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This review explores the FH tools currently available for PC and evaluates their clinical performance.Five databases were systematically searched until May 2021. Identified tools were evaluated on the following criteria: time-to-complete, integration with electronic health record (EMR) systems, patient administration, risk-assessment ability, evidence-based management recommendations, analytical and clinical validity and clinical utility.We identified 26?PC FH tools. Analytical and clinical validity was poorly reported and agreement between FH and gold standard was commonly inadequately reported and assessed. Sensitivity was acceptable; specificity was found in half of the reviewed tools to be poor. Most reviewed tools showed a capacity to successfully identify individuals with increased risk of disease (6.2-84.6% of high and/or moderate or increased risk individuals).Despite the potential of FH tools to improve risk stratification of patients in PC, clinical performance of current tools remains limited as well as their integration in EMR systems. Twenty-one FH tools are designed to be self-administered by patients.

Kidney diseases are a leading cause of death in the United States. About 37 million US adults are estimated to have CKD, and most are undiagnosed. 40% of people with severely reduced kidney function (not on dialysis) are not aware of having CKD. Talk to your doctor about getting tested if you have any of these risk factors: Diabetes, High blood pressure, Heart disease, Family history of CKD, Obesity.

Researchers are studying the risk factors for different types of breast cancer. They’re also searching for more personalized treatments. Breast cancer is caused by a combination of factors. Your genes, lifestyle, and environment all contribute to your risk. Researchers are trying to better understand how each plays a role. People with a family history of breast cancer are at increased risk for the disease. Some are born with rare versions of certain genes that put them at high risk. These include the genes BRCA1 and BRCA2. But the vast majority of patients have no known family history and no known gene that causes cancer.

Genome-wide association studies (GWASs) have revolutionized human genetics, allowing researchers to identify thousands of disease-related genes and possible drug targets. However, case-control status does not account for the fact that not all controls may have lived through their period of risk for the disorder of interest. This can be quantified by examining the age-of-onset distribution and the age of the controls or the age of onset for cases. The age-of-onset distribution may also depend on information such as sex and birth year. In addition, family history is not routinely included in the assessment of control status. Using simulation, we show that, when family history and the age-of-onset distribution are available, the proposed approach yields statistically significant power gains over LT-FH and large power gains over genome-wide association study by proxy (GWAX).

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