Radiologic discrepancies in diagnosis of fractures in a Dutch teaching emergency department: a retrospective analysis.

BACKGROUND: Missed fractures in the emergency department (ED) are common and may lead to patient morbidity. AIM: To determine the rate and nature of radiographic discrepancies between ED treating physicians, radiologists and trauma/orthopaedic surgeons and the clinical consequences of delayed diagnosis. A secondary outcome measurement is the timeframe in which most fractures were missed. METHODS: A single-centre retrospective analysis of all missed fractures in a general teaching hospital from 2012 to 2017 was performed. Data regarding missed fractures were provided by the hospital's complication list and related database. Additional data were retrieved from the electronic medical records as required for the study. RESULTS: A total of 25,957 fractures were treated at our ED. Initially, 289 fractures were missed by ED treating physicians (1.1%). The most frequently missed fractures were the elbow (28.6%) and wrist (20.8%) in children, the foot (17.2%) in adults and the pelvis and hip (37.3%) in elderly patients. Patients required surgery in 9.3% of missed fractures, received immobilization by a cast or brace in 45.7%, had no treatment alterations during the first week in 38.1%. Follow-up data were lacking for 6.9% of cases. 49% of all missed fractures took place between 4 PM and 9 PM. There is a discrepancy in percentages of correctly diagnosed fractures and missed fractures between 5 PM and 3 AM. CONCLUSION: Adequate training of ED treating physicians in radiographic interpretation is essential in order to increase diagnostic accuracy. A daily multidisciplinary radiology meeting is very effective in detecting missed fractures.

Development and validation of an admission prediction tool for emergency departments in the Netherlands.

OBJECTIVE: Early prediction of admission has the potential to reduce length of stay in the ED. The aim of this study is to create a computerised tool to predict admission probability. METHODS: The prediction rule was derived from data on all patients who visited the ED of the Rijnstate Hospital over two random weeks. Performing a multivariate logistic regression analysis factors associated with hospitalisation were explored. Using these data, a model was developed to predict admission probability. Prospective validation was performed at Rijnstate Hospital and in two regional hospitals with different baseline admission rates. The model was converted into a computerised tool that reported the admission probability for any patient at the time of triage. RESULTS: Data from 1261 visits were included in the derivation of the rule. Four contributing factors for admission that could be determined at triage were identified: age, triage category, arrival mode and main symptom. Prospective validation showed that this model reliably predicts hospital admission in two community hospitals (area under the curve (AUC) 0.87, 95% CI 0.85 to 0.89) and in an academic hospital (AUC 0.76, 95% CI 0.72 to 0.80). In the community hospitals, using a cut-off of 80% for admission probability resulted in the highest number of true positives (actual admissions) with the greatest specificity (positive predictive value (PPV): 89.6, 95% CI 84.5 to 93.6; negative predictive value (NPV): 70.3, 95% CI 67.6 to 72.9). For the academic hospital, with a higher admission rate, a 90% probability was a better cut-off (PPV: 83.0, 95% CI 73.8 to 90.0; NPV: 59.3, 95% CI 54.2 to 64.2). CONCLUSION: Admission probability for ED patients can be calculated using a prediction tool. Further research must show whether using this tool can improve patient flow in the ED.

Titanium fixtures for bone-conduction devices and the influence of type 2 diabetes mellitus.

OBJECTIVE: The aim of this study is to evaluate whether diabetes mellitus (DM) is a risk factor for titanium fixture loss in bone-conduction devices (BCDs) because of osseointegration failure. STUDY DESIGN: Retrospective case study. SETTING: Tertiary referral center. PATIENTS: All patients who received a BCD at Nijmegen between January 1, 1988, and December 31, 2007, were analyzed. The analyses were performed on 833 patients (993 implants) and a subpopulation of patients aged 40 years or older consisting of 641 patients (739 implants). METHODS: Patients received a questionnaire asking about the presence of DM at the time of implantation. Data concerning implant loss were retrieved from medical records and the Nijmegen BCD database. RESULTS: The total survival rate of the BCD implant in this population was 90.6%. The prevalence of DM was 9.3%. In the subpopulation of patients aged 40 years or older, the non-DM group lost 5.1% of their implants versus 14% of Type 2 DM patients, a statistically significantly difference (p = 0.003). Spontaneous loss, loss due to a Grade 4 Holgers skin reaction, and trauma accounted for 2.2% versus 4.7% (p = 0.13), 0.5% versus 2.3% (p = 0.1), and 0.6% versus 4.7% (p = 0.007), respectively, of implant losses in non-DM versus Type 2 DM patients. CONCLUSION: The prevalence of DM among the Nijmegen BCD population is higher than the general Dutch prevalence. A statistically significantly higher implant loss was observed during the study period for Type 2 DM patients than non-DM BCD wearers.