Abstract
Background:
Disease severity may change in the first week after acute respiratory distress syndrome (ARDS) onset. The aim of this study was to evaluate whether the reclassification of disease severity after 48 h (i.e. day 3) of ARDS onset could help in predicting mortality and determine factors associated with ARDS persistence and mortality.
Methods:
We performed a secondary analysis of a 3-year prospective, observational cohort study of ARDS in a tertiary care referral center. Disease severity was reclassified after 48 h of enrollment, and cases that still fulfilled the Berlin criteria were regarded as nonresolving ARDS.
Results:
A total of 1034 ARDS patients were analyzed. Overall hospital mortality was 57.7% (56.7%, 57.5%, and 58.6% for patients with initial mild, moderate, and severe ARDS, respectively, p = 0.189). On day 3 reclassification, the hospital mortality rates were as follows: resolved (42.1%), mild (47.9%), moderate (62.4%), and severe ARDS (76.1%) (p < 0.001). Patients with improving severity on day 3 had lower mortality (48.8%), whereas patients with the same or worsening severity on day 3 had higher mortality (62.7% and 76.3%, respectively). Patients who were older, had lower PaO2/FiO2, or higher positive end-expiratory pressure on day 1 were significantly associated with nonresolving ARDS on day 3. A Cox regression model with ARDS severity as a time-dependent covariate and competing risk analysis demonstrated that ARDS severity was independently associated with hospital mortality, and nonresolving ARDS had significantly increased hazard of death than resolved ARDS (p < 0.0001). Cumulative mortality curve for ARDS severity comparisons demonstrated significantly different (overall comparison, p < 0.001).
Conclusions:
Reclassification of disease severity after 48 h of ARDS onset could help to divide patients into subgroups with greater separation in terms of mortality.
The reviews of this paper are available via the supplemental material section.
Keywords: acute respiratory distress syndrome, reclassification, prediction, outcome, mortality
Introduction
Acute respiratory distress syndrome (ARDS) is a heterogeneous syndrome with complex pathophysiologic mechanisms characterized by severe hypoxemia and high mortality.1 Lung-protective mechanical ventilation with low tidal volume and low airway pressure has been shown to improve the outcomes among ARDS patients.1–3 LUNG SAFE (Large Observational Study to Understand the Global Impact of Severe Acute Respiratory Failure) was a recent large-scale study with the objective of obtaining epidemiologic data of ARDS patients, and reported hospital mortality rates ranging from 35% to 46%, depending on the initial categorization of severity.4
The definition of ARDS has evolved over the past few decades; however, the diagnostic criteria remain nonspecific, and not a prognostication tool for ARDS. Previous studies also concluded that second assessment of disease severity after 24 h greatly improved risk stratification of ARDS patients.5–15 The LUNG SAFE study reported that over half of ARDS patients had resolving or decreasing in severity after 24 h.4 Therefore, the severity of ARDS may change significantly during first few days after onset and initial classifications of ARDS may not reflect the evolution of disease severity and permit accurate predictions of clinical outcomes and mortality.
We hypothesized that patients presenting with ARDS initially constitute heterogenous groups with distinct disease severity evolutions, and the evolution in the first few days after ARDS onset may be associated with clinical outcome.
The LUNG SAFE study concluded that ARDS reclassification 24 h after onset was of relatively limited predictive value for mortality.8 However, one recent analysis of two large cohorts of intensive care unit (ICU) patients enrolled critically ill patients receiving mechanical ventilator for at least 48 h and demonstrated that one ventilation variable (i.e. mechanical power) is independently associated with higher in-hospital mortality. This study suggested that more severely ill patients would be selected after 48 h, including ARDS patients, and also guaranteed that patients were exposed to invasive ventilation and the primary exposure of interest for a sufficient period of time.16 Therefore, the rationale of our study was to verify whether reclassification 48 h later would improve prediction of mortality and ensure that ARDS patients were exposed to a sufficient time for response to clinical therapy and adjusted mechanical ventilator settings.
The primary outcome of this secondary analysis of a 3-year prospective study in Taiwan17,18 was to determine whether the reclassification of disease severity after 48 h of ARDS onset (i.e. day 3 rather than the initial 24 h) would improve the accuracy of predictions pertaining to clinical outcomes. The secondary outcome was to identify the risk factors associated with nonresolving ARDS at day 3 and hospital mortality.
Methods
Study design and participants
This was a retrospective analysis of the prospective observational study conducted from September 2012 to September 2015 at Chang Gung Memorial Hospital, the tertiary care referral center in Taiwan with 3700 ward beds and 278 adult ICU beds (9 medical ICUs, 7 surgical ICUs, and 1 burn ICU).17,18 All patients admitted to the ICUs with invasive mechanical ventilation were screened, and patients that met the Berlin criteria for ARDS were included.19 Exclusion criteria were as follows: (1) age < 18 years and (2) ARDS diagnosis and referral from other hospitals. The local Institutional Review Board for Human Research approved this study (CGMH IRB No. 102-1729B) and waived the need for informed consent.
Definitions
The term day 1 refers to the day on which the patient first met the Berlin definition of ARDS, irrespective of ICU admission and intubation day, whereas day 3 was defined as 48 h after ARDS onset. Clinical variables that did not fulfill the Berlin criteria on day 3 were deemed to be resolved, whereas clinical variables that still fulfilled the Berlin criteria on day 3 were regarded as nonresolving ARDS. Continuous change of ARDS severity was regarded as a time-varying covariate. If the patients were extubated and alive during the follow-up period, the ARDS severity was deemed to be resolved. The term ventilator-free days was defined as the number of days between day 1 and day 28 in which the patient breathed without assistance for at least 48 consecutive hours. Patients who did not survive to 28 days were assigned zero ventilator-free days. The term hospital mortality refers to all-cause death during the hospital stay. Patients who remained alive for 90 days after discharge from the hospital were regarded as survivors.
Data collections
Demographic variables, baseline clinical variables, and the etiology of ARDS were recorded from hospital charts at study entry. Arterial blood gas, mechanical ventilator settings including tidal volume, positive end-expiratory pressure (PEEP), peak inspiratory pressure, total respiratory rate, and FiO2, Acute Physiology and Chronic Health Evaluation (APACHE) II score, Sequential Organ Failure Assessment (SOFA) score, and lung injury score (LIS) were prospectively collected at around 10 a.m. on the day of ARDS onset as well as on days 3, 7, and 14 after the initial diagnosis. Rescue therapies included prone positioning and extracorporeal membrane oxygenation (ECMO). All enrolled ARDS patients were followed up until death in the hospital or discharge from the hospital.
Statistical analysis
Continuous variables were presented as mean ± standard deviation or median (interquartile range), and categorical variables were reported as numbers (percentages). A Student’s t test or the Mann–Whitney U test was used to compare continuous variables between groups. Categorical variables were tested using the chi-squared test for equal proportions or Fisher’s exact test. Risk factors associated with hospital mortality or nonresolving ARDS on day 3 were analyzed using univariate analysis in the first step. While considering ARDS severity variability over time, Cox proportional hazards regression model with continuous change of ARDS severity as a time-varying covariate and multivariable logistic regression model with stepwise selection procedure were performed. The results were presented as hazard ratio (HR) or odds ratio (95% confidence interval). While considering that patients who were liberated from the mechanical ventilator and still alive as the competing event, competing-risks regression based on Fine-Gray proportional subdistribution hazards model was performed. The results were presented as subdistribution hazard ratio (SHR) (95% confidence interval).20 Cumulative mortality curves were generated as a function of time using the Kaplan–Meier approach, and compared using the log-rank test. All statistical analysis was performed using SPSS 22.0 and Stata 14.2 statistical software, and a two-sided p value < 0.05 was considered statistically significant.
Results
Study population
A total of 1034 patients who fulfilled the Berlin definition of ARDS were included for analysis (Figure 1). The overall all-cause in-hospital mortality was 57.7%. Among ARDS patients on day 1, the mortality rates were as follows: mild ARDS (56.7%), moderate ARDS (57.5%), and severe ARDS (58.6%), and no significant difference was observed across the three groups (p = 0.189). At day 3 after ARDS onset, 96 patients (9.3%) had died, had been discharged from the ICU, or had missing data. Among the 938 ARDS patients remaining on day 3, the mortality rate showed significant difference between resolved and nonresolving ARDS patients (42.1% versus 59.9%, p < 0.001). In addition, prone positioning was applied to three patients, and ECMO was used in 61 patients.
Figure 1.
Flow chart showing enrollment of patients with acute respiratory distress syndrome (ARDS) and outcomes. Day 1 was defined as the day the patient first met the Berlin criteria of ARDS, and day 3 was defined as 48 h after ARDS onset.
Baseline variables between groups
Table 1 demonstrates that nonsurvivors were older, had a lower body mass index (BMI), higher APACHE II score, higher SOFA score, and higher LIS. Almost all ARDS patients received pressure-controlled ventilation, and there were no significant differences in terms of baseline ventilator settings or arterial blood gas, except for higher peak inspiratory pressure among nonsurvivors (p = 0.013). Table 2 showed that the etiologies of ARDS were not associated with the persistence of ARDS on day 3. Compared with patients with nonresolving ARDS, those with resolved ARDS tended to be younger and had lower LIS, higher PaO2/FiO2 ratios, lower PEEP, lower peak inspiratory pressure, lower total respiratory rates, and lower FiO2 values on day 1 (all p < 0.05). The APACHE II and SOFA scores on day 1 did not present significant differences.
Table 1.
Background characteristics: ARDS patients, survivors, and nonsurvivors.
| Characteristic | All patients |
Survivors |
Nonsurvivors |
p value |
|---|---|---|---|---|
| (n = 1034) | (n = 437) | (n = 597) | ||
| Age (years) | 63.1 ± 16.1 | 60.2 ± 16.9 | 65.1 ± 15.1 | <0.001 |
| Gender (male) | 715 (69.1%) | 302 (69.1%) | 413 (69.2%) | 0.98 |
| Body mass index (kg/m2) | 23.8 ± 4.5 | 24.4 ± 4.8 | 23.3 ± 4.2 | <0.001 |
| ARDS etiologies | ||||
| Bacterial pneumonia | 682 (66.0%) | 285 (65.2%) | 397 (66.5%) | 0.667 |
| Extrapulmonary sepsis | 142 (13.7%) | 52 (11.9%) | 90 (15.1%) | 0.143 |
| Aspiration pneumonia | 70 (6.8%) | 32 (7.3%) | 38 (6.4%) | 0.545 |
| Influenza pneumonia | 39 (3.8%) | 23 (5.3%) | 16 (2.7%) | 0.031 |
| Pulmonary contusion | 21 (2.0%) | 16 (3.7%) | 5 (0.8%) | 0.001 |
| Other causes | 80 (7.7%) | 29 (6.6%) | 51 (8.5%) | 0.257 |
| APACHE II score | 23.5 ± 7.2 | 21.8 ± 7.1 | 24.7 ± 7.1 | <0.001 |
| SOFA score | 9.9 ± 3.5 | 8.9 ± 3.0 | 10.7 ± 3.7 | <0.001 |
| Lung injury score | 2.89 ± 0.51 | 2.85 ± 0.53 | 2.92 ± 0.49 | 0.031 |
| Ventilator settings | ||||
| PaO2/FiO2 (mmHg) | 138.6 ± 70.9 | 138.5 ± 70.5 | 138.6 ± 71.1 | 0.839 |
| Tidal volume (ml/kg PBW) | 8.3 ± 2.1 | 8.2 ± 2.0 | 8.4 ± 2.1 | 0.387 |
| PEEP (cmH2O) | 9.9 ± 2.1 | 9.8 ± 2.2 | 9.9 ± 2.1 | 0.707 |
| Peak inspiratory pressure (cm H2O) | 29.1 ± 5.8 | 28.6 ± 5.6 | 29.5 ± 5.9 | 0.013 |
| Total respiratory rate (breaths/min) | 21.6 ± 5.9 | 21.4 ± 6.2 | 21.6 ± 5.6 | 0.533 |
| FiO2 (%) | 78.5 ± 23.2 | 78.6 ± 23.2 | 78.5 ± 23.1 | 0.975 |
| Arterial blood gas | ||||
| pH | 7.35 ± 0.13 | 7.36 ± 0.12 | 7.34 ± 0.13 | 0.027 |
| PaCO2 (mmHg) | 45.4 ± 16.4 | 44.7 ± 15.1 | 45.9 ± 17.2 | 0.23 |
| PaO2 (mmHg) | 99.4 ± 49.3 | 99.6 ± 49.4 | 99.2 ± 49.3 | 0.901 |
| HCO3 (mEq/l) | 23.7 ± 6.2 | 23.9 ± 5.7 | 23.6 ± 6.5 | 0.380 |
| Saturation (%) | 93.2 ± 9.4 | 93.2 ± 9.7 | 93.2 ± 9.2 | 0.584 |
| Mechanical ventilation(days) | 14 (8–28) | 12 (7–24.5) | 16 (8–29) | 0.013 |
| ICU length of stay (days) | 16.0 (9.0–31.0) | 16.0 (10.0–32.0) | 16.0 (8.0–30.0) | 0.088 |
| Hospital length of stay (days) | 26.0 (14.0–46.0) | 37.0 (23.0–61.0) | 18.0 (8.0–34.0) | <0.001 |
Values are presented as mean ± standard deviation, count or median (interquartile range).
APACHE, acute physiology and chronic health evaluation; ARDS, acute respiratory distress syndrome; FiO2, fraction of inspired oxygen; ICU, intensive care unit; PaCO2, partial pressure of carbon dioxide in arterial blood; PaO2, partial pressure of oxygen in arterial blood; PBW, predicted body weight; PEEP, positive end-expiratory pressure; SOFA, sequential organ failure assessment.
Table 2.
Characteristics of patients with resolved and nonresolving ARDS on day 3 after diagnosis.
| Characteristic | All patients |
Resolved |
Nonresolving |
p value |
|---|---|---|---|---|
| (n = 938) | (n = 152) | (n = 786) | ||
| Age (years) | 63.0 ± 16.2 | 60.3 ± 17.8 | 63.6 ± 15.8 | 0.025 |
| Gender (male) | 649 (69.2%) | 101 (66.4%) | 548 (69.7%) | 0.424 |
| Body mass index (kg/m2) | 23.8 ± 4.5 | 23.2 ± 4.3 | 23.9 ± 4.5 | 0.098 |
| ARDS etiologies | ||||
| Bacterial pneumonia | 626 (66.7%) | 107 (70.4%) | 519 (66.0%) | 0.296 |
| Extrapulmonary sepsis | 119 (12.7%) | 20 (13.2%) | 99 (12.6%) | 0.849 |
| Aspiration pneumonia | 61 (6.5%) | 5 (3.3%) | 56 (7.1%) | 0.079 |
| Viral pneumonia | 36 (3.8%) | 4 (2.6%) | 32 (4.1%) | 0.495 |
| Pulmonary contusion | 20 (2.1%) | 5 (3.3%) | 15 (1.9%) | 0.281 |
| Other causes | 76 (8.1%) | 11 (7.2%) | 65 (8.3%) | 0.669 |
| Organ failure score on day 1 | ||||
| APACHE II score | 23.2 ± 7.1 | 23.0 ± 7.3 | 23.3 ± 7.0 | 0.668 |
| SOFA score | 9.8 ± 3.4 | 9.4 ± 3.4 | 9.8 ± 3.4 | 0.134 |
| Lung injury score | 2.89 ± 0.51 | 2.65 ± 0.55 | 2.94 ± 0.48 | <0.001 |
| Ventilator settings on day 1 | ||||
| PaO2/FiO2 (mmHg) | 139.7 ± 71.7 | 165.7 ± 76.4 | 135.2 ± 69.8 | <0.001 |
| Tidal volume (ml/kg PBW) | 8.3 ± 2.1 | 8.5 ± 2.1 | 8.3 ± 2.1 | 0.246 |
| PEEP (cmH2O) | 9.9 ± 2.1 | 9.2 ± 1.9 | 10.0 ± 2.1 | <0.001 |
| Peak inspiratory pressure (cm H2O) | 29.3 ± 5.7 | 28.3 ± 5.8 | 29.5 ± 5.7 | 0.024 |
| Total respiratory rate (breaths/min) | 21.5 ± 5.9 | 20.6 ± 5.5 | 21.7 ± 5.9 | 0.036 |
| FiO2 (%) | 78.6 ± 23.1 | 73.8 ± 24.6 | 79.6 ± 22.7 | 0.009 |
| Arterial blood gas on day 1 | ||||
| pH | 7.35 ± 0.12 | 7.34 ± 0.13 | 7.35 ± 0.12 | 0.114 |
| PaCO2 (mmHg) | 45.4 ± 16.6 | 45.5 ± 21.2 | 45.4 ± 15.5 | 0.952 |
| PaO2 (mmHg) | 100.3 ± 50.1 | 109.6 ± 50.3 | 98.5 ± 49.8 | 0.012 |
| HCO3 (mEq/l) | 24.0 ± 6.2 | 22.6 ± 6.2 | 24.2 ± 6.1 | 0.003 |
| Saturation (%) | 93.3 ± 9.7 | 94.0 ± 10.6 | 93.3 ± 8.9 | 0.342 |
| Organ failure score on day 3 | ||||
| APACHE II score | 21.4 ± 7.4 | 18.1 ± 5.8 | 22.1 ± 7.5 | <0.001 |
| SOFA score | 9.6 ± 3.8 | 7.1 ± 3.4 | 10.0 ± 3.7 | <0.001 |
| Lung injury score | 2.72 ± 0.63 | 1.94 ± 0.41 | 2.87 ± 0.55 | <0.001 |
| Ventilator settings on day 3 | ||||
| PaO2/FiO2 (mmHg) | 203.8 ± 109.5 | 389.2 ± 105.4 | 168.7 ± 65.7 | <0.001 |
| Tidal volume (ml/kg PBW) | 8.3 ± 2.3 | 8.7 ± 2.5 | 8.2 ± 2.2 | 0.009 |
| PEEP (cm H2O) | 10.8 ± 2.6 | 9.3 ± 1.9 | 11.0 ± 2.7 | <0.001 |
| Peak inspiratory pressure (cm H2O) | 28.4 ± 6.7 | 24.3 ± 6.0 | 29.2 ± 6.6 | <0.001 |
| Total respiratory rate (breaths/min) | 22.1 ± 5.6 | 19.2 ± 5.1 | 22.6 ± 5.5 | <0.001 |
| FiO2 (%) | 53.3 ± 19.3 | 38.4 ± 7.6 | 56.1 ± 19.6 | <0.001 |
| Arterial blood gas on day 3 | ||||
| pH | 7.40 ± 0.10 | 7.44 ± 0.07 | 7.40 ± 0.11 | <0.001 |
| PaCO2 (mmHg) | 43.3 ± 13.8 | 37.2 ± 8.1 | 44.4 ± 14.3 | <0.001 |
| PaO2 (mmHg) | 96.1 ± 37.5 | 149.1 ± 47.6 | 85.9 ± 24.3 | <0.001 |
| HCO3 (mEq/l) | 26.0 ± 5.8 | 24.7 ± 5.1 | 26.3 ± 5.9 | <0.001 |
| Saturation (%) | 95.5 ± 4.6 | 98.8 ± 0.5 | 94.8 ± 4.7 | <0.001 |
| Ventilator-free days on day 28 | 0.0 (0.0–16.0) | 17.5 (0.0–21.0) | 0.0 (0.0–14.0) | <0.001 |
| ICU length of stay (days) | 17.0 (10.0–32.0) | 13.5 (9.0–25.0) | 18.0 (11.0–33.0) | 0.002 |
| Hospital length of stay (days) | 28.0 (16.0–47.0) | 30.5 (19.0–52.0) | 27.0 (14.8–47.0) | 0.148 |
Values are presented as mean ± standard deviation, count or median (interquartile range).
