The AWA Prehospital Scoring System Predicts 30-Day Mortality After Out-of-Hospital Cardiac Arrest with High Accuracy: An SOS-KANTO 2017 Registry Study

The AWA Prehospital Scoring System Predicts 30-Day Mortality After Out-of-Hospital Cardiac Arrest with High Accuracy: An SOS-KANTO 2017 Registry Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The AWA Prehospital Scoring System Predicts 30-Day Mortality After Out-of-Hospital Cardiac Arrest with High Accuracy: An SOS-KANTO 2017 Registry Study Seiya Kanou, Eiji Nakatani, Philip Hawke, Yosuke Homma, Takashi Tagami, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8513452/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Mar, 2026 Read the published version in International Journal of Emergency Medicine → Version 1 posted 12 You are reading this latest preprint version Abstract Background Out-of-hospital cardiac arrest (OHCA) has a very high mortality rate. Standard emergency responses to OHCA can result in unwanted resuscitation, particularly in elderly patients, and futile interventions can place additional strain on already limited healthcare resources. An appropriate prognostic scoring system for OHCA could reduce these adverse effects, but existing systems all rely on information unavailable in the prehospital setting. This study developed and internally validated a simple scoring system based exclusively on prehospital parameters that predicts 30-day mortality in OHCA patients with high accuracy. Results This retrospective study used the Japanese SOS-KANTO registry. The primary outcome was 30-day mortality. A multivariable logistic regression model using prehospital predictors was converted into a point-based scoring system. Model performance and internal validation were assessed. Among the 9,120 patients included in the study, the 30-day mortality rate was 93.8%. The final prediction model included age, unwitnessed arrest, and asystole as initial rhythm. It showed good discriminative ability and good calibration. On a 9-point scoring system, a total score of 9 (patients aged ≥90 years, with unwitnessed arrest, and an initial rhythm of asystole) predicted 30-day mortality with a specificity of 99.66% and a positive predictive value of 99.66%. Conclusion We have developed a practical scoring system based on three prehospital parameters—age, witness status, and asystole (AWA)—that accurately predicts 30-day mortality after OHCA. Our simple and accurate AWA scoring system facilitates rapid assessment in prehospital settings, supporting timely decision-making. out-of-hospital cardiac arrest 30-day mortality prehospital setting scoring system prediction mode Figures Figure 1 1. Background Out-of-hospital cardiac arrest (OHCA) is a leading cause of mortality worldwide, and the rapid initiation of the “chain of survival”—bystander cardiopulmonary resuscitation (CPR) 1 , followed by advanced cardiac life support and post-resuscitation intensive care—has a profound impact on patient outcome 2 , 3 . Although improvements in emergency medical systems and guideline-based interventions have led to modest improvements in survival and neurological outcomes in certain regions, overall prognosis remains suboptimal. 4 , 5 . Furthermore, with the aging of society, concerns have emerged regarding both the invasive nature of OHCA resuscitation procedures that may not be desired by patients, and the impact of such procedures on limited healthcare resources. Numerous prognostic scoring systems and risk stratification tools have been developed for OHCA. Representative systems include RACA for assessing the return of spontaneous circulation after cardiac arrest 6 , 7 , NULL-PLEASE for assessing in-hospital mortality among ICU-admitted patients 8 , 9 , and CAHP for assessing neurological outcome at discharge 10 . More recently, CRASS 11 , 12 and MIRACLE2 13 provide prognostic models for neurological outcome following OHCA. However, all of these systems require input parameters that are unavailable during prehospital care, such as CPR duration and laboratory findings obtained after hospital admission, making these systems inappropriate in prehospital situations. This study developed a simpler and more effective scoring system that enables rapid and straightforward estimation of 30-day mortality in OHCA patients in the prehospital setting, exclusively utilizing prehospital information. This approach is readily applicable in the field and has the potential to support decision-making by emergency medical service (EMS) personnel and family members. In resource-limited settings, this model will allow EMS personnel to identify patients with an extremely high likelihood of mortality at an early stage, thereby enabling the avoidance of prolonged on-scene resuscitation efforts and potentially futile invasive interventions. 2. METHODS 2.1. Study population This study is based on the SOS-KANTO registry 14 maintained by the Japanese Association for Acute Medicine Kanto, which is a registry that integrates both prehospital variables in the Utstein style and in-hospital information. The registry is a large, multi-center regional database that continuously enrolls cases of out-of-hospital cardiac arrest (OHCA) from 46 participating institutions in the Kanto area, which includes Tokyo and prefectures surrounding it. The registry was established to develop a comprehensive understanding of the nature of OHCA care in Japan and to identify outcome-improving interventions. Information is collected prospectively across the entire care continuum, from arrest onset through emergency medical services, transport, and multidisciplinary treatment to discharge/death outcome. Variables (age, witness status, initial electrocardiogram findings, etc.) are collected per protocol. The registry is designed to prevent duplicate patient identification. The registry facilitates OHCA research by enrolling patients from diverse urban/suburban backgrounds, providing comprehensive data across varying population densities and degrees of access to medical resources. In Japan, EMS personnel are generally mandated to transport OHCA patients to medical facilities, a practice that differs significantly from many Western nations. Resuscitation may only be withheld in the presence of definitive signs of death, including truncal transection, putrefaction, or rigor mortis. Cases newly registered between September 9, 2019 and March 8, 2021 were analyzed. A total of 9,909 OHCA patient records were obtained. All cases were used for model development and internal validation. External validation was not conducted due to the lack of availability of suitable test datasets. 2.2 Data preparation The raw data from the registry was initially examined for duplicates, and fundamental data quality assessments were performed. Age was categorized according to conventional groupings used in previous OHCA studies to allow for comparability 15 : ≤69 years, 70–79 years, 80–89 years, and ≥90 years. While initial cardiac rhythm was recorded in four categories—ventricular fibrillation (VF), pulseless electrical activity (PEA), pulseless ventricular tachycardia (VT), and asystole—these categories were converted into a binary variable (asystole vs. non-asystole) to align with the analytical objectives of the study. Asystole was emphasized as a key predictor for two reasons: asystole consistently results in significantly poorer outcomes than other initial rhythms 16 , and electrical activity other than asystole may heighten expectations of survival among family members, thus increasing psychological burden and thereby complicating expeditious prehospital decision-making. All other variables were used as defined in the original registry (Supplementary Table S1). 2.3 Outcome The registry primarily enrolls transported patients. The primary outcome was 30-day all-cause mortality, defined as death from any cause within 30 days after transport. Patients who were transferred or discharged within 30 days were followed up by each hospital to ascertain survival status. We did not use neurological outcome as the endpoint because we considered that termination of resuscitation in the prehospital setting on the basis of an anticipated poor neurological prognosis would not be ethically justified. 2.4 Predictors All candidate predictors were derived from information collected by EMS personnel at the scene or during transport. Age was calculated from date of birth or, when unavailable, from age reported on official identification. Witness status was determined based on accounts provided by family members or bystanders. Initial cardiac rhythm refers to the first rhythm identified by EMS upon arrival at the scene and was documented using EMS electrocardiography. 2.5. Analytical methods For categorical variables, data were compared with the chi-square test and are presented as n (%). Continuous variables were compared using the independent samples t-test (Student’s t-test), assuming normality, and are expressed as mean ± standard deviation (SD). Both univariate and multivariate logistic regression models were constructed to predict 30-day mortality. Multiple metrics were used to evaluate model performance: for overall fit, Brier score; for calibration assessment, calibration-in-the-large, calibration slope, and observed/expected ratio (O/E ratio), with calibration plots created; and for discriminative ability, the C-statistic (AUROC), pseudo-R², and likelihood ratio chi-square test (LR χ²). Internal validation was conducted using k-fold cross-validation. The final predictive values were calculated using logistic regression coefficients, thereby maintaining simplicity for prehospital implementation. Class imbalance methods were not applied during model development; instead, the original data distribution was analyzed. To construct a point-based scoring system, the regression coefficients (log (OR)) were converted into integer values. The smallest log (OR) was rounded to 1 point, and the other predictors were scaled proportionally and rounded to the nearest integer. This method balances fidelity to the underlying logistic model with clinical usability. A complete case analysis was conducted, and records with missing predictor or outcome values were excluded. A p-value threshold of 0.05 was used to determine statistical significance, with all reported p-values being two-tailed. All statistical analyses were performed using Stata/MP 18.0 (StataCorp LLC, College Station, TX, USA). This study was reported in accordance with the TRIPOD statement. 2.6. Ethical approval This study was approved by the Clinical Ethics Committee of Fujieda Municipal General Hospital (R06-49). As this is a registry study using medical records without therapeutic intervention, written informed consent was waived. Study information and withdrawal procedures were disclosed on the websites of participating institutions. 