Abstract
Objectives: To determine if functional residual capacity (FRC) and elastic power (EP) could predict outcomes in patients with acute respiratory distress syndrome (ARDS). Methods: This retrospective study included 353 ARDS patients admitted to our hospital between 2018 and 2025. Patients were categorized into good (n=251) and poor (n=102) prognosis groups based on their 28-day outcomes. FRC was measured using nitrogen washout, and EP was calculated using the formula: 0.098 × VT × RR × ½(PEEP + Pplat). After comparing these parameters between groups, multivariate regression and ROC analyses were performed to identify predictors. We validated our findings in an external cohort of 101 patients. Results: Poor outcome patients had significantly lower FRC and higher EP (both P<0.001). A positive correlation between EP and mortality was observed (rho=0.298, P<0.001), while FRC showed a significant inverse relationship (rho=-0.177, P<0.001). Multivariate analysis confirmed that EP (OR=1.251, P<0.001), FRC (OR=0.956, P=0.039), ARDS severity (OR=8.421, P<0.001), and PEEP (OR=1.338, P=0.011) were independent predictors of outcomes. A predictive model combining FRC and EP showed strong performance, with an AUC of 0.809 in internal validation and 0.819 in the external cohort. EP alone exhibited a high specificity of 0.944 at a cutoff value of 21.535 J/min. Conclusions: ARDS patients with low FRC and high EP face an increased risk of mortality. Combining these measures enhances prognostic accuracy and can help tailor ventilation strategies for improved outcomes.
Keywords: Acute respiratory distress syndrome, functional residual capacity, elastic power, mechanical ventilation, prognosis, lung mechanics
Introduction
Acute respiratory distress syndrome (ARDS) is a severe inflammatory lung condition with persistently high mortality rates despite years of study and research [1,2]. ARDS can be triggered by various factors, including pneumonia, sepsis, and aspiration, which lead to diffuse alveolar damage and impair pulmonary gas exchange [3]. Clinicians commonly employ lung-protective ventilation strategies to reduce the risk of ventilator-induced lung injury (VILI), however, predicting patient outcome remains challenging. Current clinical parameters, such as oxygenation ratios and severity scores, are often of limited accuracy in forecasting outcomes [4,5].
Recent research has increasingly focused on biomechanical parameters, which offer a more direct assessment of lung pathophysiology. Functional residual capacity (FRC) refers to the volume of air remaining in the lungs after a normal exhalation. It represents the alveolar units available for gas exchange and reflects lung tissue recruitment and pulmonary reserve. Lower FRC values typically indicate increased alveolar collapse and greater heterogeneity in lung ventilation, both of which can exacerbate hypoxemia by worsening ventilation-perfusion mismatch [6,7].
Elastic power (EP) is a newer parameter used in mechanical ventilation. It quantifies the energy required each minute to overcome the lung’s elastic recoil. EP specifically addresses the elastic component of VILI, distinct from the pressures related to airway resistance. High EP values suggest excessive energy transfer to lung tissues, which can cause damage through alveolar overstretching and cyclic recruitment-derecruitment. Recent studies have established links between EP and ARDS severity, but its potential as a standalone predictor of patient outcomes remains unclear [8,9].
In 2023, the European Society of Intensive Care Medicine (ESICM) and the American Thoracic Society (ATS) revised the definition of ARDS, emphasizing the syndrome’s phenotypic heterogeneity and its implications for managing mechanical ventilation [10]. Different lung patterns exhibit variable responses to positive end-expiratory pressure (PEEP) and lung recruitment strategies. FRC and EP contribute complementary insights into these differences, with FRC quantifying recruitable lung volume and EP reflecting mechanical stress on lung tissue [11]. This study investigates whether FRC and EP can predict 28-day mortality in ARDS patients. We hypothesize that these biomechanical markers offer independent prognostic value beyond conventional clinical parameters. Additionally, we have developed and validated a model to assess their clinical utility. A better understanding of how FRC and EP influence ARDS may lead to improved ventilator settings, ultimately enhancing patient outcomes.
Materials and methods
Case selection
This retrospective cohort study was conducted at the Intensive Care Unit of the First Affiliated Hospital of Jinzhou Medical University. It included 353 patients diagnosed with ARDS, admitted between January 2018 and January 2025. Demographic information, clinical data, FRC, and EP were collected through the hospital’s case system. All procedures followed the ethical standards outlined by the Helsinki Declaration of 1964 and its later amendments. Given the use of anonymized patient data and the absence of potential harm, the study was approved by the First Affiliated Hospital of Jinzhou Medical University’s review board and ethics committee without the need for informed consent (No. KYLL202526).
