Search Results

You are looking at 1 - 4 of 4 items for

  • Author or Editor: Oscar D. Guillamondegui x
  • All content x
Clear All Modify Search
Restricted access

Michael F. Stiefel, Alejandro Spiotta, Vincent H. Gracias, Alicia M. Garuffe, Oscar Guillamondegui, Eileen Maloney-Wilensky, Stephanie Bloom, M. Sean Grady, and Peter D. LeRoux

Object. An intracranial pressure (ICP) monitor, from which cerebral perfusion pressure (CPP) is estimated, is recommended in the care of severe traumatic brain injury (TBI). Nevertheless, optimal ICP and CPP management may not always prevent cerebral ischemia, which adversely influences patient outcome. The authors therefore determined whether the addition of a brain tissue oxygen tension (PO2) monitor in the treatment of TBI was associated with an improved patient outcome.

Methods. Patients with severe TBI (Glasgow Coma Scale [GCS] score < 8) who had been admitted to a Level I trauma center were evaluated as part of a prospective observational database. Patients treated with ICP and brain tissue PO2 monitoring were compared with historical controls matched for age, pathological features, admission GCS score, and Injury Severity Score who had undergone ICP monitoring alone. Therapy in both patient groups was aimed at maintaining an ICP less than 20 mm Hg and a CPP greater than 60 mm Hg. Among patients whose brain tissue PO2 was monitored, oxygenation was maintained at levels greater than 25 mm Hg. Twenty-five patients with a mean age of 44 ± 14 years were treated using an ICP monitor alone. Twenty-eight patients with a mean age of 38 ± 18 years underwent brain tissue PO2-directed care. The mean daily ICP and CPP levels were similar in each group. The mortality rate in patients treated using conventional ICP and CPP management was 44%. Patients who also underwent brain tissue PO2 monitoring had a significantly reduced mortality rate of 25% (p < 0.05).

Conclusions. The use of both ICP and brain tissue PO2 monitors and therapy directed at brain tissue PO2 is associated with reduced patient death following severe TBI.

Free access

Patrick D. Kelly, Pious D. Patel, Aaron M. Yengo-Kahn, Daniel I. Wolfson, Fakhry Dawoud, Ranbir Ahluwalia, Oscar D. Guillamondegui, and Christopher M. Bonfield

OBJECTIVE

Several scores estimate the prognosis for gunshot wounds to the head (GSWH) at the point of hospital admission. However, prognosis may change over the course of the hospital stay. This study measures the accuracy of the Baylor score among patients who have already survived the acute phase of hospitalization and generates conditional outcome curves for the duration of hospital stay for patients with GSWH.

METHODS

Patients in whom GSWH with dural penetration occurred between January 2009 and June 2019 were identified from a trauma registry at a level I trauma center in the southeastern US. The Baylor score was calculated using component variables. Conditional overall survival and good functional outcome (Glasgow Outcome Scale score of 4 or 5) curves were generated. The accuracy of the Baylor score in predicting mortality and functional outcome among acute-phase survivors (survival > 48 hours) was assessed using receiver operating characteristic curves and the area under the curve (AUC).

RESULTS

A total of 297 patients were included (mean age 38.0 [SD 15.7] years, 73.4% White, 85.2% male), and 129 patients survived the initial 48 hours of admission. These acute-phase survivors had a decreased mortality rate of 32.6% (n = 42) compared to 68.4% (n = 203) for all patients, and an increased rate of good functional outcome (48.1%; n = 62) compared to the rate for all patients (23.2%; n = 69). Among acute-phase survivors, the Baylor score accurately predicted mortality (AUC = 0.807) and functional outcome (AUC = 0.837). However, the Baylor score generally overestimated true mortality rates and underestimated good functional outcome. Additionally, hospital day 18 represented an inflection point of decreasing probability of good functional outcome.

CONCLUSIONS

During admission for GSWH, surviving beyond the acute phase of 48 hours doubles the rates of survival and good functional outcome. The Baylor score maintains reasonable accuracy in predicting these outcomes for acute-phase survivors, but generally overestimates mortality and underestimates good functional outcome. Future prognostic models should incorporate conditional survival to improve the accuracy of prognostication after the acute phase.

