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Original Research: CRITICAL CARE MEDICINE |

Risk Factors for ARDS in Patients Receiving Mechanical Ventilation for > 48 h* FREE TO VIEW

Xiaoming Jia, MEng; Atul Malhotra, MD, FCCP; Mohammed Saeed, PhD; Roger G. Mark, MD; Daniel Talmor, MD, MPH, FCCP
Author and Funding Information

*From the Massachusetts Institute of Technology (Mr. Jia), Cambridge; Harvard-MIT Division of Health Science and Technology (Drs. Saeed and Mark), Boston; Division of Pulmonary, Critical Care and Sleep Medicine (Dr. Malhotra), Brigham and Women’s Hospital, Harvard Medical School, Boston; and Department of Anesthesia, Critical Care and Pain Medicine (Dr. Talmor), Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA.

Correspondence to: Daniel Talmor, MD, MPH, Department of Anesthesia, Critical Care and Pain Medicine, Beth Israel Deaconess Medical Center, 1 Deaconess Rd, CC-470, Boston MA 02215; e-mail: dtalmor@bidmc.harvard.edu



Chest. 2008;133(4):853-861. doi:10.1378/chest.07-1121
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Background: Low tidal volume (Vt) ventilation for ARDS is a well-accepted concept. However, controversy persists regarding the optimal ventilator settings for patients without ARDS receiving mechanical ventilation. This study tested the hypothesis that ventilator settings influence the development of new ARDS.

Methods: Retrospective analysis of patients from the Multi Parameter Intelligent Monitoring of Intensive Care-II project database who received mechanical ventilation for ≥ 48 h between 2001 and 2005.

Results: A total of 2,583 patients required > 48 h of ventilation. Of 789 patients who did not have ARDS at hospital admission, ARDS developed in 152 patients (19%). Univariate analysis revealed high peak inspiratory pressure (odds ratio [OR], 1.53 per SD; 95% confidence interval [CI], 1.28 to 1.84), increasing positive end-expiratory pressure (OR, 1.35 per SD; 95% CI, 1.15 to 1.58), and Vt (OR, 1.36 per SD; 95% CI, 1.12 to 1.64) to be significant risk factors. Major nonventilator risk factors for ARDS included sepsis, low pH, elevated lactate, low albumin, transfusion of packed RBCs, transfusion of plasma, high net fluid balance, and low respiratory compliance. Multivariable logistic regression showed that peak pressure (OR, 1.31 per SD; 95% CI, 1.08 to 1.59), high net fluid balance (OR, 1.3 per SD; 95% CI, 1.09 to 1.56), transfusion of plasma (OR, 1.26 per SD; 95% CI, 1.07 to 1.49), sepsis (OR, 1.57; 95% CI, 1.00 to 2.45), and Vt (OR, 1.29 per SD; 95% CI, 1.02 to 1.52) were significantly associated with the development of ARDS.

Conclusions: The associations between the development of ARDS and clinical interventions, including high airway pressures, high Vt, positive fluid balance, and transfusion of blood products, suggests that ARDS may be a preventable complication in some cases.

Figures in this Article

Patients in the ICU are commonly supported using mechanical ventilation for days to weeks. Studies14 suggest that initial ventilator settings may affect patient outcome. In particular, patients with ARDS have lower mortality rates when ventilated at lower tidal volumes (Vts).,4 The idea that mechanical ventilator settings may greatly influence respiratory health and mortality has prompted clinicians to find protective measures for minimizing iatrogenic lung injuries. However, there have been few studies and no firm recommendations on the optimal settings for patients who require this intervention for reasons apart from respiratory failure. In fact, patients without respiratory failure make up a significant portion (20 to 30%) of all who are receiving mechanical ventilation in the ICU.1,3

There is increasing evidence that mechanical ventilation can trigger inflammatory pulmonary edema in both animal models5and human patients.67 Both short-term endotracheal intubation and long-term mechanical ventilation are known to increase the risk for nosocomial pneumonia.8Some studies910 have shown that higher Vts are associated with more cases of ARDS and acute lung injury in patients without the disease at the outset. Others authors11 show that ventilation with high pressures increases mortality rates in patients with ARDS. Positive end-expiratory pressure (PEEP) is hypothesized to have protective effects on the lungs during mechanical ventilation,1213 although randomized trials are equivocal in this area.

