Marcel Émond

Sensitivity of computed tomography performed within six hours of onset of headache for diagnosis of subarachnoid haemorrhage: prospective cohort study

Objective To measure the sensitivity of modern third generation computed tomography in emergency patients being evaluated for possible subarachnoid haemorrhage, especially when carried out within six hours of headache onset. Design Prospective cohort study. Setting 11 tertiary care emergency departments across Canada, 2000-9. Participants Neurologically intact adults with a new acute headache peaking in intensity within one hour of onset in whom a computed tomography was ordered by the treating physician to rule out subarachnoid haemorrhage. Main outcome measures Subarachnoid haemorrhage was defined by any of subarachnoid blood on computed tomography, xanthochromia in cerebrospinal fluid, or any red blood cells in final tube of cerebrospinal fluid collected with positive results on cerebral angiography. Results Of the 3132 patients enrolled (mean age 45.1, 2571 (82.1%) with worst headache ever), 240 had subarachnoid haemorrhage (7.7%). The sensitivity of computed tomography overall for subarachnoid haemorrhage was 92.9% (95% confidence interval 89.0% to 95.5%), the specificity was 100% (99.9% to 100%), the negative predictive value was 99.4% (99.1% to 99.6%), and the positive predictive value was 100% (98.3% to 100%). For the 953 patients scanned within six hours of headache onset, all 121 patients with subarachnoid haemorrhage were identified by computed tomography, yielding a sensitivity of 100% (97.0% to 100.0%), specificity of 100% (99.5% to 100%), negative predictive value of 100% (99.5% to 100%), and positive predictive value of 100% (96.9% to 100%). Conclusion Modern third generation computed tomography is extremely sensitive in identifying subarachnoid haemorrhage when it is carried out within six hours of headache onset and interpreted by a qualified radiologist.

Prospective validation of the ABCD2 score for patients in the emergency department with transient ischemic attack

Background: The ABCD2 score (Age, Blood pressure, Clinical features, Duration of symptoms and Diabetes) is used to identify patients having a transient ischemic attack who are at high risk for imminent stroke. However, despite its widespread implementation, the ABCD2 score has not yet been prospectively validated. We assessed the accuracy of the ABCD2 score for predicting stroke at 7 (primary outcome) and 90 days. Methods: This prospective cohort study enrolled adults from eight Canadian emergency departments who had received a diagnosis of transient ischemic attack. Physicians completed data forms with the ABCD2 score before disposition. The outcome criterion, stroke, was established by a treating neurologist or by an Adjudication Committee. We calculated the sensitivity and specificity for predicting stroke 7 and 90 days after visiting the emergency department using the original “high-risk” cutpoint of an ABCD2 score of more than 5, and the American Heart Association recommendation of a score of more than 2. Results: We enrolled 2056 patients (mean age 68.0 yr, 1046 (50.9%) women) who had a rate of stroke of 1.8% at 7 days and 3.2% at 90 days. An ABCD2 score of more than 5 had a sensitivity of 31.6% (95% confidence interval [CI] 19.1–47.5) for stroke at 7 days and 29.2% (95% CI 19.6–41.2) for stroke at 90 days. An ABCD2 score of more than 2 resulted in sensitivity of 94.7% (95% CI 82.7–98.5) for stroke at 7 days with a specificity of 12.5% (95% CI 11.2–14.1). The accuracy of the ABCD2 score as calculated by either the enrolling physician (area under the curve 0.56; 95% CI 0.47–0.65) or the coordinating centre (area under the curve 0.65; 95% CI 0.57–0.73) was poor. Interpretation: This multicentre prospective study involving patients in emergency departments with transient ischemic attack found the ABCD2 score to be inaccurate, at any cut-point, as a predictor of imminent stroke. Furthermore, the ABCD2 score of more than 2 that is recommended by the American Heart Association is nonspecific.

Rates, patterns, and determinants of unplanned readmission after traumatic injury: a multicenter cohort study.

