Antoine Léquipar, Manveer Singh, Jean Guillaume Dillinger, Antonin Trimaille, Claire Bouleti, Clément Delmas, Guillaume Schurtz, Sonia Houssany-Pissot, Reza Rossanaly Vasram, Edouard Gerbaud, Damien Millischer, Amine El Ouahidi, Nabil Bouali, Emmanuel Gall, Christophe Thuaire, François Roubille, Nathalie Noirclerc, Stéphane Andrieu, Charles Fauvel, Alexandre Lafont, Sebastian Voicu, Nathan El Bèze, Charly Alizadeh, Paul Guiraud-Chaumeil, Jeremy Florence, Solenn Toupin, Victor Aboyans, Eric Vicaut, Théo Pezel, Patrick Henry
{"title":"心肌梗死后呼气一氧化碳水平对预后预测的价值。","authors":"Antoine Léquipar, Manveer Singh, Jean Guillaume Dillinger, Antonin Trimaille, Claire Bouleti, Clément Delmas, Guillaume Schurtz, Sonia Houssany-Pissot, Reza Rossanaly Vasram, Edouard Gerbaud, Damien Millischer, Amine El Ouahidi, Nabil Bouali, Emmanuel Gall, Christophe Thuaire, François Roubille, Nathalie Noirclerc, Stéphane Andrieu, Charles Fauvel, Alexandre Lafont, Sebastian Voicu, Nathan El Bèze, Charly Alizadeh, Paul Guiraud-Chaumeil, Jeremy Florence, Solenn Toupin, Victor Aboyans, Eric Vicaut, Théo Pezel, Patrick Henry","doi":"10.1093/eurjpc/zwaf559","DOIUrl":null,"url":null,"abstract":"<p><strong>Aims: </strong>To assess the prognostic value of expiratory carbon monoxide (CO) levels in patients admitted for myocardial infarction (MI).</p><p><strong>Methods and results: </strong>In this prospective study, expiratory CO levels were measured upon admission in consecutive patients hospitalized for MI across 39 centres. The primary outcome was 1-year all-cause death. Secondary outcomes included 1-year major adverse cardiac events (MACEs: cardiovascular death and recurrent MI) and in-hospital major adverse events (MAEs: death, severe ventricular arrhythmia, cardiogenic shock, and the need for mechanical ventilation). The prognostic value of expiratory CO levels was further evaluated using machine learning (ML) analysis. Among 717 patients (64 ± 13 years; 75% males; 33% active smokers; 43% ST-elevation MI), elevated expiratory CO levels (>11 ppm) were found in 79 patients (11%). Patients with elevated CO levels had a higher rate of 1-year all-cause mortality compared with those without (16.5% vs. 5.2%, P < 0.001). Elevated CO levels were independently associated with 1-year all-cause death across various adjustment models: comorbidities [odds ratio (95% confidence interval): 3.6 (1.7-7.5)], clinical parameters of in-hospital severity [4.5 (2.3-8.8)], and respiratory parameters [6.3 (3.1-12.8)]. Elevated CO levels were also independently associated with a significant increase in 1-year MACEs and in-hospital MAEs. Machine learning analysis identified CO level as one of the most important predictors of adverse events, compared with other known prognosticators.</p><p><strong>Conclusion: </strong>This is the first study to demonstrate that elevated expiratory CO levels upon admission are independently associated with an increased risk of 1-year all-cause mortality, 1-year MACEs, and in-hospital MAEs in patients hospitalized for MI.</p>","PeriodicalId":12051,"journal":{"name":"European journal of preventive cardiology","volume":" ","pages":""},"PeriodicalIF":7.5000,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The prognostic value of expiratory carbon monoxide level for outcome prediction after myocardial infarction.\",\"authors\":\"Antoine Léquipar, Manveer Singh, Jean Guillaume Dillinger, Antonin Trimaille, Claire Bouleti, Clément Delmas, Guillaume Schurtz, Sonia Houssany-Pissot, Reza Rossanaly Vasram, Edouard Gerbaud, Damien Millischer, Amine El Ouahidi, Nabil Bouali, Emmanuel Gall, Christophe Thuaire, François Roubille, Nathalie Noirclerc, Stéphane Andrieu, Charles Fauvel, Alexandre Lafont, Sebastian Voicu, Nathan El Bèze, Charly Alizadeh, Paul Guiraud-Chaumeil, Jeremy Florence, Solenn Toupin, Victor Aboyans, Eric Vicaut, Théo Pezel, Patrick Henry\",\"doi\":\"10.1093/eurjpc/zwaf559\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Aims: </strong>To assess the prognostic value of expiratory carbon monoxide (CO) levels in patients admitted for myocardial infarction (MI).</p><p><strong>Methods and results: </strong>In this prospective study, expiratory CO levels were measured upon admission in consecutive patients hospitalized for MI across 39 centres. The primary outcome was 1-year all-cause death. Secondary outcomes included 1-year major adverse cardiac events (MACEs: cardiovascular death and recurrent MI) and in-hospital major adverse events (MAEs: death, severe ventricular arrhythmia, cardiogenic shock, and the need for mechanical ventilation). The prognostic value of expiratory CO levels was further evaluated using machine learning (ML) analysis. Among 717 patients (64 ± 13 years; 75% males; 33% active smokers; 43% ST-elevation MI), elevated expiratory CO levels (>11 ppm) were found in 79 patients (11%). Patients with elevated CO levels had a higher rate of 1-year all-cause mortality compared with those without (16.5% vs. 5.2%, P < 0.001). Elevated CO levels were independently associated with 1-year all-cause death across various adjustment models: comorbidities [odds ratio (95% confidence interval): 3.6 (1.7-7.5)], clinical parameters of in-hospital severity [4.5 (2.3-8.8)], and respiratory parameters [6.3 (3.1-12.8)]. Elevated CO levels were also independently associated with a significant increase in 1-year MACEs and in-hospital MAEs. Machine learning analysis identified CO level as one of the most important predictors of adverse events, compared with other known prognosticators.</p><p><strong>Conclusion: </strong>This is the first study to demonstrate that elevated expiratory CO levels upon admission are independently associated with an increased risk of 1-year all-cause mortality, 1-year MACEs, and in-hospital MAEs in patients hospitalized for MI.</p>\",\"PeriodicalId\":12051,\"journal\":{\"name\":\"European journal of preventive cardiology\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":7.5000,\"publicationDate\":\"2025-10-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"European journal of preventive cardiology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1093/eurjpc/zwaf559\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CARDIAC & CARDIOVASCULAR SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"European journal of preventive cardiology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1093/eurjpc/zwaf559","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CARDIAC & CARDIOVASCULAR SYSTEMS","Score":null,"Total":0}
引用次数: 0
摘要
目的:评价心肌梗死(MI)患者呼气一氧化碳(CO)水平的预后价值。方法和结果:在这项前瞻性研究中,在39个中心连续住院的心肌梗死患者入院时测量呼气CO水平。主要结局为1年内全因死亡。次要结局包括1年内主要心脏不良事件(mace:心血管性死亡和复发性心肌梗死)和院内主要不良事件(MAEs:死亡、严重室性心律失常、心源性休克和需要机械通气)。使用机器学习(ML)分析进一步评估呼气CO水平的预后价值。717例患者(64±13岁,75%为男性,33%为活跃吸烟者,43%为st段抬高心肌梗死)中,79例(11%)患者呼气中CO水平升高(bb0 ~ 11ppm)。CO水平升高的患者1年全因死亡率高于未升高的患者(16.5% vs. 5.2%, P < 0.001)。在各种调整模型中,CO水平升高与1年全因死亡独立相关:合并症[优势比(95%置信区间):3.6(1.7-7.5)]、住院严重程度临床参数[4.5(2.3-8.8)]和呼吸参数[6.3(3.1-12.8)]。升高的CO水平也与1年MAEs和院内MAEs的显著增加独立相关。与其他已知的预测因素相比,机器学习分析确定CO水平是不良事件最重要的预测因素之一。结论:这是第一个证明入院时呼气CO水平升高与心肌梗死住院患者1年全因死亡率、1年mace和住院MAEs风险增加独立相关的研究。
The prognostic value of expiratory carbon monoxide level for outcome prediction after myocardial infarction.
Aims: To assess the prognostic value of expiratory carbon monoxide (CO) levels in patients admitted for myocardial infarction (MI).
Methods and results: In this prospective study, expiratory CO levels were measured upon admission in consecutive patients hospitalized for MI across 39 centres. The primary outcome was 1-year all-cause death. Secondary outcomes included 1-year major adverse cardiac events (MACEs: cardiovascular death and recurrent MI) and in-hospital major adverse events (MAEs: death, severe ventricular arrhythmia, cardiogenic shock, and the need for mechanical ventilation). The prognostic value of expiratory CO levels was further evaluated using machine learning (ML) analysis. Among 717 patients (64 ± 13 years; 75% males; 33% active smokers; 43% ST-elevation MI), elevated expiratory CO levels (>11 ppm) were found in 79 patients (11%). Patients with elevated CO levels had a higher rate of 1-year all-cause mortality compared with those without (16.5% vs. 5.2%, P < 0.001). Elevated CO levels were independently associated with 1-year all-cause death across various adjustment models: comorbidities [odds ratio (95% confidence interval): 3.6 (1.7-7.5)], clinical parameters of in-hospital severity [4.5 (2.3-8.8)], and respiratory parameters [6.3 (3.1-12.8)]. Elevated CO levels were also independently associated with a significant increase in 1-year MACEs and in-hospital MAEs. Machine learning analysis identified CO level as one of the most important predictors of adverse events, compared with other known prognosticators.
Conclusion: This is the first study to demonstrate that elevated expiratory CO levels upon admission are independently associated with an increased risk of 1-year all-cause mortality, 1-year MACEs, and in-hospital MAEs in patients hospitalized for MI.
期刊介绍:
European Journal of Preventive Cardiology (EJPC) is an official journal of the European Society of Cardiology (ESC) and the European Association of Preventive Cardiology (EAPC). The journal covers a wide range of scientific, clinical, and public health disciplines related to cardiovascular disease prevention, risk factor management, cardiovascular rehabilitation, population science and public health, and exercise physiology. The categories covered by the journal include classical risk factors and treatment, lifestyle risk factors, non-modifiable cardiovascular risk factors, cardiovascular conditions, concomitant pathological conditions, sport cardiology, diagnostic tests, care settings, epidemiology, pharmacology and pharmacotherapy, machine learning, and artificial intelligence.