Expiratory Efforts During Insufflation are Associated with Increased Mortality in Ventilated Patients

Expiratory Efforts During Insufflation are Associated with Increased Mortality in Ventilated Patients | 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 Expiratory Efforts During Insufflation are Associated with Increased Mortality in Ventilated Patients Guillermo Gutierrez, MD, PhD, Hülya Türkan, MD, PhD This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4252169/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Breathing efforts during mechanical ventilation are associated with patient self-induced lung injury (P-SILI). We examined whether a noninvasive measure of P mus , the portion of airway pressure attributed to breathing effort during insufflation, relates to patient mortality. Methods We analyzed recorded airway signals from 267 patients on invasive mechanical ventilation monitored between six hours and five days. Patients were divided into survivor and decedent groups according to all-cause 28-day mortality. Individual P mus (t) functions, describing changes in P mus during insufflation, were generated for 13.4 million insufflations by numerical analysis of the respiratory system’s one-compartment model. P mus (t) was used to determine the magnitude and direction, expiratory or inspiratory, of peak P mus (t) (P mus Peak) and its pressure-time product (P mus PTP). Mean and cumulative P mus Peak and P mus PTP were determined for each patient and compared between the groups. Results There were 67 decedents and 200 survivors. Decedents had greater mean and cumulative expiratory P mus PTP (p < 0.05 for each) than survivors. Neither inspiratory P mus PTP nor P mus Peak differentiated between the groups. Independent predictors of mortality were age, SAPS II score, and expiratory time. Discussion We report an association between expiratory efforts during insufflation and 28-day mortality. By opposing ventilator-delivered breaths, expiratory efforts might increase alveolar pressure (P alv ), promoting P-SILI and subsequent worse outcomes. The apparent lack of association between mortality and inspiratory effort might be explained by its capacity to increase trans-pulmonary pressure without affecting P alv . Inspiratory efforts, however, could indicate air hunger. Conclusions Our findings highlight the need for further research into respiratory efforts during mechanical ventilation. Critical Care & Emergency Medicine Biomedical Engineering Pulmonology Physiology Mechanical ventilation breathing efforts acute respiratory failure static compliance dyspnea airway resistance numerical analysis Figures Figure 1 Figure 2 Figure 3 Take-home message Respiratory efforts during insufflation were calculated noninvasively from airway signals in a cohort of patients treated with invasive, positive pressure ventilation. Expiratory efforts, characterized by the pressure-time product, were associated with greater 28-day mortality rates. Conversely, inspiratory efforts were not linked to mortality, but may indicate the need for greater ventilatory support. Introduction Spontaneous breathing during mechanical ventilation has been shown to prevent diaphragmatic atrophy and to improve arterial oxygenation [ 1 ]. Conversely, forceful respiratory muscle efforts might increase transpulmonary pressures (P L ) during insufflation with ensuing lung damage [ 2 , 3 ], a condition defined as patient self-induced lung injury (P-SILI) [ 4 ]. Respiratory muscle effort (P mus ) can be calculated as the difference between esophageal pressure measurements and estimates of passive chest wall recoil pressure [ 5 ]. In addition to the invasive nature of the esophageal catheter, variability in catheter placement and chest wall mechanics may produce inconsistent results across patients. Consequently, and despite their potential utility, esophageal catheters are seldom used [ 6 ]. It is desirable, therefore, to develop techniques capable of estimating P mus accurately and noninvasively. A recursive technique has been developed to determine the static compliance (C rs ) and inspiratory resistance (R rs ) of the respiratory system from analysis of the airway flow (F aw ) and pressure (P aw ) signals [ 7 ]. This numerical method is based on the single-compartment model of the respiratory system during positive pressure ventilation [ 8 , 9 ], $${ P}_{aw}\left(t\right)= \frac{\varDelta V\left(t\right)}{{C}_{rs}}+ {R}_{rs}{F}_{aw}\left(t\right)+ {PEEP}_{a}$$ 1 where ΔV(t) denotes increases in lung volume from functional residual capacity and PEEP a represents the applied positive end expiratory pressure. The model neglects the effect of gas inertia and intrinsic PEEP (PEEP i ) on P mus (t). Eqt. 1 can be extended to calculate P mus (t), a time-dependent function describing respiratory muscle effort during insufflation [ 10 ] , $${P}_{mus}\left(t\right)= {P}_{aw}\left(t\right)-\left[\frac{\varDelta V\left(t\right)}{{C}_{rs}}+ {R}_{rs}{F}_{aw}\left(t\right)+ {PEEP}_{a}\right]$$ 2 The solution to Eqt. 2 requires prior knowledge of C rs and R rs , which can be determined numerically [ 7 ] using data collected from breaths known to be devoid of muscular effort (P mus (t) = 0). The expression enclosed in brackets in Eqt. 2 indicates the airway pressure needed for passive inflation of the respiratory system, $${P}_{mus}\left(t\right)= {P}_{aw}\left(t\right)- {P}_{passive}\left(t\right)$$ 3 According to Eqt. 3, positive values of P mus (t) signify expiratory efforts, whereas negative values indicate inspiratory efforts. The model assumes that C rs and R rs remain constant during insufflation [ 8 ], although it allows for longitudinal variations in these parameters due to treatment effects and disease progression. We applied the model of Eqt. 2 to extensive recordings of P aw (t) and F aw (t) signals obtained from patients on invasive, positive pressure mechanically ventilation. We calculated P mus (t) numerically for each recorded insufflation within this database and used it to determine its pressure-time product (P mus PTP) and peak values (P mus Peak). Our aim was to determine any potential association between all-cause 28-day mortality rate and respiratory muscle effort during insufflation, as characterized by P mus Peak and P mus PTP. Methods We analyzed a database of P aw (t) and F aw (t) recordings from patients treated with invasive, positive pressure ventilation at The George Washington University Hospital's intensive care unit from 2011 to 2018. These patients had been previously enrolled in studies approved by The George Washington University Institutional Review Board (IRB Nos.081311, 101228, 110910, and 111235) conducted in compliance with the 1964 Helsinki Declaration. Informed consent for participation in these studies was obtained from all patients, or their designated surrogates, with the use of anonymized data approved for future research. Patient population . The database included 323 patients enrolled within 24 hours of intubation and monitored during the duration of mechanical ventilation, ranging from two hours to 33 days. All patients were nasally or orotracheally intubated and ventilated using various modes of support with Servo_i or Servo_s ventilators (Getinge, Solna, Sweden). Clinicians not involved in the studies determined exclusively the manner of ventilation. We selected a priori from the database 267 patients who had been monitored for at least six hours, a period that provided adequate airway signal data for analysis. Data acquisition. P aw (t) and F aw (t) signals were sampled at 31.25 Hz with a proprietary Raspberry Pi 3B data collection system connected to the ventilator RS232 data port via a null DB9 cable. Data were segmented into sequential 2.2-minute-long epochs containing 4096 samples of each signal. Demographic data were recorded in physical notebooks that were destroyed after transferring nonidentifiable details to the database using a study number. Data processing and analysis. Custom software developed in Python 3.7 was used to process the selected cohort’s stored airway signal data. This software was designed to simulate the clinical monitoring of patients on mechanical ventilation. Excluded from analysis were epochs recorded on bi-level ventilation or airway pressure release ventilation (APRV). Data analysis spanned from the time of enrollment and the start of recording up to a maximum of five days of ventilatory support, or until the patient was weaned from the ventilator, whichever came first. All-cause mortality within 28 days of enrollment was confirmed through hospital and clinic records, and telephone interviews. Determination of P mus (t) function. Individual P mus (t) functions were generated for each recorded insufflation using Eqt. 2 and measurements of ΔV(k), F aw (k), and PEEP a obtained at sequential k points during the insufflation phase. The maximum positive and minimum negative values of each P mus (t) function were taken as the insufflation’s expiratory and inspiratory P mus Peak, respectively. P mus PTP was calculated by trapezoidal numerical integration of P mus (t) across the insufflation time. (See Supplementary Information Fig. 3e for examples of P mus (t) functions). Determination of mean and cumulative values (Fig. 1): The average expiratory and inspiratory P mus Peak and P mus PTP for all insufflations within an epoch were calculated and reported as the epoch's mean values. These were averaged over a 24-hour period to derive the daily mean values. The average of the daily means represented the patient’s P mus Peak and P mus PTP for the monitoring period. The sum of P mus Peak and P mus PTP for both expiratory and inspiratory phases were similarly compiled to evaluate the cumulative impact on 28-day mortality rate of repeated muscle effort. Other ventilatory parameters. P aw (t) and F aw (t) were used to determine each breath’s peak and mean P aw , PEEP a , mean inspired F aw , tidal volume (V T ), and inspiratory and expiratory times (Ti and Te, respectively). Driving pressure (ΔP) was computed as \(\frac{{V}_{T}}{{C}_{rs}}\) , using the static C rs derived from Eqt. 1. The plateau pressure (P plateau ) was calculated as ΔP + PEEP a , and dynamic compliance as (C dyn ) as \({C}_{dyn}= \frac{{V}_{T}}{Peak {P}_{aw }- {PEEP}_{a}}\) . All variables were averaged across all breaths within each epoch, and the average of all such epochs during the patient's ventilatory support was taken as the metric’s overall value. Mechanical power (MP) was calculated for each insufflation by trapezoidal integration of the pressure-volume curve. The sum of all MP calculations for a given epoch was multiplied by 4.45 x 10 − 2 (0.098/2.2 min/epoch) to report the epoch’s MP in J·min − 1 . The respiratory rate variability index (RRVI) for each epoch was determined by frequency analysis of the airway signals [ 11 ]. MP and RRVI were averaged across all the patient’s recorded epochs. Statistics. Data normality was evaluated with the Kolmogorov-Smirnov test and the Mann–Whitney test was used to determine significant differences between independent samples. The Benjamini–Yekutieli procedure [ 12 ] with a false discovery rate of 0.05 served to control for multiple comparisons. A logistic regression analysis was performed to predict 28-day all-cause mortality with initial predictors that included variables with univariate p 0.40 at each step. Unless otherwise specified, data are shown as median and interquartile range. All reported p values are two-sided with p < 0.05 considered significant. Results There were 200 survivors and 67 decedents in the chosen cohort. Monitoring times were 71[ 30,83] hours and 47 [25, 81] hours for decedents and survivors, respectively (p = 0.13). There were no differences in ethnicity (Table 1 e; Supplementary Information) or gender distribution (48% women). Decedents had greater prevalence of pneumonia and fewer required ICU admission for post-operative complications (Table 2 e; Supplementary Information). Table 1 Demographic Variables Univariate Differences Patients Decedents 67 Survivors 200 Total 267 Insufflations Analyzed 4,173,584 9,204,801 13,378,385 Epochs Analyzed 98,156 245,743 343,899 Demographics Statistics Age (years) 70 [59, 79] 58 [46, 70] < 0.01 BMI (kg·m − 2 ) 27 [23, 31] 28 [24, 33] N.S. SAPS II 53 [46, 62] 40 [32, 49] < 0.01 SOFA 7 [ 5 , 10 ] 6 [ 3 , 8 ] < 0.01 Measured Variables F I O 2 (%) 48 [41, 60] 41 [40, 49] < 0.01 Mean Inspired Airway Flow (L·min − 1 ) 54 [47, 60] 49 [43, 55] < 0.05 Peak Airway Pressure (cmH 2 O) 27 [23, 31] 25 [22, 29] N.S. Mean Airway Pressure (cmH 2 O) 12 [ 10 , 15 ] 10 [ 9 , 11 ] < 0.01 PEEP (cmH 2 O) 6 [ 5 , 7 ] 5 [ 5 , 6 ] < 0.05 Respiratory Rate (bpm) 19 [ 17 , 22 ] 16 [ 14 , 18 ] < 0.01 Tidal Volume/PBW (mL·kg − 1 ) 8.8 [7.8, 9.4] 8.6 [7.9, 10.0] N.S. Expiratory Time (s) 2.0 [1.5, 2.3] 2.7 [2.3, 3.1] < 0.01 Inspiratory Time (s) 1.1 [0.9, 1.2] 1.0 [0.9, 1.1] N.S. Calculated Variables Compliance - Dynamic (mL·cmH 2 O − 1 ) 29 [25, 37] 33 [28, 43] < 0.05 Compliance - Static (mL·cmH 2 O − 1 ) 35 [28, 52] 49 [38, 62] < 0.01 Plateau Pressure (cmH 2 O) 18 [ 15 , 22 ] 16 [ 13 , 18 ] < 0.01 Driving Pressure (cmH 2 O) 12 [ 10 , 16 ] 10 [ 8 , 12 ] < 0.01 Inspiratory Resistance (cmH 2 O·s·L − 1 ) 17 [ 12 , 20 ] 18 [ 14 , 21 ] N.S. Mechanical Power (J·min − 1 ) 21 [15, 27] 16 [ 13 , 20 ] < 0.01 RRVI (%) 41 [30, 48] 37 [31, 46] N.S. BMI body-mass index; SAPS II = Simplified Acute Physiology Score; SOFA = Sepsis related Organ Failure Assessment; RRVI = Respiratory Rate Variability Index. Statistics refer to Mann–Whitney test corrected with Benjamini-Yekutieli procedure for multiple comparisons. Table 2 Mean and Cumulative Respiratory Muscles Peak Pressures (PmusPeak) andPressure-Time Product (PmusPTP) According to 28-day Mortality p value Decedents n = 67 Survivors n = 200 Mann-Whitney Benjamini- Yekutieli Expiratory Efforts Mean P mus Peak (cmH 2 O) 7.1 [5.0, 9.2] 6.3 [4.7, 8.8] 0.420 N.S. Cumulative P mus Peak (cmH 2 O) 2,972 [1600, 3946] 2,355 [1586, 3405] 0.123 N.S. Mean P mus PTP (cmH 2 O·s) 11.8 [6.4, 18.3] 7.8 [5.4, 12.7] 0.004 < 0.05 Cumulative P mus PTP (cmH 2 O·s) 17,445 [5556, 31214] 8,838 [3604, 18190] 0.004 < 0.05 Inspiratory Efforts Mean P mus Peak (cmH 2 O) 3.3 [5.8, 2.5] 3.7 [5.0, 2.5] 0.961 N.S. Cumulative P mus Peak (cmH 2 O) 1,495 [835, 2114] 1,248 [881, 2089] 0.315 N.S. Mean P mus PTP (cmH 2 O·s) 4.8 [8.3, 1.6] 5.4 [9.8, 2.8] 0.084 N.S. Cumulative P mus PTP (cmH 2 O·s) 5,779 [1399,13764] 5,380 [2122, 11339] 0.801 N.S. We analyzed approximately 13.4 million insufflations contained within 343,899 epochs recorded during 600 monitoring days (Table 1 ). Each patient was monitored for an average of 2.2 days, encompassing 1,303 epochs with 50,676 insufflations. Decedents were older and had greater disease severity. Apart from peak P aw , V T , and Ti, all measured ventilator variables differed between the groups, with decedents exhibiting lower dynamic and static C rs , and higher ΔP and MP. No differences were noted in R rs or RRVI between the groups. These findings imply that patients in the decedent group were sicker and required greater ventilatory support. Patients in both groups received similar ventilation, with over 70% of epochs preferentially using pressure-regulated volume control (Table 4e; Supplementary Information). Figure 2 shows an example of an epoch obtained from a 76-year-old man with stridor on pressure support ventilation. Respiratory rate is variable with occasional double-triggering. The bottom plot shows P mus PTP as discrete values corresponding to the breaths above. These are mostly negative, indicating inhalation efforts. Figure 3 shows an epoch taken from an 89-year-old woman with aspiration pneumonia on pressure regulated volume control (PRVC). There is significant respiratory rate variability and asynchronous breathing with mostly expiratory breathing efforts during insufflation. (See Supplementary Information for additional epoch examples). Table 2 shows the mean and cumulative P mus Peak and P mus PTP during insufflation according to the respiratory effort directional phase, either expiratory or inspiratory. Decedents had greater mean and cumulative expiratory P mus PTP than survivors (p < 0.05 for each). On the other hand, there were no differences between the groups in either mean or cumulative inspiratory P mus PTP values. Similarly, no differences were noted in expiratory or inspiratory P mus Peak, mean or cumulative, between the groups. Predictors independently associated with 28-day mortality were age (OR 1.03; 1.01–1.06; p = 0.0157), Te (OR 0.36; 0.21–0.52; p = 0.0003), and SAPS II (OR 1.05; 1.02–1.08; p = 0.0009). Not independently associated with mortality were ΔP, MP, SOFA, PEEP, static and dynamic compliance and expiratory mean or cumulative P mus PTP (Table 3e; Supplementary Information). Discussion We applied a recursive numerical method to a comprehensive database of airway flow and pressure signals recorded from 267 patients treated with invasive, positive pressure ventilation for a maximum of five days. Our aim was to assess the feasibility of quantifying P mus and P mus PTP non-invasively and to determine their possible association to all-cause 28-day mortality rate. Our analysis revealed a significant direct association between expiratory efforts during insufflation and the cohort’s 28-day mortality rate. We hypothesize that sudden increases in alveolar pressure (P alv ), caused by the counteractive effect of expiratory efforts opposing ventilator-delivered breaths, might lead to injurious lung tissue strain [ 2 ], potentially resulting in poorer outcomes. This hypothesis supports the concept of early intervention in ARDS, aiming to reduce expiratory efforts through the use of paralytic agents [ 13 ]. We also noted that inspired P mus PTP was not linked to 28-day mortality. We suggest that inspiratory efforts during insufflation may work in tandem with the ventilator, leading to increased transpulmonary pressure and tidal volume without a corresponding increase in P alv . Inspiratory efforts may occur in patients experiencing air hunger [ 14 ], a psychologically distressing condition [ 15 ] experienced by nearly half of mechanically ventilated patients [ 16 ]. In contrast to P mus PTP, neither inspiratory nor expiratory P mus Peak were associated with 28-day mortality. P mus Peak measures the effort of respiratory muscles at a specific moment, whereas P mus PTP evaluates the cumulative effect of P mus (t) throughout the insufflation period, offering a more comprehensive measure of breathing effort. P mus PTP has been demonstrated to be an effective metric of respiratory effort [ 17 ] and of diaphragmatic energy expenditure [ 18 ]. Neither P mus nor P mus PTP were identified as independent predictors of mortality. Moreover, and contrasting with prior studies [ 19 , 20 ], neither were MP nor ΔP. The discrepancy might stem from the retrospective design of our study, the relatively limited size of our sample, and different methodologies used for calculating MP and ΔP. Instead of using a surrogate formula [ 21 ], we calculated MP by the geometric method and ΔP using the model-calculated static C rs , instead of the dynamic C rs . A possible confounding element