Predicting Early Death in Patients With Traumatic Bleeding
Predicting Early Death in Patients With Traumatic Bleeding
We have developed and validated a prognostic model for trauma patients by using clinical parameters that are easy to measure. The model is available as a web calculator and can be used at the point of care in its simplified form. Separate models are available for patients from low, middle, and high income countries. This simple prognostic model could inform doctors about the risk of death and guide them in the early assessment and management of trauma patients.
Our study has several strengths. Our models were based on a prospective cohort of patients with traumatic bleeding, with standardised collection of data on prognostic factors, very little missing data, and low loss to follow-up. Unlike previous prognostic models, we explored more complex relations between continuous predictors and mortality and captured non-linear relations. All of these factors provide reassurance about the internal validity of our models. The large sample size in relation to the number of prognostic variables is also an important strength. Whereas most previous models were derived from single centre studies in high income countries, we developed separate models for low, middle, and high income countries. Unlike most previous models, we did an external validation in a large cohort of trauma patients. This confirmed the discriminatory ability of the model in patients from high income countries and showed good calibration.
Another methodological strength was our use of imputation to replace missing data, which is rarely done in model validation studies. To the best of our knowledge, this is the only prognostic model for this population that is available in a web based calculator and a simplified chart that can be used at point of care. Importantly, we obtained advice from the potential users throughout its development.
The study also has some limitations. The data from which the models were developed come from a clinical trial, and this could limit external validity. For example, patients were recruited within eight hours of injury, and we cannot estimate the accuracy of the models for patients evaluated beyond this time. Nevertheless, the CRASH-2 trial was a pragmatic trial that did not require any additional tests and therefore included a diversity of “real life” patients. In addition, the relation between predictors and outcome could be different in patients included in a clinical trial and in routine practice. However, the model’s good performance in a trauma registry population provides reassurance that any potential bias (if present) was small.
Another limitation was that for the validation we used a cohort of trauma patients that were not equally defined, and we included them by using an estimation of the blood loss. In any case, this weakness could have led to underestimation of the accuracy of the model. Other potentially important variables such as pre-existing medical conditions, previous drugs, and laboratory measurements were not collected in the CRASH-2 trial and, therefore, not available for inclusion in the model. However, these are variables that are usually unavailable in the acute care trauma setting in which the model is intended to be used. The prognostic model predicts overall death rather than death due to bleeding, as death due bleeding was not available in the TARN dataset. However, bleeding would be expected to contribute to the other main causes of death in trauma patients. In addition, some deaths classified as “non-bleeding” could in fact have been due to bleeding. Finally, we observed some miscalibration; in particular, we observed overestimation for patients with predicted high risk in the internal validation. This finding might be related to the imprecision due to the low number of patients in the very high risk group. Only 100 patients (84 events) had a predicted risk of death above 90% in the CRASH-2 dataset. However, miscalibration at this high risk end of the spectrum (that is, 80% v 90% probability of death) is very unlikely to change clinical decision making.
Many trauma protocols use blood pressure as the main criterion for determining who should receive urgent intervention. However, according to our model, a 75 year old with blunt trauma and a systolic blood pressure of 110 mm Hg, heart rate of 80 beats per minute, respiratory rate of 15 breaths per minute, and Glasgow coma score of 15 has a similar risk of death to a 45 year old patient with exactly the same parameters but a systolic blood pressure of 60 mm Hg. These findings have important practical implications. According to many trauma protocols, only the younger patient would receive urgent interventions such as tranexamic acid, and the older one would be denied this lifesaving intervention. The effect of age is particularly important, bearing in mind that in high income countries the average age of trauma patients is increasing. Data from TARN show that one quarter of the deaths due to trauma in England and Wales are in patients older than 70 years. The effect of age is likely to reflect the increased incidence of coexisting diseases, particularly cardiovascular diseases. Older patients are more likely to have coronary heart disease, and the decrease in oxygen supply associated with traumatic bleeding can increase the risk of myocardial ischaemia. Another potential explanation for the increased risk of death from vascular occlusive disease is related to the trigger of the inflammation process after trauma. After trauma, a potent inflammatory response involves increased serum concentrations of interleukin-1, interleukin-2, tumour necrosis factor-α, interleukin-6, interleukin-12, and interferon-γ. In patients with traumatic bleeding, activation of plasmin occurs and plays a key role in the fibrinolytic response in the early hours after injury. Plasmin also has pro-inflammatory effects through the activation of cytokines, monocytes, neutrophils, platelets, and endothelial cells. Vascular risk may rise in short time periods of inflammatory responses to exposures such as infections or major surgery. Some of the observed prognostic role of age in trauma patients may be due to the inflammatory response to acute trauma, which might trigger acute vascular events, particularly in older patients who have a more widespread atherosclerotic condition. Furthermore, the prognostic role of age could be explained partially by a “self fulfilling prophecy” phenomenon, as age has been shown to be positively associated with “do not resuscitate” orders.