APACHE, acute physiology and chronic health evaluation; ARDS, acute respiratory distress syndrome; FiO2, fraction of inspired oxygen; ICU, intensive care unit; PaCO2, partial pressure of carbon dioxide in arterial blood; PaO2, partial pressure of oxygen in arterial blood; PBW, predicted body weight; PEEP, positive end-expiratory pressure; SOFA, sequential organ failure assessment.
Evolution of ARDS severity and clinical variables over first 48 h
Among the 938 ARDS patients who were reassessed at 48 h post-diagnosis, nearly half (48.2%) of the cases had resolved or improving severity (mortality rate: 48.8%), 31.1% presented the same severity (mortality rate: 62.7%), and 11.4% had worsening severity (mortality rate: 76.3%). The actual hospital mortality rates of patients reclassified on day 3 were as follows: resolved ARDS (42.1%), mild ARDS (47.9%), moderate ARDS (62.4%), and severe ARDS (76.1%) (p < 0.001).
In terms of organ dysfunction, patients with resolved ARDS had significantly lower APACHE II scores, lower SOFA scores, and lower LIS on day 3 than on day 1 (all p < 0.001), whereas nonresolving ARDS patients had significantly lower APACHE II scores and lower LIS but slightly higher SOFA scores on day 3 than on day 1 (Table 2).
In terms of ventilator settings, resolved ARDS patients had significantly lower peak inspiratory pressure and lower total respiratory rate on day 3 than on day 1 (p < 0.001 and p = 0.005, respectively), whereas nonresolving ARDS patients had significantly higher PEEP and higher total respiratory rates on day 3 than on day 1 (p < 0.001 and p = 0.001, respectively) (Table 2).
Comparisons of variables between resolved and nonresolving ARDS on day 3
Following reassessment on day 3, resolved ARDS patients had significantly lower APACHE II scores, lower SOFA scores, and lower LIS, as well as higher PaO2/FiO2 ratio, higher tidal volumes, lower PEEP, lower peak inspiratory pressure, lower total respiratory rates, and lower FiO2 values than did the nonresolving ARDS patients (all p < 0.05). Resolved ARDS patients also had more ventilator-free days (p < 0.001) and a shorter length of stay in the ICU (p = 0.002) (Table 2).
Factors associated with hospital mortality and ARDS persistence on day 3
After adjusting for significant confounding variables, both Cox regression model with ARDS severity as a time-varying covariate and competing risk analysis showed that ARDS severity was significantly associated with hospital mortality. Nonresolving ARDS had significantly higher mortality than resolved ARDS during the study period (mild versus resolved ARDS, HR 2.007, SHR 3.462; moderate versus resolved ARDS, HR 2.700, SHR 4.653; severe versus resolved ARDS, HR 6.963, SHR 11.648, all p < 0.0001). The hospital mortality rate also showed significant difference between moderate and mild ARDS patients (HR 1.346, p = 0.01; SHR 1.344, p = 0.007), and between severe and moderate ARDS patients (HR 2.578, p < 0.0001; SHR 2.503, p < 0.001). Age, BMI, influenza pneumonia, SOFA score on day 3 were also independently associated with hospital mortality (Table 3). Patients who were older, had lower PaO2/FiO2, and had higher PEEP on day 1 were significantly associated with nonresolving ARDS on day 3 (Table 4).
Table 3.
Factors associated with hospital mortality using Cox regression model with ARDS severity as a time-dependent covariate and incorporating time-dependent covariate in competing risk analysis.
| Variables | Time-dependent covariate model |
Competing risk model |
||
|---|---|---|---|---|
| HR (95% CI) | p value | SHR (95% CI) | p value | |
| ARDS severity | ||||
| Mild versus resolved | 2.007 (1.527–2.638) | <0.0001 | 3.462 (2.576–4.652) | <0.0001 |
| Moderate versus resolved | 2.700 (2.095–3.481) | <0.0001 | 4.653 (3.530–6.135) | <0.0001 |
| Severe versus resolved | 6.963 (5.023–9.650) | <0.0001 | 11.648 (8.197–16.553) | <0.0001 |
| Age | 1.010 (1.004–1.016) | 0.001 | 1.011 (1.005–1.017) | <0.0001 |
| Body mass index (kg/m2) | 0.944 (0.925–0.964) | <0.0001 | 0.941 (0.923–0.961) | <0.0001 |
| Influenza pneumonia | 0.418 (0.220–0.795) | 0.008 | 0.414 (0.220–0.779) | 0.006 |
| SOFA score on day 3 | 1.132 (1.103–1.162) | <0.0001 | 1.142 (1.113–1.171) | <0.0001 |
ARDS, acute respiratory distress syndrome; CI, confidence interval; HR, hazard ratio; SHR, subdistribution hazard ratio; SOFA, sequential organ failure assessment.
Table 4.
