An Automatic, Non-Invasive Method to Monitor Respiratory Muscle Effort During Mechanical Ventilation

An Automatic, Non-Invasive Method to Monitor Respiratory Muscle Effort During Mechanical Ventilation | 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 An Automatic, Non-Invasive Method to Monitor Respiratory Muscle Effort During Mechanical Ventilation Guillermo Gutierrez This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3838325/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 3 You are reading this latest preprint version Abstract Purpose This study introduces a method to non-invasively and automatically quantify respiratory muscle effort (P mus ) during mechanical ventilation (MV). The methodology hinges on numerically solving the respiratory system's equation of motion, utilizing measurements of airway pressure (P aw ) and airflow (F aw ). To evaluate the technique's effectiveness, Pmus was correlated with expected physiological responses. In volume-control (VC) mode, where tidal volume (V T ) is pre-determined, Pmus is expected to be linked to Paw fluctuations. In contrast, during pressure-control (PC) mode, where P aw is held constant, Pmus should correlate with V T variations. Methods The study utilized data from 250 patients on invasive MV. The data included detailed recordings of Paw and Faw, sampled at 31.25 Hz and saved in 131.2-second epochs, each covering 34 to 41 breaths. The algorithm identified 51,268 epochs containing breaths on either VC or PC mode exclusively. In these epochs, Pmus and its pressure-time product (P mus PTP) were computed and correlated with Paw's pressure-time product (P aw PTP) and V T , respectively. Mechanical ventilation respiratory efforts acute respiratory failure static compliance dyspnea airway resistance numerical analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction The process of mechanically ventilating the respiratory system, that includes the lungs and thoracic cage, is often influenced by a patient's level of consciousness. For heavily sedated or paralyzed patients, insufflation is entirely passive. Yet, conscious patients may exhibit an active response, such as trying to exhale during insufflation risking injurious lung strain [ 1 , 2 ], or develop forceful inhalations if experiencing air hunger [ 3 ], a stressful emotional state that may lead to long term psychologic sequela [ 4 ]. Quantification of patient effort during ventilatory support could help clinicians optimize ventilator settings and calibrate sedative administration. With that goal in mind, the current research proposes a non-invasive method to estimate the portion of airway pressure (P aw ) attributed to muscular effort (P mus ) during insufflation automatically. Model Development. The single compartment model of the respiratory system [ 5 , 6 ] during positive pressure ventilation with negligible P mus , may be expressed as [ 7 ]: $${ P}_{passive}\left(t\right)= \frac{\varDelta V\left(t\right)}{{C}_{rs}}+ {R}_{rs}{F}_{aw}\left(t\right)+ {PEEP}_{a}$$ 1 where P passive (t) is the airway pressure required to inflate the respiratory system devoid of patient assistance; ΔV(t) represents increases in lung volume from functional residual capacity; F aw (t) is airway flow; and PEEP a is the applied positive end-expiratory pressure. C rs and R rs denote the respiratory system's compliance and inspiratory resistance, respectively. In the presence of respiratory muscle activity, Eqt. 1 becomes, $${P}_{aw}\left(t\right)= \left[\frac{\varDelta V\left(t\right)}{{C}_{rs}}+ {R}_{rs}{F}_{aw}\left(t\right)+ {PEEP}_{a}\right] + {P}_{mus}\left(t\right)$$ 2 Substituting from Eqt. 1 and rearranging, $${P}_{mus}\left(t\right)= {P}_{aw}\left(t\right)- {P}_{passive}\left(t\right)$$ 3 The Pmus(t) function, which covers the duration of insufflation, is calculated using Eqt. 3 with sequential P aw (t) measurements and P passive (t) values calculated from Eq. 1 . According to Eqt.3, P mus (t) is negative for P aw (t) P passive (t), signifying expiratory muscle effort. The calculation of P passive (t) requires prior knowledge of C rs and R rs , whose values are also derived from Eqt. 1 using data from breaths with no muscle effort (P mus (t) = 0). Although Eqt. 1 by itself is indeterminate, a numerical solution has been developed [ 8 ]. This involves repeatedly solving Eqt. 1 by applying a broad spectrum of plausible C rs and R rs values to each set of measurements (ΔV(k), F aw (k) and PEEP a ) made during passive insufflation. The outcome is a C rs x R rs matrix that encompasses all possible solutions of Eqt. 1 for the given measurements, within the selected range of C rs and R rs values. A (C rs -R rs ) k function is next generated by identifying the matrix elements matching the measured P aw (k). Replicating the above process for all n measurements made during insufflation generates a family of (C rs -R rs ) n functions on the C rs -R rs plane. Since the model assumes C rs and R rs to be constant during insufflation, these (C rs -R rs ) n functions intersect at their true values, This methodology has been rigorously tested for stability and validated with clinical data [ 8 ]. Although assumed constant during the insufflation, the algorithm also recognizes that C rs and R rs may change longitudinally due to treatment or clinical factors. This is addressed by treating C rs and R rs as the mean of fixed-length vectors, operating like quasi-circular buffers. In other words, as monitoring begins, C rs and R rs values from passive insufflations are added sequentially to respective vectors. Once the vectors accumulate 180 elements, their averages are taken as initial C rs and R rs for that patient. C rs and R rs values derived from subsequent breaths meeting P mus (t) = 0 criteria are used to dynamically update these vectors with a First-In-First-Out (FIFO) method, ensuring their sizes remain constant. Model validation. It is possible to assess the validity of predicted P mus (t) by its consistency with anticipated physiological responses. Specifically, in patients ventilated with volume-control (VC) mode, where the tidal volume (V T ) is preset, P mus (t) is expected to associate with fluctuations in P aw (t). Conversely, for pressure-control (PC) mode, that provides a constant P aw (t) during the entire insufflation, P mus (t) should more closely correlate with alterations in V T . The soundness of the P mus (t) estimate is intrinsically linked to the robustness of its separate correlations with P aw (t) and V T , with a strong coefficient of determination R 2 signifying an accurate computation. Methods The algorithm was tested using F aw and P aw signals stored in a database of 250 patients treated with invasive ventilation at the Intensive Care Unit of The George Washington University Hospital. These patients had been enrolled in multiple studies approved by the Institutional Review Board (Nos. 101228, 110910, 111235) conducted between 2011 and 2015 in accordance with the 1964 Helsinki Declaration. The patients, or their appointed surrogates, gave informed consent for these studies, and the IRB allowed use of the anonymized data for subsequent research Table 1 Database Demographics and Enrollment Data (n = 250) Age (Years) 60 (18) ICU Admission type: Medical 67% Post-surgical 25% Trauma 8% Gender Male 58% Female 42% Ethnicity (% of total) : Asian 2.4% Black 54.4% Latino 4.8% Multiracial 1.6% White 36.8% Enrollment data – mean (SD) : SOFA 6 (3) SAPS II 42 (14) BMI (kg·m − 2 ) 28 (8) PEEP (cmH 2 O) 4.8 (3.7) F I O 2 (%) 51 (18) pH 7.37 (0.10) PO 2 (mmHg) 149 (73) PCO 2 (mmHg) 39 (10) SOFA = Sequential Organ Failure Assessment; SAPS II = Simplified Acute Physiological Score II; BMI = Body Mass Index; PEEP = Positive End Expiratory Pressure; F I O 2 = Fractional Inspired O 2 . Table 1 shows demographic and enrollment data for the patients in the database. There was a preponderance of medical diagnoses (67%), 58% were male, with the largest percentage of patients being of Black ethnicity (54.4%). All patients were intubated via the nasotracheal or orotracheal route and received ventilatory support using Servo_i or Servo_s ventilators (Getinge, Solna, Sweden) with various modes of ventilation. Treatment decisions were independent of the study. Enrollment occurred within 24 hours of intubation, with patients monitored for 3 [ 2 , 5 ] (median [IQR]) days. F aw and P aw signals were acquired from the ventilator data port (Computer Interface Emulator CIE, Getinge, Solna, Sweden) at 31.25 Hz and stored as sequential time-windows, termed epochs, spanning 131.1 seconds and containing 4096 samples of each P aw and F aw signal. Commencing with records starting from 2011, data from each patient were analyzed sequentially from the time of enrollment to the cessation of monitoring, with software developed according to the algorithm of Fig. 1 written in Python 3.11 programming language. The algorithm simulates the real-time patient monitoring process used in clinical settings. Excluded from analysis were epochs on bi-level ventilation and Airway Pressure Release Ventilation (APRV). Step 1 Data analysis begins by identifying epochs with a respiratory rate variability index [ 9 ] (RRVI) < 50%, a threshold observed during the N2 and N3 sleep stages [ 10 ]. Given their low RRVI, these epochs are considered to occur during times of minimal respiratory muscle activity and chosen for subsequent analysis. Step 2 : For each selected epoch, calculate C rs and R rs for every breath that meets the criteria for passive insufflation: 1) Ventilator triggered: (PEEP a – minimal P aw ) 0.8 seconds; 3) Absence of PEEP i : end exhalation (EE) F aw < 3 L·min − 1 and breath’s initial P aw (t 0 ) - prior breath’s EE P aw < 2 cmH 2 O [ 11 , 12 ], 4) No leaks in the circuit: inspired – expired V T < |30 mL|; and 5) Avoidance of lung overdistention: inspired V T < 740 mL [ 13 ]. Store calculated C rs and R rs values sequentially in respective vectors. Once the vectors are filled with 180 elements, use their averages as initial C rs and R rs for the patient. Step 3 : Determine P mus (t) for each breath in subsequent epochs. Use the calculated C rs and R rs to compute P passive (k) from Eqt. 1 and P mus (k) from Eqt. 3 for all P aw (k), F aw (k), ΔV(k), and PEEP a measurements obtained at sequential times k during the insufflation. P mus pressure-time product (P mus PTP) is calculated by numerical integration of the P mus (t) function (trapezoidal method), from the time ΔV(t) ≥ 150 mL through 90% of the insufflation’s duration, defined as the analysis time. In addition to the primary calculations, other derived metrics are: the maximum and minimum P mus , corresponding to the peak positive and negative values of P mus (P mus peak), the pressure-time product of airway pressure (P aw PTP) over the analysis period, the peak value of airway pressure (P aw peak), and tidal volume (V T ), defined as the largest volume change (ΔV(t)) achieved during insufflation. Initial C rs and R rs values are dynamically adjusted by the algorithm to reflect changes from disease progression or treatment. Epochs with RRVI < 50% are examined for breaths fulfilling the P mus = 0 criteria from Step 2. C rs and R rs determined from these breaths are added to the initial vectors (FIFO), keeping a steady tally of 180 breaths. This method allows C rs and R rs to adapt to evolving clinical conditions, while minimizing the effects of short-term variations. Correlation analysis. Upon analyzing the data from all 250 patients using the Fig. 1 algorithm, epochs were selected for correlation analysis based on specific criteria: 1) epochs ventilated on either PC or VC mode; 2) there was no indication of PEEP i , as determined by the established criteria in Step 2, and assessed as an average across all breaths within the epoch; and 3) the epoch’s data had the capacity for robust linear regression calculation, P aw (maximum - minimum) < 4 cmH 2 O for VC mode or a V T range < 100 mL for PC mode. Statistics Occasional anomalies in data acquisition giving rise to one or two univariate outliers per epoch were corrected by the z-score method [ 14 ] with z = 3. The coefficient of determination R² was calculated using Pearson’s linear regression for correlations of PmusPTP with PawPTP, and PmusPTP with V T . Normality of the R² distributions was evaluated with the Kolmogorov-Smirnov test. Depending on the normality of the data, independent sample differences were assessed using Mann–Whitney test or Student’s t-test, both corrected for multiple testing by Bonferroni’s method. Data are presented as median with interquartile