the relationship observed in the study. Our study initially found that an at-scene oxygen saturation level of less than 60% was associated with a significantly higher first 24-hour mortality rate than an at-scene oxygen saturation level of 60% or higher. The hazard ratio (cHR) for this relationship was 43.66, with a 95% confidence interval (CI) of 29.37-64.90, and the p -value was less than 0.001, indicating a strong association. However, after adjusting for other factors in the analysis, the at-scene oxygen saturation level of less than 60% was no longer statistically significant as a risk factor. Instead, it was identified as a protective factor, but this association did not reach statistical significance. One possible explanation for this finding is that patients with lower at-scene oxygen saturation levels were recognized more quickly, leading to a more rapid assessment, intubation, and ventilation. These interventions may have improved their chances of survival, ultimately resulting in no significantly different survival rates compared to patients with higher at-scene oxygen saturation levels. It’s important to note that further research and analysis may be necessary to fully understand the relationship between at-scene oxygen saturation levels, recognition time, interventions, and patient outcomes. An at-scene PR below 50 or above 120 bpm is associated with a higher mortality rate. This correlation is consistent with Dharod et al. ’s retrospective cohort study involving 6,733 patients with arteriosclerosis. 35 In that study, it was found that a PR below 50 or above 80 bpm was associated with a 2.42-fold and 3.55-fold higher mortality rate for 9-12 months. These findings were statistically significant, particularly among patients receiving medications that affect pulse, such as digitalis, beta-blockers, non-dihydropyridine calcium channel blockers, and drugs used to treat arrhythmias. Moreover, previous studies have also shown a correlation between rapid pulse (arrhythmia) and increased mortality. 36 – 41 Zhang et al. found that a rapid resting pulse rate increases the risk of all-cause mortality by 6% and cardiovascular mortality by 8%. This increase in mortality is believed to be caused by an imbalance between the vagus nerve, the sympathetic nervous system, and the autonomic nervous system, which leads to a rapid pulse. Myocardial ischemia, atherosclerosis, and arrhythmias are mentioned as conditions associated with a rapid pulse. A rapid pulse has also been linked to an increased mortality rate in patients with chronic renal failure and pulmonary hypertension. The results of this study can be applied to improve the unstable vital sign treatment process in the next operation. For example, there are increasing numbers of precaution and guidelines for patients with cardiogenic and distributive shock during the pre-hospital phase, including developing a shock monitoring procedure for the emergency room that requires continuous care from the pre-hospital team. Level of consciousness The GCS is commonly used in the EMS system to assess the level of consciousness in patients. 42 Our study demonstrated that a GCS score of 8 or lower, indicating a reduced level of consciousness, was associated with a significantly higher risk of death within the first 24 hours compared to patients with GCS scores of 13-15 after adjusting for other factors. This finding aligns with previous studies on different patient groups, including ischemic stroke, traumatic brain injury, and non-traumatic brain injury. These studies have consistently shown that a decreased GCS score is associated with a higher likelihood of admission to the intensive care unit and increased mortality rates. 43 – 45 Based on the results of our study, it is recommended to establish instructions for managing patients with a GCS score of 8 or lower at the incident scene. These instructions should focus on monitoring this specific group of patients for appropriate ventilation and conducting rapid investigations, such as a quickly computed tomography (CT) brain scan. 46 Definitive treatments, including surgery, should be promptly administered to improve the timing process and reduce patient mortality. By implementing such measures, it is hoped that the early identification and intervention for patients with a reduced level of consciousness will lead to improved outcomes and a decreased risk of death. Physical distance Interestingly, our study indicated that the distance from the scene to the hospital did not impact patient mortality. This result contradicts previous studies that suggested a correlation between distance and mortality rates. 47 , 48 One possible explanation for the observed lack of impact could be the resuscitation process during transportation. 