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Lung morphology impacts the association between ventilatory variables and mortality in patients with acute respiratory distress syndrome

Abstract

Background

Acute respiratory distress syndrome (ARDS) patients with different lung morphology have distinct pulmonary mechanical dysfunction and outcomes. Whether lung morphology impacts the association between ventilatory variables and mortality remains unclear. Moreover, the impact of a novel combined ventilator variable [(4×DP) + RR] on morality in ARDS patients needs external validation.

Methods

We obtained data from the Chinese Database in Intensive Care (CDIC), which included adult ARDS patients who received invasive mechanical ventilation for at least 24 h. Patients were further classified into two groups based on lung morphology (focal and non-focal). Ventilatory variables were collected longitudinally within the first four days of ventilation. The primary outcome was 28-day mortality. Extended Cox regression models were employed to explore the interaction between lung morphology and longitudinal ventilatory variables on mortality.

Findings

We included 396 ARDS patients with different lung morphology (64.1% non-focal). The overall 28-day mortality was 34.4%. Patients with non-focal lung morphology have more severe and persistent pulmonary mechanical dysfunction and higher mortality than those with focal lung morphology. Time-varying driving pressure (DP) was more significantly associated with 28-day mortality in patients with non-focal lung morphology compared to focal lung morphology patients (P for interaction = 0.0039). The impact of DP on mortality was more significant than that of respiratory rate (RR) only in patients with non-focal lung morphology. The hazard ratio (HR) of mortality for [(4×DP) + RR] was significant in patients with non-focal lung morphology (HR 1.036, 95% CI 1.027–1.045), not in patients with focal lung morphology (HR 1.019, 95% CI 0.999–1.039).

Interpretation

The association between ventilator variables and mortality varied among patients with different lung morphology. [(4×DP) + RR] was only associated with mortality in patients with non-focal lung morphology. Further validation is needed.

Background

Acute respiratory distress syndrome (ARDS) is a dangerous, potentially fatal respiratory condition in which the lungs sustain a serious, widespread injury that diminishes their ability to provide enough oxygen [1]. The short-term mortality ranged from 30 to 50% worldwide [2]. Lung-protective ventilation strategies have demonstrated survival benefits in randomized clinical trials involving patients with ARDS [3, 4], and recent observational studies demonstrated that mitigated driving pressure (DP) and mechanical power might increase the survival of ARDS [5, 6].

ARDS is a heterogeneous syndrome involving different phenotypes with distinct clinical and outcome characteristics. We previously identified three novel ARDS phenotypes with different severity of pulmonary mechanics and found that the association between pulmonary parameters and mortality varied within phenotypes [7]. Lung morphology is another crucial aspect in distinguishing specific ARDS phenotypes. Patients with non-focal ARDS, as assessed by chest computed tomography (CT), have higher mortality and distinct pulmonary mechanics, including lower lung compliance and a more elevated amount of recruitable lung, than patients with focal lung morphology [8,9,10]. Whether lung morphology impacts the association between ventilatory variables and mortality has never been explored.

Eduardo L.V and colleagues [11] conducted a study based on 4,549 ARDS patients and found that the impact of DP on mortality was four times as large as that of respiratory rate (RR). Accordingly, they created a novel combined ventilator variable [(4×DP) + RR], which was significantly associated with mortality and was comparable as mechanical power. However, these findings need external validation, and whether the findings vary among patients with different lung morphology is still unclear. Besides, a relatively safe threshold value for [(4×DP) + RR] is undefined.

In this study, we aim to investigate whether lung morphology would influence the association between ventilatory variables and mortality. We also independently validated the impact of [(4×DP) + RR] on mortality in patients with different lung morphology in an external cohort.

Method

Study design and participants

We conducted a retrospective observational study in which the data were extracted from the Chinses Database in Intensive Care (CDIC). The latest CDIC contains more than 12,000 patients admitted to the Department of Crit Care Medicine, Zhongda Hospital, Southeast University, China, from January 2014 to July 2022. ARDS patients who received mechanical ventilation for at least 24 h and underwent CT chest examinations within three days after the initiation of mechanical ventilation were eligible for inclusion. The diagnoses of ARDS were consistent with the Berlin definition [1] (detail in supplementary). We excluded patients younger than 18 years, and we only included the first intensive care unit (ICU) admission of each patient.