3. Results 3.1. Participants and outcome Of the 9,909 cases screened, we excluded 51 cases with missing age information, 664 without initial rhythm recordings, and 88 for whom 30-day survival information could not be confirmed, resulting in a final cohort of 9,120 patients. Among these, 566 (6.2%) were alive at 30 days, while 8,554 (93.8%) died within this period (Figure 1). Patient characteristics stratified by outcome are presented in Table 1. Advanced age (p<0.001), unwitnessed cardiac arrest (p<0.001), and asystole (p<0.001) were all significantly associated with higher 30-day mortality. Table 1: Baseline characteristics classified by survival status Variable Category Survival (n=566) Death (n=8,554) p-value Demographics Age category ≤69 years 337 (59.5) 3,095 (36.2) <0.001 70–79 years 136 (24.0) 2,222 (26.0) 80–89 years 82 (14.5) 2,486 (29.1) ≥90 years 11 (1.9) 751 (8.8) Age mean (SD), years 64.4 (16.8) 71.9 (17.6) <0.001 Sex Male 417 (73.7) 5,222 (61.0) <0.001 Cardiac arrest characteristics Call to hospital arrival Mean (SD) min 34.3 (11.8) 36.8 (12.2) <0.001 Bystander AED use No 439 (77.6) 6,912 (80.8) <0.001 Witnessed arrest No 437 (77.2) 3,654 (42.7) <0.001 Bystander CPR No 210 (37.8) 4,780 (57.4) <0.001 Initial rhythm Asystole 60 (10.6) 5,642 (66.0) <0.001 Others 506 (89.4) 2,912 (34.0) Comorbidities Myocardial infarction Yes 68 (12.4) 141 (1.7) <0.001 Heart failure Yes 37 (6.9) 148 (1.8) <0.001 Cerebral infarction Yes 47 (8.6) 187 (2.3) <0.001 Dementia Yes 30 (5.5) 185 (2.2) <0.001 Chronic obstructive pulmonary disease Yes 27 (4.9) 108 (1.3) <0.001 Diabetes without complications Yes 66 (12.1) 175 (2.1) <0.001 Diabetes with complications Yes 34 (6.2) 98 (1.2) <0.001 Paralysis Yes 9 (1.6) 20 (0.2) <0.001 Chronic kidney disease Yes 37 (6.7) 126 (1.5) <0.001 Hemodialysis Yes 12 (2.2) 61 (0.7) <0.001 Hypertension Yes 181 (33.3) 450 (5.4) <0.001 Atrial fibrillation Yes 36 (6.6) 76 (0.9) <0.001 Note : Data are presented as n (%) unless otherwise specified Abbreviations : AED, automated external defibrillator; SD, standard deviation; CPR, cardiopulmonary resuscitation; ROSC, return of spontaneous circulation; CAG, coronary angiography; PCI, percutaneous coronary intervention; TTM, targeted temperature management [Insert Table 1 here] 3.2. Predictive model for 30-day mortality As the prediction model developed in this study is intended for use in prehospital settings, we conducted univariate logistic regression analyses using only prehospital variables. Older age was significantly associated with increased 30-day mortality: compared to those aged ≤69 years, the OR for ages 70–79 was 1.78 (95% CI: 1.45–2.19), for ages 80–89 was 3.30 (95% CI: 2.58–4.23), and for ages ≥90 was 7.43 (95% CI: 4.06–13.62). An initial rhythm of asystole was also strongly associated with increased mortality (OR 16.34, 95% CI: 12.46–21.43). Unwitnessed arrest had an OR of 4.54 (95% CI: 3.72–5.55), and absence of bystander CPR had an OR of 2.22 (95% CI: 1.86–2.65) (Table 2). Table 2: Univariate logistic regression analysis for 30-day mortality after out-of-hospital cardiac arrest Variable Category Odds Ratio 95% CI p-value Age category ≤69 years Reference 70–79 years 1.78 1.45–2.19 <0.001 80–90 years 3.30 2.58–4.23 <0.001 ≥90 years 7.43 4.06–13.62 <0.001 Initial rhythm Non-asystole Reference Asystole 16.34 12.46–21.43 <0.001 Witnessed arrest Yes Reference No 4.54 3.72–5.55 <0.001 Bystander CPR Yes Reference No 2.22 1.86–2.65 <0.001 Abbreviations : CI, confidence interval; CPR, cardiopulmonary resuscitation A multivariable logistic regression analysis was performed using all the variables that were statistically significant in the univariate analysis (Table 3). Advanced age was independently associated with higher 30-day mortality: the OR for ages 70–79 was 1.84 (95% CI: 1.47–2.30), for ages 80–89 was 3.04 (95% CI: 2.35–3.94), and for ages ≥90 was 6.32 (95% CI: 3.41–11.70). Asystole as the initial rhythm was the strongest predictor (OR 12.55, 95% CI: 9.40–16.75). Unwitnessed arrest (OR 1.69, 95% CI: 1.35–2.12) and absence of bystander CPR (OR 2.15, 95% CI: 1.78–2.59) also remained significant. No multicollinearity was observed among the included variables. Table 3. Multivariable logistic regression analysis of factors associated with 30-day mortality Variable Category OR (95% CI) p-value Age ≤69 years Reference 70–79 years 1.84 (1.47–2.30) <0.001 80–89 years 3.04 (2.35–3.94) <0.001 ≥90 years 6.32 (3.41–11.70) <0.001 Initial rhythm Non-asystole Reference Asystole 12.55 (9.40–16.75) <0.001 Witnessed arrest Yes Reference No 1.69 (1.35–2.12) <0.001 Bystander CPR Yes Reference No 2.15 (1.78–2.59) <0.001 Abbreviations : OR, odds ratio; CI, confidence interval Next, a follow-up multivariable logistic regression analysis excluding bystander CPR was carried out. Although bystander CPR is known to be associated with patient outcomes 17 , it was excluded from the final prediction model due to ethical and practical considerations, as the provision of bystander CPR is highly dependent on witness behavior and not on the patient's biological condition. Using it as a predictor may pose challenges in terms of acceptability, especially for patients' families in prehospital decision-making contexts. (Table 4). Table 4. Multivariable logistic regression analysis of factors associated with 30-day mortality excluding bystander CPR Variable Category OR (95% CI) p-value Age ≤69 years Reference 70–79 years 1.76 (1.42–2.18) <0.001 80–89 years 3.04 (2.36–3.93) <0.001 ≥90 years 6.27 (3.39–11.59) <0.001 Initial rhythm Non-asystole Reference Asystole 12.18 (9.17–16.19) <0.001 Witnessed arrest Yes Reference No 1.88 (1.51–2.34) <0.001 Abbreviations : OR, odds ratio; CI, confidence interval Older age remained significantly associated with increased 30-day mortality: the OR for ages 70–79 was 1.76 (95% CI: 1.42–2.18), for ages 80–89 was 3.04 (95% CI: 2.36–3.93), and for ages ≥90 was 6.27 (95% CI: 3.39–11.59). Asystole as the initial rhythm showed a strong association with mortality (OR 12.18, 95% CI: 9.17–16.19), as did unwitnessed arrest (OR 1.88, 95% CI: 1.51–2.34). 3.3. Discrimination and calibration performance The model exhibited good discriminative ability, with an area under the receiver operating characteristic curve (AUROC) score of 0.82, indicating good capacity to distinguish between positive and negative outcomes. This discriminative performance was reinforced by a pseudo-R² value of 0.206, both suggesting substantial improvement over a null model and confirming the statistical significance of the predictors incorporated in the model (Supplementary Figure S1). The calibration metrics indicated strong alignment between predicted and observed outcomes. The calibration-in-the-large was 0.00 (95% CI: 0.09–0.09), indicating no systematic over- or under-prediction across the cohort. The calibration slope of 1.00 (95% CI: 0.92–1.09) demonstrated nearly ideal calibration with predicted probabilities closely matching observed frequencies across the risk spectrum. This was further supported by the calibration slope of 1.01 (95% CI: 0.90–1.11), confirming consistent calibration performance across different probability thresholds. The observed/expected ratio of 1.07 suggested a slight tendency toward under-prediction, though this deviation was minimal. The calibration plot visually confirmed these excellent calibration metrics, showing the relationship between mean predicted probabilities and observed proportions. The binned observations closely follow the ideal diagonal line (Supplementary Figure S2), indicating excellent agreement between predicted and observed risk across the probability range from 0.75 to 1.0 (Supplementary Table S2). 3.4. Cross-validation for assessment of internal validation We developed our clinical prediction model and assessed its internal validity using a 10-fold cross-validation approach on the 9,120 cases comprising the dataset. Given that each fold contained sufficient data, we determined that a K value of 10 was appropriate, as this mitigated potential underfitting issues while maintaining robust validation 18 . The model was trained on an average of 8,208 instances per fold. Across all folds, the likelihood ratio chi-square test (LR χ 2 ; (df=5)) yielded highly significant results (p<0.0001), with values ranging from 766.26 to 826.37, indicating strong predictive power. Pseudo-R² values showed consistent explanatory ability across folds, with an average of 0.21 (range: 0.20–0.21) (Supplementary Table S3). Discrimination performance remained strong across all folds, with AUROC values ranging from 0.77 to 0.86. The pooled AUROC was 0.83, indicating good discriminative ability. The consistency of AUROC values across folds, with overlapping confidence intervals, suggests robust model performance regardless of data partitioning. 3.5. Scoring system for 30-day mortality The point-based scoring system was constructed by converting the regression coefficients (log (OR)) obtained from logistic regression analysis into integer values: Age 70–79 years: OR 1.76, log (OR) 0.56 → 1 point Age 80–89 years: OR 3.04, log (OR) 1.11 → 2 points Age ≥90 years: OR 6.27, log (OR) 1.84 → 3 points Asystole: OR 12.18, log (OR) 2.50 → 5 points No witness: OR 1.88, log (OR) 0.63 → 1 point Point allocations were approximated to clinically convenient integer values based on the logarithmic odds ratios of each predictor. Age category points progressively increased with rising log (OR) values, while asystole, the strongest predictor, received the highest score of 5 points. Using this scoring system, patients could receive a total score ranging from 0 to 9 points. Our analysis revealed that when the cut-off value was set at 9, the model demonstrated very high specificity and positive predictive value (PPV), with a sensitivity of 3.99%, a specificity of 99.66%, a PPV of 99.66%, and a negative predictive value (NPV) of 6.37% (Table 5). Table 5. Sensitivity, specificity, positive predictive value, and negative predictive value for each cut-off value in the scoring system for 30-day mortality Cut-off ( ≥ ) Sensitivity (%) Specificity (%) PPV (%) NPV (%) 0 100.00 0.00 93.40 1 91.18 42.69 95.43 28.53 2 79.64 71.93 97.17 23.52 3 69.14 86.39 98.45 19.83 4 62.91 89.41 98.72 17.85 5 61.55 89.75 98.74 17.47 6 55.55 91.43 98.87 15.76 7 34.84 95.46 99.07 10.61 8 18.44 98.66 99.53 7.48 9 3.99 99.66 99.66 6.37 Note : Values calculated based on a disease prevalence of 93.4% Abbreviations : PPV, positive predictive value; NPV, negative predictive value 4. Discussion 4.1. Interpretation This study successfully developed and internally validated a prehospital prediction model for 30-day OHCA mortality using only large-scale registry data and prehospital information. Key findings include exceptional specificity (99.66%) and PPV (99.66%) when the total score reaches 9 points for patients aged ≥90 years with unwitnessed arrest and initial asystole, identifying a high-risk group with overwhelmingly high mortality. From a clinical perspective, avoiding misclassification of surviving patients as likely to die (false positives) was prioritized; therefore, a high decision threshold was chosen. Conversely, the sensitivity was extremely low at 3.99%. Given that survival rates in OHCA are inherently very low and that avoiding false positives is imperative, this low sensitivity must be tolerated. This precise futility identification, based solely on age ≥90 years, initial asystole, and unwitnessed arrest, is a significant advance over existing prognostic scores requiring post-hospital parameters like laboratory tests, total downtime, or medication dosages. The clinical utility of this highly specific predictive tool in prehospital settings is profound. In resource-limited contexts, this model enables EMS personnel to rapidly identify patients with minimal resuscitation potential, preventing potentially futile invasive interventions. This is particularly relevant in Japan, where EMS personnel must continue CPR until hospital arrival except with overt death signs. Japan is undergoing rapid and unprecedented demographic changes. Within the next decade, more than 30% of the population will be aged 65 years or older, while the number of healthcare workers is expected to decline, and the workforce itself to age considerably. Rural and remote areas already experience chronic and structural shortages of emergency medical resources, even under ordinary conditions. In such settings, persistent imbalances between patient needs and system capacity may increasingly resemble mass casualty triage scenarios, even in the absence of large-scale acute disasters. Although our current model does not explicitly endorse rationing of care, it may help facilitate fair and transparent prioritization in resource-constrained healthcare systems when combined with ethical guidelines, legal frameworks, and societal consensus in the future. While numerous prognostic scoring systems for OHCA exist, including the RACA score 6,7 for on-site ROSC and the CAHP score 10 for neurological outcomes at hospital discharge, they often incorporate variables requiring subjective judgment or additional diagnostic testing, which may be impractical in the earliest stages of care. For instance, scores like NULL-PLEASE 8,9 and CRASS 11,12 , while effective for predicting in-hospital mortality and neurological recovery, respectively, typically involve variables that become available only upon or after hospital admission. Our model is distinguished by relying exclusively on immediately assessable prehospital information: age, witnessed status, and initial cardiac rhythm (asystole). Its streamlined nature ensures easy implementation in dynamic field settings, allowing for rapid risk stratification upon EMS arrival. In contrast to termination-of-resuscitation protocols prevalent in some regions of the United States 19,20 , which allow EMS personnel to cease efforts based on criteria like unwitnessed arrest, non-shockable rhythm, and lack of ROSC, our model offers a predictive capacity independent of on-scene resuscitative duration. This distinction is vital, as prolonged on-scene resuscitation is highly invasive. Ethical considerations aimed at preventing harm to patients have been demonstrated to have a psychological impact on resuscitation efforts in the field 21 . Our model enables early identification of patients with a very high likelihood of mortality without necessitating prolonged on-scene resuscitation. It may serve as a high-specificity aid for recognizing cases with limited survival potential. However, it should not be used in isolation to determine whether to continue or discontinue resuscitation; rather, it should be applied in conjunction with clinical judgment, patient and family preferences, and relevant ethical and legal standards. 4.2. Limitations This study has several limitations. First, the 30-day survival rate was relatively low at 6.2%, compared with a reported survival rate of 9.3% in the United States 22 . As mentioned above, this discrepancy is attributable to differences in the Japanese EMS system. Such differences may limit the generalizability of our model to other countries. Second, the high mortality rate raises concerns about class imbalance. Nevertheless, we considered that even small shifts in predicted probabilities could carry significant clinical implications and therefore did not perform a class imbalance correction. Third, the absence of an independent test set precluded full evaluation of the external validity of our findings. Future studies should validate this scoring system in diverse international cohorts to improve generalizability. Independent test sets and strategies addressing class imbalance are needed to strengthen external validity. 5. Conclusion In this study, we have developed the AWA prehospital scoring system, a simple yet effective scoring system that accurately predicts 30-day mortality based on only three parameters, all of which are available in the prehospital setting: age, witnessed status, and the presence of asystole. Specifically, when the three conditions of age over 90 years, unwitnessed cardiac arrest, and asystole were present simultaneously, the specificity for 30-day mortality was 99.66%, and the positive predictive value was 99.66%. This simplified model facilitates rapid assessment in the prehospital setting and supports timely decision-making. External validation using independent datasets is necessary to confirm generalizability and clinical utility. Abbreviations OHCA: out-of-hospital cardiac arrest EMS: emergency medical services AUROC: area under the receiver operating characteristic curve CPR: cardiopulmonary resuscitation ROSC: return of spontaneous circulation VF: ventricular fibrillation PEA: pulseless electrical activity VT: pulseless ventricular tachycardia SD: standard deviation O/E ratio: observed/expected ratio LR χ²: likelihood ratio chi-square CAG: coronary angiography PCI: percutaneous coronary intervention TTM: targeted temperature management OR: odds ratio CI: confidence interval PPV: positive predictive value NPV: negative predictive value AED: automated external defibrillator Declarations Ethics approval and consent to participate This study was approved by the Clinical Ethics Committee of Fujieda Municipal General Hospital (R06-49). As this is a registry study using medical records without therapeutic intervention, written informed consent was waived. Study information and withdrawal procedures were disclosed on the websites of participating institutions. Consent for publication Not applicable Availability of data and materials Registry data is managed by the Japanese Association for Acute Medicine Kanto Branch and is not publicly available. Third-party access requires association approval through established procedures and ethical review. Competing interests The authors declare that they have no competing interests. Funding Not applicable. Authors' contributions Seiya Kanou, MD, MPH, conceived and designed the study, analyzed and interpreted data, drafted the manuscript, and approved the final manuscript. Eiji Nakatani, PhD, analyzed and interpreted data, revised the manuscript, and approved the final manuscript. Philip Hawke, PhD, revised the manuscript, and approved the final manuscript. Yosuke Homma, MD, MPH, collected data and approved the final manuscript. 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Validation of the ROSC after cardiac arrest (RACA) score in Pan-Asian out-of-hospital cardiac arrest patients. Resuscitation. 2020;149:53–9. Potpara TS, Mihajlovic M, Stankovic S, Jozic T, Jozic I, Asanin MR, et al. External Validation of the Simple NULL-PLEASE Clinical Score in Predicting Outcome of Out-of-Hospital Cardiac Arrest. Am J Med. 2017;130(12):1464.e13-1464.e21. Gue YX, Sayers M, Whitby BT, Kanji R, Adatia K, Smith R, et al. Usefulness of the NULL-PLEASE Score to Predict Survival in Out-of-Hospital Cardiac Arrest. Am J Med. 2020;133(11):1328–35. Adrie C, Cariou A, Mourvillier B, Laurent I, Dabbane H, Hantala F, et al. Predicting survival with good neurological recovery at hospital admission after successful resuscitation of out-of-hospital cardiac arrest: the OHCA score. Eur Hear J. 2006;27(23):2840–5. Seewald S, Wnent J, Lefering R, Fischer M, Bohn A, Jantzen T, et al. CaRdiac Arrest Survival Score (CRASS) — A tool to predict good neurological outcome after out-of-hospital cardiac arrest. Resuscitation. 2020;146:66–73. Liu N, Wnent J, Lee JW, Ning Y, Ho AFW, Siddiqui FJ, et al. Validation of the CaRdiac Arrest Survival Score (CRASS) for predicting good neurological outcome after out-of-hospital cardiac arrest in an Asian emergency medical service system. Resuscitation. 2022;176:42–50. Pareek N, Kordis P, Beckley-Hoelscher N, Pimenta D, Kocjancic ST, Jazbec A, et al. A practical risk score for early prediction of neurological outcome after out-of-hospital cardiac arrest: MIRACLE2. Eur Hear J. 2020;41(47):4508–17. Group TSK 2017 S. Changes in treatments and outcomes of out-of-hospital cardiac arrest between the SOS-KANTO 2012 and 2017 studies. Ann Clin Epidemiology. 2025;7(1):17–26. Libungan B, Lindqvist J, Strömsöe A, Nordberg P, Hollenberg J, Albertsson P, et al. Out-of-hospital cardiac arrest in the elderly: A large-scale population-based study. Resuscitation. 2015;94:28–32. Ishii J, Nishikimi M, Kikutani K, Ohki S, Ota K, Anzai T, et al. Resuscitation Attempt and Outcomes in Patients With Asystole Out-of-Hospital Cardiac Arrest. JAMA Netw Open. 2024;7(11):e2445543. O’Keefe EL, Jawad MA, Kennedy KF, Nguyen D, Ikemura N, Chan PS. Time to bystander CPR and survival for witnessed out-of-hospital cardiac arrest. Resuscitation. 2025;209:110566. Jung Y, Hu J. A K-fold averaging cross-validation procedure. J Nonparametric Stat. 2015;27(2):167–79. Association EC Subcommittees and Task Forces of the American Heart. 2005 American Heart Association Guidelines for Cardiopulmonary Resuscitation and Emergency Cardiovascular Care. Circulation. 2005;112(24 Suppl):IV1-203. Physicians NA of E. Termination of Resuscitation in Nontraumatic Cardiopulmonary Arrest. Prehospital Emerg Care. 2011;15(4):542–542. Eli K, Huxley CJ, Gardiner G, Perkins GD, Smyth MA, Griffiths F, et al. Ethical issues in termination of resuscitation decision-making: an interview study with paramedics and relatives of out-of-hospital cardiac arrest non-survivors. BMJ Open. 2024;14(11):e085132. Buaprasert P, Al-Araji R, Rajdev M, Vellano K, Carr MJ, McNally B. The past, present, and future of the Cardiac Arrest Registry to Enhance Survival (CARES). Resusc Plus. 2024;18:100624. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterialsubmit.docx Cite Share Download PDF Status: Published Journal Publication published 17 Mar, 2026 Read the published version in International Journal of Emergency Medicine → Version 1 posted Editorial decision: Revision requested 15 Feb, 2026 Reviewers agreed at journal 14 Feb, 2026 Reviews received at journal 14 Feb, 2026 Reviewers agreed at journal 12 Feb, 2026 Reviewers agreed at journal 08 Feb, 2026 Reviews received at journal 08 Feb, 2026 Reviewers agreed at journal 02 Feb, 2026 Reviewers agreed at journal 01 Feb, 2026 Reviewers invited by journal 29 Jan, 2026 Editor assigned by journal 06 Jan, 2026 Submission checks completed at journal 06 Jan, 2026 First submitted to journal 04 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8513452","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":583661401,"identity":"4bcb70a6-ff85-4b31-a289-9b4086394042","order_by":0,"name":"Seiya Kanou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYJACxgYgwQ9nMTAk4FfPBlUo2UCyFoMDCC34gfz8BuaPM2ruyBmfP8D8uXAHgzx/A8OzB/i0GBxjYJPccOyZsdmNBDbpmWcYDGccYEg3wKuFjYGN8QHb4cRtN/i/MfO2MTBuYGBIk8DrsDYG5o8P/h1O3NwPdBhQiz1BLQzHgIG1se1w4gZgOEkDtSQS1GJwLLFNcmbfYWMJsF/aJJJnHCbgF/nmw4c/9nw7LMcPclhhm41tf3tP2gO8DkOOC2YGBqCTmHnS8OtABswQiv0Y8VpGwSgYBaNgJAAA0bBGiogtIyAAAAAASUVORK5CYII=","orcid":"","institution":"Teikyo University Hospital","correspondingAuthor":true,"prefix":"","firstName":"Seiya","middleName":"","lastName":"Kanou","suffix":""},{"id":583661402,"identity":"93f21cba-3920-48d5-acb2-cb023a505da5","order_by":1,"name":"Eiji Nakatani","email":"","orcid":"","institution":"Nagoya City University","correspondingAuthor":false,"prefix":"","firstName":"Eiji","middleName":"","lastName":"Nakatani","suffix":""},{"id":583661403,"identity":"9f6279f6-91df-430d-9b61-5c32348b15ac","order_by":2,"name":"Philip Hawke","email":"","orcid":"","institution":"University of Shizuoka","correspondingAuthor":false,"prefix":"","firstName":"Philip","middleName":"","lastName":"Hawke","suffix":""},{"id":583661410,"identity":"1de0bc08-8ee1-4037-be22-24831e89746f","order_by":3,"name":"Yosuke Homma","email":"","orcid":"","institution":"Chiba Kaihin Municipal Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yosuke","middleName":"","lastName":"Homma","suffix":""},{"id":583661415,"identity":"02a4f7ef-2e8b-4d64-8c90-0906fcc39fc8","order_by":4,"name":"Takashi Tagami","email":"","orcid":"","institution":"Jikei University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Takashi","middleName":"","lastName":"Tagami","suffix":""},{"id":583661416,"identity":"ab65ba5b-1512-4a16-b009-373256fdb029","order_by":5,"name":"Yoshihiro Tanaka","email":"","orcid":"","institution":"Shizuoka Graduate University of Public Health","correspondingAuthor":false,"prefix":"","firstName":"Yoshihiro","middleName":"","lastName":"Tanaka","suffix":""}],"badges":[],"createdAt":"2026-01-04 13:53:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8513452/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8513452/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12245-026-01173-6","type":"published","date":"2026-03-17T15:59:02+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":101785505,"identity":"7d3506ed-94c8-4af9-a2d1-8eddad2282b6","added_by":"auto","created_at":"2026-02-03 15:36:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":65681,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy diagram\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8513452/v1/a981a2baed545335ca9e334b.png"},{"id":105223372,"identity":"af379067-d4c9-45da-b604-086717e34fca","added_by":"auto","created_at":"2026-03-23 16:05:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1238623,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8513452/v1/2cd8809a-ae21-4c3c-a867-90fd097f84fc.pdf"},{"id":101785506,"identity":"2b0488f4-6e29-4f13-bfd0-fa5600330135","added_by":"auto","created_at":"2026-02-03 15:36:58","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":37978,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterialsubmit.docx","url":"https://assets-eu.researchsquare.com/files/rs-8513452/v1/ec5464da97679f302b499f75.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The AWA Prehospital Scoring System Predicts 30-Day Mortality After Out-of-Hospital Cardiac Arrest with High Accuracy: An SOS-KANTO 2017 Registry Study","fulltext":[{"header":"1. Background","content":"\u003cp\u003eOut-of-hospital cardiac arrest (OHCA) is a leading cause of mortality worldwide, and the rapid initiation of the \u0026ldquo;chain of survival\u0026rdquo;\u0026mdash;bystander cardiopulmonary resuscitation (CPR)\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e, followed by advanced cardiac life support and post-resuscitation intensive care\u0026mdash;has a profound impact on patient outcome\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Although improvements in emergency medical systems and guideline-based interventions have led to modest improvements in survival and neurological outcomes in certain regions, overall prognosis remains suboptimal.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Furthermore, with the aging of society, concerns have emerged regarding both the invasive nature of OHCA resuscitation procedures that may not be desired by patients, and the impact of such procedures on limited healthcare resources.\u003c/p\u003e \u003cp\u003eNumerous prognostic scoring systems and risk stratification tools have been developed for OHCA. Representative systems include RACA for assessing the return of spontaneous circulation after cardiac arrest \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, NULL-PLEASE for assessing in-hospital mortality among ICU-admitted patients \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, and CAHP for assessing neurological outcome at discharge\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. More recently, CRASS\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e and MIRACLE2\u003csup\u003e13\u003c/sup\u003eprovide prognostic models for neurological outcome following OHCA. However, all of these systems require input parameters that are unavailable during prehospital care, such as CPR duration and laboratory findings obtained after hospital admission, making these systems inappropriate in prehospital situations.\u003c/p\u003e \u003cp\u003eThis study developed a simpler and more effective scoring system that enables rapid and straightforward estimation of 30-day mortality in OHCA patients in the prehospital setting, exclusively utilizing prehospital information. This approach is readily applicable in the field and has the potential to support decision-making by emergency medical service (EMS) personnel and family members. In resource-limited settings, this model will allow EMS personnel to identify patients with an extremely high likelihood of mortality at an early stage, thereby enabling the avoidance of prolonged on-scene resuscitation efforts and potentially futile invasive interventions.\u003c/p\u003e"},{"header":"2. METHODS","content":"\u003cp\u003e\u003cstrong\u003e2.1. Study population\u003cbr\u003e\u003c/strong\u003eThis study is based on the SOS-KANTO registry\u003csup\u003e14\u003c/sup\u003e maintained by the Japanese Association for Acute Medicine Kanto, which is a registry that integrates both prehospital variables in the Utstein style and in-hospital information. The registry is a large, multi-center regional database that continuously enrolls cases of out-of-hospital cardiac arrest (OHCA) from 46 participating institutions in the Kanto area, which includes Tokyo and prefectures surrounding it.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe registry was established to develop a comprehensive understanding of the nature of OHCA care in Japan and to identify outcome-improving interventions. Information is collected prospectively across the entire care continuum, from arrest onset through emergency medical services, transport, and multidisciplinary treatment to discharge/death outcome. Variables (age, witness status, initial electrocardiogram findings, etc.) are collected per protocol. The registry is designed to prevent duplicate patient identification. The registry facilitates OHCA research by enrolling patients from diverse urban/suburban backgrounds, providing comprehensive data across varying population densities and degrees of access to medical resources.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn Japan, EMS personnel are generally mandated to transport OHCA patients to medical facilities, a practice that differs significantly from many Western nations. Resuscitation may only be withheld in the presence of definitive signs of death, including truncal transection, putrefaction, or rigor mortis.\u003c/p\u003e\n\u003cp\u003eCases newly registered between September 9, 2019 and March 8, 2021 were analyzed. A total of 9,909 OHCA patient records were obtained. All cases were used for model development and internal validation. External validation was not conducted due to the lack of availability of suitable test datasets.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eData preparation\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw data from the registry was initially examined for duplicates, and fundamental data quality assessments were performed. Age was categorized according to conventional groupings used in previous OHCA studies to allow for comparability\u003csup\u003e15\u003c/sup\u003e: \u0026le;69 years, 70\u0026ndash;79 years, 80\u0026ndash;89 years, and \u0026ge;90 years. While initial cardiac rhythm was recorded in four categories\u0026mdash;ventricular fibrillation (VF), pulseless electrical activity (PEA), pulseless ventricular tachycardia (VT), and asystole\u0026mdash;these categories were converted into a binary variable (asystole vs. non-asystole) to align with the analytical objectives of the study. Asystole was emphasized as a key predictor for two reasons: asystole consistently results in significantly poorer outcomes than other initial rhythms \u003csup\u003e16\u003c/sup\u003e, and electrical activity other than asystole may heighten expectations of survival among family members, thus increasing psychological burden and thereby complicating expeditious prehospital decision-making. All other variables were used as defined in the original registry\u0026nbsp;(Supplementary Table S1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eOutcome\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe registry primarily enrolls transported patients. The primary outcome was 30-day all-cause mortality, defined as death from any cause within 30 days after transport. Patients who were transferred or discharged within 30 days were followed up by each hospital to ascertain survival status. We did not use neurological outcome as the endpoint because we considered that termination of resuscitation in the prehospital setting on the basis of an anticipated poor neurological prognosis would not be ethically justified.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ePredictors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll candidate predictors were derived from information collected by EMS personnel at the scene or during transport. Age was calculated from date of birth or, when unavailable, from age reported on official identification. Witness status was determined based on accounts provided by family members or bystanders. Initial cardiac rhythm refers to the first rhythm identified by EMS upon arrival at the scene and was documented using EMS electrocardiography.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5. Analytical methods\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor categorical variables, data were compared with the chi-square test and are presented as n (%). Continuous variables were compared using the independent samples t-test (Student\u0026rsquo;s t-test), assuming normality, and are expressed as mean \u0026plusmn; standard deviation (SD).