Inclusion and exclusion criteria
Inclusion Criteria: (1) Age ≥18 years; (2) Diagnosis of ARDS according to the 2023 joint criteria from the European Society of Intensive Care Medicine (ESICM) and the American Thoracic Society (ATS) [12]; (3) Diagnosis of ARDS within 48 hours of onset; (4) ARDS patients requiring invasive mechanical ventilation for >48 hours; (5) Complete case and clinical data.
Exclusion Criteria: (1) Untreated pneumothorax; (2) Use of unconventional ventilation strategies, including prone position ventilation, extracorporeal membrane oxygenation (ECMO), or severe hemodynamic instability (mean arterial pressure ≤65 mmHg); (3) Severe cardiac dysfunction (pulmonary artery wedge pressure >18 mmHg or New York Heart Association class III or higher) [13]; (4) Severe cognitive impairment preventing cooperation with clinical assessments; (5) Pregnant or lactating women.
Grouping criteria
Patients were divided into a good prognosis group (n=251) and a poor prognosis group (n=102) based on their 28-day survival status. Patients who died within 28 days were classified into the poor prognosis group. A patient was considered alive if they were discharged home with unassisted breathing within 28 days. “Home” refers to the place where the patient lived prior to hospital admission. A patient was considered expired if they died before discharge or before achieving unassisted breathing at home for 48 hours [14].
For external validation, 101 patients meeting the same inclusion criteria were included. These patients were also divided into a good prognosis group (n=64) and a poor prognosis group (n=37) based on their 28-day survival status. All patients had complete 28-day follow-up data, resulting in a follow-up rate of 100%.
Measurement of FRC and best PEEP
All patients were mechanically ventilated with sedation and analgesia, supplemented by neuromuscular blocking agents when necessary. A volume-controlled ventilation mode was used, with tidal volume (VT) set to 4-6 mL/kg, plateau airway pressure (Pplat) ≤30 cmH2O, and driving pressure (ΔP) ≤15 cmH2O. FRC was measured using the nitrogen washout technique with a GE ventilator (CARESCAPE R860, GE Healthcare, USA) equipped with an FRC module [15]. This technique provides clinically applicable bedside measurements of ventilated lung volume but may underestimate absolute FRC in cases of severe lung injury due to poor gas mixing in some regions. Measurements were taken with patients in the supine position during steady-state ventilation, with neuromuscular blockade applied as necessary.
Continuous FRC measurements were initiated at a PEEP of 15 cmH2O and titrated down to 3 cmH2O. Data were collected at PEEP levels of 15, 12, 9, 6, and 3 cmH2O, with each level maintained for 10 minutes to allow patient stabilization. A 40% drop in FRC from the maximum value after lung recruitment triggered a lung recruitment maneuver, which was conducted to reset the PEEP to the level just prior to the FRC decline. This PEEP value was considered the optimal PEEP, representing the last PEEP level before a significant decline in FRC. FRC values and optimal PEEP were recorded at 6, 12, and 48 hours after initiating mechanical ventilation, with triplicate measurements averaged for analysis. The primary FRC measurement used for analysis was recorded at 48 hours, as it best reflected the stabilized lung condition after initial therapeutic interventions. The ratio of FRC to predicted body weight (FRC/PBW) was calculated to assess changes in FRC, with predicted body weight calculated as:
For males: 50 + 0.91 × (height in cm - 152.4).
For females: 45.5 + 0.91 × (height in cm - 152.4) [16].
Measurements of EP and MP
Ventilation variables, including the highest, lowest, and average values for each 6-hour time frame during the 48-hour ventilation period, were extracted. The average of the highest and lowest values was used to compute the 24-hour ventilation variables. Variables for calculating mechanical power (MP) and EP were collected from the second 24-hour period (hours 24-48), as ventilation parameters were considered more physiologically stable during this phase [8]. Static compliance (Cst) was calculated as Cst = VT/(Pplat - PEEP), where Pplat was measured during a 3-second end-inspiratory hold maneuver in volume-controlled ventilation.
EP, which reflects the energy required to overcome the elastic resistance of the respiratory system, was calculated using the formula:
EP (J/min) = 0.098 × VT × RR × ½(PEEP + Pplat).
