scholarly journals The Ten-year Risk Prediction for Cardiovascular Disease in the National Population (Globorisk) of Malaysian Adults

2021 ◽  
Author(s):  
Kamarul Imran Musa ◽  
Che Muhammad Nur Hidayat Che Nawi ◽  
Mohd. Azahadi Omar ◽  
Thomas Keegan ◽  
Yong Poh Yu
Keyword(s):  
Cardiovascular Disease ◽  
Risk Factor ◽  
High Risk ◽  
Risk Prediction ◽  
Adult Population ◽  
Risk Scores ◽  
Average Risk ◽  
National Population ◽  
C 30 ◽  
Event Rates

Abstract Globorisk is a novel risk prediction model that predicts cardiovascular disease (CVD) in the national population of all world countries. Using Malaysia's risk factor levels and CVD event rates, we calculated the laboratory-based and office-based risk scores to predict the 10-year risk for fatal CVD and fatal plus non-fatal CVD for the Malaysian adult population. We analysed data from 8253 participants from the 2015 nationwide Malaysian National Health and Morbidity Survey (NHMS 2015). The average risk for the 10-year fatal and fatal plus non-fatal CVD was calculated, and participants were further grouped into four categories: Low Risk (<10% risk for CVD), High-Risk A (≥10%), High-Risk B (≥20%) and High-Risk C (≥30%). Results were reported for all participants and were then stratified by sex, race, region, and state. The average risks for laboratory-based fatal CVD, laboratory-based fatal plus non-fatal CVD and office-based fatal plus non-fatal CVD were 0.07 (SD = 0.10), 0.14 (SD = 0.12) and 0.11 (SD = 0.09), respectively. There were substantial differences in terms of the sex-, race- and state-specific Globorisk risk scores obtained.

scholarly journals Evaluating Cardiovascular Disease (CVD) risk scores for participants with known CVD and non-CVD in a multiracial/ethnic Caribbean sample

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8232
Author(s):  
Amalia Hosein ◽  
Valerie Stoute ◽  
Samantha Chadee ◽  
Natasha Ramroop Singh
Keyword(s):  
Cardiovascular Disease ◽  
High Risk ◽  
Trinidad And Tobago ◽  
Risk Prediction ◽  
Prediction Models ◽  
Time Frame ◽  
Risk Scores ◽  
Cvd Risk ◽  
Biometric Data ◽  
Risk Prediction Models

Background Cardiovascular Disease (CVD) risk prediction models have been useful in estimating if individuals are at low, intermediate, or high risk, of experiencing a CVD event within some established time frame, usually 10 years. Central to this is the concern in Trinidad and Tobago of using pre-existing CVD risk prediction methods, based on populations in the developed world (e.g. ASSIGN, Framingham and QRISK®2), to establish risk for its multiracial/ethnic Caribbean population. The aim of this study was to determine which pre-existing CVD risk method is best suited for predicting CVD risk for individuals in this population. Method A survey was completed by 778 participants, 526 persons with no prior CVD, and 252 who previously reported a CVD event. Lifestyle and biometric data was collected from non-CVD participants, while for CVD participants, medical records were used to collect data at the first instance of CVD. The performances of three CVD risk prediction models (ASSIGN, Framingham and QRISK®2) were evaluated using their calculated risk scores. Results All three models (ASSIGN, Framingham and QRISK®2) identified less than 62% of cases (CVD participants) with a high proportion of false-positive predictions to true predictions as can be seen by positive predictabilities ranging from 78% (ASSIGN and Framingham) to 87% (QRISK®2). Further, for all three models, individuals whose scores fell into the misclassification range were 2X more likely to be individuals who had experienced a prior CVD event as opposed to healthy individuals. Conclusion The ASSIGN, Framingham and QRISK®2 models should be utilised with caution on a Trinidad and Tobago population of intermediate and high risk for CVD since these models were found to have underestimated the risk for individuals with CVD up to 2.5 times more often than they overestimated the risk for healthy persons.

scholarly journals Assessing the clinical utility of genetic risk scores for targeted cancer screening

