Respiratory Effort Monitoring: a Novel, Bedside, Non- invasive, Real-time Method

Respiratory Effort Monitoring: a Novel, Bedside, Non- invasive, Real-time Method | 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 Respiratory Effort Monitoring: a Novel, Bedside, Non- invasive, Real-time Method Yinxia Lv, Meiling Dong, Haisong Song, Jinglei Liu, Zhiwen Huang, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5876654/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Jul, 2025 Read the published version in Critical Care → Version 1 posted 10 You are reading this latest preprint version Abstract Background Mechanical ventilation is essential for treating respiratory failure. However, ventilator over-assistance can lead to ventilator-induced diaphragm dysfunction (VIDD), and inadequate assistance can necessitate excessive effort, which can be detrimental to lung mechanics and damage diaphragm function. Current monitoring methods face clinical implementation challenges due to invasiveness and complexity. This study introduces and validates a novel non-invasive real-time respiratory muscle pressure (N-Pmus) monitoring method. Methods 1) The bench study involved developing a non-invasive, real-time respiratory muscle pressure monitoring algorithm (N-Pmus) based on respiratory mechanics equations and validated against an ASL5000 lung simulator across 270 clinical scenarios. 2) A clinical validation was conducted as a self-randomized controlled study(n = 23) comparing N-Pmus with the Pmus derived from simultaneously monitored esophageal pressure (Pes-Pmus) to assess correlation and agreement. The association between N-Pmus and the established Pmus benchmarks was analyzed using linear mixed-effects models. Bias and agreement were evaluated through Bland–Altman analysis for repeated measures. Results 1) The bench study demonstrated that N-Pmus correlated well with ASL5000-Pmus, with marginal R²=0.993 and conditional R²=0.997. The bias was − 0.23 cmH₂O, with limits of agreement ranging from − 1.51 to 1.04 cmH₂O. 2) The clinical validation revealed strong N-Pmus/Pes-Pmus agreement with marginal R²=0.97 and conditional R²=0.971. The bias was − 0.2 cmH₂O, with limits of agreement ranging from − 2.22 to 1.83 cmH₂O. Conclusions N-Pmus, a novel, non-invasive real-time monitoring method, demonstrates a strong correlation and agreement with the established Pmus benchmarks (ASL5000-Pmus or Pes-Pmus), offering an effective means of assessing respiratory effort in mechanically ventilated patients. Clinical trial retrospectively registered with www.chictr.org.cn . Registration number : ChiCTR2300076940, registered 24 October 2023. mechanical ventilation respiratory effort non-invasive monitoring respiratory muscle pressure (Pmus) Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Mechanical ventilation stands as a critical intervention for patients grappling with respiratory failure, while its improper usage can potentially lead to ventilator associated injury [ 1 – 3 ]. During the process of mechanical ventilation, ventilator over-assistance may result in insufficient breathing effort, ultimately contributing to atrophy and weakness of the diaphragm. Conversely, inadequate assistance can necessitate excessive effort, which can be detrimental to lung mechanics and damage diaphragm function. Vigorous effort can significantly elevate local lung stress and strain, potentially resulting in injurious transpulmonary pressure, pendelluft, and asynchrony with ventilator, thereby inciting or exacerbating patient self-inflicted lung injury (P-SILI)[ 4 ]. Furthermore, excessive breathing effort could incite eccentric contractions of diaphragm, which have been shown to cause sarcolemmal disruption and inflammation on a microscopic scale in animal models, so called load injury[ 5 ]. Ventilator-induced diaphragm dysfunction (VIDD) due to underuse or overuse of the diaphragm is frequently encountered in critically ill mechanical ventilated patients, which is associated with prolonged mechanical ventilation and poor prognostic outcomes[ 6 ]. Therefore, precise and reliable monitoring of respiratory effort is of paramount importance to maintain appropriate effort and prevent both VIDD and P-SILI. Currently, bedside routine methodologies for the direct assessing breathing effort include esophageal pressure (Pes) [ 7 , 8 ], end-inspiratory airway occlusion pressure (PMI) [ 9 ], and end-expiratory airway occlusion pressure (Pocc) [ 10 ]. Additionally, diaphragm electromyography (EAdi) [ 11 , 12 ], and diaphragm ultrasound[ 13 ] are employed for the indirect evaluating breathing effort. These methodologies require manual oversight by medical professionals or respiratory therapists, and the use of diaphragm electromyography and esophageal pressure catheter insertion involve invasive procedures, which carry inherent risks in clinical application. Furthermore, the assessment of diaphragm ultrasound poses specific challenges in clinical execution [ 14 , 15 ]. At present, a straightforward, non-invasive, and continual monitoring modality is lacking in clinical praxis to accurately gauge the strength of patients' spontaneous respiratory efforts. This study developed an innovative noninvasive approach for real-time dynamic monitoring of spontaneous breathing effort (N-Pmus), achieved by modeling respiratory mechanics through mathematical optimization algorithms, thereby eliminating the need for catheter insertion. Subsequently, simulated scenarios and clinical trials were conducted to compare N-Pmus with the bedside routine Pmus measurement (Pes-Pmus) to confirm their correlation and alignment. Furthermore, subgroup analyses encompassing a variety of conditions, ventilation modalities, and levels of respiratory effort were performed to further validate these findings. Methods Study design The study comprised two distinct steps (Fig. 1 ). In Step One, termed the Bench study, a real-time algorithm for monitoring N-Pmus was developed and integrated into a ventilator platform (SV800, Mindray, Shenzhen, China). An experimental setup was established using the Active Servo Lung 5000 (ASL 5000, IngMar Medical, Pittsburgh, PA, USA) and the ventilator to validate its accuracy by comparing N-Pmus with ASL5000-Pmus. Step Two involved clinical validation through an observational trial conducted in 2023 in the ICUs of West China Hospital, Sichuan University, using a single-center, self-randomized controlled design. N-Pmus was compared with simultaneously monitored Pes-Pmus to assess its clinical correlation and agreement. The study was approved by the Ethics Committee of West China Hospital, Sichuan University (Ethics Approval No. 2023 Approval (105)), and informed consent was obtained from all participants before enrollment. The study is registered under ChiCTR2300076940. Bench Study The N-Pmus algorithm was developed based on the classical respiratory mechanics equation: \(\:{\text{P}}_{\text{a}\text{w}}\left(\text{t}\right)=\dot{\text{V}}\left(\text{t}\right)\text{*}{\text{R}}_{\text{r}\text{s}}+\frac{\text{V}\left(\text{t}\right)}{{\text{C}}_{\text{r}\text{s}}}+{\text{P}}_{\text{m}\text{u}\text{s}}\left(\text{t}\right)+{\text{P}}_{0}\) (1) Here, \(\:{\text{P}}_{\text{a}\text{w}}\left(\text{t}\right)\) (airway pressure), \(\:\dot{\text{V}}\left(\text{t}\right)\) (flow), and \(\:\text{V}\left(\text{t}\right)\) (volume, derived from flow integration) are time-dependent waveforms measured by ventilator sensors. The unknowns include three constants: \(\:{\text{R}}_{\text{r}\text{s}}\) (respiratory system resistance), \(\:{\text{C}}_{\text{r}\text{s}}\) (respiratory system compliance), and \(\:{\text{P}}_{0}\) (end-expiratory pressure), as well as the time-varying respiratory muscle pressure \(\:{\text{P}}_{\text{m}\text{u}\text{s}}\left(\text{t}\right)\) . Directly solving for these variables is infeasible due to an underdetermined system (fewer equations than unknowns). To address this, \(\:{\text{P}}_{\text{m}\text{u}\text{s}}\left(\text{t}\right)\) morphology is modeled as a physiologically plausible polynomial function, reducing the number of unknowns (see Additional file: Figure E1). This reformulates the system into an overdetermined framework (more equations than unknowns), solvable via iterative least-squares optimization[ 16 ]. The experiment utilized an ASL5000 simulator in conjunction with an SV800 ventilator to simulate three clinical scenarios: normal (compliance: 50 mL/cmH 2 O; resistance: 10 cm H 2 O/L/s), restrictive (compliance: 20–40 mL/cmH 2 O; resistance: 13 cmH 2 O/L/s), and obstructive (compliance: 60 mL/cmH 2 O; resistance: 15–25 cmH 2 O/L/s), following established benchmarks[ 17 , 18 ]. Spontaneous breathing was modeled using programmable sinusoidal patient muscle pressure ( \(\:{\text{P}}_{\text{m}\text{u}\text{s}}\) ) waveforms (intensity: 3–30 cmH 2 O), with adjustable rise (10–20%) and release (10–30%) phases to replicate varied effort profiles (see Additional file: Table E1). Clinical Study The study flow, including the inclusion and exclusion criteria, is summarized in Fig. 1 . Comprehensive procedural details are documented in the Additional file. Study Population Three groups of patients were enrolled to this study: (1) acute respiratory distress syndrome (ARDS) per Berlin definition[ 19 ], (2) chronic obstructive pulmonary disease (COPD) per GOLD guidelines[ 20 ], and (3) non-ARDS/non-COPD respiratory failure. Study Protocol Study Protocol After obtaining informed consent, enrolled patients were mechanically ventilated using the Mindray SV800 ventilator. An esophageal balloon catheter (SDY-1, Mindray, China) was inserted nasally to monitor respiratory mechanics[ 8 ]. The study did not interfere with the patients' ventilation modes or parameters. Chest wall compliance (Ccw) was temporarily measured in volume-controlled ventilation mode under sedation-induced apnea. Thereafter, the ventilator automatically recorded waveforms (flow, pressure, volume, and esophageal pressure) along with the real-time calculated parameters N-Pmus and N-PTPmus. N-Pmus: The peak amplitude of Pmus(t) calculated using the novel algorithm in the bench study during the patient's spontaneous inspiratory phase. N-PTPmus: The time integral of N-Pmus(t) during inspiration, representing the magnitude of the patient's inspiratory effort. Esophageal pressure-derived metrics were used as the reference standard for validation: Pes-Pmus: Calculated as the maximal difference between the esophageal pressure (Pes) and recoil pressure of the chest wall (Pcw, volume/Ccw) [ 8 ]. Pes-PTPmus: Obtained by integrating esophageal pressure-derived Pmus(t) over the inspiratory phase. Statistical Analysis In the Bench study, each experimental scenario yielded a single averaged value, reducing the influence of repeated measurements. In the Clinical study, stable respiratory segments were selected based on predefined criteria for esophageal pressure and peak expiratory flow (see Figure E5). For each participant, 2 to 4 segments were identified, each comprising 20 consecutive breath cycles with consistent signal quality. These cycles were subsequently divided into 3 to 4 groups of 5 to 6 cycles each, and their averages were calculated to generate aggregated Pmus data. This approach effectively reduced intra-cycle variability[ 21 ] and minimized the influence of cardiac artifacts[ 22 ]. Data are presented as mean (standard deviation) or median [interquartile range], as appropriate. Linear mixed-effects models were used to evaluate the association between N-Pmus, ASL5000-Pmus, and Pes-Pmus, with patients treated as random effects[ 23 ]. Marginal R² and conditional R² were computed to quantify the variance in the dependent variable explained by the mixed models, excluding and including the variance attributed to random effects, respectively[ 24 ]. Bias and agreement were assessed using Bland–Altman analysis [ 25 ] for repeated measures. To account for repeated measures within patients, linear mixed-effects models were used to estimate within-patient limits of agreement and to compute the mean and between-patient standard deviation of the bias [ 26 ]. To address limited sample sizes, we used parametric bootstrap resampling to estimate 95% confidence intervals (CIs) [ 27 ]. Statistical analyses included the Wilcoxon and Shapiro-Wilk tests. A p-value less than 0.05 was considered significant. Offline computation and aggregation of waveform data were performed using MATLAB (The MathWorks, Inc., Natick, MA, USA). Statistical analyses were conducted in RStudio (R Foundation for Statistical Computing, Vienna, Austria). Results The primary outcomes of this study include the correlation and agreement between N-Pmus and Pmus benchmarks (ASL5000-Pmus or Pes-Pmus). Bench Study A total of 270 scenarios were evaluated in the ASL5000 simulation experiment, simulating patients with different RC types, ventilation modes, Pmus amplitudes, and Pmus patterns. N-Pmus showed excellent correlation with ASL5000-Pmus, with a marginal R² of 0.993 and a