Meaghan Zehr1, Neil Klar1, Richard A Malthaner2. 1. Department of Epidemiology and Biostatistics, Division of Thoracic Surgery, Western University, London, Ontario, Canada. 2. Department of Epidemiology and Biostatistics, Division of Thoracic Surgery, Western University, London, Ontario, Canada; Department of Surgery, Division of Thoracic Surgery, Western University, London, Ontario, Canada. Electronic address: richard.malthaner@lhsc.on.ca.
Abstract
BACKGROUND: Flail chest injuries are associated with high mortality and morbidity. Despite evidence that operative repair of flail chest is beneficial, it is rarely done. We sought to create a simple risk score using available preoperative covariates to calculate individual risk of mortality in flail chest. METHODS: A logistic regression model was trained on Ontario Trauma Registry data to generate a mortality risk score. The final model was validated for calibration and discrimination and corrected for optimism. RESULTS: The model uses five risk factors that are readily obtained during the initial assessment of the trauma patient: age, Glasgow Coma Score, ventilation, cardiopulmonary resuscitation, and number of comorbidities. It was determined that less than 6 points is consistent with 1% observed mortality, 6 to 10 points predicts 5% mortality, 11 to 15 points predicts 22% mortality, and 16 or more points predicts 46% mortality. CONCLUSIONS: We have developed a simple model that can be easily applied at bedside to predict mortality in patients with flail chest by accessing a spreadsheet program in an application or other handheld computer device. This model has the potential to be a useful tool for surgeons considering operative repair of flail chest.
BACKGROUND: Flail chest injuries are associated with high mortality and morbidity. Despite evidence that operative repair of flail chest is beneficial, it is rarely done. We sought to create a simple risk score using available preoperative covariates to calculate individual risk of mortality in flail chest. METHODS: A logistic regression model was trained on Ontario Trauma Registry data to generate a mortality risk score. The final model was validated for calibration and discrimination and corrected for optimism. RESULTS: The model uses five risk factors that are readily obtained during the initial assessment of the traumapatient: age, Glasgow Coma Score, ventilation, cardiopulmonary resuscitation, and number of comorbidities. It was determined that less than 6 points is consistent with 1% observed mortality, 6 to 10 points predicts 5% mortality, 11 to 15 points predicts 22% mortality, and 16 or more points predicts 46% mortality. CONCLUSIONS: We have developed a simple model that can be easily applied at bedside to predict mortality in patients with flail chest by accessing a spreadsheet program in an application or other handheld computer device. This model has the potential to be a useful tool for surgeons considering operative repair of flail chest.
Authors: Max R Coffey; Katelynn C Bachman; Vanessa P Ho; Stephanie G Worrell; Matthew L Moorman; Philip A Linden; Christopher W Towe Journal: Eur J Trauma Emerg Surg Date: 2021-01-26 Impact factor: 3.693