Editorials 9. Griffiths J, Hatch RA, Bishop J, et al: An exploration of social and economic outcome and associated health-related quality of life after critical illness in general intensive care unit survivors: A 12-month follow-up study. Crit Care 2013; 17:R100 10. Cox CE, Docherty SL, Brandon DH, et al: Surviving critical illness: Acute respiratory distress syndrome as experienced by patients and their caregivers. Crit Care Med 2009; 37:2702–2708 11. Lee CM, Herridge MS, Matte A, et al: Education and support needs during recovery in acute respiratory distress syndrome. Crit Care 2009; 13:R153
12. Liu V, Lei X, Prescott HC, et al: Hospital readmission and healthcare utilization following sepsis in community settings. J Hosp Med 2014; 9:502–507 13. Prescott HC, Langa KM, Liu V, et al: Increased 1-year healthcare use in survivors of severe sepsis. Am J Respir Crit Care Med 2014; 190:62–69 14. Schweickert WD, Pohlman MC, Pohlman AS, et al: Early physical and occupational therapy in mechanically ventilated, critically ill patients: A randomised controlled trial. Lancet 2009; 373:1874–1882 15. Jones C, Bäckman C, Capuzzo M, et al; RACHEL group: Intensive care diaries reduce new onset post traumatic stress disorder following critical illness: A randomised, controlled trial. Crit Care 2010; 14:R168
Predicting Outcome in Acute Respiratory Distress Syndrome—Putting Some Science Behind Crystal Gazing* Shailesh Bihari, MD Andrew D. Bersten, MD Department of Critical Care Medicine Flinders Medical Centre and Flinders University Bedford Park, SA, Australia
A
cute hypoxemic respiratory failure and acute respiratory distress syndrome (ARDS) pose many complex, and often linked, problems including 1) time-varying prognosis and 2) timing of rescue therapies. Rescue therapies, such as prone position (1), inhaled nitric oxide (2), recruitment maneuvers (3), and the use of extracorporeal membrane oxygenation (ECMO) (4), are advocated early, with minimal benefit from them if they are instituted late in the course of acute respiratory failure. Consequently, prediction techniques using readily available data would be both clinically useful and help shape research projects by allowing high-risk patients to be enrolled early enough to better test an intervention. Currently available measures of prognosis for patients with ARDS are not particularly useful for clinical or research purposes. Acute Physiology and Chronic Health Evaluation II and Simplified Acute Physiology Score II scores poorly predict mortality in ARDS, with areas under their receiver operating characteristic curves (AUC) of 0.66 (95% CI, 0.61–0.71) and 0.63 (0.58–0.69), respectively (5). The American-European Consensus Conference (6) definition of ARDS and the recent Berlin definition (7) also have poor AUCs of 0.54 (0.52–0.55) and 0.58 (0.56–0.59), respectively, barely better than chance,
*See also p. 308. Key Words: acute hypoxemic respiratory failure; Acute Physiology and Chronic Health Evaluation II; acute respiratory distress syndrome; rescue therapies; Simplified Acute Physiology Score II The authors have disclosed that they do not have any potential conflicts of interest. Copyright © 2015 by the Society of Critical Care Medicine and Lippincott Williams & Wilkins DOI: 10.1097/CCM.0000000000000728
Critical Care Medicine
