Sara Cuff

Traumatic brain injury in the elderly: Increased mortality and worse functional outcome at discharge despite lower injury severity

OBJECTIVE The purpose of this study was to compare data obtained from a statewide data set for elderly patients (age > 64 years) that presented with traumatic brain injury with data from nonelderly patients (age > 15 and < 65 years) with similar injuries. METHODS The New York State Trauma Registry from January 1994 through December 1995, from trauma centers and community hospitals excluding New York City (45,982 patients), was examined. Head-injured patients were identified by International Classification of Diseases, Ninth Revision diagnosis codes. A relative head injury severity scale (RHISS) was constructed on the basis of groups of these codes (range, 0 = none to 3 = severe). Comparisons were made with nonelderly patients for mortality, Glasgow Coma Scale (GCS) score at admission and discharge, Injury Severity Score, New Injury Severity Score, and RHISS. Outcome was assessed by a Functional Independence Measure score in three major domains: expression, locomotion, and feeding. Data were analyzed by the chi2 test and Mann-Whitney U test, with p < 0.05 considered significant. RESULTS There were 11,772 patients with International Classification of Diseases, Ninth Revision diagnosis of head injury, of which 3,244 (27%) were elderly. There were more male subjects in the nonelderly population (78% male subjects) compared with the elderly population (50% men). Mortality was 24.0% in the elderly population compared with 12.8% in the nonelderly population (risk ratio, 2.2; 95% confidence interval, 1.99-2.43). The elderly nonsurvivors were statistically older, and mortality rate increased with age. Stratified by GCS score, there was a higher percentage of nonsurvivors in the elderly population, even in the group with only moderately depressed GCS score (GCS score of 13-15; risk ratio, 7.8; 95% confidence interval, 6.1-9.9 for elderly vs. nonelderly). Functional outcome in all three domains was significantly worse in the elderly survivors compared with the nonelderly survivors. CONCLUSION Elderly traumatic brain injury patients have a worse mortality and functional outcome than nonelderly patients who present with head injury even though their head injury and overall injuries are seemingly less severe.

Preparation and achievement of American College of Surgeons level I trauma verification raises hospital performance and improves patient outcome.

OBJECTIVE The purpose of this study was to assess the impact on patient outcome and hospital performance of preparing for and achieving American College of Surgeons (ACS) Level I trauma verification. METHODS The center was a previously designated state regional trauma center located adjacent to a major metropolitan area. Preparation for ACS verification began in early 1996 and was completed in early 1998. Final verification took place in April 1999. Data were analyzed before (1994) and after (1998) the process. There was a marked increase in administrative support with trauma named one of the hospitals six centers of excellence. Two full-time board-certified trauma/critical care surgeons were added to the current six trauma surgeons. Their major focus was trauma care. Trauma support staff was also increased with case managers, a trauma nurse practitioner, additional trauma registrars, and administrative support staff. Education and continuous quality improvement were markedly expanded starting in 1996. RESULTS There were 1,098 trauma patients admitted in 1994, and 1,658 in 1998. Overall mortality decreased (1994, 7.38%; 1998, 5.37%; p < 0.05). There was a marked decrease in mortality for severely injured (Injury Severity Score > 30) patients (1994, 44% mortality [38 of 86]; 1998, 27% [22 of 80]; p < 0.04). Average length of stay also decreased (1994, 12.22 days; 1998, 9.87 days; p < 0.02). This yielded an estimated cost savings for 1998 of greater than

An artificial neural network as a model for prediction of survival in trauma patients: validation for a regional trauma area.

4,000 per patient (total saving estimate of

Development of a model for prediction of survival in pediatric trauma patients: Comparison of artificial neural networks and logistic regression ☆

7.4 million). CONCLUSION Trauma system improvement as related to achieving ACS Level I verification appeared to have a positive impact on survival and patient care. There were cost savings realized that helped alleviate the added expense of this system improvement. The process of achieving ACS Level I verification is worthwhile and can be cost effective.

Validation of a relative head injury severity scale for pediatric trauma

BACKGROUND To develop and validate an artificial neural network (ANN) for predicting survival of trauma patients based on standard prehospital variables, emergency room admission variables, and Injury Severity Score (ISS) using data derived from a regional area trauma system, and to compare this model with known trauma scoring systems. PATIENT POPULATION The study was composed of 10,609 patients admitted to 24 hospitals comprising a seven-county suburban/rural trauma region adjacent to a major metropolitan area. The data was generated as part of the New York State trauma registry. Study period was from January 1993 through December 1996 (1993-1994: 5,168 patients; 1995: 2,768 patients; 1996: 2,673 patients). METHODS A standard feed-forward back-propagation neural network was developed using Glasgow Coma Scale, systolic blood pressure, heart rate, respiratory rate, temperature, hematocrit, age, sex, intubation status, ICD-9-CM Injury E-code, and ISS as input variables. The network had a single layer of hidden nodes. Initial network development of the model was performed on the 1993-1994 data. Subsequent models were generated using the 1993, 1994, and 1995 data. The model was tested first on the 1995 and then on the 1996 data. The ANN model was tested against Trauma and Injury Severity Score (TRISS) and ISS using the receiver operator characteristic (ROC) area under the curve [ROC-A(z)], Lemeshow-Hosmer C-statistic, and calibration curves. RESULTS The ANN showed good clustering of the data, with good separation of nonsurvivors and survivors. The ROCA(z) was 0.912 for the ANN, 0.895 for TRISS, and 0.766 for ISS. The ANN exceeded TRISS with respect to calibration (Lemeshow-Hosmer C-statistic: 7.4 for ANN; 17.1 for TRISS). The prediction of survivors was good for both models. The ANN exceeded TRISS in nonsurvivor prediction. CONCLUSION An ANN developed for trauma patients using prehospital, emergency room admission data, and ISS gave good prediction of survival. It was accurate and had excellent calibration. This study expands our previous results developed at a single Level I trauma center and shows that an ANN model for predicting trauma deaths can be applied across hospitals with good results