Due to the multitude and complexity of risk factors associated with ventilator-induced ARDS, it is necessary to analyze the disorder in large patient populations. In this study, we examined a large ICU database to find physiologic and ventilator-associated risk factors for ARDS. We hypothesized that initial ventilator settings as well as other, potentially preventable, risk factors may be associated with the development of ARDS in patients receiving mechanical ventilation for > 48 h but who did not have ARDS at the outset.

The Multi Parameter Intelligent Monitoring of Intensive Care-II Project Database

The Multi Parameter Intelligent Monitoring of Intensive Care (MIMIC)-II project was approved by the institutional review boards of the Massachusetts Institute of Technology and Beth Israel Deaconess Medical Center and granted a waiver of informed consent. The MIMIC-II database includes physiologic information from bedside monitors in the adult ICUs of Beth Israel Deaconess Medical Center. These data (heart rate, BP) were validated by ICU nurses on an hourly basis. The database also contains records of arterial blood gas levels and laboratory values, nursing progress notes, IV medications, fluid intake/output, and other clinical variables. Ventilator settings were documented by respiratory therapists at intubation and as ventilator settings were adjusted. Radiologic films were evaluated by specialists at the time of patient care, and written evaluations were recorded into the database. International Classification of Diseases, Ninth Revision (ICD-9) codes were documented for specific diseases as required by hospital staff on patient discharge. Currently, the database includes > 17,000 medical records collected between 2001 and 2005.14 The distribution of patients from the MIMIC-II database is shown in Figure 1 .

Patient Selection

We examined medical records of patients who received mechanical ventilation for > 48 h and who did not have ARDS at the outset of ventilation. To rule out cardiogenic causes of pulmonary edema, we excluded patients with evidence of congestive heart failure (CHF) during their hospital stay.

Definitions

The length of mechanical ventilation was defined as the duration of the first continuous ventilation period according to recorded ventilator settings. ARDS was diagnosed using the American European consensus conference criteria15 (acute onset, Pao2/fraction of inspired oxygen [Fio2] < 200 mm Hg, bilateral infiltrates on chest radiograph, and no CHF). Pao2/Fio2 values were calculated by finding the ratio of each Pao2 measurement to the nearest Fio2 value available before the corresponding blood gas value (this difference was approximately 2 ± 1.9 h apart). Due to the absence of pulmonary wedge pressure in the majority of records (86%), patients with CHF were identified using ICD-9 code 428 and were excluded from the study. To be considered as without ARDS at the outset, patients must have had two Pao2/Fio2 values > 200 mm Hg in the first 12 h of mechanical ventilation. Development of ARDS was identified has having an acute drop in Pao2/Fio2 to < 200 mm Hg for at least 24 h with evidence of bilateral infiltrates and/or consolidations in the chest radiograph report. Reports from 24 h before to 72 h after the drop in Pao2/Fio2 ratio were independently evaluated by two expert intensivists for the presence of bilateral disease. A random sample of 25% of the radiographs was reviewed and in all cases confirmed findings from the report and were consistent with a diagnosis of ARDS. Discrepancies were settled by a joint evaluation of the overall data available but blinded to the exposure variables (eg, baseline mechanical ventilator settings).

Physiologic information and ventilator settings were collected from the first 24 h of mechanical ventilation and prior to any new lung injury, as defined by Pao2/Fio2 dropping below 200 for > 24 h. Potential risk factors for development of new ARDS (predictor variables) included the following: (1) demographic information: sex, age, weight, height; (2) underlying illness/indicators of organ health: pneumonia, sepsis, Simplified Acute Physiology Score (SAPS), creatinine, albumin, alanine aminotransferase; (3) ventilator settings: Vt, plateau pressure (Pplat), peak inspiratory pressure (PIP), PEEP, Fio2, and total respiratory rate; (4) indicators of gas exchange and metabolism: arterial pH, Pao2, Paco2, bicarbonate, and lactate; and (5) transfusion of blood products: packed RBCs, plasma, and platelets.