Free access

Aaron M. Yengo-Kahn, Pious D. Patel, Patrick D. Kelly, Daniel I. Wolfson, Fakhry Dawoud, Ranbir Ahluwalia, Christopher M. Bonfield, and Oscar D. Guillamondegui

OBJECTIVE

Gunshot wounds to the head (GSWH) are devastating injuries with a grim prognosis. Several prognostic scores have been created to estimate mortality and functional outcome, including the so-called Baylor score, an uncomplicated scoring method based on bullet trajectory, patient age, and neurological status on admission. This study aimed to validate the Baylor score within a temporally, institutionally, and geographically distinct patient population.

METHODS

Data were obtained from the trauma registry at a level I trauma center in the southeastern US. Patients with a GSWH in which dural penetration occurred were identified from data collected between January 1, 2009, and June 30, 2019. Patient demographics, medical history, bullet trajectory, intent of GSWH (e.g., suicide), admission vital signs, Glasgow Coma Scale score, pupillary response, laboratory studies, and imaging reports were collected. The Baylor score was calculated directly by using its clinical components. The ability of the Baylor score to predict mortality and good functional outcome (Glasgow Outcome Scale score 4 or 5) was assessed using the receiver operating characteristic curve and the area under the curve (AUC) as a measure of performance.

RESULTS

A total of 297 patients met inclusion criteria (mean age 38.0 [SD 15.7] years, 73.4% White, 85.2% male). A total of 205 (69.0%) patients died, whereas 69 (23.2%) patients had good functional outcome. Overall, the Baylor score showed excellent discrimination of mortality (AUC = 0.88) and good functional outcome (AUC = 0.90). Baylor scores of 3–5 underestimated mortality. Baylor scores of 0, 1, and 2 underestimated good functional outcome.

CONCLUSIONS

The Baylor score is an accurate and easy-to-use prognostic scoring tool that demonstrated relatively stable performance in a distinct cohort between 2009 and 2019. In the current era of trauma management, providers may continue to use the score at the point of admission to guide family counseling and to direct investment of healthcare resources.

Full access

Andrew T. Hale, David P. Stonko, Jaims Lim, Oscar D. Guillamondegui, Chevis N. Shannon, and Mayur B. Patel

OBJECTIVE

Pediatric traumatic brain injury (TBI) is common, but not all injuries require hospitalization. A computational tool for ruling in patients who will have a clinically relevant TBI (CRTBI) would be valuable, providing an evidence-based way to safely discharge children who are at low risk for a CRTBI. The authors hypothesized that an artificial neural network (ANN) trained on clinical and radiologist-interpreted imaging metrics could provide a tool for identifying patients likely to suffer from a CRTBI.

METHODS

The authors used the prospectively collected, publicly available, multicenter Pediatric Emergency Care Applied Research Network (PECARN) TBI data set. All patients under the age of 18 years with TBI and admission head CT imaging data were included. The authors constructed an ANN using clinical and radiologist-interpreted imaging metrics in order to predict a CRTBI, as previously defined by PECARN: 1) neurosurgical procedure, 2) intubation > 24 hours as direct result of the head trauma, 3) hospitalization ≥ 48 hours and evidence of TBI on a CT scan, or 4) death due to TBI.

RESULTS

Among 12,902 patients included in this study, 480 were diagnosed with CRTBI. The authors’ ANN had a sensitivity of 99.73% with precision of 98.19%, accuracy of 97.98%, negative predictive value of 91.23%, false-negative rate of 0.0027%, and specificity for CRTBI of 60.47%. The area under the receiver operating characteristic curve was 0.9907.

CONCLUSIONS

The authors are the first to utilize artificial intelligence to predict a CRTBI in a clinically meaningful manner, using radiologist-interpreted CT information, in order to identify pediatric patients likely to suffer from a CRTBI. This proof-of-concept study lays the groundwork for future studies incorporating iterations of this algorithm directly into the electronic medical record for real-time, data-driven predictive assistance to physicians.