Pub Date : 2025-12-04DOI: 10.1016/j.resuscitation.2025.110918
Charlotte Eickelmann, Anna Josefine Beiske, Martin Deicke, Julia Johanna Grannemann, Annika Hoyer, Lydia Johnson Kolaparambil Varghese, Bernd Strickmann, Mathini Vaseekaran, Gerrit Jansen
INTRODUCTIONThis study examines the influence of supraglottic airway (SGA) devices versus tracheal intubation (TI) on key ventilation parameters during intra-arrest-ventilation using volume-controlled-ventilation (VCV) in adult out-of-hospital cardiac arrest (OHCA).METHODSThis cohort study is based on real-world data obtained from the emergency medical service of the Gütersloh district, Germany. Ventilation data were extracted in March 2024 from emergency ventilators and combined with patient-level information from the German Resuscitation Registry. Adult OHCA cases receiving intra-arrest-ventilation 01/2019-08/2023 with VCV via either SGA or TI were included. Collected parameters included the airway device used, set tidal volume (VTset), measured expiratory tidal volume (VTe), and leakage volume (VLeak). The primary outcome was the difference between VTset-VTe. Patients were grouped according to the airway management strategy used (SGA vs. TI). Potential differences in outcomes between these groups were assessed using linear mixed regression models.RESULTSVCV was performed in n=27 individuals (682 minutes) using SGA in n=13 (330 minutes) vs. TI in n=14 (352 minutes). The mean total VTset was 562.8±58.0ml (TI=573.9±62.5ml; SGA=550.9±50.1ml). The mean VTe totaled 270.7±205.5ml (TI=348.1±215.6ml; SGA=188.2±156.6ml). The mean VLeak was 23.3±27.4% (TI=5.5±7.0%; SGA=42.3±28.4%). Compared to SGA, TI was associated with smaller VTset-VTe (regression coefficient: -128.3ml; 95%-CI: [-252.3ml; -4.3ml]; p=0.0427) as well as for a lower VLeak (regression coefficient: -32.3%; 95%-CI: [-46.1%; -18.4%]; p<0.0001) for TI.CONCLUSIONIn OHCA cases receiving mechanical intra-arrest-ventilation with VCV, TI was associated with higher delivered VTe, less deviation from VTset, and significantly lower VLeak compared to SGA.
{"title":"Tacheal intubation vs. supraglottic airway devices during mechanical intra-arrest-ventilation with volume-controlled-ventilation in out-of-hospital cardiac arrest: a cohort study","authors":"Charlotte Eickelmann, Anna Josefine Beiske, Martin Deicke, Julia Johanna Grannemann, Annika Hoyer, Lydia Johnson Kolaparambil Varghese, Bernd Strickmann, Mathini Vaseekaran, Gerrit Jansen","doi":"10.1016/j.resuscitation.2025.110918","DOIUrl":"https://doi.org/10.1016/j.resuscitation.2025.110918","url":null,"abstract":"INTRODUCTIONThis study examines the influence of supraglottic airway (SGA) devices versus tracheal intubation (TI) on key ventilation parameters during intra-arrest-ventilation using volume-controlled-ventilation (VCV) in adult out-of-hospital cardiac arrest (OHCA).METHODSThis cohort study is based on real-world data obtained from the emergency medical service of the Gütersloh district, Germany. Ventilation data were extracted in March 2024 from emergency ventilators and combined with patient-level information from the German Resuscitation Registry. Adult OHCA cases receiving intra-arrest-ventilation 01/2019-08/2023 with VCV via either SGA or TI were included. Collected parameters included the airway device used, set tidal volume (VTset), measured expiratory tidal volume (VTe), and leakage volume (VLeak). The primary outcome was the difference between VTset-VTe. Patients were grouped according to the airway management strategy used (SGA vs. TI). Potential differences in outcomes between these groups were assessed using linear mixed regression models.RESULTSVCV was performed in n=27 individuals (682 minutes) using SGA in n=13 (330 minutes) vs. TI in n=14 (352 minutes). The mean total VTset was 562.8±58.0ml (TI=573.9±62.5ml; SGA=550.9±50.1ml). The mean VTe totaled 270.7±205.5ml (TI=348.1±215.6ml; SGA=188.2±156.6ml). The mean VLeak was 23.3±27.4% (TI=5.5±7.0%; SGA=42.3±28.4%). Compared to SGA, TI was associated with smaller VTset-VTe (regression coefficient: -128.3ml; 95%-CI: [-252.3ml; -4.3ml]; p=0.0427) as well as for a lower VLeak (regression coefficient: -32.3%; 95%-CI: [-46.1%; -18.4%]; p<0.0001) for TI.CONCLUSIONIn OHCA cases receiving mechanical intra-arrest-ventilation with VCV, TI was associated with higher delivered VTe, less deviation from VTset, and significantly lower VLeak compared to SGA.","PeriodicalId":21052,"journal":{"name":"Resuscitation","volume":"31 1","pages":"110918"},"PeriodicalIF":6.5,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145689540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-03DOI: 10.1016/j.resuscitation.2025.110911