49 We hypothesized that performing resuscitation procedures in the vehicle while en route to the hospital could have alleviated the potential negative effects of longer distances on patient survival. This implies that even when the distance to the hospital is longer, the availability of skilled medical professionals and necessary equipment in the vehicle plays a crucial role in maintaining patient stability and minimizing mortality rates. However, it is essential to consider that each study has its limitations and unique circumstances, so further research is necessary to validate these findings and understand the specific factors at play in different situations. Transport time Our study found that when the transport time from the scene to the hospital was longer than 15 minutes, there was a two-fold increase in mortality within 24 hours. This finding suggests a longer transport time is associated with poorer patient outcomes. Further subgroup analysis revealed that a greater distance and extended response time were factors contributing to the longer transport time. Patients with transport times longer than 15 minutes had a 19% and 12% increase in mortality, respectively, with statistical significance. This suggests that distance and response time affect patient outcomes during transportation. The study also found that patients with a GCS score of 8 or less, indicating a more severe neurological condition, had a transport time of 15 minutes or less. These patients experienced a 49% reduction in 24-hour mortality. This finding implies that in cases where patients have unclear warning signs but are still awake with early abnormal hemodynamics, a shorter transport time can lead to improved outcomes. The shorter transport time allows for timely detection of unstable status and prompt resuscitation. Various challenges encountered during transportation can lead to delays in providing care and observation. One such challenge is the constrained space available for patient care within the ambulance, which can hinder immediate attention and observation. Additionally, the requirement for ambulance staff to sit in positions other than the designated chair further exacerbates these obstacles. If the duration of transport is excessively long, it could potentially lead to a higher mortality rate, possibly due to the obstacles in providing immediate care. Previous studies have also shown that prolonged transport times exceeding 20 minutes and prehospital times exceeding 65 minutes are associated with poor patient outcomes. 9 , 50 – 53 These findings emphasize the importance of minimizing transport time and time spent during transportation to improve patient outcomes. Synchronized operation with the BLS team The study found that prior transfer of patients by the basic-level operation team, in a synchronized operation with the BLS team, resulted in a statistically significant reduction of 24-hour deaths compared to cases where no previous transfer occurred. When the distance between the ambulance base and the scene is considerable, the dispatcher and medical commander may instruct the basic-level operation team closest to the scene to start transferring the patient along the way rather than waiting for the higher-level operations team, which might take longer to arrive. During this transfer, the patient receives primary assessment and basic life support management, including interventions like chest compressions and airway opening, without waiting for the arrival of higher-level teams. This highlights the importance of rapid resuscitation and access to basic-level operations teams. The findings of this study align with a meta-analysis conducted by Tannvik et al. in 2012. 54 Their analysis focused on primary resuscitation of patients with accidents and found that patients who received primary care, including interventions such as hemostasis and airway opening, had a 5.8% reduction in mortality compared to those who did not receive primary care. Additionally, two separate fatal and post-mortem studies supported the notion that primary resuscitation at the scene led to a reduction in accidental death rates of 4.5% and 1.8%, respectively. It should be noted that different studies may have variations in the geographical area, the composition of first responders, and incident prevalence, which can affect the mortality rate differently. However, overall, the evidence suggests that providing primary resuscitation at the scene, especially by basic-level operations teams, can significantly reduce mortality rates and improve outcomes for patients experiencing emergencies or accidents. Pre-hospital life-saving intervention The study found that patients who received prehospital life-saving interventions, including cardiopulmonary resuscitation, endotracheal intubation, rapid hydration, or resuscitative drug administration, were more severely ill and had unstable vital signs compared to those who did not receive resuscitation. Additionally, these patients were associated with a statistically significant higher risk of death within 24 hours. However, it’s important to note that this finding does not imply that out-of-hospital advanced life-support procedures in severely ill patients do not reduce mortality within 24 hours and does not contradict a previous study. 