A rigorous algorithm to detect and clean inaccurate adult height records within EHR systems.

BACKGROUND: Height is a critical variable for many biomedical analyses because it is an important component of Body Mass Index (BMI). Transforming EHR height measures into meaningful research-ready values is challenging and there is limited information available on methods for "cleaning" these data. OBJECTIVES: We sought to develop an algorithm to clean adult height data extracted from EHR using only height values and associated ages. RESULTS: The algorithm we developed is sensitive to normal decreases in adult height associated with aging, is implemented using an open-source software tool and is thus easily modifiable, and is freely available. We checked the performance of our algorithm using data from the Northwestern biobank and a replication sample from the Marshfield Clinic biobank obtained through our participation in the eMERGE consortium. The algorithm identified 1262 erroneous values from a total of 33937 records in the Northwestern sample. Replacing erroneous height values with those identified as correct by the algorithm resulted in meaningful changes in height and BMI records; median change in recorded height after cleaning was 7.6 cm and median change in BMI was 2.9 kg/m(2). Comparison of cleaned EHR height values to observer measured values showed that 94.5% (95% C.I 93.8-% - 95.2%) of cleaned values were within 3.5 cm of observer measured values. CONCLUSIONS: Our freely available height algorithm cleans EHR height data with only height and age inputs. Use of this algorithm will benefit groups trying to perform research with height and BMI data extracted from EHR.

Use of a performance algorithm improves utilization of vertebral fracture assessment in clinical practice.

SUMMARY: A performance algorithm can be successfully used by bone density technologists at the time of a bone density test to identify patients with an indication for vertebral fracture assessment (VFA). Doing so appropriately increases physician prescription of fracture prevention medication. INTRODUCTION: Densitometric spine imaging (vertebral fracture assessment, VFA) can identify prevalent vertebral fracture but is underutilized. We developed an algorithm by which DXA technologists identify patients for whom VFA should be performed. Following this algorithm, VFA was performed in patients whose lowest T-score (lumbar spine, total hip, or femoral neck) was between -1.5 and -2.4 inclusive and with one of the following: age, ≥ 65 years; height loss, ≥ 1.5 in.; or current systemic glucocorticoid therapy. Our main objectives were to assess change in VFA utilization at two other healthcare organizations after algorithm implementation, and to estimate the association of VFA results with prescription of fracture prevention medication. METHODS: The proportions of patients with an indication for VFA who had one performed before and after algorithm implementation were compared. Logistic regression was used to estimate the multivariable-adjusted association of VFA results with subsequent prescription of fracture prevention medication adjusted for healthcare organization (study site). RESULTS: After algorithm introduction, appropriate VFA use rose significantly Patients with a VFA positive for vertebral fracture had an odds ratio of 3.2 (95 % C.I., 2.1- 5.1) for being prescribed new fracture prevention medication, adjusted for age, sex, prior clinical fracture, use of glucocorticoid medication, femoral neck bone mineral density T-score, and study site. CONCLUSIONS: An algorithm to identify those for whom VFA is indicated can successfully be implemented by DXA technologists. Documentation of vertebral fracture increases prescription of fracture prevention medication for patients who otherwise lack an apparent indication for such therapy.