Lis Frykler Abazi, Sune Forsberg, Felix Böhm, Martin Jonsson, Mattias Ringh, Gabriel Riva, Ludvig Elfwén, Per Nordberg, Akil Awad, Charlotte Miedel, Anette Nord, Andreas Claesson, Nils Witt, Jacob Hollenberg
{"title":"Coronary angiography findings in relation to defibrillation refractoriness in out-of-hospital cardiac arrest - a nationwide study over 10 years","authors":"Lis Frykler Abazi, Sune Forsberg, Felix Böhm, Martin Jonsson, Mattias Ringh, Gabriel Riva, Ludvig Elfwén, Per Nordberg, Akil Awad, Charlotte Miedel, Anette Nord, Andreas Claesson, Nils Witt, Jacob Hollenberg","doi":"10.1016/j.resuscitation.2025.110911","DOIUrl":"https://doi.org/10.1016/j.resuscitation.2025.110911","url":null,"abstract":"","PeriodicalId":21052,"journal":{"name":"Resuscitation","volume":"27 1","pages":""},"PeriodicalIF":6.5,"publicationDate":"2025-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145657363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-03DOI: 10.1016/j.resuscitation.2025.110915
Zachary M. Shinar, Brian Burns
{"title":"ECPR in the futile traumatic patient: breaking paradigms or fanciful optimism?","authors":"Zachary M. Shinar, Brian Burns","doi":"10.1016/j.resuscitation.2025.110915","DOIUrl":"10.1016/j.resuscitation.2025.110915","url":null,"abstract":"","PeriodicalId":21052,"journal":{"name":"Resuscitation","volume":"218 ","pages":"Article 110915"},"PeriodicalIF":4.6,"publicationDate":"2025-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145657365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-03DOI: 10.1016/j.resuscitation.2025.110914
Per Olav Berve , Simon Orlob
{"title":"Intra-arrest ventilation: we can only improve what we measure – But what are our devices really calculating?","authors":"Per Olav Berve , Simon Orlob","doi":"10.1016/j.resuscitation.2025.110914","DOIUrl":"10.1016/j.resuscitation.2025.110914","url":null,"abstract":"","PeriodicalId":21052,"journal":{"name":"Resuscitation","volume":"218 ","pages":"Article 110914"},"PeriodicalIF":4.6,"publicationDate":"2025-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145657362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01DOI: 10.1016/j.resuscitation.2025.110851
David G. Dillon , Katherine S. Allan , Juan Carlos C. Montoy , Mika’il Visanji , Robert M. Rodriguez , Steve Lin , Ralph C. Wang
Background
Up to fifteen percent of out-of-hospital cardiac arrests (OHCAs) are precipitated by occult drug overdose – cases without history or evidence of drug use that are often attributed to a non-overdose cause. The NAloxone Cardiac ARrest Decision Instrument (NACARDI) was derived to help emergency medical service (EMS) providers rapidly identify patients at higher risk of occult opioid-associated (OA)-OHCAs during resuscitation. In this analysis we externally validate NACARDI in an independent cohort of OHCA patients.
Methods
We conducted a retrospective validation using data from EMS-attended OHCA patients and coroner records in Ontario, Canada between 2020–2021. Inclusion criteria were age ≥18 years and OHCA death with a coroner record. Exclusion criteria were EMS-suspected drug overdose or known cause of the OHCA. NACARDI consists of two criteria: patient age and unwitnessed cardiac arrest. Two cut-offs for patient age were assessed for this validation: <50 years (NACARDI-50) and <60 years (NACARDI-60). The primary outcome was coroner adjudicated cause of death. We calculated screening characteristics and receiver operating characteristic (ROC) curves using standard formulae.
