scholarly journals An integrated clinical and genetic model for predicting risk of severe COVID-19: A population-based case–control study

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0247205 ◽  
Author(s):  
Gillian S. Dite ◽  
Nicholas M. Murphy ◽  
Richard Allman
Keyword(s):  
Risk Factors ◽  
Confidence Interval ◽  
Severe Disease ◽  
Accurate Prediction ◽  
Clinical Risk Factors ◽  
Uk Biobank ◽  
Age And Gender ◽  
Clinical Risk ◽  
Increased Risk ◽  
And Gender

Up to 30% of people who test positive to SARS-CoV-2 will develop severe COVID-19 and require hospitalisation. Age, gender, and comorbidities are known to be risk factors for severe COVID-19 but are generally considered independently without accurate knowledge of the magnitude of their effect on risk, potentially resulting in incorrect risk estimation. There is an urgent need for accurate prediction of the risk of severe COVID-19 for use in workplaces and healthcare settings, and for individual risk management. Clinical risk factors and a panel of 64 single-nucleotide polymorphisms were identified from published data. We used logistic regression to develop a model for severe COVID-19 in 1,582 UK Biobank participants aged 50 years and over who tested positive for the SARS-CoV-2 virus: 1,018 with severe disease and 564 without severe disease. Model discrimination was assessed using the area under the receiver operating characteristic curve (AUC). A model incorporating the SNP score and clinical risk factors (AUC = 0.786; 95% confidence interval = 0.763 to 0.808) had 111% better discrimination of disease severity than a model with just age and gender (AUC = 0.635; 95% confidence interval = 0.607 to 0.662). The effects of age and gender are attenuated by the other risk factors, suggesting that it is those risk factors–not age and gender–that confer risk of severe disease. In the whole UK Biobank, most are at low or only slightly elevated risk, but one-third are at two-fold or more increased risk. We have developed a model that enables accurate prediction of severe COVID-19. Continuing to rely on age and gender alone (or only clinical factors) to determine risk of severe COVID-19 will unnecessarily classify healthy older people as being at high risk and will fail to accurately quantify the increased risk for younger people with comorbidities.

scholarly journals An integrated clinical and genetic model for predicting risk of severe COVID-19

2020 ◽  
Author(s):  
Gillian S Dite ◽  
Nicholas M Murphy ◽  
Richard Allman
Keyword(s):  
Risk Factors ◽  
Severe Disease ◽  
Accurate Prediction ◽  
Clinical Risk Factors ◽  
Published Data ◽  
Uk Biobank ◽  
Age And Gender ◽  
Clinical Risk ◽  
Increased Risk ◽  
And Gender

Background: Age and gender are often the only considerations in determining risk of severe COVID-19. There is an urgent need for accurate prediction of the risk of severe COVID-19 for use in workplaces and healthcare settings, and for individual risk management. Methods: Clinical risk factors and a panel of 64 single-nucleotide polymorphisms were identified from published data. We used logistic regression to develop a model for severe COVID-19 in 1,582 UK Biobank participants aged 50 years and over who tested positive for the SARS-CoV-2 virus: 1,018 with severe disease and 564 without severe disease. Model discrimination was assessed using the area under the receiver operating characteristic curve (AUC). Results: A model incorporating the SNP score and clinical risk factors (AUC=0.786) had 111% better discrimination of disease severity than a model with just age and gender (AUC=0.635). The effects of age and gender are attenuated by the other risk factors, suggesting that it is those risk factors -- not age and gender -- that confer risk of severe disease. In the whole UK Biobank, most are at low or only slightly elevated risk, but one-third are at two-fold or more increased risk. Conclusions: We have developed a model that enables accurate prediction of severe COVID-19. Continuing to rely on age and gender alone to determine risk of severe COVID-19 will unnecessarily classify healthy older people as being at high risk and will fail to accurately quantify the increased risk for younger people with comorbidities.

Abstract 13771: Combining Clinical and Polygenic Risk Improves Stroke Prediction Among Individuals With Atrial Fibrillation

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Jack W Osullivan ◽  
Anna Shcherbina ◽  
Johanne M Justesen ◽  
Mintu Turakhia ◽  
Marco V Perez ◽  
...  
Keyword(s):  
Risk Factors ◽  
Atrial Fibrillation ◽  
Ischemic Stroke ◽  
Predictive Ability ◽  
Clinical Risk Factors ◽  
Risk Scores ◽  
Uk Biobank ◽  
Polygenic Risk ◽  
Clinical Risk ◽  
Increased Risk

