End-expiratory lung volumes as a potential indicator for COVID-19 associated acute respiratory distress syndrome: a retrospective study

End-expiratory lung volumes as a potential indicator for COVID-19 associated acute respiratory distress syndrome: a retrospective study | 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 End-expiratory lung volumes as a potential indicator for COVID-19 associated acute respiratory distress syndrome: a retrospective study Shengyu Hao, Yilin Wei, Yuxian Wang, Yaxiaerjiang Muhetaer, Chujun Zhou, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3989949/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background End-expiratory lung volume (EELV) has been observed to decrease in acute respiratory distress syndrome (ARDS). Yet, research investigating EELV in patients with COVID-19 associated ARDS (CARDS) remains limited. It is unclear EELV serve as a potential metric for monitoring disease progression and identifying patients with ARDS at increased risk of adverse outcomes. Study Design and Methods: This retrospective study included mechanically ventilated patients with CARDS during the initial phase of epidemic control in Shanghai. EELV was measured within 48 hours post-intubation, followed by regular assessments every 3–4 days. Chest CT scans, performed within a 24-hour window around each EELV measurement, were analyzed using AI software. Differences in patient demographics, clinical data, respiratory mechanics, EELV, and chest CT findings were assessed using linear mixed models (LMM). Results Out of the 38 enrolled patients, 26.3% survived until discharge from the ICU. In the survivor group, EELV, EELV/PBW and EELV/preFRC were significantly higher than those in the non-survivor group (survivor group vs non-survivor group: EELV: 1455 vs 1162 ml, P = 0.049; EELV/PBW: 24.1 vs 18.5 ml/kg, P = 0.011; EELV/preFRC: 0.45 vs 0.34, P = 0.005). Follow-up assessments showed a sustained elevation of EELV/PBW and EELV/preFRC among the survivors. Additionally, EELV exhibited a positive correlation with total lung volume and residual lung volume, while demonstrating a negative correlation with lesion volume determined through chest CT scans analyzed using AI software. Conclusion EELV is a useful indicator for assessing disease severity and monitoring the prognosis of patients with CARDS. end-expiratory lung volume COVID-19 acute respiratory distress syndrome mechanical ventilation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Severe COVID-19 pneumonia has presented significant challenges for the research and medical communities. Among individuals hospitalized with COVID-19, 15–30% will progress to develop COVID-19 associated acute respiratory distress syndrome (CARDS) [ 1 ]. Autopsy studies of patients who succumbed to severe SARS CoV-2 infection reveal the presence of diffuse alveolar damage, accompanied by a higher thrombus burden in the pulmonary capillaries and fibrosing nonspecific interstitial pneumonia. These factors contribute to a reduction in functional residual capacity (FRC) and severe arterial hypoxemia[ 2 , 3 ]. It has been observed that oxygen levels do not significantly correlate with the prognosis of ARDS [ 4 , 5 ]. Additionally, the definition of ARDS has a somewhat controversial history, and the COVID-19 pandemic has further complicated the current Berlin definition of ARDS. Researchers have been advocating for and working towards improved criteria and methods for defining ARDS [ 6 ]. It should be emphasized that our treatment objective for ARDS patients should not solely focus on improving their oxygen levels or the ratio of arterial oxygen partial pressure to the fraction of inspired oxygen (PaO₂/FiO₂) [ 7 , 8 ]. Instead, we require effective and non-invasive monitoring methods to track the progression of ARDS, which are crucial for evaluating patient condition and prognosis. FRC, the amount of gas remaining in the lungs after a natural exhalation at atmospheric pressure, serves as a crucial indicator of gas exchange capacity in healthy individuals [ 9 ]. End-expiratory lung volume (EELV), which encompasses the cumulative gas volume within intubated patients, incorporates the functional residual capacity along with the additional volume introduced by positive end-expiratory pressure (PEEP) [ 10 , 11 ]. ARDS leads to a substantial decrease in EELV, resulting in higher strain at a given tidal volume (VT). For this reason, bedside EELV measurement may assist in setting ventilation parameters for protect strategies and better monitoring changes in lung injury [ 12 ]. Reproducible measurement techniques are essential for bedside use to minimize overdistention and identify which patients may benefit from recruitment strategies. While CT scans and gas-dilution techniques have been validated for lung-volume measurement, their complexity limits their practical use in clinical settings. Fortunately, ICU ventilators now offer washout/washin techniques using oxygen or nitrogen, making it convenient to measure EELV at the patient's bedside. Comparisons between EELV measurements (obtained through multiple breath nitrogen washout/washin and helium dilution) and CT scans have consistently demonstrated strong agreements in stable patients, animal models of ARDS, and artificial lungs [ 11 , 13 , 14 ]. However, the investigation of EELV and its variations, as well as their association with the prognosis of patients with CARDS, remains unexplored. In this study, we monitored the EELV and its changes in patients with CARDS, and performed a correlation analysis with CT scans. Our hypothesis posits that changes in EELV could serve as a valuable indicator of disease progression and a predictive factor for the prognosis of patients with ARDS, in contrast to relying solely on arterial blood gas measurements and CT volume analysis. 2. Materials and Methods The study protocol and informed consent forms were reviewed and approved by the ethics board of Zhongshan Hospital afflicted to Fudan University (approval code: B2023-074R). 2.1.Study population This study included patients admitted to our ICU between December 2022 to March 2023, who had been diagnosed with COVID-19 infection through confirmation via real-time reverse transcriptase-polymerase chain reaction. The inclusion criteria were as follows: (a) COVID-19 cases classified as severe in accordance with the WHO interim guidance, characterized by clinical signs of pneumonia in addition to a respiratory rate (RR) > 30 breaths/min, severe respiratory distress, and/or oxygen saturation (SpO 2 ) < 90% on room air [ 15 ]. (b) Endotracheal intubation was administered during the patient's ICU admission in response to their deteriorating condition. (c) Subsequent follow-up chest CT scans and EELV tests were conducted. Exclusion criteria encompassed severe hemodynamic instability, inability to complete the EELV test (e.g., due to a pronounced decline in SpO 2 levels observed during the evaluation), recurrent ICU admissions, and patient whose fraction of inspired oxygen (FiO 2 ) exceeded 80%. 2.2. Data collection Patients’ demographics, date of disease onset, initial symptoms, duration of hospital admission and ICU admission, disease severity, comorbidities, chronic therapy, medications and treatment received during ICU, as well as chest CT scans, were extracted from electronic patient records. The EELV test was conducted within 48 hours post-intubation, with follow-up assessments performed every 3–4 days for critically ill patients. The CT scans selected for analysis were obtained within a 24-hour window before or after the EELV measurements. EELV monitoring was discontinued for patients who underwent extubation, were discharged from ICU, or were no longer able to undergo further EELV measurements. This study involved EELV monitoring of a cohort of 38 patients: all 38 patients received an initial EELV assessment, 23 underwent a second evaluation, and 12 participated in a third round of monitoring. This resulted in a total of 73 EELV measurements across these patients. Additionally, 92 CT scans were performed, adhering to the specified temporal criteria for the study. Subsequent analysis using AI software enabled the successful identification and processing of 72 CT scans. However, the scans from 4 patients were not amenable to AI-based analysis. As a result, the final dataset for correlation analysis included 72 CT scans and their corresponding EELV values, encompassing a subset of 34 patients. The study flow is illustrated in Fig. 1 . 2.3. Ventilator parameters setting The ventilator settings were aligned with established guidelines, configuring patient ventilation modes to A/C-VC and V-SIMV. Initial VT, 6–8 ml/kg, was calibrated against ideal body weight. Continuous monitoring encompassed peak pressure, plateau pressure (Pplat), RR, minute ventilation, arterial pH, partial pressure of carbon dioxide (PaCO 2 ), SpO 2 , and partial PaO 2 . Should Pplat exceed 30 cmH 2 O, VT was stepwise reduced to 4ml/kg. FiO 2 adjustments were based on SpO 2 and PaO 2 readings. Personalized PEEP was titrated using the EIT-Costa method, keeping driving pressure (Pplat-PEEP) below 15 cmH 2 O [ 16 ]. In the event that driving pressure exceeded this threshold, further VT reduction was enacted. It's imperative to adjust RR to maintain minute ventilation when reducing VT. 2.4. EELV assessment EELV was measured utilizing the nitrogen washout-washin technique (E-sCOVX module sensor, GE Healthcare, Madison, WI, USA). Infusion of intravenous anesthetic agents and rocuronium bromide was administered to set controlled mechanical ventilation during EELV measurement. Consistency in ventilator parameters was maintained throughout the EELV monitoring including follow-up measurements. Other key ventilatory parameters, including PEEP, VT, RR, and static compliance of the respiratory system (Cstat), were also recorded from the mechanical ventilator at each measurement. Lung Strain was calculated as: Strain = VT/EELV Predicted body weight (PBW) in kilograms (kg) was determined based on patient height measurements. These measurements were taken while the patient was in a supine position, using the following formula: PBW (male) = 50 + 0.91 (height cm − 152.4) PBW (female) = 45.5 + 0.91 (height cm − 152.4) preFRC (male) = 2.34 height cm + 0.01 age year − 1.09 ± 0.99 preFRC* (female) = 2.24 height cm + 0.001 age year − 1.00 ± 0.82 * preFRC: predicted functional residual capacity. 2.5. CT image acquisition and volume analysis A chest CT scan was performed based on clinical judgment, necessitated by changes in patient condition or for follow-up examination purposes. The scans were acquired with patients in the supine position, under mechanical ventilation, covering the area from the lung bases to the apex, using a 64-slice scanner (uCT 530+, R001; United Imaging, Shanghai, China). All CT acquisitions were performed without the use of contrast medium, adhering to the following parameters: tube voltage, 120 kVp; automatic exposure control for tube current; pitch, 0.5. Images were reconstructed with 0.5 mm slice thickness using sharp kernels and standard lung window settings (width, 1000 HU; level, -600 HU). For the analysis of these chest CT scans, Dr. Pecker Diagnosis Robot (Pneumonia CT Image-Assisted Triage and Evaluation System V1.2) was employed. This system is a sophisticated chest CT imaging analysis tool, underpinned by deep learning technology. It uses a multi-task Unet network to segment the input chest CT images. Within the automatically segmented lung region and regions of interest/lesion regions, it calculates several metrics to quantify lung lesions: volumes and densities of the entire lung, individual left and right lungs, and separate lung lobes; lesion volumes, counts, densities, solid-to-total ratio, ground glass opacity ratio, as well as the ratios of bilateral lung ground glass opacity and consolidation volumes to the total lung volume. The implementation process and accuracy of this system have been validated in previously published studies [ 17 ]. 