\u003c/p\u003e\n\u003cp\u003eBoth univariate and multivariate logistic regression models were constructed to predict 30-day mortality. Multiple metrics were used to evaluate model performance: for overall fit, Brier score; for calibration assessment, calibration-in-the-large, calibration slope, and observed/expected ratio (O/E ratio), with calibration plots created; and for discriminative ability, the C-statistic (AUROC), pseudo-R\u0026sup2;, and likelihood ratio chi-square test (LR \u0026chi;\u0026sup2;). Internal validation was conducted using k-fold cross-validation. The final predictive values were calculated using logistic regression coefficients, thereby maintaining simplicity for prehospital implementation. Class imbalance methods were not applied during model development; instead, the original data distribution was analyzed.\u003c/p\u003e\n\u003cp\u003eTo construct a point-based scoring system, the regression coefficients (log (OR)) were converted into integer values. The smallest log (OR) was rounded to 1 point, and the other predictors were scaled proportionally and rounded to the nearest integer. This method balances fidelity to the underlying logistic model with clinical usability.\u003c/p\u003e\n\u003cp\u003eA complete case analysis was conducted, and records with missing predictor or outcome values were excluded.\u003c/p\u003e\n\u003cp\u003eA p-value threshold of 0.05 was used to determine statistical significance, with all reported p-values being two-tailed. All statistical analyses were performed using Stata/MP 18.0 (StataCorp LLC, College Station, TX, USA). This study was reported in accordance with the TRIPOD statement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6. Ethical approval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Clinical Ethics Committee of Fujieda Municipal General Hospital (R06-49). As this is a registry study using medical records without therapeutic intervention, written informed consent was waived. Study information and withdrawal procedures were disclosed on the websites of participating institutions.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1. Participants and outcome\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOf the 9,909 cases screened, we excluded 51 cases with missing age information, 664 without initial rhythm recordings, and 88 for whom 30-day survival information could not be confirmed, resulting in a final cohort of 9,120 patients. Among these, 566 (6.2%) were alive at 30 days, while 8,554 (93.8%) died within this period (Figure 1). Patient characteristics stratified by outcome are presented in Table 1. Advanced age (p\u0026lt;0.001), unwitnessed cardiac arrest (p\u0026lt;0.001), and asystole (p\u0026lt;0.001) were all significantly associated with higher 30-day mortality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1: Baseline characteristics classified by survival status\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSurvival (n=566)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDeath (n=8,554)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDemographics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eAge category\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e\u0026le;69 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e337 (59.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e3,095 (36.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e70\u0026ndash;79 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e136 (24.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e2,222 (26.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e80\u0026ndash;89 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e82 (14.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e2,486 (29.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e\u0026ge;90 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e11 (1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e751 (8.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003emean (SD), years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e64.4 (16.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e71.9 (17.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e417 (73.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e5,222 (61.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCardiac arrest characteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eCall to hospital arrival\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eMean (SD) min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e34.3 (11.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e36.8 (12.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eBystander AED use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e439 (77.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e6,912 (80.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eWitnessed arrest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e437 (77.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e3,654 (42.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eBystander CPR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e210 (37.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e4,780 (57.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eInitial rhythm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eAsystole\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e60 (10.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e5,642 (66.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e506 (89.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e2,912 (34.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eComorbidities\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eMyocardial infarction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e68 (12.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e141 (1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eHeart failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e37 (6.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e148 (1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eCerebral infarction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e47 (8.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e187 (2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eDementia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e30 (5.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e185 (2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eChronic obstructive pulmonary disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e27 (4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e108 (1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eDiabetes without complications\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e66 (12.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e175 (2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eDiabetes with complications\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e34 (6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e98 (1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eParalysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e9 (1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e20 (0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eChronic kidney disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e37 (6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e126 (1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eHemodialysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e12 (2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e61 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e181 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e450 (5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eAtrial fibrillation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e36 (6.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e76 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e: Data are presented as n (%) unless otherwise specified\u003cbr\u003e\u003cstrong\u003eAbbreviations\u003c/strong\u003e: AED, automated external defibrillator; SD, standard deviation; CPR, cardiopulmonary resuscitation; ROSC, return of spontaneous circulation; CAG, coronary angiography; PCI, percutaneous coronary intervention; TTM, targeted temperature management\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e[Insert Table 1 here]\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2. Predictive model for 30-day mortality\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs the prediction model developed in this study is intended for use in prehospital settings, we conducted univariate logistic regression analyses using only prehospital variables. Older age was significantly associated with increased 30-day mortality: compared to those aged \u0026le;69 years, the OR for ages 70\u0026ndash;79 was 1.78 (95% CI: 1.45\u0026ndash;2.19), for ages 80\u0026ndash;89 was 3.30 (95% CI: 2.58\u0026ndash;4.23), and for ages \u0026ge;90 was 7.43 (95% CI: 4.06\u0026ndash;13.62).\u003c/p\u003e\n\u003cp\u003eAn initial rhythm of asystole was also strongly associated with increased mortality (OR 16.34, 95% CI: 12.46\u0026ndash;21.43). Unwitnessed arrest had an OR of 4.54 (95% CI: 3.72\u0026ndash;5.55), and absence of bystander CPR had an OR of 2.22 (95% CI: 1.86\u0026ndash;2.65) (Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: Univariate logistic regression analysis for 30-day mortality after out-of-hospital cardiac arrest\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOdds Ratio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eAge category\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e\u0026le;69 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e70\u0026ndash;79 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e1.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e1.45\u0026ndash;2.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e80\u0026ndash;90 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e3.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e2.58\u0026ndash;4.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e\u0026ge;90 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e7.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e4.06\u0026ndash;13.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eInitial rhythm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003eNon-asystole\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003eAsystole\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e16.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e12.46\u0026ndash;21.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eWitnessed arrest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e4.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e3.72\u0026ndash;5.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eBystander CPR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e2.