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Caring for the Critically Ill Patient |

Referral to an Extracorporeal Membrane Oxygenation Center and Mortality Among Patients With Severe 2009 Influenza A(H1N1) FREE

Moronke A. Noah, MRCS; Giles J. Peek, FRCS (CTh), MD; Simon J. Finney, FRCA, PhD; Mark J. Griffiths, MRCP, PhD; David A. Harrison, PhD; Richard Grieve, PhD; M. Zia Sadique, PhD; Jasjeet S. Sekhon, PhD; Daniel F. McAuley, FRCP, MD; Richard K. Firmin, FRCS; Christopher Harvey, MRCS; Jeremy J. Cordingley, FRCA, MD; Susanna Price, MRCP, PhD; Alain Vuylsteke, FRCA, MD; David P. Jenkins, FRCS (CTh); David W. Noble, FFICM; Roxanna Bloomfield, FRCA; Timothy S. Walsh, FRCA, MD; Gavin D. Perkins, MRCP, MD; David Menon, FMedSci, PhD; Bruce L. Taylor, FRCA, FCICM; Kathryn M. Rowan, PhD
[+] Author Affiliations

Author Affiliations: Heartlink ECMO Centre, Glenfield Hospital, Leicester, England (Drs Noah, Peek, Firmin, and Harvey); Department of Anaesthesia, Royal Brompton Hospital, London, England (Drs Finney, Griffiths, Cordingley, and Price); Intensive Care National Audit & Research Centre, London, England (Drs Harrison and Rowan); Department of Health Services Research and Policy, London School of Hygiene and Tropical Medicine, London, England (Drs Grieve and Sadique); Travers Department of Political Science, University of California, Berkeley (Dr Sekhon); Centre for Infection and Immunity, Queens University of Belfast, Belfast, Ireland (Dr McAuley); Department of Anaesthesia, Papworth Hospital, Cambridge, England (Drs Vuylsteke and Jenkins); Department of Anaesthesia, Aberdeen Royal Infirmary, Aberdeen, Scotland (Drs Noble and Bloomfield); Department of Critical Care Medicine, University of Edinburgh, Edinburgh, Scotland (Dr Walsh); Warwick Clinical Trials Unit, University of Warwick, Coventry, England (Dr Perkins); Division of Anaesthesia, University of Cambridge, Cambridge, England (Dr Menon); and Department of Critical Care, Portsmouth Hospitals NHS Trust, Portsmouth, England (Dr Taylor).


JAMA. 2011;306(15):1659-1668. doi:10.1001/jama.2011.1471.
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Published online

Context Extracorporeal membrane oxygenation (ECMO) can support gas exchange in patients with severe acute respiratory distress syndrome (ARDS), but its role has remained controversial. ECMO was used to treat patients with ARDS during the 2009 influenza A(H1N1) pandemic.

Objective To compare the hospital mortality of patients with H1N1-related ARDS referred, accepted, and transferred for ECMO with matched patients who were not referred for ECMO.

Design, Setting, and Patients A cohort study in which ECMO-referred patients were defined as all patients with H1N1-related ARDS who were referred, accepted, and transferred to 1 of the 4 adult ECMO centers in the United Kingdom during the H1N1 pandemic in winter 2009-2010. The ECMO-referred patients and the non–ECMO-referred patients were matched using data from a concurrent, longitudinal cohort study (Swine Flu Triage study) of critically ill patients with suspected or confirmed H1N1. Detailed demographic, physiological, and comorbidity data were used in 3 different matching techniques (individual matching, propensity score matching, and GenMatch matching).

Main Outcome Measure Survival to hospital discharge analyzed according to the intention-to-treat principle.

Results Of 80 ECMO-referred patients, 69 received ECMO (86.3%) and 22 died (27.5%) prior to discharge from the hospital. From a pool of 1756 patients, there were 59 matched pairs of ECMO-referred patients and non–ECMO-referred patients identified using individual matching, 75 matched pairs identified using propensity score matching, and 75 matched pairs identified using GenMatch matching. The hospital mortality rate was 23.7% for ECMO-referred patients vs 52.5% for non–ECMO-referred patients (relative risk [RR], 0.45 [95% CI, 0.26-0.79]; P = .006) when individual matching was used; 24.0% vs 46.7%, respectively (RR, 0.51 [95% CI, 0.31-0.81]; P = .008) when propensity score matching was used; and 24.0% vs 50.7%, respectively (RR, 0.47 [95% CI, 0.31-0.72]; P = .001) when GenMatch matching was used. The results were robust to sensitivity analyses, including amending the inclusion criteria and restricting the location where the non–ECMO-referred patients were treated.

Conclusion For patients with H1N1-related ARDS, referral and transfer to an ECMO center was associated with lower hospital mortality compared with matched non–ECMO-referred patients.

Figures in this Article

Extracorporeal membrane oxygenation (ECMO) can support gas exchange independently of mechanical ventilation in patients with severe acute respiratory failure. ECMO may be used either as a rescue intervention or to minimize ventilator-associated lung injury1 and its associated multiple organ dysfunction,2 both crucial determinants of survival for patients with acute respiratory distress syndrome (ARDS).3 A recent randomized controlled study indicated that significantly more patients with severe ARDS survived without severe disability if they were transferred to a single ECMO center compared with patients who were managed conventionally.4 However, differences between the centers in mechanical ventilation and other therapies used to manage the conventionally treated patients could have affected outcome independently. Moreover, ECMO doubled hospital costs compared with conventional care.5 Hence, the role of ECMO in adults with severe ARDS remains controversial.68

A minority of patients infected during the 2009 influenza A(H1N1) pandemic developed severe, rapidly progressive ARDS, which was often associated with other organ failures.911 The severity of the respiratory failure led to some patients being supported with ECMO. In a case series from Australia and New Zealand, more than 70% of patients with respiratory failure who received ECMO survived.12 However, the interpretation of this case series is complicated by confounding factors that influence both case selection and patient outcome.

In an attempt to address this limitation, and using an opportunity presented by local variation in referral for ECMO in the United Kingdom during the H1N1 pandemic, we compared the mortality for patients that were referred, accepted, and transferred to UK ECMO centers for H1N1-related ARDS with carefully matched non–ECMO-referred patients. Data were obtained from the Swine Flu Triage study (SwiFT), which was a prospective cohort study of patients with suspected or confirmed H1N1 who were referred and assessed as requiring critical care.13

Our study used the SwiFT study13 as the main data source for ECMO-referred patients and non–ECMO-referred patients and supplementary data on ECMO-referred patients from the UK H1N1 ECMO registry. The SwiFT study was a rapid commission from the UK government to monitor the impact of the H1N1 pandemic. A minimal set of clinical data (eMethods and eTable 1) was prospectively collected from both the point of assessment for critical care and daily while patients were receiving critical care (sufficient for the sequential organ failure assessment14 and for unit outcome).

The SwiFT data were collected on suspected and confirmed H1N1 cases who were referred and assessed as requiring critical care in 192 participating acute hospitals, and entered into a dedicated, secure Web portal hosted by the Intensive Care National Audit & Research Centre (ICNARC). Data definitions (either as a data collection manual and form, or as help text and answers to frequently asked questions) and error checking were available either for download or built into the design of the Web portal. The SwiFT study was approved by the North West Research Ethics Committee and the National Information Governance Board Ethics and Confidentiality Committee. The SwiFT study data collection began on September 3, 2009, and ended on January 31, 2010.