2.6. Statistical analysis Linear mixed models (LMM), an extension of linear regression, offers a robust framework for analyzing correlated observations, such as repeated measures on the same subjects [ 18 ]. We employed LMM to assess differences in EELV, EELV/PBW and EELV/preFRC across survivor and non-survivor groups at each follow-up point (follow-up 1, 2 and 3). In this mixed model, patients were categorized as a random effect (random intercept), while time and group variables, along with their interaction term if significant, were treated as fixed effect. We also used LMM to examine the changes in EELV and their correlation with CT findings. Residual plots revealed no obvious deviations from homoscedasticity or normality. P -values, derived from likelihood-ratio tests that compare models with or without the specified effect, were considered statistically significant when P < 0.05. The agreement between preset and measured FRC gas volumes obtained through nitrogen washout/washin technique was evaluated with a Bland & Altman analysis. Continuous variables were presented as median (interquartile range), and categorical variables as frequency (%). All statistical analyses were performed using the R Project software, version 4.3.1, for macOS. Missing data were accounted for by using the mixed-effects model. 3. Results 3.1. General characteristics During the study period, 97 critically ill COVID-19 patients were admitted to the ICU. Out of these, 38 were included in the study, as depicted in Fig. 1 . Among them, 28 (73.7%) succumbed in the ICU, while 10 (26.3%) survived and were subsequently discharged to the ward. The average age of the patients was 70 years, indicating a predominantly elderly demographic. The average BMI was 25 kg/m 2 , classifying them as overweight. There were no significant differences in age and BMI between the groups of survivors and non-survivors. The median time from symptom onset to hospital admission was 11 days, and the median ICU stay was 11 days, with no significant differences between the groups of survivors and non-survivors. However, the survivors had a significantly longer total hospital stay than the non-survivors. The APACHE II score tended to be higher in the non-survivor group, though this difference was not statistically significant. Other clinical characteristics, including initial symptoms, disease severity, comorbidities, chronic therapy, and treatments received in the ICU, showed no statistical differences between the groups (Table 1 , Table S1 ). Table 1 Demographic and clinical characteristics of patients with CARDS All (N = 38) Survivors (N = 10) Non-survivors (N = 28) P- value Age 72 (67,82) 72 (70,84) 76 (65,82) 0.982 Sex (male), n (%) 24 (63.2) 6 (60.0) 18 (64.3) 1.000 Weight (kg) 70 (58,78) 67 (61,75) 70 (57,80) 0.847 Height 168 (160,17) 163 (158,170) 169 (164,173) 0.146 Body mass index (kg/m 2 ) 25 (22,27) 24 (22,28) 25 (21,27) 0.519 Length from symptom onset to hospital admission (day) 11 (7,19) 14 (4,28) 10 (7,14) 0.282 Length of hospitalization (day) 19 (14,29) 31 (28,43) 18 (14,21) 0.012* Length of ICU stay (day) 11 (7,17) 14 (12,22) 9 (7,14) 0.371 APACHE II 14 (8,19) 9 (7,13) 16 (12,19) 0.079 Charlton score 2 (1,3) 2 (1,3) 2 (1,3) 0.282 Drugs received during ICU stay Paxlovid, n (%) 19 (36.5) 2 (20.0) 17 (60.7) 0.065 Days of using Paxlovid 5 (3,5) 5 (4,5) 5 (2,5) 0.709 Tocilizumab, n (%) 8 (21.1) 1 (10.0) 7 (25.0) 0.653 Methylprednisolone, n (%) 31 (81.6) 7 (70.0) 24 (85.7) 0.351 Days of using methylprednisolone 8 (5,12) 6 (0,8) 10 (6,13) 0.346 Heparin for prevention, n (%) 7 (18.4) 2 (20.0) 5 (17.9) 1.000 Heparin for treatment, n (%) 28 (73.7) 6 (60.0) 22 (78.6) 0.404 Fondaparinux Sodium, n (%) 4 (10.5) 0 (0.0) 4 (14.3) 0.556 Thymosin, n (%) 19 (50.0) 4 (40.0) 15 (53.6) 0.713 HIG, n (%) 7 (18.4) 4 (40.0) 3 (10.7) 0.063 Days of using HIG 5 (3,6) 3 (3,3) 5 (4,7) 0.567 CRRT, n (%) 16 (47.1) 4 (40.0) 12 (50.0) 0.715 Values are median (interquartile range) or number (%). CARDS: COVID-19 associated acute respiratory distress syndrome; ICU: intensive care unit; CRRT: Continuous Renal Replacement Therapy; HIG: Human Immunoglobulin. * \(P<0.05\) . 3.2. EELV assessment The initial ventilator settings for measuring EELV and the patient's standard FRC showed no significant differences between the survivor group and the non-survivor group, except for FiO 2 (50% vs 60%, P = 0.036) (Table 2 ). Subsequently, we employed a mixed-effects model to compare EELV, EELV/PBW, and EELV/preFRC at three different time points between the groups. The survivor group consistently exhibited higher values in these measurements (EELV: 1455 vs 1162 ml, P = 0.049; EELV/PBW: 24.1 vs 18.5 ml/kg, P = 0.011; EELV/preFRC: 0.45 vs 0.34, P = 0.005), with significant statistical differences. While there were no significant variations in EELV/PBW and EELV/preFRC across the three follow-up sessions within the survivor group, a positive trend in EELV-related data over time was noted (Fig. 2 , Table S2, and Table S5). Additionally, we compared the changes in strain, PaO 2 /FiO 2 ratio, and Cstat between the groups across the three follow-up sessions. Strain was significantly lower in the survivor group (0.25 vs 0.31, P = 0.032), with notable differences in the first and third sessions but not in the second. Nevertheless, no significant temporal changes in strain were observed within either group. Differences in the overall PaO 2 /FiO 2 ratio were also noted between the survivor and the non-survivor group (169.5 vs 248 mmHg, P = 0.001), with disparities in the first two follow-up sessions but not in the third. No differences in Cstat were observed between the groups (Fig. 3 , Table S3 and Table S5). Table 2 The ventilation parameters and preFRC values at baseline: analysis between survivor and non-survivor groups. Total Survival Deceased P- value preFRC (ml) 3502.60 (2756.25, 3674.75) 3384.50 (2634.65, 3534.50) 3529.50 (2801.40, 3700.50) 0.182 VT (ml) 375.00 (350.00, 418.75) 375.00 (331.25, 418.75) 375.00 (350.00, 406.25) 0.48 Frequency (min) 19.00 (16.00, 25.00) 20.50 (15.25, 25.00) 19.00 (16.00, 25.00) 0.973 PEEP (cmH 2 O) 8.00 (6.00, 10.00) 8.00 (6.00, 8.00) 8.00 (6.00, 10.00) 0.564 FiO 2 (%) 60.00 (50.00, 65.00) 50.00 (50.00, 50.00) 60.00 (50.00, 65.00) 0.036* Cstat (ml/cmH 2 O) 22 (31,39) 31 (22,40) 30.5 (22.8,38.2) 0.952 Values are median (interquartile range). preFRC: redicted functional residual capacity; VT: tidal volume; PEEP: positive end-expiratory pressure; FiO2: fraction of inspired oxygen; Cstat: static compliance of the respiratory system. * \(P <0.05\) . To ascertain optimal cutoff values for EELV, EELV/PBW, and EELV/preFRC, we utilized the Maximally Selected Log-rank Statistic for multiple classifications. Subsequent to this, we generated survival curves from the onset of symptoms to mortality. Applying a cutoff value of 1545 ml for EELV, the median survival time in the high EELV group was notably longer (60.3 days), compared to the low EELV group (27.9 days). This significant difference in survival times was confirmed by the Log-rank test ( P < 0.05) (Fig. 4 A). Similarly, with a cutoff value of 21.7 ml/kg for EELV/PBW, the median survival time was substantially greater in the high EELV/PBW group (115.4 days) than in the low EELV/PBW group (32.7 days), with the Log-rank test indicating a significant difference ( P < 0.05) (Fig. 4 B). Likewise, utilizing a cutoff value of 0.41 for EELV/preFRC, we observed that the median survival time in the high EELV/preFRC group (60.3 days) exceeded that in the low EELV/preFRC group (33.4 days). The Log-rank test exhibited a significant difference between the groups ( P < 0.05) (Fig. 4 C). These findings suggest that patients categorized in the high EELV, EELV/PBW, or EELV/preFRC groups not only have a greater likelihood of survival at a given time point, but also exhibit better overall survival outcomes. 3.3. Comparison of EELV and AI-analyzed CT volumetry CT-graphic volumetry of total lung volume, lesion volume, and residual lung volume was performed using AI software, with comparisons drawn between the groups of survivors and non-survivors (Table S4). As illustrated in Fig. 5 , no significant differences were observed in total lung volume and residual lung volume between the groups. However, the survivor group exhibited significantly lower total lesion volume than the non-survivor group (634 vs 1313 ml, P = 0.027) (Table S5). Further analysis using LMM method was conducted to explore the correlation between EELV-related parameters and total lung volume, lesion volume, and residual lung volume calculated by AI software. Figure 6 A shows a positive correlation between EELV and both total lung volume (r = 0.90, P < 0.05) and residual lung volume (r = 0.90, P < 0.05), but no correlation with lesion volume. In Fig. 6 B, a positive correlation was found between EELV/preFRC and total lung volume (r = 0.94, P < 0.05) and residual lung volume (r = 0.94, P < 0.05). Additionally, a negative correlation was noted with injured lung volume (r = 0.82, P < 0.05). Similarly, Fig. 6 C demonstrates a positive correlation between EELV/PBW and total lung volume (r = 0.93, P < 0.05) and residual lung volume (r = 0.94, P < 0.05), and a negative correlation with injured lung volume (r = 0.93, P < 0.05). Furthermore, a notable discrepancy of 471.10 ml was identified between the residual lung volume as calculated by the AI software and the one measured by EELV. 4. Discussion We evaluated the values and changes of EELV in patients with CARDS and found certain association between EELV and their prognosis, as well as a significant correlation with AI-analyzed CT lung volumes. However, in both the survivor group and non-survivor group, solely observing changes in CT lesion volume or the PaO 2 /FiO 2 ratio did not consistently yield differences at every measurement point. While there are some reports on pulmonary function changes post-discharge, literature is limited regarding EELV and its variations in CARDS patients under invasive mechanical ventilation. To our knowledge, this study is pioneering in demonstrating that EELV can be an effective indicator of lung damage extent in CARDS patients and provide valuable insights into their prognosis. Our analysis includes comparisons of EELV differences and trends in COVID-19 patients, potentially informing assessments and prognoses for patients with ARDS from other causes. Monitoring EELV could potentially serve as an alternative to repetitive CT scans for tracking lung lesion progression in patients with CARDS, offering a quicker and more convenient method for follow-up. COVID-19 can progress to ARDS, necessitating mechanical ventilation in approximately one-third of critically ill patients[ 19 ]. Notably, during the initial wave of the pandemic, the mortality rates among patients receiving invasive mechanical ventilation varied widely, ranging from 23.3–81% [ 20 , 21 ]. In our study, we investigated the ICU mortality rate of patients with CARDS and invasive mechanical ventilation after Shanghai’s first lockdown ended. The ICU mortality rate for these patients was 73.7%. Previous studies have indicated that ARDS typically develops around 8–9 days after the onset of COVID-19 symptoms. In our cohort, the average time from symptom onset to hospital admission was 11 days, with no significant difference between the survivor and non-survivor groups. This timeline could be attributed to the overwhelming surge of COVID-19 cases, which strained healthcare resources, leading to hospital bed shortages, personnel constraints, and limited availability of medications and equipment. Consistent with previous studies, factors like advanced age, comorbidities, and obesity were associated with poorer outcomes and prognosis in our patient groups. The average age of