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Carly A. Conran ◽  
Zhuqing Shi ◽  
William Kyle Resurreccion ◽  
Rong Na ◽  
Brian T. Helfand ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Primary Care ◽  
High Risk ◽  
Risk Score ◽  
Genetic Risk ◽  
Clinical Utility ◽  
Low Risk ◽  
Risk Scores ◽  
Average Risk ◽  
Genetic Risk Scores

Abstract Background Genome-wide association studies have identified thousands of disease-associated single nucleotide polymorphisms (SNPs). A subset of these SNPs may be additively combined to generate genetic risk scores (GRSs) that confer risk for a specific disease. Although the clinical validity of GRSs to predict risk of specific diseases has been well established, there is still a great need to determine their clinical utility by applying GRSs in primary care for cancer risk assessment and targeted intervention. Methods This clinical study involved 281 primary care patients without a personal history of breast, prostate or colorectal cancer who were 40–70 years old. DNA was obtained from a pre-existing biobank at NorthShore University HealthSystem. GRSs for colorectal cancer and breast or prostate cancer were calculated and shared with participants through their primary care provider. Additional data was gathered using questionnaires as well as electronic medical record information. A t-test or Chi-square test was applied for comparison of demographic and key clinical variables among different groups. Results The median age of the 281 participants was 58 years and the majority were female (66.6%). One hundred one (36.9%) participants received 2 low risk scores, 99 (35.2%) received 1 low risk and 1 average risk score, 37 (13.2%) received 1 low risk and 1 high risk score, 23 (8.2%) received 2 average risk scores, 21 (7.5%) received 1 average risk and 1 high risk score, and no one received 2 high risk scores. Before receiving GRSs, younger patients and women reported significantly more worry about risk of developing cancer. After receiving GRSs, those who received at least one high GRS reported significantly more worry about developing cancer. There were no significant differences found between gender, age, or GRS with regards to participants’ reported optimism about their future health neither before nor after receiving GRS results. Conclusions Genetic risk scores that quantify an individual’s risk of developing breast, prostate and colorectal cancers as compared with a race-defined population average risk have potential clinical utility as a tool for risk stratification and to guide cancer screening in a primary care setting.

scholarly journals Predicting the risk of severe menopausal syndrome in perimenopausal women with hypothyroidism

2021 ◽  
Vol 17 (3) ◽  
pp. 219-225
Author(s):  
N.V. Pasechko ◽  
O.O. Chukur ◽  
A.O. Bob ◽  
A.S. Sverstiuk
Keyword(s):  
Mathematical Model ◽  
Regression Analysis ◽  
Risk Factor ◽  
High Risk ◽  
High Risk Group ◽  
Coefficient Of Determination ◽  
Endocrine Disorders ◽  
Average Risk ◽  
Menopausal Syndrome ◽  
Perimenopausal Women

Background. Every year the number of menopausal women increases. At this age, the prevalence of hypothyroidism (HT) reaches its peak. The problem of menopausal syndrome (MS) is relevant for patients with HT, concomitant endocrine disorders create a background for combination with dyshormonal factors. The purpose of the study: to propose an approach to predicting the risk of severe MS in perimenopausal women with HT according to the developed algorithm and mathematical model. Materials and methods. To predict the development of MS, 146 perimenopausal women with autoimmune HT were surveyed. ­Using multiple regression analysis, a prognostic model of the risk of severe MS was created. Results. Logistic regression analysis revealed the following most significant multicollinear risk factors for MS: smoking, alcohol consumption, adverse environmental conditions, physical activity, history of stress and anxiety, thyroid disease. A correlation matrix with calculation of regression coefficients and coefficient of determination was constructed, a mathematical model was created to determine the risk factor for the progression of MS. The predicted value of the risk factor for severe MS with a high degree of probability was determined in 72 (49.32 %) women, with an average probability — in 58 (39.73 %), and with a low probability — in 16 women (10.95 %) with HT. The correspondence of the predicted results with the theoretically expected ones in the high-risk group was recorded in 104.37 %, in the average-risk — in 94.73 %, and in the low-risk — in 89.65 % of cases. Conclusions. The developed algorithm and mathematical model for predicting severe MS on the background of HT are highly informative and allow determining in advance the group of women at high risk of severe MS for the timely implementation of appropriate preventive measures.

scholarly journals Role of Usual Healthcare Combined with Telemedicine in the Management of High-Risk Pregnancy in Hangzhou, China