Results
Of 2904 OHCA cases without an obvious cause, 791 had coroner evaluations and 121 (15.3 %) were adjudicated as occult OA-OHCA. NACARDI-60 had: sensitivity 82.6 % (95 %CI 74.9–88.4 %), specificity 77.1 % (95 %CI 73.8–80.1 %), negative predictive value 96.1 % (95 %CI 94.1–97.4 %), and positive predictive value 39.4 % (95 %CI 33.6–45.5 %). NACARDI-50 had: sensitivity 63.6 % (95 %CI 54.4–72.2 %), specificity 89.3 % (95 %CI 86.7–91.5 %), negative predictive value 93.2 % (95 %CI 90.9–95.0 %), and positive predictive value 51.7 % (95 %CI 43.4–59.9 %). ROC curves for both NACARDI-50 and NACARDI-60 demonstrated excellent discrimination for occult OA-OHCA.
Conclusion
In this external validation cohort, NACARDI had a sensitivity and specificity sufficiently high to aid in the real-time identification of occult OA-OHCA in the field. NACARDI has the potential to guide targeted interventions for OA-OHCA.
{"title":"Validation of the naloxone cardiac arrest decision instrument for identifying opioid-associated cardiac arrests","authors":"David G. Dillon , Katherine S. Allan , Juan Carlos C. Montoy , Mika’il Visanji , Robert M. Rodriguez , Steve Lin , Ralph C. Wang","doi":"10.1016/j.resuscitation.2025.110851","DOIUrl":"10.1016/j.resuscitation.2025.110851","url":null,"abstract":"<div><h3>Background</h3><div>Up to fifteen percent of out-of-hospital cardiac arrests (OHCAs) are precipitated by occult drug overdose – cases without history or evidence of drug use that are often attributed to a non-overdose cause. The NAloxone Cardiac ARrest Decision Instrument (NACARDI) was derived to help emergency medical service (EMS) providers rapidly identify patients at higher risk of occult opioid-associated (OA)-OHCAs during resuscitation. In this analysis we externally validate NACARDI in an independent cohort of OHCA patients.</div></div><div><h3>Methods</h3><div>We conducted a retrospective validation using data from EMS-attended OHCA patients and coroner records in Ontario, Canada between 2020–2021. Inclusion criteria were age ≥18 years and OHCA death with a coroner record. Exclusion criteria were EMS-suspected drug overdose or known cause of the OHCA. NACARDI consists of two criteria: patient age and unwitnessed cardiac arrest. Two cut-offs for patient age were assessed for this validation: <50 years (NACARDI-50) and <60 years (NACARDI-60). The primary outcome was coroner adjudicated cause of death. We calculated screening characteristics and receiver operating characteristic (ROC) curves using standard formulae.</div></div><div><h3>Results</h3><div>Of 2904 OHCA cases without an obvious cause, 791 had coroner evaluations and 121 (15.3 %) were adjudicated as occult OA-OHCA. NACARDI-60 had: sensitivity 82.6 % (95 %CI 74.9–88.4 %), specificity 77.1 % (95 %CI 73.8–80.1 %), negative predictive value 96.1 % (95 %CI 94.1–97.4 %), and positive predictive value 39.4 % (95 %CI 33.6–45.5 %). NACARDI-50 had: sensitivity 63.6 % (95 %CI 54.4–72.2 %), specificity 89.3 % (95 %CI 86.7–91.5 %), negative predictive value 93.2 % (95 %CI 90.9–95.0 %), and positive predictive value 51.7 % (95 %CI 43.4–59.9 %). ROC curves for both NACARDI-50 and NACARDI-60 demonstrated excellent discrimination for occult OA-OHCA.</div></div><div><h3>Conclusion</h3><div>In this external validation cohort, NACARDI had a sensitivity and specificity sufficiently high to aid in the real-time identification of occult OA-OHCA in the field. NACARDI has the potential to guide targeted interventions for OA-OHCA.</div></div>","PeriodicalId":21052,"journal":{"name":"Resuscitation","volume":"217 ","pages":"Article 110851"},"PeriodicalIF":4.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145252094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01DOI: 10.1016/j.resuscitation.2025.110900
Tatsuya Norii , Michael A. Smyth , Monica E. Kleinman , Sander van Goor , Janet E. Bray
{"title":"A comparison of adult basic life support recommendations in the latest guidelines","authors":"Tatsuya Norii , Michael A. Smyth , Monica E. Kleinman , Sander van Goor , Janet E. Bray","doi":"10.1016/j.resuscitation.2025.110900","DOIUrl":"10.1016/j.resuscitation.2025.110900","url":null,"abstract":"","PeriodicalId":21052,"journal":{"name":"Resuscitation","volume":"217 ","pages":"Article 110900"},"PeriodicalIF":4.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145534777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01DOI: 10.1016/j.resuscitation.2025.110847
Rohit S. Loomba
{"title":"Cerebral autoregulation: why predict a monitored value when it’s already being monitored?","authors":"Rohit S. Loomba","doi":"10.1016/j.resuscitation.2025.110847","DOIUrl":"10.1016/j.resuscitation.2025.110847","url":null,"abstract":"","PeriodicalId":21052,"journal":{"name":"Resuscitation","volume":"217 ","pages":"Article 110847"},"PeriodicalIF":4.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145207363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01DOI: 10.1016/j.resuscitation.2025.110853
Raphael A. Ehmann , Jim Briggs , David R. Prytherch , Ina Kostakis
Aim
Early Warning Scores are support tools intended to help clinicians prevent adverse patient outcomes. Although it has been shown that trends in a patient’s medical condition are associated with patient-outcome, the incorporation of this knowledge within early warning score development has been slow. Our goal is to find the minimal-best-performing set of predictors for logistic regression models that includes trends in a patient’s medical state.
