Skip to main content
Download PDF

Ventilatory variables and computed tomography features in COVID-19 ARDS and non–COVID-19-related ARDS: a prospective observational cohort study

European Journal of Medical Research volume 30, Article number: 57 (2025) Cite this article

Abstract

Background

This study compared the ventilatory variables and computed tomography (CT) features of patients with coronavirus disease 2019 (COVID-19) versus those of patients with pulmonary non–COVID-19-related acute respiratory distress syndrome (ARDS) during the early phase of ARDS.

Methods

This prospective, observational cohort study of ARDS patients in Taiwan was performed between February 2017 and June 2018 as well as between October 2020 and January 2024. Analysis was performed on clinical characteristics, including consecutive ventilatory variables during the first week after ARDS diagnosis. Analysis was also performed on CT scans obtained within one week after ARDS onset.

Results

A total of 222 ARDS patients were divided into a COVID-19 ARDS group (n = 44; 19.8%) and a non–COVID-19 group (all pulmonary origin) (n = 178; 80.2%). No significant difference was observed between the two groups in terms of all-cause hospital mortality (38.6% versus 47.8%, p = 0.277). Pulmonary non–COVID-19 patients presented higher values for mechanical power (MP), MP normalized to predicted body weight (MP/PBW), MP normalized to compliance (MP/compliance), ventilatory ratio (VR), peak inspiratory pressure (Ppeak), and dynamic driving pressure (∆P) as well as lower dynamic compliance from day 1 to day 7 after ARDS onset. In both groups, non-survivors exceeded survivors and presented higher values for MP, MP/PBW, MP/compliance, VR, Ppeak, and dynamic ∆P with lower dynamic compliance from day 1 to day 7 after ARDS onset. The CT severity score for each of the five lung lobes and total CT scores were all significantly higher in the non–COVID-19 group (all p < 0.05). Multivariable logistic regression models revealed that Sequential Organ Failure Assessment (SOFA) score was independently associated with mortality in the COVID-19 group. In the non–COVID-19 group, body mass index, immunocompromised status, SOFA score, MP/PBW and total CT severity scores were independently associated with mortality.

Conclusions

In the early course of ARDS, physicians should be aware of the distinctions between COVID-19-related ARDS and non–COVID-19-related ARDS in terms of ventilatory variables and CT imaging presentations. It is also important to tailor the mechanical ventilation settings according to these distinct subsets of ARDS.

Background

Roughly one-third of patients hospitalized with coronavirus disease 2019 (COVID-19) develop acute respiratory distress syndrome (ARDS), and the mortality rate of severe ARDS caused by COVID-19 can reach 50% [1, 2]. COVID-19 ARDS is distinct not only from the classical ARDS clinical phenotype in terms of pathophysiology but also likely in terms of ventilatory and radiographic features [3,4,5].

ARDS management is generally based on mechanical ventilation strategies that limit tidal volume and inspiratory pressures to protect against ventilator-induced lung injury (VILI) and reduce the risk of mortality. Nonetheless, the morbidity and mortality of ARDS remain considerable [6,7,8].

Computed tomography (CT) is a useful tool for characterizing the morphological features and pathophysiology of ARDS and determining the complex relationship between lung parenchyma and mechanical ventilation. CT scans can be used to stratify disease in terms of severity, guide ventilatory management, evaluate disease progression and cardiopulmonary complications, and predict potential sequelae in cases of COVID-19 or non–COVID-19-related ARDS [9,10,11,12].

The objective in this study was to compare ventilatory variables, radiological features, and corresponding clinical outcomes among patients with COVID-19 and pulmonary non–COVID-19-related ARDS.

Methods

Study design and participants

This prospective observational study was conducted in the intensive care unit (ICU) of a tertiary care referral center in Taiwan between February 2017 and June 2018 and between October 2020 and January 2024. All patients admitted to the ICUs were screened, and this study included cases of ARDS (COVID-19 and non–COVID-19) diagnosed at the Linkou branch of Chang Gung Memorial Hospital (CGMH). COVID-19 was diagnosed following confirmation of acute severe acute respiratory syndrome coronavirus 2 infection via positive real-time reverse transcriptase-polymerase chain reaction. The timing of CT measurements was performed within 3 days of ARDS onset. The exclusion criteria were as follows: (1) age < 20 years, (2) mortality within 7 days after ARDS onset, (3) ARDS etiology of extrapulmonary origin, (4) participants who did not receive invasive mechanical ventilation, and (5) failure to obtain informed consent. This study was conducted in accordance with the Declaration of Helsinki, and approval was obtained from the Institutional Review Board for Human Research of CGMH (CGMH IRB No. 201407524B0, 202000760A3, 202100595A3, 202201833A3, and 202300897A3).

