Two cases of spinal tanycytic ependymomas occurring in brothers with a neurofibromatosis type 2 gene mutation.

Tanycytic ependymomas are a rare spinal cord tumour arising from tanycyte cells lining the ventricle or spinal central canal. This is the first report of familial spinal tanycytic ependymoma occurring in two first degree relatives. Both patients underwent surgical resection of the intra-medullary tumours with good overall recovery. Genetic analysis identified that the brothers shared a previously unreported mutation in the NF-2 gene. NF-2 mutations in spinal tanycytic ependymomas may be more common than initially thought and consideration should be given to screening the neural axis for other tumours and genetic counselling.

Evaluating tools to support a new practical classification of diabetes: excellent control may represent misdiagnosis and omission from disease registers is associated with worse control.

AIMS: To conduct a service evaluation of usability and utility on-line clinical audit tools developed as part of a UK Classification of Diabetes project to improve the categorisation and ultimately management of diabetes. METHOD: We conducted the evaluation in eight volunteer computerised practices all achieving maximum pay-for-performance (P4P) indicators for diabetes; two allowed direct observation and videotaping of the process of running the on-line audit. We also reported the utility of the searches and the national levels of uptake. RESULTS: Once launched 4235 unique visitors accessed the download pages in the first 3 months. We had feedback about problems from 10 practices, 7 were human error. Clinical audit naive staff ran the audits satisfactorily. However, they would prefer more explanation and more user-familiar tools built into their practice computerised medical record system. They wanted the people misdiagnosed and misclassified flagged and to be convinced miscoding mattered. People with T2DM misclassified as T1DM tended to be older (mean 62 vs. 47 years old). People misdiagnosed as having T2DM have apparently 'excellent' glycaemic control mean HbA1c 5.3% (34 mmol/mol) vs. 7.2% (55 mmol/mol) (p<0.001). People with vague codes not included in the P4P register (miscoded) have worse glycaemic control [HbA1c 8.1% (65 mmol/mol) SEM=0.42 vs.7.0% (53mmol/mol) SEM=0.11, p=0.006]. CONCLUSIONS: There was scope to improve diabetes management in practice achieving quality targets. Apparently 'excellent' glycaemic control may imply misdiagnosis, while miscoding is associated with worse control. On-line clinical audit toolkits provide a rapid method of dissemination and should be added to the armamentarium of quality improvement interventions.

Automated identification of miscoded and misclassified cases of diabetes from computer records.

AIMS: To develop a computer processable algorithm, capable of running automated searches of routine data that flag miscoded and misclassified cases of diabetes for subsequent clinical review. METHOD: Anonymized computer data from the Quality Improvement in Chronic Kidney Disease (QICKD) trial (n = 942,031) were analysed using a binary method to assess the accuracy of data on diabetes diagnosis. Diagnostic codes were processed and stratified into: definite, probable and possible diagnosis of Type 1 or Type 2 diabetes. Diagnostic accuracy was improved by using prescription compatibility and temporally sequenced anthropomorphic and biochemical data. Bayesian false detection rate analysis was used to compare findings with those of an entirely independent and more complex manual sort of the first round QICKD study data (n = 760,588). RESULTS: The prevalence of definite diagnosis of Type 1 diabetes and Type 2 diabetes were 0.32% and 3.27% respectively when using the binary search method. Up to 35% of Type 1 diabetes and 0.1% of Type 2 diabetes were miscoded or misclassified on the basis of age/BMI and coding. False detection rate analysis demonstrated a close correlation between the new method and the published hand-crafted sort. Both methods had the highest false detection rate values when coding, therapeutic, anthropomorphic and biochemical filters were used (up to 90% for the new and 75% for the hand-crafted search method). CONCLUSIONS: A simple computerized algorithm achieves very similar results to more complex search strategies to identify miscoded and misclassified cases of both Type 1 diabetes and Type 2 diabetes. It has the potential to be used as an automated audit instrument to improve quality of diabetes diagnosis.