Objective:This study aimed to (i) describe unplanned readmission rates after injury according to time, reason, and place; (ii) compare observed rates with general population rates, and (iii) identify determinants of 30-day readmission. Background:Hospital readmissions represent an important burden in terms of mortality, morbidity, and resource use but information on unplanned rehospitalization after injury admissions is scarce. Methods:This multicenter retrospective cohort study was based on adults discharged alive from a Canadian provincial trauma system (1998–2010; n = 115,329). Trauma registry data were linked to hospital discharge data to obtain information on readmission up to 12 months postdischarge. Provincial admission rates were matched to study data by age and gender to obtain expected rates. Determinants of readmission were identified using multiple logistic regression. Results:Cumulative readmission rates at 30 days, 3 months, 6 months, and 12 months were 5.9%, 10.9%, 15.5%, and 21.1%, respectively. Observed rates persisted above expected rates up to 11 months postdischarge. Thirty percent of 30-day readmissions were due to potential complications of injury compared with 3% for general provincial admissions. Only 23% of readmissions were to the same hospital. The strongest independent predictors of readmission were the number of prior admissions, discharge destination, the number of comorbidities, and age. Conclusions:Unplanned readmissions after discharge from acute care for traumatic injury are frequent, persist beyond 30 days, and are often related to potential complications of injury. Several patient-, injury-, and hospital-related factors are associated with the risk of readmission. Injury readmission rates should be monitored as part of trauma quality assurance efforts.

Cumulative Incidence of Functional Decline After Minor Injuries in Previously Independent Older Canadian Individuals in the Emergency Department

To estimate the cumulative incidence of functional decline in independent older adults 3 and 6 months after a minor injury treated in the emergency department (ED) and to identify predictors of this functional decline.

A Multiple Imputation Model for Imputing Missing Physiologic Data in the National Trauma Data Bank

BACKGROUND Like most trauma registries, the National Trauma Data Bank (NTDB) is limited by the problem of missing physiologic data. Multiple imputation (MI) has been proposed to simulate missing Glasgow Coma Scale (GCS) scores, respiratory rate (RR), and systolic blood pressure (SBP). The aim of this study was to develop an MI model for missing physiologic data in the NTDB and to provide guidelines for its implementation. STUDY DESIGN The NTDB 7.0 was restricted to patients admitted in 2005 with at least one anatomic injury code. A series of auxiliary variables thought to offer information for the imputation process was selected from the NTDB by literature review and expert opinion. The relation of these variables to physiologic variables and to the fact that they were missing was examined using logistic regression. The MI model included all auxiliary variables that had a statistically significant association with physiologic variables or with the fact that they were missing (Bonferroni-corrected p value <0.05). RESULTS The NTDB sample included 373,243 observations. Glasgow Coma Scale, respiratory rate, and systolic blood pressure were missing for 20.3%, 3.9%, and 8.5% of data observations, respectively. The MI model included information on the following: gender, age, anatomic injury severity, transfer status, injury mechanism, intubation status, alcohol and drug test results, emergency department disposition, total length of stay, ICU length of stay, duration of mechanical ventilation, and discharge disposition. The MI model offered good discrimination for predicting the value of physiologic variables and the fact that they were missing (areas under the receiver operating characteristic curve between 0.832 and 0.999). CONCLUSIONS This article proposes an MI model for imputing missing physiologic data in the NTDB and provides guidelines to facilitate its use. Implementation of the model should improve the quality of research involving the NTDB. The methodology can also be adapted to other trauma registries.

