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Do more predictors improve mortality risk estimates among burn patients?
burns 35 (2009) 303–304
available at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/burns
Letter to the Editor
Do more predictors improve mortality risk estimates among burn patients? Comment on McGwin et al. McGwin et al. [1] used data from two large datasets, the National Burn Repository and the National Trauma Data Bank to develop and validate a model of mortality risk among patients admitted with burns. They concluded that the best model for estimating mortality risk included well-known predictors of mortality – age, body surface area burned (BSAB) and inhalation injury, as well as 2 additional predictors – coexistent traumatic injury, and pneumonia. McGwin and colleagues correctly noted that previous risk models have been limited by small sample sizes and data from single centers. Since 1990, 4 studies of at least 1000 patients have reported on predictors of mortality based on data available at the time of admission for burn care [2–5]. Despite the potential limitations noted by McGwin et al., the results of these studies have been generally consistent. One study found that the combination of age and BSAB optimized risk estimation [4]; 2 found that age, BSAB, and inhalation injury best predicted mortality [2,3,5]; and 1 included gender along with age, BSAB and inhalation injury [5]. The conclusion of McGwin et al. that pneumonia and co-existent trauma should be added to the mix, however, does not appear to be supported by their results. McGwin and colleagues recognized that pneumonia is a response to burn rather than a patient or burn characteristic. They did not, however, address the issue of whether or not inclusion of pneumonia in their prediction model was consistent with the overall objective of their study. If the objective of the study was to estimate mortality risk based on patient and burn characteristics plus events that occur during treatment, then the inclusion of pneumonia was justified. On the other hand, if that was indeed the objective, then the failure to include several other important prognostic variables that occur between the time of the burn and discharge or death (e.g., infection) calls into question the interpretability of model results. If the objective, on the other hand, was to estimate mortality risk associated with patient and burn characteristics, then pneumonia was a confound that should not have been included in the model.
Beyond the issue of variable selection, McGwin and colleagues did not present convincing evidence that adding pneumonia and traumatic injury variables to standard predictors – age, BSAB, and inhalation injury – improved prediction meaningfully. The area under the curve was equal for models with and without these variables, and comparisons based on the HL statistic and deviance did not suggest substantive differences between the models. One possible reason why meaningful differences between models were not found is that McGwin et al. developed and validated their model in samples that included all patients admitted for burn care. Most patients who are admitted with burn injuries, however, are never at significant risk of death, and mortality rates, as recognized by the authors, are low. When this is the case, model fit as determined by area under the ROC curve and the HL statistic will typically be good and will not readily differentiate between reasonably good models. Thus, if the case is to be made that mortality prognosis can be improved by using a less parsimonious model, stronger evidence is needed. McGwin and colleagues did not compare the model they selected as optimal with the basic 3-predictor model in the validation sample, which may have been a better test of comparative utility. Similarly, comparing the models in a subsample of patients with large burns or patients otherwise defined as having high risk for mortality may have gone farther to clarify the relative merits of the models. Based on the principle of parsimony, however, the results presented thus far by McGwin et al. argue for the status quo.
Conflict of interest statement There are no conflicts of interest to report.
Role of the funding source There was no outside funding related to this letter.
304
burns 35 (2009) 303–304
references
[1] McGwin Jr G, George RL, Cross JM, Rue LW. Improving the ability to predict mortality among burn patients. Burns 2008;34:320–7. [2] Ryan CM, Schoenfeld DA, Thorpe WP, Sheridan RL, Cassem EH, Tompkins RG. Objective estimates of the probability of death from burn injuries. N Engl J Med 1998;338:362–6. [3] Smith DL, Cairns BA, Ramadan F, Dalston JS, Fakhry SM, Rutledge R, et al. Effect of inhalation injury, burn size, and age on mortality: a study of 1447 consecutive burn patients. J Trauma 1994;37:655–9. [4] Griffe O, Gartner R, Captier G, Brabet M, Baro B, Selloumi D, et al. Evaluation of prognostic factors in the burned patient. Ann Chir Plast Esthet 2001;46:167–72. [5] O’Keefe GE, Hunt JL, Purdue GF. An evaluation of risk factors for mortality after burn trauma and the identification of gender-dependent differences in outcomes. J Am Coll Surg 2001;192:153–60.