When patient height information was available (60% of records), predicted body weight was calculated using the following formulae9:

Vt per ideal body weight was then calculated using predicted weight. Respiratory compliance was calculated as follows: Vt/(Pplat − PEEP). When more than one ventilator setting was present on a given day, the “worst” values (highest Vt, highest ventilator pressures) were selected. For all laboratory values and blood gases, the first value after the outset of ventilation was collected for analysis. Presence of pneumonia and sepsis as an underlying illness was identified by ICD-9 codes (480–486 for pneumonia, 038 for sepsis). Transfusion of blood products was recorded as the number of 375-mL units (or volume equivalent) administered on the first day of mechanical ventilation. If ARDS developed on day 1, fluids administered before the onset of ARDS were used. The net fluid balance was calculated as the difference of fluid inputs and outputs beginning at admission to the ICU until the onset of ARDS. In patients without the development of ARDS, the net fluid balance was calculated for the day closest to the average time until progression to ARDS in the ARDS cohort. The SAPS was calculated using physiologic data from the first 24 h of ICU admission: age, heart rate, systolic arterial BP, temperature, respiratory rate, urine output, BUN, hematocrit, WBC count, glucose, potassium, sodium, bicarbonate, and Glasgow coma scale score.

Statistical Analysis

Univariate and multivariate logistical regressions were performed to find correlations between predictor and outcome variables. The outcome of interest was development of ARDS after the onset of mechanical ventilation. In the univariate analysis, odds ratios (ORs) were calculated per 1-SD increase in predictor variables. Statistically significant variables (p < 0.05) were then considered for inclusion in a multivariate model if the variable contained < 20% missing data. A backward stepwise logistic regression was performed to find the optimal model in which all contained variables were statistically significant predictors of ARDS, again with p < 0.05. SAPS and initial Pao2/Fio2 were forced into the multivariate model to control for severity of illness.

Finally, significant ventilator-related variables from univariate analyses were adjusted for severity of illness using the most significant nonventilator variables found to be predictive of ARDS. A subsequent comparison of the unadjusted and adjusted ORs (with 95% confidence intervals [CIs]) was performed. In multivariate analyses, missing values were filled using the averages from all patients who did not have ARDS at the outset of ventilation. All data analyses were performed using Matlab 7.2 (The MathWorks; Natick, MA) and its supporting statistical toolboxes (http://www.mathworks.com/products/matlab/).

A total of 1,366 of 17,493 patients admitted between 2001 and 2005 required mechanical ventilation for > 48 h in the ICU and had no record of CHF during their stay. The average age of these patients was 59 years, and the mean duration of ICU stay was 13 days. When broken down by location, 29% of patients were in medical ICU, 28% in surgical ICUs, 28% in the cardiac surgery recovery unit, and 15% in coronary care units. The characteristics of this patient cohort are summarized in Table 1 . Of 789 patients without ARDS on the first day of mechanical ventilation, 177 patients had worsening Pao2/Fio2; of these, 152 patients (19%) had bilateral infiltrates and met ARDS criteria. On average, these patients had ARDS develop 3.3 days after the initiation of mechanical ventilation (25–75% quartile: 0.8 to 4.3 days; median, 2.3 days). Three hundred twenty-four of the 789 patients (41%) without ARDS at the outset received transfusion of blood products, including 101 patients (13%) who received platelets, 268 patients (34%) who received packed RBCs, and 146 patients (19%) who received fresh-frozen plasma. Five hundred seventy-seven patients had Pao2/Fio2 < 200 at the outset of ventilation and were excluded from logistic regression analyses.

Univariate continuous-variable logistical regression found the following variables to be associated with the development of ARDS: Pplat (OR, 1.5 per SD; 95% CI, 1.3 to 1.9); PIP (OR, 1.5 per SD; 95% CI, 1.3 to 1.8); patient weight (OR, 1.4 per SD; 95% CI, 1.2 to 1.7); PEEP (OR, 1.4 per SD; 95% CI, 1.2 to 1.7); sepsis (OR, 1.95; 95% CI, 1.3 to 2.9); Vt (OR, 1.4 per SD; 95% CI, 1.1 to 1.7); transfusion of packed RBCs (OR, 1.2 per SD; 95% CI, 1.1 to 1.4); transfusion of plasma (OR, 1.3 per SD; 95% CI, 1.1 to 1.5); net fluid balance (OR, 1.5 per SD; 95% CI, 1.3 to 1.8); blood pH (OR, 0.8 per SD; 95% CI, 0.6 to 0.9); albumin (OR, 0.8 per SD; 95% CI, 0.6 to 0.9); respiratory compliance (OR, 0.8 per SD; 95% CI, 0.64 to 0.97); and lactate (OR, 1.2 per SD; 95% CI, 1.0 to 1.4). In 448 patients with recorded height and Vt, Vt per predicted body weight was not found to be significantly associated (p = 0.559) with the development of ARDS. Table 2 shows the ORs and p value statistics from univariate logistical regression, comparing patients who did and did not have ARDS develop. Figure 2 examines the distribution of day 1 Pplat and the increasing percentage of patients with ARDS developing with increasing pressure. Figures 345 show similar analyses for PIP, Vt, and patient weight, respectively.