our patients was 72 years, and they generally exhibited overweight status, with a mean BMI of 25 (kg/m2). Although both the survivor and non-survivor groups had a Charlson Comorbidity Index score of 2, we observed a higher proportion of non-survivors with comorbidities such as kidney disease, cardiovascular disorders, and pulmonary diseases. Additionally, the APACHE II score tended to be higher in the non-survivor group, though it did not reach statistical significance. It should be noted, however, that the limited sample size of our study may have influenced these findings. Low lung function is recognized as a strong and independent risk factor for all-cause mortality [ 22 , 23 ]. However, previous studies have primarily focused on general populations or chronic disease cohorts, emphasizing FEV1 and FVC as the primary indicators [ 24 ]. Yet, there seems to be hesitancy in acknowledging lung function as an independent marker of disease severity. In patients discharged after severe or critical COVID-19, reduced respiratory function is a notable issue [ 25 ]. While blood gas analysis and CT scans are useful in assessing a patient's oxygenation capacity and detecting structural changes in the lungs, they fall short of providing a comprehensive evaluation of lung function. In this study, we propose that measures associated with EELV offer a more direct assessment of residual lung function, with potential correlations to patient prognosis. Our findings reveal a significant decline in EELV among patients with CARDS receiving mechanical ventilation. Dilken et al. conducted a study on 40 intubated COVID-19 patients to examine the variations in EELV while in supine and prone positions. Their study monitored changes over a single day, and reported median values of 1444 ml for EELV, 23.4 ml/kg for EELV/PBW, and 0.31 for strain in the supine position, but did not assess patient outcomes [ 26 ]. In our study, we found median values of 1287 ml for EELV, 19.96 ml/kg for EELV/PBW, and 0.30 for strain. Notably, EELV, EELV/PBW, and EELV/preFRC were consistently lower in the non-survivor group compared to the survivor group. Furthermore, the established cutoff values for EELV, EELV/PBW, and EELV/preFRC effectively differentiated patients into two distinct groups with varying survival times and prognoses. These findings suggest that EELV and its associated parameters could be vital in determining the prognostic outcomes of patients with CARDS. In our study, although EELV and its associated parameters demonstrated a strong correlation with CT-measured lung volumes (including total lung volume and residual lung volume), no significant differences were observed between the survivor and non-survivor groups based on the CT measurements alone, except in lesion volume. Additionally, although there was an overall difference in the PaO 2 /FiO 2 ratio between the groups, this difference was not statistically significant during the third follow-up measurement. Interestingly, both EELV/PBW and EELV/preFRC exhibited statistically significant differences between the survivor and non-survivor groups, both in the overall analysis and across the three measurement points. Lieuwe Bos et al. reported that while the PaO2/FiO2 ratio is an important prognostic indicator for patients with CARDS, the related mechanical ventilation parameters such as mechanical power and ventilatory ratio hold greater significance in guiding patient prognosis and classification over time [ 27 ]. Consistent with these observations, our study also noted that while PaO 2 /FiO 2 ratio did not vary significantly between the survivor and non-survivor groups over time, a growing disparity was evident in EELV-related indicators. These findings suggest that EELV measurement may offer a more effective evaluation and follow-up indicator compared to PaO 2 /FiO 2 and CT scans for assessing lung function and prognosis in CARDS patients. However, further studies are required to validate these results and understand their clinical implications. This study has some limitations. Firstly, as a single center study, its findings necessitate further validation through broader research. Secondly, although data collection was prospective, the study's retrospective nature may impact the robustness of the conclusions. The study also had a relatively small sample size. Moreover, CT and EELV measurements were not conducted in real-time but rather within a 24-hour window surrounding each intervention. This approach may not accurately reflect the rapid and dynamic changes in patient conditions. Finally, while CT scans are the gold standard for assessing functional residual capacity, in this study, patients underwent CT imaging using a transport ventilator, which raises concerns about the consistency of capturing scans at end expiration, and could potentially affect lung volume evaluations. 5. Conclusions In summary, this study represents a pioneering exploration of the changes in EELV among surviving and deceased patients with CARDS. Our findings reveal significant differences in EELV between surviving and deceased patients and establish a strong correlation between EELV and CT evaluations of lung volume. These insights contribute to our understanding of the progression of pulmonary lesions in critically ill COVID-19 patients, particularly during the follow-up of endotracheal intubation. In addition to traditional assessments like CT evaluations and the PaO 2 /FiO 2 ratio, the monitoring of EELV and related indicators may offer a novel approach for evaluating the condition and prognosis of patients with ARDS caused by other factors. Abbreviations ARDS, acute respiratory distress syndrome; CARDS, COVID-19 associated acute respiratory distress syndrome; Cstat, static compliance of the respiratory system; CT, computed tomography; EELV, end-expiratory lung volume; FRC, functional residual capacity; LMM, linear mixed models; PaO₂/FiO₂, arterial oxygen partial pressure to the fraction of inspired oxygen; PBW, predicted body weight; PEEP, positive end-expiratory pressure; Pplat, plateau pressure; preFRC, predicted functional residual capacity; RR, respiratory rate; SpO 2 , oxygen saturation; VT, tidal volume. Declarations Ethics approval: The study was conducted in accordance with the Declaration of Helsinki, and approved by the ethics board of Zhongshan Hospital afflicted to Fudan University (approval code: B2023-074R). Informed consent was obtained from all subjects involved in the study. Consent for publication : Not applicable. Availability of data and materials: Data are available on request from the corresponding author. Competing interests: The authors declare no conflicts of interest. Authors’ Contributions: S. Y. H., Y. L. W., Y. X. W., P. J., and M. Z. contributed to the design of the study. S. Y. H., Y. L. W., P. J., Y. M., and C. J. Z. contributed to the data collection. S. Y. H., Y. L. W., and Y. X. W., and S. J. Q. performed data analysis and data interpretation. M. Z. supervised the study. S.Y. H., and Y. X. W. wrote the first draft. All authors contributed to the writing and review of the main manuscript, had full access to all the data in the study, and had final responsibility for the decision to submit the manuscript for publication. Funding: This research was sponsored by the Shanghai Sailing Program (21YF1440300), the National Science Fund for Young Scholars (82200061), and the Shanghai Sailing Program (22YF1407700). Acknowledgements: The authors thank Ying Wang for review and editorial assistance. References Attaway AH, Scheraga RG, Bhimraj A, Biehl M, Hatipoğlu U. Severe covid-19 pneumonia: pathogenesis and clinical management. BMJ. 2021;372:n436. Hatabu H, Kaye KM, Christiani DC. Viral Infection, Pulmonary Fibrosis, and Long COVID. Am J Respir Crit Care Med. 2023;207(6):647–9. Shaw RJ, Bradbury C, Abrams ST, Wang G, Toh CH. COVID-19 and immunothrombosis: emerging understanding and clinical management. Br J Haematol. 2021;194(3):518–29. Gattinoni L, Carlesso E, Cressoni M. Assessing gas exchange in acute lung injury/acute respiratory distress syndrome: diagnostic techniques and prognostic relevance. Curr Opin Crit Care. 2011;17(1):18–23. Barbas CS, Isola AM, Caser EB. What is the future of acute respiratory distress syndrome after the Berlin definition? Curr Opin Crit Care. 2014;20(1):10–6. Ranieri VM, Rubenfeld G, Slutsky AS. Rethinking Acute Respiratory Distress Syndrome after COVID-19: If a Better Definition Is the Answer, What Is the Question? Am J Respir Crit Care Med. 2023;207(3):255–60. van der Wal LI, Grim CCA, Del Prado MR, van Westerloo DJ, Boerma EC, Rijnhart-de Jong HG, Reidinga AC, Loef BG, van der Heiden PLJ, Sigtermans MJ et al. Conservative versus Liberal Oxygenation Targets in Intensive Care Unit Patients (ICONIC): A Randomized Clinical Trial. Am J Respir Crit Care Med 2023. Barrot L, Asfar P, Mauny F, Winiszewski H, Montini F, Badie J, Quenot JP, Pili-Floury S, Bouhemad B, Louis G, et al. Liberal or Conservative Oxygen Therapy for Acute Respiratory Distress Syndrome. N Engl J Med. 2020;382(11):999–1008. Gommers D. Functional residual capacity and absolute lung volume. Curr Opin Crit Care. 2014;20(3):347–51. Leith DE, Brown R. Human lung volumes and the mechanisms that set them. Eur Respir J. 1999;13(2):468–72. Berger-Estilita J, Haenggi M, Ott D, Berger D. Accuracy of the end-expiratory lung volume measured by the modified nitrogen washout/washin technique: a bench study. J Transl Med. 2021;19(1):36. Dellamonica J, Lerolle N, Sargentini C, Beduneau G, Di Marco F, Mercat A, Richard JC, Diehl JL, Mancebo J, Rouby JJ, et al. Accuracy and precision of end-expiratory lung-volume measurements by automated nitrogen washout/washin technique in patients with acute respiratory distress syndrome. Crit Care. 2011;15(6):R294. Luecke T, Meinhardt JP, Herrmann P, Klemm S, Weiss A, Weisser G, Hirschl RB, Quintel M. End-expiratory lung volumes and density distribution patterns during partial liquid ventilation in healthy and oleic acid-injured sheep: a computed tomography study. Crit Care Med. 2003;31(8):2190–7. Graf J, Santos A, Dries D, Adams AB, Marini JJ. Agreement between functional residual capacity estimated via automated gas dilution versus via computed tomography in a pleural effusion model. Respir Care. 2010;55(11):1464–8. Patel A, Jernigan DB. Initial Public Health Response and Interim Clinical Guidance for the 2019 Novel Coronavirus Outbreak - United States, December 31, 2019-February 4, 2020. MMWR Morb Mortal Wkly Rep. 2020;69(5):140–6. Sella N, Pettenuzzo T, Zarantonello F, Andreatta G, De Cassai A, Schiavolin C, Simoni C, Pasin L, Boscolo A, Navalesi P. Electrical impedance tomography: A compass for the safe route to optimal PEEP. Respir Med. 2021;187:106555. Shi H, Xu Z, Cheng G, Ji H, He L, Zhu J, Hu H, Xie Z, Ao W, Wang J. CT-based radiomic nomogram for predicting the severity of patients with COVID-19. Eur J Med Res. 2022;27(1):13. Liu S, Rovine MJ, Molenaar PC. Selecting a linear mixed model for longitudinal data: repeated measures analysis of variance, covariance pattern model, and growth curve approaches. Psychol Methods. 2012;17(1):15–30. Zhou F, Yu T, Du R, Fan G, Liu Y, Liu Z, Xiang J, Wang Y, Song B, Gu X, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet. 2020;395(10229):1054–62. Armstrong RA, Kane AD, Cook TM. Outcomes from intensive care in patients with COVID-19: a systematic review and meta-analysis of observational studies. Anaesthesia. 2020;75(10):1340–9. Krause M, Douin DJ, Kim KK, Fernandez-Bustamante A, Bartels K. Characteristics and Outcomes of Mechanically Ventilated COVID-19 Patients-An Observational Cohort Study. J Intensive Care Med. 2021;36(3):271–6. Burney PG, Hooper R. Forced vital capacity, airway obstruction and survival in a general population sample from the USA. Thorax. 