Definitions

ARDS was defined in accordance with the Berlin criteria [6]. Ventilatory ratio (VR) was defined as [minute ventilation (ml/min) × PaCO2 (mm Hg)]/(predicted body weight [PBW] × 100 × 37.5) [13]. Unless otherwise specified, the peak inspiratory pressure (Ppeak), equivalent to the plateau pressure in pressure-controlled ventilation, was adopted as a surrogate for plateau pressure [14,15,16]. Dynamic driving pressure (∆P) was defined as Ppeak minus positive end-expiratory pressure (PEEP) [14, 15]. Mechanical power (MP) was calculated using the following formula: MP (Joules/minutes) (J/min) = 0.098 × tidal volume × respiratory rate × (Ppeak–1/2 × ∆P) [16, 17]. All enrolled ARDS patients were deeply sedated and did not exhibit spontaneous breathing when measuring VR, ∆P, MP and other ventilatory parameters. In this paper, MP normalized to PBW is indicated as MP/PBW, and MP normalized to compliance is indicated as MP/compliance. Hospital mortality was defined as death from any cause during the hospital stay. Patients who remained alive for 90 days after discharge from the hospital were regarded as ARDS survivors.

Data collection

Demographic data, risk factors for ARDS, underlying comorbidities, and laboratory data including interleukin-6 were available for all participants. Acute Physiology and Chronic Health Evaluation II (APACHE II) scores, Sequential Organ Failure Assessment (SOFA) scores, and lung injury scores were calculated. The dates and findings of CT scans were recorded for analysis. The dates of hospital and ICU admission, ARDS onset, mechanical ventilator initiation and liberation, ICU and hospital discharge, and time of death were also recorded. Arterial blood gas parameters and mechanical ventilator settings were recorded at the time of ARDS diagnosis and at approximately 10 a.m. on days 1, 3, and 7 after ARDS onset.

CT scanning protocol

High-resolution CT scans were captured using one of our four protocols: 640-slice CT (Aquilion ONE; Toshiba), 512-slice CT (Revolution; GE Healthcare), 160-slice CT (Aquilion Prime SP; Toshiba), or 128-slice CT (Scenaria View; Hitachi Medical System). Each scanner used a preset scanning protocol recommended by the manufacturer for routine chest imaging, which included scanning the area from the lung apex to the lung base with the patients in the supine position during a single inspiratory phase. All images were reconstructed with a slice thickness of 0.625–1.250 mm, using the same increment. CT findings were recorded as present or absent in images with a slice thickness of 1.00 or 1.25 mm.

Imaging technique and interpretation

Image analysis was performed and interpreted independently by a radiologist and thoracic clinicians (YH Juan and LC Chiu), both of whom possessed more than 15 years of experience in clinical thoracic imaging. The radiologists and clinicians were blinded to the clinical data. The images were viewed using the RA1000 workstation under the lung window settings (Centricity PACS system, GE Healthcare). A consensus by both readers was reached for all image findings based on visual interpretation. Analysis focused on the presence and extent of CT manifestations, such as ground-glass opacity (GGO), crazy-paving pattern (i.e., the presence of GGO with superimposed interlobular and intralobular septal thickening and reticulations), and consolidation. CT severity scores were calculated for each of the five lung lobes based on the methods outlined in a previous report [18]. Scores were assigned in accordance with the extent of anatomic involvement, as follows: 0 (no involvement), 1 (less than 5% involvement), 2 (5–25% involvement), 3 (26–50% involvement), 4 (51–75% involvement), and 5 (greater than 75% involvement). The total CT score was the sum of the scores of the five lung lobes, ranging from 0 (no involvement) to 25 (maximal involvement). Different ventilation areas such as overinflation, normal ventilation and collapse was assessed.

Outcome measurements

The 28-day, 60-day, 90-day, and all-cause hospital mortality were analyzed. The use of inotropic agents, acute kidney injury, renal replacement therapy, duration of mechanical ventilation, length of ICU stay, and length of hospital stay were recorded.

Statistical analysis

Continuous variables were presented as mean ± standard deviation for normally distributed variables or as median values (interquartile range) for non-normally distributed variables. A student’s t test or the Mann–Whitney U test was respectively used to compare the continuous variables of normally distributed data or the nonparametric data between groups. Categorical variables were reported as counts (percentages) and were compared using the chi-square test for equal proportions or Fisher’s exact test. Participants with missing data of clinical parameters were excluded for analysis. Propensity score matching was performed to reduce the confounding effects and the likelihood of selection bias using a nearest-neighbor algorithm with 1:2 matching without replacement and a caliper distance of less than 0.2. Matching was performed for baseline characteristics between groups. Each COVID-19 ARDS patient was matched with a non–COVID-19 patient presenting the smallest absolute difference in propensity scores. Univariable analysis was used to identify the risk factors for hospital mortality in the COVID-19 and non–COVID-19 groups in the first step, followed by the construction of multivariable logistic regression models with stepwise selection to adjust for confounding factors. The results were presented using odds ratio (OR) and 95% confidence interval (CI). All statistical analysis was performed using SPSS version 26.0 (IBM Inc., Armonk, NY), with a two-sided p value < 0.05 considered statistically significant.

Results

During the study period, a total of 5880 patients admitted to ICUs were screened, and this study finally included 222 ARDS patients who underwent mechanical ventilation. The patients were classified into a COVID-19 group (n = 44; 19.8%) and a pulmonary non–COVID-19 group (n = 178; 80.2%). The overall all-cause mortality rate was 45.9% (102 patients died) (Fig. 1).