The present study was approved by the Research Ethics Commission of Zhongda Hospital Southeast University (2022ZDSYLL385-Y01). STROBE recommendations were followed.

Data collection

For every patient, demographic data, primary lung injury of ARDS, vital signs, laboratory results and severity of illness within the first 24 h after inclusion were collected. The disease severity was measured by Acute Physiology and Chronic Health Evaluation (APACHE) II score and Sequential Organ Failure Assessment (SOFA) score. The use of extracorporeal membrane oxygenation (ECMO), prone positioning and neuromuscular blockade agents were also collected. Longitudinal data of ventilatory variables and blood gas were collected on Days 0, 1, 2 and 3. Ventilatory variables included RR, tidal volume (VT) (scaled to predicted body weight (PBW)), minute ventilation (MV), positive end-expiratory pressure (PEEP), peak pressure (Ppeak) and plateau pressure (Pplat) were obtained. All ventilatory variables were extracted under controlled mechanical ventilation. For patients who received ventilation in a volume-controlled assist mode, DP was calculated as Pplat minus PEEP, if not specified, Ppeak was considered equal to Pplat in pressure-regulated modes other than pressure support ventilation. PBW was calculated as equal to [50 + 0.91 × (centimeters of height − 152.4)] in males and [45.5 + 0.91 × (centimeters of height − 152.4)] in females. Combined ventilator variables were calculated as follows: Mechanical power was calculated as [0.098 × VT × RR × (Ppeak − 0.5 × DP)], and entilatory ratio was calculated as [MV × PaCO2 / (PBW × 37.5 × 100)].

The data in CDIC were extracted from the ICU Patient Data Management System which is used to collect patient health information, measurements of organ function parameters, results of laboratory tests and treatment parameters from ICU admission to discharge. Ventilatory variables were extracted per each time frame of 6 h after the initiation of ventilation. For each 24 h, the time-weighted average variables were calculated as the area under the variables versus the time plot. For other clinical variables, we recorded the most abnormal value if a variable was recorded more than once. We defined DP > 35 cmH20, RR > 60 (breaths min-1) and VT > 20 ml/Kg PBW as outliners, because these physiologically improbable values were assumed to be erroneous. Outliers were transformed into missingness.

Assessment of lung morphology

The lung CT of all patients was collected within three days after the initiation of mechanical ventilation. All lung CT scans were performed in the supine position. One radiologist and two intensivists characterized patients as having focal or non-focal lung morphology [8]. The focal lung morphology was defined as the presence of consolidations localized only in the lower and back parts of the lungs (Additional file 1: Fig. S1).

Outcomes

The primary outcome in the present study was 28-day mortality. Secondary outcomes included ICU mortality, in-hospital mortality and ventilation-free days in 28 days.

Statistical analyses

Values are presented as the mean (standard deviation) or median [interquartile range (IQR)] for continuous variables as appropriate and as the total number (percentage) for categorical variables. Comparisons between groups were made using the X2 test or Fisher’s exact test for categorical variables and Student’s t test or Mann–Whitney U test for continuous variables as appropriate.

We first compared the 28-day mortality of patients with different lung morphology (focal and non-focal) using Kaplan–Meier curves. The dynamic changes of PaO2/FiO2 ratio, PaCO2, DP, RR, mechanical power and the ventilatory ratio between the two groups were also compared using a liner mixed effects model.

Second, we employed extended multivariate Cox proportional hazards regression [12] to assess the interaction effect between lung morphology and longitudinal ventilator variables on mortality. To adjust for baseline disease severity, we constructed a baseline risk model based on clinical relevance and prior knowledge [11] and included ARDS primary risk factor, PaO2/FiO2 ratio, arterial pH, PaCO2 and respiratory system compliance. All subsequent analyses were pre-adjusted for the baseline risk model. Three Cox regression models (Model A to Model C) containing different ventilator variables were performed (Additional file 1: Table S1). We treated the longitudinal ventilator variables as time-varying exposures in the model. The interaction effect was assessed by the interaction term (lung morphology x ventilator variables) in each Cox regression model.