and the lung injury (Murray) score performs similarly with an AUC of 0.58 (0.53–0.64) (5). Biological markers give insights into pathophysiologic processes and may be predictive. Interleukin-4, interleukin-2, angiopoietin-2, and Krebs von den Lungen-6 are biomarkers most strongly associated with ARDS mortality (8); however, even though their combination with clinical data may strengthen their associations (9), these data remain interesting research observations. In this context, the study by Pannu et al (10) in this issue of Critical Care Medicine is a timely and a welcome development in this area. Their severe hypoxemia associated risk prediction (SHARP) model, which uses routinely available clinical data, identified patients with either acute hypoxemic respiratory failure or ARDS at increased risk of death in hospital. The full SHARP model, which has comorbid conditions (hematological malignancy, cirrhosis, and aspiration) and cardiopulmonary variables (pH, estimated physiological dead space, oxygenation index, and vasopressor use), showed good discrimination and calibration in both independent derivation (n = 464; 2005–2007) and validation (n = 568, 2008–2010) cohorts. The modified version (without comorbidities) also performed well in both derivation and validation cohorts. The methodology used was robust and their database (METRIC data mart) was previously validated (11). The AUC for hospital mortality using the full SHARP model was 0.85 (0.82–0.89) in the derivation cohort and 0.79 (0.75–0.82) in the validation cohort; similar results were found for the modified model. Increasing age was associated with mortality, but consistent with Eachempati et al, who failed to find an increase in mortality in elderly patients with ARDS (12), Pannu et al (10) were able to discard age in their model as it did not contribute to improved discrimination. The SHARP model based on data within 24 hours of the onset of acute hypoxemic respiratory failure results in a numerical score from –1 to 8. This allows earlier prediction than previous work from this group which used day 3 data (13), without loss of performance. Using a cutoff of 1, the SHARP model predicted a mortality rate between 30% and www.ccmjournal.org
481
Editorials
40% with a sensitivity of 0.87 (0.82–0.91) and specificity of 0.59 (0.52–0.64). Although a higher specificity is desirable, these data could inform both clinical estimates and assist researchers with a simple, early measure of severity. As the SHARP score rose further, so did specificity but sensitivity fell (Table 4 in [10]). For example, a score of more than or equal to 4 had a positive predictive value for hospital mortality of 0.99 (0.87–0.99) with a negative predictive value of 0.65 (0.60–0.69). Despite the potential clinical and research implications of this model, external validation is required as there are some important limitations. Between derivation (2005–2007) and validation (2008–2010) cohorts, there was a decrease in hospital mortality (41% vs 35%; p < 0.05). Similarly, the use of rescue therapies (such as high-frequency oscillatory ventilation, ECMO, prone positioning, and inhaled vasodilators) and use of higher positive end-expiratory pressure were greater in the validation cohort, indicating a change of practice during the time course of this retrospective study. Furthermore, as data for the therapy limitations, which may influence raw mortality, were not available and as more than 80% population in this study were Caucasians, racial and ethnic disparities also influence mortality from ARDS (14), further emphasizing the need for external validation before the SHARP model can be more widely used. Finally, as the mortality rates of ARDS are changing over the years, the SHARP model will require repeated validation over time. Well-managed data registries offer clinicians many opportunities, including a better understanding of their practice in a larger context, examination of trends in disease states such as sepsis and septic shock (15), and to develop tools that assist with both individual patient care and research. Over the last 2 decades, we have learnt a lot about ARDS and observed a falling mortality rate in association with key clinical trials. The SHARP model may be another step forward by giving easily accessible structure to the crystal gazing.
