TBS validation - IN VIVO
I N V I V O | Nature of study | Results | Cohort | Reference | Authors | Bone Densitometer | |
D i a g n o s t i c | All densitometric zones | TBS: enables differentiation from healthy cases to fractured cases. TBS Added Value: detection of vertebral fractures. | 135 (45 fractures) people. Age and BMD matched | Barthe, Mehsen, MED-IMAPS | QDR4500 A | ||
| Low densitometric zone | Best TBS added value is in the osteopenia zone. TBS + BMD > BMD alone; Weak correlation between BMD and TBS [r(TBS,BMD)=0.3] | 168 (42 fractures)people. Age matched | Article BONE 2009, A2 | Héraud, Grand-Lenoir, MED-IMAPS | PRODIGY | ||
| Osteopenia Zone 1 | Same results as the previous study, but verified on a much bigger and significant osteopenic cohort | 243 (81 fractures) people. Age matched | Article CTI 2010, A3 | Dufour, MED-IMAPS | PRODIGY | ||
| Osteopenia Zone 2 | TBS > BMD. Here again we have a weak correlation TBS and BMD (0.24) | 116 (29 fractures) people. Age and BMD matched | SFR 2010, P1 | Colson, MED-IMAPS | IDXA | ||
| Evaluation of Hip fracture with TBS | Spine TBS helps hip fracture prediction and as such could be a useful tool to prevent fractures, in combination with spine BMD | 191 ( 83 fractures) people. Age, weight, height and BMI matched | ECCEO 2011 | Del Rio, Di Gregorio, Cormier, MED-IMAPS | PRODIGY | ||
P r o n o s t i c | Manitoba | TBS and BMD predicts the fracture equally well (identical risks).Correlation BMD – TBS is 0.32. TBS + BMD > BMD alone. TBS identifies 1/3 of fractures that were not identified by BMD | 29 407 (1668 fractures) | Leslie, Krieg, Hans | PRODIGY | ||
| Ofely | BMD and TBS predict fractures equally well. 39% of TBS is explained by BMD. BMD and TBS are correlated to age (r=-0,17 et r=-0,49). Analysis of TBS tertile by tertile: increase of the number of fractured cases identified by TBS. | 564 (94 fractures) | Boutroy, Chapurlat | QDR4500 A | |||
Nature of study | Results | Cohort | Reference | Authors | Bone Densitometer | ||
Arthritis | No effect of arthritis on TBS, until 3.2 standard deviations from mean | 390 subjects (arthritis at L4 in 141) | Dufour, MED-IMAPS | PRODIGY | |||
Soft Tissue | Non significant effect of soft tissues for 15 < BMI < 41 | Ex-vivo + validation on 5942 subjects | MED-IMAPS | MED-IMAPS | PRODIGY | ||
Treatments | Sub-study of Manitob | Significant differences between treated and untreated in BMD and TBS. Treated: increased BMD + stable TBS. Untreated: decreased BMD and TBS. Agrees with medical literature. | 1684 subjects (534 under treatment) | Leslie, Krieg, Hans | PRODIGY | ||
Corticoïds | Secondary Osteoporosis | TBS: significant difference between treated patients and the reference population, from 5mg/day; BMD: no difference. The higher the number of fractures, the lower TBS. | 136 females on corticosteroids | Colson, MED-IMAPS | PRODIGY | ||
Hyper-parathyroïdism | Secondary Osteoporosis | After PTX: Increased BMD; No increase in TBS: no correlation between BMD increase and TBS increase. Agrees with literature. | 28 females–Follow-up before and after PTX | Cormier, MED-IMAPS | QDR4500 A | ||
Rheumatoid Arthritis | Secondary Osteoporosis | TBS brings new information into fracture risk prediction; 8 cases with a high risk of fracture identified by TBS only, thanks to TBS threshold (all with a normal BMD) | 140 subjects | Bréban, Kolta, Dougados, Fetchenbaum, Roux | QDR4500 A - DELPHI W | ||
Anorexia nervosa | TBS: differentiation of the subjects as a function of anorexia type. Physical activities exert an effect upon TBS in women until 30 years old. Agrees with literature. | 82 females (from12 to 41 years old ) | Cormier, MED-IMAPS | PRODIGY | |||
Normative data curve | Creation and validation of a normative reference curve for TBS as a function of age | 5942 subjects (from 45 to 85 years old) | Dufour, Héraud | PRODIGY | |||
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