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 111px;\"\u003e\n \u003cp\u003e1.86\u0026ndash;2.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations\u003c/strong\u003e: CI, confidence interval; CPR, cardiopulmonary resuscitation\u003c/p\u003e\n\u003cp\u003eA multivariable logistic regression analysis was performed using all the variables that were statistically significant in the univariate analysis (Table 3). Advanced age was independently associated with higher 30-day mortality: the OR for ages 70\u0026ndash;79 was 1.84 (95% CI: 1.47\u0026ndash;2.30), for ages 80\u0026ndash;89 was 3.04 (95% CI: 2.35\u0026ndash;3.94), and for ages \u0026ge;90 was 6.32 (95% CI: 3.41\u0026ndash;11.70). Asystole as the initial rhythm was the strongest predictor (OR 12.55, 95% CI: 9.40\u0026ndash;16.75). Unwitnessed arrest (OR 1.69, 95% CI: 1.35\u0026ndash;2.12) and absence of bystander CPR (OR 2.15, 95% CI: 1.78\u0026ndash;2.59) also remained significant. No multicollinearity was observed among the included variables.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Multivariable logistic regression analysis of factors associated with 30-day mortality\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026le;69 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e70\u0026ndash;79 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e1.84 (1.47\u0026ndash;2.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e80\u0026ndash;89 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e3.04 (2.35\u0026ndash;3.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026ge;90 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e6.32 (3.41\u0026ndash;11.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003eInitial rhythm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eNon-asystole\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eAsystole\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e12.55 (9.40\u0026ndash;16.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003eWitnessed arrest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e1.69 (1.35\u0026ndash;2.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003eBystander CPR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e2.15 (1.78\u0026ndash;2.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations\u003c/strong\u003e: OR, odds ratio; CI, confidence interval\u003c/p\u003e\n\u003cp\u003eNext, a follow-up multivariable logistic regression analysis excluding bystander CPR was carried out. Although bystander CPR is known to be associated with patient outcomes\u0026nbsp;\u003csup\u003e17\u003c/sup\u003e, it was excluded from the final prediction model due to ethical and practical considerations, as the provision of bystander CPR is highly dependent on witness behavior and not on the patient\u0026apos;s biological condition. Using it as a predictor may pose challenges in terms of acceptability, especially for patients\u0026apos; families in prehospital decision-making contexts. (Table 4).\u003cbr\u003e\u003cbr\u003e\u003cstrong\u003eTable 4.\u003c/strong\u003e \u003cstrong\u003eMultivariable logistic regression analysis of factors associated with 30-day mortality excluding bystander CPR\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026le;69 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e70\u0026ndash;79 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e1.76 (1.42\u0026ndash;2.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e80\u0026ndash;89 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e3.04 (2.36\u0026ndash;3.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026ge;90 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e6.27 (3.39\u0026ndash;11.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003eInitial rhythm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eNon-asystole\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eAsystole\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e12.18 (9.17\u0026ndash;16.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\n \u003cp\u003eWitnessed arrest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e1.88 (1.51\u0026ndash;2.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations\u003c/strong\u003e: OR, odds ratio; CI, confidence interval\u003c/p\u003e\n\u003cp\u003eOlder age remained significantly associated with increased 30-day mortality: the OR for ages 70\u0026ndash;79 was 1.76 (95% CI: 1.42\u0026ndash;2.18), for ages 80\u0026ndash;89 was 3.04 (95% CI: 2.36\u0026ndash;3.93), and for ages \u0026ge;90 was 6.27 (95% CI: 3.39\u0026ndash;11.59).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAsystole as the initial rhythm showed a strong association with mortality (OR 12.18, 95% CI: 9.17\u0026ndash;16.19), as did unwitnessed arrest (OR 1.88, 95% CI: 1.51\u0026ndash;2.34).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3. Discrimination and calibration performance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe model exhibited good discriminative ability, with an area under the receiver operating characteristic curve (AUROC) score of 0.82, indicating good capacity to distinguish between positive and negative outcomes. This discriminative performance was reinforced by a pseudo-R\u0026sup2; value of 0.206, both suggesting substantial improvement over a null model and confirming the statistical significance of the predictors incorporated in the model (Supplementary Figure S1).\u003c/p\u003e\n\u003cp\u003eThe calibration metrics indicated strong alignment between predicted and observed outcomes. The calibration-in-the-large was 0.00 (95% CI: 0.09\u0026ndash;0.09), indicating no systematic over- or under-prediction across the cohort. The calibration slope of 1.00 (95% CI: 0.92\u0026ndash;1.09) demonstrated nearly ideal calibration with predicted probabilities closely matching observed frequencies across the risk spectrum. This was further supported by the calibration slope of 1.01 (95% CI: 0.90\u0026ndash;1.11), confirming consistent calibration performance across different probability thresholds. The observed/expected ratio of 1.07 suggested a slight tendency toward under-prediction, though this deviation was minimal. The calibration plot visually confirmed these excellent calibration metrics, showing the relationship between mean predicted probabilities and observed proportions. The binned observations closely follow the ideal diagonal line (Supplementary Figure S2), indicating excellent agreement between predicted and observed risk across the probability range from 0.75 to 1.0 (Supplementary Table S2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4. Cross-validation for assessment of internal validation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe developed our clinical prediction model and assessed its internal validity using a 10-fold cross-validation approach on the 9,120 cases comprising the dataset. Given that each fold contained sufficient data, we determined that a K value of 10 was appropriate, as this mitigated potential underfitting issues while maintaining robust validation \u003csup\u003e18\u003c/sup\u003e.\u0026nbsp;The model was trained on an average of 8,208 instances per fold. Across all folds, the likelihood ratio chi-square test (LR \u0026chi;\u003csup\u003e2\u003c/sup\u003e; (df=5)) yielded highly significant results (p\u0026lt;0.0001), with values ranging from 766.26 to 826.37, indicating strong predictive power. Pseudo-R\u0026sup2; values showed consistent explanatory ability across folds, with an average of 0.21 (range: 0.20\u0026ndash;0.21) (Supplementary Table S3).\u003c/p\u003e\n\u003cp\u003eDiscrimination performance remained strong across all folds, with AUROC values ranging from 0.77 to 0.86. The pooled AUROC was 0.83, indicating good discriminative ability. The consistency of AUROC values across folds, with overlapping confidence intervals, suggests robust model performance regardless of data partitioning.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5. Scoring system for 30-day mortality\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe point-based scoring system was constructed by converting the regression coefficients (log (OR)) obtained from logistic regression analysis into integer values:\u003c/p\u003e\n\u003cul class=\"decimal_type\"\u003e\n \u003cli\u003eAge 70\u0026ndash;79 years: OR 1.76, log (OR) 0.56 \u0026rarr; 1 point\u003c/li\u003e\n \u003cli\u003eAge 80\u0026ndash;89 years: OR 3.04, log (OR) 1.11 \u0026rarr; 2 points\u003c/li\u003e\n \u003cli\u003eAge \u0026ge;90 years: OR 6.27, log (OR) 1.84 \u0026rarr; 3 points\u003c/li\u003e\n \u003cli\u003eAsystole: OR 12.18, log (OR) 2.50 \u0026rarr; 5 points\u003c/li\u003e\n \u003cli\u003eNo witness: OR 1.88, log (OR) 0.63 \u0026rarr; 1 point\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003ePoint allocations were approximated to clinically convenient integer values based on the logarithmic odds ratios of each predictor. Age category points progressively increased with rising log (OR) values, while asystole, the strongest predictor, received the highest score of 5 points.\u003c/p\u003e\n\u003cp\u003eUsing this scoring system, patients could receive a total score ranging from 0 to 9 points. Our analysis revealed that when the cut-off value was set at 9, the model demonstrated very high specificity and positive predictive value (PPV), with a sensitivity of 3.99%, a specificity of 99.66%, a PPV of 99.66%, and a negative predictive value (NPV) of 6.37% (Table 5).\u003cbr\u003e\u0026nbsp;\u003cbr\u003e\u003cstrong\u003eTable 5. Sensitivity, specificity, positive predictive value, and negative predictive value for each cut-off value in the scoring system for 30-day mortality\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCut-off (\u003c/strong\u003e\u003cstrong\u003e\u0026ge;\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSensitivity (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecificity (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 20px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 20px;\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e93.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 20px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 20px;\"\u003e\n \u003cp\u003e91.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e42.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e95.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e28.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 20px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 20px;\"\u003e\n \u003cp\u003e79.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e71.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e97.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e23.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 20px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 20px;\"\u003e\n \u003cp\u003e69.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e86.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e98.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e19.