REFERENCES
1. Guérin C, Reignier J, Richard JC, et al; PROSEVA Study Group: Prone positioning in severe acute respiratory distress syndrome. N Engl J Med 2013; 368:2159–2168
482
www.ccmjournal.org
2. Taylor RW, Zimmerman JL, Dellinger RP, et al; Inhaled Nitric Oxide in ARDS Study Group: Low-dose inhaled nitric oxide in patients with acute lung injury: A randomized controlled trial. JAMA 2004; 291:1603–1609 3. Hodgson CL, Tuxen DV, Davies AR, et al: A randomised controlled trial of an open lung strategy with staircase recruitment, titrated PEEP and targeted low airway pressures in patients with acute respiratory distress syndrome. Crit Care 2011; 15:R133 4. 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; 374:1351–1363 5. Kangelaris KN, Calfee CS, May AK, et al: Is there still a role for the lung injury score in the era of the Berlin definition ARDS? Ann Intensive Care 2014; 4:4 6. Bernard GR, Artigas A, Brigham KL, et al: The American-European Consensus Conference on ARDS. Definitions, mechanisms, relevant outcomes, and clinical trial coordination. Am J Respir Crit Care Med 1994; 149:818–824 7. ARDS Definition Task Force, Ranieri VM, Rubenfeld GD, et al: Acute respiratory distress syndrome: The Berlin Definition. JAMA 2012; 307:2526–2533 8. Terpstra ML, Aman J, van Nieuw Amerongen GP, et al: Plasma biomarkers for acute respiratory distress syndrome: A systematic review and meta-analysis. Crit Care Med 2014; 42:691–700 9. Calfee CS, Ware LB, Glidden DV, et al; National Heart, Blood, and Lung Institute Acute Respiratory Distress Syndrome Network: Use of risk reclassification with multiple biomarkers improves mortality prediction in acute lung injury. Crit Care Med 2011; 39:711–717 10. Pannu SR, Moreno Franco P, Li G, et al: Development and Validation of Severe Hypoxemia Associated Risk Prediction Model in 1,000 Mechanically Ventilated Patients. Crit Care Med 2015; 43: 308–317 11. Herasevich V, Pickering BW, Dong Y, et al: Informatics infrastructure for syndrome surveillance, decision support, reporting, and modeling of critical illness. Mayo Clin Proc 2010; 85:247–254 12. Eachempati SR, Hydo LJ, Shou J, et al: Outcomes of acute respiratory distress syndrome (ARDS) in elderly patients. J Trauma 2007; 63:344–350 13. Gajic O, Afessa B, Thompson BT, et al; Second International Study of Mechanical Ventilation and ARDS-net Investigators: Prediction of death and prolonged mechanical ventilation in acute lung injury. Crit Care 2007; 11:R53 14. Erickson SE, Shlipak MG, Martin GS, et al; National Institutes of Health National Heart, Lung, and Blood Institute Acute Respiratory Distress Syndrome Network: Racial and ethnic disparities in mortality from acute lung injury. Crit Care Med 2009; 37:1–6 15. Kaukonen KM, Bailey M, Suzuki S, et al: Mortality related to severe sepsis and septic shock among critically ill patients in Australia and New Zealand, 2000-2012. JAMA 2014; 311:1308–1316
February 2015 • Volume 43 • Number 2
Copyright of Critical Care Medicine is the property of Lippincott Williams & Wilkins and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
12. Liu V, Lei X, Prescott HC, et al: Hospital readmission and healthcare utilization following sepsis in community settings. J Hosp Med 2014; 9:502–507 13. Prescott HC, Langa KM, Liu V, et al: Increased 1-year healthcare use in survivors of severe sepsis. Am J Respir Crit Care Med 2014; 190:62–69 14. Schweickert WD, Pohlman MC, Pohlman AS, et al: Early physical and occupational therapy in mechanically ventilated, critically ill patients: A randomised controlled trial. Lancet 2009; 373:1874–1882 15. Jones C, Bäckman C, Capuzzo M, et al; RACHEL group: Intensive care diaries reduce new onset post traumatic stress disorder following critical illness: A randomised, controlled trial. Crit Care 2010; 14:R168
Predicting Outcome in Acute Respiratory Distress Syndrome—Putting Some Science Behind Crystal Gazing* Shailesh Bihari, MD Andrew D. Bersten, MD Department of Critical Care Medicine Flinders Medical Centre and Flinders University Bedford Park, SA, Australia
A
cute hypoxemic respiratory failure and acute respiratory distress syndrome (ARDS) pose many complex, and often linked, problems including 1) time-varying prognosis and 2) timing of rescue therapies. Rescue therapies, such as prone position (1), inhaled nitric oxide (2), recruitment maneuvers (3), and the use of extracorporeal membrane oxygenation (ECMO) (4), are advocated early, with minimal benefit from them if they are instituted late in the course of acute respiratory failure. Consequently, prediction techniques using readily available data would be both clinically useful and help shape research projects by allowing high-risk patients to be enrolled early enough to better test an intervention. Currently available measures of prognosis for patients with ARDS are not particularly useful for clinical or research purposes. Acute Physiology and Chronic Health Evaluation II and Simplified Acute Physiology Score II scores poorly predict mortality in ARDS, with areas under their receiver operating characteristic curves (AUC) of 0.66 (95% CI, 0.61–0.71) and 0.63 (0.58–0.69), respectively (5). The American-European Consensus Conference (6) definition of ARDS and the recent Berlin definition (7) also have poor AUCs of 0.54 (0.52–0.55) and 0.58 (0.56–0.59), respectively, barely better than chance,