conditional R² of 0.997 (Fig. 2 (B)). The bias was − 0.23 cmH₂O, and the limits of agreement ranged from − 1.51 to 1.04 cmH₂O (Fig. 2 (C)). Scenarios were further categorized into restrictive (across a range of compliances) and obstructive (across varying airway resistances) subgroups, with all demonstrating robust correlations (marginal R² > 0.988 and conditional R² > 0.992). Detailed clinical scenarios and simulation results are provided in Table E1 and Figure E4 in the Additional File. Clinical Study Twenty-five patients initially met the inclusion criteria, but two were excluded due to absent of spontaneous breathing or missing of esophageal pressure data. The final analysis included 23 patients, with their clinical characteristics presented in Table 1 . These patients contributed 1,380 single-cycle Pmus data pairs and 261 aggregated Pmus data pairs. The median number of aggregated data points per patient was 10 (IQR: 8–14), reflecting the number of distinct effort levels analyzed for each individual. As an example, representative tracings for Patient 16 are presented in Fig. 3 (A), with the corresponding theoretical fit obtained using the N-Pmus algorithm shown in Fig. 3 (B). Table 1 All Patient's Basic Characteristics No Sex Age (years) BMI (kg/m 2 ) Sub-group Diagnosis Depth† (cm) OccRatio‡ Ccw (mL/cmH 2 O) Ventilation mode TV (mL) PEEP (cmH 2 O) 1 M 43 28.6 ARDS Necrotizing pancreatitis 57.5 1.01 107 PSV 416 12 2 M 51 25.1 Other TAAA 60 1.09 175 V-A/C 438 10 3 M 64 22.5 Other Coronary heart disease 65 0.87 206 P-A/C 456 10 4 M 74 24.5 ARDS Severe acute pancreatitis 57.5 0.94 102 V-SIMV 420 12 5 F 23 18.7 Other Autoimmune encephalitis 60 1.08 143 P-A/C 381 8 6 M 75 15.9 COPD Emphysema 57.5 1.01 172 P-SIMV 474 8 7 M 53 26.0 ARDS Type 1 RF 60 0.98 122 P-A/C 611 5 8 M 61 25.4 ARDS Acute cerebral infarction 60 0.92 130 V-A/C 495 12 9 M 53 24.8 ARDS Type 1 RF 60 0.93 360 PSV 608 6 10 M 72 22.9 ARDS Pulmonary embolism 60 0.98 167 V-A/C 426 8 11 M 82 23.3 ARDS Sepsis, Severe pneumonia 57.5 1.08 351 P-A/C 488 5 12 F 52 17.7 Other Sepsis, Severe pneumonia 52.5 0.96 111 PSV 388 10 13 M 55 24.4 Other Space-occupying lesion 60 1.08 333 P-A/C 754 5 14 M 87 21.3 COPD Type 2 RF 65 0.98 165 P-A/C 472 8 15 F 87 28.9 COPD Invasive aspergillosis 60 1.03 69 P-A/C 405 8 16 M 53 17.7 ARDS Type 1 RF 57.5 1.07 76 PSV 391 8 17 M 56 25.6 COPD End-stage renal disease 57.5 1.07 259 P-SIMV 401 8 18 F 79 27.5 COPD AECOPD 60 0.99 84 PSV 358 8 19 F 58 20.2 Other Traumatic brain injury 57.5 1.02 131 V-A/C 550 8 20 M 24 26.7 Other Traumatic brain injury 55 1.03 222 P-A/C 503 10 21 F 74 18.7 COPD AECOPD, Type 2 RF 51 1.08 112 PSV 351 5 22 M 75 23.0 COPD AECOPD, Type 2 RF 57.5 0.96 91 V-A/C 397 8 23 M 61 26.0 COPD Sepsis, AECOPD 57.5 1.2 141 P-A/C 520 8 Definition of abbreviations: BMI = body mass index; Ccw = compliance of the chest wall; TV = measured tidal volume; PEEP = positive end expiratory pressure; ARDS = acute respiratory distress syndrome; COPD = chronic obstructive pulmonary disease; TAAA = thoracoabdominal aortic aneurysm; RF = respiratory failure; AECOPD = acute exacerbation of chronic obstructive pulmonary disease; † The transnasal esophageal balloon placement depth is measured from the nasal orifice to the catheter tip. ‡ The ΔPes/ΔPaw ratio during the Baydur (occlusion) test with spontaneous breathing or the Positive Pressure Occlusion test without spontaneous breathing. The analysis of aggregated Pmus data between N-Pmus and Pes-Pmus showed a marginal R² of 0.97 and a conditional R² of 0.971 (Fig. 4 (B)). The bias was − 0.2 cmH₂O, with limits of agreement ranging from − 2.22 to 1.83 cmH₂O (Fig. 4 (C)). In contrast, single-cycle data exhibited a correlation with a marginal R² of 0.884 and a conditional R² of 0.952 (Fig. 4 (E)). The bias was − 0.23 cmH₂O, with limits of agreement from − 2.33 to 1.88 cmH₂O (Fig. 4 (F)). Furthermore, the correlation between N-PTPmus and Pes-PTPmus showed a marginal R² of 0.861 and a conditional R² of 0.907 (Fig. 5 (B)). The bias was − 0.18 cmH₂O·s, with limits of agreement ranging from − 2.45 to 2.08 cmH₂O·s (Fig. 5 (C)). Post-hoc Subgroup Analysis Patients were stratified by disease type (COPD, ARDS, others). The ARDS subgroup demonstrated the strongest correlation between N-Pmus and Pes-Pmus (marginal R² = 0.955, conditional R² = 0.959; bias = − 0.26 cmH₂O [95% CI: −0.75 to 0.35]), with limits of agreement (LOA) ranging from − 2.87 to 2.35 cmH₂O. The COPD and other subgroups exhibited slightly lower correlations (COPD: marginal R² = 0.822, LOA = − 1.85 to 1.47 cmH₂O; others: marginal R² = 0.876, LOA = − 1.56 to 1.22 cmH₂O) (Table E2, Figure E6). Analysis across ventilation modes (P-A/C, V-A/C, PSV, SIMV) revealed robust agreement, with V-A/C showing the highest correlation (marginal R² = 0.978, conditional R² = 0.982; bias = − 0.06 cmH₂O [95% CI: −0.89 to 0.75]), and LOA ranging from − 2.88 to 2.76 cmH₂O. P-A/C, PSV, and SIMV modes demonstrated narrower LOA ranges (P-A/C: −2.04 to 1.18 cmH₂O; PSV: −2.46 to 2.17 cmH₂O; SIMV: −1.48 to 1.45 cmH₂O) (Table E3, Figure E7). Stratification by Pes-Pmus magnitude (low: 10 cmH₂O) revealed stronger correlations in the high-effort group (marginal R² = 0.895, conditional R² = 0.916; bias = − 0.12 cmH₂O [95% CI: −1.1 to 0.59]), with LOA spanning from − 3.04 to 2.79 cmH₂O. The low- and normal-effort groups exhibited reduced agreement (low: marginal R² = 0.611, LOA = − 1.46 to 0.97 cmH₂O; normal: marginal R² = 0.628, LOA = − 2.38 to 1.65 cmH₂O) (Table E4, Figure E8). Discussion In this investigation, we introduced and validated N-Pmus, an innovative non-invasive approach for continuous, real-time monitoring of respiratory effort at the bedside in mechanically ventilated patients. The essence of the N-Pmus algorithm lies in integrating a functional model with physiological constraints into the equation governing respiratory motion, solving it iteratively via the least squares method. A bench study conducted in a simulated setting demonstrated a perfect correlation of 0.99 between N-Pmus and ASL5000-Pmus, with narrow 95% limits of agreement of [-1.51, 1.04] cmH 2 O. Additionally, a subsequent clinical validation study showcased a high correlation of 0.97 between N-Pmus and Pes-Pmus, with tight 95% limits of agreement of [-2.22, 1.83] cmH 2 O. Subsequent subgroup analyses post hoc unveiled a robust correlation and excellent agreement across diverse disease categories, ventilation modalities, and degrees of respiratory effort. Feasible and reliable monitoring of respiratory effort in mechanically ventilated patients is paramount to mitigate potential lung and diaphragm injuries. Currently, the accuracy of both invasive direct monitoring techniques (EAdi, Pes, and Pdi) and noninvasive indirect methods (PMI, and Pocc) can be susceptible to various factors including procedural standards, the state of illness, and the respiratory status of patients, impeding seamless real-time monitoring. Notably, Albani et al. [ 28 , 29 ] proposed a noninvasive real-time monitoring technique for inspiratory effort by analyzing the concavity of the inspiratory flow waveform in pressure support ventilation (PSV). However, this method is restricted to pressure-control ventilation and lacks automated calculation capabilities. Pmus, recognized as a key indicator of respiratory effort, has been a focal point of research for achieving noninvasive real-time monitoring. In reviewing decades of research, methodologies for real-time Pmus calculation using respiratory mechanics equations can be categorized into two main approaches. The first approach entails solving for respiratory system resistance and compliance before back-calculating the Pmus waveform [ 16 , 30 – 35 ]. The second approach involves simultaneously determining Pmus, resistance, and compliance while incorporating constraints into the motion equations [ 36 – 38 ]. Previous studies often underestimate Pmus because active respiratory muscle effort distorts airway pressure and flow profiles, leading to inaccuracies when such activity is not fully accounted for. In contrast, our algorithm enhances Pmus estimation by employing global constrained least-squares fitting, a cubic function template to model active expiration, and advanced waveform feature recognition using both inspiratory and expiratory data. These advancements significantly improved the accuracy of Pmus peak estimation and morphology characterization, as detailed in the Additional file (see Figure E2 and Figure E3). Validation of this approach is conducted through simulation studies and clinical trials, offering promising prospects for enhanced respiratory effort monitoring in mechanically ventilated patients. The simulation experiment validated scenarios encompassing prevalent disease types, including restrictive and obstructive lung diseases, as well as typical ventilator mode settings, such as V-A/C, P-A/C, PSV, and SIMV, commonly seen in clinical practice. The high correlation, reaching up to 0.99, can be ascribed to the meticulous mathematical model of the Pmus waveform generated by the ASL5000 and the precise fitting effect of the theoretical model. The 95% limits of agreement were found to be [-1.51, 1.04] cmH 2 O. Furthermore, Silva et al. [ 38 ] simulated 49 scenarios, revealing that deviations from the absolute Pmus amplitude predominantly clustered within the quartiles (0.4 to 1.3 cmH₂O). Our simulation experiments confirmed similar outcomes across a wider range of use case scenarios, including those with elevated resistance and decreased compliance. However, it should be noted that the observed correlation may be influenced by the method of effort generation used by the ASL5000, which warrants further investigation to assess its generalizability. In our clinical trial with 23 patients, the correlation between N-Pmus and Pes-Pmus was 0.97, with 95% limits of agreement from − 2.22 to 1.83 cmH 2 O. This demonstrates significantly improved accuracy and reproducibility compared to previous studies, particularly those by Kondili et al. [ 33 ] with 11 patients (correlation of 0.83, 95% limits of agreement between − 7.23 and 5.41 cmH 2 O), and Natalini et al. [ 34 ] with 18 patients (correlation of 0.58, 95% limits of agreement between − 12 and 12 cmH 2 O). Similarly, the correlation between N-PTPmus and Pes-PTPmus was 0.86, with 95% limits of agreement from − 2.45 to 2.08 cmH 2 O·s, surpassing the findings of Kondili et al. [ 33 ] (correlation of 0.77, 95% limits of agreement between − 3.01 and 1.99 cmH 2 O·s), and Natalini et al. [ 34 ] (correlation of 0.52, 95% limits of agreement between − 8.35 and 7.74 cmH 2 O·s). In a post-hoc subgroup analysis, the ARDS subgroup showed the highest correlation between N‐Pmus and Pes‐Pmus (marginal R² = 0.955) but exhibited a wide limit of agreement (LOA: −2.87 to 2.35 cmH₂O). In contrast, the COPD and other disease subgroups demonstrated lower correlations (COPD: marginal R² = 0.822; others: marginal R² = 0.876) with narrower LOA. Stratification by respiratory effort revealed that the high-effort group, similar to the ARDS subgroup, had the highest correlation with a wider LOA, whereas the normal and low-effort groups had lower correlations and narrower LOA. These differences are primarily attributable to the influence of respiratory effort magnitude on measurement precision: in high-effort states (commonly observed in ARDS), larger Pmus values reduce the relative impact of small absolute errors—enhancing correlation—but also amplify absolute differences. In practice, the relative error remains small, yet this leads to a wider LOA. Conversely, in low-effort states (as seen in other subgroups), smaller Pmus values exaggerate the relative effect of minimal errors, leading to lower correlations but narrower LOA. Therefore, further investigations are imperative to validate the observations within this subgroup. Strengths and Limitations. This study systematically presents the derivation of the N-Pmus algorithm along with the results from laboratory simulations and clinical validation. The simulation scenarios strived to cover a broad spectrum of patient profiles, Pmus configurations, and a wide array of amplitudes, while the clinical trials aimed to confirm the efficacy across diverse patient phenotypes. Nonetheless, our study does have certain limitations. Firstly, while esophageal pressure serves as the most accurate approximation of pleural pressure, it may not accurately represent the actual intrathoracic pressure under various pathological conditions. Although chest wall compliance typically remains relatively constant, patients subjected to differing circumstances, such as controlled versus spontaneous ventilation, might exhibit slightly varying effects on this compliance. Thus, the aforementioned limitations regarding the discrepancy between Pes-Pmus in the control group and the true Pmus necessitate further refinement. Secondly, the study is constrained by its brief duration, diminutive sample size, and confinement to a single center. Finally, it omitted