Penetrating cardiac injuries

predictive model for outcomes based on 2016 patients from the National Trauma Data Bank

Juan A. Asensio, O. A. Ogun, P. Petrone, A. J. Perez-Alonso, M. Wagner, R. Bertellotti, B. Phillips, D. L. Cornell, A. O. Udekwu

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Background: Penetrating cardiac injuries are uncommon and lethal. The objectives of this study are to examine the national profile of cardiac injuries, identify independent predictors of outcome, generate, compare and validate previous predictive models for outcomes. We hypothesized that National Trauma Data Bank (NTDB) given its large number of patients, would validate these models. Methods: The NTDB was queried for data on cardiac injuries, using survival as the main outcome measure. Statistical analysis was performed utilizing univariate and stepwise logistic regression. The stepwise logistic regression model was then compared with other predictive models of outcome. Results: There were 2016 patients with penetrating cardiac injuries identified from 1,310,720 patients. Incidence: 0.16%. Mechanism of injury: GSWs—1264 (63%), SWs—716 (36%), Shotgun/impalement—19/16 (1%). Mean RTS 1.75, mean ISS 27 ± 23. Overall survival 675 (33%). 830 patients (41%) underwent ED thoracotomy, 47 survived (6%). Survival stratified by mechanism: GSWs 114/1264 (10%), SWs 564/717 (76%). Predictors of outcome for mortality—univariate analysis: vital signs, RTS, ISS, GCS: Field CPR, ED intubation, ED thoracotomy and aortic cross-clamping (p < 0.001). Stepwise logistic regression identified cardiac GSW’s (p < 0.001; AOR 26.85; 95% CI 17.21–41.89), field CPR (p = 0.003; AOR 3.65; 95% CI 1.53–8.69), the absence of spontaneous ventilation (p = 0.008; AOR 1.08, 95% CI 1.02–1.14), the presence of an associated abdominal GSW (p = 0.009; AOR 2.58, 95% CI 1.26–5.26) need for ED airway (p = 0.0003 AOR 1386.30; 95% CI 126.0–15251.71) and aortic cross-clamping (p = 0.0003 AOR 0.18; 95% CI 0.11–0.28) as independent predictors for mortality. Overall predictive power of model—93%. Conclusion: Predictors of outcome were identified. Overall survival rates are lower than prospective studies report. Predictive model from NTDB generated larger number of strong independent predictors of outcomes, correlated and validated previous predictive models.

Original languageEnglish (US)
Pages (from-to)1-7
Number of pages7
JournalEuropean Journal of Trauma and Emergency Surgery
DOIs
StateAccepted/In press - Jun 3 2017

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Databases
Wounds and Injuries
Logistic Models
Cardiopulmonary Resuscitation
Thoracotomy
Constriction
Survival
Vital Signs
Firearms
Intubation
Ventilation
Survival Rate
Outcome Assessment (Health Care)
Prospective Studies
Mortality
Incidence

All Science Journal Classification (ASJC) codes

  • Surgery
  • Emergency Medicine
  • Orthopedics and Sports Medicine
  • Critical Care and Intensive Care Medicine

Cite this

Penetrating cardiac injuries : predictive model for outcomes based on 2016 patients from the National Trauma Data Bank. / Asensio, Juan A.; Ogun, O. A.; Petrone, P.; Perez-Alonso, A. J.; Wagner, M.; Bertellotti, R.; Phillips, B.; Cornell, D. L.; Udekwu, A. O.

In: European Journal of Trauma and Emergency Surgery, 03.06.2017, p. 1-7.