*See also p. 308. Key Words: acute hypoxemic respiratory failure; Acute Physiology and Chronic Health Evaluation II; acute respiratory distress syndrome; rescue therapies; Simplified Acute Physiology Score II The authors have disclosed that they do not have any potential conflicts of interest. Copyright © 2015 by the Society of Critical Care Medicine and Lippincott Williams & Wilkins DOI: 10.1097/CCM.0000000000000728
Critical Care Medicine
and the lung injury (Murray) score performs similarly with an AUC of 0.58 (0.53–0.64) (5). Biological markers give insights into pathophysiologic processes and may be predictive. Interleukin-4, interleukin-2, angiopoietin-2, and Krebs von den Lungen-6 are biomarkers most strongly associated with ARDS mortality (8); however, even though their combination with clinical data may strengthen their associations (9), these data remain interesting research observations. In this context, the study by Pannu et al (10) in this issue of Critical Care Medicine is a timely and a welcome development in this area. Their severe hypoxemia associated risk prediction (SHARP) model, which uses routinely available clinical data, identified patients with either acute hypoxemic respiratory failure or ARDS at increased risk of death in hospital. The full SHARP model, which has comorbid conditions (hematological malignancy, cirrhosis, and aspiration) and cardiopulmonary variables (pH, estimated physiological dead space, oxygenation index, and vasopressor use), showed good discrimination and calibration in both independent derivation (n = 464; 2005–2007) and validation (n = 568, 2008–2010) cohorts. The modified version (without comorbidities) also performed well in both derivation and validation cohorts. The methodology used was robust and their database (METRIC data mart) was previously validated (11). The AUC for hospital mortality using the full SHARP model was 0.85 (0.82–0.89) in the derivation cohort and 0.79 (0.75–0.82) in the validation cohort; similar results were found for the modified model. Increasing age was associated with mortality, but consistent with Eachempati et al, who failed to find an increase in mortality in elderly patients with ARDS (12), Pannu et al (10) were able to discard age in their model as it did not contribute to improved discrimination. The SHARP model based on data within 24 hours of the onset of acute hypoxemic respiratory failure results in a numerical score from –1 to 8. This allows earlier prediction than previous work from this group which used day 3 data (13), without loss of performance. Using a cutoff of 1, the SHARP model predicted a mortality rate between 30% and www.ccmjournal.org
481
Editorials
40% with a sensitivity of 0.87 (0.82–0.91) and specificity of 0.59 (0.52–0.64). Although a higher specificity is desirable, these data could inform both clinical estimates and assist researchers with a simple, early measure of severity. As the SHARP score rose further, so did specificity but sensitivity fell (Table 4 in [10]). For example, a score of more than or equal to 4 had a positive predictive value for hospital mortality of 0.99 (0.87–0.99) with a negative predictive value of 0.65 (0.60–0.69). Despite the potential clinical and research implications of this model, external validation is required as there are some important limitations. Between derivation (2005–2007) and validation (2008–2010) cohorts, there was a decrease in hospital mortality (41% vs 35%; p < 0.05). Similarly, the use of rescue therapies (such as high-frequency oscillatory ventilation, ECMO, prone positioning, and inhaled vasodilators) and use of higher positive end-expiratory pressure were greater in the validation cohort, indicating a change of practice during the time course of this retrospective study. Furthermore, as data for the therapy limitations, which may influence raw mortality, were not available and as more than 80% population in this study were Caucasians, racial and ethnic disparities also influence mortality from ARDS (14), further emphasizing the need for external validation before the SHARP model can be more widely used. Finally, as the mortality rates of ARDS are changing over the years, the SHARP model will require repeated validation over time. Well-managed data registries offer clinicians many opportunities, including a better understanding of their practice in a larger context, examination of trends in disease states such as sepsis and septic shock (15), and to develop tools that assist with both individual patient care and research. Over the last 2 decades, we have learnt a lot about ARDS and observed a falling mortality rate in association with key clinical trials. The SHARP model may be another step forward by giving easily accessible structure to the crystal gazing.