Risk factors found to be significant in univariate analysis were considered for multivariate regression analysis if the variable had < 20% missing data. In the final multivariate model, PIP (OR, 1.31 per SD; p = 0.006), transfusion of plasma (OR, 1.26 per SD; p = 0.006), high fluid balance (OR, 1.3 per SD; p = 0.003), and Vt (OR, 1.25 per SD; p = 0.030) remained significantly associated with the development of new ARDS. Sepsis and PEEP were of borderline significance (p = 0.055 and p = 0.058, respectively). This final multivariate model, in which Pao2/Fio2 and SAPS were included to control for severity of illness, is shown in Table 3 .

Finally, in order to further define significant ventilator related risk factors for the development of ARDS, significant ventilator-related variables from univariate analysis (PIP, Pplat, Vt, PEEP) were adjusted for significant physiologic risk factors (Pao2/Fio2, pH, sepsis, respiratory compliance, transfusion of plasma, and net fluid balance). In this analysis, PIP (OR, 1.43 per SD; p < 0.001), Pplat (OR, 1.37 per SD; p = 0.007), PEEP (OR, 1.22 per SD; p = 0.016), and Vt (OR, 1.33 per SD; p = 0.006) remained significantly associated with the development of ARDS (Table 4 ).

This retrospective cohort study sheds new light on the risk factors for ARDS in patients receiving mechanical ventilation for ≥ 48 h in the ICU. Approximately 19% of patients had ARDS develop who did not have ARDS at the outset of mechanical ventilation and had no record of CHF during their hospitalization. The association between initial ventilator settings and new ARDS suggests that ventilator-associated lung injury may be a preventable illness in some cases. Ventilation with high airway pressures and Vt is an important risk factor for respiratory failure. In our analysis, high PIP, Pplat, PEEP, and Vt remained predictive of new ARDS after adjusting for Pao2/Fio2, sepsis, pH, respiratory compliance, net fluid balance, and transfusion of blood plasma. Pressure and volume are related through respiratory compliance, and in this cohort, PIP and Vt had a small but significant correlation (R2 = 0.05, p < 0.001). The multivariate model demonstrated that PIP and Vt were significant predictors of ARDS independently of each other. This may be due to the variations in respiratory compliance that allow high pressures to be injurious in some cases and high volumes to be injurious in others. In general, the finding that ventilator pressures and Vts play a role in the development of ARDS is supported by existing literature5,1618 that emphasizes the adverse effects of high airway and transpulmonary pressures.

Vt per predicted body weight was not significantly associated with ARDS (p = 0.559), contradicting previous studies910 that recognize this to be the most important ventilator-related risk factor for ARDS. It is possible that this discrepancy is influenced by the lower percentage of recorded height data and imprecision in making this measurement for the current cohort. Further studies should be conducted to clarify this discrepancy.

Pplat was significantly associated with ARDS in univariate regression analysis and after adjusting for severity of illness. However, Pplat was excluded from the multivariate model by the backward-search algorithm while PIP was included. This is likely explained by the phenomenon in which variables that are correlated (such as PIP and Pplat) have low significance levels or high p values when they are introduced into the same multivariate model. A backward-stepwise logistic regression algorithm would simply remove the less significant variable, in this case Pplat, while searching for the optimal multivariate model. Due to differences in airway resistance, Pplat and PIP do not correlate perfectly (R2 = 0.55, p < 0.001). However, a high PIP is likely to produce a high Pplat that can cause alveolar damage.

