Medical outcome after immediate computed tomography or admission for observation in patients with mild head injury: randomised controlled trial.
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ABSTRACT: Objective
To compare immediate computed tomography during triage for admission with observation in hospital in patients with mild head injury.Design
Multicentre, pragmatic, non-inferiority randomised trial.Setting
39 acute hospitals in Sweden.Participants
2602 patients (aged > or = 6) with mild head injury.Interventions
Immediate computed tomography or admission for observation.Main outcome measure
Dichotomised extended Glasgow outcome scale (1-7 v 8). The non-inferiority margin was 5 percentage points.Results
At three months, 275 patients (21.4%) in the computed tomography group had not recovered completely compared with 300 (24.2%) admitted for observation. The difference was - 2.8 percentage points, non-significantly in favour of computed tomography (95% confidence interval - 6.1% to 0.6%). The worst outcomes (mortality and more severe loss of function) were similar between the groups. In the patients admitted for observation, there was a considerable delay in time to treatment in those who required surgery. None of the patients with normal findings on immediate computed tomography had complications later. Patients' satisfaction with the two strategies was similar.Conclusions
The use of computed tomography in the management of patients with mild head injury is feasible and leads to similar clinical outcomes compared with observation in hospital.Trial registration
ISRCTN81464462.
SUBMITTER: af Geijerstam JL
PROVIDER: S-EPMC1557917 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:ObjectiveTo compare the costs of immediate computed tomography during triage for admission with those of observation in hospital in patients with mild head injury.DesignProspective cost effectiveness analysis within a multicentre, pragmatic randomised trial.Setting39 acute hospitals in SwedenParticipants2602 patients (aged > or = 6) with mild head injury.InterventionsImmediate computed tomography or admission for observation.Main outcome measuresDirect and indirect costs related to the mild head injury during the acute and three month follow-up period.ResultsOutcome after three months was similar for both strategies (non-significantly in favour of computed tomography). For the acute stage and complications, the cost was 461 euros (314 pounds sterling, 582 dollars) per patient in the computed tomography group and 677 euros (462 pounds sterling, 854 dollars) in the observation group; an average of 32% less in the computed tomography group (216 euros, 95% confidence interval -272 to -164; P < 0.001). Sensitivity analysis showed that computed tomography was the most cost effective strategy under a broad range of assumptions. After three months, total costs were 718 euros and 914 euros per patient-that is, 196 euros less in the computed tomography group (- 281 to - 114; P < 0.001). The lower cost of the computed tomography strategy at the acute stage thus remained unchanged during follow-up.ConclusionPatients with mild head injury attending an emergency department can be managed more cost effectively with computed tomography rather than admission for observation in hospital.Trial registrationISRCTN81464462.
| S-EPMC1557926 | biostudies-literature
Project description:IntroductionTraumatic brain injuries (TBIs) are very common in paediatric populations, in which they are also a leading cause of death. Computed tomography (CT) overuse in these populations results in ionization radiation exposure, which can lead to lethal malignancies. The aims of this study were to investigate the accuracy of serum S100B levels with respect to the detection of cranial injury in children with mild TBI and to determine whether decisions regarding the performance of CT can be made based on biomarker levels alone.Materials and methodsThis was a single-center prospective cohort study that was carried out from December 2016 to December 2017. A total of 80 children with mild TBI who met the inclusion criteria were included in the study. The patients were between 2 and 16 years of age. We determined S100B protein levels and performed head CTs in all the patients.ResultsPatients with cranial injury, as detected by CT, had higher S100B protein levels than those without cranial injury (p < 0.0001). We found that patients with cranial injury (head CT+) had higher mean S100B protein levels (0.527 μg L-1, 95% confidence interval (CI) 0.447-0.607 μg L-1) than did patients without cranial injury (head CT-) (0.145 μg L-1, 95% CI 0.138-0.152 μg L-1). Receiver operating characteristic (ROC) curve analysis clearly showed that S100B protein levels differed between patients with and without cranial injury at 3 hours after TBI (AUC = 0.893, 95% CI 0.786-0.987, p = 0.0001).ConclusionSerum S100B levels cannot replace clinical examinations or CT as tools for identifying paediatric patients with mild head injury; however, serum S100B levels can be used to identify low-risk patients to prevent such patients from being exposed to radiation unnecessarily.