REFERENCES
1. Guérin C, Reignier J, Richard JC, et al; PROSEVA Study Group: Prone positioning in severe acute respiratory distress syndrome. N Engl J Med 2013; 368:2159–2168
482
www.ccmjournal.org
2. Taylor RW, Zimmerman JL, Dellinger RP, et al; Inhaled Nitric Oxide in ARDS Study Group: Low-dose inhaled nitric oxide in patients with acute lung injury: A randomized controlled trial. JAMA 2004; 291:1603–1609 3. Hodgson CL, Tuxen DV, Davies AR, et al: A randomised controlled trial of an open lung strategy with staircase recruitment, titrated PEEP and targeted low airway pressures in patients with acute respiratory distress syndrome. Crit Care 2011; 15:R133 4. 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; 374:1351–1363 5. Kangelaris KN, Calfee CS, May AK, et al: Is there still a role for the lung injury score in the era of the Berlin definition ARDS? Ann Intensive Care 2014; 4:4 6. Bernard GR, Artigas A, Brigham KL, et al: The American-European Consensus Conference on ARDS. Definitions, mechanisms, relevant outcomes, and clinical trial coordination. Am J Respir Crit Care Med 1994; 149:818–824 7. ARDS Definition Task Force, Ranieri VM, Rubenfeld GD, et al: Acute respiratory distress syndrome: The Berlin Definition. JAMA 2012; 307:2526–2533 8. Terpstra ML, Aman J, van Nieuw Amerongen GP, et al: Plasma biomarkers for acute respiratory distress syndrome: A systematic review and meta-analysis. Crit Care Med 2014; 42:691–700 9. Calfee CS, Ware LB, Glidden DV, et al; National Heart, Blood, and Lung Institute Acute Respiratory Distress Syndrome Network: Use of risk reclassification with multiple biomarkers improves mortality prediction in acute lung injury. Crit Care Med 2011; 39:711–717 10. Pannu SR, Moreno Franco P, Li G, et al: Development and Validation of Severe Hypoxemia Associated Risk Prediction Model in 1,000 Mechanically Ventilated Patients. Crit Care Med 2015; 43: 308–317 11. Herasevich V, Pickering BW, Dong Y, et al: Informatics infrastructure for syndrome surveillance, decision support, reporting, and modeling of critical illness. Mayo Clin Proc 2010; 85:247–254 12. Eachempati SR, Hydo LJ, Shou J, et al: Outcomes of acute respiratory distress syndrome (ARDS) in elderly patients. J Trauma 2007; 63:344–350 13. Gajic O, Afessa B, Thompson BT, et al; Second International Study of Mechanical Ventilation and ARDS-net Investigators: Prediction of death and prolonged mechanical ventilation in acute lung injury. Crit Care 2007; 11:R53 14. Erickson SE, Shlipak MG, Martin GS, et al; National Institutes of Health National Heart, Lung, and Blood Institute Acute Respiratory Distress Syndrome Network: Racial and ethnic disparities in mortality from acute lung injury. Crit Care Med 2009; 37:1–6 15. Kaukonen KM, Bailey M, Suzuki S, et al: Mortality related to severe sepsis and septic shock among critically ill patients in Australia and New Zealand, 2000-2012. JAMA 2014; 311:1308–1316
February 2015 • Volume 43 • Number 2
Copyright of Critical Care Medicine is the property of Lippincott Williams & Wilkins and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