in our study is the omission of intrinsic PEEP (PEEPi) when calculating P mus (t) with Eqt. 2. According to Eqt. 2, ignoring PEEPi may overestimate expiratory P mus while underestimating inspiratory P mus . Given the substantial number of insufflations analyzed, however, it is unlikely that the random occurrence of PEEPi would have significantly altered our results. Caution is advised in interpreting the results due to potential changes in treatment practices over the time spanning the collection of data. On the other hand, the robustness of our findings is enhanced by the substantial dataset employed and the methodology used for analyzing patient data, with software that simulated real-time bedside monitoring. This approach enhances the clinical relevance of our work and emphasizes the value of using large, high-fidelity airway signal datasets to gain novel insights into patient-ventilator dynamics. In summary, numerical analysis of airway signals revealed a significant link between expiratory effort during insufflation and 28-day mortality. This finding suggests that the opposing interaction of expiratory efforts and ventilator-provided insufflations is a plausible mechanism of lung injury leading to P-SILI and possibly adverse patient outcomes. On the other hand, inspiratory efforts, although not associated with mortality, could indicate the presence of air hunger. The contrasting effect of inspiratory and expiratory efforts highlights the nuanced role of respiratory muscle effort in determining patient outcome. Further research is necessary to validate our observations and possibly pinpoint a harmful threshold for expiratory muscle effort. List Of Abbreviations BMI = Body-mass index. C rs = Respiratory system static compliance. ΔP = Driving pressure ΔV(t) = Lung volume change during insufflation. F aw = Airway flow. MP = Mechanical power. P alv = Alveolar pressure. P aw = Airway pressure. PEEP a = Applied positive end expiratory pressure. PEEP i = Intrinsic PEEP at end expiration. P L = Transpulmonary pressure. P alv = Alveolar pressure. P mus = Portion of P aw attributed to respiratory muscles effort. P mus Peak = Highest expiratory or inspiratory P mus value. P mus PTP = P mus pressure time product. P mus (t) = Time dependent function describing P mus during insufflation. P passive = Airway pressure required to inflate the respiratory system passively. P peak = Peak inspiratory pressure. P plateau = Plateau pressure during the end-inspiratory hold. PRVC = Pressure regulated volume control. PS = Pressure support P-SILI = Patient self-induced lung injury. rs = Subscript denoting respiratory system. R rs = Respiratory system airway resistance. RRVI = Respiratory rate variability index. SAPS II = Simplified Acute Physiological Score. SOFA = Sofa Related Organ Failure. VC = Volume controlled. V(t) = Lung volume as a function of time. V T = Tidal volume Declarations Ethical Approval and Consent to participate : The database used in the present study was collected as part of various studies conducted from 2011 to 2018, which had received approval from The George Washington University Institutional Review Board (IRB Nos. 081311, 101228, 110910, 111235). These studies were in compliance with the 1964 Helsinki Declaration. Informed consent was obtained from the patients or their designated surrogates for participation in these studies. The IRB approved the use of anonymized data from these studies for future research purposes. Consent for publication: Not applicable. Availability of supporting data : The dataset analyzed during the current study can be found in the Electronic Data Repository. Access to the database storing the raw data may be granted to qualified researchers upon reasonable request and adequate vetting. Competing interests: One of the authors (GG) has applied for a U.S. patent based on the information presented in the paper. Funding: Not applicable. Authors' contributions: GG – Designed the study, supervised the data collection, wrote the software used to calculate muscle effort, analyzed and interpreted the data, wrote the manuscript and is accountable for all aspects of the work. HT - Made substantial contributions to the acquisition of data and reviewed the manuscript. Acknowledgments: The authors thank Dr. Hatice Kaya and Dr. Katherine Rider for their help in gathering much of the information recorded in the patient database. GG thanks the Commission for Educational Exchange between the United States, Belgium and Luxembourg and the Fulbright Scholarship Board. Their support as a Fulbright Research Scholar provided him the essential time to develop the ideas foundational to this research. HT thanks TÜBİTAK, the Scientific and Technological Research Council of Türkiye for their support as a Research Scholar at The George Washington University, Division of Pulmonary and Critical Care Medicine. Disclaimer: The content is solely the authors’ responsibility and does not necessarily represent the official views of The George Washington University. 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Serpa Neto A, Deliberato RO, Johnson AEW, Bos LD, Amorim P, Pereira SM, Cazati DC, Cordioli RL, Correa TD, Pollard TJ, Schettino GPP, Timenetsky KT, Celi LA, Pelosi P, Gama de Abreu M, Schultz MJ; PROVE Network Investigators (2018) Mechanical power of ventilation is associated with mortality in critically ill patients: an analysis of patients in two observational cohorts. Intensive Care Med. 44:1914-1922. doi: 10.1007/s00134-018-5375-6. Epub 2018 Oct 5. PMID: 30291378. Amato MB, Meade MO, Slutsky AS, Brochard L, Costa EL, Schoenfeld DA, Stewart TE, Briel M, Talmor D, Mercat A, Richard JC, Carvalho CR, Brower RG (2015) Driving pressure and survival in the acute respiratory distress syndrome. N Engl J Med. 372:747-755. doi: 10.1056/NEJMsa1410639. PMID: 25693014. Chiumello D, Gotti M, Guanziroli M, Formenti P, Umbrello M, Pasticci I, Mistraletti G, Busana M (2020) Bedside calculation of mechanical power during volume- and pressure-controlled mechanical ventilation. Crit Care. 24:417. doi: 10.1186/s13054-020-03116-w. PMID: 32653011; PMCID: PMC7351639. Additional Declarations The authors declare potential competing interests as follows: The corresponding author, Guillermo Gutierrez, MD, PhD has applied for a U.S. patent related to the contents of the manuscript. Supplementary Files GutierrezExpiratoryEffortsSupplementalI.docx Cite Share Download PDF Status: Posted Version 1 posted 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-4252169","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":290105409,"identity":"81eee318-6e65-4179-aa22-cd1cd7cd5cdb","order_by":0,"name":"Guillermo Gutierrez, MD, PhD","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsUlEQVRIiWNgGAWjYJCCAwwMNhAWD3EamEFa0kjUAgSHSdAiH5F/8MDHPeftNtxIYHzwto0ILYY3khkOznh2OxmohdlwLlFaZiQzHOY5cDvZ7EYCmzQv0Vr+HDgH0sL+mygt8hJALQwHDtiBbGEmSosBz2ODgz0HkhPszzxslpxzjhhb2hMff/hxwM5esj354Ic3ZcTYcgBCJzYwMDYQoR5kC1SdPXHKR8EoGAWjYEQCAMoGPBrYl3SYAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0001-7754-5816","institution":"The George Washington University, Washington, DC","correspondingAuthor":true,"prefix":"","firstName":"","middleName":"MD Guillermo","lastName":"Gutierrez","suffix":"MD"},{"id":290105410,"identity":"20e68e1e-297f-49a8-aaf1-99e0a0d9c8cb","order_by":1,"name":"Hülya Türkan, MD, PhD","email":"","orcid":"","institution":"Gulhane School of Medicine. Ankara, Turkey","correspondingAuthor":false,"prefix":"","firstName":"","middleName":"MD Hülya","lastName":"Türkan","suffix":"MD"}],"badges":[],"createdAt":"2024-04-11 11:35:48","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":true,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-4252169/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4252169/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54595766,"identity":"723d6fce-265a-4bdf-95d0-c28fe2d6ccf9","added_by":"auto","created_at":"2024-04-12 18:58:05","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":72275,"visible":true,"origin":"","legend":"\u003cp\u003eCalculation sequence for average and cumulative expiratory and inspiratory P\u003csub\u003emus\u003c/sub\u003ePeak (muscle pressure) and P\u003csub\u003emus\u003c/sub\u003ePTP (pressure-time product). \u0026nbsp;\u003c/p\u003e","description":"","filename":"GutierrezFigure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4252169/v1/ca91dd14024db37adf43f28b.jpg"},{"id":54597212,"identity":"55178da7-4eeb-47cf-9101-62a81b7cd39a","added_by":"auto","created_at":"2024-04-12 19:14:05","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":128757,"visible":true,"origin":"","legend":"\u003cp\u003eEpoch showing data obtained from a 76-year-old man with stridor ventilated with pressure support. The top and middle plots show airway flow and pressure, respectively. The bottom plot shows calculated P\u003csub\u003emus\u003c/sub\u003ePTP values during insufflation as discrete dots. These correspond to the breaths above and are mostly inspiratory (negative) in nature.\u003c/p\u003e","description":"","filename":"GutierrezFigure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4252169/v1/0d07f0259737e6b82506f4fb.jpg"},{"id":54595768,"identity":"25fe8947-a7dc-4c67-aae3-0c27f731332a","added_by":"auto","created_at":"2024-04-12 18:58:05","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":126523,"visible":true,"origin":"","legend":"\u003cp\u003eEpoch an 81-year-old woman with aspiration pneumonia on pressure regulated volume control (PRVC) showing mostly expiratory (positive) P\u003csub\u003emus\u003c/sub\u003ePTP values during insufflation. See Fig. 1 for plot description.