2011;66(1):49–54. Reyna ME, Bedard MA, Subbarao P. Lung Function as a Biomarker of Health: An Old Concept Rediscovered. Am J Respir Crit Care Med. 2023;208(2):117–9. Leivseth L, Nilsen TI, Mai XM, Johnsen R, Langhammer A. Lung function and respiratory symptoms in association with mortality: The HUNT Study. Copd. 2014;11(1):59–80. Bellan M, Soddu D, Balbo PE, Baricich A, Zeppegno P, Avanzi GC, Baldon G, Bartolomei G, Battaglia M, Battistini S, et al. Respiratory and Psychophysical Sequelae Among Patients With COVID-19 Four Months After Hospital Discharge. JAMA Netw Open. 2021;4(1):e2036142. Dilken O, Rezoagli E, Yartaş Dumanlı G, Ürkmez S, Demirkıran O, Dikmen Y. Effect of prone positioning on end-expiratory lung volume, strain and oxygenation change over time in COVID-19 acute respiratory distress syndrome: A prospective physiological study. Front Med (Lausanne). 2022;9:1056766. Bos LDJ, Sjoding M, Sinha P, Bhavani SV, Lyons PG, Bewley AF, Botta M, Tsonas AM, Serpa Neto A, Schultz MJ, et al. Longitudinal respiratory subphenotypes in patients with COVID-19-related acute respiratory distress syndrome: results from three observational cohorts. Lancet Respir Med. 2021;9(12):1377–86. Additional Declarations No competing interests reported. Supplementary Files onlinesupplementalmaterial.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 08 Apr, 2024 Reviews received at journal 06 Apr, 2024 Reviews received at journal 23 Mar, 2024 Reviewers agreed at journal 12 Mar, 2024 Reviewers agreed at journal 06 Mar, 2024 Reviewers invited by journal 06 Mar, 2024 Editor assigned by journal 06 Mar, 2024 Editor invited by journal 06 Mar, 2024 Submission checks completed at journal 06 Mar, 2024 First submitted to journal 25 Feb, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3989949","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":276811807,"identity":"dbf6c5b1-421d-4e59-a4e3-b839f626bfc7","order_by":0,"name":"Shengyu Hao","email":"","orcid":"","institution":"Zhongshan Hospital, Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Shengyu","middleName":"","lastName":"Hao","suffix":""},{"id":276811808,"identity":"ea2145bf-c36f-48d1-88b2-17fd767e4291","order_by":1,"name":"Yilin Wei","email":"","orcid":"","institution":"Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Yilin","middleName":"","lastName":"Wei","suffix":""},{"id":276811809,"identity":"ed460584-118d-4e31-8dc7-a9baffc3e6d9","order_by":2,"name":"Yuxian Wang","email":"","orcid":"","institution":"Zhongshan Hospital, Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Yuxian","middleName":"","lastName":"Wang","suffix":""},{"id":276811810,"identity":"571267f9-af41-47b8-b625-fd1e7b8273f3","order_by":3,"name":"Yaxiaerjiang Muhetaer","email":"","orcid":"","institution":"Zhongshan Hospital, Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Yaxiaerjiang","middleName":"","lastName":"Muhetaer","suffix":""},{"id":276811811,"identity":"4891c0f8-4194-4dd8-bc9a-4bd8ae8ea391","order_by":4,"name":"Chujun Zhou","email":"","orcid":"","institution":"Zhongshan Hospital, Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Chujun","middleName":"","lastName":"Zhou","suffix":""},{"id":276811812,"identity":"40a6d5aa-6580-40da-92bc-d24e13c8a339","order_by":5,"name":"Songjie Qiong","email":"","orcid":"","institution":"Zhongshan Hospital, Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Songjie","middleName":"","lastName":"Qiong","suffix":""},{"id":276811813,"identity":"40e7b261-504c-4de6-bc0e-cf21c3bbe121","order_by":6,"name":"Pan Jiang","email":"","orcid":"","institution":"Zhongshan Hospital, Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Pan","middleName":"","lastName":"Jiang","suffix":""},{"id":276811814,"identity":"4a8c123e-a4d2-4af6-be3e-0db30362161d","order_by":7,"name":"Ming Zhong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIiWNgGAWjYDACCRjJ3tj44EOFhJw88Vp4Dh82nHHGwtiwgTgtIEZamjRvW0UiwwECOuRnNx97+DXHIk/eIcdAgneeRAJjA/PDRzfwaGGccyzdWHabRLHhgTMGBpLbJPLYGdiMjXPwaGGWyDGTBqpM3NjYY5BgCNTL2MDDJo1PCxtcSzOPwYHEORKJDQcIaOEBapH8CNQyn40tseFgAxFaJEABxQjUsoGH+TBjwzEJY8NmAn6Rn5F8TPLntrrE+fMftv/+U1MnJ8/e/PAxPi0gwMwDJAwOwLkElIMA4w+QdQ1EqBwFo2AUjIKRCQCXVErJ8KC4WgAAAABJRU5ErkJggg==","orcid":"","institution":"Zhongshan Hospital, Fudan University","correspondingAuthor":true,"prefix":"","firstName":"Ming","middleName":"","lastName":"Zhong","suffix":""}],"badges":[],"createdAt":"2024-02-26 05:03:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3989949/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3989949/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52301564,"identity":"3962acff-675f-4337-bcc2-d7177438e6bb","added_by":"auto","created_at":"2024-03-08 18:40:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":329886,"visible":true,"origin":"","legend":"\u003cp\u003eParticipant flow diagram. LMM, linear mixed model.\u003c/p\u003e","description":"","filename":"figure1final.png","url":"https://assets-eu.researchsquare.com/files/rs-3989949/v1/d47d45c57ccc21aa6d421bad.png"},{"id":52302737,"identity":"f7e53b83-4c92-4f6b-b9ce-a2babdf53582","added_by":"auto","created_at":"2024-03-08 18:48:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":423845,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in mean EELV (A), EELV/preFRC (B), and EELV/PBW (C) analyzed by LMM across three EELV tests. The horizontal line in the middle of each box (left column) indicates the median, the top and bottom borders of the box mark the 75th and 25th percentiles, the whiskers above and below the box indicate the 90th and 10th percentiles, and the points beyond the whiskers are outliers beyond the 90th or 10th percentiles. The modeled data (right column) show the standard error of marginal mean for the predicted values using the random-effects model. \u003cem\u003eP\u003c/em\u003e-values signify the group effect. Asterisks (* or **) indicate statistically significant differences between the survival and death groups at each measurement. *\u003cem\u003eP\u003c/em\u003e＜0.05, ** \u003cem\u003eP\u003c/em\u003e＜0.01.\u003c/p\u003e","description":"","filename":"figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3989949/v1/4681c8a157225b3494493310.png"},{"id":52301565,"identity":"f0013dbb-39da-4802-b5b9-b6b9ca94eeac","added_by":"auto","created_at":"2024-03-08 18:40:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":597077,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in mean strain (A), PaO\u003csub\u003e2\u003c/sub\u003e/FiO\u003csub\u003e2\u003c/sub\u003e (B), and Cstat (C) analyzed by LMM across three EELV tests over 9-11 days. The horizontal line in the middle of each box (left column) indicates the median, the top and bottom borders of the box mark the 75th and 25th percentiles, the whiskers above and below the box indicate the 90th and 10th percentiles, and the points beyond the whiskers are outliers beyond the 90th or 10th percentiles. The modeled data (right column) show the standard error of marginal mean for the predicted values using the random-effects model. \u003cem\u003eP\u003c/em\u003e-values signify the group effect. Asterisks (* or ***) indicate statistically significant differences between the survival and death groups at each measurement. *\u003cem\u003eP\u003c/em\u003e＜0.05, *** \u003cem\u003eP\u003c/em\u003e＜0.001.\u003c/p\u003e","description":"","filename":"figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3989949/v1/3d15e6b8128fe51d0f559f3a.png"},{"id":52301567,"identity":"beb1fddb-1727-4e3a-ab3b-995ece07f7cc","added_by":"auto","created_at":"2024-03-08 18:40:09","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":406469,"visible":true,"origin":"","legend":"\u003cp\u003eDetermination of cutoff values for EELV (A), EELV/PBW (B), and EELV/pre FRC (C) using maximally selected Log-rank statistics. Based on these cutoff values, survival curves were plotted. The analysis focuses on patient mortality during ICU hospitalization, with 'event' signifying death and 'time' denoting the period from symptom onset to either death or discharge for surviving patients. The dashed line on the y-axis signifies a survival probability of 0.5, intersecting with the survival curve to indicate the estimated median survival time.\u003c/p\u003e","description":"","filename":"figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-3989949/v1/9476c5a773200ad9f9316bd4.png"},{"id":52301570,"identity":"3122c7b2-ed19-49c2-b28b-4241938749c9","added_by":"auto","created_at":"2024-03-08 18:40:09","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":651521,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in mean total lung volume (A), total lesion volume (B), and residual (C) analyzed by LMM across three EELV tests over 9-11 days. The horizontal line in the middle of each box (left column) indicates the median, the top and bottom borders of the box mark the 75th and 25th percentiles, the whiskers above and below the box indicate the 90th and 10th percentiles, and the points beyond the whiskers are outliers beyond the 90th or 10th percentiles. The modeled data (right column) show the standard error of marginal mean for the predicted values using the random-effects model. \u003cem\u003eP\u003c/em\u003e-values signify the group effect. Asterisks (*) indicate statistically significant differences between the survival and death groups at each measurement. *\u003cem\u003eP\u003c/em\u003e＜0.05.\u003c/p\u003e","description":"","filename":"figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-3989949/v1/039d6b0dabb95c661f9bd2f7.png"},{"id":52302744,"identity":"4e58427b-4341-4840-8fe7-407ddc257c09","added_by":"auto","created_at":"2024-03-08 18:48:09","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":568872,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelations calculated using LMM between EELV measured by multiple breath nitrogen washout/washin method versus computed tomography. The correlations of EELV (A), EELV/preFRC (B), and EELV/PBW (C) with total lung volume (left), total lesion volume (middle), and residual volume (right).\u003c/p\u003e","description":"","filename":"figure61.png","url":"https://assets-eu.researchsquare.com/files/rs-3989949/v1/47dc0aa398d2a9a225b5b3b3.png"},{"id":52303132,"identity":"649e2c02-9a0f-44c7-894b-94027962f5ce","added_by":"auto","created_at":"2024-03-08 18:56:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1446037,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3989949/v1/e6d87c27-93f1-4b9f-b3cf-a957f0ad792e.pdf"},{"id":52301569,"identity":"4272a57c-96d0-4afe-b819-f0c8887b827a","added_by":"auto","created_at":"2024-03-08 18:40:09","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":43150,"visible":true,"origin":"","legend":"","description":"","filename":"onlinesupplementalmaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-3989949/v1/b6e6cf48db9a25fc6ec770b2.