High PEEP was associated with development of ARDS in univariate regression, but this relationship becomes less significant after adjusting for peak pressure and Pao2/Fio2. Thus, high PEEP may be responsible for higher airway pressures and may be a marker of sicker patients. Historically, PEEP is thought to be lung protective1213 and is often used to recruit collapsed alveoli.1920 However, the data presented from this cohort do not point to the protective nature of PEEP.

Transfusion of blood products is a recognized risk factor for ARDS.2123 The exact mechanism of transfusion-related ARDS is unclear, although studies2425 suggest granulocyte and human leukocyte antigen antibodies are important factors. In this study, univariate analyses identified transfusion of packed RBCs and plasma to be significant risk factors for ARDS (p = 0.007 and p < 0.001, respectively) while transfusion of platelets was of borderline significance (p = 0.051). In the multivariate model, plasma remained a significant risk factor (p = 0.006), while packed RBCs and platelets were excluded from the model due by the backward-search algorithm. These results support the conclusion that transfusion of blood products, especially plasma, is an important risk factor for ARDS.

Conservative fluid management has been proposed as a strategy to reduce lung fluid in patients with acute lung injury.26 Results from this study support the hypothesis that an elevated fluid balance is a risk factor for the development of ARDS. High net fluid balance was moderately associated with low albumin (R2 = 0.10, p < 0.001). This association may be caused by disruption of oncotic balance in circulating blood due to high fluid balance, or more likely by the effects of capillary leak in the critically ill that leads to decreased plasma oncotic pressure, hypotension, and an increased demand for fluid administration. Capillary leak produces exudates and hyaline membrane formation in the alveoli, while hydrostatic pulmonary edema produces transudation without hyaline membranes. In this study, however, there is no way to definitively distinguish between the two effects. Nevertheless, the final multivariate model shows that high net fluid balance is an ARDS risk factor independent of ventilator settings, transfusion of plasma, and severity of illness.

Physiologic risk factors for new ARDS include blood acidemia, hypoalbuminia, low respiratory compliance, and high lactate. Low pH and high lactate are characteristic of metabolic acidosis, a condition known to be predictive of acute lung injury in severely traumatized patients.27Although high lactate and low pH were associated with the development of ARDS in univariate analyses, these relationships become secondary in the multivariate context (neither variable was selected for the final multivariate model). This result suggests that the practice of using the ventilator to correct for acidosis may be a potentially harmful intervention. Hypoalbuminia, another factor known to be predictive of ARDS,28 disrupts the oncotic balance between fluids in the pulmonary circulation and lung alveoli. In the critically ill, hypoalbuminia may be a marker of leaky capillaries rather than a cause of hydrostatic edema. Low albumin was significantly associated with ARDS in univariate analysis, but the variable was not included in multivariate analysis due to insufficient data (31% missing). High patient weight was also associated with higher odds of ARDS developing. However, weight is significantly correlated with set Vt (R2 = 0.19, p < 0.001), and to a lesser extent with set peak pressure (R2 = 0.05, p < 0.001). Thus, heavier patients may require higher ventilator pressures to deliver the same Vt, producing the observed though indirect association with ARDS due to the ventilator.