\u003c/p\u003e","description":"","filename":"GutierrezFigure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4252169/v1/b3348ac0a4a6c695a91b7593.jpg"},{"id":54597562,"identity":"b58be4bd-ef85-4b34-9046-ecf52ba1e1d8","added_by":"auto","created_at":"2024-04-12 19:22:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":562833,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4252169/v1/4a511ec8-2289-4fca-8f83-20c938aedab5.pdf"},{"id":54596605,"identity":"8b12ee44-fd8a-44bd-b5e4-004699e4855d","added_by":"auto","created_at":"2024-04-12 19:06:05","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1374706,"visible":true,"origin":"","legend":"","description":"","filename":"GutierrezExpiratoryEffortsSupplementalI.docx","url":"https://assets-eu.researchsquare.com/files/rs-4252169/v1/382beb6159f1aec4fee3277b.docx"}],"financialInterests":"The authors declare potential competing interests as follows: The corresponding author, Guillermo Gutierrez, MD, PhD has applied for a U.S. patent related to the contents of the manuscript.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eExpiratory Efforts During Insufflation are Associated with Increased Mortality in Ventilated Patients\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Take-home message","content":"\u003cp\u003eRespiratory efforts during insufflation were calculated noninvasively from airway signals in a cohort of patients treated with invasive, positive pressure ventilation. Expiratory efforts, characterized by the pressure-time product, were associated with greater 28-day mortality rates. Conversely, inspiratory efforts were not linked to mortality, but may indicate the need for greater ventilatory support.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eSpontaneous breathing during mechanical ventilation has been shown to prevent diaphragmatic atrophy and to improve arterial oxygenation [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Conversely, forceful respiratory muscle efforts might increase transpulmonary pressures (P\u003csub\u003eL\u003c/sub\u003e) during insufflation with ensuing lung damage [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], a condition defined as patient self-induced lung injury (P-SILI) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRespiratory muscle effort (P\u003csub\u003emus\u003c/sub\u003e) can be calculated as the difference between esophageal pressure measurements and estimates of passive chest wall recoil pressure [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In addition to the invasive nature of the esophageal catheter, variability in catheter placement and chest wall mechanics may produce inconsistent results across patients. Consequently, and despite their potential utility, esophageal catheters are seldom used [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. It is desirable, therefore, to develop techniques capable of estimating P\u003csub\u003emus\u003c/sub\u003e accurately and noninvasively.\u003c/p\u003e \u003cp\u003eA recursive technique has been developed to determine the static compliance (C\u003csub\u003ers\u003c/sub\u003e) and inspiratory resistance (R\u003csub\u003ers\u003c/sub\u003e) of the respiratory system from analysis of the airway flow (F\u003csub\u003eaw\u003c/sub\u003e) and pressure (P\u003csub\u003eaw\u003c/sub\u003e) signals [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. This numerical method is based on the single-compartment model of the respiratory system during positive pressure ventilation [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e],\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$${ P}_{aw}\\left(t\\right)= \\frac{\\varDelta V\\left(t\\right)}{{C}_{rs}}+ {R}_{rs}{F}_{aw}\\left(t\\right)+ {PEEP}_{a}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere ΔV(t) denotes increases in lung volume from functional residual capacity and PEEP\u003csub\u003ea\u003c/sub\u003e represents the applied positive end expiratory pressure. The model neglects the effect of gas inertia and intrinsic PEEP (PEEP\u003csub\u003ei\u003c/sub\u003e) on P\u003csub\u003emus\u003c/sub\u003e(t).\u003c/p\u003e \u003cp\u003eEqt. 1 can be extended to calculate P\u003csub\u003emus\u003c/sub\u003e(t), a time-dependent function describing respiratory muscle effort during insufflation [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003csup\u003e,\u003c/sup\u003e\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$${P}_{mus}\\left(t\\right)= {P}_{aw}\\left(t\\right)-\\left[\\frac{\\varDelta V\\left(t\\right)}{{C}_{rs}}+ {R}_{rs}{F}_{aw}\\left(t\\right)+ {PEEP}_{a}\\right]$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe solution to Eqt. 2 requires prior knowledge of C\u003csub\u003ers\u003c/sub\u003e and R\u003csub\u003ers\u003c/sub\u003e, which can be determined numerically [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] using data collected from breaths known to be devoid of muscular effort (P\u003csub\u003emus\u003c/sub\u003e(t)\u0026thinsp;=\u0026thinsp;0). The expression enclosed in brackets in Eqt. 2 indicates the airway pressure needed for passive inflation of the respiratory system,\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$${P}_{mus}\\left(t\\right)= {P}_{aw}\\left(t\\right)- {P}_{passive}\\left(t\\right)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eAccording to Eqt. 3, positive values of P\u003csub\u003emus\u003c/sub\u003e(t) signify expiratory efforts, whereas negative values indicate inspiratory efforts. The model assumes that C\u003csub\u003ers\u003c/sub\u003e and R\u003csub\u003ers\u003c/sub\u003e remain constant during insufflation [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], although it allows for longitudinal variations in these parameters due to treatment effects and disease progression.\u003c/p\u003e \u003cp\u003eWe applied the model of Eqt. 2 to extensive recordings of P\u003csub\u003eaw\u003c/sub\u003e(t) and F\u003csub\u003eaw\u003c/sub\u003e(t) signals obtained from patients on invasive, positive pressure mechanically ventilation. We calculated P\u003csub\u003emus\u003c/sub\u003e(t) numerically for each recorded insufflation within this database and used it to determine its pressure-time product (P\u003csub\u003emus\u003c/sub\u003ePTP) and peak values (P\u003csub\u003emus\u003c/sub\u003ePeak). Our aim was to determine any potential association between all-cause 28-day mortality rate and respiratory muscle effort during insufflation, as characterized by P\u003csub\u003emus\u003c/sub\u003ePeak and P\u003csub\u003emus\u003c/sub\u003ePTP.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eWe analyzed a database of P\u003csub\u003eaw\u003c/sub\u003e(t) and F\u003csub\u003eaw\u003c/sub\u003e(t) recordings from patients treated with invasive, positive pressure ventilation at The George Washington University Hospital's intensive care unit from 2011 to 2018. These patients had been previously enrolled in studies approved by The George Washington University Institutional Review Board (IRB Nos.081311, 101228, 110910, and 111235) conducted in compliance with the 1964 Helsinki Declaration. Informed consent for participation in these studies was obtained from all patients, or their designated surrogates, with the use of anonymized data approved for future research.\u003c/p\u003e \u003cp\u003e \u003cem\u003ePatient population\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eThe database included 323 patients enrolled within 24 hours of intubation and monitored during the duration of mechanical ventilation, ranging from two hours to 33 days. All patients were nasally or orotracheally intubated and ventilated using various modes of support with Servo_i or Servo_s ventilators (Getinge, Solna, Sweden). Clinicians not involved in the studies determined exclusively the manner of ventilation. We selected a priori from the database 267 patients who had been monitored for at least six hours, a period that provided adequate airway signal data for analysis.\u003c/p\u003e \u003cp\u003e \u003cem\u003eData acquisition.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eP\u003csub\u003eaw\u003c/sub\u003e(t) and F\u003csub\u003eaw\u003c/sub\u003e(t) signals were sampled at 31.25 Hz with a proprietary Raspberry Pi 3B data collection system connected to the ventilator RS232 data port via a null DB9 cable. Data were segmented into sequential 2.2-minute-long epochs containing 4096 samples of each signal. Demographic data were recorded in physical notebooks that were destroyed after transferring nonidentifiable details to the database using a study number.\u003c/p\u003e \u003cp\u003e \u003cem\u003eData processing and analysis.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eCustom software developed in Python 3.7 was used to process the selected cohort\u0026rsquo;s stored airway signal data. This software was designed to simulate the clinical monitoring of patients on mechanical ventilation. Excluded from analysis were epochs recorded on bi-level ventilation or airway pressure release ventilation (APRV). Data analysis spanned from the time of enrollment and the start of recording up to a maximum of five days of ventilatory support, or until the patient was weaned from the ventilator, whichever came first. All-cause mortality within 28 days of enrollment was confirmed through hospital and clinic records, and telephone interviews.\u003c/p\u003e \u003cp\u003e \u003cem\u003eDetermination of P\u003c/em\u003e \u003csub\u003e \u003cem\u003emus\u003c/em\u003e \u003c/sub\u003e \u003cem\u003e(t) function.