55 In other words, our study does not suggest that advanced life-support procedures are ineffective in reducing mortality within the first 24 hours for severely ill patients. It’s crucial to carefully interpret the study’s results and consider other factors and variables that might affect the outcomes. Further research and analysis are necessary to fully understand the relationship between out-of-hospital advanced life-support procedures and mortality rates for severely ill patients within a specific time frame. Limitations Our study was conducted in the central region of Thailand, specifically in a suburban area. It’s important to note that the findings of this study may not apply to other areas with different topography and population distribution. The emergency medical services (EMS) system in this study relied heavily on the central hospital, and all operational team members were affiliated with a high-level hospital. Additionally, the patients involved in the research were transported to the only hospital in Saraburi that had adequate medical equipment and provided multidisciplinary care. Consequently, the mortality rate might vary in other areas due to factors related to the destination hospital. It’s crucial to consider the capabilities and resources of different hospitals when interpreting the impact on mortality rates. Furthermore, this study was observational and focused solely on identifying factors that could be associated with mortality. A predictive model may be developed to assess the mortality rate of individuals receiving EMS. Such a model could be utilized to measure and evaluate the quality of prehospital patient care, thereby enhancing the potential and effectiveness of prehospital care. It’s important to recognize the study’s limitations and consider these factors when applying its findings to other regions or making broader conclusions about EMS systems and mortality rates. Conclusions The findings suggest several factors are associated with increased mortality within 24 hours in patients receiving EMS. These factors include male sex, delayed time from symptom onset to calling for help (within two hours), longer transport time (>15 minutes), low diastolic blood pressure (DBP < 60 mmHg), abnormal heart rate (PR < 50 or ≥ 120 beats per minute), low Glasgow Coma Scale (GCS ≤8), and the presence of prehospital life-saving interventions. Identifying these factors that strongly correlate with higher mortality can help predict a patient’s risk of death. This knowledge can be valuable for responsible agencies involved in emergency medical services to design appropriate protocols, operating systems, and commands. By understanding the risk factors, these agencies can implement strategies to mitigate the patient’s risk of death more effectively. Another important finding from the study is that synchronized operation with the Basic Life Support (BLS) team was associated with a statistically significant reduction in mortality within 24 hours. This highlights the importance of coordinated efforts between different levels of emergency medical response teams. Even at distances greater than 10 km, studies have shown that mortality does not increase if basic and advanced life support is appropriately provided. This emphasizes the significance of increasing the volume and developing effective basic-level operations teams to ensure quick access to emergency care for ill populations. Subsequent referrals to advanced-level operations teams can be made for definitive hospital treatment. Overall, these findings emphasize the importance of early intervention, synchronized operations, and access to appropriate levels of care in improving patient outcomes and reducing mortality in emergency settings. Data availability Underlying data Zenodo: Rawdata EMS 24 hr mortality, https://zenodo.org/record/8006895 . 56 This project contains the following underlying data: ‐ Excel data_Factors affecting the first 24-hour mortality of patients receiving Emergency Medical Service in the sub-urban area.xlsx Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0). References 1. Sittichanbuncha Y, Prachanukool T, Sarathep P, et al. : An emergency medical service system in Thailand: providers’ perspectives. J. Med. Assoc. Thai. 2014; 97 (10): 1016–1021. PubMed Abstract 2. Peaugkham N, Tavarom A, Kriavas K: Thailand Emergency Medical Services System Management Model. NATIONAL DEFENCE STUDIES INSTITUTE JOURNAL. 2021; 2 (2): 65–76. 3. 