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 hospital's 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 $4,000 per patient (total saving estimate of $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.     

Predicting hospital mortality for patients in the intensive care unit: a comparison of artificial neural networks with logistic regression models

OBJECTIVE: Logistic regression (LR), commonly used for hospital mortality prediction, has limitations. Artificial neural networks (ANNs) have been proposed as an alternative. We compared the performance of these approaches by using stepwise reductions in sample size. DESIGN: Prospective cohort study. SETTING: Seven intensive care units (ICU) at one tertiary care center. PATIENTS: Patients were 1,647 ICU admissions for whom first-day Acute Physiology and Chronic Health Evaluation III variables were collected. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We constructed LR and ANN models on a random set of 1,200 admissions (development set) and used the remaining 447 as the validation set. We repeated model construction on progressively smaller development sets (800, 400, and 200 admissions) and retested on the original validation set (n = 447). For each development set, we constructed models from two LR and two ANN architectures, organizing the independent variables differently. With the 1,200-admission development set, all models had good fit and discrimination on the validation set, where fit was assessed by the Hosmer-Lemeshow C statistic (range, 10.6-15.3; p > or = .05) and standardized mortality ratio (SMR) (range, 0.93 [95% confidence interval, 0.79-1.15] to 1.09 [95% confidence interval, 0.89-1.38]), and discrimination was assessed by the area under the receiver operating characteristic curve (range, 0.80-0.84). As development set sample size decreased, model performance on the validation set deteriorated rapidly, although the ANNs retained marginally better fit at 800 (best C statistic was 26.3 [p = .0009] and 13.1 [p = .11] for the LR and ANN models). Below 800, fit was poor with both approaches, with high C statistics (ranging from 22.8 [p <.004] to 633 [p <.0001]) and highly biased SMRs (seven of the eight models below 800 had SMRs of <0.85, with an upper confidence interval of <1). Discrimination ranged from 0.74 to 0.84 below 800. CONCLUSIONS: When sample size is adequate, LR and ANN models have similar performance. However, development sets of < or = 800 were generally inadequate. This is concerning, given typical sample sizes used for individual ICU mortality prediction.     

Black children experience worse clinical and functional outcomes after traumatic brain injury: an analysis of the National Pediatric Trauma Registry

BACKGROUND: Recent studies suggest racial disparities in the treatment and outcomes of children with traumatic brain injury (TBI). This study aims to identify race-based clinical and functional outcome differences among pediatric TBI patients in a national database. METHODS: A total of 41,122 patients (ages 2-16 years) who were included in the National Pediatric Trauma Registry (from 1996-2001) were studied. TBI was categorized by Relative Head Injury Severity Score (RHISS) and patients with moderate to severe TBI were included. Individual race groups were compared with white as the majority group. Differences between races in functional outcomes at discharge in three domains-speech, locomotion, and feeding-were determined using multiple logistic regression. Cases were adjusted for age, sex, severity of head injury (using RHISS), severity of injury (using New Injury Severity Score and Pediatric Trauma Score), premorbidities, mechanism, and injury intent. RESULTS: A total of 7,778 children had moderate or severe TBI with or without associated injuries. All races had similar demographics. Hispanics (n=1,041) had outcomes comparable to whites (n=4,762). Black children (n=1,238) had significantly increased premorbidities, penetrating trauma, and violent intent. They also had higher unadjusted mortality and longer mean intensive care unit and floor stays. After adjustment, there was no difference in the odds of death between black and white children. However, black patients were more likely to be discharged to an inpatient rehabilitation facility and had increased odds of possessing a functional deficit at discharge for all three domains studied. CONCLUSION: Black children with traumatic brain injury have worse clinical and functional outcomes at discharge when compared with equivalently injured white children.     

Dilated cardiomyopathy following right ventricular pacing for AV block in young patients: resolution after upgrading to biventricular pacing systems

Introduction: Cardiac resynchronization therapy (CRT) has been demonstrated to result in clinical improvement in older adult patients with dilated cardiomyopathy (DCM), specifically those with left bundle branch block and prolonged QRS duration. We sought to demonstrate the benefits of CRT on improvement in cardiac function and clinical outcome in young patients that developed congestive heart failure (CHF) and DCM following cardiac pacing for AV block.
Methods and Results: We reviewed the charts of six patients who developed CHF or low cardiac output symptoms and DCM following implantation of right ventricular (RV)-based pacing systems for AV block, and subsequently underwent CRT. Patients ranged in age from 6 months to 23.7 years (mean: 11.3 ± 3.6 years). AV block was congenital (3), post-surgery (2), and acquired (1). Pacing had been performed for 0.1–14.5 (7.6 ± 2.4) years prior to development of DCM. Two patients required listing for cardiac transplantation. Following CRT: (1) QRS duration shortened from 204 ± 15 to 138 ± 10 msec, P = 0.002, (2) left ventricular ejection fraction improved from 34 ± 6 to 60 ± 2%, P = 0.003, and (3) left ventricular end diastolic dimension shortened from 5.5 ± 0.8 to 4.3 ± 0.5 cm, P =0.03. All patients demonstrated clinical improvement and have been weaned from CHF medications and listing for cardiac transplantation.
Conclusions: CRT can benefit young patients that develop CHF and DCM following RV pacing for AV block. Upgrading to biventricular pacing systems should be considered early in the management of these patients prior to listing for cardiac transplantation.     

Decreased serum transferrin concentration in children with the nephrotic syndrome: effect on lymphocyte proliferation and correlation with serum immunoglobulin levels

Recent evidence suggests that transferrin has immunoregulatory functions. In the nephrotic syndrome, excessive urinary losses can produce hypotransferrinemia. Whether low serum transferrin concentration in children with the nephrotic syndrome is related to their decreased immunoglobulin concentrations and to the decreased in vitro response of lymphocytes to a mitogen was studied. Twenty patients, 2 to 15 years of age, were studied. Fifteen patients had the nephrotic syndrome and 5 had other renal disorders. Of 13 patients with nephrotic syndrome in relapse, serum transferrin and gamma-globulin concentrations were decreased in 10 and 11 patients, respectively. Transferrin levels correlated with the concentrations of total protein (r = 0.87, P less than 0.001), albumin (r = 0.91, P less than 0.001), and gamma-globulin (r = 0.78, P less than 0.001). Urinary electrophoretic analyses suggested that hypogammaglobulinemia was not explained simply by urinary losses. In order to determine whether decreased serum transferrin concentrations might limit immunoglobulin synthesis, the effect of hypotransferrinemic sera on lymphocyte proliferation in vitro was tested. At low concentrations of serum, tritiated thymidine uptake was directly proportional to the serum transferrin concentration (r = 0.86, P less than 0.001 at 0.02% serum concentration). Addition of transferrin completely restored the ability of patients' sera to support lymphocyte proliferation. These results suggest that hypotransferrinemia might influence in vivo lymphocyte function and immunity in the nephrotic syndrome.     

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.     

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

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