range, unless noted otherwise. Two-sided p values are reported, with significance set at p 0.80) between P mus PTP and P aw PTP in VC mode, and between P mus PTP and V T in PC mode. Conversely, the hypothesis expected a weak or non-existent correlation in the opposite scenarios. Results Individual epochs examples. The following examples highlight the performance of the algorithm when applied to patient data under two different modes of ventilation, PC and VC: Figure 2 The epoch shown in Fig. 2 was obtained from a 70-year-old woman with acute heart failure. The patient was on constant flow, VC ventilation with fractional inspired O 2 (F I O 2 ) of 80%, mean V T of 450 mL, respiratory rate (RR) of 16 bpm, and PEEP a of 10 cmH 2 O. F aw and P aw signals (upper and middle panels, respectively) are uniform in timing (RRVI = 30%) and configuration, showing minor fluctuations in P aw peak. The epoch is typical of a sedated individual, with most breaths being triggered by the ventilator. The lower panel shows calculated P mus PTP as discrete points corresponding to the breaths above. P mus PTP values are positive for all insufflations, indicating the occurrence of mild expiratory efforts not readily apparent from airway signal examination. Figure 3 shows a subsequent epoch from the same patient, now on PC mode with F I O 2 = 60%, RR = 12 bpm, P aw peak = 22 cmH 2 O, and PEEP a = 5 cmH 2 O. All breaths are ventilator triggered with low RRVI (24%) and V T values ranging from 620 to 740 mL. Visual examination of the airway signals provides little insight into respiratory muscle activity, but the lower panel shows negative P mus PTP values ranging from − 1.8 to 0.3 cmH 2 O·s. The source of these inspiratory efforts is not apparent from the data, but could indicate air hunger or the presence of reverse triggering [ 15 ]. Figure 3 Figure 4 depicts the relationship between P mus PTP with P aw PTP and V T for the data of Figs. 2 and 3 . With the patient on VC ventilation, there is a strong proportional relationship between P mus PTP and P aw PTP (R² = 0.85) and none with V T (R² = 0.00). Conversely, on PC mode there is negligible correlation between P mus PTP and P aw PTP (R² = 0.10) and a strong inverse correlation between P mus PTP and V T (R² = 0.95). Overall data analysis. In the analysis of the entire 250 patient dataset, the algorithm failed to determine initial C rs and R rs in 25 patients, as they lacked sufficient breaths meeting criteria for P mus = 0. This was due to agitation following enrollment in the study in some patients and short monitoring time in others, either the result of technical difficulties or early ventilator weaning. Table 2 Number of Analyzed Epochs and Breaths Across Ventilation Modes Mode VC PC Total Patients 57 67 Analyzed Epochs 17,648 33,620 51,268 % of Total 34% 66% Analyzed Breaths 623,538 1,453,886 2,077,424 % of Total 30% 70% Outliers per Epoch 1.6 2.1 Analyzed Breaths per Epoch 34 41 VC = Volume Control; PC = Pressure Control; Patients = Number of patients in the database having at least one analyzed epoch in the designated ventilation mode. Application of the algorithm to the remaining 225 patients identified 551,642 epochs in which the algorithm could determine P mus (t) for individual breaths. From this cohort, 51,268 epochs were chosen for correlation analysis since they occurred exclusively on VC or PC ventilation modes. Table 2 displays the number of patients who were included based on having at least one epoch in the analyzed ventilation mode. Since most patients received treatment with more than one ventilation modality, it is possible for the same patient to have been included in both groups of Table 2 . There were twice as many epochs on PC mode as compared to VC mode. Outliers were < 5% of the epoch’s breaths, ensuring enough breaths remained for robust correlation analyses. Table 3 Measured Ventilation Parameters for the Analyzed Epochs Mode VC PC F I O 2 (%) 41 (10) 48 (16) * PEEP (cmH 2 O) 5.5 (0.9) 6.6 (1.9) * Peak P aw (cmH 2 O) 29 (7) 32 (6) * RR (bpm) 17 (4.3) 21 (6.1) * V T (mL) 512 (84) 566 (148) * V T /PBW (ml·kg − 1 ) 8.2 (1.2) 9.2 (2.6) * Static C rs mL·cmH 2 O − 1 46 (15) 45 (26) Inspired R rs cmH 2 O·s· L − 1 15 (7) 8 (7) * VC = Volume Control; PC = Pressure Control; F I O 2 = Fraction Inspired O 2 concentration (%); PEEP = Positive end expiratory pressure; P aw = Airway pressure; RR = Respiratory rate; V T = Tidal volume; PBW = Predicted body weight. Compliance (C rs ) and resistance (R rs ) refer to the respiratory system, including the lungs and chest wall. Figures are shown as mean (SD). * p < 0.001 two-sided t test with Bonferroni’s correction. Table 3 shows ventilation parameters stratified by ventilation mode across the analyzed epochs. The greater ventilatory assistance noted with PC mode, in terms of F I O 2 , P aw , PEEP a , V T and RR, hint at greater respiratory compromise when compared to epochs on VC mode. Table 4 Pmus Related Variables Across Ventilation Modes Directionality VC PC % of Total Efforts Inspiratory 36 (32) 31 (33) Expiratory 64 (32) 69 (33) P mus per breath (cmH 2 O) Inspiratory 1.2 (1.7) 0.7 (2.3) Expiratory 5.0 (4.6) 6.4 (4.4) * P mus PTP per breath (cmH 2 O·s) Inspiratory 1.0 (1.7) 1.1 (2.0) Expiratory 1.4 (1.4) 2.3 (2.4) * P mus PTP per minute (cmH 2 O·s·min − 1 ) § Inspiratory 17 (26) 26 (62) * Expiratory 39 (54) 89 (112) * VC = Volume Control; PC = Pressure Control; P mus = Highest inspiratory or expiratory pressure attributed to respiratory muscle effort; Inspiratory and expiratory refer to the direction of P mus; P mus PTP = P mus pressure time product; per breath = Average value of all inspiratory or all expiratory values in an epoch; § Calculated as the sum of P mus PTP (either expiratory or inspiratory) in an epoch divided by the length an epoch in minutes (2.184 minutes). Figures shown as mean (SD); * p < 0.001 comparing PC to VC; two-sided t test with Bonferroni’s correction. Table 4 presents the percentage of inspiratory and expiratory efforts per epoch, the average P mus and P mus PTP per breath, and the sum of P mus PTP values per epoch, stratified by ventilation modality and P mus directionality (inspiratory or expiratory) within an epoch. Both modes displayed a mix of inspiratory and expiratory efforts, although expiratory efforts were more vigorous, both in magnitude and frequency, in PC mode (p < 0.001). Table 5 R2 for the Correlation of PmusPTP with PawPTP and VT Mode VC PC Number of Epochs 17,648 33,620 P mus PTP vs. P aw PTP 0.91 [0.76, 0.96] * 0.06 [0.01, 0.18] * P mus PTP vs. V T 0.03 [0.01, 0.09] 0.88 [0.74, 0.94] VC = Volume Control; PC = Pressure Control; P mus = Share of airway pressure attributed to respiratory muscle effort; P mus PTP = P mus pressure time product; P aw = Airway pressure; P aw PTP = P aw pressure-time product; V T = Tidal volume. Figures shown as median [IQR]; * p < 0.001 comparing R 2 for P mus PTP vs. P aw PTP to P mus PTP vs. V T by Mann-Whitney with Bonferroni’s correction. Table 5 lists R² values for the correlation of P mus PTP with P aw PTP and V T across the analyzed epochs. In VC mode, P mus PTP demonstrates a strong positive correlation with P aw PTP (P aw PTP = 1.7 P mus PTP + 19.4; R² = 0.91; n = 17,648 epochs), while such relationship is absent for V T (R² = 0.03). Conversely, this pattern reverses in PC mode, resulting in a robust inverse association between P mus PTP and V T (V T = -43.6 P mus PTP + 615; R² = 0.88; 33,620 epochs) and a negligible one with P aw PTP (R² = 0.06). Discussion A method is proposed to estimate P mus (t) during individual insufflations in patients undergoing ventilatory support based on the numerical solution of a single-compartment model of the respiratory system. The method is non-invasive and may be used to continuously monitor patients automatically by connecting a microprocessor to the data port of a mechanical ventilator. A significant strength of the study is the extensive dataset used, comprising thousands of epochs collected continuously over several days from 250 patients mechanically ventilated using diverse ventilation modes. Specialized software assessed over two million individual breaths, consequently, the influence of sample size bias, random measurement variations, or the inclusion in the analysis of epochs with significant PEEP i levels is considered minimal. Quantifying P mus (t) is inherently difficult due to the absence of a direct method of measurement method. The present "gold standard" [ 16 ] involves the difference between esophageal pressure, measured with a fluid-filled catheter, and chest wall recoil pressure under passive conditions. However, this method is complex, as it relies on uncertain factors like chest wall elastance and a specific chest wall recoil pressure point [ 17 ]. Additionally, the variability in chest wall mechanics and the challenge in accurately distinguishing respiratory phases add to the difficulties in obtaining precise measurements of P mus (t). Given the challenges in directly measuring P mus (t), it is not unreasonable to assess the validity of its estimate indirectly by evaluating its consistency with expected physiological responses and its correlation with patient outcomes. The present study concentrated on the former. The high R² values obtained from the correlations P mus PTP vs. P aw PTP in VC mode, and P mus PTP vs. V T in PC mode, across more than 50,000 epochs, indicate a strong predictability between these variables and provides robust evidence supporting the accuracy of the predicted P mus (t). The results of the study highlight the bidirectionality of P mus during insufflation. Expiratory P mus values were predicted in more than two-thirds of insufflations in either VC or PC modes. This finding, previously noted by others [ 18 ], may be significant considering the potential for lung injury due to elevated transpulmonary pressure during expiratory efforts. On the other hand, inspiratory efforts are often indicative of air hunger, a distressing condition with long-term psychological sequelae. Confounders and limitations. A potential confounder is the possibility that epochs with significant PEEP i may have been unintentionally incorporated into the analysis. Failing to address intrinsic can PEEPi lead to an overestimation of expiratory P mus and a corresponding underestimation of inspiratory P mus . Although the automated data analysis precluded visual identification of epochs with substantial PEEP i , efforts were made to prevent this occurrence by excluding epochs meeting established criteria for this condition. Further, the database contained a limited subset of patients predisposed to the development of PEEP i with the diagnosis of asthma or chronic obstructive pulmonary disease (COPD) (8.3%) Another possible confounder is the presence of outliers related to anomalies in data acquisition or to double-triggered breaths. Outliers were systematically excluded by applying the z-score method to all P aw PTP, V T and P mus PTP datasets This approach resulted in the omission of one or two outliers per epoch, while ensuring > 30 breaths remained for regression analysis (Table 2 ). Since P mus (t) was derived directly from P aw (t) (Eq. 3 ) and indirectly from V T (Eq. 1 ), the possibility must be considered that mathematic coupling of shared variables [ 19 ] might have resulted in the robust correlations noted between P mus PTP and P aw PTP or with V T . This is an unlikely possibility, however, given the almost complete absence of association between these variables when tested for the opposite modes. Clinical application of the method is limited by the need for specialized data acquisition equipment. This concern is mitigated by the incorporation in modern ventilators of signal sampling algorithms whose output is readily accessed through a data port. Nonetheless, the sheer number of calculations needed to produce even a single breath’s P mus (t) function, makes the use of a digital computer mandatory in the clinical application of the method. Until additional studies are conducted, the performance of the method using airway signals generated by specialized ventilatory support techniques, such as bi-level ventilation and Airway Pressure Release Ventilation (APRV), remains uncertain. Conclusions The proposed method provides a non-invasive, real-time estimate of respiratory muscle activity during insufflation, one capable of distinguishing between expiratory and inspiratory efforts. This could help clinicians identify harmful respiratory patterns associated with expiratory efforts [ 20 ] that may result in injurious