A MATHEMATICAL-MODEL THAT IMPROVES THE VALIDITY OF OSTEOARTHRITIS DIAGNOSES OBTAINED FROM A COMPUTERIZED DIAGNOSTIC DATABASE

We developed an algorithm, using recursive partitioning, that utilized information from a computerized, diagnostic database to predict the d iagnosis of osteoarthritis as determined by medical record review. The complete (inpatient and outpatient) medical records for a random samp le of 400 Olmsted County, Minnesota residents with a database diagnosi s consistent with osteoarthritis were reviewed, and confirmation or re jection of the diagnosis was accomplished, Of the 387 patients in our sample, only 232 (a positive predictive value of 60%) fulfilled diagno stic criteria for osteoarthritis following medical record review. A cl assification tree was created that used information from the diagnosti c database to partition the study population according to the proporti on of individuals with a ''true'' diagnosis of osteoarthritis (based o n medical record review). The receiver operating characteristic curve generated from these data illustrated that the algorithm substantially improved the validity of the database diagnosis, yielding a positive predictive value of 89% and a negative predictive value of 70% (sensit ivity of 75% and specificity of 86%) at a selected cutoff point. This model also provides the capability of selecting the cutoff point to fa vor either specificity or sensitivity. These data demonstrate that a m athematical model can substantially improve the validity of computeriz ed diagnostic databases in osteoarthritis.