Derivation and validation of a clinical diagnostic tool for the identification of older community-dwellers with hypovitaminosis D.
OBJECTIVE
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.


METHODS
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.


METHODS
Health Examination Centers of health insurance, Lyon, France.


METHODS
A total of 1924 community-dwellers aged ≥ 65 years without vitamin D supplements, consecutively recruited between 2009 and 2012.


METHODS
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.
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.
Cedric
C
Annweiler
Division of Geriatric Medicine and Memory Clinic, Department of Neuroscience, UPRES EA 4638, UNAM, Angers University Hospital, Angers, France.
Anastasiia
A
Kabeshova
School of Medicine, University of Angers, Angers, France.
Mathilde
M
Legeay
Division of Geriatric Medicine and Memory Clinic, Department of Neuroscience, UPRES EA 4638, UNAM, Angers University Hospital, Angers, France.
Bruno
B
Fantino
Division of Geriatric Medicine and Memory Clinic, Department of Neuroscience, UPRES EA 4638, UNAM, Angers University Hospital, Angers, France.
Olivier
O
Beauchet
D000368:Aged; D005260:Female; D005602:France / Q000453:epidemiology; D015577:Geriatric Assessment / Q000379:methods*; D006801:Humans; D008297:Male; D016571:Neural Networks, Computer; D015995:Prevalence; D014808:Vitamin D Deficiency / Q000175:diagnosis* / Q000453:epidemiology
Screening; older adults; vitamin D; vitamin D deficiency