Derivation of the children's head injury algorithm for the prediction of important clinical events decision rule for head injury in children.
Ontology highlight
ABSTRACT: Background
A quarter of all patients presenting to emergency departments are children. Although there are several large, well-conducted studies on adults enabling accurate selection of patients with head injury at high risk for computed tomography scanning, no such study has derived a rule for children.Aim
To conduct a prospective multicentre diagnostic cohort study to provide a rule for selection of high-risk children with head injury for computed tomography scanning.Design
All children presenting to the emergency departments of 10 hospitals in the northwest of England with any severity of head injury were recruited. A tailor-made proforma was used to collect data on around 40 clinical variables for each child. These variables were defined from a literature review, and a pilot study was conducted before the children's head injury algorithm for the prediction of important clinical events (CHALICE) study. All children who had a clinically significant head injury (death, need for neurosurgical intervention or abnormality on a computed tomography scan) were identified. Recursive partitioning was used to create a highly sensitive rule for the prediction of significant intracranial pathology.Results
22,772 children were recruited over 2 1/2 years. 65% of these were boys and 56% were <5 years old. 281 children showed an abnormality on the computed tomography scan, 137 had a neurosurgical operation and 15 died. The CHALICE rule was derived with a sensitivity of 98% (95% confidence interval (CI) 96% to 100%) and a specificity of 87% (95% CI 86% to 87%) for the prediction of clinically significant head injury, and requires a computed tomography scan rate of 14%.Conclusion
A highly sensitive clinical decision rule is derived for the identification of children who should undergo computed tomography scanning after head injury. This rule has the potential to improve and standardise the care of children presenting with head injuries. Validation of this rule in new cohorts of patients should now be undertaken.
SUBMITTER: Dunning J
PROVIDER: S-EPMC2082967 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:BackgroundAbout 75% of patients with minor thoracic injury are discharged after an emergency department visit. However, complications such as delayed hemothorax can occur. We sought to derive and validate a clinical decision rule to predict hemothorax in patients discharged from the emergency department.MethodsWe conducted a 6-year prospective cohort study in 4 university-affiliated emergency departments. Patients aged 16 years or older presenting with a minor thoracic injury were assessed at 5 time points (initial visit and 7, 14, 30 and 90 d after the injury). Radiologists' reports were reviewed for the presence of hemothorax. We used log-binomial regression models to identify predictors of hemothorax.ResultsA total of 1382 patients were included: 830 in the derivation phase and 552 in the validation phase. Of these, 151 (10.9%) had hemothorax at the 14-day follow-up. Patients 65 years of age or older represented 25.3% (210/830) and 23.7% (131/552) of the derivation and validation cohorts, respectively. The final clinical decision rule included a combination of age (> 70 yr, 2 points; 45-70 yr, 1 point), fracture of any high to mid thorax rib (ribs 3-9, 2 points) and presence of 3 or more rib fractures (1 point). Twenty (30.8%) of the 65 high-risk patients (score ? 4) experienced hemothorax during the follow-up period. The clinical decision rule had a high specificity (90.7%, 95% confidence interval 87.7%-93.1%) in this high-risk group, thus guiding appropriate post-emergency care.InterpretationOne patient out of every 10 presented with delayed hemothorax after discharge from the emergency department. Implementation of this validated clinical decision rule for minor thoracic injury could guide emergency discharge plans.
| S-EPMC5498437 | biostudies-literature
Project description:There is no clear consensus regarding radiologic evaluation of head trauma in young children without traumatic brain injury. We conducted a study to develop and validate a clinical decision rule to identify skull fracture in young children with head trauma and no immediate need for head tomography.We performed a prospective cohort study in 3 tertiary care emergency departments in the province of Quebec. Participants were children less than 2 years old who had a head trauma and were not at high risk of clinically important traumatic brain injury (Glasgow Coma Scale score < 15, altered level of consciousness or palpable skull fracture). The primary outcome was skull fracture. For each participant, the treating physician completed a standardized report form after physical examination and before radiologic evaluation. The decision to order skull radiography was at the physician's discretion. The clinical decision rule was derived using recursive partitioning.A total of 811 patients (49 with skull fracture) were recruited during the derivation phase. The 2 predictors identified through recursive partitioning were parietal or occipital swelling or hematoma and age less than 2 months. The rule had a sensitivity of 94% (95% confidence interval [CI] 83%-99%) and a specificity of 86% (95% CI 84%-89%) in the derivation phase. During the validation phase, 856 participants (44 with skull fracture) were recruited. The rule had a sensitivity of 89% and a specificity of 87% during this phase.The clinical decision rule developed in this study identified about 90% of skull fractures among young children with mild head trauma who had no immediate indication for head tomography. Use of the rule would have reduced the number of radiologic evaluations by about 60%.
| S-EPMC4627875 | biostudies-other
Project description:IntroductionThe aim of this study was to develop and validate an easy to use clinical decision rule, applicable in the ED that limits the number of unnecessary cast immobilizations and diagnostic follow-up in suspected scaphoid injury, without increasing the risk of missing fractures.MethodsA prospective multicenter study was conducted that consisted of three components: (1) derivation of a clinical prediction model for detecting scaphoid fractures in adult patients following wrist trauma; (2) internal validation of the model; (3) design of a clinical decision rule. The predictors used were: sex, age, swelling of the anatomic snuffbox, tenderness in the anatomic snuffbox, scaphoid tubercle tenderness, painful ulnar deviation and painful axial thumb compression. The outcome measure was the presence of a scaphoid fracture, diagnosed on either initial radiographs or during re-evaluation after 1-2 weeks or on additional imaging (radiographs/MRI/CT). After multivariate logistic regression analysis and bootstrapping, the regression coefficient for each significant predictor was calculated. The effect of the rule was determined by calculating the number of missed scaphoid fractures and reduction of suspected fractures that required a cast.ResultsA consecutive series of 893 patients with acute wrist injury was included. Sixty-eight patients (7.6%) were diagnosed with a scaphoid fracture. The final prediction rule incorporated sex, swelling of the anatomic snuffbox, tenderness in the anatomic snuffbox, painful ulnar deviation and painful axial thumb compression. Internal validation of the prediction rule showed a sensitivity of 97% and a specificity of 20%. Using this rule, a 15% reduction in unnecessary immobilization and imaging could be achieved with a 50% decreased risk of missing a fracture compared with current clinical practice.ConclusionsThis dataset provided a simple clinical decision rule for scaphoid fractures following acute wrist injury that limits unnecessary immobilization and imaging with a decreased risk of missing a fracture compared to current clinical practice.Clinical prediction rule1/(1 + EXP (-(0.649662618 × if man) + (0.51353467826 × if swelling anatomic snuffbox) + (-0.79038263985 × if painful palpation anatomic snuffbox) + (0.57681198857 × if painful ulnar deviation) + (0.66499549728 × if painful thumb compression)-1.685).Trial registrationTrial register NTR 2544, www.trialregister.nl.
| S-EPMC7109163 | biostudies-literature
Project description:BACKGROUND: Numerous short-statured children are evaluated for growth hormone (GH) deficiency (GHD). In most patients, GH provocative tests are normal and are thus in retrospect unnecessary. METHODS: A retrospective cohort study was conducted to identify predictors of growth hormone (GH) deficiency (GHD) in children seen for short stature, and to construct a very sensitive and fairly specific predictive tool to avoid unnecessary GH provocative tests. GHD was defined by the presence of 2 GH concentration peaks < 10 ng/ml. Certain GHD was defined as GHD and viewing pituitary stalk interruption syndrome on magnetic resonance imaging. Independent predictors were identified with uni- and multi-variate analyses and then combined in a decision rule that was validated in another population. RESULTS: The initial study included 167 patients, 36 (22%) of whom had GHD, including 5 (3%) with certain GHD. Independent predictors of GHD were: growth rate < -1 DS (adjusted odds ratio: 3.2; 95% confidence interval [1.3-7.9]), IGF-I concentration < -2 DS (2.8 [1.1-7.3]) and BMI z-score > or = 0 (2.8 [1.2-6.5]). A clinical decision rule suggesting that patients be tested only if they had a growth rate < -1 DS and a IGF-I concentration < -2 DS achieved 100% sensitivity [48-100] for certain GHD and 63% [47-79] for GHD, and a specificity of 68% [60-76]. Applying this rule to the validation population (n = 40, including 13 patients with certain GHD), the sensitivity for certain GHD was 92% [76-100] and the specificity 70% [53-88]. CONCLUSION: We have derived and performed an internal validation of a highly sensitive decision rule that could safely help to avoid more than 2/3 of the unnecessary GH tests. External validation of this rule is needed before any application.
| S-EPMC2492843 | biostudies-other
Project description:Routine CT for patients with acute flank pain has not been shown to improve patient outcomes, and it may unnecessarily expose patients to radiation and increased costs. As preliminary steps toward the development of a guideline for selective CT, we sought to determine the prevalence of clinically important outcomes in patients with acute flank pain and derive preliminary decision rules.We analyzed data from a randomized trial of CT vs. ultrasonography for patients with acute flank pain from 15 EDs between October 2011 and February 2013. Clinically important outcomes were defined as inpatient admission for ureteral stones and alternative diagnoses. Clinically important stones were defined as stones requiring urologic intervention. We sought to derive highly sensitive decision rules for both outcomes.Of 2759 participants, 236 (8.6%) had a clinically important outcome and 143 (5.2%) had a clinically important stone. A CDR including anemia (hemoglobin <13.2g/dl), WBC count >11000/?l, age>42years, and the absence of CVAT had a sensitivity of 97.9% (95% CI 94.8-99.2%) and specificity of 18.7% (95% 17.2-20.2%) for clinically important outcome. A CDR including hydronephrosis, prior history of stone, and WBC count <8300/?l had a sensitivity of 98.6% (95% CI 94.5-99.7%) and specificity of 26.0% (95% 24.2-27.7%) for clinically important stone.We determined the prevalence of clinically important outcomes in patients with acute flank pain, and derived preliminary high sensitivity CDRs that predict them. Validation of CDRs with similar test characteristics would require prospective enrollment of 2100 patients.
| S-EPMC5701802 | biostudies-literature
Project description:We aimed to derive and validate a clinical decision rule (CDR) for suspected cardiac chest pain in the emergency department (ED). Incorporating information available at the time of first presentation, this CDR would effectively risk-stratify patients and immediately identify: (A) patients for whom hospitalisation may be safely avoided; and (B) high-risk patients, facilitating judicious use of resources.In two sequential prospective observational cohort studies at heterogeneous centres, we included ED patients with suspected cardiac chest pain. We recorded clinical features and drew blood on arrival. The primary outcome was major adverse cardiac events (MACE) (death, prevalent or incident acute myocardial infarction, coronary revascularisation or new coronary stenosis >50%) within 30 days. The CDR was derived by logistic regression, considering reliable (?>0.6) univariate predictors (p<0.05) for inclusion.In the derivation study (n=698) we derived a CDR including eight variables (high sensitivity troponin T; heart-type fatty acid binding protein; ECG ischaemia; diaphoresis observed; vomiting; pain radiation to right arm/shoulder; worsening angina; hypotension), which had a C-statistic of 0.95 (95% CI 0.93 to 0.97) implying near perfect diagnostic performance. On external validation (n=463) the CDR identified 27.0% of patients as 'very low risk' and potentially suitable for discharge from the ED. 0.0% of these patients had prevalent acute myocardial infarction and 1.6% developed MACE (n=2; both coronary stenoses without revascularisation). 9.9% of patients were classified as 'high-risk', 95.7% of whom developed MACE.The Manchester Acute Coronary Syndromes (MACS) rule has the potential to safely reduce unnecessary hospital admissions and facilitate judicious use of high dependency resources.