the inclusion of healthy subjects or individuals with specific conditions like abdominal hypertension, and it did not assess transdiaphragmatic pressure (Pdi). Subsequent validation endeavors should incorporate long term, more extensive, multicenter research initiatives. Conclusions This study introduces an innovative, bedside, non-invasive, real-time approach to quantitatively evaluate respiratory effort. Our investigation illustrated that N-Pmus showcases a robust correlation and agreement with the established Pmus benchmarks (ASL5000-Pmus or Pes-Pmus), surpassing conventional metrics for assessing respiratory effort. Further research is essential to validate this technique and explore its clinical applications. In the future, N-Pmus will enable precise monitoring and guide parameter adjustments in mechanical ventilation, potentially supporting lung- and diaphragm-protective ventilation. Abbreviations Pmus: respiratory muscle pressure; Pes: esophageal pressure; Pcw: chest wall elastic recoil pressure; Ccw: compliance of the chest wall; N-Pmus: real-time Pmus amplitude monitored by the ventilator; Pes-Pmus: the usual monitoring for Pmus amplitude, calculated from Pes and Pcw; ASL5000-Pmus: Pmus settings from ASL5000; N-PTPmus: pressure–time-product of Pmus measured by the ventilator in a single breath; Pes-PTPmus: the usual monitoring pressure–time-product of Pmus, calculated using esophageal pressure in a single breath; P-A/C: pressure-assist/control ventilation; PSV: pressure support ventilation. Declarations Ethics approval and consent to participate This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of West China Hospital, Sichuan University (Ethics Approval No. 2023 Approval (105)). Written informed consent was obtained from the legal primary decision-maker, who was the spouse of the patient, or the parent of the child in cases in which there was no spouse. The trial was registered at chictr.org.cn (ChiCTR2300076940). Consent for publication Not applicable. Availability of data and materials All data generated during this study are included in this published article and can be found in the Additional file. Competing interests BW and YL received a grant from Mindray (China), and HS, ZH, JL, and XZ are affiliated with Mindray (China). All other authors declare no competing interests. Funding This study received funding from Mindray (China). Authors' contributions Study concept and design: YL, MD, HS, ZH, YZ, JL and BW. Acquisition of data: YL, ZN, ZW and XJ. Analysis and interpretation of data: YL, MD, HS, ZH, YZ, JL and BW. Drafting of the manuscript: MD, HS and YZ. Critical revision and important intellectual contribution: YL, MD, HS, ZH, YZ, JL and BW. Statistical analysis: HS, ZH and JL. Administrative support: XZ, JL, BW and YK. Study supervision: YZ, JL, BW and YK. Acknowledgements Not applicable. References Slutsky AS, Ranieri VM: Ventilator-Induced Lung Injury . New England Journal of Medicine 2013, 369 (22):2126-2136. Amato MB, Meade MO, Slutsky AS, Brochard L, Costa EL, Schoenfeld DA, Stewart TE, Briel M, Talmor D, Mercat A et al : Driving pressure and survival in the acute respiratory distress syndrome . N Engl J Med 2015, 372 (8):747-755. Urner M, Juni P, Hansen B, Wettstein MS, Ferguson ND, Fan E: Time-varying intensity of mechanical ventilation and mortality in patients with acute respiratory failure: a registry-based, prospective cohort study . Lancet Respir Med 2020, 8 (9):905-913. Yoshida T, Grieco DL, Brochard L, Fujino Y: Patient self-inflicted lung injury and positive end-expiratory pressure for safe spontaneous breathing . Curr Opin Crit Care 2020, 26 (1):59-65. Demoule A, Molinari N, Jung B, Prodanovic H, Chanques G, Matecki S, Mayaux J, Similowski T, Jaber S: Patterns of diaphragm function in critically ill patients receiving prolonged mechanical ventilation: a prospective longitudinal study . Ann Intensive Care 2016, 6 (1):75. Goligher EC, Dres M, Fan E, Rubenfeld GD, Scales DC, Herridge MS, Vorona S, Sklar MC, Rittayamai N, Lanys A et al : Mechanical Ventilation-induced Diaphragm Atrophy Strongly Impacts Clinical Outcomes . Am J Respir Crit Care Med 2018, 197 (2):204-213. Cherniack RM, Farhi LE, Armstrong BW, Proctor DF: A comparison of esophageal and intrapleural pressure in man . J Appl Physiol 1955, 8 (2):203-211. Akoumianaki E, Maggiore SM, Valenza F, Bellani G, Jubran A, Loring SH, Pelosi P, Talmor D, Grasso S, Chiumello D et al : The application of esophageal pressure measurement in patients with respiratory failure . Am J Respir Crit Care Med 2014, 189 (5):520-531. Foti G, Cereda M, Banfi G, Pelosi P, Fumagalli R, Pesenti A: End-inspiratory airway occlusion: a method to assess the pressure developed by inspiratory muscles in patients with acute lung injury undergoing pressure support . Am J Respir Crit Care Med 1997, 156 (4 Pt 1):1210-1216. Bertoni M, Telias I, Urner M, Long M, Del Sorbo L, Fan E, Sinderby C, Beck J, Liu L, Qiu H et al : A novel non-invasive method to detect excessively high respiratory effort and dynamic transpulmonary driving pressure during mechanical ventilation . Crit Care 2019, 23 (1):346. Bellani G, Mauri T, Coppadoro A, Grasselli G, Patroniti N, Spadaro S, Sala V, Foti G, Pesenti A: Estimation of patient's inspiratory effort from the electrical activity of the diaphragm . Crit Care Med 2013, 41 (6):1483-1491. Jansen D, Jonkman AH, Roesthuis L, Gadgil S, van der Hoeven JG, Scheffer GJ, Girbes A, Doorduin J, Sinderby CS, Heunks LMA: Estimation of the diaphragm neuromuscular efficiency index in mechanically ventilated critically ill patients . Crit Care 2018, 22 (1):238. Goligher EC, Laghi F, Detsky ME, Farias P, Murray A, Brace D, Brochard LJ, Bolz SS, Rubenfeld GD, Kavanagh BP et al : Measuring diaphragm thickness with ultrasound in mechanically ventilated patients: feasibility, reproducibility and validity . Intensive Care Med 2015, 41 (4):642-649. Bertoni M, Spadaro S, Goligher EC: Monitoring Patient Respiratory Effort During Mechanical Ventilation: Lung and Diaphragm-Protective Ventilation . Crit Care 2020, 24 (1):106. Jonkman AH, de Vries HJ, Heunks LMA: Physiology of the Respiratory Drive in ICU Patients: Implications for Diagnosis and Treatment . Crit Care 2020, 24 (1):104. Iotti GA, Braschi A, Brunner JX, Smits T, Olivei M, Palo A, Veronesi R: Respiratory mechanics by least squares fitting in mechanically ventilated patients: applications during paralysis and during pressure support ventilation . Intensive Care Med 1995, 21 (5):406-413. Arnal JM, Garnero A, Saoli M, Chatburn RL: Parameters for Simulation of Adult Subjects During Mechanical Ventilation . Respir Care 2018, 63 (2):158-168. Daoud EG, Katigbak R, Ottochian M: Accuracy of the Ventilator Automated Displayed Respiratory Mechanics in Passive and Active Breathing Conditions: A Bench Study . Respir Care 2019, 64 (12):1555-1560. Force ADT, Ranieri VM, Rubenfeld GD, Thompson BT, Ferguson ND, Caldwell E, Fan E, Camporota L, Slutsky AS: Acute respiratory distress syndrome: the Berlin Definition . JAMA 2012, 307 (23):2526-2533. Disease GIfCOL: Global Strategy for the Diagnosis, Management, and Prevention of Chronic Obstructive Pulmonary Disease (2023 Report) . In . ; 2023. Bellani G, Bronco A, Arrigoni Marocco S, Pozzi M, Sala V, Eronia N, Villa G, Foti G, Tagliabue G, Eger M et al : Measurement of Diaphragmatic Electrical Activity by Surface Electromyography in Intubated Subjects and Its Relationship With Inspiratory Effort . Respir Care 2018, 63 (11):1341-1349. Qin Y, Huang Z, Zhou X, Gui S, Xiong L, Liu L, Liu J: A novel adaptive filter with a heart-rate-based reference signal for esophageal pressure signal denoising . J Clin Monit Comput 2024. Shan G, Zhang H, Jiang T: Correlation Coefficients for a Study with Repeated Measures . Comput Math Methods Med 2020, 2020 :7398324. Nakagawa S, Schielzeth H, O'Hara RB: A general and simple method for obtaining R2 from generalized linear mixed‐effects models . Methods in Ecology and Evolution 2012, 4 (2):133-142. Martin Bland J, Altman D: Statistical Methods for Assessing Agreement between Two Methods of Clinical Measurement . The Lancet 1986, 327 (8476):307-310. Myles PS, Cui J: Using the Bland-Altman method to measure agreement with repeated measures . Br J Anaesth 2007, 99 (3):309-311. Parker RA, Weir CJ, Rubio N, Rabinovich R, Pinnock H, Hanley J, McCloughan L, Drost EM, Mantoani LC, MacNee W et al : Application of Mixed Effects Limits of Agreement in the Presence of Multiple Sources of Variability: Exemplar from the Comparison of Several Devices to Measure Respiratory Rate in COPD Patients . PLoS One 2016, 11 (12):e0168321. Albani F, Fusina F, Ciabatti G, Pisani L, Lippolis V, Franceschetti ME, Giovannini A, di Mussi R, Murgolo F, Rosano A et al : Flow Index accurately identifies breaths with low or high inspiratory effort during pressure support ventilation . Crit Care 2021, 25 (1):427. Albani F, Pisani L, Ciabatti G, Fusina F, Buizza B, Granato A, Lippolis V, Aniballi E, Murgolo F, Rosano A et al : Flow Index: a novel, non-invasive, continuous, quantitative method to evaluate patient inspiratory effort during pressure support ventilation . Crit Care 2021, 25 (1):196. Gillard C, Flemale A, Dierckx JP, Themelin G: Measurement of effective elastance of the total respiratory system in ventilated patients by a computed method. Comparison with the static method . Intensive Care Med 1990, 16 (3):189-195. Lucangelo U, Bernabe F, Blanch L: Respiratory mechanics derived from signals in the ventilator circuit . Respir Care 2005, 50 (1):55-65; discussion 65-57. Younes M, Brochard L, Grasso S, Kun J, Mancebo J, Ranieri M, Richard J-C, Younes H: A method for monitoring and improving patient: ventilator interaction . Intensive Care Medicine 2007, 33 (8):1337-1346. Kondili E, Alexopoulou C, Xirouchaki N, Vaporidi K, Georgopoulos D: Estimation of inspiratory muscle pressure in critically ill patients . Intensive Care Med 2010, 36 (4):648-655. Natalini G, Buizza B, Granato A, Aniballi E, Pisani L, Ciabatti G, Lippolis V, Rosano A, Latronico N, Grasso S et al : Non-invasive assessment of respiratory muscle activity during pressure support ventilation: accuracy of end-inspiration occlusion and least square fitting methods . J Clin Monit Comput 2021, 35 (4):913-921. Gutierrez G: A non-invasive method to monitor respiratory muscle effort during mechanical ventilation . J Clin Monitor Comp 2024. Vicario F, Albanese A, Wang D, Karamolegkos N, Chbat NW, Ieee: Constrained Optimization for Noninvasive Estimation of Work of Breathing . In: 2015 37TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC). 2015: 5327-5330. Vicario F, Albanese A, Karamolegkos N, Wang D, Seiver A, Chbat NW: Noninvasive Estimation of Respiratory Mechanics in Spontaneously Breathing Ventilated Patients: A Constrained Optimization Approach . IEEE Trans Biomed Eng 2016, 63 (4):775-787. Silva DO, de Souza PN, de Araujo Sousa ML, Morais CCA, Ferreira JC, Holanda MA, Yamaguti WP, Junior LP, Costa ELV: Impact on the ability of healthcare professionals to correctly identify patient-ventilator asynchronies of the simultaneous visualization of estimated muscle pressure curves on the ventilator display: a randomized study (Pmus study) . Critical Care 2023, 27 (1). Additional Declarations No competing interests reported. Supplementary Files RevisonAdditionalfile1RespiratoryEffortMonitoringaNovelBedsideNoninvasiveRealtimeMethod.docx Cite Share Download PDF Status: Published Journal Publication published 03 Jul, 2025 Read the published version in Critical Care → Version 1 posted Editorial decision: Revision requested 19 May, 2025 Reviews received at journal 16 May, 2025 Reviews received at journal 01 May, 2025 Reviews received at journal 28 Apr, 2025 Reviewers agreed at journal 27 Apr, 2025 Reviewers agreed at journal 24 Apr, 2025 Reviewers agreed at journal 23 Apr, 2025 Reviewers invited by journal 22 Apr, 2025 Submission checks completed at journal 11 Apr, 2025 First submitted to journal 31 Mar, 2025 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-5876654","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":446152620,"identity":"5bb29aec-2a02-4a25-81c2-85278dce5ce9","order_by":0,"name":"Yinxia Lv","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Yinxia","middleName":"","lastName":"Lv","suffix":""},{"id":446152622,"identity":"2b0f1733-1872-41d2-b4ff-afb1580ecd5a","order_by":1,"name":"Meiling Dong","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Meiling","middleName":"","lastName":"Dong","suffix":""},{"id":446152623,"identity":"b2040c20-49ff-472b-a97a-6c31a0bec360","order_by":2,"name":"Haisong Song","email":"","orcid":"","institution":"Shenzhen Mindray Bio-Medical Electronics Co. Ltd","correspondingAuthor":false,"prefix":"","firstName":"Haisong","middleName":"","lastName":"Song","suffix":""},{"id":446152624,"identity":"ebd4b85f-94ed-447f-9aba-ead7f31d5e51","order_by":3,"name":"Jinglei Liu","email":"","orcid":"","institution":"Shenzhen Mindray Bio-Medical Electronics