lung strain [ 21 ], or detect severe inspiratory exertions that indicate distressing dyspnea [ 3 ]. Efforts directed at validation, as well as establishing the range of applications for this method, warrant further investigation in future studies. Abbreviations C rs = Respiratory system static compliance. ΔV(t) = Lung volume change during insufflation. F aw = Airway flow. PEEP a = Applied positive end expiratory pressure. PEEP i = Intrinsic PEEP present at end expiration. PC = Pressure control ventilation mode. PS = Pressure support ventilation mode. P aw = airway pressure. P aw Peak = Peak inspiratory pressure. P aw PTP = Paw pressure time product. P mus = Respiratory muscles pressure. Peak_P mus = Peak respiratory muscles pressure. P mus PTP = P mus pressure time product. P passive = Paw required for passively inflation of the respiratory system. rs = Respiratory system. R rs = Respiratory system inspiratory airway resistance. RRVI = respiratory rate variability. VC = Volume control ventilation mode. V T = Tidal volume. Declarations Statements and Declarations. Ethical Approval and Consent to participate : The database used in the present study was collected during the conduct of several IRB approved studies (Nos. 101228, 110910, 111235) at The George Washington University Hospital, with the IRB allowing the use of deidentified data in further studies. Consent for publication: Not applicable. Availability of supporting data : The datasets used and analyzed during the current study can be found in the Electronic Data Repository. The database storing the raw data is available from the author upon reasonable request. Acknowledgements: The author thanks the Commission for Educational Exchange between the United States, Belgium and Luxembourg and the Fulbright Scholarship Board for their generous support as a Fulbright Research Scholar at the Erasme Hospital of the Université Libre de Bruxelles. Competing interests: The author has applied for a U.S. patent based on the information presented in the manuscript. Authors' contributions: Single author manuscript. References Slutsky AS, Ranieri VM. (2013) Ventilator-induced lung injury. N Engl J Med. 369:2126–2136,. doi: 10.1056/NEJMra1208707. Erratum in: N Engl J Med. 370:1668–1669, 2014. PMID: 24283226. Marini JJ, Rocco PRM, Gattinoni L. Static and Dynamic Contributors to Ventilator-induced Lung Injury in Clinical Practice. Pressure, Energy, and Power. Am J Respir Crit Care Med. 2020;201:767–74. 10.1164/rccm.201908-1545CI . PMID: 31665612; PMCID: PMC7124710. Demoule A, Hajage D, Messika J, Jaber S, Diallo H, Coutrot M, Kouatchet A, Azoulay E, Fartoukh M, Hraiech S, Beuret P, Darmon M, Decavèle M, Ricard JD, Chanques G, Mercat A, Schmidt M, Similowski T, REVA Network (Research Network in Mechanical Ventilation). ; (2022) Prevalence, Intensity, and Clinical Impact of Dyspnea in Critically Ill Patients Receiving Invasive Ventilation. Am J Respir Crit Care Med. 205:917–926. 10.1164/rccm.202108-1857OC . PMID: 35061577. Worsham CM, Banzett RB, Schwartzstein RM. Dyspnea, Acute Respiratory Failure, Psychological Trauma, and Post-ICU Mental Health: A Caution and a Call for Research. Chest. 2021;159:749–56. 10.1016/j.chest.2020.09.251 . Epub 2020 Oct 1. PMID: 33011205; PMCID: PMC7528739. Rohrer F. Der Zusammenhang der Atemkräfte und ihre Abhängigkeit vom Dehnungszustand der Atmungsorgane. Pflüger's Arch. 1916;165:419–44. doi.org/10.1007/BF01681079 . Otis 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. Rossi 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. 10.1152/jappl.1985.58.6.1849 . PMID: 4008405. Gutierrez G. A novel method to calculate compliance and airway resistance in ventilated patients. Intensive Care Med Exp. 2022;10:55. 10.1186/s40635-022-00483-2 . PMID: 36581716; PMCID: PMC9800666. Gutierrez G, Ballarino GJ, Turkan H, Abril J, De La Cruz L, Edsall C, George B, Gutierrez S, Jha V, Ahari J. Automatic detection of patient-ventilator asynchrony by spectral analysis of airway flow. Crit Care. 2011;15:R167. 10.1186/cc10309 . PMID: 21749683; PMCID: PMC3387605. Gutierrez G, Williams J, Alrehaili GA, McLean A, Pirouz R, Amdur R, Jain V, Ahari J, Bawa A, Kimbro S. Respiratory rate variability in sleeping adults without obstructive sleep apnea. Physiol Rep. 2016;4:e12949. 10.14814/phy2.12949 . PMID: 27597768; PMCID: PMC5027356. Rossi A, Gottfried SB, Zocchi L, Higgs BD, Lennox S, Calverley PM, Begin P, Grassino A, Milic-Emili J. (1985) Measurement of static compliance of the total respiratory system in patients with acute respiratory failure during mechanical ventilation. The effect of intrinsic positive end-expiratory pressure. Am Rev Respir Dis 131:672–677. 10.1164/arrd.1985.131.5.672 . PMID: 4003913. Brochard L. Intrinsic (or auto-) PEEP during controlled mechanical ventilation. Intensive Care Med. 2002;28:1376–8. 10.1007/s00134-002-1438-8 . Servillo G, Svantesson C, Beydon L, Roupie E, Brochard L, Lemaire F, Jonson B. Pressure–volume curves in acute respiratory failure: automated low flow inflation versus occlusion. Am J Respir Crit Care Med. 1997;155:1629–36. 10.1164/ajrccm.155.5.9154868 . Rousseeuw PJ, Hubert M. Anomaly detection by robust statistics WIREs. Data Min Knowl Discov. 2018;8:e1236. 10.1002/widm.1236 . Akoumianaki E, Lyazidi A, Rey N, Matamis D, Perez-Martinez N, Giraud R, Mancebo J, Brochard L, Richard JM. (2013) Mechanical ventilation-induced reverse-triggered breaths: a frequently unrecognized form of neuromechanical coupling. Chest. 143:927–938. 10.1378/chest.12-1817 . PMID: 23187649. Jonkman AH, Telias I, Spinelli E, Akoumianaki E, Piquilloud L. The oesophageal balloon for respiratory monitoring in ventilated patients: updated clinical review and practical aspects. Eur Respir Rev. 2023;32:220186. 10.1183/16000617.0186-2022 . PMID: 37197768; PMCID: PMC10189643. Mauri 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—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. Jubran A, Van de Graaff WB, Tobin MJ. (1995) Variability of patient-ventilator interaction with pressure support ventilation in patients with chronic obstructive pulmonary disease. Am J Respir Crit Care Med. 152:129–136. 10.1164/ajrccm.152.1.7599811 . PMID: 7599811. Archie JP Jr. Mathematic coupling of data: a common source of error. Ann Surg. 1981;193:296–303. 10.1097/00000658-198103000-00008 . PMID: 7212790; PMCID: PMC1345065. Carteaux G, Parfait M, Combet M, Haudebourg AF, Tuffet S, Mekontso Dessap A. Patient-Self Inflicted Lung Injury: A Practical Review. J Clin Med. 2021;10:2738. 10.3390/jcm10122738 . PMID: 34205783; PMCID: PMC8234933. Yoshida T, Nakahashi S, Nakamura MAM, Koyama Y, Roldan R, Torsani V, De Santis RR, Gomes S, Uchiyama A, Amato MBP, Kavanagh BP, Fujino Y. (2017) Volume-controlled Ventilation Does Not Prevent Injurious Inflation during Spontaneous Effort. Am J Respir Crit Care Med. 196:590–601. 10.1164/rccm.201610-1972OC . PMID: 28212050. Yoshida 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, 2. 10.1164/rccm.201604-0748CP . PMID: 27786562. Demoule A, Hajage D, Messika J, Jaber S, Diallo H, Coutrot M, Kouatchet A, Azoulay E, Fartoukh M, Hraiech S, Beuret P, Darmon M, Decavèle M, Ricard JD, Chanques G, Mercat A, Schmidt M, Similowski T. ; REVA Network (Research Network in Mechanical Ventilation) (2022) Prevalence, Intensity, and Clinical Impact of Dyspnea in Critically Ill Patients Receiving Invasive Ventilation. Am J Respir Crit Care Med 205:917–26. doi: 10.1164/rccm.202108-1857OC. PMID: 35061577. Additional Declarations Competing interest reported. The author has applied for a U.S. patent based on the information presented in the manuscript. Supplementary Files DemographicsGutierrez.xlsx Cite Share Download PDF Status: Under Review Version 1 posted Submission checks completed at journal 09 Jan, 2024 Editor assigned by journal 09 Jan, 2024 First submitted to journal 05 Jan, 2024 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-3838325","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":266094500,"identity":"6f1d85ec-26ac-46de-9056-e604e7a9fdbe","order_by":0,"name":"Guillermo Gutierrez","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsUlEQVRIiWNgGAWjYJCCAwwMNhAWD3EamEFa0kjUAgSHSdAiH5F/8MDHPeftNtxIYHzwto0ILYY3khkOznh2OxmohdlwLlFaZiQzHOY5cDvZ7EYCmzQv0Vr+HDgH0sL+mygt8hJALQwHDtiBbGEmSosBz2ODgz0HkhPszzxslpxzjhhb2hMff/hxwM5esj354Ic3ZcTYcgBCJzYwMDYQoR5kC1SdPXHKR8EoGAWjYEQCAMoGPBrYl3SYAAAAAElFTkSuQmCC","orcid":"","institution":"The George Washington University","correspondingAuthor":true,"prefix":"","firstName":"Guillermo","middleName":"","lastName":"Gutierrez","suffix":""}],"badges":[],"createdAt":"2024-01-05 22:44:41","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3838325/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3838325/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49493604,"identity":"97124aaa-e4f2-445a-bba3-7081fd554a09","added_by":"auto","created_at":"2024-01-11 19:01:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":47638,"visible":true,"origin":"","legend":"\u003cp\u003eAlgorithm used to analyze data from 250 patients in chronologic order from 2011 to 2015.\u0026nbsp; Stage 1: Search the database for epochs with respiratory rate variability (RRVI) \u0026nbsp;\u0026lt; 50%, considered to occur when P\u003csub\u003emus\u003c/sub\u003e = 0. Stage 2: Identify all breaths in the selected epochs meeting a strict criteria for absent P\u003csub\u003emus\u003c/sub\u003e and PEEP\u003csub\u003ei\u003c/sub\u003e. Apply the numerical solution of the equation of motion to calculate breath specific C\u003csub\u003ers\u003c/sub\u003e and R\u003csub\u003ers\u003c/sub\u003e and fill respective vectors sequentially to a length of 180 elements. Stage 3: Use the mean of these vectors as estimates for C\u003csub\u003ers\u003c/sub\u003e and R\u003csub\u003ers\u003c/sub\u003e to calculate P\u003csub\u003epassive\u003c/sub\u003e from Eqt. 1 and P\u003csub\u003emus\u003c/sub\u003e from Eqt. 3. Account for longitudinal variations in C\u003csub\u003ers\u003c/sub\u003e and R\u003csub\u003ers\u003c/sub\u003e by calculating their values in subsequent epochs with RRVI \u0026lt; 50% and incorporate them in the respective vectors FIFO.\u003c/p\u003e","description":"","filename":"Fig1Gutierrez.png","url":"https://assets-eu.researchsquare.com/files/rs-3838325/v1/43e3e4838729185eddba588b.png"},{"id":49493603,"identity":"a5c9c929-0059-4c4f-b19d-7a6dd3f94ef9","added_by":"auto","created_at":"2024-01-11 19:01:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":66020,"visible":true,"origin":"","legend":"\u003cp\u003eEpoch lasting 131 seconds acquired from a patient on VC ventilation. The upper and middle panels show F\u003csub\u003eaw\u003c/sub\u003e and P\u003csub\u003eaw\u003c/sub\u003e signals, respectively. The bottom panel depicts calculated P\u003csub\u003emus\u003c/sub\u003ePTP as discrete points, each datum corresponding to the breath above.\u003c/p\u003e","description":"","filename":"Fig2Gutierrez.png","url":"https://assets-eu.researchsquare.com/files/rs-3838325/v1/6f9a5441c69c030d7d97b19d.png"},{"id":49493607,"identity":"f5f0afa6-c662-48e3-ab3e-719fe0cbb9b9","added_by":"auto","created_at":"2024-01-11 19:01:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":60078,"visible":true,"origin":"","legend":"\u003cp\u003eEpoch lasting 131 seconds acquired from a patient on PC ventilation. The upper and middle panels show F\u003csub\u003eaw\u003c/sub\u003e and P\u003csub\u003eaw\u003c/sub\u003e signals, respectively. The bottom panel depicts calculated P\u003csub\u003emus\u003c/sub\u003ePTP as discrete points, each datum corresponding to the breath above.\u003c/p\u003e","description":"","filename":"Fig3Gutierrez.png","url":"https://assets-eu.researchsquare.com/files/rs-3838325/v1/d840592a394dad33f35ddb0f.png"},{"id":49493605,"identity":"f9e7cb94-87d7-46a5-b9a8-25bac0ebbff0","added_by":"auto","created_at":"2024-01-11 19:01:28","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":46517,"visible":true,"origin":"","legend":"\u003cp\u003eRelationships of P\u003csub\u003emus\u003c/sub\u003ePTP vs. P\u003csub\u003eaw\u003c/sub\u003ePTP and P\u003csub\u003emus\u003c/sub\u003ePTP vs. V\u003csub\u003eT\u003c/sub\u003e for the examples shown in Figs. 2 and 3. With VC ventilation there is a strong correlation for P\u003csub\u003emus\u003c/sub\u003ePTP vs. P\u003csub\u003eaw\u003c/sub\u003ePTP (R\u003csup\u003e2\u003c/sup\u003e = 0.85) while being absent for P\u003csub\u003emus\u003c/sub\u003ePTP vs. V\u003csub\u003eT\u003c/sub\u003e (R\u003csup\u003e2\u003c/sup\u003e = 0.00). Conversely, PC ventilation is characterized by minimal correlation between P\u003csub\u003emus\u003c/sub\u003ePTP and P\u003csub\u003eaw\u003c/sub\u003ePTP (R\u003csup\u003e2\u003c/sup\u003e = 0.10) \u003cdel\u003ea\u003c/del\u003e\u003cu\u003ea\u003c/u\u003end a robust inverse correlation between P\u003csub\u003emus\u003c/sub\u003ePTP and V\u003csub\u003eT\u003c/sub\u003e (R\u003csup\u003e2\u003c/sup\u003e = 0.95).