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"End-expiratory lung volumes as a potential indicator for COVID-19 associated acute respiratory distress syndrome: a retrospective study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eSevere COVID-19 pneumonia has presented significant challenges for the research and medical communities. Among individuals hospitalized with COVID-19, 15\u0026ndash;30% will progress to develop COVID-19 associated acute respiratory distress syndrome (CARDS) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Autopsy studies of patients who succumbed to severe SARS CoV-2 infection reveal the presence of diffuse alveolar damage, accompanied by a higher thrombus burden in the pulmonary capillaries and fibrosing nonspecific interstitial pneumonia. These factors contribute to a reduction in functional residual capacity (FRC) and severe arterial hypoxemia[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. It has been observed that oxygen levels do not significantly correlate with the prognosis of ARDS [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Additionally, the definition of ARDS has a somewhat controversial history, and the COVID-19 pandemic has further complicated the current Berlin definition of ARDS. Researchers have been advocating for and working towards improved criteria and methods for defining ARDS [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. It should be emphasized that our treatment objective for ARDS patients should not solely focus on improving their oxygen levels or the ratio of arterial oxygen partial pressure to the fraction of inspired oxygen (PaO₂/FiO₂) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Instead, we require effective and non-invasive monitoring methods to track the progression of ARDS, which are crucial for evaluating patient condition and prognosis. FRC, the amount of gas remaining in the lungs after a natural exhalation at atmospheric pressure, serves as a crucial indicator of gas exchange capacity in healthy individuals [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. End-expiratory lung volume (EELV), which encompasses the cumulative gas volume within intubated patients, incorporates the functional residual capacity along with the additional volume introduced by positive end-expiratory pressure (PEEP) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. ARDS leads to a substantial decrease in EELV, resulting in higher strain at a given tidal volume (VT). For this reason, bedside EELV measurement may assist in setting ventilation parameters for protect strategies and better monitoring changes in lung injury [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eReproducible measurement techniques are essential for bedside use to minimize overdistention and identify which patients may benefit from recruitment strategies. While CT scans and gas-dilution techniques have been validated for lung-volume measurement, their complexity limits their practical use in clinical settings. Fortunately, ICU ventilators now offer washout/washin techniques using oxygen or nitrogen, making it convenient to measure EELV at the patient's bedside. Comparisons between EELV measurements (obtained through multiple breath nitrogen washout/washin and helium dilution) and CT scans have consistently demonstrated strong agreements in stable patients, animal models of ARDS, and artificial lungs [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, the investigation of EELV and its variations, as well as their association with the prognosis of patients with CARDS, remains unexplored.\u003c/p\u003e \u003cp\u003eIn this study, we monitored the EELV and its changes in patients with CARDS, and performed a correlation analysis with CT scans. Our hypothesis posits that changes in EELV could serve as a valuable indicator of disease progression and a predictive factor for the prognosis of patients with ARDS, in contrast to relying solely on arterial blood gas measurements and CT volume analysis.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e The study protocol and informed consent forms were reviewed and approved by the ethics board of Zhongshan Hospital afflicted to Fudan University (approval code: B2023-074R).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1.Study population\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThis study included patients admitted to our ICU between December 2022 to March 2023, who had been diagnosed with COVID-19 infection through confirmation via real-time reverse transcriptase-polymerase chain reaction. The inclusion criteria were as follows: (a) COVID-19 cases classified as severe in accordance with the WHO interim guidance, characterized by clinical signs of pneumonia in addition to a respiratory rate (RR)\u0026thinsp;\u0026gt;\u0026thinsp;30 breaths/min, severe respiratory distress, and/or oxygen saturation (SpO\u003csub\u003e2\u003c/sub\u003e)\u0026thinsp;\u0026lt;\u0026thinsp;90% on room air [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. (b) Endotracheal intubation was administered during the patient's ICU admission in response to their deteriorating condition. (c) Subsequent follow-up chest CT scans and EELV tests were conducted. Exclusion criteria encompassed severe hemodynamic instability, inability to complete the EELV test (e.g., due to a pronounced decline in SpO\u003csub\u003e2\u003c/sub\u003e levels observed during the evaluation), recurrent ICU admissions, and patient whose fraction of inspired oxygen (FiO\u003csub\u003e2\u003c/sub\u003e) exceeded 80%.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Data collection\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003ePatients\u0026rsquo; demographics, date of disease onset, initial symptoms, duration of hospital admission and ICU admission, disease severity, comorbidities, chronic therapy, medications and treatment received during ICU, as well as chest CT scans, were extracted from electronic patient records. The EELV test was conducted within 48 hours post-intubation, with follow-up assessments performed every 3\u0026ndash;4 days for critically ill patients. The CT scans selected for analysis were obtained within a 24-hour window before or after the EELV measurements. EELV monitoring was discontinued for patients who underwent extubation, were discharged from ICU, or were no longer able to undergo further EELV measurements.\u003c/p\u003e \u003cp\u003eThis study involved EELV monitoring of a cohort of 38 patients: all 38 patients received an initial EELV assessment, 23 underwent a second evaluation, and 12 participated in a third round of monitoring. This resulted in a total of 73 EELV measurements across these patients. Additionally, 92 CT scans were performed, adhering to the specified temporal criteria for the study. Subsequent analysis using AI software enabled the successful identification and processing of 72 CT scans. However, the scans from 4 patients were not amenable to AI-based analysis. As a result, the final dataset for correlation analysis included 72 CT scans and their corresponding EELV values, encompassing a subset of 34 patients. The study flow is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Ventilator parameters setting\u003c/h2\u003e \u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e The ventilator settings were aligned with established guidelines, configuring patient ventilation modes to A/C-VC and V-SIMV. Initial VT, 6\u0026ndash;8 ml/kg, was calibrated against ideal body weight. Continuous monitoring encompassed peak pressure, plateau pressure (Pplat), RR, minute ventilation, arterial pH, partial pressure of carbon dioxide (PaCO\u003csub\u003e2\u003c/sub\u003e), SpO\u003csub\u003e2\u003c/sub\u003e, and partial PaO\u003csub\u003e2\u003c/sub\u003e. Should Pplat exceed 30 cmH\u003csub\u003e2\u003c/sub\u003eO, VT was stepwise reduced to 4ml/kg. FiO\u003csub\u003e2\u003c/sub\u003e adjustments were based on SpO\u003csub\u003e2\u003c/sub\u003e and PaO\u003csub\u003e2\u003c/sub\u003e readings. Personalized PEEP was titrated using the EIT-Costa method, keeping driving pressure (Pplat-PEEP) below 15 cmH\u003csub\u003e2\u003c/sub\u003eO [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In the event that driving pressure exceeded this threshold, further VT reduction was enacted. It's imperative to adjust RR to maintain minute ventilation when reducing VT.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. EELV assessment\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eEELV was measured utilizing the nitrogen washout-washin technique (E-sCOVX module sensor, GE Healthcare, Madison, WI, USA). Infusion of intravenous anesthetic agents and rocuronium bromide was administered to set controlled mechanical ventilation during EELV measurement. Consistency in ventilator parameters was maintained throughout the EELV monitoring including follow-up measurements. Other key ventilatory parameters, including PEEP, VT, RR, and static compliance of the respiratory system (Cstat), were also recorded from the mechanical ventilator at each measurement.\u003c/p\u003e \u003cp\u003eLung Strain was calculated as:\u003c/p\u003e \u003cp\u003eStrain\u0026thinsp;=\u0026thinsp;VT/EELV\u003c/p\u003e \u003cp\u003ePredicted body weight (PBW) in kilograms (kg) was determined based on patient height measurements. These measurements were taken while the patient was in a supine position, using the following formula:\u003c/p\u003e \u003cp\u003ePBW (male)\u0026thinsp;=\u0026thinsp;50\u0026thinsp;+\u0026thinsp;0.91 (height \u003csub\u003ecm\u003c/sub\u003e \u0026minus;\u0026thinsp;152.4)\u003c/p\u003e \u003cp\u003ePBW (female)\u0026thinsp;=\u0026thinsp;45.5\u0026thinsp;+\u0026thinsp;0.91 (height \u003csub\u003ecm\u003c/sub\u003e \u0026minus;\u0026thinsp;152.4)\u003c/p\u003e \u003cp\u003epreFRC (male)\u0026thinsp;=\u0026thinsp;2.34 height \u003csub\u003ecm\u003c/sub\u003e + 0.01 age \u003csub\u003eyear\u003c/sub\u003e \u0026minus;\u0026thinsp;1.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.99\u003c/p\u003e \u003cp\u003epreFRC* (female)\u0026thinsp;=\u0026thinsp;2.24 height \u003csub\u003ecm\u003c/sub\u003e + 0.001 age \u003csub\u003eyear\u003c/sub\u003e \u0026minus;\u0026thinsp;1.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.82\u003c/p\u003e \u003cp\u003e* preFRC: predicted functional residual capacity.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. CT image acquisition and volume analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eA chest CT scan was performed based on clinical judgment, necessitated by changes in patient condition or for follow-up examination purposes. The scans were acquired with patients in the supine position, under mechanical ventilation, covering the area from the lung bases to the apex, using a 64-slice scanner (uCT 530+, R001; United Imaging, Shanghai, China). All CT acquisitions were performed without the use of contrast medium, adhering to the following parameters: tube voltage, 120 kVp; automatic exposure control for tube current; pitch, 0.5. Images were reconstructed with 0.5 mm slice thickness using sharp kernels and standard lung window settings (width, 1000 HU; level, -600 HU).\u003c/p\u003e \u003cp\u003eFor the analysis of these chest CT scans, Dr. Pecker Diagnosis Robot (Pneumonia CT Image-Assisted Triage and Evaluation System V1.2) was employed. This system is a sophisticated chest CT imaging analysis tool, underpinned by deep learning technology. It uses a multi-task Unet network to segment the input chest CT images. Within the automatically segmented lung region and regions of interest/lesion regions, it calculates several metrics to quantify lung lesions: volumes and densities of the entire lung, individual left and right lungs, and separate lung lobes; lesion volumes, counts, densities, solid-to-total ratio, ground glass opacity ratio, as well as the ratios of bilateral lung ground glass opacity and consolidation volumes to the total lung volume. The implementation process and accuracy of this system have been validated in previously published studies [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Statistical analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eLinear mixed models (LMM), an extension of linear regression, offers a robust framework for analyzing correlated observations, such as repeated measures on the same subjects [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. We employed LMM to assess differences in EELV, EELV/PBW and EELV/preFRC across survivor and non-survivor groups at each follow-up point (follow-up 1, 2 and 3). In this mixed model, patients were categorized as a random effect (random intercept), while time and group variables, along with their interaction term if significant, were treated as fixed effect. We also used LMM to examine the changes in EELV and their correlation with CT findings. Residual plots revealed no obvious deviations from homoscedasticity or normality. \u003cem\u003eP\u003c/em\u003e-values, derived from likelihood-ratio tests that compare models with or without the specified effect, were considered statistically significant when \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The agreement between preset and measured FRC gas volumes obtained through nitrogen washout/washin technique was evaluated with a Bland \u0026amp; Altman analysis. Continuous variables were presented as median (interquartile range), and categorical variables as frequency (%). All statistical analyses were performed using the R Project software, version 4.3.1, for macOS. Missing data were accounted for by using the mixed-effects model.