Co. Ltd","correspondingAuthor":false,"prefix":"","firstName":"Jinglei","middleName":"","lastName":"Liu","suffix":""},{"id":446152626,"identity":"fb25b768-f3af-4fd4-b950-9a409aa56e23","order_by":4,"name":"Zhiwen Huang","email":"","orcid":"","institution":"Shenzhen Mindray Bio-Medical Electronics Co. Ltd","correspondingAuthor":false,"prefix":"","firstName":"Zhiwen","middleName":"","lastName":"Huang","suffix":""},{"id":446152627,"identity":"3f5f53ff-1e85-422b-bae8-89ac3a5946b8","order_by":5,"name":"Zhong Ni","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Zhong","middleName":"","lastName":"Ni","suffix":""},{"id":446152628,"identity":"cd2ac653-3f6f-477d-99e4-84d107a32f3b","order_by":6,"name":"Zhen Wang","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Zhen","middleName":"","lastName":"Wang","suffix":""},{"id":446152634,"identity":"449d99dd-d7dd-4549-8c40-70ba4752dc44","order_by":7,"name":"Xiaorong Jing","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Xiaorong","middleName":"","lastName":"Jing","suffix":""},{"id":446152637,"identity":"ea7de9a1-42fb-4590-a414-5918e4dc9b53","order_by":8,"name":"Xiaoyong Zhou","email":"","orcid":"","institution":"Shenzhen Mindray Bio-Medical Electronics Co. Ltd","correspondingAuthor":false,"prefix":"","firstName":"Xiaoyong","middleName":"","lastName":"Zhou","suffix":""},{"id":446152641,"identity":"2d28cc87-d2f6-4d29-ad74-77d67d7825ab","order_by":9,"name":"Yongfang Zhou","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Yongfang","middleName":"","lastName":"Zhou","suffix":""},{"id":446152643,"identity":"3b6b1422-777c-4908-a13f-250b7bad8812","order_by":10,"name":"Yan Kang","email":"","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Kang","suffix":""},{"id":446152647,"identity":"0d113ee8-c279-4310-b5b6-74e8a0476a7a","order_by":11,"name":"Bo Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYDCCA3CSh/FBQkUNaVqYDR6cOUaaFjbJhy3MhHXw3T5j+Lng1x05g+Nnj1UkNrAx8Ld3J+DVInkux1h6Zt8zY4MzeWk3EnfIMEicObsBrxaDM7wbpHl7Diduu8FjdiPxDBuDgUQuQS2bf8O0FCS2MROlZZs0zw+IFgaitEie4f9mzdvwzNj+TI6xRMKZYzwE/cJ3hi35Ns+fO3KS7WcMP/6oqJHjb+/FrwUMGNsQbB7CysHgD5HqRsEoGAWjYGQCAPvxT8d1qGE/AAAAAElFTkSuQmCC","orcid":"","institution":"West China Hospital of Sichuan University","correspondingAuthor":true,"prefix":"","firstName":"Bo","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-01-22 02:08:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5876654/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5876654/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13054-025-05514-4","type":"published","date":"2025-07-03T15:58:23+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":81541808,"identity":"c5d58ef7-d1e6-45f5-ba6d-fcae087f207e","added_by":"auto","created_at":"2025-04-28 11:15:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":238923,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart of the research progress\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1Flowchartoftheresearchprogress.png","url":"https://assets-eu.researchsquare.com/files/rs-5876654/v1/259f0efbcc07c90c18b88d0b.png"},{"id":81541870,"identity":"3e5735cf-5b13-46d6-9ac9-f2f9613e131d","added_by":"auto","created_at":"2025-04-28 11:16:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":619863,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation and agreement analysis of Pmus parameter verfication across ASL5000 test scenarios.\u003c/strong\u003e (A) Violin plot depicting the Pmus measured by the ventilator (N-Pmus) and the Pmus settings from ASL5000 (ASL500-Pmus), with “NS” indicating no significant difference (p\u0026gt;0.05). (B) Linear mixed-effects model fit plot showing the correlation between N-Pmus and ASL500-Pmus (marginal R²=0.993, conditional R²=0.997). (C) Corresponding Bland–Altman plot with a bias of -0.23 cmH₂O (95% CI: -0.36 to -0.12), L-LOA of −1.51 cmH₂O (95% CI: −1.69 to −1.33), and U-LOA of 1.04 cmH₂O (95% CI: 0.85 to 1.21). The linear regression of the bias is shown as the black line, illustrating the trend in the bias.\u003c/p\u003e","description":"","filename":"Figure2CorrelationandagreementanalysisofPmusparameterverficationacrossASL5000testscenarios.png","url":"https://assets-eu.researchsquare.com/files/rs-5876654/v1/3f994802620f8ba67aeae712.png"},{"id":81543469,"identity":"0c9a1ef4-d740-46c2-b25e-e99fc309ed23","added_by":"auto","created_at":"2025-04-28 11:23:58","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":952967,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRepresentative Tracings and Fitting Results of Pmus in Stable Breathing.\u003c/strong\u003e (A) Representative tracings for Patient 16 during stable breathing, obtained through ventilator measurements or calculations, including airway pressure (Paw), flow, volume, and esophageal pressure (Pes) signals recorded by the ventilator. Pmus, estimated as the difference between Chest wall elastic recoil pressure (Pcw) and Pes (baseline Pmus defined as 0 cm H₂O), was quantified by the peak inspiratory muscle pressure (Pes-Pmus) and the pressure–time product of Pmus per breath (Pes-PTPmus). (B) Typical fitting results using the non-invasive Pmus algorithm. The algorithm calculates the onset (t\u003csub\u003eo\u003c/sub\u003e) and offset (t\u003csub\u003em\u003c/sub\u003e) of respiratory muscle activity. The red solid line represents the Pes-Pmus waveform, while the black solid line represents the N-Pmus waveform, with N-Pmus quantified identically to Pes-Pmus for data analysis.\u003c/p\u003e","description":"","filename":"Figure3RepresentativeTracingsandFittingResultsofPmusinStableBreathing.png","url":"https://assets-eu.researchsquare.com/files/rs-5876654/v1/ee683892a28ab263a5f97b8a.png"},{"id":81544681,"identity":"f3090675-aee5-4958-a064-cda5c19a110d","added_by":"auto","created_at":"2025-04-28 11:31:59","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1861978,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation and agreement analysis of Pmus parameter verification for clinical trial patient data. \u003c/strong\u003e(A), (B), and (C) display the analysis results for aggregated Pmus data across varying levels of inspiratory effort, while (D), (E), and (F) show the analysis results for single-cycle Pmus data. (A) and (D) are violin plots depicting the Pmus measured by the ventilator (N-Pmus) and the gold-standard Pmus calculated using esophageal pressure (Pes-Pmus), with “NS” indicating no significant difference (p\u0026gt;0.05). (B) and (E) show linear mixed-effects model fit plots demonstrating the correlation between N-Pmus and Pes-Pmus, where (B) has marginal R²=0.97 and conditional R²=0.971, and (E) has marginal R²=0.884 and conditional R²=0.952. (C) and (F) are the corresponding Bland–Altman plots, where (C) has a bias of -0.2 cmH₂O (95% CI: -0.4 to -0.05), L-LOA of −2.22 cmH₂O (95% CI: −2.52 to −1.89), and U-LOA of 1.83 cmH₂O (95% CI: 1.54 to 2.18) , while (F) has a bias of -0.23 cmH₂O (95% CI: −0.37 to −0.08), L-LOA of −2.33 cmH₂O (95% CI: −2.51 to −2.16), and U-LOA of 1.88 cmH₂O (95% CI: 1.71 to 2.06) . The linear regression of the bias is shown as the black line, illustrating the trend in the bias.\u003c/p\u003e","description":"","filename":"Figure4CorrelationandagreementanalysisofPmusparameterverificationforclinicaltrialpatientdata.png","url":"https://assets-eu.researchsquare.com/files/rs-5876654/v1/170d82c1b294ba2fbb3b007d.png"},{"id":81543457,"identity":"fe5c9210-5c08-4343-8097-d99f320880c2","added_by":"auto","created_at":"2025-04-28 11:23:57","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":625373,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation and agreement analysis of PTPmus parameter verification for clinical trial patient data.\u003c/strong\u003e (A) Violin plot depicting the PTPmus values measured by the ventilator (N-PTPmus) and the usual monitoring PTPmus values calculated using esophageal pressure (Pes-PTPmus), with “NS” indicating no significant difference (p\u0026gt;0.05). (B) Linear mixed-effects model fit plot showing the correlation between N-PTPmus and Pes-PTPmus (marginal R²=0.861, conditional R²=0.907). (C) Corresponding Bland–Altman plot with a bias of -0.18 cmH2O·s (95% CI: −0.48 to −0.15), L-LOA of −2.45 cmH2O·s (95% CI: −2.93 to −1.99), and U-LOA of 2.08 cmH2O·s (95% CI: 1.63 to 2.53). The linear regression of the bias is shown as the black line, illustrating the trend in the bias.\u003c/p\u003e","description":"","filename":"Figure5CorrelationandagreementanalysisofPTPmusparameterverificationforclinicaltrialpatientdata.png","url":"https://assets-eu.researchsquare.com/files/rs-5876654/v1/111e526653d39066ad0bfb42.png"},{"id":86180300,"identity":"f13b59fa-ee48-4199-a142-523613aa9a33","added_by":"auto","created_at":"2025-07-07 16:22:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6903201,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5876654/v1/58ad877a-23df-467f-91e5-02dbca0fe75e.pdf"},{"id":81541817,"identity":"a8edb2e7-f4d2-4325-998c-4112dd62e238","added_by":"auto","created_at":"2025-04-28 11:15:57","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":5882617,"visible":true,"origin":"","legend":"","description":"","filename":"RevisonAdditionalfile1RespiratoryEffortMonitoringaNovelBedsideNoninvasiveRealtimeMethod.docx","url":"https://assets-eu.researchsquare.com/files/rs-5876654/v1/df0fb9166c9a59082c8eef35.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Respiratory Effort Monitoring: a Novel, Bedside, Non- invasive, Real-time Method","fulltext":[{"header":"Background","content":"\u003cp\u003eMechanical ventilation stands as a critical intervention for patients grappling with respiratory failure, while its improper usage can potentially lead to ventilator associated injury [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. During the process of mechanical ventilation, ventilator over-assistance may result in insufficient breathing effort, ultimately contributing to atrophy and weakness of the diaphragm. Conversely, inadequate assistance can necessitate excessive effort, which can be detrimental to lung mechanics and damage diaphragm function. Vigorous effort can significantly elevate local lung stress and strain, potentially resulting in injurious transpulmonary pressure, pendelluft, and asynchrony with ventilator, thereby inciting or exacerbating patient self-inflicted lung injury (P-SILI)[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Furthermore, excessive breathing effort could incite eccentric contractions of diaphragm, which have been shown to cause sarcolemmal disruption and inflammation on a microscopic scale in animal models, so called load injury[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eVentilator-induced diaphragm dysfunction (VIDD) due to underuse or overuse of the diaphragm is frequently encountered in critically ill mechanical ventilated patients, which is associated with prolonged mechanical ventilation and poor prognostic outcomes[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Therefore, precise and reliable monitoring of respiratory effort is of paramount importance to maintain appropriate effort and prevent both VIDD and P-SILI. Currently, bedside routine methodologies for the direct assessing breathing effort include esophageal pressure (Pes) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], end-inspiratory airway occlusion pressure (PMI) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], and end-expiratory airway occlusion pressure (Pocc) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Additionally, diaphragm electromyography (EAdi) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], and diaphragm ultrasound[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] are employed for the indirect evaluating breathing effort. These methodologies require manual oversight by medical professionals or respiratory therapists, and the use of diaphragm electromyography and esophageal pressure catheter insertion involve invasive procedures, which carry inherent risks in clinical application. Furthermore, the assessment of diaphragm ultrasound poses specific challenges in clinical execution [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. At present, a straightforward, non-invasive, and continual monitoring modality is lacking in clinical praxis to accurately gauge the strength of patients' spontaneous respiratory efforts.\u003c/p\u003e \u003cp\u003eThis study developed an innovative noninvasive approach for real-time dynamic monitoring of spontaneous breathing effort (N-Pmus), achieved by modeling respiratory mechanics through mathematical optimization algorithms, thereby eliminating the need for catheter insertion. Subsequently, simulated scenarios and clinical trials were conducted to compare N-Pmus with the bedside routine Pmus measurement (Pes-Pmus) to confirm their correlation and alignment. Furthermore, subgroup analyses encompassing a variety of conditions, ventilation modalities, and levels of respiratory effort were performed to further validate these findings.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eThe study comprised two distinct steps (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In Step One, termed the Bench study, a real-time algorithm for monitoring N-Pmus was developed and integrated into a ventilator platform (SV800, Mindray, Shenzhen, China). An experimental setup was established using the Active Servo Lung 5000 (ASL 5000, IngMar Medical, Pittsburgh, PA, USA) and the ventilator to validate its accuracy by comparing N-Pmus with ASL5000-Pmus. Step Two involved clinical validation through an observational trial conducted in 2023 in the ICUs of West China Hospital, Sichuan University, using a single-center, self-randomized controlled design. N-Pmus was compared with simultaneously monitored Pes-Pmus to assess its clinical correlation and agreement. The study was approved by the Ethics Committee of West China Hospital, Sichuan University (Ethics Approval No. 2023 Approval (105)), and informed consent was obtained from all participants before enrollment. The study is registered under ChiCTR2300076940.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eBench Study\u003c/h3\u003e\n\u003cp\u003eThe N-Pmus algorithm was developed based on the classical respiratory mechanics equation:\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\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 \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{P}}_{\\text{a}\\text{w}}\\left(\\text{t}\\right)=\\dot{\\text{V}}\\left(\\text{t}\\right)\\text{*}{\\text{R}}_{\\text{r}\\text{s}}+\\frac{\\text{V}\\left(\\text{t}\\right)}{{\\text{C}}_{\\text{r}\\text{s}}}+{\\text{P}}_{\\text{m}\\text{u}\\text{s}}\\left(\\text{t}\\right)+{\\text{P}}_{0}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\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\u003eHere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{P}}_{\\text{a}\\text{w}}\\left(\\text{t}\\right)\\)\u003c/span\u003e\u003c/span\u003e (airway pressure), \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\dot{\\text{V}}\\left(\\text{t}\\right)\\)\u003c/span\u003e\u003c/span\u003e (flow), and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{V}\\left(\\text{t}\\right)\\)\u003c/span\u003e\u003c/span\u003e (volume, derived from flow integration) are time-dependent waveforms measured by ventilator sensors. The unknowns include three constants: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{R}}_{\\text{r}\\text{s}}\\)\u003c/span\u003e\u003c/span\u003e (respiratory system resistance), \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{C}}_{\\text{r}\\text{s}}\\)\u003c/span\u003e\u003c/span\u003e (respiratory system compliance), and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{P}}_{0}\\)\u003c/span\u003e\u003c/span\u003e (end-expiratory pressure), as well as the time-varying respiratory muscle pressure \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{P}}_{\\text{m}\\text{u}\\text{s}}\\left(\\text{t}\\right)\\)\u003c/span\u003e\u003c/span\u003e. Directly solving for these variables is infeasible due to an underdetermined system (fewer equations than unknowns). To address this, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{P}}_{\\text{m}\\text{u}\\text{s}}\\left(\\text{t}\\right)\\)\u003c/span\u003e\u003c/span\u003e morphology is modeled as a physiologically plausible polynomial function, reducing the number of unknowns (see Additional file: Figure E1). This reformulates the system into an overdetermined framework (more equations than unknowns), solvable via iterative least-squares optimization[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe experiment utilized an ASL5000 simulator in conjunction with an SV800 ventilator to simulate three clinical scenarios: normal (compliance: 50 mL/cmH\u003csub\u003e2\u003c/sub\u003eO; resistance: 10 cm H\u003csub\u003e2\u003c/sub\u003eO/L/s), restrictive (compliance: 20\u0026ndash;40 mL/cmH\u003csub\u003e2\u003c/sub\u003eO; resistance: 13 cmH\u003csub\u003e2\u003c/sub\u003eO/L/s), and obstructive (compliance: 60 mL/cmH\u003csub\u003e2\u003c/sub\u003eO; resistance: 15\u0026ndash;25 cmH\u003csub\u003e2\u003c/sub\u003eO/L/s), following established benchmarks[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Spontaneous breathing was modeled using programmable sinusoidal patient muscle pressure (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{P}}_{\\text{m}\\text{u}\\text{s}}\\)\u003c/span\u003e\u003c/span\u003e) waveforms (intensity: 3\u0026ndash;30 cmH\u003csub\u003e2\u003c/sub\u003eO), with adjustable rise (10\u0026ndash;20%) and release (10\u0026ndash;30%) phases to replicate varied effort profiles (see Additional file: Table E1).\u003c/p\u003e\n\u003ch3\u003eClinical Study\u003c/h3\u003e\n\u003cp\u003eThe study flow, including the inclusion and exclusion criteria, is summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Comprehensive procedural details are documented in the Additional file.\u003c/p\u003e\n\u003ch3\u003eStudy Population\u003c/h3\u003e\n\u003cp\u003eThree groups of patients were enrolled to this study: (1) acute respiratory distress syndrome (ARDS) per Berlin definition[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], (2) chronic obstructive pulmonary disease (COPD) per GOLD guidelines[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], and (3) non-ARDS/non-COPD respiratory failure.\u003c/p\u003e\n\u003ch3\u003eStudy Protocol\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003eStudy Protocol\u003c/div\u003e \u003cp\u003eAfter obtaining informed consent, enrolled patients were mechanically ventilated using the Mindray SV800 ventilator. An esophageal balloon catheter (SDY-1, Mindray, China) was inserted nasally to monitor respiratory mechanics[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The study did not interfere with the patients' ventilation modes or parameters. Chest wall compliance (Ccw) was temporarily measured in volume-controlled ventilation mode under sedation-induced apnea.\u003c/p\u003e \u003cp\u003eThereafter, the ventilator automatically recorded waveforms (flow, pressure, volume, and esophageal pressure) along with the real-time calculated parameters N-Pmus and N-PTPmus.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eN-Pmus: The peak amplitude of Pmus(t) calculated using the novel algorithm in the bench study during the patient's spontaneous inspiratory phase.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eN-PTPmus: The time integral of N-Pmus(t) during inspiration, representing the magnitude of the patient's inspiratory effort.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eEsophageal pressure-derived metrics were used as the reference standard for validation:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003ePes-Pmus: Calculated as the maximal difference between the esophageal pressure (Pes) and recoil pressure of the chest wall (Pcw, volume/Ccw) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePes-PTPmus: Obtained by integrating esophageal pressure-derived Pmus(t) over the inspiratory phase.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eIn the Bench study, each experimental scenario yielded a single averaged value, reducing the influence of repeated measurements. In the Clinical study, stable respiratory segments were selected based on predefined criteria for esophageal pressure and peak expiratory flow (see Figure E5). For each participant, 2 to 4 segments were identified, each comprising 20 consecutive breath cycles with consistent signal quality. These cycles were subsequently divided into 3 to 4 groups of 5 to 6 cycles each, and their averages were calculated to generate aggregated Pmus data. This approach effectively reduced intra-cycle variability[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] and minimized the influence of cardiac artifacts[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eData are presented as mean (standard deviation) or median [interquartile range], as appropriate.\u003c/p\u003e \u003cp\u003eLinear mixed-effects models were used to evaluate the association between N-Pmus, ASL5000-Pmus, and Pes-Pmus, with patients treated as random effects[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Marginal R\u0026sup2; and conditional R\u0026sup2; were computed to quantify the variance in the dependent variable explained by the mixed models, excluding and including the variance attributed to random effects, respectively[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBias and agreement were assessed using Bland\u0026ndash;Altman analysis [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] for repeated measures. To account for repeated measures within patients, linear mixed-effects models were used to estimate within-patient limits of agreement and to compute the mean and between-patient standard deviation of the bias [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. To address limited sample sizes, we used parametric bootstrap resampling to estimate 95% confidence intervals (CIs) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eStatistical analyses included the Wilcoxon and Shapiro-Wilk tests. A p-value less than 0.05 was considered significant. Offline computation and aggregation of waveform data were performed using MATLAB (The MathWorks, Inc., Natick, MA, USA). Statistical analyses were conducted in RStudio (R Foundation for Statistical Computing, Vienna, Austria).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe primary outcomes of this study include the correlation and agreement between N-Pmus and Pmus benchmarks (ASL5000-Pmus or Pes-Pmus).\u003c/p\u003e\n\u003ch3\u003eBench Study\u003c/h3\u003e\n\u003cp\u003eA total of 270 scenarios were evaluated in the ASL5000 simulation experiment, simulating patients with different RC types, ventilation modes, Pmus amplitudes, and Pmus patterns. N-Pmus showed excellent correlation with ASL5000-Pmus, with a marginal R\u0026sup2; of 0.993 and a conditional R\u0026sup2; of 0.997 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e(B)). The bias was \u0026minus;\u0026thinsp;0.23 cmH₂O, and the limits of agreement ranged from \u0026minus;\u0026thinsp;1.51 to 1.04 cmH₂O (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e(C)). Scenarios were further categorized into restrictive (across a range of compliances) and obstructive (across varying airway resistances) subgroups, with all demonstrating robust correlations (marginal R\u0026sup2; \u0026gt; 0.988 and conditional R\u0026sup2; \u0026gt; 0.992). Detailed clinical scenarios and simulation results are provided in Table E1 and Figure E4 in the Additional File.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eClinical Study\u003c/h2\u003e \u003cp\u003eTwenty-five patients initially met the inclusion criteria, but two were excluded due to absent of spontaneous breathing or missing of esophageal pressure data. The final analysis included 23 patients, with their clinical characteristics presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. These patients contributed 1,380 single-cycle Pmus data pairs and 261 aggregated Pmus data pairs. The median number of aggregated data points per patient was 10 (IQR: 8\u0026ndash;14), reflecting the number of distinct effort levels analyzed for each individual. As an example, representative tracings for Patient 16 are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e(A), with the corresponding theoretical fit obtained using the N-Pmus algorithm shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e(B).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAll Patient's Basic Characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"14\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003cp\u003e(years)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003cp\u003e(kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSub-group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDiagnosis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDepth\u0026dagger;\u003c/p\u003e \u003cp\u003e(cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" colname=\"c8\"\u003e \u003cp\u003eOccRatio\u0026Dagger;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eCcw\u003c/p\u003e \u003cp\u003e(mL/cmH\u003csub\u003e2\u003c/sub\u003eO)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003eVentilation mode\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eTV\u003c/p\u003e \u003cp\u003e(mL)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003ePEEP\u003c/p\u003e \u003cp\u003e(cmH\u003csub\u003e2\u003c/sub\u003eO)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eARDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNecrotizing pancreatitis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e57.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003ePSV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTAAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eV-A/C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCoronary heart disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eP-A/C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eARDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSevere acute pancreatitis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e57.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eV-SIMV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAutoimmune encephalitis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eP-A/C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCOPD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEmphysema\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e57.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eP-SIMV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eARDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eType 1 RF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eP-A/C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eARDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAcute cerebral infarction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eV-A/C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eARDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eType 1 RF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003ePSV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eARDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePulmonary embolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eV-A/C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eARDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSepsis, Severe pneumonia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e57.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eP-A/C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSepsis, Severe pneumonia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e52.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003ePSV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSpace-occupying lesion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eP-A/C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCOPD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eType 2 RF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eP-A/C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e472\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCOPD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eInvasive aspergillosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eP-A/C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eARDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eType 1 RF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e57.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003ePSV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCOPD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEnd-stage renal disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e57.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eP-SIMV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCOPD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAECOPD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003ePSV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTraumatic brain injury\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e57.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eV-A/C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTraumatic brain injury\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eP-A/C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCOPD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAECOPD, Type 2 RF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003ePSV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCOPD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAECOPD, Type 2 RF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e57.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eV-A/C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCOPD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSepsis, AECOPD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e57.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eP-A/C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"14\"\u003eDefinition of abbreviations: BMI\u0026thinsp;=\u0026thinsp;body mass index; Ccw\u0026thinsp;=\u0026thinsp;compliance of the chest wall; TV\u0026thinsp;=\u0026thinsp;measured tidal volume; PEEP\u0026thinsp;=\u0026thinsp;positive end expiratory pressure; ARDS\u0026thinsp;=\u0026thinsp;acute respiratory distress syndrome; COPD\u0026thinsp;=\u0026thinsp;chronic obstructive pulmonary disease; TAAA\u0026thinsp;=\u0026thinsp;thoracoabdominal aortic aneurysm; RF\u0026thinsp;=\u0026thinsp;respiratory failure; AECOPD\u0026thinsp;=\u0026thinsp;acute exacerbation of chronic obstructive pulmonary disease;\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"14\"\u003e\u0026dagger; The transnasal esophageal balloon placement depth is measured from the nasal orifice to the catheter tip.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"14\"\u003e\u0026Dagger; The ΔPes/ΔPaw ratio during the Baydur (occlusion) test with spontaneous breathing or the Positive Pressure Occlusion test without spontaneous breathing.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe analysis of aggregated Pmus data between N-Pmus and Pes-Pmus showed a marginal R\u0026sup2; of 0.97 and a conditional R\u0026sup2; of 0.971 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e(B)). The bias was \u0026minus;\u0026thinsp;0.2 cmH₂O, with limits of agreement ranging from \u0026minus;\u0026thinsp;2.22 to 1.83 cmH₂O (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e(C)). In contrast, single-cycle data exhibited a correlation with a marginal R\u0026sup2; of 0.884 and a conditional R\u0026sup2; of 0.952 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e(E)). The bias was \u0026minus;\u0026thinsp;0.23 cmH₂O, with limits of agreement from \u0026minus;\u0026thinsp;2.33 to 1.88 cmH₂O (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e(F)).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFurthermore, the correlation between N-PTPmus and Pes-PTPmus showed a marginal R\u0026sup2; of 0.861 and a conditional R\u0026sup2; of 0.907 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e(B)). The bias was \u0026minus;\u0026thinsp;0.18 cmH₂O\u0026middot;s, with limits of agreement ranging from \u0026minus;\u0026thinsp;2.45 to 2.08 cmH₂O\u0026middot;s (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e(C)).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePost-hoc Subgroup Analysis\u003c/h2\u003e \u003cp\u003ePatients were stratified by disease type (COPD, ARDS, others). The ARDS subgroup demonstrated the strongest correlation between N-Pmus and Pes-Pmus (marginal R\u0026sup2; = 0.955, conditional R\u0026sup2; = 0.959; bias\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.26 cmH₂O [95% CI: \u0026minus;0.75 to 0.35]), with limits of agreement (LOA) ranging from \u0026minus;\u0026thinsp;2.87 to 2.35 cmH₂O. The COPD and other subgroups exhibited slightly lower correlations (COPD: marginal R\u0026sup2; = 0.822, LOA\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;1.85 to 1.47 cmH₂O; others: marginal R\u0026sup2; = 0.876, LOA\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;1.56 to 1.22 cmH₂O) (Table E2, Figure E6).\u003c/p\u003e \u003cp\u003e Analysis across ventilation modes (P-A/C, V-A/C, PSV, SIMV) revealed robust agreement, with V-A/C showing the highest correlation (marginal R\u0026sup2; = 0.978, conditional R\u0026sup2; = 0.982; bias\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.06 cmH₂O [95% CI: \u0026minus;0.89 to 0.75]), and LOA ranging from \u0026minus;\u0026thinsp;2.88 to 2.76 cmH₂O. P-A/C, PSV, and SIMV modes demonstrated narrower LOA ranges (P-A/C: \u0026minus;2.04 to 1.18 cmH₂O; PSV: \u0026minus;2.46 to 2.17 cmH₂O; SIMV: \u0026minus;1.48 to 1.45 cmH₂O) (Table E3, Figure E7).\u003c/p\u003e \u003cp\u003eStratification by Pes-Pmus magnitude (low: \u0026lt;4 cmH₂O; normal: 4\u0026ndash;10 cmH₂O; high: \u0026gt;10 cmH₂O) revealed stronger correlations in the high-effort group (marginal R\u0026sup2; = 0.895, conditional R\u0026sup2; = 0.916; bias\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.12 cmH₂O [95% CI: \u0026minus;1.1 to 0.59]), with LOA spanning from \u0026minus;\u0026thinsp;3.04 to 2.79 cmH₂O. The low- and normal-effort groups exhibited reduced agreement (low: marginal R\u0026sup2; = 0.611, LOA\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;1.46 to 0.97 cmH₂O; normal: marginal R\u0026sup2; = 0.628, LOA\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;2.38 to 1.65 cmH₂O) (Table E4, Figure E8).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this investigation, we introduced and validated N-Pmus, an innovative non-invasive approach for continuous, real-time monitoring of respiratory effort at the bedside in mechanically ventilated patients. The essence of the N-Pmus algorithm lies in integrating a functional model with physiological constraints into the equation governing respiratory motion, solving it iteratively via the least squares method. A bench study conducted in a simulated setting demonstrated a perfect correlation of 0.99 between N-Pmus and ASL5000-Pmus, with narrow 95% limits of agreement of [-1.51, 1.04] cmH\u003csub\u003e2\u003c/sub\u003eO. Additionally, a subsequent clinical validation study showcased a high correlation of 0.97 between N-Pmus and Pes-Pmus, with tight 95% limits of agreement of [-2.22, 1.83] cmH\u003csub\u003e2\u003c/sub\u003eO. Subsequent subgroup analyses post hoc unveiled a robust correlation and excellent agreement across diverse disease categories, ventilation modalities, and degrees of respiratory effort.\u003c/p\u003e \u003cp\u003eFeasible and reliable monitoring of respiratory effort in mechanically ventilated patients is paramount to mitigate potential lung and diaphragm injuries. Currently, the accuracy of both invasive direct monitoring techniques (EAdi, Pes, and Pdi) and noninvasive indirect methods (PMI, and Pocc) can be susceptible to various factors including procedural standards, the state of illness, and the respiratory status of patients, impeding seamless real-time monitoring. Notably, Albani et al. [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] proposed a noninvasive real-time monitoring technique for inspiratory effort by analyzing the concavity of the inspiratory flow waveform in pressure support ventilation (PSV). However, this method is restricted to pressure-control ventilation and lacks automated calculation capabilities. Pmus, recognized as a key indicator of respiratory effort, has been a focal point of research for achieving noninvasive real-time monitoring.\u003c/p\u003e \u003cp\u003eIn reviewing decades of research, methodologies for real-time Pmus calculation using respiratory mechanics equations can be categorized into two main approaches. The first approach entails solving for respiratory system resistance and compliance before back-calculating the Pmus waveform [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan additionalcitationids=\"CR31 CR32 CR33 CR34\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The second approach involves simultaneously determining Pmus, resistance, and compliance while incorporating constraints into the motion equations [\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Previous studies often underestimate Pmus because active respiratory muscle effort distorts airway pressure and flow profiles, leading to inaccuracies when such activity is not fully accounted for. In contrast, our algorithm enhances Pmus estimation by employing global constrained least-squares fitting, a cubic function template to model active expiration, and advanced waveform feature recognition using both inspiratory and expiratory data. These advancements significantly improved the accuracy of Pmus peak estimation and morphology characterization, as detailed in the Additional file (see Figure E2 and Figure E3). Validation of this approach is conducted through simulation studies and clinical trials, offering promising prospects for enhanced respiratory effort monitoring in mechanically ventilated patients.\u003c/p\u003e \u003cp\u003eThe simulation experiment validated scenarios encompassing prevalent disease types, including restrictive and obstructive lung diseases, as well as typical ventilator mode settings, such as V-A/C, P-A/C, PSV, and SIMV, commonly seen in clinical practice. The high correlation, reaching up to 0.99, can be ascribed to the meticulous mathematical model of the Pmus waveform generated by the ASL5000 and the precise fitting effect of the theoretical model. The 95% limits of agreement were found to be [-1.51, 1.04] cmH\u003csub\u003e2\u003c/sub\u003eO. Furthermore, Silva et al. [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] simulated 49 scenarios, revealing that deviations from the absolute Pmus amplitude predominantly clustered within the quartiles (0.4 to 1.3 cmH₂O). Our simulation experiments confirmed similar outcomes across a wider range of use case scenarios, including those with elevated resistance and decreased compliance. However, it should be noted that the observed correlation may be influenced by the method of effort generation used by the ASL5000, which warrants further investigation to assess its generalizability.\u003c/p\u003e \u003cp\u003eIn our clinical trial with 23 patients, the correlation between N-Pmus and Pes-Pmus was 0.97, with 95% limits of agreement from \u0026minus;\u0026thinsp;2.22 to 1.83 cmH\u003csub\u003e2\u003c/sub\u003eO. This demonstrates significantly improved accuracy and reproducibility compared to previous studies, particularly those by Kondili et al. [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] with 11 patients (correlation of 0.83, 95% limits of agreement between \u0026minus;\u0026thinsp;7.23 and 5.41 cmH\u003csub\u003e2\u003c/sub\u003eO), and Natalini et al. [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] with 18 patients (correlation of 0.58, 95% limits of agreement between \u0026minus;\u0026thinsp;12 and 12 cmH\u003csub\u003e2\u003c/sub\u003eO). Similarly, the correlation between N-PTPmus and Pes-PTPmus was 0.86, with 95% limits of agreement from \u0026minus;\u0026thinsp;2.45 to 2.08 cmH\u003csub\u003e2\u003c/sub\u003eO\u0026middot;s, surpassing the findings of Kondili et al. [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] (correlation of 0.77, 95% limits of agreement between \u0026minus;\u0026thinsp;3.01 and 1.99 cmH\u003csub\u003e2\u003c/sub\u003eO\u0026middot;s), and Natalini et al. [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] (correlation of 0.52, 95% limits of agreement between \u0026minus;\u0026thinsp;8.35 and 7.74 cmH\u003csub\u003e2\u003c/sub\u003eO\u0026middot;s).\u003c/p\u003e \u003cp\u003eIn a post-hoc subgroup analysis, the ARDS subgroup showed the highest correlation between N‐Pmus and Pes‐Pmus (marginal R\u0026sup2; = 0.955) but exhibited a wide limit of agreement (LOA: \u0026minus;2.87 to 2.35 cmH₂O). In contrast, the COPD and other disease subgroups demonstrated lower correlations (COPD: marginal R\u0026sup2; = 0.822; others: marginal R\u0026sup2; = 0.876) with narrower LOA. Stratification by respiratory effort revealed that the high-effort group, similar to the ARDS subgroup, had the highest correlation with a wider LOA, whereas the normal and low-effort groups had lower correlations and narrower LOA. These differences are primarily attributable to the influence of respiratory effort magnitude on measurement precision: in high-effort states (commonly observed in ARDS), larger Pmus values reduce the relative impact of small absolute errors\u0026mdash;enhancing correlation\u0026mdash;but also amplify absolute differences. In practice, the relative error remains small, yet this leads to a wider LOA. Conversely, in low-effort states (as seen in other subgroups), smaller Pmus values exaggerate the relative effect of minimal errors, leading to lower correlations but narrower LOA. Therefore, further investigations are imperative to validate the observations within this subgroup.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStrengths and Limitations.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis study systematically presents the derivation of the N-Pmus algorithm along with the results from laboratory simulations and clinical validation. The simulation scenarios strived to cover a broad spectrum of patient profiles, Pmus configurations, and a wide array of amplitudes, while the clinical trials aimed to confirm the efficacy across diverse patient phenotypes. Nonetheless, our study does have certain limitations. Firstly, while esophageal pressure serves as the most accurate approximation of pleural pressure, it may not accurately represent the actual intrathoracic pressure under various pathological conditions. Although chest wall compliance typically remains relatively constant, patients subjected to differing circumstances, such as controlled versus spontaneous ventilation, might exhibit slightly varying effects on this compliance. Thus, the aforementioned limitations regarding the discrepancy between Pes-Pmus in the control group and the true Pmus necessitate further refinement. Secondly, the study is constrained by its brief duration, diminutive sample size, and confinement to a single center. Finally, it omitted the inclusion of healthy subjects or individuals with specific conditions like abdominal hypertension, and it did not assess transdiaphragmatic pressure (Pdi). Subsequent validation endeavors should incorporate long term, more extensive, multicenter research initiatives.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study introduces an innovative, bedside, non-invasive, real-time approach to quantitatively evaluate respiratory effort. Our investigation illustrated that N-Pmus showcases a robust correlation and agreement with the established Pmus benchmarks (ASL5000-Pmus or Pes-Pmus), surpassing conventional metrics for assessing respiratory effort. Further research is essential to validate this technique and explore its clinical applications. In the future, N-Pmus will enable precise monitoring and guide parameter adjustments in mechanical ventilation, potentially supporting lung- and diaphragm-protective ventilation.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003ePmus:\u003c/strong\u003e respiratory muscle pressure; \u003cstrong\u003ePes:\u003c/strong\u003e esophageal pressure; \u003cstrong\u003ePcw:\u003c/strong\u003e chest wall elastic recoil pressure; \u003cstrong\u003eCcw:\u003c/strong\u003e compliance of the chest wall;\u003cstrong\u003e\u0026nbsp;N-Pmus:\u003c/strong\u003e real-time Pmus amplitude monitored by the ventilator; \u003cstrong\u003ePes-Pmus:\u003c/strong\u003e the usual monitoring for Pmus amplitude, calculated from Pes and Pcw; \u003cstrong\u003eASL5000-Pmus:\u003c/strong\u003e Pmus settings from ASL5000; \u003cstrong\u003eN-PTPmus:\u003c/strong\u003e pressure\u0026ndash;time-product of Pmus measured by the ventilator in a single breath; \u003cstrong\u003ePes-PTPmus:\u003c/strong\u003e the usual monitoring pressure\u0026ndash;time-product of Pmus, calculated using esophageal pressure in a single breath; \u003cstrong\u003eP-A/C:\u003c/strong\u003e pressure-assist/control ventilation; \u003cstrong\u003ePSV:\u003c/strong\u003e pressure support ventilation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of West China Hospital, Sichuan University (Ethics Approval No. 2023 Approval (105)). Written informed consent was obtained from the legal primary decision-maker, who was the spouse of the patient, or the parent of the child in cases in which there was no spouse. The trial was registered at chictr.org.cn (ChiCTR2300076940).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated during this study are included in this published article and can be found in the Additional file.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBW and YL received a grant from Mindray (China), and HS, ZH, JL, and XZ are affiliated with Mindray (China). All other authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study received funding from Mindray (China).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudy concept and design: YL, MD, HS, ZH, YZ, JL and BW. Acquisition of data: YL, ZN, ZW and XJ. Analysis and interpretation of data: YL, MD, HS, ZH, YZ, JL and BW. Drafting of the manuscript: MD, HS and YZ. Critical revision and important intellectual contribution: YL, MD, HS, ZH, YZ, JL and BW. Statistical analysis: HS, ZH and JL. Administrative support: XZ, JL, BW and YK. Study supervision: YZ, JL, BW and YK.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSlutsky AS, Ranieri VM: \u003cstrong\u003eVentilator-Induced Lung Injury\u003c/strong\u003e. \u003cem\u003eNew England Journal of Medicine \u003c/em\u003e2013, \u003cstrong\u003e369\u003c/strong\u003e(22):2126-2136.\u003c/li\u003e\n\u003cli\u003eAmato MB, Meade MO, Slutsky AS, Brochard L, Costa EL, Schoenfeld DA, Stewart TE, Briel M, Talmor D, Mercat A\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eDriving pressure and survival in the acute respiratory distress syndrome\u003c/strong\u003e. \u003cem\u003eN Engl J Med \u003c/em\u003e2015, \u003cstrong\u003e372\u003c/strong\u003e(8):747-755.\u003c/li\u003e\n\u003cli\u003eUrner M, Juni P, Hansen B, Wettstein MS, Ferguson ND, Fan E: \u003cstrong\u003eTime-varying intensity of mechanical ventilation and mortality in patients with acute respiratory failure: a registry-based, prospective cohort study\u003c/strong\u003e. \u003cem\u003eLancet Respir Med \u003c/em\u003e2020, \u003cstrong\u003e8\u003c/strong\u003e(9):905-913.\u003c/li\u003e\n\u003cli\u003eYoshida T, Grieco DL, Brochard L, Fujino Y: \u003cstrong\u003ePatient self-inflicted lung injury and positive end-expiratory pressure for safe spontaneous breathing\u003c/strong\u003e. \u003cem\u003eCurr Opin Crit Care \u003c/em\u003e2020, \u003cstrong\u003e26\u003c/strong\u003e(1):59-65.\u003c/li\u003e\n\u003cli\u003eDemoule A, Molinari N, Jung B, Prodanovic H, Chanques G, Matecki S, Mayaux J, Similowski T, Jaber S: \u003cstrong\u003ePatterns of diaphragm function in critically ill patients receiving prolonged mechanical ventilation: a prospective longitudinal study\u003c/strong\u003e. \u003cem\u003eAnn Intensive Care \u003c/em\u003e2016, \u003cstrong\u003e6\u003c/strong\u003e(1):75.\u003c/li\u003e\n\u003cli\u003eGoligher EC, Dres M, Fan E, Rubenfeld GD, Scales DC, Herridge MS, Vorona S, Sklar MC, Rittayamai N, Lanys A\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eMechanical Ventilation-induced Diaphragm Atrophy Strongly Impacts Clinical Outcomes\u003c/strong\u003e. \u003cem\u003eAm J Respir Crit Care Med \u003c/em\u003e2018, \u003cstrong\u003e197\u003c/strong\u003e(2):204-213.\u003c/li\u003e\n\u003cli\u003eCherniack RM, Farhi LE, Armstrong BW, Proctor DF: \u003cstrong\u003eA comparison of esophageal and intrapleural pressure in man\u003c/strong\u003e. \u003cem\u003eJ Appl Physiol \u003c/em\u003e1955, \u003cstrong\u003e8\u003c/strong\u003e(2):203-211.\u003c/li\u003e\n\u003cli\u003eAkoumianaki E, Maggiore SM, Valenza F, Bellani G, Jubran A, Loring SH, Pelosi P, Talmor D, Grasso S, Chiumello D\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eThe application of esophageal pressure measurement in patients with respiratory failure\u003c/strong\u003e. \u003cem\u003eAm J Respir Crit Care Med \u003c/em\u003e2014, \u003cstrong\u003e189\u003c/strong\u003e(5):520-531.\u003c/li\u003e\n\u003cli\u003eFoti G, Cereda M, Banfi G, Pelosi P, Fumagalli R, Pesenti A: \u003cstrong\u003eEnd-inspiratory airway occlusion: a method to assess the pressure developed by inspiratory muscles in patients with acute lung injury undergoing pressure support\u003c/strong\u003e. \u003cem\u003eAm J Respir Crit Care Med \u003c/em\u003e1997, \u003cstrong\u003e156\u003c/strong\u003e(4 Pt 1):1210-1216.\u003c/li\u003e\n\u003cli\u003eBertoni M, Telias I, Urner M, Long M, Del Sorbo L, Fan E, Sinderby C, Beck J, Liu L, Qiu H\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eA novel non-invasive method to detect excessively high respiratory effort and dynamic transpulmonary driving pressure during mechanical ventilation\u003c/strong\u003e. \u003cem\u003eCrit Care \u003c/em\u003e2019, \u003cstrong\u003e23\u003c/strong\u003e(1):346.\u003c/li\u003e\n\u003cli\u003eBellani G, Mauri T, Coppadoro A, Grasselli G, Patroniti N, Spadaro S, Sala V, Foti G, Pesenti A: \u003cstrong\u003eEstimation of patient\u0026apos;s inspiratory effort from the electrical activity of the diaphragm\u003c/strong\u003e. \u003cem\u003eCrit Care Med \u003c/em\u003e2013, \u003cstrong\u003e41\u003c/strong\u003e(6):1483-1491.\u003c/li\u003e\n\u003cli\u003eJansen D, Jonkman AH, Roesthuis L, Gadgil S, van der Hoeven JG, Scheffer GJ, Girbes A, Doorduin J, Sinderby CS, Heunks LMA: \u003cstrong\u003eEstimation of the diaphragm neuromuscular efficiency index in mechanically ventilated critically ill patients\u003c/strong\u003e. \u003cem\u003eCrit Care \u003c/em\u003e2018, \u003cstrong\u003e22\u003c/strong\u003e(1):238.\u003c/li\u003e\n\u003cli\u003eGoligher EC, Laghi F, Detsky ME, Farias P, Murray A, Brace D, Brochard LJ, Bolz SS, Rubenfeld GD, Kavanagh BP\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eMeasuring diaphragm thickness with ultrasound in mechanically ventilated patients: feasibility, reproducibility and validity\u003c/strong\u003e. \u003cem\u003eIntensive Care Med \u003c/em\u003e2015, \u003cstrong\u003e41\u003c/strong\u003e(4):642-649.\u003c/li\u003e\n\u003cli\u003eBertoni M, Spadaro S, Goligher EC: \u003cstrong\u003eMonitoring Patient Respiratory Effort During Mechanical Ventilation: Lung and Diaphragm-Protective Ventilation\u003c/strong\u003e. \u003cem\u003eCrit Care \u003c/em\u003e2020, \u003cstrong\u003e24\u003c/strong\u003e(1):106.\u003c/li\u003e\n\u003cli\u003eJonkman AH, de Vries HJ, Heunks LMA: \u003cstrong\u003ePhysiology of the Respiratory Drive in ICU Patients: Implications for Diagnosis and Treatment\u003c/strong\u003e. \u003cem\u003eCrit Care \u003c/em\u003e2020, \u003cstrong\u003e24\u003c/strong\u003e(1):104.