MP was calculated as:
MP (J/min) = 0.098 × VT × RR × (Ppeak - 0.5 × ΔP), where ΔP = Pplat - PEEP, RR stands for respiratory rate Ppeak is the peak airway pressure.
Statistical methods
Data analysis was performed using SPSS 29.0 (SPSS Inc., Chicago, IL, USA). Categorical data are presented as [n (%)], and comparisons between groups were made using Chi-square tests. For small sample sizes or frequencies <1, Fisher’s exact test was used. Continuous data with normal distribution are expressed as mean ± standard deviation and compared using t-tests. Pearson or Spearman correlation analysis was performed for continuous and categorical variables, respectively. Univariate and multivariate logistic regression were performed to identify independent predictors of poor ARDS prognosis. Variables showing significant associations in univariate analysis were included in the multivariate model to control for confounding factors. Results are presented as odds ratios (ORs) with 95% confidence intervals (CIs) and p-values. A p-value <0.05 was considered statistically significant. A predictive model for poor prognosis was specifically developed by integrating FRC and EP. These two variables were selected for the model as they individually demonstrated the highest AUC values and represent complementary pathophysiological aspects. The model’s performance was then evaluated using calibration, decision curve, clinical impact, and receiver operating characteristic (ROC) curves.
Results
Analysis of differences in general information of patients
Demographic characteristics were largely comparable between the two patient groups, as shown in Table 1. Most parameters did not reach statistical significance. Age (P=0.089), BMI (P=0.622), gender distribution (P=0.915), predicted body weight (P=0.197), hypertension (P=0.713), and diabetes (P=0.943) showed no marked differences. Similarly, there were no significant differences in residence type (P=0.824), smoking history (P=0.784), or drinking status (P=0.297). These findings suggest that baseline demographic characteristics and lifestyle factors were comparable between the good prognosis and poor prognosis groups.
Table 1.
Comparison of demographic characteristics between the two groups of patients
| Parameters | Good prognosis Group (n=251) | Poor prognosis Group (n=102) | t/χ2 | P |
|---|---|---|---|---|
| Age (years) | 61.74±5.65 | 62.86±5.44 | 1.706 | 0.089 |
| BMI (kg/m2) | 24.45±3.00 | 24.63±3.30 | 0.493 | 0.622 |
| Gender [n (%)] | 0.011 | 0.915 | ||
| Male | 132 (52.59%) | 53 (51.96%) | ||
| Female | 119 (47.41%) | 49 (48.04%) | ||
| PBW (kg) | 63.95±5.45 | 63.28±4.01 | 1.292 | 0.197 |
| Hypertension [n (%)] | 125 (49.80%) | 53 (51.96%) | 0.135 | 0.713 |
| Diabetes [n (%)] | 154 (61.35%) | 63 (61.76%) | 0.005 | 0.943 |
| Residence [n (%)] | 0.050 | 0.824 | ||
| Urban | 137 (54.58%) | 57 (55.88%) | ||
| Rural | 114 (45.42%) | 45 (44.12%) | ||
| Smoking [n (%)] | 161 (64.14%) | 67 (65.69%) | 0.075 | 0.784 |
| Drinking [n (%)] | 173 (68.92%) | 76 (74.51%) | 1.089 | 0.297 |
Note: BMI: Body Mass Index; PBW: Predicted Body Weight.
Clinical features showed more significant differences between the two groups (Table 2). ARDS severity (P<0.001) and ICU length of stay (P=0.019) were significantly different. The distribution of mild, moderate, and severe ARDS cases clearly distinguished the prognostic groups. However, no significant differences were observed in SOFA score (P=0.122), SAPS II score (P=0.113), or ARDS etiology (P=0.615). Length of hospital stay (P=0.289), body temperature (P=0.218), and days of invasive ventilation (P=0.599) were also comparable. These observations suggest that ARDS severity and ICU stay length are important factors influencing patient outcomes.
Table 2.