| S-EPMC5937551 | biostudies-literature
Project description:Hematoma expansion (HE) is a modifiable risk factor and a potential treatment target in patients with intracerebral hemorrhage (ICH). We aimed to train and validate deep-learning models for high-confidence prediction of supratentorial ICH expansion, based on admission non-contrast head Computed Tomography (CT). Applying Monte Carlo dropout and entropy of deep-learning model predictions, we estimated the model uncertainty and identified patients at high risk of HE with high confidence. Using the receiver operating characteristics area under the curve (AUC), we compared the deep-learning model prediction performance with multivariable models based on visual markers of HE determined by expert reviewers. We randomly split a multicentric dataset of patients (4-to-1) into training/cross-validation (n = 634) versus test (n = 159) cohorts. We trained and tested separate models for prediction of ≥6 mL and ≥3 mL ICH expansion. The deep-learning models achieved an AUC = 0.81 for high-confidence prediction of HE≥6 mL and AUC = 0.80 for prediction of HE≥3 mL, which were higher than visual maker models AUC = 0.69 for HE≥6 mL (p = 0.036) and AUC = 0.68 for HE≥3 mL (p = 0.043). Our results show that fully automated deep-learning models can identify patients at risk of supratentorial ICH expansion based on admission non-contrast head CT, with high confidence, and more accurately than benchmark visual markers.
| S-EPMC10847454 | biostudies-literature
Project description:Predicting outcomes of children after cardiac arrest (CA) remains challenging. To identify useful prognostic markers for pediatric CA, we retrospectively analyzed the early findings of head computed tomography (CT) of patients. Subjects were non-traumatic, out-of-hospital CA patients < 16 years of age who underwent the first head CT within 24 h in our institute from 2006 to 2018 (n = 70, median age: 4 months, range 0-163). Of the 24 patients with return of spontaneous circulation, 14 survived up to 30 days after CA. The degree of brain damage was quantitatively measured with modified methods of the Alberta Stroke Program Early CT Score (mASPECTS) and simplified gray-matter-attenuation-to-white-matter-attenuation ratio (sGWR). The 14 survivors showed higher mASPECTS values than the 56 non-survivors (p = 0.035). All 3 patients with mASPECTS scores ≥ 20 survived, while an sGWR ≥ 1.14 indicated a higher chance of survival than an sGWR < 1.14 (54.5% vs. 13.6%). Follow-up magnetic resonance imaging for survivors validated the correlation of the mASPECTS < 15 with severe brain damage. Thus, low mASPECTS scores were associated with unfavorable neurological outcomes on the Pediatric Cerebral Performance Category scale. A quantitative analysis of early head CT findings might provide clues for predicting survival of pediatric CA.
| S-EPMC8187472 | biostudies-literature
Project description:BackgroundComputed tomography (CT) is commonly used in children with mild head injuries. People in Japan are concerned about radiation exposure and radiation-induced cancer because of the Fukushima Daiichi Nuclear Power Plant accident on March 11, 2011. This study investigated whether the accident influenced the use of CT in children with mild head injuries.MethodsUsing the Japan Medical Data Center database, we identified patients aged ≤15 years visiting hospitals because of mild head injuries from January 1, 2008, to December 31, 2013. We excluded patients who were admitted to the hospital or received other medical examinations. Regression discontinuity analysis was used to compare proportions of patients undergoing head CT and having clinically important traumatic brain injury (ciTBI) overlooked before versus after the accident, adjusting for patient characteristics, secular trends, and hospital effect.ResultsEligible patients (n = 40,440) were classified as visiting the hospital before (n = 11,659) or after (n = 28,781) the accident. The regression discontinuity analysis showed that the accident was associated with a reduction in the proportion of patients undergoing head CT (odds ratio [OR] 0.73; 95% confidence interval [CI], 0.63-0.86), whereas the accident was not associated with an increase in cases where ciTBI was overlooked (OR 0.72; 95% CI, 0.13-4.00).ConclusionsThe use of CT in children with mild head injuries declined after the Fukushima Daiichi Nuclear Power Plant accident. Improving awareness of radiation exposure risks among patients and physicians could reduce unnecessary CT.