The UK H1N1 ECMO registry pooled data on all H1N1 patients referred for ECMO during the pandemic. The data were maintained at the Heartlink ECMO center at Glenfield Hospital in Leicester, England. Detailed demographic and physiological data from the time of referral plus technical ECMO and outcome data were collected. The UK National Research Ethics Service designated the registry as a service evaluation; requiring neither ethics approval nor patient consent.

ECMO-referred patients and non–ECMO-referred patients were initially identified from the SwiFT study. The UK ECMO registry ensured total capture of ECMO-referred patients from the SwiFT study during the H1N1 pandemic and provided more detailed data on ECMO-referred patients. All data for both ECMO-referred patients and non–ECMO-referred patients used in these analyses were collected according to SwiFT study definitions.

ECMO-Referred Patients

ECMO-referred patients were defined as adults with suspected or confirmed H1N1-associated respiratory failure who were referred, accepted, and transferred to 1 of 4 UK ECMO centers between July 14, 2009, and February 19, 2010. Adult ECMO support was provided by the National H1N1 ECMO service and led and coordinated by the Heartlink ECMO center. This center has been providing ECMO support since 1989 and is the only permanently designated respiratory ECMO center for adult patients in the United Kingdom. It cares for approximately 50 patients per year.

Due to the expected, increased demand, 3 other hospitals (Royal Brompton Hospital, Papworth Hospital, and Aberdeen Royal Infirmary) were evaluated, quality assured, and commissioned by the UK government's Department of Health as additional providers of ECMO. Temporary commissioning of the 3 additional centers followed onsite reviews to ensure that they met the national standards of being designated respiratory ECMO centers in relation to equipment, clinical skills, workforce, training, and governance. Prior to the pandemic, all 3 additional centers were using ECMO in various clinical contexts.

In the United Kingdom, suitability of adult patients for ECMO support was defined using the Conventional Ventilatory Support Versus Extracorporeal Membrane Oxygenation for Severe Adult Respiratory Failure (CESAR) trial4 entry criteria (eMethods), and referral was left to local, clinical decision making. Following transfer to the ECMO center, ECMO was instituted if adequate gas exchange could not be achieved with conventional lung-protective ventilation. ECMO aimed to maintain arterial oxygen saturation levels above 85% and arterial carbon dioxide tensions between 30 and 45 mm Hg. Mechanical ventilation during ECMO was reduced to a respiratory rate of 10 breaths per minute, peak inspiratory pressure of less than 30 cm H2O (ideally 25 cm H2O), positive end-expiratory pressure of 10 to 15 cm H2O, and fraction of inspired oxygen (FIO2) of 0.3. Other therapeutic strategies considered included neuraminidase inhibitors,15 conservative use of fluids,16 and corticosteroids.17,18

Non–ECMO-Referred Patients

Non–ECMO-referred patients were defined as adults with suspected or confirmed H1N1-associated respiratory failure who were not referred, accepted, or transferred to 1 of the 4 ECMO centers. Potential non–ECMO-referred patients were excluded if they were (1) not suitable for ECMO (age <16 years or >70 years, not mechanically ventilated, FIO2 never >0.7 and/or ratio of PaO2 to FIO2 never <100 mm Hg, or having chronic respiratory organ dysfunction sufficient to severely impair activities of daily living), (2) treated with ECMO at an undesignated center, (3) referred but not accepted for transfer for ECMO due to a presupposed lack of benefit, (4) missing data either for matching or for the primary outcome. As per usual critical care delivery in the United Kingdom, all non–ECMO-referred patients were treated in intensivist-led, closed critical care units.

The primary outcome was survival to acute hospital discharge determined by linkage to the UK ECMO registry for ECMO-referred patients and to the ICNARC Case Mix Programme (or by telephone follow-up) for non–ECMO-referred patients.

Statistical Analysis

Matched cohort analyses were performed using 3 statistical approaches: individual matching, propensity score matching, and GenMatch matching. The variables selected for matching were those anticipated a priori to be associated with ECMO use and hospital mortality, but only those that were available in the SwiFT study data set were chosen. ECMO-referred patients and non–ECMO-referred patients were matched at a similar time point in the natural history of their illness by matching on the number of days of mechanical ventilation received prior to referral in the ECMO-referred patients with the equivalent number of days of mechanical ventilation in the non–ECMO-referred patients. Non–ECMO-referred patients were not considered for matching on days when the patient was not mechanically ventilated, required an FIO2 of less than 0.7, had a ratio of PaO2 to FIO2 of more than 100 mm Hg, or had been ventilated for more than 20 days; these patients would not be suitable for ECMO (eMethods).

Individual case matching compared individual factors in the following order: (1) the number of days of continuous mechanical ventilation (nearest in absolute value to a maximum difference of 2 days); (2) FIO2 (1.0 or 0.70-0.99) associated with the arterial blood gas with the lowest PaO2; (3) ratio of PaO2 to FIO2 from the arterial blood gas with the lowest PaO2; (4) Sequential Organ Failure Assessment Score (nearest in absolute value to a maximum difference of 3 points); (5) age (nearest in absolute value to a maximum difference of 10 years); (6) pregnancy status (defined as currently pregnant, pregnant within the previous 42 days, or not pregnant); and (7) body mass index (BMI) category (calculated either from recorded weight in kilograms divided by height in meters squared or assessed subjectively; very thin or thin: BMI <18.6; average weight, overweight, or obese: BMI, 18.6-39.9; or morbidly obese: BMI ≥40).

Propensity score matching was undertaken by estimating the likelihood of referral, acceptance, and transfer for ECMO using a logistic regression model including the following variables: number of days of continuous mechanical ventilation; FIO2; ratio of PaO2 to FIO2; Sequential Organ Failure Assessment Score; pregnancy; BMI category; H1N1 status (suspected or confirmed); prior use of inhaled nitric oxide, high-frequency oscillation, or prone positioning; advanced cardiovascular support; renal support; antiviral therapy; and age. Each ECMO-referred patient was matched with a non–ECMO-referred patient with the closest absolute propensity score (predicted log odds of referral, acceptance, and transfer for ECMO).

GenMatch is a matching technique that combines propensity score matching with multivariate matching.1921 Unlike individual matching, the multivariate matching used by GenMatch does not drop observations that cannot be exactly matched but seeks to make the multivariate distribution of covariates in the matched groups as similar as possible (ie, maximizing the balance of the observed covariates). GenMatch weights the propensity score and the observed individual variables based on an automated search algorithm. The GenMatch algorithm iteratively checks the balance and directs the search toward the best matches (those that optimize balance).22,23

GenMatch selects matched pairs using a generalized Mahalanobis distance metric,24 which weights each baseline covariate included in the matching. The weights define alternative distance metrics that differ in the relative importance given to matching each covariate. The automated search algorithm selects those weights, and hence the corresponding distance metric, that gives the best covariate balance in the matched samples. The balance statistics are chosen a priori from recommended measures such as t statistics from paired t tests, D statistics from Kolmogorov-Smirnov tests, and weighted standardized differences.25

The search algorithm improves covariate balance to the extent possible given the data.19,21 Compared with matching on propensity score alone, GenMatch matching has been shown to reduce covariate imbalance and bias from confounding.19,20 Previous cohort studies have assessed the relative effectiveness of other clinical interventions using GenMatch matching to balance baseline covariates.20,26,27 In this study, GenMatch matching was based on the propensity score and the same individual covariates included in the propensity score model but aimed to improve covariate balance by comparing the distribution of each covariate across the groups using paired t tests and Kolmogorov-Smirnov tests.

All matching was performed on a 1-to-1 basis with replacement. Nearest neighbor matching was applied within a caliper of 1 standard deviation on the propensity score and for the number of days of mechanical ventilation. The balance between ECMO-referred patients and matched non–ECMO-referred patients was assessed using t statistics from paired t tests and D statistics from Kolmogorov-Smirnov tests and was reported for the same number of ECMO-referred patients and non–ECMO-referred patients before and after matching with the propensity score and GenMatch.