The Trauma Risk Adjustment Model: A New Model for Evaluating Trauma Care

Summary Background Data:The trauma injury severity score (TRISS) has been used for over 20 years for retrospective risk assessment in trauma populations. The TRISS has serious limitations, which may compromise the validity of trauma care evaluations. Objective:To derive and validate a new mortality prediction model, the trauma risk adjustment model (TRAM), and to compare the performance of the TRAM to that of the TRISS in terms of predictive validity and risk adjustment. Methods:The Quebec Trauma Registry (1998–2005), based on the mandatory participation of 59 designated provincial trauma centers, was used to derive the model. The American National Trauma Data Bank (2000–2005), based on the voluntary participation of any US hospitals treating trauma, was used for the validation phase. Adult patients with blunt trauma respecting at least one of the following criteria were included: hospital stay >2 days, intensive care unit admission, death, or hospital transfer. Hospital mortality was modeled with logistic generalized additive models using cubic smoothing splines to accommodate nonlinear relations to mortality. Predictive validity was assessed with model discrimination and calibration. Risk adjustment was assessed using comparisons of risk-adjusted mortality between hospitals. Results:The TRAM generated an area under the receiving operator curve of 0.944 and a Hosmer-Lemeshow statistic of 42 in the derivation phase. In the validation phase, the TRAM demonstrated better model discrimination and calibration than the TRISS (area under the receiving operator curve = 0.942 and 0.928, P < 0.001; Hosmer-Lemeshow statistics = 127 and 256, respectively). Replacing the TRISS with the TRAM led to a mean change of 28% in hospital risk-adjusted odds ratios of mortality. Conclusions:Our results suggest that adopting the TRAM could improve the validity of trauma care evaluations and trauma outcome research.

Using Information on Preexisting Conditions to Predict Mortality From Traumatic Injury

STUDY OBJECTIVE Preexisting conditions have been found to be an independent predictor of mortality after trauma. However, no consensus has been reached as to what indicator of preexisting condition status should be used, and the contribution of preexisting conditions to mortality prediction models is unclear. This study aims to identify the most accurate way to model preexisting condition status to predict inhospital trauma mortality and to evaluate the potential gain of adding preexisting condition status to a standard trauma mortality prediction model. METHODS The study comprised all patients from the trauma registries of 4 Level I trauma centers. Information provided by individual preexisting conditions was compared to 3 commonly used summary measures: (1) absence/presence of any preexisting condition, (2) number of preexisting conditions, and (3) Charlson Comorbidity Index. The impact of adding preexisting condition status to 2 baseline risk models, the current standard Trauma and Injury Severity Score model and an improved model based on nonparametric transformations of quantitative variables, was evaluated by the area under the receiver operating characteristics curve. RESULTS Discrimination for predicting mortality in the improved model was as follows: baseline risk model: area under the receiver operating characteristics curve=0.935; baseline risk model+individually modeled preexisting conditions: area under the receiver operating characteristics curve=0.941; baseline risk model+presence of any preexisting condition: area under the receiver operating characteristics curve=0.937; baseline risk model+number of preexisting conditions: area under the receiver operating characteristics curve=0.939; baseline risk model+Charlson Comorbidity Index: area under the receiver operating characteristics curve=0.938. CONCLUSION Preexisting condition status is an independent predictor of mortality from trauma that provides a modest improvement in mortality prediction. The total number of preexisting conditions is a good summary measure of preexisting condition status. The Charlson Comorbidity Index is no better than the total number of preexisting conditions and is therefore not recommended for use in trauma mortality modeling.

Pediatric trauma mortality by type of designated hospital in a mature inclusive trauma system

Background: Previous studies have shown divergent results regarding the survival of injured children treated at pediatric trauma centers (PTC) and adult trauma centers (ATC). Aims: (1) To document, in a regionalized inclusive trauma system, at which level of trauma centers were the injured children treated and (2) to compare the in-hospital mortality over five levels of trauma care, ranging from pediatric level I trauma centers (PTC) to designated local trauma hospitals (level IV) for the whole study sample and for subgroups of severely injured children and head trauma. Materials and Methods: A retrospective analysis included data on 11,053 injured children (age ≤16 years) treated between April 1998 and March 2005 in 58 designated trauma hospitals in the province of Quebec, Canada. Multiple imputation was used to handle missing physiological data and multivariate logistic regression was used to compare mortality over levels of care. Results: PTC treated 52.2% of the children. Children treated at PTC were more often transferred from another hospital (73%) and were more severely injured. ATC level I, II, III and IV centers treated, respectively, 3.0%, 16.2%, 24.3% and 4.3% of children. Compared with children treated at a PTC, the risk of mortality was higher for children treated at each other ATC, i.e. level I (adjusted odds ratio [OR] = 3.1; 95% confidence interval [CI]: 1.3–7.5), level II (OR = 2.5; 95% CI: 1.3–5.0), level III (OR = 5.2; 95% CI: 2.1–13.1) and level IV (OR = 9.9; 95% CI: 2.4–41.3). Similar findings were observed among the subsamples of children who were more severely injured (Injury Severity Score >15) and who sustained head injuries. Conclusions: In our trauma system, PTC cared for more than half of the injured children and patients treated there have better survival than those treated at all other levels of ATC.

