Fracture risk prediction using clinical risk factors in comparison to BMD measurements of the calcaneus and other skeletal areas in Austrian males and females

Bone ◽  
2011 ◽  
Vol 48 ◽  
pp. S204-S205
Author(s):  
C. Bittighofer ◽  
C. Muschitz ◽  
A. Trubrich ◽  
F. Kühne ◽  
J. Haschka ◽  
...  
Keyword(s):  
Risk Factors ◽  
Fracture Risk ◽  
Risk Prediction ◽  
Clinical Risk Factors ◽  
Clinical Risk ◽  
Males And Females ◽  
Fracture Risk Prediction

scholarly journals Fracture risk prediction using BMD and clinical risk factors in early postmenopausal women: Sensitivity of the WHO FRAX tool

2010 ◽  
Vol 25 (5) ◽  
pp. 1002-1009 ◽  
Author(s):  
Florence A Trémollieres ◽  
Jean-Michel Pouillès ◽  
Nicolas Drewniak ◽  
Jacques Laparra ◽  
Claude A Ribot ◽  
...  
Keyword(s):  
Risk Factors ◽  
Fracture Risk ◽  
Postmenopausal Women ◽  
Risk Prediction ◽  
Clinical Risk Factors ◽  
Clinical Risk ◽  
Early Postmenopausal Women ◽  
Fracture Risk Prediction

scholarly journals Improved prediction of fracture risk leveraging a genome-wide polygenic risk score

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Tianyuan Lu ◽  
Vincenzo Forgetta ◽  
Julyan Keller-Baruch ◽  
Maria Nethander ◽  
Derrick Bennett ◽  
...  
Keyword(s):  
Risk Factors ◽  
Fracture Risk ◽  
Risk Prediction ◽  
Osteoporotic Fracture ◽  
Clinical Risk Factors ◽  
European Ancestry ◽  
Major Osteoporotic Fracture ◽  
Risk Of Fracture ◽  
Clinical Risk ◽  
Genome Wide

Abstract Background Accurately quantifying the risk of osteoporotic fracture is important for directing appropriate clinical interventions. While skeletal measures such as heel quantitative speed of sound (SOS) and dual-energy X-ray absorptiometry bone mineral density are able to predict the risk of osteoporotic fracture, the utility of such measurements is subject to the availability of equipment and human resources. Using data from 341,449 individuals of white British ancestry, we previously developed a genome-wide polygenic risk score (PRS), called gSOS, that captured 25.0% of the total variance in SOS. Here, we test whether gSOS can improve fracture risk prediction. Methods We examined the predictive power of gSOS in five genome-wide genotyped cohorts, including 90,172 individuals of European ancestry and 25,034 individuals of Asian ancestry. We calculated gSOS for each individual and tested for the association between gSOS and incident major osteoporotic fracture and hip fracture. We tested whether adding gSOS to the risk prediction models had added value over models using other commonly used clinical risk factors. Results A standard deviation decrease in gSOS was associated with an increased odds of incident major osteoporotic fracture in populations of European ancestry, with odds ratios ranging from 1.35 to 1.46 in four cohorts. It was also associated with a 1.26-fold (95% confidence interval (CI) 1.13–1.41) increased odds of incident major osteoporotic fracture in the Asian population. We demonstrated that gSOS was more predictive of incident major osteoporotic fracture (area under the receiver operating characteristic curve (AUROC) = 0.734; 95% CI 0.727–0.740) and incident hip fracture (AUROC = 0.798; 95% CI 0.791–0.805) than most traditional clinical risk factors, including prior fracture, use of corticosteroids, rheumatoid arthritis, and smoking. We also showed that adding gSOS to the Fracture Risk Assessment Tool (FRAX) could refine the risk prediction with a positive net reclassification index ranging from 0.024 to 0.072. Conclusions We generated and validated a PRS for SOS which was associated with the risk of fracture. This score was more strongly associated with the risk of fracture than many clinical risk factors and provided an improvement in risk prediction. gSOS should be explored as a tool to improve risk stratification to identify individuals at high risk of fracture.

scholarly journals WHO absolute fracture risk models (FRAX): Do clinical risk factors improve fracture prediction in older women without osteoporosis?