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Publisher Full Text Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 28 Jul 2023 ADD YOUR COMMENT Comment Author details Author details 1 Department of Emergency Medicine, Saraburi Hospital, Mueang Saraburi District, Saraburi, Thailand 2 Center of Excellence in Stroke, Faculty of Medicine, Thammasat University, Pathum Thani, 12120, Thailand 3 Department of Emergency Medicine, Lampang Hospital, Lampang, Thailand 4 Chaiprakarn Hospital, Chaing Mai, Thailand 5 Chaki Naruebodindra Medical Institute, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Samut Prakan, Thailand Patcharee Duongthong Roles: Conceptualization, Data Curation, Formal Analysis, Methodology, Resources, Writing – Original Draft Preparation, Writing – Review & Editing Sombat Muengtaweepongsa Roles: Conceptualization, Funding Acquisition, Investigation, Methodology, Project Administration, Supervision, Writing – Original Draft Preparation, Writing – Review & Editing Thanin Lokeskrawee Roles: Data Curation, Supervision, Validation, Visualization Suppachai Lawanaskol Roles: Data Curation, Resources, Validation, Visualization Natee Sakornyutthadej Roles: Data Curation, Supervision, Validation, Visualization Patraporn Thongyong Roles: Data Curation, Validation, Visualization Competing interests No competing interests were disclosed. Grant information This study was supported by the Center of Excellence in Stroke, Thammasat University, Thailand. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Article Versions (1) version 1 Published: 28 Jul 2023, 12:899 https://doi.org/10.12688/f1000research.137744.1 Copyright © 2023 Duongthong P et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Duongthong P, Muengtaweepongsa S, Lokeskrawee T et al. Factors affecting the first 24-hour mortality of patients receiving emergency medical service (EMS) in a sub-urban area: a retrospective cohort study [version 1; peer review: 1 approved with reservations] . F1000Research 2023, 12 :899 ( https://doi.org/10.12688/f1000research.137744.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 1 VERSION 1 PUBLISHED 28 Jul 2023 Views 0 Cite How to cite this report: Wittayachamnankul B. Reviewer Report For: Factors affecting the first 24-hour mortality of patients receiving emergency medical service (EMS) in a sub-urban area: a retrospective cohort study [version 1; peer review: 1 approved with reservations] . F1000Research 2023, 12 :899 ( https://doi.org/10.5256/f1000research.150909.r392689 ) The direct URL for this report is: https://f1000research.com/articles/12-899/v1#referee-response-392689 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 14 Aug 2025 Borwon Wittayachamnankul , Chiang Mai University, Chiang Mai, Thailand Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.150909.r392689 1. Multicollinearity and Variable Redundancy The inclusion of multiple closely related physiological variables in the multivariable model (e.g., SBP and DBP, PR and RR, GCS and oxygen saturation) raises concerns about multicollinearity. The manuscript would benefit from ... Continue reading READ ALL 1. Multicollinearity and Variable Redundancy The inclusion of multiple closely related physiological variables in the multivariable model (e.g., SBP and DBP, PR and RR, GCS and oxygen saturation) raises concerns about multicollinearity. The manuscript would benefit from a clear explanation of whether correlation diagnostics (e.g., correlation matrix, VIF) were conducted. Additionally, simplifying the model by retaining only the most predictive or clinically relevant variable from each group would reduce overfitting and improve interpretability. 2. Inclusion of Non-significant Variables Without Justification Several variables that were not statistically significant in the univariate analysis (e.g., SBP, RR, oxygen saturation) were included in the adjusted model without explanation. The rationale for including these variables should be clearly stated—whether due to theoretical importance, confounding control, or variable selection strategy (e.g., stepwise or full model). Otherwise, their inclusion may reduce the precision of effect estimates. 3. Discussion Should Prioritize Key Findings The discussion section would be clearer if it began with a concise summary of the key predictors identified in the adjusted model, followed by more detailed discussion of individual factors. Currently, the flow mixes background with result interpretation, which may obscure the main findings. A clearer structure would enhance the impact of the discussion. 4. Ambiguity in Model Building Strategy The manuscript does not clearly describe how the multivariable model was constructed. Was a full model used? Were variables screened via univariate p-values, or were clinically relevant variables pre-specified? Was any model selection (e.g., AIC-based) performed? Adding this detail would improve reproducibility and transparency. 