Third, to validate whether the impact of DP on mortality was four times as large as that of RR, we used Model D (Additional file 1: Table S1) to compare the effect size of DP and RR on 28-day mortality. We also employed restricted cubic splines to visualize the above association. Model E (Additional file 1: Table S1) was constructed to validate the association between the combined ventilator variable [(4×DP) + RR] and mortality. We identified the lowest [(4×DP) + RR] that corresponded to an adjusted hazard ratio estimate of more than 1.00 from Model E. Model F (Additional file 1: Table S1) was used to compare which variable ([(4×DP) + RR] or mechanical power) had a stronger association with mortality. All the above analyses were performed in the whole population and patients with different lung morphology.

Fourth, to further explore the discriminatory performance of each variable on mortality, we calculated the C-index (equivalent to the area under the receiver operating characteristics (AUROC) curve) of each Cox regression model (except for Model D and Model F). To avoid bias induced by missing data (Additional file 1: Table S2) in all Cox regression modes, we used multiple imputations by chained equation (MICE) to account for the missing data (detail in supplementary).

Finally, considering that ECMO could influence the impact of ventilatory variables on mortality, we performed a sensitivity analysis after excluding patients receiving ECMO during the study period.

The p-value was calculated to evaluate the differences between groups, and P < 0.05 was considered statistically significant. All statistical analyses were performed using R (version 4.0.3).

Results

Patients in study

A total of 396 ARDS patients with different lung morphology were enrolled in the final analysis. The flow diagram of study patients is presented in Additional file 1: Fig. S2. The patients had a median age of 64 years (IQR 52–74 years), and 273 (68.9%) were male. Within the first 24 h of ventilation, the DP was 16 cmH20 (IQR:13–20 cmH20), RR was 25 breaths min−1 (IQR: 21–30 breaths min−1), mechanical power was 20.2 J/min (IQR: 14.8–25.9 J/min), and the ventilatory ratio was 1.86 (IQR: 1.37–2.43). Pneumonia was the leading cause of ARDS (62.1%). The 28-day all-cause mortality was 34.4%. Additional details are summarized in Table 1.

Table 1 Clinical variables and outcomes in ARDS patients with different lung morphology

Comparison between patients with focal and non-focal lung morphology

Of the study cohort, 254 patients (64.1%) were classified as having non-focal lung morphology, and the remaining were focal lung morphology (n = 142). The comparison between focal and non-focal lung morphology is presented in Table 1 and Additional file 1: Fig. S3-S5. Patients in the non-focal lung morphology group had a significantly lower PaO2/FiO2 ratio, respiratory compliance, and a higher RR within the first 24 h of ventilation than those in the focal lung morphology group. In the liner mixed effects model, most ventilatory variables are changed dynamically during the first four days of mechanical ventilation, and there was a significant difference between the two groups regarding dynamic changes of PaO2/FiO2 ratio, PaCO2, DP and mechanical power (Fig. 1).

Fig. 1
figure 1

Temporal changes of ventilatory variables between patients with focal and non-focal lung morphology within the first four days of mechanical ventilation. Figure shows temporal changes in PaO2/FiO2 ratio (A), PaCO2 (B), Driving pressure (C), Respiratory rate (D), Mechanical Power (E) and Ventilatory ratio (F)

The 28-day mortality rate was significantly higher in patients with non-focal lung morphology than those with focal lung morphology (42.5% vs. 19.7%, P < 0.001). Kaplan–Meier survival curves also showed that the 28-day mortality was higher in the non-focal lung morphology group (Additional file 1: Fig. S6).

Interaction effect between lung morphology and ventilator variables on mortality

After adjusting for the baseline risk model, time-varying DP was more significantly associated with 28-day mortality in patients with non-focal lung morphology compared to patients with focal lung morphology (P for interaction = 0.0039). Time-varying mechanical power was associated with increased 28-day mortality in patients with focal and non-focal lung morphology. Although the association between time-varying ventilatory ratio and 28-day mortality seemed to be stronger among patients with focal lung morphology, no significant interaction was detected (P for interaction = 0.25) (Table 2 and Fig. 2).

Table 2 Association between time-varying ventilatory variables and mortality in ARDS patients with different lung morphology
Fig. 2
figure 2

The association between time-varying ventilatory variables and 28-day mortality in patients with focal and non-focal lung morphology. DP: Driving pressure; RR: Respiratory rate

Validation of the association between [(4×DP) + RR] and 28-day mortality

In Model D, we found that both DP and RR were associated with 28-day mortality, and the effect size of each 1 cmH20 increase in DP was larger than that of each 1 breath/min increase in RR in the whole population (approximately 2.8 times), while these associations only remained in patients with non-focal lung morphology (approximately 4.6 times) (Fig. 3 and Additional file 1: Table S3).

Fig. 3
figure 3

The hazard ratio of 28-day mortality for driving pressure and respiratory rate in whole patients (A), patients with focal lung morphology (B) and patients with non-focal lung morphology (C)

In Model E, the combined ventilator variable [(4×DP) + RR] was significantly associated with mortality in whole patients and patients with non-focal lung morphology, but not in patients with focal lung morphology, the interaction p value was 0.093 (Fig. 4 and Table 2). After including mechanical power and [(4×DP) + RR] in the same model (Model F), [(4×DP) + RR] remained a significant predictor of mortality in patients with non-focal lung morphology (Table 2). This analysis was consistent with a comparable discriminatory performance of [(4×DP) + RR] as compared to mechanical power in the non-focal group (Additional file 1: Fig. S7).

Fig. 4
figure 4

The association between [(4×DP) + RR] and 28-day mortality in whole patients (A), patients with focal lung morphology (B) and patients with non-focal lung morphology (C)

The lowest [(4×DP) + RR], during the first four days of mechanical ventilation, that was associated with an adjusted HR of more than 1.00 for 28-day mortality was 95 in the whole patients and 99 in patients with non-focal lung morphology (Fig. 4).

Sensitivity analysis

The results of the sensitivity analysis were generally consistent with the primary analysis (Additional file 1: Table S4). Specifically, both DP and [(4×DP) + RR] were more significantly associated with 28-day mortality in patients with non-focal lung morphology compared to patients with focal lung morphology.

Discussion

The novel findings of our study can be summarized as follows. First, patients with non-focal lung morphology have more severe and persistent pulmonary mechanical dysfunction and higher mortality than those with focal lung morphology. Second, lung morphology significantly impacts the association between time-varying driving pressure and mortality. Third, only in patients with non-focal lung morphology, the impact of DP on mortality was more significant than that of RR, and the combined ventilator variable [(4×DP) + RR] was comparable to mechanical power in predicting mortality.

Consistent with previous research, patients with non-focal lung morphology were distinct from patients with focal lung morphology in clinical and outcomes characteristics. Luciano Gattinoni and colleagues [9] grouped patients into lower (similar to focal lung morphology) and higher (similar to non-focal lung morphology) percentages of potentially recruitable lung based on quantitative analysis of lung CT at 5 and 45 cmH20 PEEP levels, and concluded that patients with a higher percentage of potentially recruitable lung had a lower PaO2/FiO2 ratio, respiratory compliance and a higher PaCO2, dead space and mortality rate than those in the group with a lower percentage of the potentially recruitable lung. Additionally, our study also detected that the differences in most respiratory variables between groups persisted through the first four days of mechanical ventilation.

The association between ventilatory variables and mortality among patients with different lung morphology was not always in accordance. There are several explanations for our results. First, patients in the focal lung morphology group were accomplished with less pulmonary mechanical dysfunction (higher DP) but more extra-pulmonary dysfunction (more elevated creatinine, total bilirubin and lactate), suggesting that patients with focal lung morphology have worse outcomes independently from DP, but may dependently from other makers of organ dysfunction [13]. Second, mechanical power is composed of multiple respiratory variables, which are both mathematically and physiologically coupled [14]. In focal lung morphology patients, RR was significantly associated with mortality, whereas DP was the opposite, which could explain, in part, the significant effect of mechanical power on mortality in patients with different lung morphology. Finally, since our study was a retrospective cohort study, important predictors could have insignificant associations ascribed to other measured or unmeasured confounders. More external validations are needed.

Gattinoni and colleagues first demonstrated mechanical power as a compound indicator in ventilator-induced lung injury (VILI). They found that the same percentage increase of DP and RR can produce an identical exponential increase of mechanical power, with an exponent of 2.0 and 1.4, respectively [15]. Recently, a muti-database study identified that the impact of DP on mortality was four times as large as that of RR in ARDS patients and derived a novel ventilator variable [(4×DP) + RR] accordingly, which was as informative as mechanical power and easier to assess at the bedside [11]. These findings were validated in our study, but only in patients with non-focal lung morphology, and the times was 4.6. Whether the combined variable [(4×DP) + RR] is applicable depends on the different weights of DP and RR on mortality. In patients with focal lung morphology, RR was valuable while DP was not, the effects of RR on mortality could be counteracted by [4×DP] and therefore [(4×DP) + RR] was insignificant. Additionally, whether “four times” was generalized in all ARDS remains uncertain and needs more prospective and extensive sample size studies.

There was a different impact between mechanical power with [(4×DP) + RR] on mortality in the focal lung morphology group. Mechanical power was consisting of elastic dynamic, elastic static and resistive power, which represents the energy transferred from the ventilator to the respiratory system essentially [15], regardless of the lung morphology. Whereas the [(4×DP) + RR] could only reflect elastic dynamic (related to DP), and the value of DP in the focal lung morphology group is limited as we discussed above.

The current study has some implications for clinical practice. For patients with non-focal lung morphology, ventilatory strategies including high respiratory rate and relatively low VT may be beneficial. The upper limit of RR can be calculated based on the threshold value of [(4×DP) + RR]. In contrast, patients with focal lung morphology may benefit more from a relatively high VT, low respiratory rate and early prone position [16, 17]. The survival benefit of these individual ventilator settings has been proved in ARDS patients after accounting for the misclassification of lung morphology [18].

Several limitations in the present study should be considered. First, the present study was a retrospective observational study based on a single center, the sample size was limited, and we considered only segmental measured confounders, the residual measured confounders and unmeasured confounders cannot be fully included, such as the prone position ventilation, use of neuromuscular blockade agents. Second, although the lung morphologies were classified as focal and non-focal by one radiologist and two intensivists, we cannot exclude the possibility of misclassification of lung morphology. Additionally, the whole lung CT was only collected once within three days after initiation of mechanical ventilation, we can only assume that the lung morphology would not change significantly during the early phase of ventilation. Third, DP was calculated as Ppeak minus PEEP in some patients, which may lead to underestimating mechanical power. In addition, the intrinsic PEEP was limited in the present study, in the presence of intrinsic PEEP, the ideal combination of RR and DP might be unattainable [19]. Finally, we identified the threshold of [(4×DP) + RR] based on an increased risk of death at 28 days. Give the single-center and limited sample size; the threshold may not always be applicable in other patients. Long-term outcomes of ARDS also be influenced by the high intensity of mechanical ventilation [20], while the threshold of [(4×DP) + RR] for long-term outcomes may be different from that we have identified.

Conclusion

In conclusion, lung morphology impacts the association between ventilatory variables and mortality in ARDS patients, especially for driving pressure. The combined ventilator variable [(4×DP) + RR] was informative in patients with non-focal lung morphology, not in patients with focal lung morphology.

Availability of data and material

For data in CDIC cohort, data are available upon reasonable request and with the approval from the Department of Critical Care Medicine, Zhongda Hospital, School of Medicine, Southeast University.

Abbreviations

ARDS:

Acute respiratory distress syndrome

DP:

Driving pressure

CT:

Computed tomography

RR:

Respiratory rate

ICU:

Intensive care unit

APACHE II:

Acute Physiology and Chronic Health Evaluation

SOFA:

Sequential Organ Failure Assessment

VT :

Tidal volume

PBW:

Predicted body weight

MV:

Minute ventilation

PEEP:

Positive end-expiratory pressure

Ppeak :

Peak pressure

Pplat :

Plateau pressure

AUROC:

Area under the receiver operating characteristics curve

MICE:

Multiple imputations by chained equation

ECMO:

Extracorporeal membrane oxygenation

VILI:

Ventilator-induced lung injury

References

  1. Bellani G, Laffey JG, Pham T, Fan E, Brochard L, Esteban A, et al. Epidemiology, patterns of care, and mortality for patients with acute respiratory distress syndrome in intensive care units in 50 countries. JAMA. 2016;315(8):788–800.

    Article  CAS  PubMed  Google Scholar 

  2. Liu L, Yang Y, Gao Z, Li M, Mu X, Ma X, et al. Practice of diagnosis and management of acute respiratory distress syndrome in mainland China: a cross-sectional study. J Thorac Dis. 2018;10(9):5394–404.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Villar J, Kacmarek RM, Pérez-Méndez L, Aguirre-Jaime A. A high positive end-expiratory pressure, low tidal volume ventilatory strategy improves outcome in persistent acute respiratory distress syndrome: a randomized, controlled trial. Crit Care Med. 2006;34(5):1311–8.

    Article  PubMed  Google Scholar 

  4. Brower RG, Matthay MA, Morris A, Schoenfeld D, Thompson BT, Wheeler A. Ventilation with lower tidal volumes as compared with traditional tidal volumes for acute lung injury and the acute respiratory distress syndrome. N Engl J Med. 2000;342(18):1301–8.

    Article  PubMed  Google Scholar 

  5. Serpa Neto A, Deliberato RO, Johnson AEW, Bos LD, Amorim P, Pereira SM, et al. Mechanical power of ventilation is associated with mortality in critically ill patients: an analysis of patients in two observational cohorts. Intensive Care Med. 2018;44(11):1914–22.

    Article  CAS  PubMed  Google Scholar 

  6. Amato MB, Meade MO, Slutsky AS, Brochard L, Costa EL, Schoenfeld DA, et al. Driving pressure and survival in the acute respiratory distress syndrome. N Engl J Med. 2015;372(8):747–55.

    Article  CAS  PubMed  Google Scholar 

  7. Chen H, Yu Q, Xie J, Liu S, Pan C, Liu L, et al. Longitudinal phenotypes in patients with acute respiratory distress syndrome: a multi-database study. Crit Care. 2022;26(1):340.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Puybasset L, Cluzel P, Gusman P, Grenier P, Preteux F, Rouby J. Regional distribution of gas and tissue in acute respiratory distress syndrome. Consequences for lung morphology. Intensive Care Med. 2000;26:857–69.

    Article  CAS  PubMed  Google Scholar 

  9. Gattinoni L, Caironi P, Cressoni M, Chiumello D, Ranieri VM, Quintel M, et al. Lung recruitment in patients with the acute respiratory distress syndrome. N Engl J Med. 2006;354:1775–86.

    Article  CAS  PubMed  Google Scholar 

  10. Jabaudon M, Blondonnet R, Lutz J, Roszyk L, Bouvier D, Guérin R, et al. Net alveolar fluid clearance is associated with lung morphology phenotypes in acute respiratory distress syndrome. Anaesth Crit Care Pain Med. 2016;35:81–6.

    Article  PubMed  Google Scholar 

  11. 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 

  12. Zhang Z, Reinikainen J, Adeleke KA, Pieterse ME, Groothuis-Oudshoorn CGM. Time-varying covariates and coefficients in Cox regression models. Ann Transl Med. 2018;6(7):121.

    Article  PubMed  PubMed Central  Google Scholar 

  13. De Jong A, Cossic J, Verzilli D, Monet C, Carr J, Conseil M, et al. Impact of the driving pressure on mortality in obese and non-obese ARDS patients: a retrospective study of 362 cases. Intensive Care Med. 2018;44(7):1106–14.

    Article  PubMed  Google Scholar 

  14. Fan E, Rubenfeld GD. Driving pressure-the emperor’s new clothes. Crit Care Med. 2017;45(5):919–20.

    Article  PubMed  Google Scholar 

  15. 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(10):1567–75.

    Article  CAS  PubMed  Google Scholar 

  16. Taccone P, Pesenti A, Latini R, Polli F, Vagginelli F, Mietto C, et al. Prone positioning in patients with moderate and severe acute respiratory distress syndrome: a randomized controlled trial. JAMA. 2009;302:1977–84.

    Article  CAS  PubMed  Google Scholar 

  17. Rouby JJ, Puybasset L, Nieszkowska A, Lu Q. Acute respiratory distress syndrome: lessons from computed tomography of the whole lung. Crit Care Med. 2003;31(4 Suppl):S285–95.

    Article  PubMed  Google Scholar 

  18. Constantin JM, Jabaudon M, Lefrant JY, Jaber S, Quenot JP, Langeron O, et al. Personalised mechanical ventilation tailored to lung morphology versus low positive end-expiratory pressure for patients with acute respiratory distress syndrome in France (the LIVE study): a multicentre, single-blind, randomised controlled trial. Lancet Respir Med. 2019;7(10):870–80.

    Article  PubMed  Google Scholar 

  19. Aguirre-Bermeo H, Morán I, Bottiroli M, Italiano S, Parrilla FJ, Plazolles E, et al. End-inspiratory pause prolongation in acute respiratory distress syndrome patients: effects on gas exchange and mechanics. Ann Intensive Care. 2016;6(1):81.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Toufen Junior C, De Santis Santiago RR, Hirota AS, Carvalho ARS, Gomes S, Amato MBP, et al. Driving pressure and long-term outcomes in moderate/severe acute respiratory distress syndrome. Ann Intensive Care. 2018;8(1):119.

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

None.

Funding

The present study is supported by the National Key Research and Development Plan (2022YFC2504400 & 2021YFC2500804), the Jiangsu Provincial Key Research and Development Program (BE2022854 & BE2020786) and the Yilu “Gexin”—Fluid Therapy Research Fund Project (YLGX-ZZ-2020002).

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Contributions

HQ, LL and YY had the idea of the study, conceptualized the research aims, HC, QS and YC design the study and take responsibility for the integrity of the data and the accuracy of the data analysis. QS, YC, YL and QY contributed to the acquisition of data and classification of lung morphology, HC doing the statistical analysis, HC and QS wrote the first draft of the paper, and other authors provided comments and approved the final manuscript.

Corresponding authors

Correspondence to Ling Liu or Yi Yang.

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Ethic approval and consent to participate

The present study was approved by the Research Ethics Commission of Zhongda Hospital Southeast University (2022ZDSYLL385-Y01).

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The authors declare that they have no competing interests.

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Supplementary Information

Additional file 1

: Table S1. Different multivariate Cox proportional hazards regression models; Table S2. Percentage of missing data in the variables of interest at baseline; Table S3. The impact of Driving pressure and respiratory rate on 28-day mortality in Model D; Table S4. Association between time-varying ventilatory variables and mortality in ARDS patients with different lung morphology after excluding patients receiving ECMO (n=352); Fig. S1. Lung morphology of ARDS patients based on lung CT: non-focal lung morphology (A); focal lung morphology (B); Fig. S2 patients selection in the CDIC cohort. Fig. S3. The distribution of ventilatory variables between patients with focal lung morphology and patients with non-focal lung morphology; Fig. S4. The correlation between Driving pressure and PF ratio (A), respiratory compliance (B), Tidal volume (C) and [(4×DP)+RR] (D) in patients with different lung morphology; Fig. S5. The correlation between mechanical power and PF ratio (A), Respiratory compliance (B), tidal volume (C) and [(4×DP)+RR] (D) in patients with different lung morphology; Fig. S6. Mortality by lung morphology in patients with ARDS; Fig. S7. The C-index of each Cox regression Model in total patients and patients with different with lung morphology (DOCX 8.7M).

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Chen, H., Sun, Q., Chao, Y. et al. Lung morphology impacts the association between ventilatory variables and mortality in patients with acute respiratory distress syndrome. Crit Care 27, 59 (2023). https://doi.org/10.1186/s13054-023-04350-8

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