2011 ◽  
Vol 26 (8) ◽  
pp. 1774-1782 ◽  
Author(s):  
Teresa A Hillier ◽  
Jane A Cauley ◽  
Joanne H Rizzo ◽  
Kathryn L Pedula ◽  
Kristine E Ensrud ◽  
...  
Keyword(s):  
Risk Factors ◽  
Fracture Risk ◽  
Older Women ◽  
Fracture Prediction ◽  
Clinical Risk Factors ◽  
Risk Models ◽  
Clinical Risk

scholarly journals Standardised measurement of coronary inflammation using cardiovascular CT: integration in clinical care as a prognostic medical device

2021 ◽  
Author(s):  
Evangelos K Oikonomou ◽  
Alexios S Antonopoulos ◽  
David Schottlander ◽  
Mohammad Marwan ◽  
Chris Mathers ◽  
...  
Keyword(s):  
Risk Factors ◽  
Cardiovascular Risk ◽  
Coronary Artery ◽  
Medical Device ◽  
Risk Prediction ◽  
Clinical Care ◽  
Clinical Risk Factors ◽  
Clinical Risk ◽  
Cardiovascular Risk Prediction ◽  
Perivascular Fat

Abstract Aims Coronary CT angiography (CCTA) is a first-line modality in the investigation of suspected coronary artery disease (CAD). Mapping of perivascular Fat Attenuation Index (FAI) on routine CCTA enables the non-invasive detection of coronary artery inflammation by quantifying spatial changes in perivascular fat composition. We now report the performance of a new medical device, CaRi-Heart®, which integrates standardised FAI mapping together with clinical risk factors and plaque metrics to provide individualised cardiovascular risk prediction. Methods and Results The study included 3912 consecutive patients undergoing CCTA as part of clinical care in the United States (n = 2040) and Europe (n = 1872). These cohorts were used to generate age-specific nomograms and percentile curves as reference maps for the standardised interpretation of FAI. The first output of CaRi-Heart® is the FAI-Score of each coronary artery, which provides a measure of coronary inflammation adjusted for technical, biological and anatomical characteristics. FAI-Score is then incorporated into a risk prediction algorithm together with clinical risk factors and CCTA-derived coronary plaque metrics to generate the CaRi-Heart® Risk that predicts the likelihood of a fatal cardiac event at 8 years. CaRi-Heart® Risk was trained in the US population and its performance was validated externally in the European population. It improved risk discrimination over a clinical risk factor-based model (Δ[C-statistic] of 0.085, P = 0.01 in the US Cohort and 0.149, P < 0.001 in the European cohort) and had a consistent net clinical benefit on decision curve analysis above a baseline traditional risk factor-based model across the spectrum of cardiac risk. Conclusion CaRi-Heart® reliably improves cardiovascular risk prediction by incorporating traditional cardiovascular risk factors along with comprehensive CCTA coronary plaque and perivascular adipose tissue phenotyping. This integration advances the prognostic utility of CCTA for individual patients and paves the way for its use as a screening tool among patients referred for CCTA. Translational Perspective Mapping of perivascular Fat Attenuation Index (FAI) on coronary computed tomography angiography (CCTA) enables the non-invasive detection of coronary artery inflammation by quantifying spatial changes in perivascular fat composition. We now report the performance of a new medical device, CaRi-Heart®, which integrates standardised FAI mapping together with clinical risk factors and plaque metrics to provide age-standardised reference maps and individualised cardiovascular risk prediction. This integration advances the prognostic value of CCTA and paves the way for its use as a screening tool among patients referred for CCTA.

scholarly journals Genetic risk score for risk prediction of diabetic nephropathy in Han Chinese type 2 diabetes patients

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Li-Na Liao ◽  
Tsai-Chung Li ◽  
Chia-Ing Li ◽  
Chiu-Shong Liu ◽  
Wen-Yuan Lin ◽  
...  
Keyword(s):  
Risk Factors ◽  
Type 2 Diabetes ◽  
Prediction Model ◽  
Risk Prediction ◽  
Genetic Risk Score ◽  
Clinical Risk Factors ◽  
Clinical Risk ◽  
Conventional Risk Factors ◽  
Diabetes Patients

AbstractWe evaluated whether genetic information could offer improvement on risk prediction of diabetic nephropathy (DN) while adding susceptibility variants into a risk prediction model with conventional risk factors in Han Chinese type 2 diabetes patients. A total of 995 (including 246 DN cases) and 519 (including 179 DN cases) type 2 diabetes patients were included in derivation and validation sets, respectively. A genetic risk score (GRS) was constructed with DN susceptibility variants based on findings of our previous genome-wide association study. In derivation set, areas under the receiver operating characteristics (AUROC) curve (95% CI) for model with clinical risk factors only, model with GRS only, and model with clinical risk factors and GRS were 0.75 (0.72–0.78), 0.64 (0.60–0.68), and 0.78 (0.75–0.81), respectively. In external validation sample, AUROC for model combining conventional risk factors and GRS was 0.70 (0.65–0.74). Additionally, the net reclassification improvement was 9.98% (P = 0.001) when the GRS was added to the prediction model of a set of clinical risk factors. This prediction model enabled us to confirm the importance of GRS combined with clinical factors in predicting the risk of DN and enhanced identification of high-risk individuals for appropriate management of DN for intervention.

scholarly journals Pelvic Organ Prolapse and Relationship with Skeletal Integrity

2009 ◽  
Vol 5 (3) ◽  
pp. 325-333 ◽  
Author(s):  
Lubna Pal
Keyword(s):  
Risk Factors ◽  
Pelvic Organ Prolapse ◽  
Fracture Risk ◽  
Postmenopausal Women ◽  
Pelvic Organ ◽  
Clinical Risk Factors ◽  
Mineral Density ◽  
Female Population ◽  
Clinical Risk ◽  
Age Related

Health burden related to osteoporotic fractures in an aging female population far exceeds that imposed by other chronic disorders such as cardiovascular disease and breast cancer. Bone mineral density assessment and clinical risk factors provide independent insights into fracture risk in individuals. A finite list of clinical risk factors are identified as prognostic of fracture risk, namely among aging women, including low body mass, compromised reproductive physiology (e.g., prolonged periods of amenorrhea and early menopause), parental and personal histories of fracture, and alcohol and tobacco use. Pelvic organ prolapse is a common gynecologic entity and a contributor to age-related morbidities. The purpose of this review is to communicate data identifying pelvic organ prolapse as another clinical risk factor for fracture risk in postmenopausal women and to increase the caregiver's vigilance in anticipating and instituting preventive care strategies to a population (i.e., postmenopausal women with clinically appreciable pelvic organ prolapse) that may be at an enhanced lifetime risk for skeletal fractures.

scholarly journals Fracture risk assessment in frail older people using clinical risk factors

2008 ◽  
Vol 37 (5) ◽  
pp. 536-541 ◽  
Author(s):  
J. S. Chen ◽  
J. M. Simpson ◽  
L. M. March ◽  
I. D. Cameron ◽  
R. G. Cumming ◽  
...  
Keyword(s):  
Risk Factors ◽  
Risk Assessment ◽  
Older People ◽  
Fracture Risk ◽  
Fracture Risk Assessment ◽  
Clinical Risk Factors ◽  
Frail Older People ◽  
Clinical Risk

scholarly journals One leg standing time predicts fracture risk in older women independent of clinical risk factors and BMD

2021 ◽  
Author(s):  
B.A.M. Larsson ◽  
L. Johansson ◽  
D. Mellström ◽  
H. Johansson ◽  
K.F. Axelsson ◽  
...  
Keyword(s):  
Risk Factors ◽  
Fracture Risk ◽  
Older Women ◽  
Clinical Risk Factors ◽  
Standing Time ◽  
Clinical Risk

scholarly journals Comparisons of Polyexposure, Polygenic, and Clinical Risk Scores in Risk Prediction of Type 2 Diabetes

2021 ◽  
Author(s):  
Yixuan He ◽  
Chirag M Lakhani ◽  
Danielle Rasooly ◽  
Arjun K Manrai ◽  
Ioanna Tzoulaki ◽  
...  
Keyword(s):  
Risk Factors ◽  
Risk Prediction ◽  
Risk Score ◽  
Prediction Models ◽  
Disease Risk ◽  
Clinical Risk Factors ◽  
Risk Scores ◽  
Polygenic Risk Score ◽  
Polygenic Risk ◽  
Clinical Risk

OBJECTIVE: <p>Establish a polyexposure score for T2D incorporating 12 non-genetic exposure and examine whether a polyexposure and/or a polygenic risk score improves diabetes prediction beyond traditional clinical risk factors.</p> <h2><a></a>RESEARCH DESIGN AND METHODS:</h2> <p>We identified 356,621 unrelated individuals from the UK Biobank of white British ancestry with no prior diagnosis of T2D and normal HbA1c levels. Using self-reported and hospital admission information, we deployed a machine learning procedure to select the most predictive and robust factors out of 111 non-genetically ascertained exposure and lifestyle variables for the polyexposure risk score (PXS) in prospective T2D. We computed the clinical risk score (CRS) and polygenic risk score (PGS) by taking a weighted sum of eight established clinical risk factors and over six million SNPs, respectively.</p> <h2><a></a>RESULTS:</h2> <p>In the study population, 7,513 had incident T2D. The C-statistics for the PGS, PXS, and CRS models were 0.709, 0.762, and 0.839, respectively. Hazard ratios (HR) associated with risk score values in the top 10% percentile versus the remaining population is 2.00, 5.90, and 9.97 for PGS, PXS, and CRS respectively. Addition of PGS and PXS to CRS improves T2D classification accuracy with a continuous net reclassification index of 15.2% and 30.1% for cases, respectively, and 7.3% and 16.9% for controls, respectively. </p> <h2><a></a>CONCLUSIONS:</h2> <p>For T2D, the PXS provides modest incremental predictive value over established clinical risk factors. The concept of PXS merits further consideration in T2D risk stratification and is likely to be useful in other chronic disease risk prediction models.</p>

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