A prospective cohort study of patients with transient ischemic attack to identify high-risk clinical characteristics.

Background and Purpose— The occurrence of a transient ischemic attack (TIA) increases an individual’s risk for subsequent stroke. The objectives of this study were to determine clinical features of patients with TIA associated with impending (⩽7 days) stroke and to develop a clinical prediction score for impending stroke. Methods— We conducted a prospective cohort study at 8 Canadian emergency departments for 5 years. We enrolled patients with a new TIA. Our outcome was subsequent stroke within 7 days of TIA diagnosis. Results— We prospectively enrolled 3906 patients, of which 86 (2.2%) experienced a stroke within 7 days. Clinical features strongly correlated with having an impending stroke included first-ever TIA, language disturbance, longer duration, weakness, gait disturbance, elevated blood pressure, atrial fibrillation on ECG, infarction on computed tomography, and elevated blood glucose. Variables less associated with having an impending stroke included vertigo, lightheadedness, and visual loss. From this cohort, we derived the Canadian TIA Score which identifies the risk of subsequent stroke ⩽7 days and consists of 13 variables. This model has good discrimination with a c-statistic of 0.77 (95% confidence interval, 0.73–0.82). Conclusions— Patients with TIA with their first TIA, language disturbance, duration of symptoms ≥10 minutes, gait disturbance, atrial fibrillation, infarction on computed tomography, elevated platelets or glucose, unilateral weakness, history of carotid stenosis, and elevated diastolic blood pressure are at higher risk for an impending stroke. Patients with vertigo and no high-risk features are at low risk. The Canadian TIA Score quantifies the impending stroke risk following TIA.

Consensus or Data-Derived Anatomic Injury Severity Scoring?

BACKGROUND Anatomic injury severity scores can be grouped into two classes; consensus-derived and data-derived. The former, including the Injury Severity Score (ISS), the New Injury Severity Score (NISS), and the Anatomic Profile Score (APS), are based on the severity score of the Abbreviated Injury Scale (AIS), assigned by clinical experts. The latter, including the International Classification of Disease Injury Severity Score (ICISS) and the Trauma Registry Abbreviated Injury Scale Score (TRAIS) are based on survival probabilities calculated in large trauma databases. We aimed to compare the predictive accuracy of consensus-derived and data-derived severity scores when considered alone and in combination with age and physiologic status. METHODS Analyses were based on 25,111 patients from the trauma registries of the four Level I trauma centers in the province of Quebec, Canada, abstracted between April 1998 and March 2005. The predictive validity of each severity score was evaluated in logistic regression models predicting hospital mortality using measures of discrimination (Area Under the Receiver Operating Characteristics curve [AUC]) and calibration (Hosmer-Lemeshow statistic [HL]). RESULTS Data-derived scores had consistently better predictive accuracy than consensus-derived scores in univariate models (p < 0.0001) but very little difference between scores was observed in models including information on age and physiologic status. The difference in AUC between the least accurate severity score (ISS) and the most accurate severity score (TRAIS) was 15% in anatomic-only models but fell to 2% in models including age and physiologic status. CONCLUSIONS Data-derived scores provide more accurate mortality prediction than consensus-derived scores do when only anatomic injury severity is considered but offer little advantage if age and physiologic status are taken into account. This may be because of the fact that data-derived scores are not an independent measure of anatomic injury severity.