Online Letters to the Editor
Association of Body Mass Index With Hospital Mortality in ICU Patients To the Editor:
I
n an observational cohort study including 154,308 ICU patients, Pickkers et al (1) showed that obese and seriously obese patients had the lowest risk of hospital death in a recent article in Critical Care Medicine. Strengths of this study include the large sample of patients and adjust for most of known risk factors that can affect mortality of ICU patients. Also, Pickkers et al (1) openly discuss the limitations of their work. However, the study employed observational designs, which are subject to uncontrolled confounding. In our view, several issues in the study design may confound interpretation of the results. First, illness severity is one of mostly important determinants for hospital death of ICU patients (2). Pickkers et al (1) have attempted to control illness severity differences among patients with different body mass index (BMI) by adjusting the Simplified Acute Physiology Score II (SAPS II), but a significant shortcoming of the SAPS II is inability to distinguish chronic disease from acute disease. This presents a potential problem in controlling for illness severity. It is generally believed that chronic diseases are more common in the obese patients. In our opinion, no matter how refined the adjustment is for differences in illness burden, it is never possible to ensure a complete adjustment for illness severity differences among ICU patients with various BMI and admission reasons. For example, an obese patient with chronic cardiac insufficiency and chronic obstructive pulmonary disease may qualify for a SAPS II score of 16 at baseline, but this patient would have a better short-term outcome than an underweight or normal patient with acute cardiac failure and acute respiratory insufficiency despite an equivalent SAPS II score. That is, a chronically higher SAPS II score in obese patients may manifest as a survival benefit. Thus, we argue that not taking impacts of acute versus chronic disease scoring on the study outcomes into account is injustice to conclude association between obesity and hospital mortality in the ICU patients. Second, transfusion and ethnicity were not included in adjusted potential confounders. It has been shown that transfusions independently contribute to increased risk for hospital death of ICU patients (3). Furthermore, available evidence shows the existence of important ethnic differences in therapies of ICU patients (4) and that black patients are almost three times more likely than white patients to die in-hospital following admission to the ICU (5). Additionally, lack of health insurance is associated with increased risk of hospital mortality in ICU patients. Thus, we cannot exclude possibility that these factors would have contributed to their results. Third, their study design did not include the detail about therapies of ICU patients. Consequently, it is difficult to estimate the degree to which interventions by ICU physicians might have influenced outcomes. From a clinical standpoint, obese patients have more physical care requirements. e80
www.ccmjournal.org
Furthermore, they may have an improved rate of chemical thromboprophylaxis, which is independently associated with survival (2). These bring to light the possibility that obese patients may be triaged to higher care standards. Thus, differences in care standards between obese and nonobese patients could account for subsequent differences in hospital death. The authors have disclosed that they do not have any potential conflicts of interest. Fu-Shan Xue, MD, Shi Yu Wang, MD, Rui Ping Li, MD Department of Anesthesiology, Plastic Surgery Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People’s Republic of China
REFERENCES
1. Pickkers P, de Keizer N, Dusseljee J, et al: Body Mass Index Is Associated With Hospital Mortality in Critically Ill Patients: An Observational Cohort Study. Crit Care Med 2013; 41:1878–1883 2. Kiraly L, Hurt RT, Van Way CW 3rd: The outcomes of obese patients in critical care. J Parenter Enteral Nutr 2011; 35(5 Suppl):29S–35S 3. Zilberberg MD, Stern LS, Wiederkehr DP, et al: Anemia, transfusions and hospital outcomes among critically ill patients on prolonged acute mechanical ventilation: A retrospective cohort study. Crit Care 2008; 12:R60 4. Williams JF, Zimmerman JE, Wagner DP, et al: African-American and white patients admitted to the intensive care unit: Is there a difference in therapy and outcome? Crit Care Med 1995; 23:626–636 5. Horner RD, Lawler FH, Hainer BL: Relationship between patient race and survival following admission to intensive care among patients of primary care physicians. Health Serv Res 1991; 26:531–542 DOI: 10.1097/01.ccm.0000435673.83682.58
The “Obesity-Mortality Paradox” Phenomenon in Critically Ill Patients: One Size Does Not Fit All To the Editor:
I