Factors associated with nonresolving ARDS on day 3 after diagnosis using multivariable logistic regression model.
| Clinical variables | Odds ratio (95% CI) | p value |
|---|---|---|
| Age | 1.014 (1.002–1.025) | 0.020 |
| PaO2/FiO2 (mmHg) on day 1 | 0.995 (0.993–0.998) | < 0.001 |
| PEEP (cm H2O) on day 1 | 1.160 (1.048–1.284) | 0.004 |
ARDS, acute respiratory distress syndrome; CI, confidence interval; FiO2, fraction of inspired oxygen; PaO2, partial pressure of oxygen in arterial blood; PEEP, positive end-expiratory pressure.
Continuous change of ARDS severity and hospital mortality
Cumulative mortality curve using the Kaplan–Meier approach for ARDS categorical comparisons without and with accounting for ARDS severity as a time-dependent covariate and competing risk analysis demonstrated significant difference (Figure 2(a) and (b)). Log-rank test for ARDS category comparisons: overall comparison, p < 0.001 (Figure 2(a)); p < 0.001 (Figure 2(b)).
Figure 2.
Cumulative mortality curve using the Kaplan–Meier approach among patients with acute respiratory distress syndrome (ARDS) without (a) and with (b) accounting for ARDS severity as a time-dependent covariate and competing risk analysis. Log-rank test for ARDS category comparisons: overall comparison, (a) p < 0.001; (b) p < 0.001.
Discussion
This is the secondary analysis of the prospective observational 3-year cohort study in patients with ARDS in Taiwan. No significant differences in hospital mortality rates were observed among the ARDS severity groups as diagnosed on day 1. However, among the 938 remaining ARDS patients on day 3 (48 h later), there was a significant difference in hospital mortality rates between resolved and nonresolving ARDS patients (42.1% versus 59.9%, p < 0.001). Continuous change of ARDS severity was significantly associated with hospital mortality and mortality rates among the distinct ARDS severity was significantly different.
The hospital mortality rate of this study was 57.7%, which was higher than the figures reported in recent epidemiologic studies.4,21 Our hospital is the tertiary care referral center in Taiwan and we did not exclude patients with malignancy and severe comorbidities, such as chronic heart failure, advanced liver disease, chronic lung or kidney diseases, or terminal illness in the present study. The LUNG SAFE study reported that overall hospital mortality was 38%, and 24% patients no longer fulfilled ARDS definition after 24 h (i.e. resolved ARDS) with mortality rate of 31%. Compared with patients with the LUNG SAFE study,4 our enrolled ARDS patients were older, had more chronic diseases, and receiving higher airway pressure, and these factors may cause higher mortality in our study.
Lung-protective mechanical ventilation strategies using lower tidal volumes, optimal PEEP values, and lower airway pressure have been shown to decrease ARDS-related mortality,1–3 mitigate the effects of ventilator-induced lung injury (VILI), and reduce multiple organ failure.22,23 Most ARDS patients did not receive low tidal volume ventilation in clinical practice. The mean tidal volume was 7.6 ml/kg predicted body weight in the LUNG SAFE study4 and 8.3 ml/kg in the present study. Both values exceeded the recommended 6 ml/kg but were far below levels deemed injurious (12 ml/kg).2 However, tidal volume in this study was not significantly associated with ARDS persistence on day 3 or with hospital mortality.
On day 1, the PEEP value, peak inspiratory pressure, and total respiratory rate of nonresolving ARDS patients were significantly higher than those of resolved ARDS patients (all p < 0.05). Gattinoni et al. identified these three parameters as aspects of mechanical power, which has been linked to the development of VILI.24 One recent study reported that high mechanical power is independently associated with higher in-hospital mortality rates among critically ill patients receiving invasive ventilation for at least 48 h.16 In the current study, there was a significant difference in the mortality rates of resolved ARDS and nonresolving ARDS patients at 48 h after ARDS onset (42.1% versus 59.9%, p < 0.001). It is reasonable to assume that higher mechanical power on day 1 could increase the risk of VILI and would therefore be associated with ARDS persistence on day 3 and hospital mortality.
On day 3, the PEEP value, peak inspiratory pressure, and total respiratory rate of nonresolving ARDS patients were still higher than those of resolved ARDS patients (all p < 0.001). We also compared the changes in ventilator settings from day 1 to day 3. The peak inspiratory pressure and total respiratory rate of resolved ARDS patients were significantly lower on day 3 than on day 1, whereas the PEEP values and total respiratory rate of nonresolving ARDS patients were significantly higher on day 3 than on day 1. These changes may be indicative of oxygenation levels, the severity of lung damage, and the corresponding adjustments of the ventilator settings.
The most common cause of death among ARDS patients is multiorgan failure,1 and previous studies have shown that the degree of systemic organ failure is correlated with ARDS outcome.6,8,21,25 Secondary analysis of the LUNG SAFE study revealed that the SOFA score is independently associated with hospital mortality in ARDS patients.8,25 In our study, the APACHE II and SOFA scores of survivors were significantly lower than those of nonsurvivors on day 1 (p < 0.001). On day 1, there was no significant difference between resolved and nonresolving ARDS patients in terms of APACHE II or SOFA scores; however, on day 3, there was a significant difference in these scores (p < 0.001) as well as hospital mortality (42.1% versus 59.9%; p < 0.001). This indicates that organ dysfunction was correlated with clinical outcome. Moreover, SOFA scores on day 3 were independently associated with hospital mortality in a multivariable Cox model.
Villar et al. reported significant differences in mortality among ARDS patients classified by responses to standard ventilatory settings (PEEP ⩾ 10 cm H2O and FiO2 ⩾ 0.5) at 24 h after ARDS onset.7,9,10,12,13 Several studies have also reported that reassessing disease severity and clinical parameters at 24–72 h after ARDS diagnosis might have a significant effect on predicting mortality.6–15 A persistently low PaO2/FiO2 ratio is associated with poor outcomes and could potentially indicate a failure to respond to conventional therapy.14 No significant difference in hospital mortality was observed between patients with different severity levels at ARDS onset in the present study, it indicates that initial PaO2/FiO2 ratio cannot discriminate subphenotype of ARDS patients precisely in terms of mortality in our study. However, patients with resolved or mild ARDS 48 h later faced a relatively lower risk of mortality (42.1% and 47.9%, respectively), whereas those with moderate or severe ARDS at 48 h faced a higher risk of mortality (62.4% and 76.1%, respectively). In addition, mortality according to the evolution of ARDS severity between day 1 and day 3 demonstrated that patients with resolved or improving severity on day 3 had lower mortality (48.8%), whereas patients with the same or worsening severity on day 3 had higher mortality (62.7% and 76.3%, respectively).
