An algorithmic approach to chronic XXXX .

@entity1 @entity1353
QUESTION: The objective of the study was to prospectively evaluate an algorithmic approach to the cause(s) of chronic @entity1353 . MATERIALS/ @entity1 /METHODS: Prospective observational study. The study group consisted of 123 @entity1 with a chief complaint of @entity1353 of unknown cause present for >8 weeks. Dyspnea severity scores were documented at entry and after therapy. @entity1 underwent an algorithmic approach to @entity1353 . Therapy could be instituted at any time that data supported a treatable diagnosis. Whenever possible, accuracy of diagnosis was confirmed with an improvement in @entity1353 after therapy. Tests required, spectrum and frequency of diagnoses, and the values of individual tests were determined. RESULTS: Cause(s) was(were) diagnosed in 122/123 @entity1 (99%); 97 @entity1 had one diagnosis and 25 two diagnoses. Fifty-three percent of diagnoses were respiratory and 47% were non-respiratory. Following therapy, @entity1353 improved in 63% of @entity1 . CONCLUSIONS: The prospective algorithmic approach led to diagnoses in 99% of cases. A third of @entity1 were diagnosed with each tier of the algorithm, thus minimizing the need for invasive testing. Specific diagnoses led to improvement in @entity1353 in the majority of cases. Based on the results of this study, the algorithm can be revised to further minimize unnecessary tests without loss of diagnostic accuracy.