n a recent issue of Critical Care Medicine, I read with great interest the article by Pickkers et al (1), which reported that a large observational database derived from the Dutch National Intensive Care Evaluation registry shows an inverse association between obesity and hospital mortality in critically ill patients that could not be explained by a variety of known confounders. Similarly, a short-term obesity-related survival benefit was also concluded in some previous meta-analyses for patients with or without surgical intervention in intensive care. Notably, these meta-analyses were statistically very heterogeneous, which indicates the need for caution in interpreting pooled estimates (1). As is known, types of specific diseases, severity of sickness, and management in critically ill patients may have diverse impacts of body mass index (BMI) on the hospital mortality. For instance, some meta-analyses conversely demonstrated that obesity is associated with higher risks of ICU death among severely traumatic patients and those with 2009 H1N1 infection (2, 3). In addition, we have identified the severity of Glasgow Coma Scale or Acute Physiology and Chronic Health Examination January 2014 • Volume 42 • Number 1
Online Letters to the Editor
II score seems to be the most powerful predictors to hospital mortality regardless of BMI levels in critical patients with tuberculous meningitis (4). Finally, as to the effect of therapeutic improvement in acute coronary syndrome with time, the obesity-mortality paradox was mainly described in bare-metal stent era before 2003. Since the introduction of drug-eluting stent and new generation of antiplatelet therapy, the effect of BMI on the hospital mortality gradually attenuated (5). In conclusion, in my opinion, the obesity-mortality paradox in critical patents should be specifically interpreted in variety of conditions. The author has disclosed that he does not have any potential conflicts of interest. Gen-Min Lin, MD, MPH, Department of Medicine, Hualien Armed Forces General Hospital, Hualien, Taiwan
REFERENCES
1. Pickkers P, de Keizer N, Dusseljee J, et al: Body Mass Index Is Associated With Hospital Mortality in Critically Ill Patients: An Observational Cohort Study. Crit Care Med 2013; 41:1878–1883 2. Liu T, Chen JJ, Bai XJ, et al: The effect of obesity on outcomes in trauma patients: A meta-analysis. Injury 2013; 44:1145–1152 3. Fezeu L, Julia C, Henegar A, et al: Obesity is associated with higher risk of intensive care unit admission and death in influenza A (H1N1) patients: A systematic review and meta-analysis. Obes Rev 2011; 12:653–659 4. Chou CH, Lin GM, Ku CH, et al: Comparison of the APACHE II, GCS and MRC scores in predicting outcomes in patients with tuberculous meningitis. Int J Tuberc Lung Dis 2010; 14:86–92 5. Lin GM, Li YH, Lin CL, et al: Relation of body mass index to mortality among patients with percutaneous coronary intervention in the drugeluting stent era: A systematic review and meta-analysis. Int J Cardiol 2013; 168:4459–4466 DOI: 10.1097CCM.0000000000000019
The authors reply:
W
e thank Xue et al (1), Lin (2), and Claessen et al (3) for their interest in our study (4). Xue et al (1) highlight the possibility of uncontrolled confounding in an observational study such as ours, an important issue that we also mentioned in the discussion of our article. We like to emphasize that in the previous meta-analyses on this subject (see [4]), results were not corrected for any other prognostic covariate, whereas in our database, it was possible to do so for most known risk factors, but obviously not all. In our view, registration of over a 100 patient characteristics and with over 90% of all Dutch ICUs participating, the Netherlands Intensive Care Evaluation (NICE) database allows us to explore associations between patient characteristics and outcome in a sufficiently sophisticated manner. To specifically address the issues raised by Xue et al (1): the Simplified Acute Physiology Score II includes 17 variables: 12 physiology variables, age, type of admission (scheduled surgical, unscheduled surgical, or medical), and three underlying disease variables (AIDS, metastatic cancer, and hematologic malignancy) (5). Its performance in the Dutch population is comparable to Acute Physiology and Chronic Health Evaluation
Critical Care Medicine
II and IV, but data are available for a longer time period (6). We did not correct for chronic diagnoses, such as heart failure, as it might be the consequence, not the cause, of obesity. This is different from chronic diagnosis such as AIDS or cancer, where weight loss is clearly related to the chronic diagnosis and correction is justified. Second, transfusion data are, unfortunately, not collected in the NICE database, but in our view, it appears unlikely that physicians would use different transfusion triggers based on the body mass index (BMI) of the patient. Also ethnicity is not collected, but with 3–4% blacks in The Netherlands (7), it unlikely represents a potential bias of relevance. Finally, all residents in the Netherlands are obliged by law to purchase health insurance coverage, resulting in only 0.8% of people without insurance (8), also suggesting that the relevance of possible bias induced by uninsured patients is limited. The