Medical Record Review to Differentiate between @entity191 and XXXX : A Danish Record Linkage Study with 10 Years of Follow-Up.

@entity1 @entity1726 @entity304 @entity191
Background. The electronic medical records provide new and unprecedented opportunities for large population-based and clinical studies if valid and reliable diagnoses can be obtained, to determine what information is needed to distinguish idiopathic @entity1726 from @entity304 in electronic medical records. Methods. Chart review of complete medical records of 2,446 @entity1 with a hospital discharge diagnosis of @entity1726 , who, between 1996 and 2009, were registered in the Danish National Hospital Register as idiopathic @entity1726 . All @entity1 were examined in neurology departments. Clinical features were abstracted from charts to determine @entity191 phenotypes and disease course, using predefined criteria for idiopathic @entity1726 . Results. Chart review verified that 2,068 (84.5%) @entity1 met criteria for idiopathic @entity1726 . The most distinguishing features of idiopathic @entity1726 @entity1 were asymmetric onset, and fewer atypical features at onset or follow-up compared to @entity304 , and the area under the curve (AUC) for these items alone is moderate (0.74-0.77) and the highest AUC (0.91) was achieved when using all clinical features recorded in addition to @entity1726 medication use and a follow-up of 5 years or more. Conclusion. To reduce disease misclassification, information extracted from medical record review with at least 5 years of follow-up after first diagnosis was key to improve diagnostic accuracy.