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1. General characteristics\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eDuring the study period, 97 critically ill COVID-19 patients were admitted to the ICU. Out of these, 38 were included in the study, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Among them, 28 (73.7%) succumbed in the ICU, while 10 (26.3%) survived and were subsequently discharged to the ward. The average age of the patients was 70 years, indicating a predominantly elderly demographic. The average BMI was 25 kg/m\u003csup\u003e2\u003c/sup\u003e, classifying them as overweight. There were no significant differences in age and BMI between the groups of survivors and non-survivors. The median time from symptom onset to hospital admission was 11 days, and the median ICU stay was 11 days, with no significant differences between the groups of survivors and non-survivors. However, the survivors had a significantly longer total hospital stay than the non-survivors. The APACHE II score tended to be higher in the non-survivor group, though this difference was not statistically significant. Other clinical characteristics, including initial symptoms, disease severity, comorbidities, chronic therapy, and treatments received in the ICU, showed no statistical differences between the groups (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic and clinical characteristics of patients with CARDS\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll (N\u0026thinsp;=\u0026thinsp;38)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSurvivors (N\u0026thinsp;=\u0026thinsp;10)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNon-survivors (N\u0026thinsp;=\u0026thinsp;28)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72 (67,82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72 (70,84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76 (65,82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.982\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (male), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 (63.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (60.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (64.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70 (58,78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67 (61,75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70 (57,80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.847\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e168 (160,17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e163 (158,170)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e169 (164,173)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody mass index (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (22,27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (22,28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25 (21,27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.519\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLength from symptom onset to hospital admission (day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (7,19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (4,28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (7,14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.282\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLength of hospitalization (day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (14,29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (28,43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (14,21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.012*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLength of ICU stay (day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (7,17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (12,22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (7,14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.371\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPACHE II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (8,19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (7,13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (12,19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCharlton score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (1,3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (1,3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (1,3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.282\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDrugs received during ICU stay\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePaxlovid, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (36.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (60.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDays of using Paxlovid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (3,5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (4,5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (2,5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.709\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTocilizumab, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (21.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.653\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMethylprednisolone, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31 (81.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (70.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 (85.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.351\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDays of using methylprednisolone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (5,12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (0,8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (6,13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.346\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeparin for prevention, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (18.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (17.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeparin for treatment, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28 (73.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (60.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (78.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.404\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFondaparinux Sodium, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (10.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (14.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.556\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThymosin, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (40.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (53.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.713\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHIG, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (18.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (40.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (10.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDays of using HIG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (3,6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (3,3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (4,7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.567\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRRT, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (47.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (40.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.715\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eValues are median (interquartile range) or number (%). CARDS: COVID-19 associated acute respiratory distress syndrome; ICU: intensive care unit; CRRT: Continuous Renal Replacement Therapy; HIG: Human Immunoglobulin. *\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(P\u0026lt;0.05\\)\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2. EELV assessment\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe initial ventilator settings for measuring EELV and the patient's standard FRC showed no significant differences between the survivor group and the non-survivor group, except for FiO\u003csub\u003e2\u003c/sub\u003e (50% vs 60%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.036) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Subsequently, we employed a mixed-effects model to compare EELV, EELV/PBW, and EELV/preFRC at three different time points between the groups. The survivor group consistently exhibited higher values in these measurements (EELV: 1455 vs 1162 ml, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.049; EELV/PBW: 24.1 vs 18.5 ml/kg, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011; EELV/preFRC: 0.45 vs 0.34, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005), with significant statistical differences. While there were no significant variations in EELV/PBW and EELV/preFRC across the three follow-up sessions within the survivor group, a positive trend in EELV-related data over time was noted (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Table S2, and Table S5). Additionally, we compared the changes in strain, PaO\u003csub\u003e2\u003c/sub\u003e/FiO\u003csub\u003e2\u003c/sub\u003e ratio, and Cstat between the groups across the three follow-up sessions. Strain was significantly lower in the survivor group (0.25 vs 0.31, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.032), with notable differences in the first and third sessions but not in the second. Nevertheless, no significant temporal changes in strain were observed within either group. Differences in the overall PaO\u003csub\u003e2\u003c/sub\u003e/FiO\u003csub\u003e2\u003c/sub\u003e ratio were also noted between the survivor and the non-survivor group (169.5 vs 248 mmHg, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), with disparities in the first two follow-up sessions but not in the third. No differences in Cstat were observed between the groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Table S3 and Table S5).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe ventilation parameters and preFRC values at baseline: analysis between survivor and non-survivor groups.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDeceased\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epreFRC (ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3502.60 \u003c/p\u003e \u003cp\u003e(2756.25, 3674.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3384.50 \u003c/p\u003e \u003cp\u003e(2634.65, 3534.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3529.50 \u003c/p\u003e \u003cp\u003e(2801.40, 3700.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVT (ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e375.00 \u003c/p\u003e \u003cp\u003e(350.00, 418.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e375.00 \u003c/p\u003e \u003cp\u003e(331.25, 418.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e375.00 \u003c/p\u003e \u003cp\u003e(350.00, 406.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrequency (min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.00 \u003c/p\u003e \u003cp\u003e(16.00, 25.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.50 \u003c/p\u003e \u003cp\u003e(15.25, 25.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.00 \u003c/p\u003e \u003cp\u003e(16.00, 25.