authors are correct that we do not have details available about the therapies the patients receive during their stay in the ICU. Lin (2) highlights the possibility that the obesity paradox might not be present in specific subgroups of critically ill patients. We agree that it appears to be plausible that the presence of the obesity paradox is related to the diagnosis. In our article, we wished to demonstrate the absence/presence of the obesity paradox in ICU patients using our database of over 150,000 patients to have sufficient statistical power. The study by Lin (2) refers to apply dichotomous analyses (at a BMI of 30 kg/m2), and in view of the inverse J-shape outcome, this may dilute putative beneficial associations between BMI and outcome. For example, in the H1N1 study cited (3,059 patients), an increased risk of dying was found in patients with a BMI more than 40, whereas for patients with a BMI more than 30, this did not reach statistical significance. In the study in 43 patients with tuberculous meningitis, BMI is not mentioned as a covariate, and the study would be underpowered to detect a possible effect of BMI on outcome. Considering patients with acute coronary syndrome, the obesity paradox might be attenuated in the drug-eluting stent (DES) era considering long-term mortality; however, 30-day hospital mortality was similar before and after the DES introduction, showing a better outcome for obese patients reaching statistical significance pre-DES (with 22,338 patients included), and a similar effect, but not reaching statistical significance, with 5,073 patients post-DES. Again, statistical power appears to be of importance. In our statistical analysis, the BMI was included in the logistic regression model as a continuous nonlinear predictor in a restricted regression spline transformation. Claessen et al (3) suggest in their letter that it would have been more appropriate to model the BMI as a categorical variable. Yet, multiple studies have demonstrated that categorization of continuous variables is rarely defensible and often will yield biased results (9–11). Categorization is also less efficient (i.e., requires more estimates to be made from the same data) than spline transformation. We, therefore, believe that spline transformation is the better choice. Furthermore, also when patients with a BMI below 20 kg/m2 are discarded, the association between a higher BMI and survival remains, indicating that the p of the complete curve is not dependent on the patients with a BMI below 20 kg/m2. www.ccmjournal.org
e81
Association of Body Mass Index With Hospital Mortality in ICU Patients To the Editor:
I
n an observational cohort study including 154,308 ICU patients, Pickkers et al (1) showed that obese and seriously obese patients had the lowest risk of hospital death in a recent article in Critical Care Medicine. Strengths of this study include the large sample of patients and adjust for most of known risk factors that can affect mortality of ICU patients. Also, Pickkers et al (1) openly discuss the limitations of their work. However, the study employed observational designs, which are subject to uncontrolled confounding. In our view, several issues in the study design may confound interpretation of the results. First, illness severity is one of mostly important determinants for hospital death of ICU patients (2). Pickkers et al (1) have attempted to control illness severity differences among patients with different body mass index (BMI) by adjusting the Simplified Acute Physiology Score II (SAPS II), but a significant shortcoming of the SAPS II is inability to distinguish chronic disease from acute disease. This presents a potential problem in controlling for illness severity. It is generally believed that chronic diseases are more common in the obese patients. In our opinion, no matter how refined the adjustment is for differences in illness burden, it is never possible to ensure a complete adjustment for illness severity differences among ICU patients with various BMI and admission reasons. For example, an obese patient with chronic cardiac insufficiency and chronic obstructive pulmonary disease may qualify for a SAPS II score of 16 at baseline, but this patient would have a better short-term outcome than an underweight or normal patient with acute cardiac failure and acute respiratory insufficiency despite an equivalent SAPS II score. That is, a chronically higher SAPS II score in obese patients may manifest as a survival benefit. Thus, we argue that not taking impacts of acute versus chronic disease scoring on the study outcomes into account is injustice to conclude association between obesity and hospital mortality in the ICU patients. Second, transfusion and ethnicity were not included in adjusted potential confounders. It has been shown that transfusions independently contribute to increased risk for hospital death of ICU patients (3). Furthermore, available evidence shows the existence of important ethnic differences in therapies of ICU patients (4) and that black patients are almost three times more likely than white patients to die in-hospital following admission to the ICU (5). Additionally, lack of health insurance is associated with increased risk of hospital mortality in ICU patients. Thus, we cannot exclude possibility that these factors would have contributed to their results. Third, their study design did not include the detail about therapies of ICU patients. Consequently, it is difficult to estimate the degree to which interventions by ICU physicians might have influenced outcomes. From a clinical standpoint, obese patients have more physical care requirements. e80