Brett D. Thombs* Department of Psychiatry, McGill University and Sir Mortimer B. Davis - Jewish General Hospital, Montreal, Quebec, Canada *Correspondence to: Institute of Community and Family Psychiatry, SMBD-Jewish General Hospital, 4333 Cote Ste Catherine Road, Montreal, Quebec, Canada H3T 1E4. Tel.: +1 514 340 8222x5112; fax: +1 514 340 8124 E-mail address: [email protected] 0305-4179/$36.00 # 2008 Elsevier Ltd and ISBI. All rights reserved. doi:10.1016/j.burns.2008.04.013
available at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/burns
Letter to the Editor
Do more predictors improve mortality risk estimates among burn patients? Comment on McGwin et al. McGwin et al. [1] used data from two large datasets, the National Burn Repository and the National Trauma Data Bank to develop and validate a model of mortality risk among patients admitted with burns. They concluded that the best model for estimating mortality risk included well-known predictors of mortality – age, body surface area burned (BSAB) and inhalation injury, as well as 2 additional predictors – coexistent traumatic injury, and pneumonia. McGwin and colleagues correctly noted that previous risk models have been limited by small sample sizes and data from single centers. Since 1990, 4 studies of at least 1000 patients have reported on predictors of mortality based on data available at the time of admission for burn care [2–5]. Despite the potential limitations noted by McGwin et al., the results of these studies have been generally consistent. One study found that the combination of age and BSAB optimized risk estimation [4]; 2 found that age, BSAB, and inhalation injury best predicted mortality [2,3,5]; and 1 included gender along with age, BSAB and inhalation injury [5]. The conclusion of McGwin et al. that pneumonia and co-existent trauma should be added to the mix, however, does not appear to be supported by their results. McGwin and colleagues recognized that pneumonia is a response to burn rather than a patient or burn characteristic. They did not, however, address the issue of whether or not inclusion of pneumonia in their prediction model was consistent with the overall objective of their study. If the objective of the study was to estimate mortality risk based on patient and burn characteristics plus events that occur during treatment, then the inclusion of pneumonia was justified. On the other hand, if that was indeed the objective, then the failure to include several other important prognostic variables that occur between the time of the burn and discharge or death (e.g., infection) calls into question the interpretability of model results. If the objective, on the other hand, was to estimate mortality risk associated with patient and burn characteristics, then pneumonia was a confound that should not have been included in the model.
Beyond the issue of variable selection, McGwin and colleagues did not present convincing evidence that adding pneumonia and traumatic injury variables to standard predictors – age, BSAB, and inhalation injury – improved prediction meaningfully. The area under the curve was equal for models with and without these variables, and comparisons based on the HL statistic and deviance did not suggest substantive differences between the models. One possible reason why meaningful differences between models were not found is that McGwin et al. developed and validated their model in samples that included all patients admitted for burn care. Most patients who are admitted with burn injuries, however, are never at significant risk of death, and mortality rates, as recognized by the authors, are low. When this is the case, model fit as determined by area under the ROC curve and the HL statistic will typically be good and will not readily differentiate between reasonably good models. Thus, if the case is to be made that mortality prognosis can be improved by using a less parsimonious model, stronger evidence is needed. McGwin and colleagues did not compare the model they selected as optimal with the basic 3-predictor model in the validation sample, which may have been a better test of comparative utility. Similarly, comparing the models in a subsample of patients with large burns or patients otherwise defined as having high risk for mortality may have gone farther to clarify the relative merits of the models. Based on the principle of parsimony, however, the results presented thus far by McGwin et al. argue for the status quo.
Conflict of interest statement There are no conflicts of interest to report.
Role of the funding source There was no outside funding related to this letter.
304
burns 35 (2009) 303–304
references
[1] McGwin Jr G, George RL, Cross JM, Rue LW. Improving the ability to predict mortality among burn patients. Burns 2008;34:320–7. [2] Ryan CM, Schoenfeld DA, Thorpe WP, Sheridan RL, Cassem EH, Tompkins RG. Objective estimates of the probability of death from burn injuries. N Engl J Med 1998;338:362–6. [3] Smith DL, Cairns BA, Ramadan F, Dalston JS, Fakhry SM, Rutledge R, et al. Effect of inhalation injury, burn size, and age on mortality: a study of 1447 consecutive burn patients. J Trauma 1994;37:655–9. [4] Griffe O, Gartner R, Captier G, Brabet M, Baro B, Selloumi D, et al. Evaluation of prognostic factors in the burned patient. Ann Chir Plast Esthet 2001;46:167–72. [5] O’Keefe GE, Hunt JL, Purdue GF. An evaluation of risk factors for mortality after burn trauma and the identification of gender-dependent differences in outcomes. J Am Coll Surg 2001;192:153–60.
Brett D. Thombs* Department of Psychiatry, McGill University and Sir Mortimer B. Davis - Jewish General Hospital, Montreal, Quebec, Canada *Correspondence to: Institute of Community and Family Psychiatry, SMBD-Jewish General Hospital, 4333 Cote Ste Catherine Road, Montreal, Quebec, Canada H3T 1E4. Tel.: +1 514 340 8222x5112; fax: +1 514 340 8124 E-mail address: [email protected] 0305-4179/$36.00 # 2008 Elsevier Ltd and ISBI. All rights reserved. doi:10.1016/j.burns.2008.04.013