Comparison of clinical characteristics between the two groups of patients
| Parameters | Good prognosis Group (n=251) | Poor prognosis Group (n=102) | t/χ2 | P |
|---|---|---|---|---|
| ARDS severity [n (%)] | 87.66 | <0.001 | ||
| Mild | 72 (28.69%) | 0 (0.00%) | ||
| Moderate | 133 (52.99%) | 34 (33.33%) | ||
| Severe | 46 (18.33%) | 68 (66.67%) | ||
| SOFA score | 6.45±1.56 | 6.73±1.56 | 1.551 | 0.122 |
| SAPS II (score) | 46.28±5.01 | 45.34±5.12 | 1.589 | 0.113 |
| Causes of ARDS [n (%)] | 1.799 | 0.615 | ||
| Sepsis | 67 (26.69%) | 31 (30.39%) | ||
| Respiratory disease | 164 (65.34%) | 62 (60.78%) | ||
| Trauma | 5 (1.99%) | 4 (3.92%) | ||
| Others | 15 (5.98%) | 5 (4.90%) | ||
| Length of ICU stay (days) | 11.52±2.45 | 12.24±2.85 | 2.360 | 0.019 |
| Length of hospitalization (days) | 19.91±1.61 | 20.07±1.12 | 1.062 | 0.289 |
| Temperature (°C) | 36.92±1.67 | 36.73±1.08 | 1.235 | 0.218 |
| Duration of IMV (days) | 6.34±1.46 | 6.43±1.57 | 0.527 | 0.599 |
Note: ARDS: Acute Respiratory Distress Syndrome; SOFA: Sequential Organ Failure Assessment; SAPS II: Simplified Acute Physiology Score II; ICU: Intensive Care Unit; IMV: Invasive Mechanical Ventilation.
Comparison of FRC and best PEEP
FRC and the best PEEP levels showed clear differences between the two groups (Figure 1). The good prognosis group had significantly higher FRC compared to the poor prognosis group (P<0.001). FRC was measured in mL per kg of predicted body weight. Best PEEP levels also differed significantly (P=0.006), with the poor prognosis group requiring slightly higher PEEP settings. These results suggest that lower FRC values and the need for higher PEEP are associated with worse patient prognosis.
Figure 1.
Comparison of FRC and best PEEP between the two groups of patients. A: FRC (mL/kg PBW); B: PEEP (cmH2O). FRC: Functional Residual Capacity; PEEP: Positive End-Expiratory Pressure. **: P<0.01; ***: P<0.001.
Comparison of EP and MP
Several ventilatory parameters showed significant differences between the two groups (Figure 2), including ΔP (P=0.023), VT (P=0.029), Cst (P=0.004), MP (P=0.002), and EP (P<0.001). The poor prognosis group had higher ΔP, lower VT, and lower Cst. Additionally, both MP and elastic power were elevated in this group. However, no significant differences were observed in RR (P=0.071) or Pplat (P=0.065). These findings suggest that breathing mechanics and the energy delivered during ventilation may be strongly linked to patient outcomes.
Figure 2.
Comparison of EP and MP between two groups of patients. A: ΔP (cmH2O); B: VT (mL/kg PBW); C: RR (bpm); D: Pplat (cmH2O); E: Cst (ml/cmH2O); F: MP (J/min); G: EP (J/min). ΔP: Driving Pressure; VT: Tidal Volume; RR: Respiratory Rate; Pplat: Plateau Pressure; Cst: Static Compliance; MP: Mechanical Power; EP: Elastic Power. ns: no significant difference; *: P<0.05; **: P<0.01; ***: P<0.001.
Correlation analysis
Several factors were found to correlate with poor prognosis in ARDS patients (Table 3). ARDS severity had a strong positive correlation with poor prognosis (rho=0.493, P<0.001). Other factors associated with worse outcomes included longer ICU stays (rho=0.131, P=0.014), higher PEEP (rho=0.150, P=0.005), increased ΔP (rho=0.129, P=0.016), higher MP (rho=0.159, P=0.003), and elevated elastic power (EP, rho=0.298, P<0.001). In contrast, better prognosis was associated with higher values of FRC (rho=-0.177, P<0.001), tidal volume (VT, rho=-0.123, P=0.020), and Cst (rho=-0.167, P=0.002).
Table 3.
Correlation analysis between ARDS severity, length of ICU stay, FRC, PEEP, ΔP, VT, Cst, MP and EP on poor prognosis in ARDS
| Variable | rho | P |
|---|---|---|
| ARDS severity | 0.493 | <0.001 |
| Length of ICU stay | 0.131 | 0.014 |
| FRC | -0.177 | <0.001 |
| PEEP | 0.150 | 0.005 |
| ΔP | 0.129 | 0.016 |
| VT | -0.123 | 0.020 |
| Cst | -0.167 | 0.002 |
| MP | 0.159 | 0.003 |
| EP | 0.298 | <0.001 |
Note: ARDS: Acute Respiratory Distress Syndrome; ICU: Intensive Care Unit; FRC: Functional Residual Capacity; PEEP: Positive End-Expiratory Pressure; ΔP: Driving Pressure; VT: Tidal Volume; Cst: Static Compliance; MP: Mechanical Power; EP: Elastic Power.