www.ccmjournal.org
Furthermore, they may have an improved rate of chemical thromboprophylaxis, which is independently associated with survival (2). These bring to light the possibility that obese patients may be triaged to higher care standards. Thus, differences in care standards between obese and nonobese patients could account for subsequent differences in hospital death. The authors have disclosed that they do not have any potential conflicts of interest. Fu-Shan Xue, MD, Shi Yu Wang, MD, Rui Ping Li, MD Department of Anesthesiology, Plastic Surgery Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People’s Republic of China
REFERENCES
1. Pickkers P, de Keizer N, Dusseljee J, et al: Body Mass Index Is Associated With Hospital Mortality in Critically Ill Patients: An Observational Cohort Study. Crit Care Med 2013; 41:1878–1883 2. Kiraly L, Hurt RT, Van Way CW 3rd: The outcomes of obese patients in critical care. J Parenter Enteral Nutr 2011; 35(5 Suppl):29S–35S 3. Zilberberg MD, Stern LS, Wiederkehr DP, et al: Anemia, transfusions and hospital outcomes among critically ill patients on prolonged acute mechanical ventilation: A retrospective cohort study. Crit Care 2008; 12:R60 4. Williams JF, Zimmerman JE, Wagner DP, et al: African-American and white patients admitted to the intensive care unit: Is there a difference in therapy and outcome? Crit Care Med 1995; 23:626–636 5. Horner RD, Lawler FH, Hainer BL: Relationship between patient race and survival following admission to intensive care among patients of primary care physicians. Health Serv Res 1991; 26:531–542 DOI: 10.1097/01.ccm.0000435673.83682.58
The “Obesity-Mortality Paradox” Phenomenon in Critically Ill Patients: One Size Does Not Fit All To the Editor:
I
n a recent issue of Critical Care Medicine, I read with great interest the article by Pickkers et al (1), which reported that a large observational database derived from the Dutch National Intensive Care Evaluation registry shows an inverse association between obesity and hospital mortality in critically ill patients that could not be explained by a variety of known confounders. Similarly, a short-term obesity-related survival benefit was also concluded in some previous meta-analyses for patients with or without surgical intervention in intensive care. Notably, these meta-analyses were statistically very heterogeneous, which indicates the need for caution in interpreting pooled estimates (1). As is known, types of specific diseases, severity of sickness, and management in critically ill patients may have diverse impacts of body mass index (BMI) on the hospital mortality. For instance, some meta-analyses conversely demonstrated that obesity is associated with higher risks of ICU death among severely traumatic patients and those with 2009 H1N1 infection (2, 3). In addition, we have identified the severity of Glasgow Coma Scale or Acute Physiology and Chronic Health Examination January 2014 • Volume 42 • Number 1
Online Letters to the Editor
II score seems to be the most powerful predictors to hospital mortality regardless of BMI levels in critical patients with tuberculous meningitis (4). Finally, as to the effect of therapeutic improvement in acute coronary syndrome with time, the obesity-mortality paradox was mainly described in bare-metal stent era before 2003. Since the introduction of drug-eluting stent and new generation of antiplatelet therapy, the effect of BMI on the hospital mortality gradually attenuated (5). In conclusion, in my opinion, the obesity-mortality paradox in critical patents should be specifically interpreted in variety of conditions. The author has disclosed that he does not have any potential conflicts of interest. Gen-Min Lin, MD, MPH, Department of Medicine, Hualien Armed Forces General Hospital, Hualien, Taiwan
REFERENCES
1. Pickkers P, de Keizer N, Dusseljee J, et al: Body Mass Index Is Associated With Hospital Mortality in Critically Ill Patients: An Observational Cohort Study. Crit Care Med 2013; 41:1878–1883 2. Liu T, Chen JJ, Bai XJ, et al: The effect of obesity on outcomes in trauma patients: A meta-analysis. Injury 2013; 44:1145–1152 3. Fezeu L, Julia C, Henegar A, et al: Obesity is associated with higher risk of intensive care unit admission and death in influenza A (H1N1) patients: A systematic review and meta-analysis. Obes Rev 2011; 12:653–659 4. Chou CH, Lin GM, Ku CH, et al: Comparison of the APACHE II, GCS and MRC scores in predicting outcomes in patients with tuberculous meningitis. Int J Tuberc Lung Dis 2010; 14:86–92 5. Lin GM, Li YH, Lin CL, et al: Relation of body mass index to mortality among patients with percutaneous coronary intervention in the drugeluting stent era: A systematic review and meta-analysis. Int J Cardiol 2013; 168:4459–4466 DOI: 10.1097CCM.0000000000000019