We acknowledge that estimating the risk of death in a trauma patient with bleeding is challenging. It is an ongoing process that uses not only physiological variables but other variables such as laboratory measurements and response to treatments. A prognostic model would never replace clinical judgment, but it can support it.
We found that trauma patients in low and middle income countries were at higher risk of death compared with those from high income countries. We emphasise that the income classification refers to the country and not to individual patients. Some of the effect of classification of income might be the consequence of the differences in healthcare settings. Other studies have shown similar results, but to our knowledge this is the first one to include a large number of low and middle income countries. Although we did not have enough information to explore the causes of these differences, the rapid increase in the number of trauma patients combined with the lack of resources in poorer countries is probably among the most important reasons. Scaling up cost effective interventions in these settings could save hundreds of thousands of lives every year.
The relation between age and mortality needs further exploration. A better understanding of the mechanism by which age is associated with increasing mortality could lead to effective interventions to improve the outcome in this vulnerable population. As we were able to validate the model only in patients from high income regions, future studies should also explore its performance in low and middle income countries. Finally, future research should evaluate whether the use of this prognostic model in clinical practice has an effect on the management and outcomes of trauma patients.
Discussion
We have developed and validated a prognostic model for trauma patients by using clinical parameters that are easy to measure. The model is available as a web calculator and can be used at the point of care in its simplified form. Separate models are available for patients from low, middle, and high income countries. This simple prognostic model could inform doctors about the risk of death and guide them in the early assessment and management of trauma patients.
Strengths and Limitations
Our study has several strengths. Our models were based on a prospective cohort of patients with traumatic bleeding, with standardised collection of data on prognostic factors, very little missing data, and low loss to follow-up. Unlike previous prognostic models, we explored more complex relations between continuous predictors and mortality and captured non-linear relations. All of these factors provide reassurance about the internal validity of our models. The large sample size in relation to the number of prognostic variables is also an important strength. Whereas most previous models were derived from single centre studies in high income countries, we developed separate models for low, middle, and high income countries. Unlike most previous models, we did an external validation in a large cohort of trauma patients. This confirmed the discriminatory ability of the model in patients from high income countries and showed good calibration.
Another methodological strength was our use of imputation to replace missing data, which is rarely done in model validation studies. To the best of our knowledge, this is the only prognostic model for this population that is available in a web based calculator and a simplified chart that can be used at point of care. Importantly, we obtained advice from the potential users throughout its development.
The study also has some limitations. The data from which the models were developed come from a clinical trial, and this could limit external validity. For example, patients were recruited within eight hours of injury, and we cannot estimate the accuracy of the models for patients evaluated beyond this time. Nevertheless, the CRASH-2 trial was a pragmatic trial that did not require any additional tests and therefore included a diversity of “real life” patients. In addition, the relation between predictors and outcome could be different in patients included in a clinical trial and in routine practice. However, the model’s good performance in a trauma registry population provides reassurance that any potential bias (if present) was small.
Another limitation was that for the validation we used a cohort of trauma patients that were not equally defined, and we included them by using an estimation of the blood loss. In any case, this weakness could have led to underestimation of the accuracy of the model. Other potentially important variables such as pre-existing medical conditions, previous drugs, and laboratory measurements were not collected in the CRASH-2 trial and, therefore, not available for inclusion in the model. However, these are variables that are usually unavailable in the acute care trauma setting in which the model is intended to be used. The prognostic model predicts overall death rather than death due to bleeding, as death due bleeding was not available in the TARN dataset. However, bleeding would be expected to contribute to the other main causes of death in trauma patients. In addition, some deaths classified as “non-bleeding” could in fact have been due to bleeding. Finally, we observed some miscalibration; in particular, we observed overestimation for patients with predicted high risk in the internal validation. This finding might be related to the imprecision due to the low number of patients in the very high risk group. Only 100 patients (84 events) had a predicted risk of death above 90% in the CRASH-2 dataset. However, miscalibration at this high risk end of the spectrum (that is, 80% v 90% probability of death) is very unlikely to change clinical decision making.
Implications of Study
Many trauma protocols use blood pressure as the main criterion for determining who should receive urgent intervention. However, according to our model, a 75 year old with blunt trauma and a systolic blood pressure of 110 mm Hg, heart rate of 80 beats per minute, respiratory rate of 15 breaths per minute, and Glasgow coma score of 15 has a similar risk of death to a 45 year old patient with exactly the same parameters but a systolic blood pressure of 60 mm Hg. These findings have important practical implications. According to many trauma protocols, only the younger patient would receive urgent interventions such as tranexamic acid, and the older one would be denied this lifesaving intervention. The effect of age is particularly important, bearing in mind that in high income countries the average age of trauma patients is increasing. Data from TARN show that one quarter of the deaths due to trauma in England and Wales are in patients older than 70 years. The effect of age is likely to reflect the increased incidence of coexisting diseases, particularly cardiovascular diseases. Older patients are more likely to have coronary heart disease, and the decrease in oxygen supply associated with traumatic bleeding can increase the risk of myocardial ischaemia. Another potential explanation for the increased risk of death from vascular occlusive disease is related to the trigger of the inflammation process after trauma. After trauma, a potent inflammatory response involves increased serum concentrations of interleukin-1, interleukin-2, tumour necrosis factor-α, interleukin-6, interleukin-12, and interferon-γ. In patients with traumatic bleeding, activation of plasmin occurs and plays a key role in the fibrinolytic response in the early hours after injury. Plasmin also has pro-inflammatory effects through the activation of cytokines, monocytes, neutrophils, platelets, and endothelial cells. Vascular risk may rise in short time periods of inflammatory responses to exposures such as infections or major surgery. Some of the observed prognostic role of age in trauma patients may be due to the inflammatory response to acute trauma, which might trigger acute vascular events, particularly in older patients who have a more widespread atherosclerotic condition. Furthermore, the prognostic role of age could be explained partially by a “self fulfilling prophecy” phenomenon, as age has been shown to be positively associated with “do not resuscitate” orders.
We acknowledge that estimating the risk of death in a trauma patient with bleeding is challenging. It is an ongoing process that uses not only physiological variables but other variables such as laboratory measurements and response to treatments. A prognostic model would never replace clinical judgment, but it can support it.
We found that trauma patients in low and middle income countries were at higher risk of death compared with those from high income countries. We emphasise that the income classification refers to the country and not to individual patients. Some of the effect of classification of income might be the consequence of the differences in healthcare settings. Other studies have shown similar results, but to our knowledge this is the first one to include a large number of low and middle income countries. Although we did not have enough information to explore the causes of these differences, the rapid increase in the number of trauma patients combined with the lack of resources in poorer countries is probably among the most important reasons. Scaling up cost effective interventions in these settings could save hundreds of thousands of lives every year.
Future Research
The relation between age and mortality needs further exploration. A better understanding of the mechanism by which age is associated with increasing mortality could lead to effective interventions to improve the outcome in this vulnerable population. As we were able to validate the model only in patients from high income regions, future studies should also explore its performance in low and middle income countries. Finally, future research should evaluate whether the use of this prognostic model in clinical practice has an effect on the management and outcomes of trauma patients.