Regression analysis of poor prognosis in ARDS
Our univariate analysis showed that several factors had a significant connection to patient outcomes (Table 4). These included ARDS severity (P<0.001), FRC (P<0.001), PEEP (P=0.006), ΔP (P=0.024), VT (P=0.030), Cst (P=0.011), MP (P<0.001), and EP (P<0.001). We found that poorer outcomes were tied to increased ARDS severity, PEEP, ΔP, MP, and EP, along with lower VT and Cst. Meanwhile, a higher FRC was tied to better outcomes.
Table 4.
Univariate and ARDS severity, FRC, PEEP, ΔP, VT, Cst, MP and EP on poor prognosis in ARDS
| Parameters | Univariate analysis | Multivariate analysis | ||||
|---|---|---|---|---|---|---|
|
|
|
|||||
| P | OR | 95% CI | P | OR | 95% CI | |
| ARDS severity | <0.001 | 7.331 | 4.661-11.955 | <0.001 | 8.421 | 4.901-14.867 |
| FRC | <0.001 | 0.937 | 0.900-0.973 | 0.039 | 0.956 | 0.915-0.998 |
| PEEP | 0.006 | 1.261 | 1.070-1.494 | 0.011 | 1.338 | 1.068-1.677 |
| ΔP | 0.024 | 1.187 | 1.024-1.381 | 0.031 | 1.241 | 1.021-1.509 |
| VT | 0.030 | 0.584 | 0.356-0.946 | 0.067 | 0.561 | 0.299-1.052 |
| Cst | 0.011 | 0.929 | 0.877-0.982 | / | / | / |
| MP | <0.001 | 1.162 | 1.068-1.266 | 0.004 | 1.185 | 1.056-1.330 |
| EP | <0.001 | 1.278 | 1.187-1.386 | <0.001 | 1.251 | 1.142-1.371 |
Note: ARDS: Acute Respiratory Distress Syndrome; ICU: Intensive Care Unit; FRC: Functional Residual Capacity; PEEP: Positive End-Expiratory Pressure; ΔP: Driving Pressure; VT: Tidal Volume; Cst: Static Compliance; MP: Mechanical Power; EP: Elastic Power; OR: Odds Ratio; CI: Confidence Interval.
Multivariate analysis further confirmed that ARDS severity (P<0.001, OR=8.421), PEEP (P=0.011, OR=1.338), ΔP (P=0.031, OR=1.241), FRC (P=0.039, OR=0.956), MP (P=0.004, OR=1.185), and EP (P<0.001, OR=1.251) were independent predictors of poor prognosis. To avoid multicollinearity, Cst was excluded from the final multivariate model, as it is mathematically derived from ΔP. VT was significant in the initial analysis but not in the final model (P=0.067). These results indicate that ARDS severity, PEEP, ΔP, MP, and EP are key factors influencing patient outcomes.
ROC curve analysis for prognostic factors in ARDS
Table 5 shows the ROC curve analysis results for various factors influencing poor prognosis in ARDS. ARDS severity exhibited a high AUC of 0.790, with a threshold of 1.500, demonstrating strong predictive ability. Sensitivity and specificity were 0.667 and 0.817, respectively. EP also demonstrated good predictive utility (AUC=0.689), with the best cutoff at 21.535, sensitivity of 0.480, and very high specificity of 0.944, meaning that patients with EP below this threshold rarely experienced poor outcomes. Other parameters, including FRC (AUC=0.612), Cst (AUC=0.606), MP (AUC=0.601), and PEEP (AUC=0.595), had more moderate diagnostic accuracy. The best cutoffs for these parameters were as follows:
Table 5.
