Cedric Annweiler1, Anastasiia Kabeshova2, Mathilde Legeay3, Bruno Fantino2, Olivier Beauchet2. 1. Division of Geriatric Medicine and Memory Clinic, Department of Neuroscience, UPRES EA 4638, UNAM, Angers University Hospital, Angers, France; Department of Medical Biophysics, Robarts Research Institute, Schulich School of Medicine and Dentistry, The University of Western Ontario, London, Ontario, Canada. Electronic address: CeAnnweiler@chu-angers.fr. 2. Division of Geriatric Medicine and Memory Clinic, Department of Neuroscience, UPRES EA 4638, UNAM, Angers University Hospital, Angers, France. 3. School of Medicine, University of Angers, Angers, France.
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.
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.
Authors: T Merlijn; K M A Swart; P Lips; M W Heymans; E Sohl; N M Van Schoor; C J Netelenbos; P J M Elders Journal: Osteoporos Int Date: 2018-05-28 Impact factor: 4.507