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.973\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEEP (cmH\u003csub\u003e2\u003c/sub\u003eO)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.00 \u003c/p\u003e \u003cp\u003e(6.00, 10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.00 \u003c/p\u003e \u003cp\u003e(6.00, 8.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.00 \u003c/p\u003e \u003cp\u003e(6.00, 10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.564\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFiO\u003csub\u003e2\u003c/sub\u003e (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60.00 \u003c/p\u003e \u003cp\u003e(50.00, 65.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.00 \u003c/p\u003e \u003cp\u003e(50.00, 50.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60.00 \u003c/p\u003e \u003cp\u003e(50.00, 65.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.036*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCstat (ml/cmH\u003csub\u003e2\u003c/sub\u003eO)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003cp\u003e(31,39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31\u003c/p\u003e \u003cp\u003e(22,40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.5\u003c/p\u003e \u003cp\u003e(22.8,38.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.952\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eValues are median (interquartile range). preFRC: redicted functional residual capacity; VT: tidal volume; PEEP: positive end-expiratory pressure; FiO2: fraction of inspired oxygen; Cstat: static compliance of the respiratory system. *\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(P \u0026lt;0.05\\)\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eTo ascertain optimal cutoff values for EELV, EELV/PBW, and EELV/preFRC, we utilized the Maximally Selected Log-rank Statistic for multiple classifications. Subsequent to this, we generated survival curves from the onset of symptoms to mortality. Applying a cutoff value of 1545 ml for EELV, the median survival time in the high EELV group was notably longer (60.3 days), compared to the low EELV group (27.9 days). This significant difference in survival times was confirmed by the Log-rank test (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Similarly, with a cutoff value of 21.7 ml/kg for EELV/PBW, the median survival time was substantially greater in the high EELV/PBW group (115.4 days) than in the low EELV/PBW group (32.7 days), with the Log-rank test indicating a significant difference (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Likewise, utilizing a cutoff value of 0.41 for EELV/preFRC, we observed that the median survival time in the high EELV/preFRC group (60.3 days) exceeded that in the low EELV/preFRC group (33.4 days). The Log-rank test exhibited a significant difference between the groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). These findings suggest that patients categorized in the high EELV, EELV/PBW, or EELV/preFRC groups not only have a greater likelihood of survival at a given time point, but also exhibit better overall survival outcomes.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Comparison of EELV and AI-analyzed CT volumetry\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eCT-graphic volumetry of total lung volume, lesion volume, and residual lung volume was performed using AI software, with comparisons drawn between the groups of survivors and non-survivors (Table S4). As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, no significant differences were observed in total lung volume and residual lung volume between the groups. However, the survivor group exhibited significantly lower total lesion volume than the non-survivor group (634 vs 1313 ml, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.027) (Table S5). Further analysis using LMM method was conducted to explore the correlation between EELV-related parameters and total lung volume, lesion volume, and residual lung volume calculated by AI software. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA shows a positive correlation between EELV and both total lung volume (r\u0026thinsp;=\u0026thinsp;0.90, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and residual lung volume (r\u0026thinsp;=\u0026thinsp;0.90, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), but no correlation with lesion volume. In Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB, a positive correlation was found between EELV/preFRC and total lung volume (r\u0026thinsp;=\u0026thinsp;0.94, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and residual lung volume (r\u0026thinsp;=\u0026thinsp;0.94, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Additionally, a negative correlation was noted with injured lung volume (r\u0026thinsp;=\u0026thinsp;0.82, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Similarly, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC demonstrates a positive correlation between EELV/PBW and total lung volume (r\u0026thinsp;=\u0026thinsp;0.93, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and residual lung volume (r\u0026thinsp;=\u0026thinsp;0.94, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and a negative correlation with injured lung volume (r\u0026thinsp;=\u0026thinsp;0.93, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Furthermore, a notable discrepancy of 471.10 ml was identified between the residual lung volume as calculated by the AI software and the one measured by EELV.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eWe evaluated the values and changes of EELV in patients with CARDS and found certain association between EELV and their prognosis, as well as a significant correlation with AI-analyzed CT lung volumes. However, in both the survivor group and non-survivor group, solely observing changes in CT lesion volume or the PaO\u003csub\u003e2\u003c/sub\u003e/FiO\u003csub\u003e2\u003c/sub\u003e ratio did not consistently yield differences at every measurement point. While there are some reports on pulmonary function changes post-discharge, literature is limited regarding EELV and its variations in CARDS patients under invasive mechanical ventilation. To our knowledge, this study is pioneering in demonstrating that EELV can be an effective indicator of lung damage extent in CARDS patients and provide valuable insights into their prognosis. Our analysis includes comparisons of EELV differences and trends in COVID-19 patients, potentially informing assessments and prognoses for patients with ARDS from other causes. Monitoring EELV could potentially serve as an alternative to repetitive CT scans for tracking lung lesion progression in patients with CARDS, offering a quicker and more convenient method for follow-up.\u003c/p\u003e \u003cp\u003eCOVID-19 can progress to ARDS, necessitating mechanical ventilation in approximately one-third of critically ill patients[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Notably, during the initial wave of the pandemic, the mortality rates among patients receiving invasive mechanical ventilation varied widely, ranging from 23.3\u0026ndash;81% [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In our study, we investigated the ICU mortality rate of patients with CARDS and invasive mechanical ventilation after Shanghai\u0026rsquo;s first lockdown ended. The ICU mortality rate for these patients was 73.7%. Previous studies have indicated that ARDS typically develops around 8\u0026ndash;9 days after the onset of COVID-19 symptoms. In our cohort, the average time from symptom onset to hospital admission was 11 days, with no significant difference between the survivor and non-survivor groups. This timeline could be attributed to the overwhelming surge of COVID-19 cases, which strained healthcare resources, leading to hospital bed shortages, personnel constraints, and limited availability of medications and equipment. Consistent with previous studies, factors like advanced age, comorbidities, and obesity were associated with poorer outcomes and prognosis in our patient groups. The average age of our patients was 72 years, and they generally exhibited overweight status, with a mean BMI of 25 (kg/m2). Although both the survivor and non-survivor groups had a Charlson Comorbidity Index score of 2, we observed a higher proportion of non-survivors with comorbidities such as kidney disease, cardiovascular disorders, and pulmonary diseases. Additionally, the APACHE II score tended to be higher in the non-survivor group, though it did not reach statistical significance. It should be noted, however, that the limited sample size of our study may have influenced these findings.\u003c/p\u003e \u003cp\u003eLow lung function is recognized as a strong and independent risk factor for all-cause mortality [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. However, previous studies have primarily focused on general populations or chronic disease cohorts, emphasizing FEV1 and FVC as the primary indicators [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Yet, there seems to be hesitancy in acknowledging lung function as an independent marker of disease severity. In patients discharged after severe or critical COVID-19, reduced respiratory function is a notable issue [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. While blood gas analysis and CT scans are useful in assessing a patient's oxygenation capacity and detecting structural changes in the lungs, they fall short of providing a comprehensive evaluation of lung function. In this study, we propose that measures associated with EELV offer a more direct assessment of residual lung function, with potential correlations to patient prognosis. Our findings reveal a significant decline in EELV among patients with CARDS receiving mechanical ventilation. Dilken et al. conducted a study on 40 intubated COVID-19 patients to examine the variations in EELV while in supine and prone positions. Their study monitored changes over a single day, and reported median values of 1444 ml for EELV, 23.4 ml/kg for EELV/PBW, and 0.31 for strain in the supine position, but did not assess patient outcomes [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In our study, we found median values of 1287 ml for EELV, 19.96 ml/kg for EELV/PBW, and 0.30 for strain. Notably, EELV, EELV/PBW, and EELV/preFRC were consistently lower in the non-survivor group compared to the survivor group. Furthermore, the established cutoff values for EELV, EELV/PBW, and EELV/preFRC effectively differentiated patients into two distinct groups with varying survival times and prognoses. These findings suggest that EELV and its associated parameters could be vital in determining the prognostic outcomes of patients with CARDS.\u003c/p\u003e \u003cp\u003eIn our study, although EELV and its associated parameters demonstrated a strong correlation with CT-measured lung volumes (including total lung volume and residual lung volume), no significant differences were observed between the survivor and non-survivor groups based on the CT measurements alone, except in lesion volume. Additionally, although there was an overall difference in the PaO\u003csub\u003e2\u003c/sub\u003e/FiO\u003csub\u003e2\u003c/sub\u003e ratio between the groups, this difference was not statistically significant during the third follow-up measurement. Interestingly, both EELV/PBW and EELV/preFRC exhibited statistically significant differences between the survivor and non-survivor groups, both in the overall analysis and across the three measurement points. Lieuwe Bos et al. reported that while the PaO2/FiO2 ratio is an important prognostic indicator for patients with CARDS, the related mechanical ventilation parameters such as mechanical power and ventilatory ratio hold greater significance in guiding patient prognosis and classification over time [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Consistent with these observations, our study also noted that while PaO\u003csub\u003e2\u003c/sub\u003e/FiO\u003csub\u003e2\u003c/sub\u003e ratio did not vary significantly between the survivor and non-survivor groups over time, a growing disparity was evident in EELV-related indicators. These findings suggest that EELV measurement may offer a more effective evaluation and follow-up indicator compared to PaO\u003csub\u003e2\u003c/sub\u003e/FiO\u003csub\u003e2\u003c/sub\u003e and CT scans for assessing lung function and prognosis in CARDS patients. However, further studies are required to validate these results and understand their clinical implications.