Fig. 1
figure 1

Flowchart of participants with COVID-19 ARDS and non–COVID-19 ARDS. ARDS, acute respiratory distress syndrome; COVID-19, coronavirus disease 2019; ICU, intensive care unit

Full size image

Comparison of COVID-19 and non–COVID-19 ARDS groups

As shown in Table 1, the mean age of patients was higher in the COVID-19 group than in the non–COVID-19 group. There were no significant differences between the two groups in terms of sex or body mass index. The rate of chronic heart disease was significantly higher in the COVID-19 group, whereas the incidence rate of immunocompromised status was significantly higher in the non–COVID-19 group.

Table 1 Background characteristics and clinical variables at the time of ARDS diagnosis
Full size table

APACHE II scores and SOFA scores were significantly higher in the non–COVID-19 group than in the COVID-19 group (both p < 0.05). White blood cell counts, C-reactive protein levels, and interleukin-6 levels were higher in the non–COVID-19 group than in the COVID-19 group. There were no differences between the groups in terms of prone positioning or ECMO use.

Ventilatory variables in COVID-19 and non-COVID-19 ARDS groups

As shown in Table 2, there were no significant differences between COVID-19 ARDS and non–COVID-19 ARDS in terms of most ventilator settings variables at the time of ARDS diagnosis. The one exception was mean FiO2 value, which was significantly higher in the COVID-19 group at the time of ARDS diagnosis and significantly higher in the non–COVID-19 group at days 3 and 7.

Table 2 Ventilatory variables at day 1, 3, and 7 after ARDS onset
Full size table

In both groups, the MP and airway pressure values presented decreasing trends from the onset of ARDS (i.e., day 1) until day 7. At days 1, 3, and 7, all mean MP, MP/PBW, MP/compliance, VR, Ppeak, and dynamic ∆P values in the non–COVID-19 group were higher whereas dynamic compliance was lower. The differences in these values between the two groups reached the level of significances at day 7 (all p < 0.05) (Table 2 and Fig. 2). The median PaO2/FiO2 value was initially higher in the non–COVID-19 group (at day 1); however, these values were higher in the COVID-19 group at days 3 and 7, reaching the level of significance at day 7 (p = 0.040).

Fig. 2
figure 2

Serial changes in ventilatory variables during the first week of ARDS onset: A MP; B VR; C PEEP; D Ppeak; E Dynamic ∆P; and F Dynamic compliance. Dark line and gray line respectively indicate pulmonary non–COVID-19 ARDS and COVID-19 ARDS. Error bars denote mean ± standard error. * p value < 0.05 comparing COVID-19 ARDS and pulmonary non–COVID-19 ARDS. ARDS, acute respiratory distress syndrome; COVID-19, coronavirus disease 2019; MP, mechanical power; PEEP, positive end-expiratory pressure; Ppeak, peak inspiratory pressure; ∆P, driving pressure; VR, ventilatory ratio

Full size image

CT features in COVID-19 and non–COVID-19 ARDS groups

A total of 32 COVID-19 ARDS patients and 116 non–COVID-19 ARDS patients underwent CT examinations at the time of the initial ARDS diagnosis (Table 3). The median time from ARDS onset to CT scan in both groups was one day after ARDS onset. The percentage of GGO was significantly higher in the COVID-19 group (84.4%), whereas the percentages of consolidation (94%), air bronchogram (94%) and pleural effusion (76.7%) were significantly higher in the non–COVID-19 group (all p < 0.05). The main CT pattern in the COVID-19 group was GGO (62.5%), whereas the main CT pattern in the non-COVID-19 group was consolidation (75%). The percentage of collapse was significantly higher in the non–COVID-19 group than in the COVID-19 group (72.4% versus 21.9%, p < 0.001).

Table 3 Computed tomographic features of COVID-19-related ARDS versus non–COVID-19 ARDS
Full size table

In both groups, lung opacity involvement (in bilateral lungs) was more severe in the lower lobes than in the upper and middle lobes (i.e., higher CT scores). The total CT severity scores were significantly higher in the non–COVID-19 group than in the COVID-19 group (18.0 ± 4.6 versus 10.5 ± 3.7, p < 0.001), as were the CT severity scores of all the lung lobes (all p < 0.05). Figure 3 presents exemplar CT scans of participants with COVID-19 and non–COVID-19.

Fig. 3
figure 3

Unenhanced axial computed tomographic images of study participants: A Multifocal ground-glass opacity with central and peripheral distribution in the bilateral upper lobes in a 68-year-old male with COVID-19 ARDS; B Crazy-paving pattern in the bilateral middle and lower lobes in a 70-year-old female with COVID-19 ARDS; and C Consolidation in the bilateral lower lobes in a 63-year-old male with pulmonary non–COVID-19 ARDS. ARDS, acute respiratory distress syndrome; COVID-19, coronavirus disease 2019

Full size image

Correlations between patient survival and ventilatory variables, ventilation areas, or CT severity scores

Table 4 compares the ventilatory variables and CT severity scores of survivors versus those of non-survivors at the time of ARDS diagnosis. Among COVID-19 ARDS patients, non-survivors presented higher MP, MP/PBW, MP/compliance, VR, Ppeak, and ∆P values as well as lower dynamic compliance at days 1, 3, and 7 after ARDS onset. Note however that none of the differences met the level of significance (see Table 4 and Supplementary Tables 1 and 2). No significant differences were observed between survivors and non-survivors in terms of different ventilation areas such as overinflation, normal ventilation or collapse, total CT severity score or the CT severity scores for the individual five lung lobes.