The main objective of this study was to assess prognostic factors and factors associated with nonresolving ARDS, not to evaluate the effect of factors associated with nonresolving ARDS (i.e. mediator) on mortality outcome. Therefore, we used multivariable regression models, including a Cox regression model with ARDS severity as a time-varying covariate and competing risk analysis that do not provide any estimate of causal relationship (even if this link could exist). Our results demonstrated that serial change of ARDS severity was significantly associated with hospital mortality, and nonresolving ARDS had significantly increased hazard of death than resolved ARDS. Cumulative mortality curve demonstrated significant differences in hospital mortality rates among the distinct ARDS severity groups.
In all the studies mentioned previously, it appears that the initial definition of ARDS is insufficient to obtain an accurate assessment of disease severity or derive reliable predictions of mortality. We therefore recommend the reclassification at some point after ARDS onset (e.g. 24–48 h) to categorize more homogeneous subpopulations of patients according to disease prognosis and mortality.
This study was hindered by several limitations. First, this study was conducted in one tertiary care referral center with retrospective analysis, and we did not exclude patients with malignancy or severe comorbidities, which cause higher mortality in the present study, thereby limiting generalizability to other ICUs or hospitals. Second, there was no standard protocol for ventilator settings among the enrolled ICUs, and our enrolled ARDS patients received higher tidal volume and higher FiO2 than other studies. These two limitations make external validation of our study to other ARDS cohorts problematic to perform. Furthermore, throughout the ICU stay, ventilator settings were recorded only once a day (at around 10 a.m.) and therefore do not necessarily manifest dynamic changes in ventilator status. Third, during the first 48 h after ARDS onset, we analyzed only the ventilator settings, arterial blood gas, and organ dysfunction score. Other clinical variables that could be used to predict hospital mortality or nonresolving ARDS on day 3 have yet to be confirmed. Fourth, we did not exclude the 96 patients (9.3%) who had died, had been discharged from the ICU, or had missing data within first 48 h for survival predictors analysis. Fifth, causes of mortality were not reported, and patients may not die from ARDS, but from the underlying diseases. Finally, there may be unmeasured residual (i.e. confounding) variables, such as daily fluid balance, that were not included in this study. Finally, prone positioning and ECMO were underutilized as these rescue therapies might have saved many of the patients with persistent severe ARDS.
Conclusion
Our findings indicated that the reclassification of ARDS severity after 48 h could improve the accuracy of clinical outcome predictions. Continuous change of ARDS severity was significantly associated with hospital mortality and mortality rate was significantly different among distinct ARDS severity. Our study is valuable for clinical trials in the future to include more homogeneous ARDS patients in terms of mortality and help to identify severe cases warranting aggressive clinical intervention or additional rescue therapies.
Supplemental Material
Supplemental material, Author_Response_1_ for Reclassifying severity after 48 hours could better predict mortality in acute respiratory distress syndrome by Li-Chung Chiu, Shih-Wei Lin, Pi-Hua Liu, Li-Pang Chuang, Chih-Hao Chang, Chen-Yiu Hung, Shih-Hong Li, Chung-Shu Lee, Huang-Pin Wu, Chung-Chi Huang, Hsin-Hsien Li, Kuo-Chin Kao and Han-Chung Hu in Therapeutic Advances in Respiratory Disease
Supplemental material, Author_Response_2 for Reclassifying severity after 48 hours could better predict mortality in acute respiratory distress syndrome by Li-Chung Chiu, Shih-Wei Lin, Pi-Hua Liu, Li-Pang Chuang, Chih-Hao Chang, Chen-Yiu Hung, Shih-Hong Li, Chung-Shu Lee, Huang-Pin Wu, Chung-Chi Huang, Hsin-Hsien Li, Kuo-Chin Kao and Han-Chung Hu in Therapeutic Advances in Respiratory Disease
Supplemental material, Reviewer_1_v.1_ for Reclassifying severity after 48 hours could better predict mortality in acute respiratory distress syndrome by Li-Chung Chiu, Shih-Wei Lin, Pi-Hua Liu, Li-Pang Chuang, Chih-Hao Chang, Chen-Yiu Hung, Shih-Hong Li, Chung-Shu Lee, Huang-Pin Wu, Chung-Chi Huang, Hsin-Hsien Li, Kuo-Chin Kao and Han-Chung Hu in Therapeutic Advances in Respiratory Disease
Supplemental material, Reviewer_2_v.1 for Reclassifying severity after 48 hours could better predict mortality in acute respiratory distress syndrome by Li-Chung Chiu, Shih-Wei Lin, Pi-Hua Liu, Li-Pang Chuang, Chih-Hao Chang, Chen-Yiu Hung, Shih-Hong Li, Chung-Shu Lee, Huang-Pin Wu, Chung-Chi Huang, Hsin-Hsien Li, Kuo-Chin Kao and Han-Chung Hu in Therapeutic Advances in Respiratory Disease
Supplemental material, Reviewer_2_v.2 for Reclassifying severity after 48 hours could better predict mortality in acute respiratory distress syndrome by Li-Chung Chiu, Shih-Wei Lin, Pi-Hua Liu, Li-Pang Chuang, Chih-Hao Chang, Chen-Yiu Hung, Shih-Hong Li, Chung-Shu Lee, Huang-Pin Wu, Chung-Chi Huang, Hsin-Hsien Li, Kuo-Chin Kao and Han-Chung Hu in Therapeutic Advances in Respiratory Disease
Acknowledgments
The authors would like to thank Chiu-Hua Wang and Shin-Wen Bai for data management and appreciate the patients and staff of ICUs at Chang Gung Memorial Hospital, Taiwan.
Footnotes
Author contribution(s): Li-Chung Chiu: Conceptualization; Formal analysis; Investigation; Methodology; Resources; Supervision; Writing-original draft; Writing-review & editing.
Shih-Wei Lin: Data curation; Methodology; Resources; Software; Writing-original draft; Writing-review & editing.
Pi-Hua Liu: Data curation; Formal analysis; Methodology; Software; Supervision; Writing-review & editing.
Li-Pang Chuang: Data curation; Formal analysis; Software; Supervision; Writing-original draft.