The relative risks (RRs) of death prior to hospital discharge for ECMO-referred patients compared with matched non–ECMO-referred patients were estimated by Poisson regression and were conditional on the matched data. Standard errors were estimated using the nonparametric bootstrapping method. The analysis was performed according to the intention-to-treat principle for ECMO.

A priori–agreed sensitivity analyses were conducted to ascertain whether inclusion criteria and restricting the location where the non–ECMO-referred patients were treated influenced the base-case findings. For the former, analyses were repeated excluding ECMO-referred patients and non–ECMO-referred patients that met the following criteria: (1) FIO2 of less than 1.0 for the arterial blood gas with lowest PaO2 on the calendar day of referral; (2) ECMO-referred patients transferred for ECMO who did not subsequently receive ECMO; (3) ECMO-referred patients in whom H1N1 infection was suspected but not confirmed; and (4) all of these criteria combined.

For the latter, the analyses were repeated to limit comparisons of ECMO-referred patients with non–ECMO-referred patients who had been treated in critical care units with characteristics generally associated with good outcomes; data from before the pandemic (January to December 2008) were used from the ICNARC Case Mix Programme. The non–ECMO-referred patient pool was therefore limited to critical care units with relatively low mortality rates (standardized hospital mortality ratio of <1.0 estimated using the ICNARC model) and to those with a higher volume of patients receiving ventilation (above the median).

Additional sensitivity analyses were undertaken that excluded those patients who were admitted during the first influenza wave prior to October 15, 2009. Multilevel models were applied postmatching to allow for the hierarchical nature of the data (ie, that outcomes may be more similar within than across critical care units) and to adjust for any residual differences in patient factors between the groups. Finally, the sensitivity of the results to unmeasured confounding was assessed.28

Statistical analyses were conducted using Stata software version 11 (StataCorp, College Station, Texas) and R software version 2.10.1 (R Foundation for Statistical Computing, http://www.r-project.org/). Two-sided testing was used with a P value significance level of less than .05.

Eighty patients were referred, accepted, and transferred to 1 of the 4 UK ECMO centers (eTable 2), of whom 69 received ECMO (86.3%; eTable 3). No patients were refused transfer for ECMO based on lack of bed availability. Of the 80 patients referred, accepted, and transferred to 1 of the 4 UK ECMO centers, 10 were prior to and 3 were after the SwiFT study. The lead center at Glenfield Hospital (Heartlink ECMO center) received 51 ECMO-referred patients. The Royal Brompton Hospital received 18 ECMO-referred patients, the Papworth Hospital received 7 ECMO-referred patients, and the Aberdeen Royal Infirmary received 4 ECMO-referred patients. Of 1676 potential non–ECMO-referred patients, 195 were eligible for matching after exclusion criteria had been applied (Figure 1). The majority (n = 1441; 86.0%) of exclusions were for patients who were not suitable for ECMO.

Place holder to copy figure label and caption
Figure 1. Enrollment and Matching of Patients
Graphic Jump Location

ECMO indicates extracorporeal membrane oxygenation; FIO2 fraction of inspired oxygen; SwiFT, Swine Flu Triage.

Prior to matching, there were differences between the ECMO-referred patients and non–ECMO-referred patients for all patient characteristics apart from BMI (Table 1). ECMO-referred patients were younger, more likely to be currently or recently pregnant, had received longer duration of mechanical ventilation including use of alternative ventilation strategies, and had worse respiratory physiological characteristics.

Table Graphic Jump LocationTable 1. Characteristics of ECMO-Referred and Non–ECMO-Referred Patients Before and After Matching

The individual matching technique identified 59 non–ECMO-referred patients for 59 ECMO-referred patients, but excluded many pregnant or postpartum women. Propensity score and GenMatch matching identified 75 non–ECMO-referred patients for 75 ECMO-referred patients. All of the matching methods improved covariate balance (Table 1). Following propensity score matching, there were proportionally more ECMO-referred patients currently or recently pregnant compared with non–ECMO-referred patients. Following GenMatch and individual matching, this potential confounder was well-balanced.

Outcome

Twenty-two patients (27.5%) who had been transferred to 1 of the 4 UK ECMO centers died. The hospital mortality rate was 23.7% for ECMO-referred patients vs 52.5% for non–ECMO-referred patients (RR, 0.45 [95% CI, 0.26-0.79]; P = .006) when individual matching was used; 24.0% vs 46.7%, respectively (RR, 0.51 [95% CI, 0.31-0.84]; P = .008) when propensity score matching was used; and 24.0% vs 50.7%, respectively (RR, 0.47 [95% CI, 0.31-0.72]; P = .001) when GenMatch matching was used (Table 2). The survival curves indicate a considerable number of early deaths among the non–ECMO-referred patients (Figure 2). The benefit of ECMO persisted after repeating the survival analysis and excluding the matched pairs in which either the ECMO-referred patient or the non–ECMO-referred patient died during the first 48 hours (eFigure).

Place holder to copy figure label and caption
Figure 2. Survival Curves for ECMO-Referred Patients vs Matched Non–ECMO-Referred Patients
Graphic Jump Location

Study entry was defined as the day of transfer to an extracorporeal membrane oxygenation (ECMO) center for ECMO-referred patients and the equivalent day of mechanical ventilation for matched non–ECMO-referred patients.

Table Graphic Jump LocationTable 2. Deaths Analyzed by Matching Methods

For ECMO-referred patients transferred to an ECMO center (from the UK ECMO registry), 10 died while receiving ECMO therapy (7 had cerebral hemorrhage, 1 had precannulation cardiac arrest, 1 had multiorgan failure, and 1 had massive pulmonary hemorrhage), 6 died after receiving ECMO therapy and prior to discharge from the center (1 had neutropenic sepsis, 2 had irrecoverable lung damage, 1 had rhabdomyolysis, and 2 had multiorgan failure), 4 died after being transferred back to the referring hospital (1 had pulmonary embolism, 1 had cerebrovascular accident, and 2 had multiorgan failure), and 2 died after being managed without ECMO (1 intracranial and 1 pulmonary hemorrhage).

Sensitivity Analyses

The sensitivity analyses indicated that the results were robust to alternative exclusion criteria (Figure 3A). The mean RRs of death for ECMO-referred patients vs non–ECMO-referred patients remained between 0.4 and 0.6 when the analyses were restricted to patients with confirmed H1N1 infection; ECMO-referred patients receiving ECMO; and patients with an FIO2 of 1.0. When all restrictions were applied, the mean risk of death for ECMO-referred patients vs non–ECMO-referred patients was between 0.5 and 0.9. In some of these analyses, the sample size was much reduced and the 95% CIs for the RRs of death spanned unity, but these findings were still consistent with the base-case result.

Place holder to copy figure label and caption
Figure 3. Sensitivity Analyses for ECMO-Referred Patients vs Matched Non–ECMO-Referred Patients
Graphic Jump Location

Vertical dashed lines indicate the relative risks (RRs) in the base-case analyses. ECMO indicates extracorporeal membrane oxygenation; FIO2 fraction of inspired oxygen; H1N1, 2009 influenza A(H1N1); SMR, standardized hospital mortality ratio. aThe median volume of patients receiving ventilation was 233.