Materials and methods
We used a large data set obtained from a single large hospital in the south of England and logistic regression modelling to search for the smallest possible set of predictors that simultaneously has a high predictive performance. Efficiency curves were used to estimate the trade-off between clinical workload and the sensitivity of the models and to compare performance with the National Early Warning Score (NEWS), the Laboratory-Decision Tree Early Warning Score (LDTEWS) and LDTEWS:NEWS.
Results
Comparing the efficiency curves of the different models showed, that the number of consecutive observations (2 to 5 observations) had little impact on model performance. Even in the simplest scenario, using 2 consecutive observations, the best model identified between 17 and 293 more deteriorating patients per 1000 patients compared to established non-trend early warning systems, at a comparable clinical workload. This best model uses linear regression coefficients obtained from consecutive NEWS values, the current LDTEWS value as well as the mean of the respiratory rates.
Conclusions
The results of this study confirm that, not only can the performance of models predicting clinical deterioration be increased by including trends, but that a logistic regression-based model with very few predictors can predict the risk of deterioration better than current non-trend models. Thus, models incorporating trends have the potential to prevent deterioration in more patients than contemporary early warning scores, however further validation is necessary.
{"title":"Improving predictive performance of Early Warning Scores by including trends in observations","authors":"Raphael A. Ehmann , Jim Briggs , David R. Prytherch , Ina Kostakis","doi":"10.1016/j.resuscitation.2025.110853","DOIUrl":"10.1016/j.resuscitation.2025.110853","url":null,"abstract":"<div><h3>Aim</h3><div>Early Warning Scores are support tools intended to help clinicians prevent adverse patient outcomes. Although it has been shown that trends in a patient’s medical condition are associated with patient-outcome, the incorporation of this knowledge within early warning score development has been slow. Our goal is to find the minimal-best-performing set of predictors for logistic regression models that includes trends in a patient’s medical state.</div></div><div><h3>Materials and methods</h3><div>We used a large data set obtained from a single large hospital in the south of England and logistic regression modelling to search for the smallest possible set of predictors that simultaneously has a high predictive performance. Efficiency curves were used to estimate the trade-off between clinical workload and the sensitivity of the models and to compare performance with the National Early Warning Score (NEWS), the Laboratory-Decision Tree Early Warning Score (LDTEWS) and LDTEWS:NEWS.</div></div><div><h3>Results</h3><div>Comparing the efficiency curves of the different models showed, that the number of consecutive observations (2 to 5 observations) had little impact on model performance. Even in the simplest scenario, using 2 consecutive observations, the best model identified between 17 and 293 more deteriorating patients per 1000 patients compared to established non-trend early warning systems, at a comparable clinical workload. This best model uses linear regression coefficients obtained from consecutive NEWS values, the current LDTEWS value as well as the mean of the respiratory rates.</div></div><div><h3>Conclusions</h3><div>The results of this study confirm that, not only can the performance of models predicting clinical deterioration be increased by including trends, but that a logistic regression-based model with very few predictors can predict the risk of deterioration better than current non-trend models. Thus, models incorporating trends have the potential to prevent deterioration in more patients than contemporary early warning scores, however further validation is necessary.</div></div>","PeriodicalId":21052,"journal":{"name":"Resuscitation","volume":"217 ","pages":"Article 110853"},"PeriodicalIF":4.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145239426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01DOI: 10.1016/j.resuscitation.2025.110907
Claudio Sandroni , Jerry Paul Nolan , Tobias Cronberg
{"title":"Authors’ reply to: “Prognostic significance of post-anoxic myoclonus: time for a reappraisal?” by Pia De Stefano et al.","authors":"Claudio Sandroni , Jerry Paul Nolan , Tobias Cronberg","doi":"10.1016/j.resuscitation.2025.110907","DOIUrl":"10.1016/j.resuscitation.2025.110907","url":null,"abstract":"","PeriodicalId":21052,"journal":{"name":"Resuscitation","volume":"217 ","pages":"Article 110907"},"PeriodicalIF":4.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145559705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Out-of-hospital cardiac arrest affects 130,000 individuals annually in Japan, with favorable neurological outcomes of <4 %. Despite widespread AED deployment (>670,000 devices), bystander utilization remains suboptimal at 5 % in witnessed cases.
