Derivation and Validation of a Clinical Diagnostic Tool for the Identification of Older Community-Dwellers With Hypovitaminosis D.
Document Type
Article
Publication Date
4-23-2015
Journal
Journal of the American Medical Directors Association
URL with Digital Object Identifier
doi: 10.1016/j.jamda.2015.03.008
Abstract
OBJECTIVES: Hypovitaminosis D is highly prevalent among seniors. Although evidence is insufficient to recommend routine vitamin D screening in seniors, universal vitamin D supplementation is not desirable either. To rationalize vitamin D determination, our objective was to elaborate and test a clinical diagnostic tool for the identification of seniors with hypovitaminosis D without using a blood test.
DESIGN: Derivation of a clinical diagnostic tool using artificial neural networks (multilayer perceptron; MLP) in randomized training subgroup of Prévention des Chutes, Réseau 4' cohort, and validation in randomized testing subgroup.
SETTING: Health Examination Centers of health insurance, Lyon, France.
PARTICIPANTS: A total of 1924 community-dwellers aged ≥65 years without vitamin D supplements, consecutively recruited between 2009 and 2012.
MEASUREMENTS: Hypovitaminosis D defined as serum 25-hydroxyvitamin (25OHD) concentration ≤ 75 nmol/L, ≤50 nmol/L, or ≤25 nmol/L. A set of clinical variables (age, gender, living alone, individual deprivation, body mass index, undernutrition, polymorbidity, number of drugs used daily, psychoactive drugs, biphosphonates, strontium, calcium supplements, falls, fear of falling, vertebral fractures, Timed Up and Go, walking aids, lower-limb proprioception, handgrip strength, visual acuity, wearing glasses, cognitive disorders, sad mood) were recorded. Several MLPs, based on varying amounts of variables according to their relative importance, were tested consecutively.
RESULTS: A total of 1729 participants (89.9%) had 25OHD ≤75 nmol/L, 1288 (66.9%) had 25OHD ≤50 nmol/L, and 525 (27.2%) had 25OHD ≤25 nmol/L. MLP using 16 clinical variables was able to diagnose hypovitaminosis D ≤ 75 nmol/L with accuracy = 96.3%, area under curve (AUC) = 0.938, and κ = 79.3 indicating almost perfect agreement. It was also able to diagnose hypovitaminosis D ≤ 50 nmol/L with accuracy = 81.5, AUC = 0.867, and κ = 57.8 (moderate agreement); and hypovitaminosis D ≤ 25 nmol/L with accuracy = 82.5, AUC = 0.385, and κ = 55.0 (moderate agreement).
CONCLUSIONS: We elaborated an algorithm able to identify, from 16 clinical variables, seniors with hypovitaminosis D.