Introduction: Atrial fibrillation (AF) is associated with a five-fold increased risk of ischemic stroke. A portion of this risk is heritable, however current risk stratification tools (CHA 2 DS 2 -VASc) don’t include family history or genetic risk. Hypothesis: A polygenic risk scores (PRS) is both independently, and in integrated with clinical risk factors, predictive of ischemic stroke in patients with Atrial Fibrillation. Methods: Using data from the largest available GWAS in Europeans, we combined over half a million genetic variants to construct a PRS to predict ischemic stroke in patients with AF. We externally validated this PRS in independent data from the UK Biobank (UK Biobank), both independently and integrated with clinical risk factors. Results: The integrated PRS and clinical risk factors risk tool had the greatest predictive ability. Compared with the currently recommended risk tool (CHA 2 DS 2 -VASc), the integrated tool significantly improved net reclassification (NRI: 2.3% (95%CI: 1.3% to 3.0%)), and fit (χ2 P =0.002). Independently, PRS was a significant predictor of ischemic stroke in patients with AF prospectively (Hazard Ratio: 1.13 per 1 SD (95%CI: 1.04 to 1.21)). Lastly, polygenic risk scores were uncorrelated with clinical risk factors (Pearson’s correlation coefficient: -0.018). Conclusions: In patients with AF, there appears to be a significant association between PRS and risk of ischemic stroke. The greatest predictive ability was found with the integration of PRS and clinical risk factors, however the prediction of stroke remains challenging.

scholarly journals Combining clinical and polygenic risk improves stroke prediction among individuals with atrial fibrillation

2020 ◽  
Author(s):  
Jack W. O’Sullivan ◽  
Anna Shcherbina ◽  
Johanne M Justesen ◽  
Mintu Turakhia ◽  
Marco Perez ◽  
...  
Keyword(s):  
Risk Factors ◽  
Atrial Fibrillation ◽  
Ischemic Stroke ◽  
Predictive Ability ◽  
Clinical Risk Factors ◽  
Risk Scores ◽  
Uk Biobank ◽  
Polygenic Risk ◽  
Clinical Risk ◽  
Increased Risk

AbstractBackgroundAtrial fibrillation (AF) is associated with a five-fold increased risk of ischemic stroke. A portion of this risk is heritable, however current risk stratification tools (CHA2DS2-VASc) don’t include family history or genetic risk. We hypothesized that we could improve ischemic stroke prediction in patients with AF by incorporating polygenic risk scores (PRS).ObjectivesTo construct and test a PRS to predict ischemic stroke in patients with AF, both independently and integrated with clinical risk factors.MethodsUsing data from the largest available GWAS in Europeans, we combined over half a million genetic variants to construct a PRS to predict ischemic stroke in patients with AF. We externally validated this PRS in independent data from the UK Biobank (UK Biobank), both independently and integrated with clinical risk factors.ResultsThe integrated PRS and clinical risk factors risk tool had the greatest predictive ability. Compared with the currently recommended risk tool (CHA2DS2-VASc), the integrated tool significantly improved net reclassification (NRI: 2.3% (95%CI: 1.3% to 3.0%)), and fit (χ2 P =0.002). Using this improved tool, >115,000 people with AF would have improved risk classification in the US. Independently, PRS was a significant predictor of ischemic stroke in patients with AF prospectively (Hazard Ratio: 1.13 per 1 SD (95%CI: 1.06 to 1.23))). Lastly, polygenic risk scores were uncorrelated with clinical risk factors (Pearson’s correlation coefficient: −0.018).ConclusionsIn patients with AF, there appears to be a significant association between PRS and risk of ischemic stroke. The greatest predictive ability was found with the integration of PRS and clinical risk factors, however the prediction of stroke remains challenging.

scholarly journals Identification of patients at increased risk of suicide in hepatobiliary and pancreatic cancer: an analysis of epidemiologic and clinical risk factors

HPB ◽  
2017 ◽  
Vol 19 ◽  
pp. S55
Author(s):  
P. Martinez Quinones ◽  
A. Talukder ◽  
N. Walsh ◽  
A. Lawson ◽  
A. Jones ◽  
...  
Keyword(s):  
Risk Factors ◽  
Pancreatic Cancer ◽  
Clinical Risk Factors ◽  
Clinical Risk ◽  
Increased Risk

scholarly journals Donor race has no role in predicting allograft and patient survival among kidney transplant recipients

2021 ◽  
Author(s):  
Kelly Chong ◽  
Igor Litvinovich ◽  
Shan Shan Chen ◽  
Yiliang Zhu ◽  
Christos Argyropoulos ◽  
...  
Keyword(s):  
Risk Factors ◽  
Patient Survival ◽  
Risk Index ◽  
Deceased Donor ◽  
Clinical Risk Factors ◽  
Kidney Transplant Recipients ◽  
Modeling And Analysis ◽  
Clinical Risk ◽  
Heated Debate ◽  
And Gender

A heated debate in creatinine-based estimated glomerular filtration rate (eGFR) calculation is the inclusion of race alongside biological factors, such as age and gender. Similarly, the race variable was included in the calculation of the Kidney Donor Risk Index (KDRI) as deceased donor kidneys from black donors have historically been shown to be associated with lower allograft or patient survival. Given the current climate of uncertainty with the use of race in nephrology, we sought to answer the question of whether removing the donor race variable from the KDRI would alter its validity to assess allograft and patient survival. Our modeling and analysis showed that removing donor race from the original KDRI did not alter the overall model predictability of allograft failure or patient mortality. Clinical risk factors included in the KDRI have largely accounted for differential risk between black and other donors. Adding donor race into the KDRI only shifts how risk is attributed to these clinical risk factors, without yielding better prediction of outcomes than the model without race.