ROC curve analysis of ARDS severity, length of ICU stay, FRC, PEEP, ΔP, VT, Cst, MP and EP on poor prognosis in ARDS
| Parameters | Best threshold | Sensitivities | Specificities | AUC | Youden index | F1 score |
|---|---|---|---|---|---|---|
| ARDS severity | 1.500 | 0.667 | 0.817 | 0.790 | 0.484 | 0.630 |
| Length of ICU stay | 12.975 | 0.422 | 0.737 | 0.583 | 0.159 | 0.408 |
| FRC | 26.760 | 0.814 | 0.414 | 0.612 | 0.228 | 0.181 |
| PEEP | 5.950 | 0.637 | 0.562 | 0.595 | 0.199 | 0.469 |
| ΔP | 13.305 | 0.539 | 0.673 | 0.582 | 0.212 | 0.460 |
| VT | 7.820 | 0.500 | 0.681 | 0.579 | 0.181 | 0.315 |
| Cst | 41.455 | 0.745 | 0.514 | 0.606 | 0.259 | 0.202 |
| MP | 22.505 | 0.343 | 0.869 | 0.601 | 0.212 | 0.412 |
| EP | 21.535 | 0.480 | 0.944 | 0.689 | 0.424 | 0.594 |
Note: ARDS: Acute Respiratory Distress Syndrome; ICU: Intensive Care Unit; FRC: Functional Residual Capacity; PEEP: Positive End-Expiratory Pressure; ΔP: Driving Pressure; VT: Tidal Volume; Cst: Static Compliance; MP: Mechanical Power; EP: Elastic Power; AUC: Area Under the Curve; ROC: Receiver Operating Characteristic.
FRC: 26.760 (sensitivity =0.814, specificity =0.414);
Cst: 41.455 (sensitivity =0.745, specificity =0.514);
MP: 22.505 (sensitivity =0.343, specificity =0.869);
PEEP: 5.950 (sensitivity =0.637, specificity =0.562).
ICU stay length (AUC=0.583), ΔP (AUC=0.582), and VT (AUC=0.579) had lower diagnostic accuracy, with thresholds and sensitivities/specificities as shown in the Table 5.
Development of a predictive model for FRC and EP in ARDS prognosis
The predictive model for poor prognosis in ARDS, based on FRC and EP, is illustrated in Figure 3, which includes calibration (A), decision curve (B), clinical impact (C), and ROC (D) curves. The model integrates FRC and EP, which showed the highest individual AUC values in the ROC analysis and represent complementary pathophysiological aspects.
Figure 3.
Development of a prediction model for FRC and EP influencing poor prognosis in ARDS. A: Calibration curve; B: Decision curve; C: Clinical impact curve; D: ROC curve. AUC: Area Under the Curve; ROC: Receiver Operating Characteristic.
Calibration Curve (Figure 3A): The bias-corrected line closely aligns with the ideal line, indicating good model fit without significant overfitting. The mean absolute error of 0.085 confirms minimal difference between predicted and observed outcomes. The Hosmer-Lemeshow test yielded a p-value of 0.456, supporting the model’s fit.
Decision Curve Analysis (Figure 3B): The net benefit of three strategies - EP alone, all predictors combined, and no model - was compared. The blue line for EP alone consistently stayed above the red line for all predictors, indicating that EP alone adds more value in predicting poor prognosis. The black line (no model) showed the lowest net benefit.
Clinical Impact Curve (Figure 3C): This curve shows the number of patients identified as high-risk at different thresholds. Patients with a predicted probability above 0.375 were considered high-risk. The solid red line shows the total number of high-risk patients, while the blue dashed line shows how many had actual poor outcomes. As the cost-benefit ratio increases, fewer patients are identified as high-risk, while the number of true positives remains stable, indicating good model performance.
ROC Curve (Figure 3D): The model shows strong diagnostic accuracy, with an AUC of 0.809. At a cutoff of 0.375, sensitivity is 0.884 and specificity is 0.549, balancing correct identifications and false positives. This two-parameter model performed almost as well as a more complex model (AUC=0.827), suggesting its practical utility.
External validation of the predictive model
We validated the model using an external cohort, comparing prognosis groups. Significant differences were observed in several measures, including ARDS severity (P<0.001), ICU stay length (P=0.047), FRC (P=0.023), and EP (P=0.004) (Table 6). The poor prognosis group had a higher proportion of severe ARDS cases, longer ICU stays, lower FRC, and higher EP, indicating poorer lung function and increased energy expenditure. Demographic factors (age, BMI, gender distribution, PBW, hypertension, diabetes, residence, smoking, and drinking habits) showed no significant differences (all P>0.05). Similarly, there were no significant differences in SOFA score, SAPS II score, ARDS etiology, length of hospitalization, temperature, or invasive mechanical ventilation duration (all P>0.05).