5. Overinterpretation of Adjusted Results Some adjusted hazard ratios, such as for SBP < 90 mmHg and oxygen saturation ≤ 60%, were not statistically significant but are still discussed as though they are meaningful. This may mislead readers. Non-significant adjusted findings should be interpreted cautiously and explicitly acknowledged as such in the discussion. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly Competing Interests: No competing interests were disclosed. Reviewer Expertise: Emergency Medicine I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Wittayachamnankul B. Reviewer Report For: Factors affecting the first 24-hour mortality of patients receiving emergency medical service (EMS) in a sub-urban area: a retrospective cohort study [version 1; peer review: 1 approved with reservations] . F1000Research 2023, 12 :899 ( https://doi.org/10.5256/f1000research.150909.r392689 ) The direct URL for this report is: https://f1000research.com/articles/12-899/v1#referee-response-392689 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 28 Jul 2023 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 Version 1 28 Jul 23 read Borwon Wittayachamnankul , Chiang Mai University, Chiang Mai, Thailand Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Wittayachamnankul B. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 14 Aug 2025 | for Version 1 Borwon Wittayachamnankul , Chiang Mai University, Chiang Mai, Thailand 0 Views copyright © 2025 Wittayachamnankul B. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions 1. Multicollinearity and Variable Redundancy The inclusion of multiple closely related physiological variables in the multivariable model (e.g., SBP and DBP, PR and RR, GCS and oxygen saturation) raises concerns about multicollinearity. The manuscript would benefit from a clear explanation of whether correlation diagnostics (e.g., correlation matrix, VIF) were conducted. Additionally, simplifying the model by retaining only the most predictive or clinically relevant variable from each group would reduce overfitting and improve interpretability. 2. Inclusion of Non-significant Variables Without Justification Several variables that were not statistically significant in the univariate analysis (e.g., SBP, RR, oxygen saturation) were included in the adjusted model without explanation. The rationale for including these variables should be clearly stated—whether due to theoretical importance, confounding control, or variable selection strategy (e.g., stepwise or full model). Otherwise, their inclusion may reduce the precision of effect estimates. 3. Discussion Should Prioritize Key Findings The discussion section would be clearer if it began with a concise summary of the key predictors identified in the adjusted model, followed by more detailed discussion of individual factors. Currently, the flow mixes background with result interpretation, which may obscure the main findings. A clearer structure would enhance the impact of the discussion. 4. Ambiguity in Model Building Strategy The manuscript does not clearly describe how the multivariable model was constructed. Was a full model used? Were variables screened via univariate p-values, or were clinically relevant variables pre-specified? Was any model selection (e.g., AIC-based) performed? Adding this detail would improve reproducibility and transparency. 5. Overinterpretation of Adjusted Results Some adjusted hazard ratios, such as for SBP < 90 mmHg and oxygen saturation ≤ 60%, were not statistically significant but are still discussed as though they are meaningful. This may mislead readers. Non-significant adjusted findings should be interpreted cautiously and explicitly acknowledged as such in the discussion. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly Competing Interests No competing interests were disclosed. Reviewer Expertise Emergency Medicine I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (0) Wittayachamnankul B. Peer Review Report For: Factors affecting the first 24-hour mortality of patients receiving emergency medical service (EMS) in a sub-urban area: a retrospective cohort study [version 1; peer review: 1 approved with reservations] . F1000Research 2023, 12 :899 ( https://doi.org/10.5256/f1000research.150909.r392689) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/12-899/v1#referee-response-392689 Alongside their report, reviewers assign a status to the article: Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions Adjust parameters to alter display View on desktop for interactive features Includes Interactive Elements View on desktop for interactive features Competing Interests Policy Provide sufficient details of any financial or non-financial competing interests to enable users to assess whether your comments might lead a reasonable person to question your impartiality. 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