Dataset Information

Detecting scaphoid fractures in wrist injury: a clinical decision rule.

ABSTRACT: INTRODUCTION:The 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. METHODS:A 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. RESULTS:A 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. CONCLUSIONS:This 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 RULE:1/(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 REGISTRATION:Trial register NTR 2544, www.trialregister.nl.

SUBMITTER: Mallee WH

PROVIDER: S-EPMC7109163 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Detecting scaphoid fractures in wrist injury: a clinical decision rule.

Mallee Wouter H WH Walenkamp M M J MMJ Mulders M A M MAM Goslings J C JC Schep N W L NWL

Archives of orthopaedic and trauma surgery 20200303 4

<h4>Introduction</h4>The 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.<h4>Methods</h4>A 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 w ...[more]

PMID: 32125528

Similar Datasets

Project description:BackgroundAcute trauma of the wrist is one of the most frequent reasons for visiting the Emergency Department. These patients are routinely referred for radiological examination. Most X-rays however, do not reveal any fractures. A clinical decision rule determining the need for X-rays in patients with acute wrist trauma may help to percolate and select patients with fractures.Methods/designThis study will be a multi-center observational diagnostic study in which the data will be collected cross-sectionally. The study population will consist of all consecutive adult patients (≥18 years) presenting with acute wrist trauma at the Emergency Department in the participating hospitals. This research comprises two components: one study will be conducted to determine which clinical parameters are predictive for the presence of a distal radius fracture in adult patients presenting to the Emergency Department following acute wrist trauma. These clinical parameters are defined by trauma-mechanism, physical examination, and functional testing. This data will be collected in two of the three participating hospitals and will be assessed by using logistic regression modelling to estimate the regression coefficients after which a reduced model will be created by means of a log likelihood ratio test. The accuracy of the model will be estimated by a goodness of fit test and an ROC curve. The final model will be validated internally through bootstrapping and by shrinking it, an adjusted model will be generated. In the second component of this study, the developed prediction model will be validated in a new dataset consisting of a population of patients from the third hospital. If necessary, the model will be calibrated using the data from the validation study.DiscussionWrist trauma is frequently encountered at the Emergency Department. However, to this date, no decision rule regarding this type of trauma has been created. Ideally, radiographs are obtained of all patients entering one of the participating hospitals with trauma to the wrist. However, this is ethically and logistically not feasible and one could argue that patients, for whom no radiography is required according to their physician, are not suspected of having a distal radius fracture and thus are not part of the domain.Trial registrationThis study is registered at the Netherlands Trial Register (NTR 2544) and was granted permission by the Medical Ethical Committee of the Academic Medical Center Amsterdam on 06-01-2011.

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).

Project description:ObjectivesTo assess how an artificial intelligence (AI) algorithm performs against five experienced musculoskeletal radiologists in diagnosing scaphoid fractures and whether it aids their diagnosis on conventional multi-view radiographs.MethodsFour datasets of conventional hand, wrist, and scaphoid radiographs were retrospectively acquired at two hospitals (hospitals A and B). Dataset 1 (12,990 radiographs from 3353 patients, hospital A) and dataset 2 (1117 radiographs from 394 patients, hospital B) were used for training and testing a scaphoid localization and laterality classification component. Dataset 3 (4316 radiographs from 840 patients, hospital A) and dataset 4 (688 radiographs from 209 patients, hospital B) were used for training and testing the fracture detector. The algorithm was compared with the radiologists in an observer study. Evaluation metrics included sensitivity, specificity, positive predictive value (PPV), area under the characteristic operating curve (AUC), Cohen's kappa coefficient (κ), fracture localization precision, and reading time.ResultsThe algorithm detected scaphoid fractures with a sensitivity of 72%, specificity of 93%, PPV of 81%, and AUC of 0.88. The AUC of the algorithm did not differ from each radiologist (0.87 [radiologists' mean], p ≥ .05). AI assistance improved five out of ten pairs of inter-observer Cohen's κ agreements (p < .05) and reduced reading time in four radiologists (p < .001), but did not improve other metrics in the majority of radiologists (p ≥ .05).ConclusionsThe AI algorithm detects scaphoid fractures on conventional multi-view radiographs at the level of five experienced musculoskeletal radiologists and could significantly shorten their reading time.Key points• An artificial intelligence algorithm automatically detects scaphoid fractures on conventional multi-view radiographs at the same level of five experienced musculoskeletal radiologists. • There is preliminary evidence that automated scaphoid fracture detection can significantly shorten the reading time of musculoskeletal radiologists.