Research output: Contribution to journalArticle

Asensio, Juan A. ; Ogun, O. A. ; Petrone, P. ; Perez-Alonso, A. J. ; Wagner, M. ; Bertellotti, R. ; Phillips, B. ; Cornell, D. L. ; Udekwu, A. O. / Penetrating cardiac injuries : predictive model for outcomes based on 2016 patients from the National Trauma Data Bank. In: European Journal of Trauma and Emergency Surgery. 2017 ; pp. 1-7.
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abstract = "Background: Penetrating cardiac injuries are uncommon and lethal. The objectives of this study are to examine the national profile of cardiac injuries, identify independent predictors of outcome, generate, compare and validate previous predictive models for outcomes. We hypothesized that National Trauma Data Bank (NTDB) given its large number of patients, would validate these models. Methods: The NTDB was queried for data on cardiac injuries, using survival as the main outcome measure. Statistical analysis was performed utilizing univariate and stepwise logistic regression. The stepwise logistic regression model was then compared with other predictive models of outcome. Results: There were 2016 patients with penetrating cardiac injuries identified from 1,310,720 patients. Incidence: 0.16{\%}. Mechanism of injury: GSWs—1264 (63{\%}), SWs—716 (36{\%}), Shotgun/impalement—19/16 (1{\%}). Mean RTS 1.75, mean ISS 27 ± 23. Overall survival 675 (33{\%}). 830 patients (41{\%}) underwent ED thoracotomy, 47 survived (6{\%}). Survival stratified by mechanism: GSWs 114/1264 (10{\%}), SWs 564/717 (76{\%}). Predictors of outcome for mortality—univariate analysis: vital signs, RTS, ISS, GCS: Field CPR, ED intubation, ED thoracotomy and aortic cross-clamping (p < 0.001). Stepwise logistic regression identified cardiac GSW’s (p < 0.001; AOR 26.85; 95{\%} CI 17.21–41.89), field CPR (p = 0.003; AOR 3.65; 95{\%} CI 1.53–8.69), the absence of spontaneous ventilation (p = 0.008; AOR 1.08, 95{\%} CI 1.02–1.14), the presence of an associated abdominal GSW (p = 0.009; AOR 2.58, 95{\%} CI 1.26–5.26) need for ED airway (p = 0.0003 AOR 1386.30; 95{\%} CI 126.0–15251.71) and aortic cross-clamping (p = 0.0003 AOR 0.18; 95{\%} CI 0.11–0.28) as independent predictors for mortality. Overall predictive power of model—93{\%}. Conclusion: Predictors of outcome were identified. Overall survival rates are lower than prospective studies report. Predictive model from NTDB generated larger number of strong independent predictors of outcomes, correlated and validated previous predictive models.",
author = "Asensio, {Juan A.} and Ogun, {O. A.} and P. Petrone and Perez-Alonso, {A. J.} and M. Wagner and R. Bertellotti and B. Phillips and Cornell, {D. L.} and Udekwu, {A. O.}",
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T2 - predictive model for outcomes based on 2016 patients from the National Trauma Data Bank

AU - Asensio, Juan A.

AU - Ogun, O. A.

AU - Petrone, P.

AU - Perez-Alonso, A. J.

AU - Wagner, M.

AU - Bertellotti, R.

AU - Phillips, B.

AU - Cornell, D. L.

AU - Udekwu, A. O.

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N2 - Background: Penetrating cardiac injuries are uncommon and lethal. The objectives of this study are to examine the national profile of cardiac injuries, identify independent predictors of outcome, generate, compare and validate previous predictive models for outcomes. We hypothesized that National Trauma Data Bank (NTDB) given its large number of patients, would validate these models. Methods: The NTDB was queried for data on cardiac injuries, using survival as the main outcome measure. Statistical analysis was performed utilizing univariate and stepwise logistic regression. The stepwise logistic regression model was then compared with other predictive models of outcome. Results: There were 2016 patients with penetrating cardiac injuries identified from 1,310,720 patients. Incidence: 0.16%. Mechanism of injury: GSWs—1264 (63%), SWs—716 (36%), Shotgun/impalement—19/16 (1%). Mean RTS 1.75, mean ISS 27 ± 23. Overall survival 675 (33%). 830 patients (41%) underwent ED thoracotomy, 47 survived (6%). Survival stratified by mechanism: GSWs 114/1264 (10%), SWs 564/717 (76%). Predictors of outcome for mortality—univariate analysis: vital signs, RTS, ISS, GCS: Field CPR, ED intubation, ED thoracotomy and aortic cross-clamping (p < 0.001). Stepwise logistic regression identified cardiac GSW’s (p < 0.001; AOR 26.85; 95% CI 17.21–41.89), field CPR (p = 0.003; AOR 3.65; 95% CI 1.53–8.69), the absence of spontaneous ventilation (p = 0.008; AOR 1.08, 95% CI 1.02–1.14), the presence of an associated abdominal GSW (p = 0.009; AOR 2.58, 95% CI 1.26–5.26) need for ED airway (p = 0.0003 AOR 1386.30; 95% CI 126.0–15251.71) and aortic cross-clamping (p = 0.0003 AOR 0.18; 95% CI 0.11–0.28) as independent predictors for mortality. Overall predictive power of model—93%. Conclusion: Predictors of outcome were identified. Overall survival rates are lower than prospective studies report. Predictive model from NTDB generated larger number of strong independent predictors of outcomes, correlated and validated previous predictive models.

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