| S-EPMC7661337 | biostudies-literature
Project description:BACKGROUND:Reliably abstracting outcomes from free-text electronic health records remains a challenge. While automated classification of free text has been a popular medical informatics topic, performance validation using real-world clinical data has been limited. The two main approaches are linguistic (natural language processing [NLP]) and statistical (machine learning). The authors have developed a hybrid system for abstracting computed tomography (CT) reports for specified outcomes. OBJECTIVES:The objective was to measure performance of a hybrid NLP and machine learning system for automated outcome classification of emergency department (ED) CT imaging reports. The hypothesis was that such a system is comparable to medical personnel doing the data abstraction. METHODS:A secondary analysis was performed on a prior diagnostic imaging study on 3,710 blunt facial trauma victims. Staff radiologists dictated CT reports as free text, which were then deidentified. A trained data abstractor manually coded the reference standard outcome of acute orbital fracture, with a random subset double-coded for reliability. The data set was randomly split evenly into training and testing sets. Training patient reports were used as input to the Medical Language Extraction and Encoding (MedLEE) NLP tool to create structured output containing standardized medical terms and modifiers for certainty and temporal status. Findings were filtered for low certainty and past/future modifiers and then combined with the manual reference standard to generate decision tree classifiers using data mining tools Waikato Environment for Knowledge Analysis (WEKA) 3.7.5 and Salford Predictive Miner 6.6. Performance of decision tree classifiers was evaluated on the testing set with or without NLP processing. RESULTS:The performance of machine learning alone was comparable to prior NLP studies (sensitivity = 0.92, specificity = 0.93, precision = 0.95, recall = 0.93, f-score = 0.94), and the combined use of NLP and machine learning showed further improvement (sensitivity = 0.93, specificity = 0.97, precision = 0.97, recall = 0.96, f-score = 0.97). This performance is similar to, or better than, that of medical personnel in previous studies. CONCLUSIONS:A hybrid NLP and machine learning automated classification system shows promise in coding free-text electronic clinical data.
| S-EPMC3898888 | biostudies-literature
Project description:ObjectivesThis study examined the performance of serum glial fibrillary acidic protein (GFAP) in detecting traumatic intracranial lesions on computed tomography (CT) scan in children and youth with mild and moderate traumatic brain injury (TBI) and assessed its performance in trauma control patients without head trauma.MethodsThis prospective cohort study enrolled children and youth presenting to three Level I trauma centers following blunt head trauma with Glasgow Coma Scale (GCS) scores of 9 to 15, as well as trauma control patients with GCS scores of 15 who did not have blunt head trauma. The primary outcome measure was the presence of intracranial lesions on initial CT scan. Blood samples were obtained in all patients within 6 hours of injury and measured by enzyme-linked immunosorbent assay for GFAP (ng/mL).ResultsA total of 257 children and youth were enrolled in the study and had serum samples drawn within 6 hours of injury for analysis: 197 had blunt head trauma and 60 were trauma controls. CT scan of the head was performed in 152 patients and traumatic intracranial lesions on CT scan were evident in 18 (11%), all of whom had GCS scores of 13 to 15. When serum levels of GFAP were compared in children and youth with traumatic intracranial lesions on CT scan to those without CT lesions, median GFAP levels were significantly higher in those with intracranial lesions (1.01, interquartile range [IQR] = 0.59 to 1.48) than those without lesions (0.18, IQR = 0.06 to 0.47). The area under the receiver operating characteristic curve (AUC) for GFAP in detecting children and youth with traumatic intracranial lesions on CT was 0.82 (95% confidence interval [CI] = 0.71 to 0.93). In those presenting with GCS scores of 15, the AUC for detecting lesions was 0.80 (95% CI = 0.68 to 0.92). Similarly, in children under 5 years old the AUC was 0.83 (95% CI = 0.56 to 1.00). Performance for detecting intracranial lesions at a GFAP cutoff level of 0.15 ng/mL yielded a sensitivity of 94%, a specificity of 47%, and a negative predictive value of 98%.ConclusionsIn children and youth of all ages, GFAP measured within 6 hours of injury was associated with traumatic intracranial lesions on CT and with severity of TBI. Further study is required to validate these findings before clinical application.