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eIndividual P\u003csub\u003emus\u003c/sub\u003e(t) functions were generated for each recorded insufflation using Eqt. 2 and measurements of ΔV(k), F\u003csub\u003eaw\u003c/sub\u003e(k), and PEEP\u003csub\u003ea\u003c/sub\u003e obtained at sequential k points during the insufflation phase. The maximum positive and minimum negative values of each P\u003csub\u003emus\u003c/sub\u003e(t) function were taken as the insufflation\u0026rsquo;s expiratory and inspiratory P\u003csub\u003emus\u003c/sub\u003ePeak, respectively. P\u003csub\u003emus\u003c/sub\u003ePTP was calculated by trapezoidal numerical integration of P\u003csub\u003emus\u003c/sub\u003e(t) across the insufflation time. (See Supplementary Information Fig.\u0026nbsp;3e for examples of P\u003csub\u003emus\u003c/sub\u003e(t) functions).\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDetermination of mean and cumulative values (Fig.\u0026nbsp;1):\u003c/h2\u003e \u003cp\u003eThe average expiratory and inspiratory P\u003csub\u003emus\u003c/sub\u003ePeak and P\u003csub\u003emus\u003c/sub\u003ePTP for all insufflations within an epoch were calculated and reported as the epoch's mean values. These were averaged over a 24-hour period to derive the daily mean values. The average of the daily means represented the patient\u0026rsquo;s P\u003csub\u003emus\u003c/sub\u003ePeak and P\u003csub\u003emus\u003c/sub\u003ePTP for the monitoring period. The sum of P\u003csub\u003emus\u003c/sub\u003ePeak and P\u003csub\u003emus\u003c/sub\u003ePTP for both expiratory and inspiratory phases were similarly compiled to evaluate the cumulative impact on 28-day mortality rate of repeated muscle effort.\u003c/p\u003e \u003cp\u003e \u003cem\u003eOther ventilatory parameters.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eP\u003csub\u003eaw\u003c/sub\u003e(t) and F\u003csub\u003eaw\u003c/sub\u003e(t) were used to determine each breath\u0026rsquo;s peak and mean P\u003csub\u003eaw\u003c/sub\u003e, PEEP\u003csub\u003ea\u003c/sub\u003e, mean inspired F\u003csub\u003eaw\u003c/sub\u003e, tidal volume (V\u003csub\u003eT\u003c/sub\u003e), and inspiratory and expiratory times (Ti and Te, respectively). Driving pressure (ΔP) was computed as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{{V}_{T}}{{C}_{rs}}\\)\u003c/span\u003e\u003c/span\u003e, using the static C\u003csub\u003ers\u003c/sub\u003e derived from Eqt. 1. The plateau pressure (P\u003csub\u003eplateau\u003c/sub\u003e) was calculated as ΔP\u0026thinsp;+\u0026thinsp;PEEP\u003csub\u003ea\u003c/sub\u003e, and dynamic compliance as (C\u003csub\u003edyn\u003c/sub\u003e) as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({C}_{dyn}= \\frac{{V}_{T}}{Peak {P}_{aw }- {PEEP}_{a}}\\)\u003c/span\u003e\u003c/span\u003e. All variables were averaged across all breaths within each epoch, and the average of all such epochs during the patient's ventilatory support was taken as the metric\u0026rsquo;s overall value.\u003c/p\u003e \u003cp\u003eMechanical power (MP) was calculated for each insufflation by trapezoidal integration of the pressure-volume curve. The sum of all MP calculations for a given epoch was multiplied by 4.45 x 10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e (0.098/2.2 min/epoch) to report the epoch\u0026rsquo;s MP in J\u0026middot;min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. The respiratory rate variability index (RRVI) for each epoch was determined by frequency analysis of the airway signals [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. MP and RRVI were averaged across all the patient\u0026rsquo;s recorded epochs.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStatistics.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eData normality was evaluated with the Kolmogorov-Smirnov test and the Mann\u0026ndash;Whitney test was used to determine significant differences between independent samples. The Benjamini\u0026ndash;Yekutieli procedure [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] with a false discovery rate of 0.05 served to control for multiple comparisons. A logistic regression analysis was performed to predict 28-day all-cause mortality with initial predictors that included variables with univariate p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. A manual stepwise procedure was used to eliminate variables with p\u0026thinsp;\u0026gt;\u0026thinsp;0.40 at each step. Unless otherwise specified, data are shown as median and interquartile range. All reported p values are two-sided with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThere were 200 survivors and 67 decedents in the chosen cohort. Monitoring times were 71[ 30,83] hours and 47 [25, 81] hours for decedents and survivors, respectively (p\u0026thinsp;=\u0026thinsp;0.13). There were no differences in ethnicity (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee; Supplementary Information) or gender distribution (48% women). Decedents had greater prevalence of pneumonia and fewer required ICU admission for post-operative complications (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee; Supplementary Information).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic Variables Univariate Differences\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatients\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDecedents\u003c/p\u003e \u003cp\u003e67\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSurvivors\u003c/p\u003e \u003cp\u003e200\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e267\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsufflations Analyzed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,173,584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9,204,801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13,378,385\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEpochs Analyzed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98,156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e245,743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e343,899\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDemographics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStatistics\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70 [59, 79]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58 [46, 70]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg\u0026middot;m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 [23, 31]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 [24, 33]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN.S.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAPS II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53 [46, 62]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40 [32, 49]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOFA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMeasured Variables\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003csub\u003eI\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48 [41, 60]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41 [40, 49]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean Inspired Airway Flow (L\u0026middot;min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54 [47, 60]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49 [43, 55]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeak Airway Pressure (cmH\u003csub\u003e2\u003c/sub\u003eO)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 [23, 31]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 [22, 29]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN.S.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean Airway Pressure (cmH\u003csub\u003e2\u003c/sub\u003eO)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEEP (cmH\u003csub\u003e2\u003c/sub\u003eO)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiratory Rate (bpm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTidal Volume/PBW (mL\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.8 [7.8, 9.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.6 [7.9, 10.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN.S.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExpiratory Time (s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.0 [1.5, 2.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.7 [2.3, 3.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInspiratory Time (s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.1 [0.9, 1.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0 [0.9, 1.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN.S.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCalculated Variables\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompliance - Dynamic (mL\u0026middot;cmH\u003csub\u003e2\u003c/sub\u003eO \u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 [25, 37]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 [28, 43]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompliance - Static (mL\u0026middot;cmH\u003csub\u003e2\u003c/sub\u003eO \u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35 [28, 52]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49 [38, 62]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlateau Pressure (cmH\u003csub\u003e2\u003c/sub\u003eO)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDriving Pressure (cmH\u003csub\u003e2\u003c/sub\u003eO)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInspiratory Resistance (cmH\u003csub\u003e2\u003c/sub\u003eO\u0026middot;s\u0026middot;L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN.S.