We acknowledge the following limitations in the current study. First, although data were validated at collection by the bedside nurses and respiratory therapists, they were not collected by scientific investigators and thus may contain errors. The large size of the database should correct for such random errors, assuming inaccuracies are random rather than systematic. Secondly, we assume random variability in initial ventilator settings, but there always exists the possibility that higher pressures and Vts were chosen deliberately to correct underlying hypoxemia, acidemia, and noncardiogenic pulmonary edema. It has been proposed that the association between ventilator settings and development of ARDS may be driven by a group of sicker patients who received higher Vts and pressures. To test this possibility, we examined significant ventilator variables before and after being adjusted for severity of illness at baseline. The ORs for the ventilator settings decreased slightly after such adjustments while retaining significant 95% CIs. This suggests that the observed relationship between ventilation settings and development of ARDS may be in part due to sicker patients with lower lung compliance (due to pneumonia or another respiratory illness). Thirdly, although we used a large patient population in this study, data were not complete in all records. The variables with the least complete data included height (40% missing), albumin (31%), alanine aminotransferase (30%), and lactate (15%). All other variables had < 7% missing data. The absence of height in many records prevented us from calculating Vt per predicted body weight in many patients. The diagnosis of sepsis and pneumonia were based solely on ICD-9 codes, making it difficult to determine whether the disease appeared before or after ARDS. Microbiology reports were unavailable at the time of this study, and future studies should use more than ICD-9 codes to identify the underlying illness. Fourth, the final multivariate model only explains a small fraction of the total variance observed (R2 = 0.11). This suggests that the risk factors thus identified cannot be used, by themselves, to predict the development of ARDS with great sensitivity and specificity. Further, it points to the possibility that there exist additional risk factors for ARDS, genetic or environmental, that yet to be identified. Finally, Pao2/Fio2 values were available only when arterial blood gas levels were measured, and the accuracy of our patient classifications (no ARDS or ARDS at the outset of ventilation) depended on the presence and validity of these values. In general, any misclassification would bias toward the null hypothesis, making it more difficult to show a relationship between initial ventilator settings and worsening gas exchange in the lungs. Future studies could use pulmonary artery wedge pressure or biomarkers such as brain natriuretic peptide in addition to Pao2/Fio2 values and chest radiograph reports when diagnosing ARDS. Most importantly, a randomized trial is needed to verify the suggestion that high ventilator pressures play a causal role in the development of ARDS.

Development of new-onset ARDS is a relatively common complication (152 of 798 patients) in those receiving mechanical ventilation for ≥ 48 h in the ICU. In this study, high airway pressure and Vt were the most important ventilator-associated risk factors for the development of new ARDS. Transfusion of blood products, especially of fresh-frozen plasma, and high net fluid balance are also strongly associated with new ARDS. Thus, it may be possible to reduce the occurrence of ARDS with careful ventilator and fluid management. Prospective randomized prospective studies are needed to support this hypothesis. Meanwhile, practitioners should consider avoiding these potentially injurious interventions in patients without acute lung injury at the onset of mechanical ventilation.

Abbreviations: CHF = congestive heart failure; CI = confidence interval; Fio2 = fraction of inspired oxygen; ICD-9 = International Classification of Diseases, Ninth Revision; MIMIC = Multi Parameter Intelligent Monitoring of Intensive Care; OR = odds ratio; PEEP = positive end-expiratory pressure; PIP = peak inspiratory pressure; Pplat = plateau pressure; SAPS = Simplified Acute Physiology Score; Vt = tidal volume

This work was supported in part by National Institutes of Health grant R01-EB001659.

None of the authors have any financial interests or potential conflicts of interest to disclose.

Figure Jump LinkFigure 1. Patient distribution from the MIMIC-II database. Gray boxes indicate patients examined in this study.Grahic Jump Location
Table Graphic Jump Location
Table 1. Characteristics of the Patient Cohort, Grouped by Initial Lung Health*
* 

Data are presented as mean ± SD (range), No. (%), or mean ± SD.

 

Excludes patients with CHF according to ICD-9 codes.

 

SAPS score based on data from first 24 h of ICU admission.

§ 

Length of stay in a single care unit.

 

Length of continuous mechanical ventilation.

Table Graphic Jump Location
Table 2. Univariate Logistical Analysis of Risk Factors for ARDS in 789 Patients Without ARDS at the Outset of Mechanical Ventilation*
* 

Data are presented as mean ± SD or No. (%) unless otherwise indicated.

 

ORs calculated for presence of the disease.

 

SD of variable in all 789 patients without ARDS at the outset.

§ 

Statistically significant.

Figure Jump LinkFigure 2. Distribution of initial Pplat in 789 patients without ARDS at the outset of ventilation (top), and the risk of ARDS developing as a function of Pplat (bottom) [ARDS developed eventually in 152 patients].Grahic Jump Location
Figure Jump LinkFigure 3. Distribution of initial PIPs in 789 patients without ARDS at the outset of ventilation (top), and the risk of ARDS developing as a function of PIP (bottom) [ARDS developed eventually in 152 patients].Grahic Jump Location
Figure Jump LinkFigure 4. Distribution of initial Vt in 789 patients without ARDS at the outset of ventilation (top), and the risk of ARDS developing as a function of Vt (bottom) [ARDS developed eventually in 152 patients].Grahic Jump Location
Figure Jump LinkFigure 5. Distribution of weight in 789 patients without ARDS at the outset of ventilation (top), and the risk of ARDS developing ARDS as a function of weight (bottom) [ARDS developed eventually in 152 patients].Grahic Jump Location
Table Graphic Jump Location
Table 3. Multivariate Analysis of ARDS Risk Factors in 789 Patients Without ARDS at the Outset*
* 

The optimal multivariable model reached from backward search on statistically significant variables derived from univariate analysis. SAPS and Pao2/Fio2 ratio were added to control for severity of illness. In this final model, R2 = 0.11 (proportion of total variance explained by the model).