The results also were robust to the location where non–ECMO-referred patients were treated (Figure 3B). The mean RRs of death for ECMO-referred patients vs non–ECMO-referred patients remained between 0.4 and 0.7 when the non–ECMO-referred patient pool was limited to critical care units with a standardized hospital mortality ratio of less than 1.0 and a volume of patients receiving ventilation above the median of 233 per year. Similarly, limiting analyses to patients admitted during the second pandemic wave (after October 15, 2009) yielded mean RRs between 0.4 and 0.7.

The overall results were robust to postmatching regression (eg, the postmatching multilevel models before and after adjusting for differences in patient factors reported RRs of death of 0.39 [P = .04] and 0.48 [P = .01]). Finally, the sensitivity analysis assessing the potential impact of an unmeasured confounder showed that to change the finding that transfer for ECMO was associated with lower hospital mortality, the odds ratio for this confounder would have to exceed 1.8.

ECMO-Related Adverse Events

All patients survived transfer to the ECMO center. One patient had cardiac perforation and tamponade during cannulation; this was successfully repaired surgically. Hemorrhagic complications associated with ECMO included intracranial hemorrhage (n = 8), cesarean delivery wound hematoma (n = 5), laparotomy wound hematoma (n = 1), fatal pulmonary hemorrhage (n = 1), cannula site hematoma (n = 3), spontaneous intraperitoneal hemorrhage (n = 1), hemothorax (n = 4), retroperitoneal hemorrhage (n = 2), minor upper airway bleeding (n = 9), and gastrointestinal tract bleeding (n = 2). One patient developed heparin-induced thrombocytopenia.

In a cohort of 80 patients with severe H1N1-related ARDS who were referred, accepted, and transferred to UK ECMO centers, 27.5% died before hospital discharge. Hospital mortality for matched non–ECMO-referred patients was approximately twice that of the ECMO-referred patients. This result was consistent across 3 alternative matching methods and robust to a priori–determined sensitivity analyses, including restriction to non–ECMO-referred patients treated in critical care units with characteristics generally associated with good outcomes.

Survival in UK ECMO centers was similar to other patients with H1N1-related ARDS who had been treated with ECMO, both from the Extracorporeal Life Support Organization registry (of 167 patients, 102 survivors [61%] aged ≥15 years [Peter Rycus, MPH, written communication, October 2, 2010]) and from Australia and New Zealand (of 68 patients, 54 survivors [79%] at the time of reporting; however, 22 patients [32%] remained in the hospital and 2 patients [3%] remained on ECMO).12 However, the UK ECMO-referred patients may have had more organ dysfunction compared with the case series from Australia and New Zealand because they were receiving more vasopressors and renal support at referral (eTable 2).

The unique value of this study lies in the homogeneity of the patients with H1N1-related ARDS and the matching methods used. The patient characteristics included in the matching were defined a priori and included those that have an effect on outcome (age,29 degree of hypoxemia,30,31 organ dysfunction,3,32 pregnancy,33,34 obesity,35 and the use of alternative ventilatory strategies). Individual matching ensured perfect balance on some variables, but resulted in nearly one-quarter of the sample, including almost half of the currently or recently pregnant ECMO-referred patients, remaining unmatched. Propensity score and GenMatch matching matched most of the ECMO-referred patients, with GenMatch matching achieving the best balance on all observed covariates.

This study has several limitations. First, despite attempting to minimize confounding at both the design and analysis stages, the role of unobserved confounders in explaining the differences in outcome cannot be discounted. It is possible that the non–ECMO-referred patient group included patients judged to be “too sick for ECMO,” and while the matching attempted to compensate for this, matching was limited to those variables available from the SwiFT study. These included few data characterizing respiratory function (lowest PaO2 and associated FIO2); for example, a single worst ratio of PaO2 to FIO2 may have been affected by fluid overload or suboptimal mechanical ventilation. However, a sensitivity analysis reported that an unmeasured confounder, with a perfect association with hospital mortality, would have to be relatively prevalent in ECMO-referred patients rather than in non–ECMO-referred patients (with an odds ratio >1.8) before a conclusion of no difference in hospital mortality was inferred. An analogous odds ratio for pretreatment high-frequency oscillation is 1.3. Hence, any unmeasured confounder would have to be much more imbalanced at baseline to overturn the study's results.

Second, management of non–ECMO-referred patients was not part of the study's protocol. It is not possible to ascertain whether lung protective ventilation was used. One indication for using ECMO is that it enables lung protective ventilation despite life-threatening hypoxemia or hypercarbia. Further reductions in tidal volume and plateau pressure may confer additional advantage.3638

Caution must be exercised in generalizing these results. First, the patients with H1N1 who became ECMO-referred patients were not representative of all patients with H1N1. ECMO-referred patients were younger, more likely to be currently or recently pregnant, had received longer durations of mechanical ventilation including use of alternative ventilation strategies, and had worse respiratory physiological characteristics compared with eligible non–ECMO-referred patients.

Second, the survival benefit associated with transfer for ECMO could be attributed to other factors associated with 4 specialized, highly resourced centers. Such factors may be related, for example, to available facilities, the number, availability, and skill of the clinical staff, differences in ventilatory and other care processes, and volume of ECMO-referred patients.39 However, when the analyses were restricted to comparison with non–ECMO-referred patients treated in critical care units with characteristics generally associated with good outcomes following standard care, the association with survival remained.

Furthermore, patients with H1N1 infection may have been particularly likely to benefit from transfer for ECMO because the infection was likely to resolve with appropriate antiviral therapy15,34 and the patients were relatively young. Indeed, while the survival in the non–ECMO-referred patients in this and in the CESAR study were almost identical, the survival rates in the ECMO-referred patients were 73% and 63%, respectively.4 Whether advances in ECMO technology in the intervening period between these studies account for some of this improvement is not known.

The role of ECMO in ARDS is debated.68 Several reports5,12,4042 and our study demonstrate that ECMO can be undertaken without the prohibitive morbidity and adverse events seen in the 1970s.43 The data from our study are complementary to those of the CESAR randomized trial,4 which demonstrated a reduction in the composite outcome of death and severe disability at 6 months in patients with severe ARDS who were transferred for consideration for ECMO, of whom 76% were supported with ECMO.

During the influenza pandemic of 2009-2010 and despite global concern, no randomized clinical trial for patients with H1N1 was funded, established, and completed. Instead, our study uses 3 different forms of case-matching to minimize confounding when estimating effectiveness from observational data. Our study found that transfer to an ECMO center for patients with H1N1-related ARDS was associated with lower hospital mortality compared with matched non–ECMO-referred patients. This finding was consistent across all 3 matching methods used.

Corresponding Author: Giles J. Peek, FRCS (CTh), MD, Glenfield Hospital, Groby Road, Leicester LE3 9QP, England (giles.peek@uhl-tr.nhs.uk).

Published Online: October 5, 2011. doi:10.1001/jama.2011.1471

Author Contributions: Dr Rowan had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Noah, Peek, Finney, Griffiths, Harrison, Grieve, McAuley, Noble, Menon, Rowan.

Acquisition of data: Noah, Peek, Finney, Griffiths, Harrison, Firmin, Harvey, Cordingley, Price, Vuylsteke, Jenkins, Noble, Bloomfield, Walsh, Perkins, Menon, Rowan.

Analysis and interpretation of data: Peek, Finney, Griffiths, Harrison, Grieve, Sadique, Sekhon, McAuley, Vuylsteke, Jenkins, Walsh, Perkins, Taylor, Rowan.

Drafting of the manuscript: Noah, Peek, Finney, Griffiths, Harrison, Grieve, Sadique, Vuylsteke, Jenkins, Menon, Taylor, Rowan.

Critical revision of the manuscript for important intellectual content: Noah, Peek, Finney, Griffiths, Harrison, Grieve, Sadique, Sekhon, McAuley, Firmin, Harvey, Cordingley, Price, Vuylsteke, Jenkins, Noble, Bloomfield, Walsh, Perkins, Menon, Rowan.

Statistical analysis: Harrison, Grieve, Sadique, Sekhon.

Obtained funding: Harrison, McAuley, Perkins, Menon, Rowan.

Administrative, technical, or material support: Peek, Griffiths, Firmin, Harvey, Cordingley, Price, Vuylsteke, Taylor.

Study supervision: Peek, Jenkins, Menon, Rowan.

Conflict of Interest Disclosures: All authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Dr Peek reported that money has been paid to his institution (Glenfield Hospital) as grants from the Society of Critical Care Medicine, the European Society of Intensive Care Medicine, Avalon Labs, and Chalice Medical for travel and accommodation to facilitate his attendance at international conferences. Dr Griffiths reported that money was paid to him from GlaxoSmithKline for development of assets for acute lung injury and he has grants pending with GlaxoSmithKline for such development of assets. Dr Harrison reported that money has been paid to his institution (Intensive Care National Audit & Research Centre) from the National Institute for Health Research as research grant funding for the SwiFT study. Dr McAuley reported that he has consulted for, sat on advisory boards for, and received lecture fees from GlaxoSmithKline and received lecture fees from AstraZeneca for educational meetings. Dr Harvey reported that money has been paid to him from Avalon Labs for the cost of flights to attend an extracorporeal membrane oxygenation (ECMO) conference. Dr Price reported that she has received royalties as a textbook editor for cardiovascular critical care from Wiley-Blackwell; payment for the development of educational materials and honoraria from Medtronic; and reimbursement from Medtronic for travel undertaken to give educational lectures and accommodations for the European Society of Cardiology meeting in 2010 and 2011. Dr Jenkins reported that he has received reimbursement from Chalice Medical (UK distributor for Levitronix ECMO pump) for travel expenses to the Extracorporeal Life Support Organization conference in 2010. Dr Perkins reported that money was paid to his institution (University of Warwick) from GlaxoSmithKline for his work as a member of an advisory board for novel treatment of acute respiratory distress syndrome; and he received lecture fees from GlaxoSmithKline. Dr Menon reported that his institution has received grant support from the National Institute for Health Research, UK Health Technology; he serves as a paid consultant or member of a data and safety monitoring board for Solvay, GlaxoSmithKline, Brainscope, Ornim Medical, Shire Medical, and Neurovive; he has received an honorarium from GlaxoSmithKline for one lecture at the London Hospital; money is paid to his institution for a patent registered for a new positron emission tomography ligand for assessing mitochondrial function; and he receives royalties from the University of Cambridge Press as a co-editor of a textbook on neuroanesthesia and critical care. Dr Rowan reported that money was paid to her institution (Intensive Care National Audit & Research Centre) from the National Institute for Health Research as research grant funding for the SwiFT study. No other authors reported disclosures.

Funding/Support: The SwiFT study was funded by the National Institute for Health Research and has been published in full in the Health Technology Assessment journal series.

Role of the Sponsor: The National Institute for Health Research had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; or in the preparation, review, or approval of the manuscript.