\u003c/li\u003e\n\u003cli\u003eIotti GA, Braschi A, Brunner JX, Smits T, Olivei M, Palo A, Veronesi R: \u003cstrong\u003eRespiratory mechanics by least squares fitting in mechanically ventilated patients: applications during paralysis and during pressure support ventilation\u003c/strong\u003e. \u003cem\u003eIntensive Care Med \u003c/em\u003e1995, \u003cstrong\u003e21\u003c/strong\u003e(5):406-413.\u003c/li\u003e\n\u003cli\u003eArnal JM, Garnero A, Saoli M, Chatburn RL: \u003cstrong\u003eParameters for Simulation of Adult Subjects During Mechanical Ventilation\u003c/strong\u003e. \u003cem\u003eRespir Care \u003c/em\u003e2018, \u003cstrong\u003e63\u003c/strong\u003e(2):158-168.\u003c/li\u003e\n\u003cli\u003eDaoud EG, Katigbak R, Ottochian M: \u003cstrong\u003eAccuracy of the Ventilator Automated Displayed Respiratory Mechanics in Passive and Active Breathing Conditions: A Bench Study\u003c/strong\u003e. \u003cem\u003eRespir Care \u003c/em\u003e2019, \u003cstrong\u003e64\u003c/strong\u003e(12):1555-1560.\u003c/li\u003e\n\u003cli\u003eForce ADT, Ranieri VM, Rubenfeld GD, Thompson BT, Ferguson ND, Caldwell E, Fan E, Camporota L, Slutsky AS: \u003cstrong\u003eAcute respiratory distress syndrome: the Berlin Definition\u003c/strong\u003e. \u003cem\u003eJAMA \u003c/em\u003e2012, \u003cstrong\u003e307\u003c/strong\u003e(23):2526-2533.\u003c/li\u003e\n\u003cli\u003eDisease GIfCOL: \u003cstrong\u003eGlobal Strategy for the Diagnosis, Management, and Prevention of Chronic Obstructive Pulmonary Disease (2023 Report)\u003c/strong\u003e. In\u003cem\u003e.\u003c/em\u003e; 2023.\u003c/li\u003e\n\u003cli\u003eBellani G, Bronco A, Arrigoni Marocco S, Pozzi M, Sala V, Eronia N, Villa G, Foti G, Tagliabue G, Eger M\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eMeasurement of Diaphragmatic Electrical Activity by Surface Electromyography in Intubated Subjects and Its Relationship With Inspiratory Effort\u003c/strong\u003e. \u003cem\u003eRespir Care \u003c/em\u003e2018, \u003cstrong\u003e63\u003c/strong\u003e(11):1341-1349.\u003c/li\u003e\n\u003cli\u003eQin Y, Huang Z, Zhou X, Gui S, Xiong L, Liu L, Liu J: \u003cstrong\u003eA novel adaptive filter with a heart-rate-based reference signal for esophageal pressure signal denoising\u003c/strong\u003e. \u003cem\u003eJ Clin Monit Comput \u003c/em\u003e2024.\u003c/li\u003e\n\u003cli\u003eShan G, Zhang H, Jiang T: \u003cstrong\u003eCorrelation Coefficients for a Study with Repeated Measures\u003c/strong\u003e. \u003cem\u003eComput Math Methods Med \u003c/em\u003e2020, \u003cstrong\u003e2020\u003c/strong\u003e:7398324.\u003c/li\u003e\n\u003cli\u003eNakagawa S, Schielzeth H, O\u0026apos;Hara RB: \u003cstrong\u003eA general and simple method for obtaining R2 from generalized linear mixed‐effects models\u003c/strong\u003e. \u003cem\u003eMethods in Ecology and Evolution \u003c/em\u003e2012, \u003cstrong\u003e4\u003c/strong\u003e(2):133-142.\u003c/li\u003e\n\u003cli\u003eMartin Bland J, Altman D: \u003cstrong\u003eStatistical Methods for Assessing Agreement between Two Methods of Clinical Measurement\u003c/strong\u003e. \u003cem\u003eThe Lancet \u003c/em\u003e1986, \u003cstrong\u003e327\u003c/strong\u003e(8476):307-310.\u003c/li\u003e\n\u003cli\u003eMyles PS, Cui J: \u003cstrong\u003eUsing the Bland-Altman method to measure agreement with repeated measures\u003c/strong\u003e. \u003cem\u003eBr J Anaesth \u003c/em\u003e2007, \u003cstrong\u003e99\u003c/strong\u003e(3):309-311.\u003c/li\u003e\n\u003cli\u003eParker RA, Weir CJ, Rubio N, Rabinovich R, Pinnock H, Hanley J, McCloughan L, Drost EM, Mantoani LC, MacNee W\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eApplication of Mixed Effects Limits of Agreement in the Presence of Multiple Sources of Variability: Exemplar from the Comparison of Several Devices to Measure Respiratory Rate in COPD Patients\u003c/strong\u003e. \u003cem\u003ePLoS One \u003c/em\u003e2016, \u003cstrong\u003e11\u003c/strong\u003e(12):e0168321.\u003c/li\u003e\n\u003cli\u003eAlbani F, Fusina F, Ciabatti G, Pisani L, Lippolis V, Franceschetti ME, Giovannini A, di Mussi R, Murgolo F, Rosano A\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eFlow Index accurately identifies breaths with low or high inspiratory effort during pressure support ventilation\u003c/strong\u003e. \u003cem\u003eCrit Care \u003c/em\u003e2021, \u003cstrong\u003e25\u003c/strong\u003e(1):427.\u003c/li\u003e\n\u003cli\u003eAlbani F, Pisani L, Ciabatti G, Fusina F, Buizza B, Granato A, Lippolis V, Aniballi E, Murgolo F, Rosano A\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eFlow Index: a novel, non-invasive, continuous, quantitative method to evaluate patient inspiratory effort during pressure support ventilation\u003c/strong\u003e. \u003cem\u003eCrit Care \u003c/em\u003e2021, \u003cstrong\u003e25\u003c/strong\u003e(1):196.\u003c/li\u003e\n\u003cli\u003eGillard C, Flemale A, Dierckx JP, Themelin G: \u003cstrong\u003eMeasurement of effective elastance of the total respiratory system in ventilated patients by a computed method. Comparison with the static method\u003c/strong\u003e. \u003cem\u003eIntensive Care Med \u003c/em\u003e1990, \u003cstrong\u003e16\u003c/strong\u003e(3):189-195.\u003c/li\u003e\n\u003cli\u003eLucangelo U, Bernabe F, Blanch L: \u003cstrong\u003eRespiratory mechanics derived from signals in the ventilator circuit\u003c/strong\u003e. \u003cem\u003eRespir Care \u003c/em\u003e2005, \u003cstrong\u003e50\u003c/strong\u003e(1):55-65; discussion 65-57.\u003c/li\u003e\n\u003cli\u003eYounes M, Brochard L, Grasso S, Kun J, Mancebo J, Ranieri M, Richard J-C, Younes H: \u003cstrong\u003eA method for monitoring and improving patient: ventilator interaction\u003c/strong\u003e. \u003cem\u003eIntensive Care Medicine \u003c/em\u003e2007, \u003cstrong\u003e33\u003c/strong\u003e(8):1337-1346.\u003c/li\u003e\n\u003cli\u003eKondili E, Alexopoulou C, Xirouchaki N, Vaporidi K, Georgopoulos D: \u003cstrong\u003eEstimation of inspiratory muscle pressure in critically ill patients\u003c/strong\u003e. \u003cem\u003eIntensive Care Med \u003c/em\u003e2010, \u003cstrong\u003e36\u003c/strong\u003e(4):648-655.\u003c/li\u003e\n\u003cli\u003eNatalini G, Buizza B, Granato A, Aniballi E, Pisani L, Ciabatti G, Lippolis V, Rosano A, Latronico N, Grasso S\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eNon-invasive assessment of respiratory muscle activity during pressure support ventilation: accuracy of end-inspiration occlusion and least square fitting methods\u003c/strong\u003e. \u003cem\u003eJ Clin Monit Comput \u003c/em\u003e2021, \u003cstrong\u003e35\u003c/strong\u003e(4):913-921.\u003c/li\u003e\n\u003cli\u003eGutierrez G: \u003cstrong\u003eA non-invasive method to monitor respiratory muscle effort during mechanical ventilation\u003c/strong\u003e. \u003cem\u003eJ Clin Monitor Comp \u003c/em\u003e2024.\u003c/li\u003e\n\u003cli\u003eVicario F, Albanese A, Wang D, Karamolegkos N, Chbat NW, Ieee: \u003cstrong\u003eConstrained Optimization for Noninvasive Estimation of Work of Breathing\u003c/strong\u003e. In: \u003cem\u003e2015 37TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC).\u003c/em\u003e 2015: 5327-5330.\u003c/li\u003e\n\u003cli\u003eVicario F, Albanese A, Karamolegkos N, Wang D, Seiver A, Chbat NW: \u003cstrong\u003eNoninvasive Estimation of Respiratory Mechanics in Spontaneously Breathing Ventilated Patients: A Constrained Optimization Approach\u003c/strong\u003e. \u003cem\u003eIEEE Trans Biomed Eng \u003c/em\u003e2016, \u003cstrong\u003e63\u003c/strong\u003e(4):775-787.\u003c/li\u003e\n\u003cli\u003eSilva DO, de Souza PN, de Araujo Sousa ML, Morais CCA, Ferreira JC, Holanda MA, Yamaguti WP, Junior LP, Costa ELV: \u003cstrong\u003eImpact on the ability of healthcare professionals to correctly identify patient-ventilator asynchronies of the simultaneous visualization of estimated muscle pressure curves on the ventilator display: a randomized study (Pmus study)\u003c/strong\u003e. \u003cem\u003eCritical Care \u003c/em\u003e2023, \u003cstrong\u003e27\u003c/strong\u003e(1).\u003c/li\u003e\n\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":"critical-care","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cric","sideBox":"Learn more about [Critical Care](http://ccforum.biomedcentral.com/)","snPcode":"13054","submissionUrl":"https://submission.nature.com/new-submission/13054/3","title":"Critical Care","twitterHandle":"@Crit_Care","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"mechanical ventilation, respiratory effort, non-invasive monitoring, respiratory muscle pressure (Pmus)","lastPublishedDoi":"10.21203/rs.3.rs-5876654/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5876654/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMechanical ventilation is essential for treating respiratory failure. However, ventilator over-assistance can lead to ventilator-induced diaphragm dysfunction (VIDD), and inadequate assistance can necessitate excessive effort, which can be detrimental to lung mechanics and damage diaphragm function. Current monitoring methods face clinical implementation challenges due to invasiveness and complexity. This study introduces and validates a novel non-invasive real-time respiratory muscle pressure (N-Pmus) monitoring method.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1) The bench study involved developing a non-invasive, real-time respiratory muscle pressure monitoring algorithm (N-Pmus) based on respiratory mechanics equations and validated against an ASL5000 lung simulator across 270 clinical scenarios. 2) A clinical validation was conducted as a self-randomized controlled study(n = 23) comparing N-Pmus with the Pmus derived from simultaneously monitored esophageal pressure (Pes-Pmus) to assess correlation and agreement. The association between N-Pmus and the established Pmus benchmarks was analyzed using linear mixed-effects models. Bias and agreement were evaluated through Bland–Altman analysis for repeated measures.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1) The bench study demonstrated that N-Pmus correlated well with ASL5000-Pmus, with marginal R²=0.993 and conditional R²=0.997. The bias was − 0.23 cmH₂O, with limits of agreement ranging from − 1.51 to 1.04 cmH₂O. 2) The clinical validation revealed strong N-Pmus/Pes-Pmus agreement with marginal R²=0.97 and conditional R²=0.971. The bias was − 0.2 cmH₂O, with limits of agreement ranging from − 2.22 to 1.83 cmH₂O.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eN-Pmus, a novel, non-invasive real-time monitoring method, demonstrates a strong correlation and agreement with the established Pmus benchmarks (ASL5000-Pmus or Pes-Pmus), offering an effective means of assessing respiratory effort in mechanically ventilated patients.\u003c/p\u003e\n\u003cp\u003eClinical trial retrospectively registered with www.chictr.org.cn . Registration number : ChiCTR2300076940, registered 24 October 2023.\u003c/p\u003e","manuscriptTitle":"Respiratory Effort Monitoring: a Novel, Bedside, Non- invasive, Real-time Method","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-28 11:15:50","doi":"10.21203/rs.3.rs-5876654/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-19T06:42:38+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-16T23:22:27+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-01T15:06:05+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-28T07:36:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"110288474378563123593844452906552847645","date":"2025-04-27T07:45:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"68600765435814956033951697969238817798","date":"2025-04-24T10:42:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"163522145972680738483842031160179380943","date":"2025-04-23T08:09:51+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-22T07:27:14+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-11T12:53:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"Critical Care","date":"2025-03-31T05:00:59+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"critical-care","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cric","sideBox":"Learn more about [Critical Care](http://ccforum.biomedcentral.com/)","snPcode":"13054","submissionUrl":"https://submission.nature.com/new-submission/13054/3","title":"Critical Care","twitterHandle":"@Crit_Care","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6cab6ac5-0aae-4bdb-82d7-abbf7766e8c4","owner":[],"postedDate":"April 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-07-07T16:17:00+00:00","versionOfRecord":{"articleIdentity":"rs-5876654","link":"https://doi.org/10.1186/s13054-025-05514-4","journal":{"identity":"critical-care","isVorOnly":false,"title":"Critical Care"},"publishedOn":"2025-07-03 15:58:23","publishedOnDateReadable":"July 3rd, 2025"},"versionCreatedAt":"2025-04-28 11:15:50","video":"","vorDoi":"10.1186/s13054-025-05514-4","vorDoiUrl":"https://doi.org/10.1186/s13054-025-05514-4","workflowStages":[]},"version":"v1","identity":"rs-5876654","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5876654","identity":"rs-5876654","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}