Methods
We used machine learning-generated synthetic data from 2022 All-Japan Utstein Registry to project population-level impacts of universal early AED access through 2075. The synthetic dataset (n = 966,493) enabled population-level projections, while time-to-AED data were available only for witnessed cases. The intervention scenario modeled bystander AED application within 5 min for all witnessed arrests, compared with current patterns. Classification and Regression Trees incorporated Japan’s demographic projections. Primary outcomes were one-month survival and favorable neurological outcome (CPC 1or 2). Rate ratios with 95 % confidence intervals were estimated using modified Poisson regression.
Results
Universal early AED implementation showed peak effectiveness in 2055–2060: favorable neurological outcome RR 2.16 (95% CI, 2.07–2.25; 116% relative increase). Over the 45-year projection period (2030–2075), universal early AED implementation could prevent approximately 235,000 deaths (95% CI, 210,500–261,500) and result in 160,000 more survivors with good neurological recovery after OHCA (95% CI, 142,000–181,000). Analyses estimated a number needed to treat of 19–29 (median 24) witnessed OHCA patients receiving bystander AED within 5 min to achieve one additional favorable neurological outcome.
Conclusions
Machine learning-based modeling projects that universal bystander AED application in Japan over a period of four and a half decades could prevent approximately 235,000 deaths and result in 160,000 more survivors with good neurological recovery after OHCA.
{"title":"Population-level impact of universal early AED implementation on out-of-hospital cardiac arrest outcomes: a machine learning analysis using synthetic patient data of 45-year projection study from Japan","authors":"Atsushi Kubo , Shinobu Tamura , Atsushi Hiraide , Daigo Morioka , Ryu Murakami , Kenko Fukui , Shigeaki Inoue","doi":"10.1016/j.resuscitation.2025.110904","DOIUrl":"10.1016/j.resuscitation.2025.110904","url":null,"abstract":"<div><h3>Background</h3><div>Out-of-hospital cardiac arrest affects 130,000 individuals annually in Japan, with favorable neurological outcomes of <4 %. Despite widespread AED deployment (>670,000 devices), bystander utilization remains suboptimal at 5 % in witnessed cases.</div></div><div><h3>Methods</h3><div>We used machine learning-generated synthetic data from 2022 All-Japan Utstein Registry to project population-level impacts of universal early AED access through 2075. The synthetic dataset (n = 966,493) enabled population-level projections, while time-to-AED data were available only for witnessed cases. The intervention scenario modeled bystander AED application within 5 min for all witnessed arrests, compared with current patterns. Classification and Regression Trees incorporated Japan’s demographic projections. Primary outcomes were one-month survival and favorable neurological outcome (CPC 1or 2). Rate ratios with 95 % confidence intervals were estimated using modified Poisson regression.</div></div><div><h3>Results</h3><div>Universal early AED implementation showed peak effectiveness in 2055–2060: favorable neurological outcome RR 2.16 (95% CI, 2.07–2.25; 116% relative increase). Over the 45-year projection period (2030–2075), universal early AED implementation could prevent approximately 235,000 deaths (95% CI, 210,500–261,500) and result in 160,000 more survivors with good neurological recovery after OHCA (95% CI, 142,000–181,000). Analyses estimated a number needed to treat of 19–29 (median 24) witnessed OHCA patients receiving bystander AED within 5 min to achieve one additional favorable neurological outcome.</div></div><div><h3>Conclusions</h3><div>Machine learning-based modeling projects that universal bystander AED application in Japan over a period of four and a half decades could prevent approximately 235,000 deaths and result in 160,000 more survivors with good neurological recovery after OHCA.</div></div>","PeriodicalId":21052,"journal":{"name":"Resuscitation","volume":"217 ","pages":"Article 110904"},"PeriodicalIF":4.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145559706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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