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Project description:IntroductionHead computed tomography (CT) interpretation is a vital skill for emergency physicians. Existing literature shows poor concordance between emergency physicians and radiologists in head CT interpretation. Prior studies have used passive learning methods to address this knowledge gap. We created an active learning curriculum for teaching head CT interpretation to emergency medicine (EM) residents and compared its effectiveness to a passive learning strategy.MethodsWe conducted a prospective, randomized controlled study of EM residents at a single institution. Three educational sessions were delivered over a three-month period via video conference. The active learning cohort (ALC) scrolled through head CT teaching cases we designed on Pascbin, a web-based radiology picture archiving and communication system. The passive learning cohort (PLC) watched instructional videos that scrolled through the same cases. Both cohorts were given equal time to review the cases and ask an instructor questions. Residents took pre-intervention and post-intervention tests on head CT interpretation. We analyzed scores using paired and unpaired t-tests.ResultsForty-two residents took the pre-intervention test. Mean pre- and post-test scores for the ALC were 43.8% and 59.0% (P <0.001), and for the PLC were 41.7% and 45.3% (P = 0.29). The difference in ALC and PLC post-test scores was statistically significant (P = 0.009) with a large effect size (Cohen's d = 1.34).ConclusionOur active learning head CT curriculum using Pacsbin showed superior learning outcomes when compared to a passive learning strategy and required no additional time or resources. This intervention offers a more effective and learner-centric method for implementing radiology curricula in EM residency programs.
| S-EPMC8782134 | biostudies-literature
Project description:IntroductionOur goal was to investigate trends in computed tomography (CT) utilization in emergency departments (EDs) and its association with hospitalization.MethodsWe conducted an analysis of an administrative claims database of U.S. privately insured and Medicare Advantage enrollees. We identified ED visits from 2005 through 2013 and assessed for CT use, associated factors, and hospitalization after CT, along with patient demographics. We used both descriptive methods and regression models adjusted for year, age, sex, race, geographic region, and Hwang comorbidity score to explore associations among CT use, year, demographic characteristics, and hospitalization.ResultsWe identified 33,144,233 ED visits; 5,901,603 (17.8%) involved CT. Over time, CT use during ED visits increased 59.9%. CT use increased in all age groups but decreased in children since 2010. In propensity-matching analysis, odds of hospitalization increased with age, comorbidities, male sex, and CT use (odds ratio, 2.38). Odds of hospitalization over time decreased more quickly for patients with CT.ConclusionCT utilization in the ED has increased significantly from 2005 through 2013. For children, CT use after 2010 decreased, indicating caution about CT use. Male sex, older age, and higher number of comorbidities were predictors of CT in the ED. Over time, odds of hospitalization decreased more quickly for patients with CT.
| S-EPMC5576619 | biostudies-literature
Project description:Triage is crucial for patient's management and estimation of the required intensive care unit (ICU) beds is fundamental for health systems during the COVID-19 pandemic. We assessed whether chest computed tomography (CT) of COVID-19 pneumonia has an incremental role in predicting patient's admission to ICU. We performed volumetric and texture analysis of the areas of the affected lung in CT of 115 outpatients with COVID-19 infection presenting to the emergency room with dyspnea and unresponsive hypoxyemia. Admission blood laboratory including lymphocyte count, serum lactate dehydrogenase, D-dimer and C-reactive protein and the ratio between the arterial partial pressure of oxygen and inspired oxygen were collected. By calculating the areas under the receiver-operating characteristic curves (AUC), we compared the performance of blood laboratory-arterial gas analyses features alone and combined with the CT features in two hybrid models (Hybrid radiological and Hybrid radiomics)for predicting ICU admission. Following a machine learning approach, 63 patients were allocated to the training and 52 to the validation set. Twenty-nine (25%) of patients were admitted to ICU. The Hybrid radiological model comprising the lung %consolidation performed significantly (p = 0.04) better in predicting ICU admission in the validation (AUC = 0.82; 95% confidence interval 0.73-0.97) set than the blood laboratory-arterial gas analyses features alone (AUC = 0.71; 95% confidence interval 0.56-0.86). A risk calculator for ICU admission was derived and is available at: https://github.com/cgplab/covidapp . The volume of the consolidated lung in CT of patients with COVID-19 pneumonia has a mild but significant incremental value in predicting ICU admission.
| S-EPMC8329253 | biostudies-literature