SwiFT Study Investigators: Aberdeen Royal Infirmary (Stephen Stott), Addenbrookes Hospital (David Menon), Airedale General Hospital (John Scriven), Alder Hey Hospital (Paul Baines), Alexandra Hospital (Gareth Sellors), Altnagelvin Area Hospital (Lynn Gilfeather), Antrim Area Hospital (Ronnie Bailie), Arrowe Park Hospital (Ranjeev Mehra), Barnsley Hospital (Ak Bowry), Basildon Hospital (David Lowe), Basingstoke & North Hampshire Hospital (Patrick Creagh Barry), Bassetlaw Hospital (Anthony Dixon), Bedford Hospital (David Niblett), Birmingham Children's Hospital (Heather Duncan), Birmingham Heartlands Hospital (Gavin Perkins), Borders General Hospital (Jonathan Aldridge), Bristol Royal Infirmary (Tim Gould), Broomfield Hospital (Dilsh Arawwawala), Chelsea & Westminster Hospital (Richard Keays), Cheltenham General Hospital (Rob Orme), Chesterfield Royal Hospital (Nick Spittle), Chorley & South Ribble Hospital (Irfan Chaudry), Christie Hospital (Phil Haji-Michael), City Hospital (Nick Sherwood), Colchester District General Hospital (Andrew Timmins), Conquest Hospital (Rhian Edwards), Crosshouse Hospital (Roger White), Darent Valley Hospital (Mike Protopapas), Darlington Memorial Hospital (Alan Murray), Derriford Hospital (Peter Robbins), Dewsbury and District Hospital (Christine Hildyard), Doncaster Royal Infirmary (Benjamin Williams), Dorset County Hospital (Andrew Ball), Dumfries & Galloway Royal Infirmary (Dewi Williams), East Surrey Hospital (Barbara Bray), Eastbourne District General Hospital (Rhian Edwards), Epsom General Hospital (Larry Mulleague), Freeman Hospital (Jon Walton), Frenchay Hospital (Gareth Wrathall), Friarage Hospital (Steven Bonner), Frimley Park Hospital (Rhiannon Jackson), Glasgow Royal Infirmary (Tara Quasim), Glenfield Hospital (Iain Maclaren), Gloucestershire Royal Hospital (Steven Twigg), Good Hope Hospital (Gavin Perkins), Great Ormond Street Hospital (Mark Peters), Guy's Hospital (Andrew Jones), Hairmyres Hospital (Veronica Watson), Harrogate District Hospital (David Earl), Hereford County Hospital (Richard Harding), Hinchingbrooke Hospital (George Koshi), Homerton University Hospital (David Watson), Hull Royal Infirmary (Ian Smith), Inverclyde Hospital (Fiona Munro), James Cook University Hospital (Steven Bonner), John Radcliffe Hospital (Duncan Young), Kent & Canterbury Hospital (Tony Beaumont), Kettering General Hospital (Phil Watt), King George Hospital (Farrel Igielman), King's College Hospital (Andre Vercueil), King's Mill Hospital (Mervyn Ross), Kingston Hospital (Jim Zwaal), Leicester Royal Infirmary (Iain Maclaren), Lincoln County Hospital (Murray Spittal), Lister Hospital (Ruth Van Hoogstraten), Liverpool Heart and Chest Hospital (Omar Al-Rawi), Luton and Dunstable Hospital (Steve Brosnan), Macclesfield District General Hospital (John Hunter), Maidstone Hospital (Lee Baldwin), Manchester Royal Infirmary (Jane Eddleston), Mater Misericordiae University Hospital (Brian Marsh), Mayday University Hospital (Arif Moghal), Medway Maritime Hospital (David Simpson), Mid Western Regional Hospital Limerick (Catherine Motherway), Milton Keynes Hospital (Hamid Manji), Monklands Hospital (Jim Ruddy), Musgrove Park Hospital (Andy Daykin), Nevill Hall Hospital (Stephen Edwards), New Cross Hospital (Simon Hester), Newcastle General Infirmary (Alan Sweenie), Newham General Hospital (Stuart Withington), Ninewells Hospital & Medical School (Stephen Cole), Norfolk & Norwich University Hospital (Tim Leary), North Devon District Hospital (Tim Cobby), North Middlesex University Hospital (Ah-Fee Chan), North Staffordshire University Hospital (Nick Coleman), North Tyneside General Hospital (Karen Connelly), Northwick Park Hospital (Gary Wares), Nottingham City Hospital (Bob Winter), Papworth Hospital (Alain Vuylsteke), Perth Royal Infirmary (Shelagh Winship), Peterborough District Hospital (Stephen Short), Pilgrim Hospital (Murray Spittal), Poole Hospital (Ken Power), Princess Alexandra Hospital (Jeff Phillips), Princess Royal Hospital (David Christmas), Queen Alexandra Hospital (Bruce Taylor), Queen Elizabeth Hospital, Gateshead (Lynn Swanson), Queen Elizabeth Hospital, King's Lynn (Parvez Moondi), Queen Elizabeth II Hospital (Ruth Van Hoogstraten), Queen Margaret Hospital (Marcia McDougall), Queen Mary's Hospital, London (Chris Palin), Queen Victoria Hospital (Alison Munday), Queen's Hospital, Burton (Paul Stewart), Queen's Hospital, Romford (Rajesh Jain), Queen's Medical Center, Nottingham (Bob Winter), Raigmore Hospital (Jonathan Whiteside), Rotherham General Hospital (David Harling), Royal Berkshire Hospital (Chris Danbury), Royal Blackburn Hospital (Anton Krige), Royal Bournemouth General Hospital (Rob McCormick), Royal Brompton Hospital (Simon Finney), Royal Cornwall Hospital (Cate Powell), Royal Derby Hospital (Nick Reynolds), Royal Devon & Exeter Hospital (Chris Day), Royal Gwent Hospital (Jack Parry Jones), Royal Hampshire County Hospital (Arthur Goldsmith), Royal Infirmary of Edinburgh (Tim Walsh), Royal Lancaster Infirmary (David Highley), Royal Liverpool University Hospital (Richard Wenstone), Royal Preston Hospital (Irfan Chaudry), Royal Shrewsbury Hospital (David Christmas), Royal Sussex County Hospital (James Yassin), Royal United Hospital, Bath (Jerry Nolan), Royal Victoria Hospital, Belfast (Danny McAuley), Royal Victoria Infirmary, Newcastle (Ian Clement), Russells Hall Hospital (Helga Becker), Sandwell Hospital (Nick Sherwood), Scarborough General Hospital (Jo Jaidev), Sheffield Children's Hospital (Anton Mayer), Sligo General Hospital (Ronan O’Hare), Solihull Hospital (Gavin Perkins), South Tyneside District Hospital (Govindan Balaraj), Southampton General Hospital (Rebecca Cusack), Southend University Hospital (David Higgins), Southern General Hospital (Gregor Imrie), Southmead Hospital (Gareth Wrathall), Southport & Formby District General Hospital (Michael Vangikar), St George's Hospital (Barbara Philips), St Helier Hospital (Larry Mulleague), St John's Hospital (Samantha Moultrie), St Peters Hospital (Barry Sellick), St Richards Hospital (Michael Margarson), St Thomas' Hospital (Andrew Jones), St Lukes Hospital, Kilkenny (Niall Kavanagh), Stafford Hospital (Greg Earnshaw), Stirling Royal Infirmary (Chris Cairns), Stobhill Hospital (Carol Murdoch), Stoke Mandeville Hospital (Kumar Panikkar), Sunderland Royal Hospital (Andy Morrison), Great Western Hospital (Malcolm Watters), Ipswich Hospital (Richard Lloyd), James Paget University Hospital (Darell Tupper-Carey), Royal London Hospital (Marie Healy), Whittington Hospital (Chris Hargreaves), Torbay Hospital (John Carlisle), Trafford General Hospital (John Barnes), Ulster Hospital (Claire Jamison), University College Hospital, Galway (John Bates), University College Hospital, London (Sara Polhill), University Hospital Aintree (Tristan Cope), University Hospital Coventry (Buddhavarapu Murthy), University Hospital of Hartlepool (Vijay Gupta), University Hospital of North Durham (Alan Murray), University Hospital of North Tees (Vijay Gupta), University Hospital of Wales (Gareth Scholey), Victoria Hospital, Blackpool (Chris Clarke), Walton Center for Neurology & Neurosurgery (Ian Tweedie), Warrington Hospital (Jennifer Tytler), Warwick Hospital (Tim Long), Waterford Regional Hospital (Brian O’Brien), Watford General Hospital (Thomas Stambach), West Cumberland Hospital (Ian Ulyett), West Middlesex University Hospital (Thomas Mphanza), West Suffolk Hospital (John Cardy), Western General Hospital (Charles Wallis), Weston General Hospital (Ioannis Tsagurnis), Wexford General Hospital (David Honan), Wexham Park Hospital (Rob Loveland), Whipps Cross Hospital (Frieda Keane), Whiston Hospital (Christina Jones), Wishaw General Hospital (Lorraine Bell), Worcestershire Royal Hospital (Jeremy Thomas), Worthing Hospital (Shaun Anderson), Wrexham Maelor Hospital (David Southern), Wycombe Hospital (Timothy Dexter), Wythenshawe Hospital (Andrew Bentley), Yeovil District Hospital (Jeremy Reid), York Hospital (Rinus Pretorius), Ysbyty Glan Clwyd (Richard Pugh), and Ysbyty Gwynedd (Pierre Peyrasse).

Disclaimer: The views and opinions expressed in this article are those of the authors and do not necessarily reflect those of the UK Department of Health.

Additional Contributions: We acknowledge the support of NHS Specialised Services of the UK Department of Health for supporting increased capacity to provide extracorporeal membrane oxygenation (ECMO) for adult patients during the 2009 influenza A(H1N1) pandemic. The National ECMO Service is indebted to dedication of the coordinators at Glenfield Hospital, Leicester, England, led by Gail Faulkner, RGN, and Maggie Hickey, BSc. We are indebted to the relevant staff at all of the hospitals participating in the SwiFT study. Mss Faulkner and Hickey were compensated for their contributions as employees of Glenfield Hospital but they also gave a considerable amount of their own time for which they were not compensated.