scholarly journals Uncovering clinical risk factors and prediction of severe COVID-19: A machine learning approach based on UK Biobank data

2020 ◽  
Author(s):  
Kenneth C.Y. WONG ◽  
Hon-Cheong So
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Renal Function ◽  
Population Level ◽  
Clinical Risk Factors ◽  
Health Concern ◽  
Targeted Prevention ◽  
Uk Biobank ◽  
Clinical Risk ◽  
Glutamyl Transferase

Background: COVID-19 is a major public health concern. Given the extent of the pandemic, it is urgent to identify risk factors associated with severe disease. Accurate prediction of those at risk of developing severe infections is also important clinically. Methods: Based on the UK Biobank (UKBB data), we built machine learning(ML) models to predict the risk of developing severe or fatal infections, and to evaluate the major risk factors involved. We first restricted the analysis to infected subjects, then performed analysis at a population level, considering those with no known infections as controls. Hospitalization was used as a proxy for severity. Totally 93 clinical variables (collected prior to the COVID-19 outbreak) covering demographic variables, comorbidities, blood measurements (e.g. hematological/liver and renal function/metabolic parameters etc.), anthropometric measures and other risk factors (e.g. smoking/drinking habits) were included as predictors. XGboost (gradient boosted trees) was used for prediction and predictive performance was assessed by cross-validation. Variable importance was quantified by Shapley values and accuracy gain. Shapley dependency and interaction plots were used to evaluate the pattern of relationship between risk factors and outcomes. Results: A total of 1191 severe and 358 fatal cases were identified. For the analysis among infected individuals (N=1747), our prediction model achieved AUCs of 0.668 and 0.712 for severe and fatal infections respectively. Since only pre-diagnostic clinical data were available, the main objective of this analysis was to identify baseline risk factors. The top five contributing factors for severity were age, waist-hip ratio(WHR), HbA1c, number of drugs taken(cnt_tx) and gamma-glutamyl transferase levels. For prediction of mortality, the top features were age, systolic blood pressure, waist circumference (WC), urea and WHR. In subsequent analyses involving the whole UKBB population (N for controls=489987), the corresponding AUCs for severity and fatality were 0.669 and 0.749. The same top five risk factors were identified for both outcomes, namely age, cnt_tx, WC, WHR and cystatin C. We also uncovered other features of potential relevance, including testosterone, IGF-1 levels, red cell distribution width (RDW) and lymphocyte percentage. Conclusions: We identified a number of baseline clinical risk factors for severe/fatal infection by an ML approach. For example, age, central obesity, impaired renal function, multi-comorbidities and cardiometabolic abnormalities may predispose to poorer outcomes. The presented prediction models may be useful at a population level to help identify those susceptible to developing severe/fatal infections, hence facilitating targeted prevention strategies. Further replications in independent cohorts are required to verify our findings.

scholarly journals Redefining osteoporosis treatment: who to treat and how long to treat

2006 ◽  
Vol 50 (4) ◽  
pp. 694-704 ◽  
Author(s):  
E. Michael Lewiecki ◽  
Stuart L. Silverman
Keyword(s):  
Risk Factors ◽  
Fracture Risk ◽  
Weight Bearing ◽  
Pharmacological Therapy ◽  
Clinical Risk Factors ◽  
Common Disease ◽  
Mineral Density ◽  
Clinical Risk ◽  
Increased Risk ◽  
Bmd Testing

Osteoporosis is a common disease that is associated with increased risk of fractures and serious clinical consequences. Bone mineral density (BMD) testing is used to diagnose osteoporosis, estimate the risk of fracture, and monitor changes in BMD over time. Combining clinical risk factors for fracture with BMD is a better predictor of fracture risk than BMD or clinical risk factors alone. Methodologies are being developed to use BMD and validated risk factors to estimate the 10-year probability of fracture, and then combine fracture probability with country-specific economic assumptions to determine cost-effective intervention thresholds. The decision to treat is based on factors that also include availability of therapy, patient preferences, and co-morbidities. All patients benefit from nonpharmacological lifestyle treatments such a weight-bearing exercise, adequate intake of calcium and vitamin D, fall prevention, avoidance of cigarette smoking and bone-toxic drugs, and moderation of alcohol intake. Patients at high risk for fracture should be considered for pharmacological therapy, which can reduce fracture risk by about 50%.

scholarly journals Atrial inflammation in different atrial fibrillation subtypes and its relation with clinical risk factors

2020 ◽  
Vol 109 (10) ◽  
pp. 1271-1281
Author(s):  
Linghe Wu ◽  
R. W. Emmens ◽  
J. van Wezenbeek ◽  
W. Stooker ◽  
C. P. Allaart ◽  
...  
Keyword(s):  
Risk Factors ◽  
Atrial Fibrillation ◽  
Left Atrial ◽  
Artery Bypass ◽  
Inflammatory Cells ◽  
Clinical Risk Factors ