Pub Date : 2025-04-08DOI: 10.1016/j.chstcc.2025.100158
Anna K. Barker MD, PhD , Emily A. Harlan MD , Meeta Prasad Kerlin MD, MSCE , Thomas S. Valley MD , Michael W. Sjoding MD
Background
Prone positioning is underused, despite mortality benefits. Prior studies highlight that patient-independent factors may influence prone positioning rates, but attending-specific contributions are unknown.
Research Question
Does significant variability in prone positioning rates exist among attending physicians?
Study Design and Methods
This is a retrospective cohort study of 514 adults receiving mechanical ventilation in a tertiary-care medical or surgical ICU from January 1, 2015, through June 30, 2024. Inclusion criteria included Pao2 to Fio2 ratio of ≤ 150 with Fio2 of ≥ 60% and positive end-expiratory pressure of ≥ 5 cm H2O within 0 to 36 hours and 36 to 72 hours of intubation. The primary outcome was prone positioning within 72 hours of intubation or 24 hours of meeting prone positioning criteria. We hypothesized that attending variability was a significant predictor of prone positioning. We fit a mixed-effects logistic regression model to evaluate attending-level variability in prone positioning use, adjusting for 6 potential patient-centered prone positioning barriers and facilitators (age, BMI, COVID-19 status, code status, Pao2 to Fio2 ratio, and vasopressor use) and ICU location (medical or surgical).
Results
Among 514 patients eligible for prone positioning, 87 patients (17%) underwent prone positioning. Significant attending-level variability in prone positioning was noted among the 48 attendings included in the analysis, with risk- and reliability-adjusted rates ranging from 14.9% to 74.2% and a median OR among attending physicians of 2.6 (95% CI, 1.7-5.2). This effect size was associated more strongly with prone positioning than a 30-mm Hg decrease in Pao2 to Fio2 ratio. Even among patients with clinical documentation of ARDS on the day of prone positioning eligibility, the median OR among attending physicians was 2.4 (95% CI, 1.5-7.3). Additional patient factors predicting prone positioning included COVID-19 status, code status, and Pao2 to Fio2 ratio.
Interpretation
Our results show that large variation in prone positioning practices exists among attending providers, and future work should consider attending-focused and system-wide interventions as potential novel targets to improve prone positioning rates.
{"title":"Role of Attending Practice Variability in Prone Positioning Initiation","authors":"Anna K. Barker MD, PhD , Emily A. Harlan MD , Meeta Prasad Kerlin MD, MSCE , Thomas S. Valley MD , Michael W. Sjoding MD","doi":"10.1016/j.chstcc.2025.100158","DOIUrl":"10.1016/j.chstcc.2025.100158","url":null,"abstract":"<div><h3>Background</h3><div>Prone positioning is underused, despite mortality benefits. Prior studies highlight that patient-independent factors may influence prone positioning rates, but attending-specific contributions are unknown.</div></div><div><h3>Research Question</h3><div>Does significant variability in prone positioning rates exist among attending physicians?</div></div><div><h3>Study Design and Methods</h3><div>This is a retrospective cohort study of 514 adults receiving mechanical ventilation in a tertiary-care medical or surgical ICU from January 1, 2015, through June 30, 2024. Inclusion criteria included Pa<span>o</span><sub>2</sub> to F<span>io</span><sub>2</sub> ratio of ≤ 150 with F<span>io</span><sub>2</sub> of ≥ 60% and positive end-expiratory pressure of ≥ 5 cm H<sub>2</sub>O within 0 to 36 hours and 36 to 72 hours of intubation. The primary outcome was prone positioning within 72 hours of intubation or 24 hours of meeting prone positioning criteria. We hypothesized that attending variability was a significant predictor of prone positioning. We fit a mixed-effects logistic regression model to evaluate attending-level variability in prone positioning use, adjusting for 6 potential patient-centered prone positioning barriers and facilitators (age, BMI, COVID-19 status, code status, Pa<span>o</span><sub>2</sub> to F<span>io</span><sub>2</sub> ratio, and vasopressor use) and ICU location (medical or surgical).</div></div><div><h3>Results</h3><div>Among 514 patients eligible for prone positioning, 87 patients (17%) underwent prone positioning. Significant attending-level variability in prone positioning was noted among the 48 attendings included in the analysis, with risk- and reliability-adjusted rates ranging from 14.9% to 74.2% and a median OR among attending physicians of 2.6 (95% CI, 1.7-5.2). This effect size was associated more strongly with prone positioning than a 30-mm Hg decrease in Pa<span>o</span><sub>2</sub> to F<span>io</span><sub>2</sub> ratio. Even among patients with clinical documentation of ARDS on the day of prone positioning eligibility, the median OR among attending physicians was 2.4 (95% CI, 1.5-7.3). Additional patient factors predicting prone positioning included COVID-19 status, code status, and Pa<span>o</span><sub>2</sub> to F<span>io</span><sub>2</sub> ratio.</div></div><div><h3>Interpretation</h3><div>Our results show that large variation in prone positioning practices exists among attending providers, and future work should consider attending-focused and system-wide interventions as potential novel targets to improve prone positioning rates.</div></div>","PeriodicalId":93934,"journal":{"name":"CHEST critical care","volume":"3 3","pages":"Article 100158"},"PeriodicalIF":0.0,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144654096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-03DOI: 10.1016/j.chstcc.2025.100155
Whitney A. Kiker MD , Si Cheng PhD , Erin K. Kross MD , Joseph E. Tonna MD , Claire J. Creutzfeldt MD , Jenelle Badulak MD , Daniel Brodie MD
Background
Predictors of death resulting from extracorporeal membrane oxygenation (ECMO) withdrawal, in-hospital death after ECMO liberation, and survival to hospital discharge have been evaluated incompletely, despite the prognostic insight they provide.
Research Question
What are the predictors of 3 vital outcomes after venoarterial ECMO: (1) death in the context of ECMO withdrawal, (2) ECMO liberation followed by in-hospital death, and (3) survival to hospital discharge?
Study Design
This retrospective observational study using Extracorporeal Life Support Organization registry data included adults supported by venoarterial ECMO from 2018 through 2022 at 325 North American sites. Three generalized linear mixed models (each comparing 2 outcomes) measured associations between predictors and outcomes, using random intercepts to address data clustering by site.
Results
Of 23,177 patients, 10,122 patients (43.7%) died in the context of ECMO withdrawal, 3,510 patients (15.1%) died in the hospital after ECMO liberation, and 9,545 patients (41.2%) survived to hospital discharge. Statistical analysis was performed for 16,277 patients supported for ≥ 24 hours with complete data available (32.5% female; mean age, 55.7 years; and 62.7% White, 16.4% Black, 6.1% Hispanic, and 3.2% Asian). Older age, higher BMI, cardiac arrest before ECMO initiation, and renal failure were associated with increased odds of death in the context of ECMO withdrawal and death after liberation compared with survival. Higher pH and male sex also were associated with increased odds of survival relative to withdrawal. Among decedents, death in the context of ECMO withdrawal was less common than death after ECMO liberation when patients were male and pH was higher.
Interpretation
Patients who were older, had higher BMI, or experienced cardiac arrest or renal failure before ECMO initiation seemed to have increased risk of in-hospital death, both in the context of ECMO withdrawal and after ECMO liberation. Male individuals were shown to be less likely to experience ECMO withdrawal. These findings offer prognostic associations that may inform how to support patients and families after ECMO initiation.
背景：退出体外膜氧合（ECMO）导致的死亡、ECMO解除后的院内死亡以及存活至出院的预测因素一直没有得到完整的评估，尽管它们提供了预后见解。研究问题：静脉动脉ECMO后3个重要结局的预测因素是什么：(1)退出ECMO后死亡，(2)ECMO解放后院内死亡，(3)存活至出院？这项回顾性观察性研究使用了体外生命支持组织（Extracorporeal Life Support Organization）注册数据，包括2018年至2022年在北美325个地点接受静脉ECMO支持的成年人。三个广义线性混合模型（每个模型比较2个结果）测量预测因子和结果之间的关联，使用随机截距来解决按地点的数据聚类问题。结果23177例患者中，10122例（43.7%）患者在退出ECMO时死亡，3510例（15.1%）患者在ECMO解除后院内死亡，9545例（41.2%）患者存活至出院。对16277例支持≥24小时且数据完整的患者进行统计分析(32.5%为女性；平均年龄55.7岁；白人62.7%，黑人16.4%，西班牙裔6.1%，亚裔3.2%)。与生存相比，年龄较大、BMI较高、ECMO启动前心脏骤停和肾功能衰竭与退出ECMO时死亡和解放后死亡的几率增加有关。相对于停药，较高的pH值和男性也与更高的生存几率有关。在死者中，当患者为男性且pH较高时，退出ECMO后的死亡比解除ECMO后的死亡更少。年龄较大、BMI较高或在ECMO开始前经历过心脏骤停或肾功能衰竭的患者，无论是在ECMO退出的情况下还是在ECMO解除后，院内死亡的风险似乎都增加。男性个体被证明不太可能经历ECMO退出。这些发现提供了预后关联，可以告知如何在ECMO启动后支持患者和家属。
{"title":"Prevalence and Predictors of 3 Vital Outcomes After Venoarterial Extracorporeal Membrane Oxygenation","authors":"Whitney A. Kiker MD , Si Cheng PhD , Erin K. Kross MD , Joseph E. Tonna MD , Claire J. Creutzfeldt MD , Jenelle Badulak MD , Daniel Brodie MD","doi":"10.1016/j.chstcc.2025.100155","DOIUrl":"10.1016/j.chstcc.2025.100155","url":null,"abstract":"<div><h3>Background</h3><div>Predictors of death resulting from extracorporeal membrane oxygenation (ECMO) withdrawal, in-hospital death after ECMO liberation, and survival to hospital discharge have been evaluated incompletely, despite the prognostic insight they provide.</div></div><div><h3>Research Question</h3><div>What are the predictors of 3 vital outcomes after venoarterial ECMO: (1) death in the context of ECMO withdrawal, (2) ECMO liberation followed by in-hospital death, and (3) survival to hospital discharge?</div></div><div><h3>Study Design</h3><div>This retrospective observational study using Extracorporeal Life Support Organization registry data included adults supported by venoarterial ECMO from 2018 through 2022 at 325 North American sites. Three generalized linear mixed models (each comparing 2 outcomes) measured associations between predictors and outcomes, using random intercepts to address data clustering by site.</div></div><div><h3>Results</h3><div>Of 23,177 patients, 10,122 patients (43.7%) died in the context of ECMO withdrawal, 3,510 patients (15.1%) died in the hospital after ECMO liberation, and 9,545 patients (41.2%) survived to hospital discharge. Statistical analysis was performed for 16,277 patients supported for ≥ 24 hours with complete data available (32.5% female; mean age, 55.7 years; and 62.7% White, 16.4% Black, 6.1% Hispanic, and 3.2% Asian). Older age, higher BMI, cardiac arrest before ECMO initiation, and renal failure were associated with increased odds of death in the context of ECMO withdrawal and death after liberation compared with survival. Higher pH and male sex also were associated with increased odds of survival relative to withdrawal. Among decedents, death in the context of ECMO withdrawal was less common than death after ECMO liberation when patients were male and pH was higher.</div></div><div><h3>Interpretation</h3><div>Patients who were older, had higher BMI, or experienced cardiac arrest or renal failure before ECMO initiation seemed to have increased risk of in-hospital death, both in the context of ECMO withdrawal and after ECMO liberation. Male individuals were shown to be less likely to experience ECMO withdrawal. These findings offer prognostic associations that may inform how to support patients and families after ECMO initiation.</div></div>","PeriodicalId":93934,"journal":{"name":"CHEST critical care","volume":"3 3","pages":"Article 100155"},"PeriodicalIF":0.0,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144830198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-18DOI: 10.1016/j.chstcc.2025.100153
Andrea Castellví-Font MD , Tai Pham MD, PhD , Bhakti Patel MD , Eddy Fan MD, PhD
Professor Luciano Gattinoni’s contributions to critical care medicine transformed the management of ARDS and mechanical ventilation, shaping the foundation of modern intensive care. Among his landmark achievements, the so-called baby lung concept redefined ARDS as a condition characterized by reduced functional lung volume, rather than lung stiffness, leading to the development of lung-protective ventilation strategies that prioritize minimizing ventilator-induced lung injury. His work on positive end-expiratory pressure advanced the understanding of lung aeration, atelectasis, and recruitment, highlighting the role of CT imaging in respiratory research. His research on prone positioning elucidated its physiologic benefits and demonstrated its lifesaving potential for patients with severe ARDS, culminating in its widespread adoption. Additionally, his work on mechanical power provided a unifying framework for assessing ventilator-induced lung injury risk, although challenges in its bedside application remain. Through his relentless pursuit of integrating respiratory physiology into clinical practice, Professor Gattinoni inspired generations of clinicians and researchers, leaving an indelible legacy that continues to guide advancements in critical care worldwide.
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Pub Date : 2025-03-18DOI: 10.1016/j.chstcc.2025.100152
Tessa A. Mulder MD , Linda Becude MD , Jorge E. Lopez Matta MD , Wilbert B. van den Hout PhD , David J. van Westerloo MD, PhD , Martijn P. Bauer MD, PhD
{"title":"Response","authors":"Tessa A. Mulder MD , Linda Becude MD , Jorge E. Lopez Matta MD , Wilbert B. van den Hout PhD , David J. van Westerloo MD, PhD , Martijn P. Bauer MD, PhD","doi":"10.1016/j.chstcc.2025.100152","DOIUrl":"10.1016/j.chstcc.2025.100152","url":null,"abstract":"","PeriodicalId":93934,"journal":{"name":"CHEST critical care","volume":"3 2","pages":"Article 100152"},"PeriodicalIF":0.0,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143943174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-18DOI: 10.1016/j.chstcc.2025.100150
V. Eric Kerchberger MD , J. Brennan McNeil BS , Neil Zheng MD , Diana Chang PhD , Carrie M. Rosenberger PhD , Angela J. Rogers MD , Julie A. Bastarache MD , QiPing Feng PhD , Wei-Qi Wei MD, PhD , Lorraine B. Ware MD
Background
Large population-based DNA biobanks linked to electronic health records (EHRs) may provide novel opportunities to identify genetic drivers of ARDS.
Research Question
Can a computerized algorithm identify ARDS in a large EHR biobank database, and can this be used to identify ARDS genetic risk factors?
Study Design and Methods
We developed a classifier algorithm to identify a diagnosis of ARDS as identified from the electronic health record (EHR-ARDS) using diagnostic billing codes, laboratory test results, and chest radiography report text. The classifier model performance was evaluated against investigator-adjudicated ARDS using standard classification metrics including sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and the Cohen κ value. After confirming acceptable classifier performance, we evaluated the association between EHR-ARDS and the MUC5B promoter polymorphism rs35705950 in 2 parallel genotyped cohorts: a prospective biomarker cohort of critically ill adults (Validating Acute Lung Injury Biomarkers for Diagnosis [VALID]) and a retrospective cohort from our institution’s de-identified EHR biobank, BioVU.
Results
We included 2,795 patients from VALID and 9,025 hospitalized participants from BioVU. EHR-ARDS showed moderate agreement with investigator-adjudicated ARDS (VALID: sensitivity, 0.86; specificity, 0.70; PPV, 0.49; NPV, 0.93; and κ, 0.45; BioVU: sensitivity, 0.94; specificity, 0.81; PPV, 0.66; NPV, 0.97; and κ, 0.67). We observed a significant age-gene interaction effect for EHR-ARDS in VALID: among older patients, rs35705950 was associated with increased EHR-ARDS risk (OR, 1.37; 95% CI, 1.05-1.78; P = .019), whereas among younger patients, this association was absent (OR, 0.92; 95% CI, 0.70-1.21; P = .55). In BioVU, rs35705950 was associated with EHR-ARDS among all participants (OR, 1.20; 95% CI, 1.01-1.43; P = .043); however, this effect did not vary by age.
Interpretation
The MUC5B promoter polymorphism was associated with EHR-ARDS in 2 parallel cohorts of at-risk adults. An age-gene effect modification was observed in VALID, whereas BioVU identified a consistent association between MUC5B and EHR-ARDS regardless of age. Our study highlights the potential for EHR biobanks to enable precision medicine ARDS studies.
{"title":"A Computable Electronic Health Record ARDS Classifier and the Association Between the MUC5B Promoter Polymorphism and ARDS in Critically Ill Adults","authors":"V. Eric Kerchberger MD , J. Brennan McNeil BS , Neil Zheng MD , Diana Chang PhD , Carrie M. Rosenberger PhD , Angela J. Rogers MD , Julie A. Bastarache MD , QiPing Feng PhD , Wei-Qi Wei MD, PhD , Lorraine B. Ware MD","doi":"10.1016/j.chstcc.2025.100150","DOIUrl":"10.1016/j.chstcc.2025.100150","url":null,"abstract":"<div><h3>Background</h3><div>Large population-based DNA biobanks linked to electronic health records (EHRs) may provide novel opportunities to identify genetic drivers of ARDS.</div></div><div><h3>Research Question</h3><div>Can a computerized algorithm identify ARDS in a large EHR biobank database, and can this be used to identify ARDS genetic risk factors?</div></div><div><h3>Study Design and Methods</h3><div>We developed a classifier algorithm to identify a diagnosis of ARDS as identified from the electronic health record (EHR-ARDS) using diagnostic billing codes, laboratory test results, and chest radiography report text. The classifier model performance was evaluated against investigator-adjudicated ARDS using standard classification metrics including sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and the Cohen κ value. After confirming acceptable classifier performance, we evaluated the association between EHR-ARDS and the <em>MUC5B</em> promoter polymorphism rs35705950 in 2 parallel genotyped cohorts: a prospective biomarker cohort of critically ill adults (Validating Acute Lung Injury Biomarkers for Diagnosis [VALID]) and a retrospective cohort from our institution’s de-identified EHR biobank, BioVU.</div></div><div><h3>Results</h3><div>We included 2,795 patients from VALID and 9,025 hospitalized participants from BioVU. EHR-ARDS showed moderate agreement with investigator-adjudicated ARDS (VALID: sensitivity, 0.86; specificity, 0.70; PPV, 0.49; NPV, 0.93; and κ, 0.45; BioVU: sensitivity, 0.94; specificity, 0.81; PPV, 0.66; NPV, 0.97; and κ, 0.67). We observed a significant age-gene interaction effect for EHR-ARDS in VALID: among older patients, rs35705950 was associated with increased EHR-ARDS risk (OR, 1.37; 95% CI, 1.05-1.78; <em>P</em> = .019), whereas among younger patients, this association was absent (OR, 0.92; 95% CI, 0.70-1.21; <em>P</em> = .55). In BioVU, rs35705950 was associated with EHR-ARDS among all participants (OR, 1.20; 95% CI, 1.01-1.43; <em>P</em> = .043); however, this effect did not vary by age.</div></div><div><h3>Interpretation</h3><div>The <em>MUC5B</em> promoter polymorphism was associated with EHR-ARDS in 2 parallel cohorts of at-risk adults. An age-gene effect modification was observed in VALID, whereas BioVU identified a consistent association between <em>MUC5B</em> and EHR-ARDS regardless of age. Our study highlights the potential for EHR biobanks to enable precision medicine ARDS studies.</div></div>","PeriodicalId":93934,"journal":{"name":"CHEST critical care","volume":"3 3","pages":"Article 100150"},"PeriodicalIF":0.0,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144654097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-18DOI: 10.1016/j.chstcc.2025.100151
Yonatan Y. Greenstein MD, FCCP, Keith Guevarra DO, FCCP
{"title":"The SONIC CENTRAL Study Does Not See the Forest for the Trees","authors":"Yonatan Y. Greenstein MD, FCCP, Keith Guevarra DO, FCCP","doi":"10.1016/j.chstcc.2025.100151","DOIUrl":"10.1016/j.chstcc.2025.100151","url":null,"abstract":"","PeriodicalId":93934,"journal":{"name":"CHEST critical care","volume":"3 2","pages":"Article 100151"},"PeriodicalIF":0.0,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143943113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
High respiratory effort may be common in invasively ventilated patients receiving pressure support ventilation, but its epidemiologic characteristics are unclear.
Research Question
What are the epidemiologic characteristics of high respiratory efforts in critically ill patients, does agreement exist between high respiratory drive and high respiratory effort, what are clinician responses during such events, and what is the relationship between those with clinical parameters and outcomes?
Study Design and Methods
This clinician-masked, prospective, observational study in 2 centers measured the drop in airway pressure during the first 100 ms of an inspiratory effort with an occluded airway (P0.1), a validated noninvasive measure of respiratory drive, in patients receiving pressure support ventilation for > 24 hours. We also measured estimated respiratory muscle pressure (ePmusc), a validated surrogate of inspiratory effort. We measured ePmusc and P0.1 twice daily.
Results
Of 528 ventilated patients, 80 patients received pressure support ventilation for > 24 hours. Among them, 33 patients (41%) exhibited high respiratory effort, which was more common in COVID-19 ARDS, with 19 of such patients (58%) reached the predefined threshold vs 14 patients (27%) in the non-COVID-19 cohort (OR, 5.0; 95% CI, 1.9-14.9; P = .001). Moreover, 36% of P0.1 values were ≥ 4 cm H2O, indicating high respiratory drive. Moderate agreement was found between ePmusc and P0.1 measurements (intraclass correlation coefficient, 0.65), suggesting significant discrepancies between those 2 parameters. Clinician-directed management based on usual clinical observations (but masked to P0.1 and ePmusc) rarely changed in the presence of high respiratory effort. Higher ePmusc and its concomitant elevation with P0.1 were associated with worse blood gas parameters and respiratory mechanics. A concomitant elevation of both ePmusc and P0.1 was associated independently with a decreased likelihood of being alive and ventilator-free up to day 28 (OR, 0.26; 95% CI, 0.06-0.87; P = .037).
Interpretation
In this study, many critical care patients receiving invasive pressure support ventilation exhibited high respiratory efforts. In these patients, adjustments to ventilator settings were uncommon, despite association with worse clinical parameters and outcomes.
{"title":"High Respiratory Effort During Invasive Pressure Support Ventilation","authors":"Anis Chaba MD , Joanna W.Y. Chow MBBS , Atthaphong Phongphithakchai MD , Wisam Al-Bassam MD , Fumitaka Yanase PhD , Zachary O’Brien MBBS , Glenn Eastwood PhD , Ahmad Bassam MD , Stefanos Hadzakis MD , Sofia Spano MD , Akinori Maeda MD , Lucinda Roberts MD , Rinaldo Bellomo PhD , Ary Serpa Neto PhD","doi":"10.1016/j.chstcc.2025.100147","DOIUrl":"10.1016/j.chstcc.2025.100147","url":null,"abstract":"<div><h3>Background</h3><div>High respiratory effort may be common in invasively ventilated patients receiving pressure support ventilation, but its epidemiologic characteristics are unclear.</div></div><div><h3>Research Question</h3><div>What are the epidemiologic characteristics of high respiratory efforts in critically ill patients, does agreement exist between high respiratory drive and high respiratory effort, what are clinician responses during such events, and what is the relationship between those with clinical parameters and outcomes?</div></div><div><h3>Study Design and Methods</h3><div>This clinician-masked, prospective, observational study in 2 centers measured the drop in airway pressure during the first 100 ms of an inspiratory effort with an occluded airway (P<sub>0.1</sub>), a validated noninvasive measure of respiratory drive, in patients receiving pressure support ventilation for > 24 hours. We also measured estimated respiratory muscle pressure (<sub>e</sub>P<sub>musc</sub>), a validated surrogate of inspiratory effort. We measured <sub>e</sub>P<sub>musc</sub> and P<sub>0.1</sub> twice daily.</div></div><div><h3>Results</h3><div>Of 528 ventilated patients, 80 patients received pressure support ventilation for > 24 hours. Among them, 33 patients (41%) exhibited high respiratory effort, which was more common in COVID-19 ARDS, with 19 of such patients (58%) reached the predefined threshold vs 14 patients (27%) in the non-COVID-19 cohort (OR, 5.0; 95% CI, 1.9-14.9; <em>P</em> = .001). Moreover, 36% of P<sub>0.1</sub> values were ≥ 4 cm H<sub>2</sub>O, indicating high respiratory drive. Moderate agreement was found between <sub>e</sub>P<sub>musc</sub> and P<sub>0.1</sub> measurements (intraclass correlation coefficient, 0.65), suggesting significant discrepancies between those 2 parameters. Clinician-directed management based on usual clinical observations (but masked to P<sub>0.1</sub> and <sub>e</sub>P<sub>musc</sub>) rarely changed in the presence of high respiratory effort. Higher <sub>e</sub>P<sub>musc</sub> and its concomitant elevation with P<sub>0.1</sub> were associated with worse blood gas parameters and respiratory mechanics. A concomitant elevation of both <sub>e</sub>P<sub>musc</sub> and P<sub>0.1</sub> was associated independently with a decreased likelihood of being alive and ventilator-free up to day 28 (OR, 0.26; 95% CI, 0.06-0.87; <em>P</em> = .037).</div></div><div><h3>Interpretation</h3><div>In this study, many critical care patients receiving invasive pressure support ventilation exhibited high respiratory efforts. In these patients, adjustments to ventilator settings were uncommon, despite association with worse clinical parameters and outcomes.</div></div>","PeriodicalId":93934,"journal":{"name":"CHEST critical care","volume":"3 2","pages":"Article 100147"},"PeriodicalIF":0.0,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144146831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-05DOI: 10.1016/j.chstcc.2025.100145
Robin L. Goossen MD , Daan F.L. Filippini MD , Relin van Vliet MD , Laura A. Buiteman-Kruizinga RN, PhD , Markus W. Hollmann MD, PhD , Sheila N. Myatra MD , Ary Serpa Neto MD, PhD , Peter E. Spronk MD, PhD , Meta C.E. van der Woude MD, PhD , Marcus J. Schultz MD, PhD , David M.P. van Meenen MD, PhD , Frederique Paulus PhD , Lieuwe D.J. Bos MD, PhD , Practice of Ventilation and Adjunctive Therapies in ICU Patients With COVID-19 Investigators
Background
In patients with ARDS, positive end-expiratory pressure (PEEP) titration remains a challenge and recommendations are not in agreement. In mechanically ventilated patients with COVID-19, subphenotypes based on different respiratory trajectories have been identified, but their heterogeneity in response to PEEP/Fio2 strategy remains understudied.
Research Question
Can these previously determined subphenotypes be detected early in the course of mechanical ventilation, and do these subphenotypes moderate the association between PEEP and Fio2 ventilation strategy and mortality?
Study Design and Methods
Retrospective analysis of invasively ventilated patients with COVID-19. Patients were categorized into 2 treatment groups: high PEEP/low Fio2 strategy and low PEEP/high Fio2 strategy. To replicate previously described longitudinal respiratory subphenotypes, hereafter named the low-power or high-power subphenotype, a prediction model was created. The primary outcome was the interaction between PEEP/Fio2 strategy and subphenotype, with mortality as the dependent variable.
Results
Of the 1,464 patients included in this analysis, 361 patients (25%) were allocated into the high PEEP/low Fio2 strategy and 1,103 patients (75%) were allocated into the low PEEP/high Fio2 strategy. A prediction model consisting of respiratory data of the first 2 days of invasive ventilation (area under the receiver operating characteristics curve, 0.88) assigned 908 patients (62%) to the low-power subphenotype and 556 patients (38%) to the high-power subphenotype. The high-power subphenotype was characterized by higher minute volume, mechanical power, ventilatory ratio, and driving pressure. The association between PEEP/Fio2 ventilation strategy and ICU mortality was moderated by the subphenotype (P = .03), with high PEEP/low Fio2 ventilation being associated with lower mortality in the low-power subphenotype (OR, 0.46; 95% CI, 0.31-0.67; P < .001) and not in the high-power subphenotype (OR, 0.85; 95% CI, 0.57-1.28; P = .44).
Interpretation
In this study, high PEEP/low Fio2 ventilation was associated with improved mortality only in one of the subphenotypes, suggesting that such subphenotypes influence heterogeneity of PEEP and Fio2 effect and should be considered in personalized ventilation strategies.
{"title":"Longitudinal Respiratory Subphenotypes and Differences in Response to Positive End-Expiratory Pressure and Fio2 Ventilation Strategy in COVID-19 ARDS","authors":"Robin L. Goossen MD , Daan F.L. Filippini MD , Relin van Vliet MD , Laura A. Buiteman-Kruizinga RN, PhD , Markus W. Hollmann MD, PhD , Sheila N. Myatra MD , Ary Serpa Neto MD, PhD , Peter E. Spronk MD, PhD , Meta C.E. van der Woude MD, PhD , Marcus J. Schultz MD, PhD , David M.P. van Meenen MD, PhD , Frederique Paulus PhD , Lieuwe D.J. Bos MD, PhD , Practice of Ventilation and Adjunctive Therapies in ICU Patients With COVID-19 Investigators","doi":"10.1016/j.chstcc.2025.100145","DOIUrl":"10.1016/j.chstcc.2025.100145","url":null,"abstract":"<div><h3>Background</h3><div>In patients with ARDS, positive end-expiratory pressure (PEEP) titration remains a challenge and recommendations are not in agreement. In mechanically ventilated patients with COVID-19, subphenotypes based on different respiratory trajectories have been identified, but their heterogeneity in response to PEEP/F<span>io</span><sub>2</sub> strategy remains understudied.</div></div><div><h3>Research Question</h3><div>Can these previously determined subphenotypes be detected early in the course of mechanical ventilation, and do these subphenotypes moderate the association between PEEP and F<span>io</span><sub>2</sub> ventilation strategy and mortality?</div></div><div><h3>Study Design and Methods</h3><div>Retrospective analysis of invasively ventilated patients with COVID-19. Patients were categorized into 2 treatment groups: high PEEP/low F<span>io</span><sub>2</sub> strategy and low PEEP/high F<span>io</span><sub>2</sub> strategy. To replicate previously described longitudinal respiratory subphenotypes, hereafter named the <em>low-power</em> or <em>high-power</em> subphenotype, a prediction model was created. The primary outcome was the interaction between PEEP/F<span>io</span><sub>2</sub> strategy and subphenotype, with mortality as the dependent variable.</div></div><div><h3>Results</h3><div>Of the 1,464 patients included in this analysis, 361 patients (25%) were allocated into the high PEEP/low F<span>io</span><sub>2</sub> strategy and 1,103 patients (75%) were allocated into the low PEEP/high F<span>io</span><sub>2</sub> strategy. A prediction model consisting of respiratory data of the first 2 days of invasive ventilation (area under the receiver operating characteristics curve, 0.88) assigned 908 patients (62%) to the low-power subphenotype and 556 patients (38%) to the high-power subphenotype. The high-power subphenotype was characterized by higher minute volume, mechanical power, ventilatory ratio, and driving pressure. The association between PEEP/F<span>io</span><sub>2</sub> ventilation strategy and ICU mortality was moderated by the subphenotype (<em>P = .</em>03), with high PEEP/low F<span>io</span><sub>2</sub> ventilation being associated with lower mortality in the low-power subphenotype (OR, 0.46; 95% CI, 0.31-0.67; <em>P < .</em>001) and not in the high-power subphenotype (OR, 0.85; 95% CI, 0.57-1.28; <em>P = .</em>44).</div></div><div><h3>Interpretation</h3><div>In this study, high PEEP/low F<span>io</span><sub>2</sub> ventilation was associated with improved mortality only in one of the subphenotypes, suggesting that such subphenotypes influence heterogeneity of PEEP and F<span>io</span><sub>2</sub> effect and should be considered in personalized ventilation strategies.</div></div><div><h3>Clinical Trial Registry</h3><div><span><span>ClinicalTrials.gov</span><svg><path></path></svg></span>; No.: NCT05954351; URL: <span><span>www.clinicaltrials.gov</span><svg><path></path></svg></span></div></div>","PeriodicalId":93934,"journal":{"name":"CHEST critical care","volume":"3 2","pages":"Article 100145"},"PeriodicalIF":0.0,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144166487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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