2019 ◽  
Vol 2019 ◽  
pp. 1-7 ◽  
Author(s):  
Xu-Hong Zhu ◽  
Jing Tao ◽  
Li-Yuan Jiang ◽  
Zhi-Feng Zhang
Keyword(s):  
Risk Factor ◽  
High Risk ◽  
Maternal Health ◽  
Pregnant Women ◽  
High Risk Pregnancy ◽  
Risk Scores ◽  
Public Health Services ◽  
Risk Pregnancy ◽  
High Risk Factor

Background. Maternal health is an important part of basic public health services in China’s medical reform. Effective management is an important guarantee of maternal health. Telemedicine has been widely used in maternal health management. Objective. This study explores the role of usual healthcare combined with telemedicine in the management of high-risk pregnancy. Methods. The study was a retrospective. Data were obtained from Hangzhou Maternity Hospital between October 2012 and September 2016, including 93465 pregnant women who were in usual high-risk pregnancy management (usual group) and 134884 pregnant women who were in telemedicine combined with usual high-risk pregnancy management (telemedicine group). The differences in high-risk scores and pregnancy outcomes between the usual and the telemedicine groups were compared. Results. The high-risk factors were analyzed, and the results showed that the first fixed high-risk factor was scar uterus and the first dynamic high-risk factor was hepatitis B. Comparing the data of two groups, the number of prenatal visits increased significantly in the telemedicine group (p value <0.05). Although the critical proportion of high-risk women was 2.13% in the usual group and 5.88% in the telemedicine group, respectively (p value <0.01), maternal mortality decreased in the telemedicine group (p value <0.05). Conclusion. The combination of telemedicine and usual healthcare can urge the pregnant women to carry out antenatal visits on time, which is one of the important factors to improve the outcome of high-risk pregnancy.

scholarly journals Impact of sample size on the stability of risk scores from clinical prediction models: a case study in cardiovascular disease

2020 ◽  
Vol 4 (1) ◽  
Author(s):  
Alexander Pate ◽  
Richard Emsley ◽  
Matthew Sperrin ◽  
Glen P. Martin ◽  
Tjeerd van Staa
Keyword(s):  
Cardiovascular Disease ◽  
Prediction Model ◽  
Sample Size ◽  
Risk Prediction ◽  
Prediction Models ◽  
Disease Risk ◽  
Cardiovascular Disease Risk ◽  
Risk Scores ◽  
Model Derivation ◽  
The Stability

Abstract Background Stability of risk estimates from prediction models may be highly dependent on the sample size of the dataset available for model derivation. In this paper, we evaluate the stability of cardiovascular disease risk scores for individual patients when using different sample sizes for model derivation; such sample sizes include those similar to models recommended in the national guidelines, and those based on recently published sample size formula for prediction models. Methods We mimicked the process of sampling N patients from a population to develop a risk prediction model by sampling patients from the Clinical Practice Research Datalink. A cardiovascular disease risk prediction model was developed on this sample and used to generate risk scores for an independent cohort of patients. This process was repeated 1000 times, giving a distribution of risks for each patient. N = 100,000, 50,000, 10,000, Nmin (derived from sample size formula) and Nepv10 (meets 10 events per predictor rule) were considered. The 5–95th percentile range of risks across these models was used to evaluate instability. Patients were grouped by a risk derived from a model developed on the entire population (population-derived risk) to summarise results. Results For a sample size of 100,000, the median 5–95th percentile range of risks for patients across the 1000 models was 0.77%, 1.60%, 2.42% and 3.22% for patients with population-derived risks of 4–5%, 9–10%, 14–15% and 19–20% respectively; for N = 10,000, it was 2.49%, 5.23%, 7.92% and 10.59%, and for N using the formula-derived sample size, it was 6.79%, 14.41%, 21.89% and 29.21%. Restricting this analysis to models with high discrimination, good calibration or small mean absolute prediction error reduced the percentile range, but high levels of instability remained. Conclusions Widely used cardiovascular disease risk prediction models suffer from high levels of instability induced by sampling variation. Many models will also suffer from overfitting (a closely linked concept), but at acceptable levels of overfitting, there may still be high levels of instability in individual risk. Stability of risk estimates should be a criterion when determining the minimum sample size to develop models.

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