The authors reply:
W
e thank Xue et al (1), Lin (2), and Claessen et al (3) for their interest in our study (4). Xue et al (1) highlight the possibility of uncontrolled confounding in an observational study such as ours, an important issue that we also mentioned in the discussion of our article. We like to emphasize that in the previous meta-analyses on this subject (see [4]), results were not corrected for any other prognostic covariate, whereas in our database, it was possible to do so for most known risk factors, but obviously not all. In our view, registration of over a 100 patient characteristics and with over 90% of all Dutch ICUs participating, the Netherlands Intensive Care Evaluation (NICE) database allows us to explore associations between patient characteristics and outcome in a sufficiently sophisticated manner. To specifically address the issues raised by Xue et al (1): the Simplified Acute Physiology Score II includes 17 variables: 12 physiology variables, age, type of admission (scheduled surgical, unscheduled surgical, or medical), and three underlying disease variables (AIDS, metastatic cancer, and hematologic malignancy) (5). Its performance in the Dutch population is comparable to Acute Physiology and Chronic Health Evaluation
Critical Care Medicine
II and IV, but data are available for a longer time period (6). We did not correct for chronic diagnoses, such as heart failure, as it might be the consequence, not the cause, of obesity. This is different from chronic diagnosis such as AIDS or cancer, where weight loss is clearly related to the chronic diagnosis and correction is justified. Second, transfusion data are, unfortunately, not collected in the NICE database, but in our view, it appears unlikely that physicians would use different transfusion triggers based on the body mass index (BMI) of the patient. Also ethnicity is not collected, but with 3–4% blacks in The Netherlands (7), it unlikely represents a potential bias of relevance. Finally, all residents in the Netherlands are obliged by law to purchase health insurance coverage, resulting in only 0.8% of people without insurance (8), also suggesting that the relevance of possible bias induced by uninsured patients is limited. The authors are correct that we do not have details available about the therapies the patients receive during their stay in the ICU. Lin (2) highlights the possibility that the obesity paradox might not be present in specific subgroups of critically ill patients. We agree that it appears to be plausible that the presence of the obesity paradox is related to the diagnosis. In our article, we wished to demonstrate the absence/presence of the obesity paradox in ICU patients using our database of over 150,000 patients to have sufficient statistical power. The study by Lin (2) refers to apply dichotomous analyses (at a BMI of 30 kg/m2), and in view of the inverse J-shape outcome, this may dilute putative beneficial associations between BMI and outcome. For example, in the H1N1 study cited (3,059 patients), an increased risk of dying was found in patients with a BMI more than 40, whereas for patients with a BMI more than 30, this did not reach statistical significance. In the study in 43 patients with tuberculous meningitis, BMI is not mentioned as a covariate, and the study would be underpowered to detect a possible effect of BMI on outcome. Considering patients with acute coronary syndrome, the obesity paradox might be attenuated in the drug-eluting stent (DES) era considering long-term mortality; however, 30-day hospital mortality was similar before and after the DES introduction, showing a better outcome for obese patients reaching statistical significance pre-DES (with 22,338 patients included), and a similar effect, but not reaching statistical significance, with 5,073 patients post-DES. Again, statistical power appears to be of importance. In our statistical analysis, the BMI was included in the logistic regression model as a continuous nonlinear predictor in a restricted regression spline transformation. Claessen et al (3) suggest in their letter that it would have been more appropriate to model the BMI as a categorical variable. Yet, multiple studies have demonstrated that categorization of continuous variables is rarely defensible and often will yield biased results (9–11). Categorization is also less efficient (i.e., requires more estimates to be made from the same data) than spline transformation. We, therefore, believe that spline transformation is the better choice. Furthermore, also when patients with a BMI below 20 kg/m2 are discarded, the association between a higher BMI and survival remains, indicating that the p of the complete curve is not dependent on the patients with a BMI below 20 kg/m2. www.ccmjournal.org
e81