\u003c/p\u003e","description":"","filename":"Fig4Gutierrez.png","url":"https://assets-eu.researchsquare.com/files/rs-3838325/v1/16f281b0d0f34fa5f73d367f.png"},{"id":49493873,"identity":"aecb65e7-195c-4229-9b23-516caf75a82d","added_by":"auto","created_at":"2024-01-11 19:09:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1179255,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3838325/v1/65c7d5de-0e84-4561-9c76-f7e202bb616f.pdf"},{"id":49493606,"identity":"4fc357e9-2957-466b-ba32-f7a1a5584e1a","added_by":"auto","created_at":"2024-01-11 19:01:28","extension":"xlsx","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":109041,"visible":true,"origin":"","legend":"","description":"","filename":"DemographicsGutierrez.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3838325/v1/3a796d7c66adf5ff787b3a5f.xlsx"}],"financialInterests":"Competing interest reported. The author has applied for a U.S. patent based on the information presented in the manuscript.","formattedTitle":"An Automatic, Non-Invasive Method to Monitor Respiratory Muscle Effort During Mechanical Ventilation","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe process of mechanically ventilating the respiratory system, that includes the lungs and thoracic cage, is often influenced by a patient's level of consciousness. For heavily sedated or paralyzed patients, insufflation is entirely passive. Yet, conscious patients may exhibit an active response, such as trying to exhale during insufflation risking injurious lung strain [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], or develop forceful inhalations if experiencing air hunger [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], a stressful emotional state that may lead to long term psychologic sequela [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eQuantification of patient effort during ventilatory support could help clinicians optimize ventilator settings and calibrate sedative administration. With that goal in mind, the current research proposes a non-invasive method to estimate the portion of airway pressure (P\u003csub\u003eaw\u003c/sub\u003e) attributed to muscular effort (P\u003csub\u003emus\u003c/sub\u003e) during insufflation automatically.\u003c/p\u003e \u003cp\u003e \u003cb\u003eModel Development.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe single compartment model of the respiratory system [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] during positive pressure ventilation with negligible P\u003csub\u003emus\u003c/sub\u003e, may be expressed as [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$${ P}_{passive}\\left(t\\right)= \\frac{\\varDelta V\\left(t\\right)}{{C}_{rs}}+ {R}_{rs}{F}_{aw}\\left(t\\right)+ {PEEP}_{a}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere P\u003csub\u003epassive\u003c/sub\u003e(t) is the airway pressure required to inflate the respiratory system devoid of patient assistance; ΔV(t) represents increases in lung volume from functional residual capacity; F\u003csub\u003eaw\u003c/sub\u003e(t) is airway flow; and PEEP\u003csub\u003ea\u003c/sub\u003e is the applied positive end-expiratory pressure. C\u003csub\u003ers\u003c/sub\u003e and R\u003csub\u003ers\u003c/sub\u003e denote the respiratory system's compliance and inspiratory resistance, respectively.\u003c/p\u003e \u003cp\u003eIn the presence of respiratory muscle activity, Eqt. 1 becomes,\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$${P}_{aw}\\left(t\\right)= \\left[\\frac{\\varDelta V\\left(t\\right)}{{C}_{rs}}+ {R}_{rs}{F}_{aw}\\left(t\\right)+ {PEEP}_{a}\\right] + {P}_{mus}\\left(t\\right)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eSubstituting from Eqt. 1 and rearranging,\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\u003eThe Pmus(t) function, which covers the duration of insufflation, is calculated using Eqt. 3 with sequential P\u003csub\u003eaw\u003c/sub\u003e(t) measurements and P\u003csub\u003epassive\u003c/sub\u003e(t) values calculated from Eq.\u0026nbsp;\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. According to Eqt.3, P\u003csub\u003emus\u003c/sub\u003e(t) is negative for P\u003csub\u003eaw\u003c/sub\u003e (t)\u0026thinsp;\u0026lt;\u0026thinsp;P\u003csub\u003epassive\u003c/sub\u003e(t), indicating inspiratory muscle activity, and positive for P\u003csub\u003eaw\u003c/sub\u003e (t)\u0026thinsp;\u0026gt;\u0026thinsp;P\u003csub\u003epassive\u003c/sub\u003e(t), signifying expiratory muscle effort.\u003c/p\u003e \u003cp\u003eThe calculation of P\u003csub\u003epassive\u003c/sub\u003e(t) requires prior knowledge of C\u003csub\u003ers\u003c/sub\u003e and R\u003csub\u003ers\u003c/sub\u003e, whose values are also derived from Eqt. 1 using data from breaths with no muscle effort (P\u003csub\u003emus\u003c/sub\u003e(t)\u0026thinsp;=\u0026thinsp;0). Although Eqt. 1 by itself is indeterminate, a numerical solution has been developed [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. This involves repeatedly solving Eqt. 1 by applying a broad spectrum of plausible C\u003csub\u003ers\u003c/sub\u003e and R\u003csub\u003ers\u003c/sub\u003e values to each set of measurements (ΔV(k), F\u003csub\u003eaw\u003c/sub\u003e(k) and PEEP\u003csub\u003ea\u003c/sub\u003e) made during passive insufflation. The outcome is a C\u003csub\u003ers\u003c/sub\u003e x R\u003csub\u003ers\u003c/sub\u003e matrix that encompasses all possible solutions of Eqt. 1 for the given measurements, within the selected range of C\u003csub\u003ers\u003c/sub\u003e and R\u003csub\u003ers\u003c/sub\u003e values. A (C\u003csub\u003ers\u003c/sub\u003e-R\u003csub\u003ers\u003c/sub\u003e)\u003csub\u003ek\u003c/sub\u003e function is next generated by identifying the matrix elements matching the measured P\u003csub\u003eaw\u003c/sub\u003e(k).\u003c/p\u003e \u003cp\u003eReplicating the above process for all n measurements made during insufflation generates a family of (C\u003csub\u003ers\u003c/sub\u003e-R\u003csub\u003ers\u003c/sub\u003e)\u003csub\u003en\u003c/sub\u003e functions on the C\u003csub\u003ers\u003c/sub\u003e-R\u003csub\u003ers\u003c/sub\u003e plane. Since the model assumes C\u003csub\u003ers\u003c/sub\u003e and R\u003csub\u003ers\u003c/sub\u003e to be constant during insufflation, these (C\u003csub\u003ers\u003c/sub\u003e-R\u003csub\u003ers\u003c/sub\u003e)\u003csub\u003en\u003c/sub\u003e functions intersect at their true values, This methodology has been rigorously tested for stability and validated with clinical data [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough assumed constant during the insufflation, the algorithm also recognizes that C\u003csub\u003ers\u003c/sub\u003e and R\u003csub\u003ers\u003c/sub\u003e may change longitudinally due to treatment or clinical factors. This is addressed by treating C\u003csub\u003ers\u003c/sub\u003e and R\u003csub\u003ers\u003c/sub\u003e as the mean of fixed-length vectors, operating like quasi-circular buffers. In other words, as monitoring begins, C\u003csub\u003ers\u003c/sub\u003e and R\u003csub\u003ers\u003c/sub\u003e values from passive insufflations are added sequentially to respective vectors. Once the vectors accumulate 180 elements, their averages are taken as initial C\u003csub\u003ers\u003c/sub\u003e and R\u003csub\u003ers\u003c/sub\u003e for that patient. C\u003csub\u003ers\u003c/sub\u003e and R\u003csub\u003ers\u003c/sub\u003e values derived from subsequent breaths meeting P\u003csub\u003emus\u003c/sub\u003e(t)\u0026thinsp;=\u0026thinsp;0 criteria are used to dynamically update these vectors with a First-In-First-Out (FIFO) method, ensuring their sizes remain constant.\u003c/p\u003e \u003cp\u003e \u003cb\u003eModel validation.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIt is possible to assess the validity of predicted P\u003csub\u003emus\u003c/sub\u003e(t) by its consistency with anticipated physiological responses. Specifically, in patients ventilated with volume-control (VC) mode, where the tidal volume (V\u003csub\u003eT\u003c/sub\u003e) is preset, P\u003csub\u003emus\u003c/sub\u003e(t) is expected to associate with fluctuations in P\u003csub\u003eaw\u003c/sub\u003e(t). Conversely, for pressure-control (PC) mode, that provides a constant P\u003csub\u003eaw\u003c/sub\u003e(t) during the entire insufflation, P\u003csub\u003emus\u003c/sub\u003e(t) should more closely correlate with alterations in V\u003csub\u003eT\u003c/sub\u003e. The soundness of the P\u003csub\u003emus\u003c/sub\u003e(t) estimate is intrinsically linked to the robustness of its separate correlations with P\u003csub\u003eaw\u003c/sub\u003e(t) and V\u003csub\u003eT\u003c/sub\u003e, with a strong coefficient of determination R\u003csup\u003e2\u003c/sup\u003e signifying an accurate computation.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThe algorithm was tested using F\u003csub\u003eaw\u003c/sub\u003e and P\u003csub\u003eaw\u003c/sub\u003e signals stored in a database of 250 patients treated with invasive ventilation at the Intensive Care Unit of The George Washington University Hospital. These patients had been enrolled in multiple studies approved by the Institutional Review Board (Nos. 101228, 110910, 111235) conducted between 2011 and 2015 in accordance with the 1964 Helsinki Declaration. The patients, or their appointed surrogates, gave informed consent for these studies, and the IRB allowed use of the anonymized data for subsequent research\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDatabase Demographics and Enrollment Data (n\u0026thinsp;=\u0026thinsp;250)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"2\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAge (Years)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e60 (18)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eICU Admission type:\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedical\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePost-surgical\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTrauma\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEthnicity (% of total)\u003c/strong\u003e:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAsian\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBlack\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLatino\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMultiracial\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWhite\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEnrollment data \u0026ndash; mean (SD)\u003c/strong\u003e:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSOFA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSAPS II\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42 (14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI (kg\u0026middot;m\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e\u0026minus;\u0026thinsp;2\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28 (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePEEP (cmH\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003eO)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.8 (3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003eI\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003eO\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sub\u003e \u003cstrong\u003e(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51 (18)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003epH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.37 (0.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePO\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sub\u003e \u003cstrong\u003e(mmHg)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e149 (73)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePCO\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sub\u003e \u003cstrong\u003e(mmHg)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003e\u003cstrong\u003eSOFA\u0026thinsp;=\u0026thinsp;Sequential Organ Failure Assessment; SAPS II\u0026thinsp;=\u0026thinsp;Simplified Acute Physiological Score II; BMI\u0026thinsp;=\u0026thinsp;Body Mass Index; PEEP\u0026thinsp;=\u0026thinsp;Positive End Expiratory Pressure; F\u003c/strong\u003e \u003csub\u003e\u0026nbsp;\u003cstrong\u003eI\u003c/strong\u003e\u0026nbsp;\u003c/sub\u003e \u003cstrong\u003eO\u003c/strong\u003e \u003csub\u003e\u0026nbsp;\u003cstrong\u003e2\u003c/strong\u003e\u0026nbsp;\u003c/sub\u003e \u003cstrong\u003e= Fractional Inspired O\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sub\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e shows demographic and enrollment data for the patients in the database. There was a preponderance of medical diagnoses (67%), 58% were male, with the largest percentage of patients being of Black ethnicity (54.4%).\u003c/p\u003e\n\u003cp\u003eAll patients were intubated via the nasotracheal or orotracheal route and received ventilatory support using Servo_i or Servo_s ventilators (Getinge, Solna, Sweden) with various modes of ventilation. Treatment decisions were independent of the study. Enrollment occurred within 24 hours of intubation, with patients monitored for 3 [\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e] (median [IQR]) days.\u003c/p\u003e\n\u003cp\u003eF\u003csub\u003eaw\u003c/sub\u003e and P\u003csub\u003eaw\u003c/sub\u003e signals were acquired from the ventilator data port (Computer Interface Emulator CIE, Getinge, Solna, Sweden) at 31.25 Hz and stored as sequential time-windows, termed epochs, spanning 131.1 seconds and containing 4096 samples of each P\u003csub\u003eaw\u003c/sub\u003e and F\u003csub\u003eaw\u003c/sub\u003e signal. Commencing with records starting from 2011, data from each patient were analyzed sequentially from the time of enrollment to the cessation of monitoring, with software developed according to the algorithm of Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e written in Python 3.11 programming language. The algorithm simulates the real-time patient monitoring process used in clinical settings. Excluded from analysis were epochs on bi-level ventilation and Airway Pressure Release Ventilation (APRV).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStep 1\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData analysis begins by identifying epochs with a respiratory rate variability index [\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e] (RRVI)\u0026thinsp;\u0026lt;\u0026thinsp;50%, a threshold observed during the N2 and N3 sleep stages [\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e]. Given their low RRVI, these epochs are considered to occur during times of minimal respiratory muscle activity and chosen for subsequent analysis.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStep 2\u003c/em\u003e: For each selected epoch, calculate C\u003csub\u003ers\u003c/sub\u003e and R\u003csub\u003ers\u003c/sub\u003e for every breath that meets the criteria for passive insufflation: 1) Ventilator triggered: (PEEP\u003csub\u003ea\u003c/sub\u003e \u0026ndash; minimal P\u003csub\u003eaw\u003c/sub\u003e)\u0026thinsp;\u0026lt;\u0026thinsp;0.3 cmH\u003csub\u003e2\u003c/sub\u003eO; 2) Full volume breaths: V\u003csub\u003eT\u003c/sub\u003e \u0026ge; 250 mL with insufflation time (Ti)\u0026thinsp;\u0026gt;\u0026thinsp;0.8 seconds; 3) Absence of PEEP\u003csub\u003ei\u003c/sub\u003e: end exhalation (EE) F\u003csub\u003eaw\u003c/sub\u003e \u0026lt; 3 L\u0026middot;min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and breath\u0026rsquo;s initial P\u003csub\u003eaw\u003c/sub\u003e(t\u003csub\u003e0\u003c/sub\u003e) - prior breath\u0026rsquo;s EE P\u003csub\u003eaw\u003c/sub\u003e \u0026lt; 2 cmH\u003csub\u003e2\u003c/sub\u003eO [\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e], 4) No leaks in the circuit: inspired \u0026ndash; expired V\u003csub\u003eT\u003c/sub\u003e \u0026lt; |30 mL|; and 5) Avoidance of lung overdistention: inspired V\u003csub\u003eT\u003c/sub\u003e \u0026lt; 740 mL [\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e]. Store calculated C\u003csub\u003ers\u003c/sub\u003e and R\u003csub\u003ers\u003c/sub\u003e values sequentially in respective vectors. Once the vectors are filled with 180 elements, use their averages as initial C\u003csub\u003ers\u003c/sub\u003e and R\u003csub\u003ers\u003c/sub\u003e for the patient.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStep 3\u003c/em\u003e: Determine P\u003csub\u003emus\u003c/sub\u003e(t) for each breath in subsequent epochs. Use the calculated C\u003csub\u003ers\u003c/sub\u003e and R\u003csub\u003ers\u003c/sub\u003e to compute P\u003csub\u003epassive\u003c/sub\u003e(k) from Eqt. 1 and P\u003csub\u003emus\u003c/sub\u003e(k) from Eqt. 3 for all P\u003csub\u003eaw\u003c/sub\u003e(k), F\u003csub\u003eaw\u003c/sub\u003e(k), \u0026Delta;V(k), and PEEP\u003csub\u003ea\u003c/sub\u003e measurements obtained at sequential times k during the insufflation. P\u003csub\u003emus\u003c/sub\u003e pressure-time product (P\u003csub\u003emus\u003c/sub\u003ePTP) is calculated by numerical integration of the P\u003csub\u003emus\u003c/sub\u003e(t) function (trapezoidal method), from the time \u0026Delta;V(t)\u0026thinsp;\u0026ge;\u0026thinsp;150 mL through 90% of the insufflation\u0026rsquo;s duration, defined as the analysis time. In addition to the primary calculations, other derived metrics are: the maximum and minimum P\u003csub\u003emus\u003c/sub\u003e, corresponding to the peak positive and negative values of P\u003csub\u003emus\u003c/sub\u003e (P\u003csub\u003emus\u003c/sub\u003epeak), the pressure-time product of airway pressure (P\u003csub\u003eaw\u003c/sub\u003ePTP) over the analysis period, the peak value of airway pressure (P\u003csub\u003eaw\u003c/sub\u003epeak), and tidal volume (V\u003csub\u003eT\u003c/sub\u003e), defined as the largest volume change (\u0026Delta;V(t)) achieved during insufflation.\u003c/p\u003e\n\u003cp\u003eInitial C\u003csub\u003ers\u003c/sub\u003e and R\u003csub\u003ers\u003c/sub\u003e values are dynamically adjusted by the algorithm to reflect changes from disease progression or treatment. Epochs with RRVI\u0026thinsp;\u0026lt;\u0026thinsp;50% are examined for breaths fulfilling the P\u003csub\u003emus\u003c/sub\u003e = 0 criteria from Step 2. C\u003csub\u003ers\u003c/sub\u003e and R\u003csub\u003ers\u003c/sub\u003e determined from these breaths are added to the initial vectors (FIFO), keeping a steady tally of 180 breaths. This method allows C\u003csub\u003ers\u003c/sub\u003e and R\u003csub\u003ers\u003c/sub\u003e to adapt to evolving clinical conditions, while minimizing the effects of short-term variations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelation analysis.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUpon analyzing the data from all 250 patients using the Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e algorithm, epochs were selected for correlation analysis based on specific criteria: 1) epochs ventilated on either PC or VC mode; 2) there was no indication of PEEP\u003csub\u003ei\u003c/sub\u003e, as determined by the established criteria in Step 2, and assessed as an average across all breaths within the epoch; and 3) the epoch\u0026rsquo;s data had the capacity for robust linear regression calculation, P\u003csub\u003eaw\u003c/sub\u003e (maximum - minimum)\u0026thinsp;\u0026lt;\u0026thinsp;4 cmH\u003csub\u003e2\u003c/sub\u003eO for VC mode or a V\u003csub\u003eT\u003c/sub\u003e range\u0026thinsp;\u0026lt;\u0026thinsp;100 mL for PC mode.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOccasional anomalies in data acquisition giving rise to one or two univariate outliers per epoch were corrected by the z-score method [\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e] with z\u0026thinsp;=\u0026thinsp;3. The coefficient of determination R\u0026sup2; was calculated using Pearson\u0026rsquo;s linear regression for correlations of PmusPTP with PawPTP, and PmusPTP with V\u003csub\u003eT\u003c/sub\u003e. Normality of the R\u0026sup2; distributions was evaluated with the Kolmogorov-Smirnov test. Depending on the normality of the data, independent sample differences were assessed using Mann\u0026ndash;Whitney test or Student\u0026rsquo;s t-test, both corrected for multiple testing by Bonferroni\u0026rsquo;s method. Data are presented as median with interquartile range, unless noted otherwise. Two-sided p values are reported, with significance set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e\u003cem\u003eHypothesis testing\u003c/em\u003e:\u003c/h2\u003e\n \u003cp\u003eMethod validation relied on establishing a strong correlation (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.80) between P\u003csub\u003emus\u003c/sub\u003ePTP and P\u003csub\u003eaw\u003c/sub\u003ePTP in VC mode, and between P\u003csub\u003emus\u003c/sub\u003ePTP and V\u003csub\u003eT\u003c/sub\u003e in PC mode. Conversely, the hypothesis expected a weak or non-existent correlation in the opposite scenarios.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cem\u003eIndividual epochs examples.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e The following examples highlight the performance of the algorithm when applied to patient data under two different modes of ventilation, PC and VC:\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003c/p\u003e \u003cp\u003eThe epoch shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e was obtained from a 70-year-old woman with acute heart failure. The patient was on constant flow, VC ventilation with fractional inspired O\u003csub\u003e2\u003c/sub\u003e (F\u003csub\u003eI\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e) of 80%, mean V\u003csub\u003eT\u003c/sub\u003e of 450 mL, respiratory rate (RR) of 16 bpm, and PEEP\u003csub\u003ea\u003c/sub\u003e of 10 cmH\u003csub\u003e2\u003c/sub\u003eO.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eF\u003csub\u003eaw\u003c/sub\u003e and P\u003csub\u003eaw\u003c/sub\u003e signals (upper and middle panels, respectively) are uniform in timing (RRVI\u0026thinsp;=\u0026thinsp;30%) and configuration, showing minor fluctuations in P\u003csub\u003eaw\u003c/sub\u003epeak. The epoch is typical of a sedated individual, with most breaths being triggered by the ventilator. The lower panel shows calculated P\u003csub\u003emus\u003c/sub\u003ePTP as discrete points corresponding to the breaths above. P\u003csub\u003emus\u003c/sub\u003ePTP values are positive for all insufflations, indicating the occurrence of mild expiratory efforts not readily apparent from airway signal examination.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows a subsequent epoch from the same patient, now on PC mode with F\u003csub\u003eI\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;60%, RR\u0026thinsp;=\u0026thinsp;12 bpm, P\u003csub\u003eaw\u003c/sub\u003epeak = 22 cmH\u003csub\u003e2\u003c/sub\u003eO, and PEEP\u003csub\u003ea\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;5 cmH\u003csub\u003e2\u003c/sub\u003eO. All breaths are ventilator triggered with low RRVI (24%) and V\u003csub\u003eT\u003c/sub\u003e values ranging from 620 to 740 mL. Visual examination of the airway signals provides little insight into respiratory muscle activity, but the lower panel shows negative P\u003csub\u003emus\u003c/sub\u003ePTP values ranging from \u0026minus;\u0026thinsp;1.8 to 0.3 cmH\u003csub\u003e2\u003c/sub\u003eO\u0026middot;s. The source of these inspiratory efforts is not apparent from the data, but could indicate air hunger or the presence of reverse triggering [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e depicts the relationship between P\u003csub\u003emus\u003c/sub\u003ePTP with P\u003csub\u003eaw\u003c/sub\u003ePTP and V\u003csub\u003eT\u003c/sub\u003e for the data of Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. With the patient on VC ventilation, there is a strong proportional relationship between P\u003csub\u003emus\u003c/sub\u003ePTP and P\u003csub\u003eaw\u003c/sub\u003ePTP (R\u0026sup2; = 0.85) and none with V\u003csub\u003eT\u003c/sub\u003e (R\u0026sup2; = 0.00). Conversely, on PC mode there is negligible correlation between P\u003csub\u003emus\u003c/sub\u003ePTP and P\u003csub\u003eaw\u003c/sub\u003ePTP (R\u0026sup2; = 0.10) and a strong inverse correlation between P\u003csub\u003emus\u003c/sub\u003ePTP and V\u003csub\u003eT\u003c/sub\u003e (R\u0026sup2; = 0.95).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eOverall data analysis.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eIn the analysis of the entire 250 patient dataset, the algorithm failed to determine initial C\u003csub\u003ers\u003c/sub\u003e and R\u003csub\u003ers\u003c/sub\u003e in 25 patients, as they lacked sufficient breaths meeting criteria for P\u003csub\u003emus\u003c/sub\u003e = 0. This was due to agitation following enrollment in the study in some patients and short monitoring time in others, either the result of technical difficulties or early ventilator weaning.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNumber of Analyzed Epochs and Breaths Across Ventilation Modes\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMode\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePatients\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67\u003c/p\u003e \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\u003e\u003cb\u003eAnalyzed Epochs\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17,648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33,620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e51,268\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e% of Total\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66%\u003c/p\u003e \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\u003e\u003cb\u003eAnalyzed Breaths\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e623,538\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,453,886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2,077,424\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e% of Total\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70%\u003c/p\u003e \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\u003e\u003cb\u003eOutliers per Epoch\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.1\u003c/p\u003e \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\u003e\u003cb\u003eAnalyzed Breaths per Epoch\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003cb\u003eVC\u0026thinsp;=\u0026thinsp;Volume Control; PC\u0026thinsp;=\u0026thinsp;Pressure Control; Patients\u0026thinsp;=\u0026thinsp;Number of patients in the database having at least one analyzed epoch in the designated ventilation mode.\u003c/b\u003e \u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eApplication of the algorithm to the remaining 225 patients identified 551,642 epochs in which the algorithm could determine P\u003csub\u003emus\u003c/sub\u003e(t) for individual breaths. From this cohort, 51,268 epochs were chosen for correlation analysis since they occurred exclusively on VC or PC ventilation modes. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e displays the number of patients who were included based on having at least one epoch in the analyzed ventilation mode. Since most patients received treatment with more than one ventilation modality, it is possible for the same patient to have been included in both groups of Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e. There were twice as many epochs on PC mode as compared to VC mode. Outliers were \u0026lt;\u0026thinsp;5% of the epoch\u0026rsquo;s breaths, ensuring enough breaths remained for robust correlation analyses.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e Measured Ventilation Parameters for the Analyzed Epochs\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\u003eMode\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eF\u003c/b\u003e\u003csub\u003e\u003cb\u003eI\u003c/b\u003e\u003c/sub\u003e\u003cb\u003eO\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48 (16) *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePEEP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e(cmH\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e\u003cb\u003eO)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.5 (0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.6 (1.9) *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePeak P\u003c/b\u003e\u003csub\u003e\u003cb\u003eaw\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e(cmH\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e\u003cb\u003eO)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32 (6) *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e(bpm)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (6.1) *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eV\u003c/b\u003e\u003csub\u003e\u003cb\u003eT\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e(mL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e512 (84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e566 (148) *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eV\u003c/b\u003e\u003csub\u003e\u003cb\u003eT\u003c/b\u003e\u003c/sub\u003e\u003cb\u003e/PBW\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e(ml\u0026middot;kg\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;1\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.2 (1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.2 (2.6) *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStatic C\u003c/b\u003e\u003csub\u003e\u003cb\u003ers\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003emL\u0026middot;cmH\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e\u003cb\u003eO\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;1\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45 (26)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInspired R\u003c/b\u003e\u003csub\u003e\u003cb\u003ers\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ecmH\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e\u003cb\u003eO\u0026middot;s\u0026middot; L\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;1\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (7) *\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\u003e \u003cb\u003eVC\u0026thinsp;=\u0026thinsp;Volume Control; PC\u0026thinsp;=\u0026thinsp;Pressure Control; F\u003c/b\u003e \u003csub\u003e \u003cb\u003eI\u003c/b\u003e \u003c/sub\u003e \u003cb\u003eO\u003c/b\u003e \u003csub\u003e \u003cb\u003e2\u003c/b\u003e \u003c/sub\u003e \u003cb\u003e= Fraction Inspired O\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e \u003cb\u003econcentration (%); PEEP\u0026thinsp;=\u0026thinsp;Positive end expiratory pressure; P\u003c/b\u003e\u003csub\u003e\u003cb\u003eaw\u003c/b\u003e\u003c/sub\u003e \u003cb\u003e= Airway pressure; RR\u0026thinsp;=\u0026thinsp;Respiratory rate; V\u003c/b\u003e\u003csub\u003e\u003cb\u003eT\u003c/b\u003e\u003c/sub\u003e \u003cb\u003e= Tidal volume; PBW\u0026thinsp;=\u0026thinsp;Predicted body weight. Compliance (C\u003c/b\u003e\u003csub\u003e\u003cb\u003ers\u003c/b\u003e\u003c/sub\u003e\u003cb\u003e) and resistance (R\u003c/b\u003e\u003csub\u003e\u003cb\u003ers\u003c/b\u003e\u003c/sub\u003e\u003cb\u003e) refer to the respiratory system, including the lungs and chest wall. Figures are shown as mean (SD). * p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 two-sided t test with Bonferroni\u0026rsquo;s correction.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows ventilation parameters stratified by ventilation mode across the analyzed epochs. The greater ventilatory assistance noted with PC mode, in terms of F\u003csub\u003eI\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e, P\u003csub\u003eaw\u003c/sub\u003e, PEEP\u003csub\u003ea\u003c/sub\u003e, V\u003csub\u003eT\u003c/sub\u003e and RR, hint at greater respiratory compromise when compared to epochs on VC mode.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePmus Related Variables Across Ventilation Modes\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\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDirectionality\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e% of Total Efforts\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eInspiratory\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 (32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31 (33)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eExpiratory\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64 (32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69 (33)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP\u003c/b\u003e\u003csub\u003e\u003cb\u003emus\u003c/b\u003e\u003c/sub\u003e \u003cb\u003eper breath (cmH\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e\u003cb\u003eO)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eInspiratory\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.2 (1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7 (2.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eExpiratory\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0 (4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.4 (4.4) *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP\u003c/b\u003e\u003csub\u003e\u003cb\u003emus\u003c/b\u003e\u003c/sub\u003e\u003cb\u003ePTP per breath (cmH\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e\u003cb\u003eO\u0026middot;s)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eInspiratory\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0 (1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.1 (2.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eExpiratory\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.4 (1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.3 (2.4) *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP\u003c/b\u003e\u003csub\u003e\u003cb\u003emus\u003c/b\u003e\u003c/sub\u003e\u003cb\u003ePTP per minute (cmH\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e\u003cb\u003eO\u0026middot;s\u0026middot;min\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;1\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e) \u0026sect;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eInspiratory\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26 (62) *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eExpiratory\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39 (54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89 (112) *\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\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003cb\u003eVC\u0026thinsp;=\u0026thinsp;Volume Control; PC\u0026thinsp;=\u0026thinsp;Pressure Control; P\u003c/b\u003e \u003csub\u003e \u003cb\u003emus\u003c/b\u003e \u003c/sub\u003e \u003cb\u003e= Highest inspiratory or expiratory pressure attributed to respiratory muscle effort; Inspiratory and expiratory refer to the direction of P\u003c/b\u003e\u003csub\u003e\u003cb\u003emus;\u003c/b\u003e\u003c/sub\u003e \u003cb\u003eP\u003c/b\u003e\u003csub\u003e\u003cb\u003emus\u003c/b\u003e\u003c/sub\u003e\u003cb\u003ePTP = P\u003c/b\u003e\u003csub\u003e\u003cb\u003emus\u003c/b\u003e\u003c/sub\u003e \u003cb\u003epressure time product; per breath\u0026thinsp;=\u0026thinsp;Average value of all inspiratory or all expiratory values in an epoch;\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003e\u0026sect; Calculated as the sum of P\u003c/b\u003e \u003csub\u003e \u003cb\u003emus\u003c/b\u003e \u003c/sub\u003e \u003cb\u003ePTP (either expiratory or inspiratory) in an epoch divided by the length an epoch in minutes (2.184 minutes).\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eFigures shown as mean (SD); * p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 comparing PC to VC; two-sided t test with Bonferroni\u0026rsquo;s correction.\u003c/b\u003e \u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the percentage of inspiratory and expiratory efforts per epoch, the average P\u003csub\u003emus\u003c/sub\u003e and P\u003csub\u003emus\u003c/sub\u003ePTP per breath, and the sum of P\u003csub\u003emus\u003c/sub\u003ePTP values per epoch, stratified by ventilation modality and P\u003csub\u003emus\u003c/sub\u003e directionality (inspiratory or expiratory) within an epoch. Both modes displayed a mix of inspiratory and expiratory efforts, although expiratory efforts were more vigorous, both in magnitude and frequency, in PC mode (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e R2 for the Correlation of PmusPTP with PawPTP and VT\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMode\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of Epochs\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17,648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33,620\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP\u003c/b\u003e\u003csub\u003e\u003cb\u003emus\u003c/b\u003e\u003c/sub\u003e\u003cb\u003ePTP vs. P\u003c/b\u003e\u003csub\u003e\u003cb\u003eaw\u003c/b\u003e\u003c/sub\u003e\u003cb\u003ePTP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.91 [0.76, 0.96] *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06 [0.01, 0.18] *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP\u003c/b\u003e\u003csub\u003e\u003cb\u003emus\u003c/b\u003e\u003c/sub\u003e\u003cb\u003ePTP vs. V\u003c/b\u003e\u003csub\u003e\u003cb\u003eT\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.03 [0.01, 0.09]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.88 [0.74, 0.94]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cb\u003eVC\u0026thinsp;=\u0026thinsp;Volume Control; PC\u0026thinsp;=\u0026thinsp;Pressure Control; P\u003c/b\u003e\u003csub\u003e\u003cb\u003emus\u003c/b\u003e\u003c/sub\u003e \u003cb\u003e= Share of airway pressure attributed to respiratory muscle effort; P\u003c/b\u003e\u003csub\u003e\u003cb\u003emus\u003c/b\u003e\u003c/sub\u003e\u003cb\u003ePTP = P\u003c/b\u003e\u003csub\u003e\u003cb\u003emus\u003c/b\u003e\u003c/sub\u003e \u003cb\u003epressure time product; P\u003c/b\u003e\u003csub\u003e\u003cb\u003eaw\u003c/b\u003e\u003c/sub\u003e \u003cb\u003e= Airway pressure; P\u003c/b\u003e\u003csub\u003e\u003cb\u003eaw\u003c/b\u003e\u003c/sub\u003e\u003cb\u003ePTP = P\u003c/b\u003e\u003csub\u003e\u003cb\u003eaw\u003c/b\u003e\u003c/sub\u003e \u003cb\u003epressure-time product; V\u003c/b\u003e\u003csub\u003e\u003cb\u003eT\u003c/b\u003e\u003c/sub\u003e \u003cb\u003e= Tidal volume. Figures shown as median [IQR];\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003cb\u003e* p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 comparing R\u003c/b\u003e \u003csup\u003e \u003cb\u003e2\u003c/b\u003e \u003c/sup\u003e \u003cb\u003efor P\u003c/b\u003e\u003csub\u003e\u003cb\u003emus\u003c/b\u003e\u003c/sub\u003e\u003cb\u003ePTP vs. P\u003c/b\u003e\u003csub\u003e\u003cb\u003eaw\u003c/b\u003e\u003c/sub\u003e\u003cb\u003ePTP to P\u003c/b\u003e\u003csub\u003e\u003cb\u003emus\u003c/b\u003e\u003c/sub\u003e\u003cb\u003ePTP vs. V\u003c/b\u003e\u003csub\u003e\u003cb\u003eT\u003c/b\u003e\u003c/sub\u003e \u003cb\u003eby Mann-Whitney with Bonferroni\u0026rsquo;s correction.\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e5\u003c/span\u003e lists R\u0026sup2; values for the correlation of P\u003csub\u003emus\u003c/sub\u003ePTP with P\u003csub\u003eaw\u003c/sub\u003ePTP and V\u003csub\u003eT\u003c/sub\u003e across the analyzed epochs. In VC mode, P\u003csub\u003emus\u003c/sub\u003ePTP demonstrates a strong positive correlation with P\u003csub\u003eaw\u003c/sub\u003ePTP (P\u003csub\u003eaw\u003c/sub\u003ePTP = 1.7 P\u003csub\u003emus\u003c/sub\u003ePTP + 19.4; R\u0026sup2; = 0.91; n\u0026thinsp;=\u0026thinsp;17,648 epochs), while such relationship is absent for V\u003csub\u003eT\u003c/sub\u003e (R\u0026sup2; = 0.03). Conversely, this pattern reverses in PC mode, resulting in a robust inverse association between P\u003csub\u003emus\u003c/sub\u003ePTP and V\u003csub\u003eT\u003c/sub\u003e (V\u003csub\u003eT\u003c/sub\u003e = -43.6 P\u003csub\u003emus\u003c/sub\u003ePTP + 615; R\u0026sup2; = 0.88; 33,620 epochs) and a negligible one with P\u003csub\u003eaw\u003c/sub\u003ePTP (R\u0026sup2; = 0.06).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eA method is proposed to estimate P\u003csub\u003emus\u003c/sub\u003e(t) during individual insufflations in patients undergoing ventilatory support based on the numerical solution of a single-compartment model of the respiratory system. The method is non-invasive and may be used to continuously monitor patients automatically by connecting a microprocessor to the data port of a mechanical ventilator.\u003c/p\u003e \u003cp\u003eA significant strength of the study is the extensive dataset used, comprising thousands of epochs collected continuously over several days from 250 patients mechanically ventilated using diverse ventilation modes. Specialized software assessed over two million individual breaths, consequently, the influence of sample size bias, random measurement variations, or the inclusion in the analysis of epochs with significant PEEP\u003csub\u003ei\u003c/sub\u003e levels is considered minimal.\u003c/p\u003e \u003cp\u003eQuantifying P\u003csub\u003emus\u003c/sub\u003e(t) is inherently difficult due to the absence of a direct method of measurement method. The present \"gold standard\" [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] involves the difference between esophageal pressure, measured with a fluid-filled catheter, and chest wall recoil pressure under passive conditions. However, this method is complex, as it relies on uncertain factors like chest wall elastance and a specific chest wall recoil pressure point [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Additionally, the variability in chest wall mechanics and the challenge in accurately distinguishing respiratory phases add to the difficulties in obtaining precise measurements of P\u003csub\u003emus\u003c/sub\u003e(t).\u003c/p\u003e \u003cp\u003eGiven the challenges in directly measuring P\u003csub\u003emus\u003c/sub\u003e(t), it is not unreasonable to assess the validity of its estimate indirectly by evaluating its consistency with expected physiological responses and its correlation with patient outcomes. The present study concentrated on the former. The high R\u0026sup2; values obtained from the correlations P\u003csub\u003emus\u003c/sub\u003ePTP vs. P\u003csub\u003eaw\u003c/sub\u003ePTP in VC mode, and P\u003csub\u003emus\u003c/sub\u003ePTP vs. V\u003csub\u003eT\u003c/sub\u003e in PC mode, across more than 50,000 epochs, indicate a strong predictability between these variables and provides robust evidence supporting the accuracy of the predicted P\u003csub\u003emus\u003c/sub\u003e(t).\u003c/p\u003e \u003cp\u003eThe results of the study highlight the bidirectionality of P\u003csub\u003emus\u003c/sub\u003e during insufflation. Expiratory P\u003csub\u003emus\u003c/sub\u003e values were predicted in more than two-thirds of insufflations in either VC or PC modes. This finding, previously noted by others [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], may be significant considering the potential for lung injury due to elevated transpulmonary pressure during expiratory efforts. On the other hand, inspiratory efforts are often indicative of air hunger, a distressing condition with long-term psychological sequelae.\u003c/p\u003e \u003cp\u003e \u003cem\u003eConfounders and limitations.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eA potential confounder is the possibility that epochs with significant PEEP\u003csub\u003ei\u003c/sub\u003e may have been unintentionally incorporated into the analysis. Failing to address intrinsic can PEEPi lead to an overestimation of expiratory P\u003csub\u003emus\u003c/sub\u003e and a corresponding underestimation of inspiratory P\u003csub\u003emus\u003c/sub\u003e. Although the automated data analysis precluded visual identification of epochs with substantial PEEP\u003csub\u003ei\u003c/sub\u003e, efforts were made to prevent this occurrence by excluding epochs meeting established criteria for this condition. Further, the database contained a limited subset of patients predisposed to the development of PEEP\u003csub\u003ei\u003c/sub\u003e with the diagnosis of asthma or chronic obstructive pulmonary disease (COPD) (8.3%)\u003c/p\u003e \u003cp\u003eAnother possible confounder is the presence of outliers related to anomalies in data acquisition or to double-triggered breaths. Outliers were systematically excluded by applying the z-score method to all P\u003csub\u003eaw\u003c/sub\u003ePTP, V\u003csub\u003eT\u003c/sub\u003e and P\u003csub\u003emus\u003c/sub\u003ePTP datasets This approach resulted in the omission of one or two outliers per epoch, while ensuring\u0026thinsp;\u0026gt;\u0026thinsp;30 breaths remained for regression analysis (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSince P\u003csub\u003emus\u003c/sub\u003e(t) was derived directly from P\u003csub\u003eaw\u003c/sub\u003e(t) (Eq.\u0026nbsp;\u003cspan refid=\"Equ3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) and indirectly from V\u003csub\u003eT\u003c/sub\u003e (Eq.\u0026nbsp;\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), the possibility must be considered that mathematic coupling of shared variables [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] might have resulted in the robust correlations noted between P\u003csub\u003emus\u003c/sub\u003ePTP and P\u003csub\u003eaw\u003c/sub\u003ePTP or with V\u003csub\u003eT\u003c/sub\u003e. This is an unlikely possibility, however, given the almost complete absence of association between these variables when tested for the opposite modes.\u003c/p\u003e \u003cp\u003eClinical application of the method is limited by the need for specialized data acquisition equipment. This concern is mitigated by the incorporation in modern ventilators of signal sampling algorithms whose output is readily accessed through a data port. Nonetheless, the sheer number of calculations needed to produce even a single breath\u0026rsquo;s P\u003csub\u003emus\u003c/sub\u003e(t) function, makes the use of a digital computer mandatory in the clinical application of the method.\u003c/p\u003e \u003cp\u003eUntil additional studies are conducted, the performance of the method using airway signals generated by specialized ventilatory support techniques, such as bi-level ventilation and Airway Pressure Release Ventilation (APRV), remains uncertain.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe proposed method provides a non-invasive, real-time estimate of respiratory muscle activity during insufflation, one capable of distinguishing between expiratory and inspiratory efforts. This could help clinicians identify harmful respiratory patterns associated with expiratory efforts [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] that may result in injurious lung strain [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], or detect severe inspiratory exertions that indicate distressing dyspnea [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Efforts directed at validation, as well as establishing the range of applications for this method, warrant further investigation in future studies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eC\u003csub\u003ers\u003c/sub\u003e = Respiratory system static compliance.\u003c/p\u003e\n\u003cp\u003e\u0026Delta;V(t) = Lung volume change during insufflation.\u003c/p\u003e\n\u003cp\u003eF\u003csub\u003eaw\u003c/sub\u003e = Airway flow.\u0026nbsp;\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 present at end expiration.\u003c/p\u003e\n\u003cp\u003ePC = Pressure control ventilation mode.\u003c/p\u003e\n\u003cp\u003ePS = Pressure support ventilation mode.\u003c/p\u003e\n\u003cp\u003eP\u003csub\u003eaw\u003c/sub\u003e = airway pressure.\u003c/p\u003e\n\u003cp\u003eP\u003csub\u003eaw\u003c/sub\u003ePeak = Peak inspiratory pressure.\u003c/p\u003e\n\u003cp\u003eP\u003csub\u003eaw\u003c/sub\u003ePTP\u003csub\u003e\u0026nbsp;\u003c/sub\u003e= Paw pressure time product.\u003c/p\u003e\n\u003cp\u003eP\u003csub\u003emus\u003c/sub\u003e = Respiratory muscles pressure.\u003c/p\u003e\n\u003cp\u003ePeak_P\u003csub\u003emus\u003c/sub\u003e = Peak respiratory muscles pressure.\u003c/p\u003e\n\u003cp\u003eP\u003csub\u003emus\u003c/sub\u003ePTP\u003csub\u003e\u0026nbsp;\u003c/sub\u003e= P\u003csub\u003emus\u003c/sub\u003e pressure time product.\u003c/p\u003e\n\u003cp\u003eP\u003csub\u003epassive\u003c/sub\u003e =\u0026nbsp;Paw required for passively inflation of the respiratory system.\u003c/p\u003e\n\u003cp\u003ers = Respiratory system.\u003c/p\u003e\n\u003cp\u003eR\u003csub\u003ers\u003c/sub\u003e = Respiratory system inspiratory airway resistance.\u003c/p\u003e\n\u003cp\u003eRRVI = respiratory rate variability.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eVC = Volume control ventilation mode.\u003c/p\u003e\n\u003cp\u003eV\u003csub\u003eT\u0026nbsp;\u003c/sub\u003e= Tidal volume.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eStatements and Declarations.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval and Consent to participate\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eThe database used in the present study was collected during the conduct of several IRB approved studies (Nos. 101228, 110910, 111235) at The George Washington University Hospital,\u0026nbsp;with the IRB allowing the use of deidentified data in further studies.\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 datasets used and analyzed during the current study can be found in the Electronic Data Repository. The database storing the raw data is available from the author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eThe author thanks the Commission for Educational Exchange between the United States, Belgium and Luxembourg and the Fulbright Scholarship Board for their generous support as a Fulbright Research Scholar at the Erasme Hospital of the Universit\u0026eacute; Libre de Bruxelles.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eThe author has applied for a U.S. patent based on the information presented in the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions:\u0026nbsp;\u003c/strong\u003eSingle author manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSlutsky AS, Ranieri VM. (2013) Ventilator-induced lung injury. N Engl J Med. 369:2126\u0026ndash;2136,. doi: 10.1056/NEJMra1208707. Erratum in: N Engl J Med. 370:1668\u0026ndash;1669, 2014. PMID: 24283226.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarini JJ, Rocco PRM, Gattinoni L. Static and Dynamic Contributors to Ventilator-induced Lung Injury in Clinical Practice. Pressure, Energy, and Power. Am J Respir Crit Care Med. 2020;201:767\u0026ndash;74. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1164/rccm.201908-1545CI\u003c/span\u003e\u003cspan address=\"10.1164/rccm.201908-1545CI\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 31665612; PMCID: PMC7124710.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\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). ; (2022) Prevalence, Intensity, and Clinical Impact of Dyspnea in Critically Ill Patients Receiving Invasive Ventilation. Am J Respir Crit Care Med. 205:917\u0026ndash;926. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1164/rccm.202108-1857OC\u003c/span\u003e\u003cspan address=\"10.1164/rccm.202108-1857OC\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 35061577.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorsham CM, Banzett RB, Schwartzstein RM. Dyspnea, Acute Respiratory Failure, Psychological Trauma, and Post-ICU Mental Health: A Caution and a Call for Research. Chest. 2021;159:749\u0026ndash;56. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.chest.2020.09.251\u003c/span\u003e\u003cspan address=\"10.1016/j.chest.2020.09.251\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2020 Oct 1. PMID: 33011205; PMCID: PMC7528739.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRohrer F. Der Zusammenhang der Atemkr\u0026auml;fte und ihre Abh\u0026auml;ngigkeit vom Dehnungszustand der Atmungsorgane. Pfl\u0026uuml;ger's Arch. 1916;165:419\u0026ndash;44. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1007/BF01681079\u003c/span\u003e\u003cspan address=\"10.1007/BF01681079\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOtis A, Fenn W, Rahn H. (1950) Mechanics of breathing in man. J Appl Physiol. 2:592\u0026ndash;607. doi: 10.1152/jappl.1950.2.11.592. PMID: 15436363.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\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\u0026ndash;1858. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1152/jappl.1985.58.6.1849\u003c/span\u003e\u003cspan address=\"10.1152/jappl.1985.58.6.1849\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 4008405.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGutierrez G. A novel method to calculate compliance and airway resistance in ventilated patients. Intensive Care Med Exp. 2022;10:55. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s40635-022-00483-2\u003c/span\u003e\u003cspan address=\"10.1186/s40635-022-00483-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 36581716; PMCID: PMC9800666.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGutierrez G, Ballarino GJ, Turkan H, Abril J, De La Cruz L, Edsall C, George B, Gutierrez S, Jha V, Ahari J. Automatic detection of patient-ventilator asynchrony by spectral analysis of airway flow. Crit Care. 2011;15:R167. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/cc10309\u003c/span\u003e\u003cspan address=\"10.1186/cc10309\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 21749683; PMCID: PMC3387605.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGutierrez G, Williams J, Alrehaili GA, McLean A, Pirouz R, Amdur R, Jain V, Ahari J, Bawa A, Kimbro S. Respiratory rate variability in sleeping adults without obstructive sleep apnea. Physiol Rep. 2016;4:e12949. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.14814/phy2.12949\u003c/span\u003e\u003cspan address=\"10.14814/phy2.12949\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 27597768; PMCID: PMC5027356.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRossi A, Gottfried SB, Zocchi L, Higgs BD, Lennox S, Calverley PM, Begin P, Grassino A, Milic-Emili J. (1985) Measurement of static compliance of the total respiratory system in patients with acute respiratory failure during mechanical ventilation. The effect of intrinsic positive end-expiratory pressure. Am Rev Respir Dis 131:672\u0026ndash;677. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1164/arrd.1985.131.5.672\u003c/span\u003e\u003cspan address=\"10.1164/arrd.1985.131.5.672\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 4003913.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrochard L. Intrinsic (or auto-) PEEP during controlled mechanical ventilation. Intensive Care Med. 2002;28:1376\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00134-002-1438-8\u003c/span\u003e\u003cspan address=\"10.1007/s00134-002-1438-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eServillo G, Svantesson C, Beydon L, Roupie E, Brochard L, Lemaire F, Jonson B. Pressure\u0026ndash;volume curves in acute respiratory failure: automated low flow inflation versus occlusion. Am J Respir Crit Care Med. 1997;155:1629\u0026ndash;36. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1164/ajrccm.155.5.9154868\u003c/span\u003e\u003cspan address=\"10.1164/ajrccm.155.5.9154868\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRousseeuw PJ, Hubert M. Anomaly detection by robust statistics WIREs. Data Min Knowl Discov. 2018;8:e1236. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/widm.1236\u003c/span\u003e\u003cspan address=\"10.1002/widm.1236\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkoumianaki E, Lyazidi A, Rey N, Matamis D, Perez-Martinez N, Giraud R, Mancebo J, Brochard L, Richard JM. (2013) Mechanical ventilation-induced reverse-triggered breaths: a frequently unrecognized form of neuromechanical coupling. Chest. 143:927\u0026ndash;938. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1378/chest.12-1817\u003c/span\u003e\u003cspan address=\"10.1378/chest.12-1817\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 23187649.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJonkman AH, Telias I, Spinelli E, Akoumianaki E, Piquilloud L. The oesophageal balloon for respiratory monitoring in ventilated patients: updated clinical review and practical aspects. Eur Respir Rev. 2023;32:220186. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1183/16000617.0186-2022\u003c/span\u003e\u003cspan address=\"10.1183/16000617.0186-2022\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 37197768; PMCID: PMC10189643.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\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\u0026ndash;1373. doi: 10.1007/s00134-016-4400-x. Epub 2016 Jun 22. PMID: 27334266.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJubran A, Van de Graaff WB, Tobin MJ. (1995) Variability of patient-ventilator interaction with pressure support ventilation in patients with chronic obstructive pulmonary disease. Am J Respir Crit Care Med. 152:129\u0026ndash;136. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1164/ajrccm.152.1.7599811\u003c/span\u003e\u003cspan address=\"10.1164/ajrccm.152.1.7599811\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 7599811.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArchie JP Jr. Mathematic coupling of data: a common source of error. Ann Surg. 1981;193:296\u0026ndash;303. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/00000658-198103000-00008\u003c/span\u003e\u003cspan address=\"10.1097/00000658-198103000-00008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 7212790; PMCID: PMC1345065.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarteaux G, Parfait M, Combet M, Haudebourg AF, Tuffet S, Mekontso Dessap A. Patient-Self Inflicted Lung Injury: A Practical Review. J Clin Med. 2021;10:2738. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/jcm10122738\u003c/span\u003e\u003cspan address=\"10.3390/jcm10122738\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 34205783; PMCID: PMC8234933.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYoshida T, Nakahashi S, Nakamura MAM, Koyama Y, Roldan R, Torsani V, De Santis RR, Gomes S, Uchiyama A, Amato MBP, Kavanagh BP, Fujino Y. (2017) Volume-controlled Ventilation Does Not Prevent Injurious Inflation during Spontaneous Effort. Am J Respir Crit Care Med. 196:590\u0026ndash;601. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1164/rccm.201610-1972OC\u003c/span\u003e\u003cspan address=\"10.1164/rccm.201610-1972OC\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 28212050.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\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\u0026ndash;992, 2. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1164/rccm.201604-0748CP\u003c/span\u003e\u003cspan address=\"10.1164/rccm.201604-0748CP\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 27786562.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\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) (2022) Prevalence, Intensity, and Clinical Impact of Dyspnea in Critically Ill Patients Receiving Invasive Ventilation. Am J Respir Crit Care Med 205:917\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003edoi: 10.1164/rccm.202108-1857OC. PMID: 35061577.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-clinical-monitoring-and-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Journal of Clinical Monitoring and Computing](https://www.springer.com/journal/10877)","snPcode":"10877","submissionUrl":"https://submission.nature.com/new-submission/10877/3","title":"Journal of Clinical Monitoring and Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Mechanical ventilation, respiratory efforts, acute respiratory failure, static compliance, dyspnea, airway resistance, numerical analysis","lastPublishedDoi":"10.21203/rs.3.rs-3838325/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3838325/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eThis study introduces a method to non-invasively and automatically quantify respiratory muscle effort (P\u003csub\u003emus\u003c/sub\u003e) during mechanical ventilation (MV). The methodology hinges on numerically solving the respiratory system's equation of motion, utilizing measurements of airway pressure (P\u003csub\u003eaw\u003c/sub\u003e) and airflow (F\u003csub\u003eaw\u003c/sub\u003e). To evaluate the technique's effectiveness, Pmus was correlated with expected physiological responses. In volume-control (VC) mode, where tidal volume (V\u003csub\u003eT\u003c/sub\u003e) is pre-determined, Pmus is expected to be linked to Paw fluctuations. In contrast, during pressure-control (PC) mode, where P\u003csub\u003eaw\u003c/sub\u003e is held constant, Pmus should correlate with V\u003csub\u003eT\u003c/sub\u003e variations.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe study utilized data from 250 patients on invasive MV. The data included detailed recordings of Paw and Faw, sampled at 31.25 Hz and saved in 131.2-second epochs, each covering 34 to 41 breaths. The algorithm identified 51,268 epochs containing breaths on either VC or PC mode exclusively. In these epochs, Pmus and its pressure-time product (P\u003csub\u003emus\u003c/sub\u003ePTP) were computed and correlated with Paw's pressure-time product (P\u003csub\u003eaw\u003c/sub\u003ePTP) and V\u003csub\u003eT\u003c/sub\u003e, respectively.\u003c/p\u003e","manuscriptTitle":"An Automatic, Non-Invasive Method to Monitor Respiratory Muscle Effort During Mechanical Ventilation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-11 19:01:23","doi":"10.21203/rs.3.rs-3838325/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"checksComplete","content":"","date":"2024-01-09T11:36:37+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-01-09T11:36:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Clinical Monitoring and Computing","date":"2024-01-05T22:37:51+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-clinical-monitoring-and-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Journal of Clinical Monitoring and Computing](https://www.springer.com/journal/10877)","snPcode":"10877","submissionUrl":"https://submission.nature.com/new-submission/10877/3","title":"Journal of Clinical Monitoring and Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"cc82aa70-5187-4a78-b23d-273801ef2bda","owner":[],"postedDate":"January 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-04-08T07:05:16+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-11 19:01:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3838325","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3838325","identity":"rs-3838325","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}