| S-EPMC4174131 | biostudies-literature
Project description:BackgroundA novel algorithm and clinical prediction rule (CPR), with 18 variables, was created in 2014. The CPR generated a bone stress injury (BSI) score, which was used to determine the necessity of imaging in suspected BSI. To date, there are no validated algorithms for imaging selection in patients with suspected BSI.HypothesisA simplified CPR will assist clinicians with diagnosis and decision making in patients with suspected BSI.Study designProspective cohort study.Level of evidenceLevel 3.MethodsA total of 778 military trainees with lower extremity pain were enrolled. All trainees were evaluated for 18 clinical variables suggesting BSI. Participants were monitored via electronic medical record review. Then, a prediction model was developed using logistic regression to identify clinical variables with the greatest predictive value and assigned appropriate weight. Test characteristics for various BSI score thresholds were calculated.ResultsOf the enrolled trainees, 204 had imaging-confirmed BSI in or distal to the femoral condyles. The optimized CPR selected 4 clinical variables (weighted score): bony tenderness (3), prior history of BSI (2), pes cavus (2), and increased walking/running volume (1). The optimized CPR with a score ≥3 yielded 97.5% sensitivity, 54.2% specificity, and 98.2% negative predictive value. An isolated measure, bony tenderness, demonstrated similar statistical performance.ConclusionThe optimized CPR, which uses bony tenderness, prior history of BSI, pes cavus, and increased walking/running volume, is valid for detecting BSI in or distal to the femoral condyles. However, bony tenderness alone provides a simpler criterion with an equally strong negative predictive value for BSI decision making.Clinical relevanceFor suspected BSI in or distal to the femoral condyles, imaging can be deferred when there is no bony tenderness. When bony tenderness is present in the setting of 1 or more proven risk factors and no clinical evidence of high-risk bone involvement, presumptive treatment for BSI and serial radiographs may be appropriate.
| S-EPMC7485018 | biostudies-literature
Project description:Acute lung injury (ALI) is a serious postoperative complication with limited treatment options. A preoperative risk-prediction model would assist clinicians and scientists interested in ALI. The objective of this investigation was to develop a surgical lung injury prediction (SLIP) model to predict risk of postoperative ALI based on readily available preoperative risk factors.Secondary analysis of a prospective cohort investigation including adult patients undergoing high-risk surgery. Preoperative risk factors for postoperative ALI were identified and evaluated for inclusion in the SLIP model. Multivariate logistic regression was used to develop the model. Model performance was assessed with the area under the receiver operating characteristic curve and the Hosmer-Lemeshow goodness-of-fit test.Out of 4,366 patients, 113 (2.6%) developed early postoperative ALI. Predictors of postoperative ALI in multivariate analysis that were maintained in the final SLIP model included high-risk cardiac, vascular, or thoracic surgery, diabetes mellitus, chronic obstructive pulmonary disease, gastroesophageal reflux disease, and alcohol abuse. The SLIP score distinguished patients who developed early postoperative ALI from those who did not with an area under the receiver operating characteristic curve (95% CI) of 0.82 (0.78-0.86). The model was well calibrated (Hosmer-Lemeshow, P = 0.55). Internal validation using 10-fold cross-validation noted minimal loss of diagnostic accuracy with a mean ± SD area under the receiver operating characteristic curve of 0.79 ± 0.08.Using readily available preoperative risk factors, we developed the SLIP scoring system to predict risk of early postoperative ALI.