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMechanical Power (J\u0026middot;min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 [15, 27]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRRVI (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41 [30, 48]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37 [31, 46]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN.S.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eBMI body-mass index; SAPS II\u0026thinsp;=\u0026thinsp;Simplified Acute Physiology Score; SOFA\u0026thinsp;=\u0026thinsp;Sepsis related Organ Failure Assessment; RRVI\u0026thinsp;=\u0026thinsp;Respiratory Rate Variability Index. Statistics refer to Mann\u0026ndash;Whitney test corrected with Benjamini-Yekutieli procedure for multiple comparisons.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMean and Cumulative Respiratory Muscles Peak Pressures (PmusPeak) andPressure-Time Product (PmusPTP) According to 28-day Mortality\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDecedents\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;67\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSurvivors\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;200\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMann-Whitney\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBenjamini- Yekutieli\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExpiratory Efforts\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean P\u003csub\u003emus\u003c/sub\u003ePeak (cmH\u003csub\u003e2\u003c/sub\u003eO)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.1 [5.0, 9.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.3 [4.7, 8.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN.S.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCumulative P\u003csub\u003emus\u003c/sub\u003ePeak (cmH\u003csub\u003e2\u003c/sub\u003eO)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,972 [1600, 3946]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,355 [1586, 3405]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN.S.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean P\u003csub\u003emus\u003c/sub\u003ePTP (cmH\u003csub\u003e2\u003c/sub\u003eO\u0026middot;s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.8 [6.4, 18.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.8 [5.4, 12.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCumulative P\u003csub\u003emus\u003c/sub\u003ePTP (cmH\u003csub\u003e2\u003c/sub\u003eO\u0026middot;s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17,445 [5556, 31214]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8,838 [3604, 18190]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInspiratory Efforts\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean P\u003csub\u003emus\u003c/sub\u003ePeak (cmH\u003csub\u003e2\u003c/sub\u003eO)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.3 [5.8, 2.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.7 [5.0, 2.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.961\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN.S.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCumulative P\u003csub\u003emus\u003c/sub\u003ePeak (cmH\u003csub\u003e2\u003c/sub\u003eO)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,495 [835, 2114]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,248 [881, 2089]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN.S.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean P\u003csub\u003emus\u003c/sub\u003ePTP (cmH\u003csub\u003e2\u003c/sub\u003eO\u0026middot;s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.8 [8.3, 1.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.4 [9.8, 2.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN.S.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCumulative P\u003csub\u003emus\u003c/sub\u003ePTP (cmH\u003csub\u003e2\u003c/sub\u003eO\u0026middot;s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,779 [1399,13764]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,380 [2122, 11339]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN.S.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWe analyzed approximately 13.4\u0026nbsp;million insufflations contained within 343,899 epochs recorded during 600 monitoring days (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Each patient was monitored for an average of 2.2 days, encompassing 1,303 epochs with 50,676 insufflations. Decedents were older and had greater disease severity. Apart from peak P\u003csub\u003eaw\u003c/sub\u003e, V\u003csub\u003eT\u003c/sub\u003e, and Ti, all measured ventilator variables differed between the groups, with decedents exhibiting lower dynamic and static C\u003csub\u003ers\u003c/sub\u003e, and higher ΔP and MP. No differences were noted in R\u003csub\u003ers\u003c/sub\u003e or RRVI between the groups. These findings imply that patients in the decedent group were sicker and required greater ventilatory support. Patients in both groups received similar ventilation, with over 70% of epochs preferentially using pressure-regulated volume control (Table\u0026nbsp;4e; Supplementary Information).\u003c/p\u003e \u003cp\u003eFigure 2 shows an example of an epoch obtained from a 76-year-old man with stridor on pressure support ventilation. Respiratory rate is variable with occasional double-triggering. The bottom plot shows P\u003csub\u003emus\u003c/sub\u003ePTP as discrete values corresponding to the breaths above. These are mostly negative, indicating inhalation efforts. Figure\u0026nbsp;3 shows an epoch taken from an 89-year-old woman with aspiration pneumonia on pressure regulated volume control (PRVC). There is significant respiratory rate variability and asynchronous breathing with mostly expiratory breathing efforts during insufflation. (See Supplementary Information for additional epoch examples).\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the mean and cumulative P\u003csub\u003emus\u003c/sub\u003ePeak and P\u003csub\u003emus\u003c/sub\u003ePTP during insufflation according to the respiratory effort directional phase, either expiratory or inspiratory. Decedents had greater mean and cumulative expiratory P\u003csub\u003emus\u003c/sub\u003ePTP than survivors (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for each). On the other hand, there were no differences between the groups in either mean or cumulative inspiratory P\u003csub\u003emus\u003c/sub\u003ePTP values. Similarly, no differences were noted in expiratory or inspiratory P\u003csub\u003emus\u003c/sub\u003ePeak, mean or cumulative, between the groups.\u003c/p\u003e \u003cp\u003ePredictors independently associated with 28-day mortality were age (OR 1.03; 1.01\u0026ndash;1.06; p\u0026thinsp;=\u0026thinsp;0.0157), Te (OR 0.36; 0.21\u0026ndash;0.52; p\u0026thinsp;=\u0026thinsp;0.0003), and SAPS II (OR 1.05; 1.02\u0026ndash;1.08; p\u0026thinsp;=\u0026thinsp;0.0009). Not independently associated with mortality were ΔP, MP, SOFA, PEEP, static and dynamic compliance and expiratory mean or cumulative P\u003csub\u003emus\u003c/sub\u003ePTP (Table\u0026nbsp;3e; Supplementary Information).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe applied a recursive numerical method to a comprehensive database of airway flow and pressure signals recorded from 267 patients treated with invasive, positive pressure ventilation for a maximum of five days. Our aim was to assess the feasibility of quantifying P\u003csub\u003emus\u003c/sub\u003e and P\u003csub\u003emus\u003c/sub\u003ePTP non-invasively and to determine their possible association to all-cause 28-day mortality rate.\u003c/p\u003e \u003cp\u003eOur analysis revealed a significant direct association between expiratory efforts during insufflation and the cohort\u0026rsquo;s 28-day mortality rate. We hypothesize that sudden increases in alveolar pressure (P\u003csub\u003ealv\u003c/sub\u003e), caused by the counteractive effect of expiratory efforts opposing ventilator-delivered breaths, might lead to injurious lung tissue strain [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], potentially resulting in poorer outcomes. This hypothesis supports the concept of early intervention in ARDS, aiming to reduce expiratory efforts through the use of paralytic agents [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe also noted that inspired P\u003csub\u003emus\u003c/sub\u003ePTP was not linked to 28-day mortality. We suggest that inspiratory efforts during insufflation may work in tandem with the ventilator, leading to increased transpulmonary pressure and tidal volume without a corresponding increase in P\u003csub\u003ealv\u003c/sub\u003e. Inspiratory efforts may occur in patients experiencing air hunger [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], a psychologically distressing condition [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] experienced by nearly half of mechanically ventilated patients [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn contrast to P\u003csub\u003emus\u003c/sub\u003ePTP, neither inspiratory nor expiratory P\u003csub\u003emus\u003c/sub\u003ePeak were associated with 28-day mortality. P\u003csub\u003emus\u003c/sub\u003ePeak measures the effort of respiratory muscles at a specific moment, whereas P\u003csub\u003emus\u003c/sub\u003ePTP evaluates the cumulative effect of P\u003csub\u003emus\u003c/sub\u003e(t) throughout the insufflation period, offering a more comprehensive measure of breathing effort. P\u003csub\u003emus\u003c/sub\u003ePTP has been demonstrated to be an effective metric of respiratory effort [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] and of diaphragmatic energy expenditure [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNeither P\u003csub\u003emus\u003c/sub\u003e nor P\u003csub\u003emus\u003c/sub\u003ePTP were identified as independent predictors of mortality. Moreover, and contrasting with prior studies [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], neither were MP nor ΔP. The discrepancy might stem from the retrospective design of our study, the relatively limited size of our sample, and different methodologies used for calculating MP and ΔP. Instead of using a surrogate formula [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], we calculated MP by the geometric method and ΔP using the model-calculated static C\u003csub\u003ers\u003c/sub\u003e, instead of the dynamic C\u003csub\u003ers\u003c/sub\u003e.