Clinical height measurements are unreliable: a call for improvement

Impact of predictor measurement heterogeneity across settings on the performance of prediction models: A measurement error perspective

It is widely acknowledged that the predictive performance of clinical prediction models should be studied in patients that were not part of the data in which the model was derived. Out‐of‐sample performance can be hampered when predictors are measured differently at derivation and external validation. This may occur, for instance, when predictors are measured using different measurement protocols or when tests are produced by different manufacturers. Although such heterogeneity in predictor measurement between derivation and validation data is common, the impact on the out‐of‐sample performance is not well studied. Using analytical and simulation approaches, we examined out‐of‐sample performance of prediction models under various scenarios of heterogeneous predictor measurement. These scenarios were defined and clarified using an established taxonomy of measurement error models. The results of our simulations indicate that predictor measurement heterogeneity can induce miscalibration of prediction and affects discrimination and overall predictive accuracy, to extents that the prediction model may no longer be considered clinically useful. The measurement error taxonomy was found to be helpful in identifying and predicting effects of heterogeneous predictor measurements between settings of prediction model derivation and validation. Our work indicates that homogeneity of measurement strategies across settings is of paramount importance in prediction research.

Validity of height loss as a predictor for prevalent vertebral fractures, low bone mineral density, and vitamin D deficiency

Many osteoporosis-related vertebral fractures are unappreciated but their detection is important as their presence increases future fracture risk. We found height loss is a useful tool in detecting patients with vertebral fractures, low bone mineral density, and vitamin D deficiency which may lead to improvements in patient care.

INTRODUCTION

This study aimed to determine if/how height loss can be used to identify patients with vertebral fractures, low bone mineral density, and vitamin D deficiency.

METHODS

A hospital database search in which four patient groups including those with a diagnosis of osteoporosis-related vertebral fracture, osteoporosis, osteopenia, or vitamin D deficiency and a control group were evaluated for chart-documented height loss over an average 3 1/2 to 4-year time period. Data was retrieved from 66,021 patients (25,792 men and 40,229 women).

RESULTS

A height loss of 1, 2, 3, and 4 cm had a sensitivity of 42, 32, 19, and 14% in detecting vertebral fractures, respectively. Positive likelihood ratios for detecting vertebral fractures were 1.73, 2.35, and 2.89 at 2, 3, and 4 cm of height loss, respectively. Height loss had lower sensitivities and positive likelihood ratios for detecting low bone mineral density and vitamin D deficiency compared to vertebral fractures. Specificity of 1, 2, 3, and 4 cm of height loss was 70, 82, 92, and 95%, respectively. The odds ratios for a patient who loses 1 cm of height being in one of the four diagnostic groups compared to a patient who loses no height was higher for younger and male patients.

CONCLUSIONS

This study demonstrated that prospective height loss is an effective tool to identify patients with vertebral fractures, low bone mineral density, and vitamin D deficiency although a lack of height loss does not rule out these diagnoses. If significant height loss is present, the high positive likelihood ratios support a further workup.