| S-EPMC3986041 | biostudies-literature
Project description:ObjectiveWhile existing guidelines support the treatment of intracranial hypertension in severe traumatic brain injury (TBI), it is unclear when to suspect and initiate treatment for high intracranial pressure (ICP). The objective of this study was to derive a clinical decision rule that accurately predicts intracranial hypertension.MethodsUsing Delphi methods, the authors identified a set of potential predictors of intracranial hypertension and a clinical decision rule a priori by consensus among a group of 43 neurosurgeons and intensivists who have extensive experience managing severe TBI without ICP monitoring. To validate these predictors, the authors used data from a Latin American trial (n = 150; BEST TRIP). To report on the performance of the rule, they calculated sensitivity, specificity, and positive and negative predictive values with 95% confidence intervals. In a secondary analysis, the rule was validated using data from a North American trial (n = 131; COBRIT).ResultsThe final predictors and the clinical decision rule were approved by 97% of participants in the consensus working group. The predictors are divided into major and minor criteria. High ICP would be considered suspected in the presence of 1 major or ≥ 2 minor criteria. Major criteria are: compressed cisterns (CT classification of Marshall diffuse injury [DI] III), midline shift > 5 mm (Marshall DI IV), or nonevacuated mass lesion. Minor criteria are: Glasgow Coma Scale (GCS) motor score ≤ 4, pupillary asymmetry, abnormal pupillary reactivity, or Marshall DI II. The area under the curve for the logistic regression model that contains all the predictors was 0.86. When high ICP was defined as > 22 mm Hg, the decision rule performed with a sensitivity of 93.9% (95% CI 85.0%-98.3%), a specificity of 42.3% (95% CI 31.7%-53.6%), a positive predictive value of 55.5% (95% CI 50.7%-60.2%), and a negative predictive value of 90% (95% CI 77.1%-96.0%). The sensitivity of the clinical decision rule improved with higher ICP cutoffs up to a sensitivity of 100% when intracranial hypertension was defined as ICP > 30 mm Hg. Similar results were found in the North American cohort.ConclusionsA simple clinical decision rule based on a combination of clinical and imaging findings was found to be highly sensitive in distinguishing patients with severe TBI who would suffer intracranial hypertension. It could be used to identify patients who require ICP monitoring in high-resource settings or start ICP-lowering treatment in environments where resource limitations preclude invasive monitoring.Clinical trial registration no.: NCT02059941 (clinicaltrials.gov).
| S-EPMC6586526 | biostudies-literature
Project description:Spontaneous prediction of malignant ventricular arrhythmia (MVA) is useful to avoid delay in rescue operations. Recently, researchers have developed several algorithms to predict MVA using various features derived from electrocardiogram (ECG). However, there are several unresolved issues regarding MVA prediction such as the effect of number of ECG features on a prediction remaining unclear, possibility that an alert for occurring MVA may arrive very late and uncertainty in the performance of the algorithm predicting MVA minutes before onset. To overcome the aforementioned problems, this research conducts an in-depth study on the number and types of ECG features that are implemented in a decision tree classifier. In addition, this research also investigates an algorithm's execution time before the occurrence of MVA to minimize delays in warnings for MVA. Lastly, this research aims to study both the sensitivity and specificity of an algorithm to reveal the performance of MVA prediction algorithms from time to time. To strengthen the results of analysis, several classifiers such as support vector machine and naive Bayes are also examined for the purpose of comparison study. There are three phases required to achieve the objectives. The first phase is literature review on existing relevant studies. The second phase deals with design and development of four modules for predicting MVA. Rigorous experiments are performed in the feature selection and classification modules. The results show that eight ECG features with decision tree classifier achieved good prediction performance in terms of execution time and sensitivity. In addition, the results show that the highest percentage for sensitivity and specificity is 95% and 90% respectively, in the fourth 5-minute interval (15.1 minutes-20 minutes) that preceded the onset of an arrhythmia event. Such results imply that the fourth 5-minute interval would be the best time to perform prediction.
| S-EPMC7224460 | biostudies-literature