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 20px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 20px;\"\u003e\n \u003cp\u003e62.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e89.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e98.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e17.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 20px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 20px;\"\u003e\n \u003cp\u003e61.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e89.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e98.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e17.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 20px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 20px;\"\u003e\n \u003cp\u003e55.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e91.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e98.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e15.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 20px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 20px;\"\u003e\n \u003cp\u003e34.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e95.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e99.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e10.61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 20px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 20px;\"\u003e\n \u003cp\u003e18.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e98.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e99.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e7.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 20px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 20px;\"\u003e\n \u003cp\u003e3.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e99.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e99.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e6.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e: Values calculated based on a disease prevalence of 93.4%\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations\u003c/strong\u003e: PPV, positive predictive value; NPV, negative predictive value\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003e\u003cstrong\u003e4.1. Interpretation\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study successfully developed and internally validated a prehospital prediction model for 30-day OHCA mortality using only large-scale registry data and prehospital information. Key findings include exceptional specificity (99.66%) and PPV (99.66%) when the total score reaches 9 points for patients aged \u0026ge;90 years with unwitnessed arrest and initial asystole, identifying a high-risk group with overwhelmingly high mortality.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFrom a clinical perspective, avoiding misclassification of surviving patients as likely to die (false positives) was prioritized; therefore, a high decision threshold was chosen. Conversely, the sensitivity was extremely low at 3.99%. Given that survival rates in OHCA are inherently very low and that avoiding false positives is imperative, this low sensitivity must be tolerated. This precise futility identification, based solely on age \u0026ge;90 years, initial asystole, and unwitnessed arrest, is a significant advance over existing prognostic scores requiring post-hospital parameters like laboratory tests, total downtime, or medication dosages.\u003c/p\u003e\n\u003cp\u003eThe clinical utility of this highly specific predictive tool in prehospital settings is profound. In resource-limited contexts, this model enables EMS personnel to rapidly identify patients with minimal resuscitation potential, preventing potentially futile invasive interventions. This is particularly relevant in Japan, where EMS personnel must continue CPR until hospital arrival except with overt death signs. Japan is undergoing rapid and unprecedented demographic changes. Within the next decade, more than 30% of the population will be aged 65 years or older, while the number of healthcare workers is expected to decline, and the workforce itself to age considerably. Rural and remote areas already experience chronic and structural shortages of emergency medical resources, even under ordinary conditions. In such settings, persistent imbalances between patient needs and system capacity may increasingly resemble mass casualty triage scenarios, even in the absence of large-scale acute disasters. Although our current model does not explicitly endorse rationing of care, it may help facilitate fair and transparent prioritization in resource-constrained healthcare systems when combined with ethical guidelines, legal frameworks, and societal consensus in the future.\u003c/p\u003e\n\u003cp\u003eWhile numerous prognostic scoring systems for OHCA exist, including the RACA score\u003csup\u003e6,7\u003c/sup\u003e for on-site ROSC and the CAHP score\u003csup\u003e10\u003c/sup\u003e for neurological outcomes at hospital discharge, they often incorporate variables requiring subjective judgment or additional diagnostic testing, which may be impractical in the earliest stages of care. For instance, scores like NULL-PLEASE\u003csup\u003e8,9\u003c/sup\u003e and CRASS \u003csup\u003e11,12\u003c/sup\u003e, while effective for predicting in-hospital mortality and neurological recovery, respectively, typically involve variables that become available only upon or after hospital admission. Our model is distinguished by relying exclusively on immediately assessable prehospital information: age, witnessed status, and initial cardiac rhythm (asystole). Its streamlined nature ensures easy implementation in dynamic field settings, allowing for rapid risk stratification upon EMS arrival.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn contrast to termination-of-resuscitation protocols prevalent in some regions of the United States \u003cstrong\u003e\u003csup\u003e19,20\u003c/sup\u003e\u003c/strong\u003e, which allow EMS personnel to cease efforts based on criteria like unwitnessed arrest, non-shockable rhythm, and lack of ROSC, our model offers a predictive capacity independent of on-scene resuscitative duration. This distinction is vital, as prolonged on-scene resuscitation is highly invasive. Ethical considerations aimed at preventing harm to patients have been demonstrated to have a psychological impact on resuscitation efforts in the field \u003csup\u003e21\u003c/sup\u003e. Our model enables early identification of patients with a very high likelihood of mortality without necessitating prolonged on-scene resuscitation. It may serve as a high-specificity aid for recognizing cases with limited survival potential. However, it should not be used in isolation to determine whether to continue or discontinue resuscitation; rather, it should be applied in conjunction with clinical judgment, patient and family preferences, and relevant ethical and legal standards.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2. Limitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study has several limitations. First, the 30-day survival rate was relatively low at 6.2%, compared with a reported survival rate of 9.3% in the United States\u003csup\u003e22\u003c/sup\u003e. As mentioned above, this discrepancy is attributable to differences in the Japanese EMS system. Such differences may limit the generalizability of our model to other countries. Second, the high mortality rate raises concerns about class imbalance. Nevertheless, we considered that even small shifts in predicted probabilities could carry significant clinical implications and therefore did not perform a class imbalance correction. Third, the absence of an independent test set precluded full evaluation of the external validity of our findings. Future studies should validate this scoring system in diverse international cohorts to improve generalizability. Independent test sets and strategies addressing class imbalance are needed to strengthen external validity.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn this study, we have developed the AWA prehospital scoring system, a simple yet effective scoring system that accurately predicts 30-day mortality based on only three parameters, all of which are available in the prehospital setting: age, witnessed status, and the presence of asystole. Specifically, when the three conditions of age over 90 years, unwitnessed cardiac arrest, and asystole were present simultaneously, the specificity for 30-day mortality was 99.66%, and the positive predictive value was 99.66%. This simplified model facilitates rapid assessment in the prehospital setting and supports timely decision-making. External validation using independent datasets is necessary to confirm generalizability and clinical utility.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eOHCA: out-of-hospital cardiac arrest\u003c/p\u003e\n\u003cp\u003eEMS: emergency medical services\u003c/p\u003e\n\u003cp\u003eAUROC: area under the receiver operating characteristic curve\u003c/p\u003e\n\u003cp\u003eCPR: cardiopulmonary resuscitation\u003c/p\u003e\n\u003cp\u003eROSC: return of spontaneous circulation\u003c/p\u003e\n\u003cp\u003eVF: ventricular fibrillation\u003c/p\u003e\n\u003cp\u003ePEA: pulseless electrical activity\u003c/p\u003e\n\u003cp\u003eVT: pulseless ventricular tachycardia\u003c/p\u003e\n\u003cp\u003eSD: standard deviation\u003c/p\u003e\n\u003cp\u003eO/E ratio: observed/expected ratio\u003c/p\u003e\n\u003cp\u003eLR \u0026chi;\u0026sup2;: likelihood ratio chi-square\u003c/p\u003e\n\u003cp\u003eCAG: coronary angiography\u003c/p\u003e\n\u003cp\u003ePCI: percutaneous coronary intervention\u003c/p\u003e\n\u003cp\u003eTTM: targeted temperature management\u003c/p\u003e\n\u003cp\u003eOR: odds ratio\u003c/p\u003e\n\u003cp\u003eCI: confidence interval\u003c/p\u003e\n\u003cp\u003ePPV: positive predictive value\u003c/p\u003e\n\u003cp\u003eNPV: negative predictive value\u003c/p\u003e\n\u003cp\u003eAED: automated external defibrillator\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Clinical Ethics Committee of Fujieda Municipal General Hospital (R06-49). As this is a registry study using medical records without therapeutic intervention, written informed consent was waived. Study information and withdrawal procedures were disclosed on the websites of participating institutions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRegistry data is managed by the Japanese Association for Acute Medicine Kanto Branch and is not publicly available. Third-party access requires association approval through established procedures and ethical review.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSeiya Kanou, MD, MPH, conceived and designed the study, analyzed and interpreted data, drafted the manuscript, and approved the final manuscript. Eiji Nakatani, PhD, analyzed and interpreted data, revised the manuscript, and approved the final manuscript. Philip Hawke, PhD, revised the manuscript, and approved the final manuscript. Yosuke Homma, MD, MPH, collected data and approved the final manuscript. Takashi Tagami, MD, MPH, PhD, performed data cleaning and approved the final manuscript. Yoshihiro Tanaka, MD, PhD, analyzed and interpreted data, revised the manuscript, and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eIwami T, Nichol G, Hiraide A, Hayashi Y, Nishiuchi T, Kajino K, et al. Continuous Improvements in \u0026ldquo;Chain of Survival\u0026rdquo; Increased Survival After Out-of-Hospital Cardiac Arrests. Circulation. 