Table 4 Ventilatory variables and CT severity scores at the onset of ARDS: Survivors versus non-survivors
Full size table

Among non–COVID-19 ARDS patients, non-survivors presented significantly higher MP, MP/PBW, MP/compliance, VR, Ppeak, and ∆P at days 1, 3, and 7 after ARDS onset (all p < 0.05) (Table 4 and Supplementary Table 1 and 2). The percentage of collapse was significantly higher among non-survivors than among survivors (91.1% versus 60.6%, p < 0.001). The total CT severity scores were significantly higher among non-survivors than among survivors (16.4 ± 4.5 versus 20.7 ± 3.5, p < 0.001), as were the CT severity scores of all five lung lobes.

Clinical outcomes of COVID-19 and non–COVID-19 ARDS patients

As shown in Table 5, overall all-cause in-hospital mortality was 38.6% in the COVID-19 group and 47.8% in the non–COVID-19 group (p = 0.277). No significant differences were observed between the two groups in terms of 28-day, 60-day, 90-day, or all-cause hospital mortality. The risk of acute kidney injury was significantly higher in the non–COVID-19 group (p = 0.043). There were no significant differences between the two groups in terms of the duration of mechanical ventilation, length of ICU stay, or length of hospital stay.

Table 5 Clinical outcomes of patients with COVID-19-related ARDS and non–COVID-19 ARDS
Full size table

Comparisons of ventilatory variables, CT severity score, and mortality after matching

After performing a 1:2 case–control match on propensity score for age, APACHE II score and SOFA score, we included 44 patients in the COVID-19 group and 88 patients in the non–COVID-19 group. No significant differences were found between the two groups in terms of baseline characteristics and clinical variables at the time of ARDS diagnosis, and hospital mortality was not significantly different between two groups (38.6% versus 42%, p = 0.596).

The mean values of MP, MP/PBW, MP/compliance, VR, Ppeak, and dynamic ∆P in the non–COVID-19 group were higher whereas dynamic compliance was lower at days 1, 3, and 7, and the differences were significant at day 7 (all p < 0.05). The CT severity scores of all the lung lobes and total CT severity scores were significantly higher in the non–COVID-19 group (all p < 0.05). (Supplementary Tables 3, 4, and 5).

Factors associated with hospital mortality

After adjusting for significant confounding variables, multivariable logistic regression models revealed that SOFA score was independently associated with hospital mortality in the COVID-19 group. In non–COVID-19 group, body mass index, immunocompromised status, SOFA score, MP/PBW, and total CT severity scores were independently associated with hospital mortality (Table 6).

Table 6 Multivariable logistic regression analysis of factors associated with all cause hospital mortality
Full size table

Discussion

The key finding of this study was the considerable difference between the two categories of ARDS cases (COVID-19-induced vs. pulmonary non–COVID-19- induced) in terms of ventilatory variables and CT features during the early phase of ARDS. Patients with pulmonary non–COVID-19 ARDS presented higher MP, VR, and airway pressure values as well as lower respiratory system compliance than did patients with COVID-19 ARDS. Total CT scores and CT severity scores of all five lung lobes were all significantly higher in cases of non–COVID-19 ARDS.

VR is a simple yet practical bedside surrogate to monitor impaired ventilatory efficiency and is positively correlated with the fraction of pulmonary dead space (i.e.,VD/VT). VR is independently correlated with an elevated risk of hospital mortality in cases of ARDS [13, 19]. Researchers have previously reported that minute ventilation in cases of COVID-19 ARDS is lower than bacterial and culture-negative direct ARDS, and VR was independently associated with hospital mortality in COVID-19 ARDS [4, 20]. In the current study, VR and PaCO2 values were significantly higher in the pulmonary non–COVID-19 group than in the COVID-19 group at days 3 and 7 after ARDS onset. In both groups, the VR of non-survivors was higher than that of survivors from days 1 to 7 after ARDS onset. This suggests that patients with pulmonary non–COVID-19 ARDS have a higher percentage of non-aerated lung tissue and higher dead space fraction than do patients with COVID-19 ARDS. This also suggests that VR has predictive value for mortality in both groups.

The term baby lung is commonly used in cases of ARDS in reference to the portion of the lung that remains aerated and functional despite widespread lung injury. Respiratory system compliance can indicate the size of baby lung, as it is directly correlated with the number of aerated lung units available for tidal ventilation (i.e., functional lung size) [14, 16, 21]. Several studies have reported that COVID-19 ARDS is an atypical form, characterized by relatively high respiratory system compliance despite severe hypoxemia [3, 5, 22]; however, other studies reported no such distinction [4, 23]. In the current study, the dissociation between hypoxemia and respiratory compliance in COVID-19 ARDS was not observed. These findings suggest that non–COVID-19 ARDS is associated with a higher proportion of non-inflated tissue and smaller functional lung size, both of which could exacerbate hypoxemia.

MP is a combination of ventilatory variables used to estimate the amount of energy load delivered to the respiratory system per unit of time during mechanical ventilation [17]. MP can be used in assessing patients with ARDS to predict clinical outcomes, such as mortality and the risk of VILI, providing greater precision than can be achieved using a single ventilatory variable [16, 24, 25].

Note that VILI development depends not only on the energy load (i.e., MP) but also on the pathophysiological characteristics of the lungs (e.g., functional lung size, extent of inhomogeneity, and recruitability). This means that MP must be normalized to PBW or compliance if it is to reflect the actual energy load applied to the respiratory system in order to gain a reasonable estimate as to the risk of developing VILI [16, 21, 25, 26].

In the current study, we found that MP, MP/PBW, and MP/compliance values were higher in the non–COVID-19 group than in the COVID-19 group from days 1 to 7 after ARDS onset. Like previous reports, we did not observe a significant difference in terms of hospital mortality rate between the two groups [4, 5, 20, 23]. In both groups, MP, MP/PBW, and MP/compliance values were higher among non-survivors than among survivors from days 1 to 7 after ARDS onset. Thus, it is reasonable to speculate that patients with pulmonary non–COVID-19 ARDS received a higher ventilator load (i.e., MP) and faced a correspondingly higher risk of VILI. Overall, higher MP, MP/PBW, and MP/compliance values were associated with higher mortality, regardless of ARDS origin.

CT scans can be used to monitor disease evolution and predict the prognosis of ARDS [10, 27], and can complement nucleic acid amplification tests to enable to timely diagnosis of COVID-19 and/or identify situations involving a rapid worsening of respiratory status [9, 11, 12]. The hallmark of COVID-19 pneumonia on CT scans is bilateral GGO with a predominantly peripheral and subpleural distribution. [9, 11, 28, 29]. The hallmark of typical non–COVID-19 ARDS is diffuse ground-glass attenuation and alveolar consolidation with an inhomogeneous distribution. This is commonly associated with a gravitationally dependent gradient, with more extensive consolidation typically located in posterobasal dependent areas [10].

In the current study, 66.7% of the ARDS patients (n = 148) underwent CT scans during the early phase of ARDS (median time one day after ARDS onset). Those scans revealed that COVID-19 ARDS patients had a significantly higher percentage of peripheral GGO, whereas pulmonary non–COVID-19 ARDS patients had a significantly higher percentage of diffuse distribution of opacification in both the peripheral and central areas with dense consolidation predominantly in dorsal-dependent regions. In the current study, the main CT features of COVID-19 were GGO (62.5%), followed by consolidation (31.3%) and crazy-paving (6.3%). Note that the percentage of GGO and consolidation in COVID-19 cases was consistent with previous reports. In differentiating between the type L and type H phenotypes, it has been reported that the type H phenotype, characterized by extensive consolidation and high elastance (low compliance), high right-to-left shunt, high lung weight, and high lung recruitability, has been identified in 20–30% of COVID-19 cases [30, 31].

CT scans can accurately determine the pathophysiology and anatomical characteristics of the lungs by quantifying the amount of inflated functional lung tissue and estimating lung inhomogeneity or recruitability. The extent of well-aerated lung regions can significantly affect clinical outcomes in cases of COVID-19 or ARDS [32, 33]. Previous research has shown that lung gas volumes in each lung segment are higher in cases of COVID-19 ARDS than in cases of non–COVID-19 ARDS, with the most notable differences in the most dependent lung regions (i.e., the posterior parts of the lungs of patients in a supine position) [3, 22].

Previous studies have demonstrated the practicality of high-resolution CT scores and visually assessed CT severity scores in predicting disease severity, clinical outcomes, and mortality in patients with ARDS, regardless of COVID-19 or non–COVID-19 related [18, 34, 35]. In the current study, total CT scores and CT severity scores for all lung segments were significantly higher in the non–COVID-19 group than in the COVID-19 group (all p < 0.05). Among non–COVID-19 ARDS patients, non-survivors generated significantly higher CT severity scores in each lung segment as well as significantly higher total CT scores, compared with survivors.

When considering both radiological and ventilatory features, the non–COVID-19 group presented a significantly higher proportion of diffuse distribution, featuring mainly dorsal consolidation in dependent regions and more extensive pulmonary involvement, as indicated by significantly higher CT severity scores. This suggests that the higher percentages of non-inflated tissue (i.e., lower functional lung size) and dead space fraction contributed to impaired ventilation, leading to higher ventilation workload and an elevated risk of VILI (i.e., MP, MP/PBW, and MP/compliance), as reflected by higher VR, higher airway pressures, and lower respiratory system compliance in the non–COVID-19 group. This means that personalized protective mechanical ventilation settings should be formulated to deal with these two distinct subphenotypes of ARDS (i.e., COVID-19 and non–COVID-19 ARDS).

This study was subject to several limitations. First, this cohort study was conducted at a single medical center in Taiwan, which introduces potential selection bias related to regional and clinical practice variations and may limit the external validity and generalizability of our findings. The number of patients enrolled in the COVID-19 group was relatively small, which may reduce the statistical power potentially affecting the reliability and significance of the results. Second, we compared the two groups in terms of CT severity scores and CT imaging features based on visual assessments; however, we did not perform quantitative anatomical assessments, such as lung weight, lung gas volume, the amount of aerated lung tissue, percentage of inhomogeneity, or recruitability. This may affect the objectivity and reproducibility of the CT scores. Incorporating quantitative CT analysis tools (e.g., artificial intelligence algorithms or automated software) could improve the objectivity and reproducibility of imaging evaluations. Moreover, the widespread clinical use of CT scan examinations is limited by the need to transfer patients from the ICU to the radiology department, despite the unstable hemodynamics of ARDS patients and the risk of transmission. This may be the reason that only 65% of the ARDS patients in this study underwent CT examinations. Finally, this study focused exclusively on the early-stage mechanical ventilation and CT features of ARDS but lacks follow-up data on patients after discharge. Thus, the lack of serial CT examinations prevented our analysis of residual lung involvement throughout the disease course until recovery, and our findings have limited implications for long-term prognosis and rehabilitation management.

Conclusions

Our findings revealed that COVID-19-related ARDS differs from non–COVID-19-related ARDS in terms of important radiological and physiological features during the early course of ARDS. A protective mechanical ventilation strategy should be tailored to individual patients based on the unique mechanics of their respiratory system and status.

Availability of data and materials

No datasets were generated or analysed during the current study.

Abbreviations

APACHE:

Acute physiology and chronic health evaluation

ARDS:

Acute respiratory distress syndrome

CI:

Confidence interval

COVID-19:

Coronavirus disease 2019

CT:

Computed tomography

GGO:

Ground-glass opacity

ICU:

Intensive care unit

MP:

Mechanical power

OR:

Odds ratio

PaCO2 :

Partial pressure of carbon dioxide in arterial blood

PBW:

Predicted body weight

PEEP:

Positive end-expiratory pressure

Ppeak:

Peak inspiratory pressure

∆P :

Driving pressure

SOFA:

Sequential organ failure assessment

VILI:

Ventilator-induced lung injury

VR:

Ventilatory ratio

References

  1. Tzotzos SJ, Fischer B, Fischer H, Zeitlinger M. Incidence of ARDS and outcomes in hospitalized patients with COVID-19: a global literature survey. Crit Care. 2020;24(1):516.

    Article  PubMed  PubMed Central  Google Scholar 

  2. COVID-ICU Group on behalf of the REVA Network and the COVID-ICU Investigators. Clinical characteristics and day-90 outcomes of 4244 critically ill adults with COVID-19: a prospective cohort study. Intensive Care Med. 2021;47(1):60–73.

    Article  Google Scholar 

  3. Gattinoni L, Coppola S, Cressoni M, Busana M, Rossi S, Chiumello D. COVID-19 does not lead to a “Typical” acute respiratory distress syndrome. Am J Respir Crit Care Med. 2020;201(10):1299–300.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Bain W, Yang H, Shah FA, Suber T, Drohan C, Al-Yousif N, et al. COVID-19 versus Non-COVID-19 acute respiratory distress syndrome: comparison of demographics, physiologic parameters, inflammatory biomarkers, and clinical outcomes. Ann Am Thorac Soc. 2021;18(7):1202–10.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Beloncle F, Studer A, Seegers V, Richard JC, Desprez C, Fage N, et al. Longitudinal changes in compliance, oxygenation and ventilatory ratio in COVID-19 versus non-COVID-19 pulmonary acute respiratory distress syndrome. Crit Care. 2021;25(1):248.

    Article  PubMed  PubMed Central  Google Scholar 

  6. ARDS Definition Task Force, Ranieri VM, Rubenfeld GD, Thompson BT, Ferguson ND, Caldwell E, et al. Acute respiratory distress syndrome: the Berlin definition. JAMA. 2012;307:2526–33.

    Google Scholar 

  7. Qadir N, Sahetya S, Munshi L, Summers C, Abrams D, Beitler J, et al. An update on management of adult patients with acute respiratory distress syndrome: an official American thoracic society clinical practice guideline. Am J Respir Crit Care Med. 2024;209(1):24–36.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Gorman EA, O’Kane CM, McAuley DF. Acute respiratory distress syndrome in adults: diagnosis, outcomes, long-term sequelae, and management. Lancet. 2022;400(10358):1157–70.

    Article  PubMed  Google Scholar 

  9. Jeong YJ, Wi YM, Park H, Lee JE, Kim SH, Lee KS. Current and emerging knowledge in COVID-19. Radiology. 2023;306(2): e222462.

    Article  PubMed  Google Scholar 

  10. Bitker L, Talmor D, Richard JC. Imaging the acute respiratory distress syndrome: past, present and future. Intensive Care Med. 2022;48(8):995–1008.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Kwee TC, Kwee RM. Chest CT in COVID-19: what the radiologist needs to know. Radiographics. 2020;40(7):1848–65.