Pinhu L, Whitehead T, Evans T, Griffiths M. Ventilator-associated lung injury.  Lancet. 2003;361(9354):332-340
PubMed   |  Link to Article
Imai Y, Parodo J, Kajikawa O,  et al.  Injurious mechanical ventilation and end-organ epithelial cell apoptosis and organ dysfunction in an experimental model of acute respiratory distress syndrome.  JAMA. 2003;289(16):2104-2112
PubMed   |  Link to Article
Acute Respiratory Distress Syndrome Network.  Ventilation with lower tidal volumes as compared with traditional tidal volumes for acute lung injury and the acute respiratory distress syndrome.  N Engl J Med. 2000;342(18):1301-1308
PubMed   |  Link to Article
Peek GJ, Mugford M, Tiruvoipati R,  et al; CESAR Trial Collaboration.  Efficacy and economic assessment of Conventional Ventilatory Support Versus Extracorporeal Membrane Oxygenation for Severe Adult Respiratory Failure (CESAR): a multicentre randomised controlled trial.  Lancet. 2009;374(9698):1351-1363
PubMed   |  Link to Article
Peek GJ, Elbourne D, Mugford M,  et al.  Randomised controlled trial and parallel economic evaluation of Conventional Ventilatory Support Versus Extracorporeal Membrane Oxygenation for Severe Adult Respiratory Failure (CESAR).  Health Technol Assess. 2010;14(35):1-46
PubMed
Zwischenberger JB, Lynch JE. Will CESAR answer the adult ECMO debate?  Lancet. 2009;374(9698):1307-1308
PubMed   |  Link to Article
Park PK, Dalton HJ, Bartlett RH. Point: efficacy of extracorporeal membrane oxygenation in 2009 influenza A(H1N1): sufficient evidence?  Chest. 2010;138(4):776-778
PubMed   |  Link to Article
Morris AH, Hirshberg E, Miller RR III, Statler KD, Hite RD. Counterpoint: efficacy of extracorporeal membrane oxygenation in 2009 influenza A(H1N1): sufficient evidence?  Chest. 2010;138(4):778-784
PubMed   |  Link to Article
Domínguez-Cherit G, Lapinsky SE, Macias AE,  et al.  Critically Ill patients with 2009 influenza A(H1N1) in Mexico.  JAMA. 2009;302(17):1880-1887
PubMed   |  Link to Article
Kumar A, Zarychanski R, Pinto R,  et al; Canadian Critical Care Trials Group H1N1 Collaborative.  Critically ill patients with 2009 influenza A(H1N1) infection in Canada.  JAMA. 2009;302(17):1872-1879
PubMed   |  Link to Article
Webb SA, Pettilä V, Seppelt I,  et al; ANZIC Influenza Investigators.  Critical care services and 2009 H1N1 influenza in Australia and New Zealand.  N Engl J Med. 2009;361(20):1925-1934
PubMed   |  Link to Article
Davies A, Jones D, Bailey M,  et al; Australia and New Zealand Extracorporeal Membrane Oxygenation (ANZ ECMO) Influenza Investigators.  Extracorporeal membrane oxygenation for 2009 influenza A(H1N1) acute respiratory distress syndrome.  JAMA. 2009;302(17):1888-1895
PubMed   |  Link to Article
Rowan KM, Harrison DA, Walsh TS,  et al.  The Swine Flu Triage (SwiFT) study: development and ongoing refinement of a triage tool to provide regular information to guide immediate policy and practice for the use of critical care services during the H1N1 swine influenza pandemic.  Health Technol Assess. 2010;14(55):335-492
PubMed
Vincent JL, Moreno R, Takala J,  et al; Working Group on Sepsis-Related Problems of the European Society of Intensive Care Medicine.  The SOFA (Sepsis-related Organ Failure Assessment) score to describe organ dysfunction/failure.  Intensive Care Med. 1996;22(7):707-710
PubMed   |  Link to Article
Jain S, Kamimoto L, Bramley AM,  et al; 2009 Pandemic Influenza A (H1N1) Virus Hospitalizations Investigation Team.  Hospitalized patients with 2009 H1N1 influenza in the United States, April-June 2009.  N Engl J Med. 2009;361(20):1935-1944
PubMed   |  Link to Article
Wiedemann HP, Wheeler AP, Bernard GR,  et al; National Heart, Lung, and Blood Institute Acute Respiratory Distress Syndrome (ARDS) Clinical Trials Network.  Comparison of two fluid-management strategies in acute lung injury.  N Engl J Med. 2006;354(24):2564-2575
PubMed   |  Link to Article
Meduri GU, Golden E, Freire AX,  et al.  Methylprednisolone infusion in early severe ARDS: results of a randomized controlled trial.  Chest. 2007;131(4):954-963
PubMed   |  Link to Article
Tang BM, Craig JC, Eslick GD, Seppelt I, McLean AS. Use of corticosteroids in acute lung injury and acute respiratory distress syndrome: a systematic review and meta-analysis.  Crit Care Med. 2009;37(5):1594-1603
PubMed   |  Link to Article
Diamond A, Sekhon JS. Genetic matching for estimating causal effects: a general multivariate matching method for achieving balance in observational studies. http://sekhon.berkeley.edu/papers/GenMatch.pdf. Accessed September 1, 2011
Sekhon JS, Grieve RD. A matching method for improving covariate balance in cost-effectiveness analyses [published ahead of print June 2, 2011].  Health Econ
PubMed  |  Link to Article
Sekhon JS. Matching: multivariate and propensity score matching with automated balance search.  J Stat Softw. 2011;42(11):1-52
Sekhon JS, Mebane WRJ. Genetic optimization using derivatives: theory and application to nonlinear models.  Polit Anal. 1998;7(1):187-210
Link to Article
Mebane WRJ, Sekhon JS. Genetic optimization using derivatives: the rgenoud package for R.  J Stat Softw. 2011;42(7):1-26
Glance LG, Osler TM, Mukamel DB, Dick AW. Use of a matching algorithm to evaluate hospital coronary artery bypass grafting performance as an alternative to conventional risk adjustment.  Med Care. 2007;45(4):292-299
PubMed   |  Link to Article
Austin PC. Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples.  Stat Med. 2009;28(25):3083-3107
PubMed   |  Link to Article
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PubMed   |  Link to Article
Ramsahai R, Grieve R, Sekhon JS. Extending iterative matching methods: an approach to improving covariate balance that allows prioritisation [published online ahead of print September 14, 2011].  Health Serv Outcomes Res MethodolLink to Article
Rosenbaum PR. Covariance adjustment in randomised experiments and observational studies.  Stat Sci. 2002;17(3):286-304
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PubMed
Bellingan G. Hooray for hypoxia?  PLoS Med. 2005;2(6):e130
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Monchi M, Bellenfant F, Cariou A,  et al.  Early predictive factors of survival in the acute respiratory distress syndrome: a multivariate analysis.  Am J Respir Crit Care Med. 1998;158(4):1076-1081
PubMed   |  Link to Article
Suchyta MR, Orme JF Jr, Morris AH. The changing face of organ failure in ARDS.  Chest. 2003;124(5):1871-1879
PubMed   |  Link to Article
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PubMed   |  Link to Article
Siston AM, Rasmussen SA, Honein MA,  et al; Pandemic H1N1 Influenza in Pregnancy Working Group.  Pandemic 2009 influenza A(H1N1) virus illness among pregnant women in the United States.  JAMA. 2010;303(15):1517-1525
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Morgan OW, Bramley A, Fowlkes A,  et al.  Morbid obesity as a risk factor for hospitalization and death due to 2009 pandemic influenza A(H1N1) disease.  PLoS One. 2010;5(3):e9694
PubMed   |  Link to Article
Hager DN, Krishnan JA, Hayden DL, Brower RG.ARDS Clinical Trials Network.  Tidal volume reduction in patients with acute lung injury when plateau pressures are not high.  Am J Respir Crit Care Med. 2005;172(10):1241-1245
PubMed   |  Link to Article
Terragni PP, Del Sorbo L, Mascia L,  et al.  Tidal volume lower than 6 mL/kg enhances lung protection: role of extracorporeal carbon dioxide removal.  Anesthesiology. 2009;111(4):826-835
PubMed   |  Link to Article
Terragni PP, Rosboch G, Tealdi A,  et al.  Tidal hyperinflation during low tidal volume ventilation in acute respiratory distress syndrome.  Am J Respir Crit Care Med. 2007;175(2):160-166
PubMed   |  Link to Article
Kahn JM, Goss CH, Heagerty PJ, Kramer AA, O’Brien CR, Rubenfeld GD. Hospital volume and the outcomes of mechanical ventilation.  N Engl J Med. 2006;355(1):41-50
PubMed   |  Link to Article
Freed DH, Henzler D, White CW,  et al; Canadian Critical Care Trials Group.  Extracorporeal lung support for patients who had severe respiratory failure secondary to influenza A (H1N1) 2009 infection in Canada.  Can J Anaesth. 2010;57(3):240-247
PubMed   |  Link to Article
Roch A, Lepaul-Ercole R, Grisoli D,  et al.  Extracorporeal membrane oxygenation for severe influenza A (H1N1) acute respiratory distress syndrome: a prospective observational comparative study.  Intensive Care Med. 2010;36(11):1899-1905
PubMed   |  Link to Article
Peek GJ, Moore HM, Moore N, Sosnowski AW, Firmin RK. Extracorporeal membrane oxygenation for adult respiratory failure.  Chest. 1997;112(3):759-764
PubMed   |  Link to Article
Zapol WM, Snider MT, Hill JD,  et al.  Extracorporeal membrane oxygenation in severe acute respiratory failure: a randomized prospective study.  JAMA. 1979;242(20):2193-2196
PubMed   |  Link to Article

Figures

Place holder to copy figure label and caption
Figure 1. Enrollment and Matching of Patients
Graphic Jump Location

ECMO indicates extracorporeal membrane oxygenation; FIO2 fraction of inspired oxygen; SwiFT, Swine Flu Triage.