\u003c/p\u003e \u003cp\u003eA possible confounding element in our study is the omission of intrinsic PEEP (PEEPi) when calculating P\u003csub\u003emus\u003c/sub\u003e(t) with Eqt. 2. According to Eqt. 2, ignoring PEEPi may overestimate expiratory P\u003csub\u003emus\u003c/sub\u003e while underestimating inspiratory P\u003csub\u003emus\u003c/sub\u003e. Given the substantial number of insufflations analyzed, however, it is unlikely that the random occurrence of PEEPi would have significantly altered our results.\u003c/p\u003e \u003cp\u003eCaution is advised in interpreting the results due to potential changes in treatment practices over the time spanning the collection of data. On the other hand, the robustness of our findings is enhanced by the substantial dataset employed and the methodology used for analyzing patient data, with software that simulated real-time bedside monitoring. This approach enhances the clinical relevance of our work and emphasizes the value of using large, high-fidelity airway signal datasets to gain novel insights into patient-ventilator dynamics.\u003c/p\u003e \u003cp\u003eIn summary, numerical analysis of airway signals revealed a significant link between expiratory effort during insufflation and 28-day mortality. This finding suggests that the opposing interaction of expiratory efforts and ventilator-provided insufflations is a plausible mechanism of lung injury leading to P-SILI and possibly adverse patient outcomes. On the other hand, inspiratory efforts, although not associated with mortality, could indicate the presence of air hunger.\u003c/p\u003e \u003cp\u003eThe contrasting effect of inspiratory and expiratory efforts highlights the nuanced role of respiratory muscle effort in determining patient outcome. Further research is necessary to validate our observations and possibly pinpoint a harmful threshold for expiratory muscle effort.\u003c/p\u003e"},{"header":"List Of Abbreviations","content":"\u003cp\u003eBMI = Body-mass index.\u003c/p\u003e\n\u003cp\u003eC\u003csub\u003ers\u003c/sub\u003e = Respiratory system static compliance.\u003c/p\u003e\n\u003cp\u003e\u0026Delta;P = \u0026nbsp;Driving pressure\u003c/p\u003e\n\u003cp\u003e\u0026Delta;V(t) = \u0026nbsp;Lung volume change during insufflation.\u003c/p\u003e\n\u003cp\u003eF\u003csub\u003eaw\u003c/sub\u003e = Airway flow.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMP = Mechanical power.\u003c/p\u003e\n\u003cp\u003eP\u003csub\u003ealv\u003c/sub\u003e = Alveolar pressure.\u003c/p\u003e\n\u003cp\u003eP\u003csub\u003eaw\u003c/sub\u003e = Airway pressure.\u003c/p\u003e\n\u003cp\u003ePEEP\u003csub\u003ea\u003c/sub\u003e = Applied positive end expiratory pressure.\u003c/p\u003e\n\u003cp\u003ePEEP\u003csub\u003ei\u003c/sub\u003e = Intrinsic PEEP at end expiration.\u003c/p\u003e\n\u003cp\u003eP\u003csub\u003eL\u003c/sub\u003e = Transpulmonary pressure.\u003c/p\u003e\n\u003cp\u003eP\u003csub\u003ealv\u003c/sub\u003e = Alveolar pressure.\u003c/p\u003e\n\u003cp\u003eP\u003csub\u003emus\u003c/sub\u003e = \u0026nbsp;\u0026nbsp;Portion of P\u003csub\u003eaw\u003c/sub\u003e attributed to respiratory muscles effort.\u003c/p\u003e\n\u003cp\u003eP\u003csub\u003emus\u003c/sub\u003ePeak = Highest expiratory or inspiratory P\u003csub\u003emus\u003c/sub\u003e value.\u003c/p\u003e\n\u003cp\u003eP\u003csub\u003emus\u003c/sub\u003ePTP = P\u003csub\u003emus\u003c/sub\u003e pressure time product.\u003c/p\u003e\n\u003cp\u003eP\u003csub\u003emus\u003c/sub\u003e(t) = Time dependent function describing P\u003csub\u003emus\u003c/sub\u003e during insufflation.\u003c/p\u003e\n\u003cp\u003eP\u003csub\u003epassive\u003c/sub\u003e = Airway pressure required to inflate the respiratory system passively.\u003c/p\u003e\n\u003cp\u003eP\u003csub\u003epeak\u003c/sub\u003e =\u0026nbsp;\u0026nbsp; Peak inspiratory pressure.\u003c/p\u003e\n\u003cp\u003eP\u003csub\u003eplateau\u003c/sub\u003e = Plateau pressure during the end-inspiratory hold.\u003c/p\u003e\n\u003cp\u003ePRVC = Pressure regulated volume control.\u003c/p\u003e\n\u003cp\u003ePS = \u0026nbsp;Pressure support\u003c/p\u003e\n\u003cp\u003eP-SILI = Patient self-induced lung injury.\u003c/p\u003e\n\u003cp\u003ers = \u0026nbsp;Subscript denoting respiratory system.\u003c/p\u003e\n\u003cp\u003eR\u003csub\u003ers\u003c/sub\u003e = \u0026nbsp; \u0026nbsp; Respiratory system airway resistance.\u003c/p\u003e\n\u003cp\u003eRRVI =\u0026nbsp;\u0026nbsp; Respiratory rate variability index.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSAPS II = Simplified Acute Physiological Score.\u003c/p\u003e\n\u003cp\u003eSOFA = Sofa Related Organ Failure.\u003c/p\u003e\n\u003cp\u003eVC = Volume controlled.\u003c/p\u003e\n\u003cp\u003eV(t) = \u0026nbsp;Lung volume as a function of time.\u003c/p\u003e\n\u003cp\u003eV\u003csub\u003eT\u0026nbsp;\u003c/sub\u003e= \u0026nbsp;Tidal volume\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval and Consent to participate\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eThe\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003edatabase used in the present study was collected as part of various studies conducted from 2011 to 2018, which had received approval from The George Washington University Institutional Review Board (IRB Nos. 081311, 101228, 110910, 111235). These studies were in compliance with the 1964 Helsinki Declaration. Informed consent was obtained from the patients or their designated surrogates for participation in these studies. The IRB approved the use of anonymized data from these studies for future research purposes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of supporting data\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e The dataset analyzed during the current study can be found in the Electronic Data Repository. Access to the database storing the raw data may be granted to qualified researchers upon reasonable request and adequate vetting.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eOne of the authors (GG) has applied for a U.S. patent based on the information presented in the paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGG \u0026ndash;\u0026nbsp;\u003c/strong\u003eDesigned the study, supervised the data collection, wrote the software used to calculate muscle effort, analyzed and interpreted the data, wrote the manuscript and is accountable for all aspects of the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHT -\u0026nbsp;\u003c/strong\u003eMade substantial contributions to the acquisition of data and reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u0026nbsp;\u003c/strong\u003eThe authors thank Dr. Hatice Kaya and Dr. Katherine Rider for their help in gathering much of the information recorded in the patient database.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eGG thanks the Commission for Educational Exchange between the United States, Belgium and Luxembourg and the Fulbright Scholarship Board. Their support as a Fulbright Research Scholar provided him the essential time to develop the ideas foundational to this research. HT thanks T\u0026Uuml;BİTAK, the Scientific and Technological Research Council of T\u0026uuml;rkiye for their support as a Research Scholar at The George Washington University, Division of Pulmonary and Critical Care Medicine.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclaimer:\u003c/strong\u003e The content is solely the authors\u0026rsquo; responsibility and does not necessarily represent the official views of The George Washington University.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003ePutensen C, Mutz NJ, Putensen-Himmer G, Zinserling J (1999) Spontaneous breathing during ventilatory support improves ventilation-perfusion distributions in patients with acute respiratory distress syndrome. Am J Respir Crit Care Med 159:1241-1248. doi: 10.1164/ajrccm.159.4.9806077. PMID: 10194172.\u003c/li\u003e\n\u003cli\u003eYoshida T, Fujino Y, Amato MB, Kavanagh BP (2017) Fifty Years of Research in ARDS. Spontaneous Breathing during Mechanical Ventilation. Risks, Mechanisms, and Management. Am J Respir Crit Care Med. 195:985-992. doi: 10.1164/rccm.201604-0748CP. PMID: 27786562.\u003c/li\u003e\n\u003cli\u003eChiumello D, Carlesso E, Cadringher P, Caironi P, Valenza F, Polli F, Tallarini F, Cozzi P, Cressoni M, Colombo A, Marini JJ, Gattinoni L (2008) Lung stress and strain during mechanical ventilation for acute respiratory distress syndrome. Am J Respir Crit Care Med 178:346-355. doi: 10.1164/rccm.200710-1589OC. Epub 2008 May 1.\u003c/li\u003e\n\u003cli\u003eCarteaux G, Parfait M, Combet M, Haudebourg AF, Tuffet S, Mekontso Dessap A (2021) Patient-Self Inflicted Lung Injury: A Practical Review. J Clin Med. 10:2738. doi: 10.3390/jcm10122738. PMID: 34205783; PMCID: PMC8234933.