    Article  PubMed  Google Scholar 

  12. Rubin GD, Ryerson CJ, Haramati LB, Sverzellati N, Kanne JP, Raoof S, et al. The role of chest imaging in patient management during the COVID-19 pandemic: a multinational consensus statement from the Fleischner society. Chest. 2020;158(1):106–16.

    Article  CAS  PubMed  Google Scholar 

  13. Sinha P, Calfee CS, Beitler JR, Soni N, Ho K, Matthay MA, et al. Physiologic analysis and clinical performance of the ventilatory ratio in acute respiratory distress syndrome. Am J Respir Crit Care Med. 2019;199(3):333–41.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Chiu LC, Hu HC, Hung CY, Chang CH, Tsai FC, Yang CT, et al. Dynamic driving pressure associated mortality in acute respiratory distress syndrome with extracorporeal membrane oxygenation. Ann Intensive Care. 2017;7:12.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Urner M, Jüni P, Hansen B, Wettstein MS, Ferguson ND, Fan E. Time-varying intensity of mechanical ventilation and mortality in patients with acute respiratory failure: a registry-based, prospective cohort study. Lancet Respir Med. 2020;8:905–13.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Chiu LC, Lin SW, Chuang LP, Li HH, Liu PH, Tsai FC, et al. Mechanical power during extracorporeal membrane oxygenation and hospital mortality in patients with acute respiratory distress syndrome. Crit Care. 2021;25(1):13.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Gattinoni L, Tonetti T, Cressoni M, Cadringher P, Herrmann P, Moerer O, et al. Ventilator-related causes of lung injury: the mechanical power. Intensive Care Med. 2016;42:1567–75.

    Article  CAS  PubMed  Google Scholar 

  18. Pan F, Ye T, Sun P, Gui S, Liang B, Li L, et al. Time course of lung changes at chest CT during recovery from coronavirus disease 2019 (COVID-19). Radiology. 2020;295(3):715–21.

    Article  PubMed  Google Scholar 

  19. Morales-Quinteros L, Schultz MJ, Bringué J, Calfee CS, Camprubí M, Cremer OL, et al. Estimated dead space fraction and the ventilatory ratio are associated with mortality in early ARDS. Ann Intensive Care. 2019;9(1):128.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Torres A, Motos A, Riera J, Fernández-Barat L, Ceccato A, Pérez-Arnal R, et al. The evolution of the ventilatory ratio is a prognostic factor in mechanically ventilated COVID-19 ARDS patients. Crit Care. 2021;25(1):331.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Marini JJ, Rocco PRM, Gattinoni L. Static and dynamic contributors to ventilator-induced lung injury in clinical practice. Pressure, energy, and power. Am J Respir Crit Care Med. 2020;201:767–74.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Chiumello D, Busana M, Coppola S, Romitti F, Formenti P, Bonifazi M, et al. Physiological and quantitative CT-scan characterization of COVID-19 and typical ARDS: a matched cohort study. Intensive Care Med. 2020;46(12):2187–96.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Ferrando C, Suarez-Sipmann F, Mellado-Artigas R, Hernández M, Gea A, Arruti E, COVID-19 Spanish ICU Network, et al. Clinical features, ventilatory management, and outcome of ARDS caused by COVID-19 are similar to other causes of ARDS. Intensive Care Med. 2020;46(12):2200–11.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Costa ELV, Slutsky AS, Brochard LJ, Brower R, Serpa-Neto A, Cavalcanti AB, et al. Ventilatory variables and mechanical power in patients with acute respiratory distress syndrome. Am J Respir Crit Care Med. 2021;204(3):303–11.

    Article  PubMed  Google Scholar 

  25. Coppola S, Caccioppola A, Froio S, Formenti P, De Giorgis V, Galanti V, et al. Effect of mechanical power on intensive care mortality in ARDS patients. Crit Care. 2020;24:246.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Gattinoni L, Collino F, Camporota L. Mechanical power: meaning, uses and limitations. Intensive Care Med. 2023;49(4):465–7.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Maiolo G, Collino F, Vasques F, Rapetti F, Tonetti T, Romitti F, et al. Reclassifying acute respiratory distress syndrome. Am J Respir Crit Care Med. 2018;197:1586–95.

    Article  CAS  PubMed  Google Scholar 

  28. Hani C, Trieu NH, Saab I, Dangeard S, Bennani S, Chassagnon G, et al. COVID-19 pneumonia: a review of typical CT findings and differential diagnosis. Diagn Interv Imaging. 2020;101(5):263–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Carotti M, Salaffi F, Sarzi-Puttini P, Agostini A, Borgheresi A, Minorati D, et al. Chest CT features of coronavirus disease 2019 (COVID-19) pneumonia: key points for radiologists. Radiol Med. 2020;125(7):636–46.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Gattinoni L, Chiumello D, Caironi P, Busana M, Romitti F, Brazzi L, et al. COVID-19 pneumonia: different respiratory treatments for different phenotypes? Intensive Care Med. 2020;46(6):1099–102.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Marini JJ, Gattinoni L. Management of COVID-19 respiratory distress. JAMA. 2020;323(22):2329–30.

    Article  PubMed  Google Scholar 

  32. Colombi D, Bodini FC, Petrini M, Maffi G, Morelli N, Milanese G, et al. Well-aerated lung on admitting chest CT to predict adverse outcome in COVID-19 pneumonia. Radiology. 2020;296(2):E86–96.