Place holder to copy figure label and caption
Figure 2. Survival Curves for ECMO-Referred Patients vs Matched Non–ECMO-Referred Patients
Graphic Jump Location

Study entry was defined as the day of transfer to an extracorporeal membrane oxygenation (ECMO) center for ECMO-referred patients and the equivalent day of mechanical ventilation for matched non–ECMO-referred patients.

Place holder to copy figure label and caption
Figure 3. Sensitivity Analyses for ECMO-Referred Patients vs Matched Non–ECMO-Referred Patients
Graphic Jump Location

Vertical dashed lines indicate the relative risks (RRs) in the base-case analyses. ECMO indicates extracorporeal membrane oxygenation; FIO2 fraction of inspired oxygen; H1N1, 2009 influenza A(H1N1); SMR, standardized hospital mortality ratio. aThe median volume of patients receiving ventilation was 233.

Tables

Table Graphic Jump LocationTable 1. Characteristics of ECMO-Referred and Non–ECMO-Referred Patients Before and After Matching
Table Graphic Jump LocationTable 2. Deaths Analyzed by Matching Methods

References

Pinhu L, Whitehead T, Evans T, Griffiths M. Ventilator-associated lung injury.  Lancet. 2003;361(9354):332-340
PubMed   |  Link to Article
Imai Y, Parodo J, Kajikawa O,  et al.  Injurious mechanical ventilation and end-organ epithelial cell apoptosis and organ dysfunction in an experimental model of acute respiratory distress syndrome.  JAMA. 2003;289(16):2104-2112
PubMed   |  Link to Article
Acute Respiratory Distress Syndrome Network.  Ventilation with lower tidal volumes as compared with traditional tidal volumes for acute lung injury and the acute respiratory distress syndrome.  N Engl J Med. 2000;342(18):1301-1308
PubMed   |  Link to Article
Peek GJ, Mugford M, Tiruvoipati R,  et al; CESAR Trial Collaboration.  Efficacy and economic assessment of Conventional Ventilatory Support Versus Extracorporeal Membrane Oxygenation for Severe Adult Respiratory Failure (CESAR): a multicentre randomised controlled trial.  Lancet. 2009;374(9698):1351-1363
PubMed   |  Link to Article
Peek GJ, Elbourne D, Mugford M,  et al.  Randomised controlled trial and parallel economic evaluation of Conventional Ventilatory Support Versus Extracorporeal Membrane Oxygenation for Severe Adult Respiratory Failure (CESAR).  Health Technol Assess. 2010;14(35):1-46
PubMed
Zwischenberger JB, Lynch JE. Will CESAR answer the adult ECMO debate?  Lancet. 2009;374(9698):1307-1308
PubMed   |  Link to Article
Park PK, Dalton HJ, Bartlett RH. Point: efficacy of extracorporeal membrane oxygenation in 2009 influenza A(H1N1): sufficient evidence?  Chest. 2010;138(4):776-778
PubMed   |  Link to Article
Morris AH, Hirshberg E, Miller RR III, Statler KD, Hite RD. Counterpoint: efficacy of extracorporeal membrane oxygenation in 2009 influenza A(H1N1): sufficient evidence?  Chest. 2010;138(4):778-784
PubMed   |  Link to Article
Domínguez-Cherit G, Lapinsky SE, Macias AE,  et al.  Critically Ill patients with 2009 influenza A(H1N1) in Mexico.  JAMA. 2009;302(17):1880-1887
PubMed   |  Link to Article
Kumar A, Zarychanski R, Pinto R,  et al; Canadian Critical Care Trials Group H1N1 Collaborative.  Critically ill patients with 2009 influenza A(H1N1) infection in Canada.  JAMA. 2009;302(17):1872-1879
PubMed   |  Link to Article
Webb SA, Pettilä V, Seppelt I,  et al; ANZIC Influenza Investigators.  Critical care services and 2009 H1N1 influenza in Australia and New Zealand.  N Engl J Med. 2009;361(20):1925-1934
PubMed   |  Link to Article
Davies A, Jones D, Bailey M,  et al; Australia and New Zealand Extracorporeal Membrane Oxygenation (ANZ ECMO) Influenza Investigators.  Extracorporeal membrane oxygenation for 2009 influenza A(H1N1) acute respiratory distress syndrome.  JAMA. 2009;302(17):1888-1895
PubMed   |  Link to Article
Rowan KM, Harrison DA, Walsh TS,  et al.  The Swine Flu Triage (SwiFT) study: development and ongoing refinement of a triage tool to provide regular information to guide immediate policy and practice for the use of critical care services during the H1N1 swine influenza pandemic.  Health Technol Assess. 2010;14(55):335-492
PubMed
Vincent JL, Moreno R, Takala J,  et al; Working Group on Sepsis-Related Problems of the European Society of Intensive Care Medicine.  The SOFA (Sepsis-related Organ Failure Assessment) score to describe organ dysfunction/failure.  Intensive Care Med. 1996;22(7):707-710
PubMed   |  Link to Article
Jain S, Kamimoto L, Bramley AM,  et al; 2009 Pandemic Influenza A (H1N1) Virus Hospitalizations Investigation Team.  Hospitalized patients with 2009 H1N1 influenza in the United States, April-June 2009.  N Engl J Med. 2009;361(20):1935-1944
PubMed   |  Link to Article
Wiedemann HP, Wheeler AP, Bernard GR,  et al; National Heart, Lung, and Blood Institute Acute Respiratory Distress Syndrome (ARDS) Clinical Trials Network.  Comparison of two fluid-management strategies in acute lung injury.  N Engl J Med. 2006;354(24):2564-2575
PubMed   |  Link to Article
Meduri GU, Golden E, Freire AX,  et al.  Methylprednisolone infusion in early severe ARDS: results of a randomized controlled trial.  Chest. 2007;131(4):954-963
PubMed   |  Link to Article
Tang BM, Craig JC, Eslick GD, Seppelt I, McLean AS. Use of corticosteroids in acute lung injury and acute respiratory distress syndrome: a systematic review and meta-analysis.  Crit Care Med. 2009;37(5):1594-1603
PubMed   |  Link to Article
Diamond A, Sekhon JS. Genetic matching for estimating causal effects: a general multivariate matching method for achieving balance in observational studies. http://sekhon.berkeley.edu/papers/GenMatch.pdf. Accessed September 1, 2011
Sekhon JS, Grieve RD. A matching method for improving covariate balance in cost-effectiveness analyses [published ahead of print June 2, 2011].  Health Econ
PubMed  |  Link to Article
Sekhon JS. Matching: multivariate and propensity score matching with automated balance search.  J Stat Softw. 2011;42(11):1-52
Sekhon JS, Mebane WRJ. Genetic optimization using derivatives: theory and application to nonlinear models.  Polit Anal. 1998;7(1):187-210
Link to Article
Mebane WRJ, Sekhon JS. Genetic optimization using derivatives: the rgenoud package for R.  J Stat Softw. 2011;42(7):1-26
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