Chih-Hao Chang: Data curation; Methodology; Writing-original draft.
Chen-Yiu Hung: Data curation; Methodology; Writing-original draft.
Shih-Hong Li: Methodology; Validation; Writing-original draft.
Chung-Shu Lee: Formal analysis; Writing-original draft.
Huang-Pin Wu: Methodology; Resources; Supervision; Writing-original draft.
Chung-Chi Huang: Formal analysis; Investigation; Supervision; Writing-original draft.
Hsin-Hsien Li: Formal analysis; Methodology; Writing-original draft.
Kuo-Chin Kao: Formal analysis; Resources; Supervision; Writing-original draft; Writing-review & editing.
Han-Chung Hu: Data curation; Formal analysis; Methodology; Supervision; Writing-original draft; Writing-review & editing.
Funding: The authors received no financial support for the research, authorship, and/or publication of this article.
Conflict of interest statement: The authors declare that there is no conflict of interest.
ORCID iDs: Li-Chung Chiu 
https://orcid.org/0000-0003-1660-7715
Han-Chung Hu
https://orcid.org/0000-0003-1603-868X
Supplemental material: The reviews of this paper are available via the supplemental material section.
Contributor Information
Li-Chung Chiu, Department of Thoracic Medicine, Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Taoyuan; Graduate Institute of Clinical Medical Sciences, College of Medicine, Chang Gung University, Taoyuan; Department of Thoracic Medicine, New Taipei Municipal TuCheng Hospital and Chang Gung University, Taoyuan.
Shih-Wei Lin, Department of Thoracic Medicine, Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Taoyuan.
Pi-Hua Liu, Clinical Informatics and Medical Statistics Research Center, College of Medicine, Chang Gung University, Taoyuan; Department of Internal Medicine, Division of Endocrinology and Metabolism, Chang Gung Memorial Hospital, Taoyuan.
Li-Pang Chuang, Department of Thoracic Medicine, Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Taoyuan.
Chih-Hao Chang, Department of Thoracic Medicine, Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Taoyuan; Department of Thoracic Medicine, New Taipei Municipal TuCheng Hospital and Chang Gung University, Taoyuan.
Chen-Yiu Hung, Department of Thoracic Medicine, Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Taoyuan; Department of Thoracic Medicine, New Taipei Municipal TuCheng Hospital and Chang Gung University, Taoyuan.
Shih-Hong Li, Department of Thoracic Medicine, Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Taoyuan.
Chung-Shu Lee, Department of Thoracic Medicine, Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Taoyuan.
Huang-Pin Wu, Division of Pulmonary, Critical Care and Sleep Medicine, Chang Gung Memorial Hospital, Keelung.
Chung-Chi Huang, Department of Thoracic Medicine, Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Taoyuan; Department of Respiratory Therapy, Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Taoyuan; Department of Respiratory Therapy, Chang Gung University College of Medicine, Taoyuan.
Hsin-Hsien Li, Department of Respiratory Therapy, Chang Gung University College of Medicine, Taoyuan.
Kuo-Chin Kao, Department of Thoracic Medicine, Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Taoyuan; Department of Respiratory Therapy, Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Taoyuan; Department of Respiratory Therapy, Chang Gung University College of Medicine, Taoyuan; Department of Intensive Care, Xiamen Chang Gung Hospital, China.
Han-Chung Hu, Department of Thoracic Medicine, Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Linkou, No. 5, Fu-Shing St., GuiShan, Taoyua 333; Department of Respiratory Therapy, Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Taoyuan; Department of Respiratory Therapy, Chang Gung University College of Medicine, Taoyuan.
References
- 1. Thompson BT, Chambers RC, Liu KD. Acute respiratory distress syndrome. N Engl J Med 2017; 377: 562–572. [DOI] [PubMed] [Google Scholar]
- 2. Acute Respiratory Distress Syndrome Network, Brower RG, Matthay MA, et al. 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: 1301–1308. [DOI] [PubMed] [Google Scholar]
- 3. Fan E, Del Sorbo L, Goligher EC, et al. An official American thoracic society/European society of intensive care medicine/society of critical care medicine clinical practice guideline: mechanical ventilation in adult patients with acute respiratory distress syndrome. Am J Respir Crit Care Med 2017; 195: 1253–1263. [DOI] [PubMed] [Google Scholar]
- 4. Bellani G, Laffey JG, Pham T, et al. LUNG SAFE investigators; ESICM trials group: epidemiology, patterns of care, and mortality for patients with acute respiratory distress syndrome in intensive care units in 50 countries. JAMA 2016; 315: 788–800. [DOI] [PubMed] [Google Scholar]
- 5. Schenck EJ, Oromendia C, Torres LK, et al. Rapidly improving ARDS in therapeutic randomized controlled trials. Chest 2019; 155: 474–482. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. Estenssoro E, Dubin A, Laffaire E, et al. Incidence, clinical course, and outcome in 217 patients with acute respiratory distress syndrome. Crit Care Med 2002; 30: 2450–2456. [DOI] [PubMed] [Google Scholar]
- 7. Villar J, Ambrós A, Soler JA, et al. Stratification and outcome of acute respiratory distress syndrome (STANDARDS) network: age, PaO2/FiO2, and plateau pressure score: a proposal for a simple outcome score in patients with the acute respiratory distress syndrome. Crit Care Med 2016; 44: 1361–1369. [DOI] [PubMed] [Google Scholar]
- 8. Madotto F, Pham T, Bellani G, et al. LUNG SAFE investigators and the ESICM trials group: resolved versus confirmed ARDS after 24 h: insights from the LUNG SAFE study. Intensive Care Med 2018; 44: 564–577. [DOI] [PubMed] [Google Scholar]
- 9. Villar J, Pérez-Méndez L, Blanco J, et al. Spanish initiative for epidemiology, stratification, and therapies for ARDS (SIESTA) network: a universal definition of ARDS: the PaO2/FiO2 ratio under a standard ventilatory setting–a prospective, multicenter validation study. Intensive Care Med 2013; 39: 583–592. [DOI] [PubMed] [Google Scholar]
- 10. Villar J, Pérez-Méndez L, López J, et al. HELP network: an early PEEP/FIO2 trial identifies different degrees of lung injury in patients with acute respiratory distress syndrome. Am J Respir Crit Care Med 2007; 176: 795–804. [DOI] [PubMed] [Google Scholar]
- 11. Bos LD, Cremer OL, Ong DS, et al. MARS consortium: external validation confirms the legitimacy of a new clinical classification of ARDS for predicting outcome. Intensive Care Med 2015; 41: 2004–2005. [DOI] [PubMed] [Google Scholar]
- 12. Villar J, Blanco J, del Campo R, et al. Spanish initiative for epidemiology, stratification & therapies for ARDS (SIESTA) network: assessment of PaO₂/FiO₂ for stratification of patients with moderate and severe acute respiratory distress syndrome. BMJ Open 2015; 5: e006812. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Villar J, Fernández RL, Ambrós A, et al. Acute Lung injury epidemiology and natural history network: a clinical classification of the acute respiratory distress syndrome for predicting outcome and guiding medical therapy. Crit Care Med 2015; 43: 346–353. [DOI] [PubMed] [Google Scholar]
- 14. Ware LB. Prognostic determinants of acute respiratory distress syndrome in adults: impact on clinical trial design. Crit Care Med 2005; 33: S217–S222. [DOI] [PubMed] [Google Scholar]
- 15. Pham T, Serpa Neto A, Pelosi P, et al. LUNG SAFE investigators and the European society of intensive care medicine trials group: outcomes of patients presenting with mild acute respiratory distress syndrome: insights from the LUNG SAFE study. Anesthesiology 2019; 130: 263–283. [DOI] [PubMed] [Google Scholar]
- 16. Serpa Neto A, Deliberato RO, Johnson AEW, et al. PROVE network investigators: 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: 1914–1922. [DOI] [PubMed] [Google Scholar]
- 17. Kao KC, Chiu LC, Hung CY, et al. Coinfection and mortality in pneumonia-related acute respiratory distress syndrome patients with bronchoalveolar lavage. A prospective observational study. Shock 2017; 47: 615–620. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18. Kao KC, Hsieh MJ, Lin SW, et al. Survival predictors in elderly patients with acute respiratory distress syndrome: a prospective observational cohort study. Sci Rep 2018; 8: 13459. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. Ranieri VM, Rubenfeld GD, Thompson BT, et al. ARDS definition task force: acute respiratory distress syndrome: the Berlin definition. JAMA 2012; 307: 2526–2533. [DOI] [PubMed] [Google Scholar]
- 20. Fine JP, Gray RJ. A proportional hazards model for the subdistribution of a competing risk. J Am Stat Assoc 1999; 94: 496–509. [Google Scholar]
- 21. Pham T, Rubenfeld GD. Fifty years of research in ARDS. The Epidemiology of acute respiratory distress syndrome. A 50th birthday review. Am J Respir Crit Care Med 2017; 195: 860–870. [DOI] [PubMed] [Google Scholar]
- 22. Slutsky AS, Ranieri VM. Ventilator-induced lung injury. N Engl J Med 2013; 369: 2126–2136. [DOI] [PubMed] [Google Scholar]
- 23. Sahetya SK, Goligher EC, Brower RG. Fifty years of research in ARDS. Setting positive end-expiratory pressure in acute respiratory distress syndrome. Am J Respir Crit Care Med 2017; 195: 1429–1438. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24. Gattinoni L, Tonetti T, Cressoni M, et al. Ventilator-related causes of lung injury: the mechanical power. Intensive Care Med 2016; 42: 1567–1575. [DOI] [PubMed] [Google Scholar]
- 25. Laffey JG, Bellani G, Pham T, et al. LUNG SAFE investigators and the ESICM trials group: potentially modifiable factors contributing to outcome from acute respiratory distress syndrome: the LUNG SAFE study. Intensive Care Med 2016; 42: 1865–1876. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supplemental material, Author_Response_1_ for Reclassifying severity after 48 hours could better predict mortality in acute respiratory distress syndrome by Li-Chung Chiu, Shih-Wei Lin, Pi-Hua Liu, Li-Pang Chuang, Chih-Hao Chang, Chen-Yiu Hung, Shih-Hong Li, Chung-Shu Lee, Huang-Pin Wu, Chung-Chi Huang, Hsin-Hsien Li, Kuo-Chin Kao and Han-Chung Hu in Therapeutic Advances in Respiratory Disease
Supplemental material, Author_Response_2 for Reclassifying severity after 48 hours could better predict mortality in acute respiratory distress syndrome by Li-Chung Chiu, Shih-Wei Lin, Pi-Hua Liu, Li-Pang Chuang, Chih-Hao Chang, Chen-Yiu Hung, Shih-Hong Li, Chung-Shu Lee, Huang-Pin Wu, Chung-Chi Huang, Hsin-Hsien Li, Kuo-Chin Kao and Han-Chung Hu in Therapeutic Advances in Respiratory Disease
Supplemental material, Reviewer_1_v.1_ for Reclassifying severity after 48 hours could better predict mortality in acute respiratory distress syndrome by Li-Chung Chiu, Shih-Wei Lin, Pi-Hua Liu, Li-Pang Chuang, Chih-Hao Chang, Chen-Yiu Hung, Shih-Hong Li, Chung-Shu Lee, Huang-Pin Wu, Chung-Chi Huang, Hsin-Hsien Li, Kuo-Chin Kao and Han-Chung Hu in Therapeutic Advances in Respiratory Disease
Supplemental material, Reviewer_2_v.1 for Reclassifying severity after 48 hours could better predict mortality in acute respiratory distress syndrome by Li-Chung Chiu, Shih-Wei Lin, Pi-Hua Liu, Li-Pang Chuang, Chih-Hao Chang, Chen-Yiu Hung, Shih-Hong Li, Chung-Shu Lee, Huang-Pin Wu, Chung-Chi Huang, Hsin-Hsien Li, Kuo-Chin Kao and Han-Chung Hu in Therapeutic Advances in Respiratory Disease
Supplemental material, Reviewer_2_v.2 for Reclassifying severity after 48 hours could better predict mortality in acute respiratory distress syndrome by Li-Chung Chiu, Shih-Wei Lin, Pi-Hua Liu, Li-Pang Chuang, Chih-Hao Chang, Chen-Yiu Hung, Shih-Hong Li, Chung-Shu Lee, Huang-Pin Wu, Chung-Chi Huang, Hsin-Hsien Li, Kuo-Chin Kao and Han-Chung Hu in Therapeutic Advances in Respiratory Disease