\u003c/p\u003e \u003cp\u003eThis study has some limitations. Firstly, as a single center study, its findings necessitate further validation through broader research. Secondly, although data collection was prospective, the study's retrospective nature may impact the robustness of the conclusions. The study also had a relatively small sample size. Moreover, CT and EELV measurements were not conducted in real-time but rather within a 24-hour window surrounding each intervention. This approach may not accurately reflect the rapid and dynamic changes in patient conditions. Finally, while CT scans are the gold standard for assessing functional residual capacity, in this study, patients underwent CT imaging using a transport ventilator, which raises concerns about the consistency of capturing scans at end expiration, and could potentially affect lung volume evaluations.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIn summary, this study represents a pioneering exploration of the changes in EELV among surviving and deceased patients with CARDS. Our findings reveal significant differences in EELV between surviving and deceased patients and establish a strong correlation between EELV and CT evaluations of lung volume. These insights contribute to our understanding of the progression of pulmonary lesions in critically ill COVID-19 patients, particularly during the follow-up of endotracheal intubation. In addition to traditional assessments like CT evaluations and the PaO\u003csub\u003e2\u003c/sub\u003e/FiO\u003csub\u003e2\u003c/sub\u003e ratio, the monitoring of EELV and related indicators may offer a novel approach for evaluating the condition and prognosis of patients with ARDS caused by other factors.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eARDS, acute respiratory distress syndrome;\u003c/p\u003e\n\u003cp\u003eCARDS, COVID-19 associated acute respiratory distress syndrome;\u003c/p\u003e\n\u003cp\u003eCstat, static compliance of the respiratory system;\u003c/p\u003e\n\u003cp\u003eCT, computed tomography;\u003c/p\u003e\n\u003cp\u003eEELV, end-expiratory lung volume;\u003c/p\u003e\n\u003cp\u003eFRC, functional residual capacity;\u003c/p\u003e\n\u003cp\u003eLMM, linear mixed models;\u003c/p\u003e\n\u003cp\u003ePaO₂/FiO₂, arterial oxygen partial pressure to the fraction of inspired oxygen;\u003c/p\u003e\n\u003cp\u003ePBW, predicted body weight;\u003c/p\u003e\n\u003cp\u003ePEEP, positive end-expiratory pressure;\u003c/p\u003e\n\u003cp\u003ePplat, plateau pressure;\u003c/p\u003e\n\u003cp\u003epreFRC, predicted functional residual capacity;\u003c/p\u003e\n\u003cp\u003eRR, respiratory rate;\u003c/p\u003e\n\u003cp\u003eSpO\u003csub\u003e2\u003c/sub\u003e, oxygen saturation;\u003c/p\u003e\n\u003cp\u003eVT, tidal volume.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval:\u0026nbsp;\u003c/strong\u003eThe study was conducted in accordance with the Declaration of Helsinki, and approved by the ethics board of Zhongshan Hospital afflicted to Fudan University (approval code: B2023-074R). Informed consent was obtained from all subjects involved in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eData are available on request from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions:\u003c/strong\u003e S. Y. H., Y. L. W., Y. X. W., P. J., and M. Z. contributed to the design of the study. S. Y. H., Y. L. W., P. J., Y. M., and C. J. Z. contributed to the data collection. S. Y. H., Y. L. W., and Y. X. W., and S. J. Q. performed data analysis and data interpretation. M. Z. supervised the study. S.Y. H., and Y. X. W. wrote the first draft. All authors contributed to the writing and review of the main manuscript, had full access to all the data in the study, and had final responsibility for the decision to submit the manuscript for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This research was sponsored by the Shanghai Sailing Program (21YF1440300), the National Science Fund for Young Scholars (82200061), and the Shanghai Sailing Program (22YF1407700).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eThe authors thank Ying Wang for review and editorial assistance.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAttaway AH, Scheraga RG, Bhimraj A, Biehl M, Hatipoğlu U. Severe covid-19 pneumonia: pathogenesis and clinical management. 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Initial Public Health Response and Interim Clinical Guidance for the 2019 Novel Coronavirus Outbreak - United States, December 31, 2019-February 4, 2020. MMWR Morb Mortal Wkly Rep. 2020;69(5):140\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSella N, Pettenuzzo T, Zarantonello F, Andreatta G, De Cassai A, Schiavolin C, Simoni C, Pasin L, Boscolo A, Navalesi P. Electrical impedance tomography: A compass for the safe route to optimal PEEP. Respir Med. 2021;187:106555.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShi H, Xu Z, Cheng G, Ji H, He L, Zhu J, Hu H, Xie Z, Ao W, Wang J. CT-based radiomic nomogram for predicting the severity of patients with COVID-19. Eur J Med Res. 2022;27(1):13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu S, Rovine MJ, Molenaar PC. Selecting a linear mixed model for longitudinal data: repeated measures analysis of variance, covariance pattern model, and growth curve approaches. Psychol Methods. 2012;17(1):15\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou F, Yu T, Du R, Fan G, Liu Y, Liu Z, Xiang J, Wang Y, Song B, Gu X, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet. 2020;395(10229):1054\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArmstrong RA, Kane AD, Cook TM. Outcomes from intensive care in patients with COVID-19: a systematic review and meta-analysis of observational studies. Anaesthesia. 2020;75(10):1340\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKrause M, Douin DJ, Kim KK, Fernandez-Bustamante A, Bartels K. Characteristics and Outcomes of Mechanically Ventilated COVID-19 Patients-An Observational Cohort Study. J Intensive Care Med. 2021;36(3):271\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurney PG, Hooper R. Forced vital capacity, airway obstruction and survival in a general population sample from the USA. Thorax. 2011;66(1):49\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReyna ME, Bedard MA, Subbarao P. Lung Function as a Biomarker of Health: An Old Concept Rediscovered. Am J Respir Crit Care Med. 2023;208(2):117\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeivseth L, Nilsen TI, Mai XM, Johnsen R, Langhammer A. Lung function and respiratory symptoms in association with mortality: The HUNT Study. Copd. 2014;11(1):59\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBellan M, Soddu D, Balbo PE, Baricich A, Zeppegno P, Avanzi GC, Baldon G, Bartolomei G, Battaglia M, Battistini S, et al. Respiratory and Psychophysical Sequelae Among Patients With COVID-19 Four Months After Hospital Discharge. JAMA Netw Open. 2021;4(1):e2036142.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDilken O, Rezoagli E, Yartaş Dumanlı G, \u0026Uuml;rkmez S, Demirkıran O, Dikmen Y. Effect of prone positioning on end-expiratory lung volume, strain and oxygenation change over time in COVID-19 acute respiratory distress syndrome: A prospective physiological study. Front Med (Lausanne). 2022;9:1056766.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBos LDJ, Sjoding M, Sinha P, Bhavani SV, Lyons PG, Bewley AF, Botta M, Tsonas AM, Serpa Neto A, Schultz MJ, et al. Longitudinal respiratory subphenotypes in patients with COVID-19-related acute respiratory distress syndrome: results from three observational cohorts. Lancet Respir Med. 2021;9(12):1377\u0026ndash;86.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"end-expiratory lung volume, COVID-19, acute respiratory distress syndrome, mechanical ventilation","lastPublishedDoi":"10.21203/rs.3.rs-3989949/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3989949/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eEnd-expiratory lung volume (EELV) has been observed to decrease in acute respiratory distress syndrome (ARDS). Yet, research investigating EELV in patients with COVID-19 associated ARDS (CARDS) remains limited. It is unclear EELV serve as a potential metric for monitoring disease progression and identifying patients with ARDS at increased risk of adverse outcomes.\u003c/p\u003e\u003ch2\u003eStudy Design and Methods:\u003c/h2\u003e \u003cp\u003eThis retrospective study included mechanically ventilated patients with CARDS during the initial phase of epidemic control in Shanghai. EELV was measured within 48 hours post-intubation, followed by regular assessments every 3\u0026ndash;4 days. Chest CT scans, performed within a 24-hour window around each EELV measurement, were analyzed using AI software. Differences in patient demographics, clinical data, respiratory mechanics, EELV, and chest CT findings were assessed using linear mixed models (LMM).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOut of the 38 enrolled patients, 26.3% survived until discharge from the ICU. In the survivor group, EELV, EELV/PBW and EELV/preFRC were significantly higher than those in the non-survivor group (survivor group vs non-survivor group: EELV: 1455 vs 1162 ml, P\u0026thinsp;=\u0026thinsp;0.049; EELV/PBW: 24.1 vs 18.5 ml/kg, P\u0026thinsp;=\u0026thinsp;0.011; EELV/preFRC: 0.45 vs 0.34, P\u0026thinsp;=\u0026thinsp;0.005). Follow-up assessments showed a sustained elevation of EELV/PBW and EELV/preFRC among the survivors. Additionally, EELV exhibited a positive correlation with total lung volume and residual lung volume, while demonstrating a negative correlation with lesion volume determined through chest CT scans analyzed using AI software.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eEELV is a useful indicator for assessing disease severity and monitoring the prognosis of patients with CARDS.\u003c/p\u003e","manuscriptTitle":"End-expiratory lung volumes as a potential indicator for COVID-19 associated acute respiratory distress syndrome: a retrospective study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-08 18:40:04","doi":"10.21203/rs.3.rs-3989949/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-04-08T09:40:16+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-06T21:11:22+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-03-23T23:42:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"1cb9e005-a2e7-4230-adea-5487fad59069","date":"2024-03-12T18:04:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"889e7526-1954-48ab-bb70-f82dd7c7bfb6","date":"2024-03-06T13:24:44+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-03-06T13:23:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-03-06T13:19:22+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-03-06T06:07:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-06T06:06:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pulmonary Medicine","date":"2024-02-26T04:34:17+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"86d9a479-5a35-4bf3-b48c-e85ab1e33713","owner":[],"postedDate":"March 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-06-19T20:08:19+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-08 18:40:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3989949","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3989949","identity":"rs-3989949","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}