    Article  PubMed  Google Scholar 

  33. Nishiyama A, Kawata N, Yokota H, Sugiura T, Matsumura Y, Higashide T, et al. A predictive factor for patients with acute respiratory distress syndrome: CT lung volumetry of the well-aerated region as an automated method. Eur J Radiol. 2020;122: 108748.

    Article  PubMed  Google Scholar 

  34. Ichikado K, Muranaka H, Gushima Y, Kotani T, Nader HM, Fujimoto K, et al. Fibroproliferative changes on high-resolution CT in the acute respiratory distress syndrome predict mortality and ventilator dependency: a prospective observational cohort study. BMJ Open. 2012;2(2): e000545.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Yang R, Li X, Liu H, Zhen Y, Zhang X, Xiong Q, et al. Chest CT severity score: an imaging tool for assessing severe COVID-19. Radiol Cardiothorac Imaging. 2020;2(2): e200047.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors would like to express their appreciation for the patients and staff in the ICUs at Chang Gung Memorial Hospital.

Funding

This study was supported by grants from Chang Gung Memorial Hospital (CMRPG3K1151, CMRPG3L0821, CMRPG3L0822, CORPG3M0331, and CMRPG3N1121) and the Taiwan Ministry of Science and Technology (MOST 111-2314-B-182A-148).

Author information

Authors and Affiliations

  1. Department of Thoracic Medicine, Chang Gung Memorial Hospital, Linkou Branch, No. 5, Fu-Shing St., GuiShan, Taoyuan, Taiwan

    Li-Chung Chiu, Hsin-Hsien Li, How-Wen Ko, Scott Chih-Hsi Kuo, Chung-Shu Lee, Li-Pang Chuang, Han-Chung Hu, Kuo-Chin Kao & Ping-Chih Hsu

  2. College of Medicine, Chang Gung University, Taoyuan, Taiwan

    Li-Chung Chiu, Yu-Hsiang Juan, How-Wen Ko, Scott Chih-Hsi Kuo, Chung-Shu Lee, Tien-Ming Chan, Li-Pang Chuang, Han-Chung Hu, Kuo-Chin Kao & Ping-Chih Hsu

  3. Department of Respiratory Therapy, Chang Gung University College of Medicine, Taoyuan, Taiwan

    Hsin-Hsien Li

  4. Department of Medical Imaging and Intervention, Institute for Radiological Research, Chang Gung Memorial Hospital at Linkou and Taoyuan, Chang Gung University, Taoyuan, Taiwan

    Yu-Hsiang Juan

  5. Department of Thoracic Medicine, New Taipei Municipal TuCheng Hospital and Chang Gung University, Taoyuan, Taiwan

    Chung-Shu Lee

  6. Division of Rheumatology, Allergy, and Immunology, Chang Gung Memorial Hospital, Linkou Branch, Taoyuan, Taiwan

    Tien-Ming Chan

  7. Research Services Center for Health Information, Chang Gung University, Taoyuan, Taiwan

    Yu-Jr Lin

Authors
  1. Li-Chung Chiu
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Hsin-Hsien Li
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Yu-Hsiang Juan
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. How-Wen Ko
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Scott Chih-Hsi Kuo
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Chung-Shu Lee
    View author publications

    You can also search for this author inPubMed Google Scholar

  7. Tien-Ming Chan
    View author publications

    You can also search for this author inPubMed Google Scholar

  8. Yu-Jr Lin
    View author publications

    You can also search for this author inPubMed Google Scholar

  9. Li-Pang Chuang
    View author publications

    You can also search for this author inPubMed Google Scholar

  10. Han-Chung Hu
    View author publications

    You can also search for this author inPubMed Google Scholar

  11. Kuo-Chin Kao
    View author publications

    You can also search for this author inPubMed Google Scholar

  12. Ping-Chih Hsu
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

LCC and PCH assumed responsibility for the accuracy of the data analysis and drafting of the manuscript. LCC, HHL, YHJ, HWK, SCHK, CSL, and PCH performed the study design and data acquisition. LCC, TMC, YJL, LPC, HCH, KCK, and PCH were responsible for the statistical analysis of data. LCC, HHL, YHJ, and PCH performed the interpretation of the results. All authors contributed to the completion of the manuscript and have approved the final version.

Corresponding author

Correspondence to Ping-Chih Hsu.

Ethics declarations

Ethics approval and consent to participate

The study was conducted in accordance with the Declaration of Helsinki and was approved by the Institutional Review Board for Human Research of Chang Gung Memorial Hospital (CGMH IRB No. 201407524B0, 202000760A3, 202100595A3, 202201833A3, and 202300897A3), and informed consent was obtained from all subjects involved in the study.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chiu, LC., Li, HH., Juan, YH. et al. Ventilatory variables and computed tomography features in COVID-19 ARDS and non–COVID-19-related ARDS: a prospective observational cohort study. Eur J Med Res 30, 57 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s40001-025-02303-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s40001-025-02303-1

Keywords

European Journal of Medical Research

ISSN: 2047-783X

Contact us