\u003c/li\u003e\n\u003cli\u003eMauri T, Yoshida T, Bellani G, Goligher EC, Carteaux G, Rittayamai N, Mojoli F, Chiumello D, Piquilloud L, Grasso S, Jubran A, Laghi F, Magder S, Pesenti A, Loring S, Gattinoni L, Talmor D, Blanch L, Amato M, Chen L, Brochard L, Mancebo J; PLeUral pressure working Group (PLUG\u0026mdash;Acute Respiratory Failure section of the European Society of Intensive Care Medicine) (2016) Esophageal and transpulmonary pressure in the clinical setting: meaning, usefulness and perspectives. Intensive Care Med. 42:1360-1373. doi: 10.1007/s00134-016-4400-x. Epub 2016 Jun 22. PMID: 27334266.\u003c/li\u003e\n\u003cli\u003eBellani G, Laffey JG, Pham T, Fan E, Brochard L, Esteban A, Gattinoni L, van Haren F, Larsson A, McAuley DF, Ranieri M, Rubenfeld G, Thompson BT, Wrigge H, Slutsky AS, Pesenti A; LUNG SAFE Investigators; ESICM Trials Group (2016) Epidemiology, Patterns of Care, and Mortality for Patients With Acute Respiratory Distress Syndrome in Intensive Care Units in 50 Countries. JAMA. 315:788-800. doi: 10.1001/jama.2016.0291. Erratum in: JAMA. 2016 Jul 19;316(3):350. Erratum in: JAMA. 2016 Jul 19;316(3):350. PMID: 26903337.\u003c/li\u003e\n\u003cli\u003eGutierrez G (2022). A novel method to calculate compliance and airway resistance in ventilated patients. Intensive Care Med Exp. 10:55,. doi: 10.1186/s40635-022-00483-2. PMID: 36581716; PMCID: PMC9800666.\u003c/li\u003e\n\u003cli\u003eOtis A, Fenn W, Rahn H (1950) Mechanics of breathing in man. J Appl Physiol. 2:592-607. doi: 10.1152/jappl.1950.2.11.592. PMID: 15436363.\u003c/li\u003e\n\u003cli\u003eRossi A, Gottfried SB, Higgs BD, Zocchi L, Grassino A, Milic-Emili J (1985) Respiratory mechanics in mechanically ventilated patients with respiratory failure. J Appl Physiol 58:1849-1858. doi: 10.1152/jappl.1985.58.6.1849. PMID: 4008405.\u003c/li\u003e\n\u003cli\u003eGutierrez G (2024) A Non-Invasive Method to Monitor Respiratory Muscle Effort During Mechanical Ventilation. J Clin Monit Comput. (In Press) https://doi.org/10.21203/rs.3.rs-3838325/v1\u003c/li\u003e\n\u003cli\u003eGutierrez G, Das A, Ballarino G, Beyzaei-Arani A, T\u0026uuml;rkan H, Wulf-Gutierrez M, Rider K, Kaya H, Amdur R (2013) Decreased respiratory rate variability during mechanical ventilation is associated with increased mortality. Intensive Care Med 39:1359-67. doi: 10.1007/s00134-013-2937-5. Epub 2013 Jun 7. PMID: 23743521.\u003c/li\u003e\n\u003cli\u003eBenjamini, Y. and Yekutieli, D (2001) The control of the false discovery rate in multiple testing under dependency. \u003cem\u003eAnn. Statist.\u003c/em\u003e, 29, 1165\u0026ndash;1188, 2001.\u003c/li\u003e\n\u003cli\u003ePapazian L, Forel JM, Gacouin A, Penot-Ragon C, Perrin G, Loundou A, Jaber S, Arnal JM, Perez D, Seghboyan JM, Constantin JM, Courant P, Lefrant JY, Gu\u0026eacute;rin C, Prat G, Morange S, Roch A; ACURASYS Study Investigators (2010) Neuromuscular blockers in early acute respiratory distress syndrome. N Engl J Med. 363:1107-1116. doi: 10.1056/NEJMoa1005372. PMID: 20843245.\u003c/li\u003e\n\u003cli\u003eDemoule A, Hajage D, Messika J, Jaber S, Diallo H, Coutrot M, Kouatchet A, Azoulay E, Fartoukh M, Hraiech S, Beuret P, Darmon M, Decav\u0026egrave;le M, Ricard JD, Chanques G, Mercat A, Schmidt M, Similowski T; REVA Network (Research Network in Mechanical Ventilation). Prevalence, Intensity, and Clinical Impact of Dyspnea in Critically Ill Patients Receiving Invasive Ventilation. Am J Respir Crit Care Med. 205:917-926, 2022. doi: 10.1164/rccm.202108-1857OC. PMID: 35061577.\u003c/li\u003e\n\u003cli\u003eWorsham CM, Banzett RB, Schwartzstein RM (2021) Dyspnea, Acute Respiratory Failure, Psychological Trauma, and Post-ICU Mental Health: A Caution and a Call for Research. Chest. 159:749-756. doi: 10.1016/j.chest.2020.09.251. Epub 2020 Oct 1. PMID: 33011205; PMCID: PMC7528739.\u003c/li\u003e\n\u003cli\u003eDemoule A, Decavele M, Antonelli M, Camporota L, Abroug F, Adler D, Azoulay E, Basoglu M, Campbell M, Grasselli G, Herridge M, Johnson MJ, Naccache L, Navalesi P, Pelosi P, Schwartzstein R, Williams C, Windisch W, Heunks L, Similowski T. Dyspnoea in acutely ill mechanically ventilated adult patients: an ERS/ESICM statement. Eur Respir J. 2024 Feb 22;63(2):2300347. doi: 10.1183/13993003.00347-2023. PMID: 38387998. \u003c/li\u003e\n\u003cli\u003eJubran A, Tobin MJ (1997) Pathophysiologic basis of acute respiratory distress in patients who fail a trial of weaning from mechanical ventilation. Am J Respir Crit Care Med. 1997 155:906-915.doi: 10.1164/ajrccm.155.3.9117025. PMID: 9117025.\u003c/li\u003e\n\u003cli\u003eField S, Sanci S, Grassino A (1984) Respiratory muscle oxygen consumption estimated by the diaphragm pressure-time index. J Appl Physiol Respir Environ Exerc Physiol 57:44-51. doi: 10.1152/jappl.1984.57.1.44. PMID: 6469790.\u003c/li\u003e\n\u003cli\u003eSerpa Neto A, Deliberato RO, Johnson AEW, Bos LD, Amorim P, Pereira SM, Cazati DC, Cordioli RL, Correa TD, Pollard TJ, Schettino GPP, Timenetsky KT, Celi LA, Pelosi P, Gama de Abreu M, Schultz MJ; PROVE Network Investigators (2018) Mechanical power of ventilation is associated with mortality in critically ill patients: an analysis of patients in two observational cohorts. Intensive Care Med. 44:1914-1922. doi: 10.1007/s00134-018-5375-6. Epub 2018 Oct 5. PMID: 30291378.\u003c/li\u003e\n\u003cli\u003eAmato MB, Meade MO, Slutsky AS, Brochard L, Costa EL, Schoenfeld DA, Stewart TE, Briel M, Talmor D, Mercat A, Richard JC, Carvalho CR, Brower RG (2015) Driving pressure and survival in the acute respiratory distress syndrome. N Engl J Med. 372:747-755. doi: 10.1056/NEJMsa1410639. PMID: 25693014.\u003c/li\u003e\n\u003cli\u003eChiumello D, Gotti M, Guanziroli M, Formenti P, Umbrello M, Pasticci I, Mistraletti G, Busana M (2020) Bedside calculation of mechanical power during volume- and pressure-controlled mechanical ventilation. Crit Care. 24:417. doi: 10.1186/s13054-020-03116-w. PMID: 32653011; PMCID: PMC7351639.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"The George Washington University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Mechanical ventilation, breathing efforts, acute respiratory failure, static compliance, dyspnea, airway resistance, numerical analysis","lastPublishedDoi":"10.21203/rs.3.rs-4252169/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4252169/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eBreathing efforts during mechanical ventilation are associated with patient self-induced lung injury (P-SILI). We examined whether a noninvasive measure of P\u003csub\u003emus\u003c/sub\u003e, the portion of airway pressure attributed to breathing effort during insufflation, relates to patient mortality.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe analyzed recorded airway signals from 267 patients on invasive mechanical ventilation monitored between six hours and five days. Patients were divided into survivor and decedent groups according to all-cause 28-day mortality. Individual P\u003csub\u003emus\u003c/sub\u003e(t) functions, describing changes in P\u003csub\u003emus\u003c/sub\u003e during insufflation, were generated for 13.4\u0026nbsp;million insufflations by numerical analysis of the respiratory system\u0026rsquo;s one-compartment model. P\u003csub\u003emus\u003c/sub\u003e(t) was used to determine the magnitude and direction, expiratory or inspiratory, of peak P\u003csub\u003emus\u003c/sub\u003e(t) (P\u003csub\u003emus\u003c/sub\u003ePeak) and its pressure-time product (P\u003csub\u003emus\u003c/sub\u003ePTP). Mean and cumulative P\u003csub\u003emus\u003c/sub\u003ePeak and P\u003csub\u003emus\u003c/sub\u003ePTP were determined for each patient and compared between the groups.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThere were 67 decedents and 200 survivors. Decedents had greater mean and cumulative expiratory P\u003csub\u003emus\u003c/sub\u003ePTP (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for each) than survivors. Neither inspiratory P\u003csub\u003emus\u003c/sub\u003ePTP nor P\u003csub\u003emus\u003c/sub\u003ePeak differentiated between the groups. Independent predictors of mortality were age, SAPS II score, and expiratory time.\u003c/p\u003e\u003ch2\u003eDiscussion\u003c/h2\u003e \u003cp\u003eWe report an association between expiratory efforts during insufflation and 28-day mortality. By opposing ventilator-delivered breaths, expiratory efforts might increase alveolar pressure (P\u003csub\u003ealv\u003c/sub\u003e), promoting P-SILI and subsequent worse outcomes. The apparent lack of association between mortality and inspiratory effort might be explained by its capacity to increase trans-pulmonary pressure without affecting P\u003csub\u003ealv\u003c/sub\u003e. Inspiratory efforts, however, could indicate air hunger.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOur findings highlight the need for further research into respiratory efforts during mechanical ventilation.\u003c/p\u003e","manuscriptTitle":"Expiratory Efforts During Insufflation are Associated with Increased Mortality in Ventilated Patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-12 18:58:00","doi":"10.21203/rs.3.rs-4252169/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cc82aa70-5187-4a78-b23d-273801ef2bda","owner":[],"postedDate":"April 12th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":30553284,"name":"Critical Care \u0026 Emergency Medicine"},{"id":30553285,"name":"Biomedical Engineering"},{"id":30553286,"name":"Pulmonology"},{"id":30553287,"name":"Physiology"}],"tags":[],"updatedAt":"2024-04-12T18:58:00+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-12 18:58:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4252169","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4252169","identity":"rs-4252169","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}