Project description:BackgroundA 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.AimTo conduct a prospective multicentre diagnostic cohort study to provide a rule for selection of high-risk children with head injury for computed tomography scanning.DesignAll 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.Results22,772 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%.ConclusionA 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.

Project description:ImportancePenicillin allergy is a significant public health issue for patients, antimicrobial stewardship programs, and health services. Validated clinical decision rules are urgently needed to identify low-risk penicillin allergies that potentially do not require penicillin skin testing by a specialist.ObjectiveTo develop and validate a penicillin allergy clinical decision rule that enables point-of-care risk assessment of patient-reported penicillin allergies.Design, setting, and participantsIn this diagnostic study, a multicenter prospective antibiotic allergy-tested cohort of 622 patients from 2 tertiary care sites in Melbourne, Australia (Austin Health and Peter MacCallum Cancer Centre) was used for derivation and internal validation of a penicillin allergy decision rule. Backward stepwise logistic regression was used to derive the model, including clinical variables predictive of a positive penicillin allergy test result. Internal validation of the final model used bootstrapped samples and the model scoring derived from the coefficients. External validation was performed in retrospective penicillin allergy-tested cohorts consisting of 945 patients from Sydney and Perth, Australia, and Nashville, Tennessee. Patients who reported a penicillin allergy underwent penicillin allergy testing using skin prick, intradermal, or patch testing and/or oral challenge (direct or after skin testing). Data were collected from June 26, 2008, to June 3, 2019, and analyzed from January 9 to 12, 2019.Main outcomes and measuresThe primary outcome for the model was any positive result of penicillin allergy testing performed during outpatient or inpatient assessment.ResultsFrom an internal derivation and validation cohort of 622 patients (367 female [59.0%]; median age, 60 [interquartile range{IQR}, 48-71] years) and an external validation cohort of 945 patients (662 female [70.1%]; median age, 55 [IQR, 38-68] years), the 4 features associated with a positive penicillin allergy test result on multivariable analysis were summarized in the mnemonic PEN-FAST: penicillin allergy, five or fewer years ago, anaphylaxis/angioedema, severe cutaneous adverse reaction (SCAR), and treatment required for allergy episode. The major criteria included an allergy event occurring 5 or fewer years ago (2 points) and anaphylaxis/angioedema or SCAR (2 points); the minor criterion (1 point), treatment required for an allergy episode. Internal validation showed minimal mean optimism of 0.003 with internally validated area under the curve of 0.805. A cutoff of less than 3 points for PEN-FAST was chosen to classify a low risk of penicillin allergy, for which only 17 of 460 patients (3.7%) had positive results of allergy testing, with a negative predictive value of 96.3% (95% CI, 94.1%-97.8%). External validation resulted in similar findings.Conclusions and relevanceIn this study, PEN-FAST was found to be a simple rule that accurately identified low-risk penicillin allergies that do not require formal allergy testing. The results suggest that a PEN-FAST score of less than 3, associated with a high negative predictive value, could be used by clinicians and antimicrobial stewardship programs to identify low-risk penicillin allergies at the point of care.

Dataset Information

Detecting scaphoid fractures in wrist injury: a clinical decision rule.

Publications

Detecting scaphoid fractures in wrist injury: a clinical decision rule.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets