| Title |
External Validation of a Tool Predicting 7-Year Risk of Developing Cardiovascular Disease, Type 2 Diabetes or Chronic Kidney Disease
|
|---|---|
| Published in |
Journal of General Internal Medicine, December 2017
|
| DOI | 10.1007/s11606-017-4231-7 |
| Pubmed ID | |
| Authors |
Simone P. Rauh, Femke Rutters, Amber A. W. A. van der Heijden, Thomas Luimes, Marjan Alssema, Martijn W. Heymans, Dianna J. Magliano, Jonathan E. Shaw, Joline W. Beulens, Jacqueline M. Dekker |
| Abstract |
Chronic cardiometabolic diseases, including cardiovascular disease (CVD), type 2 diabetes (T2D) and chronic kidney disease (CKD), share many modifiable risk factors and can be prevented using combined prevention programs. Valid risk prediction tools are needed to accurately identify individuals at risk. We aimed to validate a previously developed non-invasive risk prediction tool for predicting the combined 7-year-risk for chronic cardiometabolic diseases. The previously developed tool is stratified for sex and contains the predictors age, BMI, waist circumference, use of antihypertensives, smoking, family history of myocardial infarction/stroke, and family history of diabetes. This tool was externally validated, evaluating model performance using area under the receiver operating characteristic curve (AUC)-assessing discrimination-and Hosmer-Lemeshow goodness-of-fit (HL) statistics-assessing calibration. The intercept was recalibrated to improve calibration performance. The risk prediction tool was validated in 3544 participants from the Australian Diabetes, Obesity and Lifestyle Study (AusDiab). Discrimination was acceptable, with an AUC of 0.78 (95% CI 0.75-0.81) in men and 0.78 (95% CI 0.74-0.81) in women. Calibration was poor (HL statistic: p < 0.001), but improved considerably after intercept recalibration. Examination of individual outcomes showed that in men, AUC was highest for CKD (0.85 [95% CI 0.78-0.91]) and lowest for T2D (0.69 [95% CI 0.65-0.74]). In women, AUC was highest for CVD (0.88 [95% CI 0.83-0.94)]) and lowest for T2D (0.71 [95% CI 0.66-0.75]). Validation of our previously developed tool showed robust discriminative performance across populations. Model recalibration is recommended to account for different disease rates. Our risk prediction tool can be useful in large-scale prevention programs for identifying those in need of further risk profiling because of their increased risk for chronic cardiometabolic diseases. |
X Demographics
The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Australia | 1 | 25% |
| United States | 1 | 25% |
| Unknown | 2 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 75% |
| Science communicators (journalists, bloggers, editors) | 1 | 25% |
Mendeley readers
The data shown below were compiled from readership statistics for 55 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 55 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 8 | 15% |
| Student > Master | 6 | 11% |
| Researcher | 4 | 7% |
| Student > Ph. D. Student | 4 | 7% |
| Student > Postgraduate | 3 | 5% |
| Other | 8 | 15% |
| Unknown | 22 | 40% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 18 | 33% |
| Nursing and Health Professions | 5 | 9% |
| Agricultural and Biological Sciences | 3 | 5% |
| Engineering | 2 | 4% |
| Social Sciences | 1 | 2% |
| Other | 2 | 4% |
| Unknown | 24 | 44% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 08 November 2019.
All research outputs
#14,223,569
of 23,911,072 outputs
Outputs from Journal of General Internal Medicine
#5,262
of 7,806 outputs
Outputs of similar age
#223,718
of 445,835 outputs
Outputs of similar age from Journal of General Internal Medicine
#59
of 88 outputs
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