 

Net fluid balance before onset of ARDS (fluid balance on day 3 of mechanical ventilation was used for patients who did not have ARDS develop).

 

Transfusion of blood products on day 1 of mechanical ventilation.

§ 

OR calculated for presence of sepsis as underlying illness.

Table Graphic Jump Location
Table 4. Analysis of Ventilator-Associated Risk Factors for ARDS in 789 Patients Without ARDS at the Onset of Mechanical Ventilation*
* 

Unadjusted = univariate; adjusted = controlled for initial Pao2/Fio2, blood pH, static respiratory compliance, presence of sepsis, transfusion of plasma, and net fluid balance.

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Figures

Figure Jump LinkFigure 1. Patient distribution from the MIMIC-II database. Gray boxes indicate patients examined in this study.Grahic Jump Location
Figure Jump LinkFigure 2. Distribution of initial Pplat in 789 patients without ARDS at the outset of ventilation (top), and the risk of ARDS developing as a function of Pplat (bottom) [ARDS developed eventually in 152 patients].Grahic Jump Location
Figure Jump LinkFigure 3. Distribution of initial PIPs in 789 patients without ARDS at the outset of ventilation (top), and the risk of ARDS developing as a function of PIP (bottom) [ARDS developed eventually in 152 patients].Grahic Jump Location
Figure Jump LinkFigure 4. Distribution of initial Vt in 789 patients without ARDS at the outset of ventilation (top), and the risk of ARDS developing as a function of Vt (bottom) [ARDS developed eventually in 152 patients].Grahic Jump Location
Figure Jump LinkFigure 5. Distribution of weight in 789 patients without ARDS at the outset of ventilation (top), and the risk of ARDS developing ARDS as a function of weight (bottom) [ARDS developed eventually in 152 patients].Grahic Jump Location

Tables

Table Graphic Jump Location
Table 1. Characteristics of the Patient Cohort, Grouped by Initial Lung Health*
* 

Data are presented as mean ± SD (range), No. (%), or mean ± SD.

 

Excludes patients with CHF according to ICD-9 codes.

 

SAPS score based on data from first 24 h of ICU admission.

§ 

Length of stay in a single care unit.

 

Length of continuous mechanical ventilation.

Table Graphic Jump Location
Table 2. Univariate Logistical Analysis of Risk Factors for ARDS in 789 Patients Without ARDS at the Outset of Mechanical Ventilation*
* 

Data are presented as mean ± SD or No. (%) unless otherwise indicated.

 

ORs calculated for presence of the disease.

 

SD of variable in all 789 patients without ARDS at the outset.

§ 

Statistically significant.

Table Graphic Jump Location
Table 3. Multivariate Analysis of ARDS Risk Factors in 789 Patients Without ARDS at the Outset*
* 

The optimal multivariable model reached from backward search on statistically significant variables derived from univariate analysis. SAPS and Pao2/Fio2 ratio were added to control for severity of illness. In this final model, R2 = 0.11 (proportion of total variance explained by the model).

 

Net fluid balance before onset of ARDS (fluid balance on day 3 of mechanical ventilation was used for patients who did not have ARDS develop).

 

Transfusion of blood products on day 1 of mechanical ventilation.

§ 

OR calculated for presence of sepsis as underlying illness.

Table Graphic Jump Location
Table 4. Analysis of Ventilator-Associated Risk Factors for ARDS in 789 Patients Without ARDS at the Onset of Mechanical Ventilation*
* 

Unadjusted = univariate; adjusted = controlled for initial Pao2/Fio2, blood pH, static respiratory compliance, presence of sepsis, transfusion of plasma, and net fluid balance.

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