Table 6.
Comparison of the two groups in the external test set
| Parameters | Good prognosis Group (n=64) | Poor prognosis Group (n=37) | t/χ2 | P |
|---|---|---|---|---|
| Age (years) | 60.73±5.25 | 61.02±5.57 | 0.259 | 0.797 |
| BMI (kg/m2) | 24.31±3.46 | 24.42±3.36 | 0.149 | 0.882 |
| Gender [n (%)] | 0.105 | 0.746 | ||
| Male | 35 (54.69%) | 19 (51.35%) | ||
| Female | 29 (45.31%) | 18 (48.65%) | ||
| PBW (kg) | 63.52±5.25 | 63.55±4.32 | 0.029 | 0.977 |
| Hypertension [n (%)] | 31 (48.44%) | 17 (45.95%) | 0.058 | 0.809 |
| Diabetes [n (%)] | 36 (56.25%) | 21 (56.76%) | 0.002 | 0.961 |
| Residence [n (%)] | 0.026 | 0.871 | ||
| Urban | 27 (42.19%) | 15 (40.54%) | ||
| Rural | 37 (57.81%) | 22 (59.46%) | ||
| Smoking [n (%)] | 41 (64.06%) | 22 (59.46%) | 0.212 | 0.645 |
| Drinking [n (%)] | 42 (65.62%) | 20 (54.05%) | 1.324 | 0.250 |
| ARDS severity [n (%)] | 37.685 | <0.001 | ||
| Mild | 17 (26.56%) | 0 (0.00%) | ||
| Moderate | 39 (60.94%) | 11 (29.73%) | ||
| Severe | 8 (12.50%) | 26 (70.27%) | ||
| SOFA score | 6.52±1.42 | 6.57±1.31 | 0.191 | 0.849 |
| SAPS II (score) | 45.47±5.42 | 45.34±5.57 | 0.119 | 0.906 |
| Causes of ARDS [n (%)] | 2.408 | 0.492 | ||
| Sepsis | 18 (28.12%) | 13 (35.14%) | ||
| Respiratory disease | 37 (57.81%) | 22 (59.46%) | ||
| Trauma | 2 (3.12%) | 1 (2.70%) | ||
| Others | 7 (10.94%) | 1 (2.70%) | ||
| Length of ICU stay (days) | 11.75±2.42 | 12.76±2.41 | 2.015 | 0.047 |
| Length of hospitalization (days) | 19.63±2.16 | 20.04±2.52 | 0.869 | 0.387 |
| Temperature (°C) | 36.52±1.33 | 36.43±1.52 | 0.304 | 0.762 |
| Duration of IMV (days) | 6.53±1.14 | 6.66±1.52 | 0.438 | 0.663 |
| FRC (mL/kg PBW) | 25.52±6.45 | 22.63±5.25 | 2.317 | 0.023 |
| EP (J/min) | 17.63±2.52 | 20.64±5.73 | 3.026 | 0.004 |
Note: BMI: Body Mass Index; PBW: Predicted Body Weight; ARDS: Acute Respiratory Distress Syndrome; SOFA: Sequential Organ Failure Assessment; SAPS II: Simplified Acute Physiology Score II; ICU: Intensive Care Unit; IMV: Invasive Mechanical Ventilation; FRC: Functional Residual Capacity; EP: Elastic Power.
External validation ROC
Figure 4 presents the external validation ROC curve, demonstrating the model’s diagnostic performance in distinguishing between good and poor prognosis in ARDS. The AUC was 0.819, indicating strong discriminatory ability. At a cutoff of 0.457, sensitivity was 0.859 and specificity was 0.676, with a 95% confidence interval for sensitivity of 0.742 to 0.896. These external validation results confirm that the model accurately predicts poor prognosis in ARDS patients.
Figure 4.
External validation ROC curve. AUC: Area Under the Curve; ROC: Receiver Operating Characteristic.
Discussion
Our findings highlight the importance of lung biomechanics, specifically FRC and EP, as independent predictors of outcomes in ARDS. Low FRC and high EP are associated with a higher risk of mortality, underscoring their potential as valuable biomarkers for guiding ventilation therapy in clinical practice. By incorporating both measures, clinicians can gain a more comprehensive understanding of the mechanical state of the lungs, improving risk stratification and treatment decisions.
FRC represents the air left in the lungs after a normal exhalation, indicating how many alveoli remain functional for gas exchange. A lower FRC suggests alveolar collapse, which leads to inefficient gas exchange. This mismatch between air and blood flow and the wasted volume of each breath often necessitate higher ventilation pressures to improve oxygenation [17,18]. Additionally, areas of the lung that experience both collapsed and inflated alveoli become focal points of stress. These regions are more susceptible to shear forces during ventilation, contributing to further lung injury and poorer outcomes [19,20].
EP measures the effort required to inflate the alveoli during mechanical ventilation. Elevated EP values indicate excessive energy being transferred to the lung structures, focusing mechanical stress on the alveolar-capillary interface, which is vulnerable to injury. Repeated overstretching disrupts cell structure and opens ion channels, initiating inflammatory pathways via NF-κB and worsening alveolar leakage [21]. These processes promote lung injury by increasing cytokine release, recruiting neutrophils, and accumulating protein-rich edema [22,23].
Together, FRC and EP offer complementary insights into lung mechanics. FRC indicates the available lung volume for breathing, while EP quantifies the mechanical stress acting on that volume [24]. The combination of these two parameters provides a more complete picture of the lung’s functional and structural integrity than either alone. Patients with low FRC and high EP are particularly vulnerable, as they not only have reduced lung reserve but also face increased mechanical strain [25]. This combination contributes to the worst outcomes, where even normal ventilation can induce damaging stress points in the lung [26].
These findings have practical implications for guiding ventilation therapy. By monitoring FRC, clinicians can identify patients who may benefit from interventions aimed at reopening collapsed lung areas, such as adjusting PEEP settings. These steps can help improve lung volume and enhance gas exchange. Additionally, EP evaluation allows for adjusting ventilator settings to minimize harmful energy transfer to the lungs. Reducing VT, RR, or Pplat can lower EP, thereby protecting the lungs while maintaining adequate ventilation [27]. Recent studies suggest that keeping EP below 17 J/min may prevent VILI while still supporting breathing. Moreover, our predictive model, incorporating both FRC and EP, can identify high-risk patients earlier, allowing for timely interventions such as neuromuscular blockade, prone positioning, or ECMO [28,29].
Our results align with existing research on preventing VILI, but provide new insights. Previous studies have highlighted ΔP and MP as key targets in ventilation strategies. However, our findings suggest that EP more accurately reflects the damaging energy transferred to the lungs, as it remained a significant predictor in our multivariate analysis [26]. This supports recent theoretical models that separate elastic and resistive energy components in understanding VILI [30].
Unlike previous studies that focused on single parameters, our approach combines FRC and EP, representing both the lung’s anatomical state and the dynamic injurious energy. This dual approach significantly improves risk assessment compared to using either parameter alone.
While our study offers valuable insights, there are several limitations to consider. First, the study was conducted at a single center, limiting its generalizability despite external validation. Additionally, specialized equipment to measure FRC is not available in all hospitals, and the nitrogen washout technique may underestimate lung volume in severely ill patients. EP, which relies on a linear pressure-volume relationship, may not be suitable for all ARDS phenotypes, potentially affecting its applicability in some cases. Furthermore, while EP shows high specificity at a threshold of 21.535 J/min, its low sensitivity suggests that it should not be the sole criterion for assessing patient risk.
This study was also retrospective, and the lack of a standardized ventilation protocol may limit the consistency of findings. Future research should focus on large, prospective studies across multiple centers to confirm our results. These studies should also evaluate how neuromuscular blockers interact with ventilation mechanics and investigate the effectiveness of therapies aimed at normalizing FRC and EP values.
In conclusion, FRC and EP are independent predictors of mortality in ARDS. Low FRC signals a reduced lung reserve, while high EP reflects harmful energy transfer to lung structures. Together, these biomarkers provide a robust prognostic model, validated across different patient groups. Their use in clinical practice could significantly improve risk assessment and guide personalized ventilation strategies. Further research is necessary to determine if controlling these parameters within optimal ranges can improve patient survival and prevent long-term lung damage.
Acknowledgements
This study was supported by the 2024 Liaoning Provincial Science and Technology Plan Project (Grant No.: 2024JH2/102500035), the 2024 “Clinical Research Special Program” of Wu Jieping Medical Foundation (Grant No. 320.6750.2024-23-22), and the 2025 Liaoning Provincial Education Science “14th Five-Year Plan” Research Project (Grant No.: JG25DB171).
Disclosure of conflict of interest
None.
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