2009;119(5):728\u0026ndash;34.\u003c/li\u003e\n\u003cli\u003eWyckoff MH, Greif R, Morley PT, Ng KC, Olasveengen TM, Singletary EM, et al. 2022 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science With Treatment Recommendations: Summary From the Basic Life Support; Advanced Life Support; Pediatric Life Support; Neonatal Life Support; Education, Implementation, and Teams; and First Aid Task Forces. Resuscitation. 2022;181:208\u0026ndash;88.\u003c/li\u003e\n\u003cli\u003ePerman SM, Elmer J, Maciel CB, Uzendu A, May T, Mumma BE, et al. 2023 American Heart Association Focused Update on Adult Advanced Cardiovascular Life Support: An Update to the American Heart Association Guidelines for Cardiopulmonary Resuscitation and Emergency Cardiovascular Care. Circulation. 2024;149(5):e254\u0026ndash;73.\u003c/li\u003e\n\u003cli\u003eBerdowski J, Berg RA, Tijssen JGP, Koster RW. Global incidences of out-of-hospital cardiac arrest and survival rates: Systematic review of 67 prospective studies. Resuscitation. 2010;81(11):1479\u0026ndash;87.\u003c/li\u003e\n\u003cli\u003eChin YH, Yaow CYL, Teoh SE, Foo MZQ, Luo N, Graves N, et al. Long-term outcomes after out-of-hospital cardiac arrest: A systematic review and meta-analysis. Resuscitation. 2022;171:15\u0026ndash;29.\u003c/li\u003e\n\u003cli\u003eGr\u0026auml;sner JT, Meybohm P, Lefering R, Wnent J, Bahr J, Messelken M, et al. ROSC after cardiac arrest\u0026mdash;the RACA score to predict outcome after out-of-hospital cardiac arrest. Eur Hear J. 2011;32(13):1649\u0026ndash;56.\u003c/li\u003e\n\u003cli\u003eLiu N, Ong MEH, Ho AFW, Pek PP, Lu TC, Khruekarnchana P, et al. Validation of the ROSC after cardiac arrest (RACA) score in Pan-Asian out-of-hospital cardiac arrest patients. Resuscitation. 2020;149:53\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003ePotpara TS, Mihajlovic M, Stankovic S, Jozic T, Jozic I, Asanin MR, et al. External Validation of the Simple NULL-PLEASE Clinical Score in Predicting Outcome of Out-of-Hospital Cardiac Arrest. Am J Med. 2017;130(12):1464.e13-1464.e21.\u003c/li\u003e\n\u003cli\u003eGue YX, Sayers M, Whitby BT, Kanji R, Adatia K, Smith R, et al. Usefulness of the NULL-PLEASE Score to Predict Survival in Out-of-Hospital Cardiac Arrest. Am J Med. 2020;133(11):1328\u0026ndash;35.\u003c/li\u003e\n\u003cli\u003eAdrie C, Cariou A, Mourvillier B, Laurent I, Dabbane H, Hantala F, et al. Predicting survival with good neurological recovery at hospital admission after successful resuscitation of out-of-hospital cardiac arrest: the OHCA score. Eur Hear J. 2006;27(23):2840\u0026ndash;5.\u003c/li\u003e\n\u003cli\u003eSeewald S, Wnent J, Lefering R, Fischer M, Bohn A, Jantzen T, et al. CaRdiac Arrest Survival Score (CRASS) \u0026mdash; A tool to predict good neurological outcome after out-of-hospital cardiac arrest. Resuscitation. 2020;146:66\u0026ndash;73.\u003c/li\u003e\n\u003cli\u003eLiu N, Wnent J, Lee JW, Ning Y, Ho AFW, Siddiqui FJ, et al. Validation of the CaRdiac Arrest Survival Score (CRASS) for predicting good neurological outcome after out-of-hospital cardiac arrest in an Asian emergency medical service system. Resuscitation. 2022;176:42\u0026ndash;50.\u003c/li\u003e\n\u003cli\u003ePareek N, Kordis P, Beckley-Hoelscher N, Pimenta D, Kocjancic ST, Jazbec A, et al. A practical risk score for early prediction of neurological outcome after out-of-hospital cardiac arrest: MIRACLE2. Eur Hear J. 2020;41(47):4508\u0026ndash;17.\u003c/li\u003e\n\u003cli\u003eGroup TSK 2017 S. Changes in treatments and outcomes of out-of-hospital cardiac arrest between the SOS-KANTO 2012 and 2017 studies. Ann Clin Epidemiology. 2025;7(1):17\u0026ndash;26.\u003c/li\u003e\n\u003cli\u003eLibungan B, Lindqvist J, Str\u0026ouml;ms\u0026ouml;e A, Nordberg P, Hollenberg J, Albertsson P, et al. Out-of-hospital cardiac arrest in the elderly: A large-scale population-based study. Resuscitation. 2015;94:28\u0026ndash;32.\u003c/li\u003e\n\u003cli\u003eIshii J, Nishikimi M, Kikutani K, Ohki S, Ota K, Anzai T, et al. Resuscitation Attempt and Outcomes in Patients With Asystole Out-of-Hospital Cardiac Arrest. JAMA Netw Open. 2024;7(11):e2445543.\u003c/li\u003e\n\u003cli\u003eO\u0026rsquo;Keefe EL, Jawad MA, Kennedy KF, Nguyen D, Ikemura N, Chan PS. Time to bystander CPR and survival for witnessed out-of-hospital cardiac arrest. Resuscitation. 2025;209:110566.\u003c/li\u003e\n\u003cli\u003eJung Y, Hu J. A K-fold averaging cross-validation procedure. J Nonparametric Stat. 2015;27(2):167\u0026ndash;79.\u003c/li\u003e\n\u003cli\u003eAssociation EC Subcommittees and Task Forces of the American Heart. 2005 American Heart Association Guidelines for Cardiopulmonary Resuscitation and Emergency Cardiovascular Care. Circulation. 2005;112(24 Suppl):IV1-203.\u003c/li\u003e\n\u003cli\u003ePhysicians NA of E. Termination of Resuscitation in Nontraumatic Cardiopulmonary Arrest. Prehospital Emerg Care. 2011;15(4):542\u0026ndash;542.\u003c/li\u003e\n\u003cli\u003eEli K, Huxley CJ, Gardiner G, Perkins GD, Smyth MA, Griffiths F, et al. Ethical issues in termination of resuscitation decision-making: an interview study with paramedics and relatives of out-of-hospital cardiac arrest non-survivors. BMJ Open. 2024;14(11):e085132.\u003c/li\u003e\n\u003cli\u003eBuaprasert P, Al-Araji R, Rajdev M, Vellano K, Carr MJ, McNally B. The past, present, and future of the Cardiac Arrest Registry to Enhance Survival (CARES). Resusc Plus. 2024;18:100624.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-emergency-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijem","sideBox":"Learn more about [International Journal of Emergency Medicine](https://intjem.biomedcentral.com/)","snPcode":"12245","submissionUrl":"https://submission.nature.com/new-submission/12245/3","title":"International Journal of Emergency Medicine","twitterHandle":"@IntJEmergMed","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"out-of-hospital cardiac arrest, 30-day mortality, prehospital setting, scoring system, prediction mode","lastPublishedDoi":"10.21203/rs.3.rs-8513452/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8513452/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground \u003cbr\u003e\n \u003c/strong\u003eOut-of-hospital cardiac arrest (OHCA) has a very high mortality rate. Standard emergency responses to OHCA can result in unwanted resuscitation, particularly in elderly patients, and futile interventions can place additional strain on already limited healthcare resources. An appropriate prognostic scoring system for OHCA could reduce these adverse effects, but existing systems all rely on information unavailable in the prehospital setting. This study developed and internally validated a simple scoring system based exclusively on prehospital parameters that predicts 30-day mortality in OHCA patients with high accuracy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults \u0026nbsp;\u003cbr\u003e\n \u003c/strong\u003eThis retrospective study used the Japanese SOS-KANTO registry. The primary outcome was 30-day mortality. A multivariable logistic regression model using prehospital predictors was converted into a point-based scoring system. Model performance and internal validation were assessed.\u003c/p\u003e\n\u003cp\u003eAmong the 9,120 patients included in the study, the 30-day mortality rate was 93.8%. The final prediction model included age, unwitnessed arrest, and asystole as initial rhythm. It showed good discriminative ability and good calibration. On a 9-point scoring system, a total score of 9 (patients aged ≥90 years, with unwitnessed arrest, and an initial rhythm of asystole) predicted 30-day mortality with a specificity of 99.66% and a positive predictive value of 99.66%. \u003cstrong\u003e\u003cbr\u003e\nConclusion \u003cbr\u003e\n \u003c/strong\u003eWe have developed a practical scoring system based on three prehospital parameters—age, witness status, and asystole (AWA)—that accurately predicts 30-day mortality after OHCA. Our simple and accurate AWA scoring system facilitates rapid assessment in prehospital settings, supporting timely decision-making.\u003c/p\u003e","manuscriptTitle":"The AWA Prehospital Scoring System Predicts 30-Day Mortality After Out-of-Hospital Cardiac Arrest with High Accuracy: An SOS-KANTO 2017 Registry Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-03 15:36:53","doi":"10.21203/rs.3.rs-8513452/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-15T06:30:26+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"41331026168038985730691711206551836091","date":"2026-02-14T22:13:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-14T06:58:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"6665406067306362213480304106813534995","date":"2026-02-13T00:20:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"99596619655984584673620239132849619920","date":"2026-02-08T22:02:18+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-08T15:46:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"15715613106689000560450257914054362068","date":"2026-02-02T15:48:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"305933294369600872104424154352793640137","date":"2026-02-01T23:57:26+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-29T07:43:59+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-06T05:29:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-06T05:27:02+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal of Emergency Medicine","date":"2026-01-04T13:46:12+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-emergency-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijem","sideBox":"Learn more about [International Journal of Emergency Medicine](https://intjem.biomedcentral.com/)","snPcode":"12245","submissionUrl":"https://submission.nature.com/new-submission/12245/3","title":"International Journal of Emergency Medicine","twitterHandle":"@IntJEmergMed","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7f707105-f29b-45c0-b6be-6e4c5241f64d","owner":[],"postedDate":"February 3rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-23T16:02:18+00:00","versionOfRecord":{"articleIdentity":"rs-8513452","link":"https://doi.org/10.1186/s12245-026-01173-6","journal":{"identity":"international-journal-of-emergency-medicine","isVorOnly":false,"title":"International Journal of Emergency Medicine"},"publishedOn":"2026-03-17 15:59:02","publishedOnDateReadable":"March 17th, 2026"},"versionCreatedAt":"2026-02-03 15:36:53","video":"","vorDoi":"10.1186/s12245-026-01173-6","vorDoiUrl":"https://doi.org/10.1186/s12245-026-01173-6","workflowStages":[]},"version":"v1","identity":"rs-8513452","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8513452","identity":"rs-8513452","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}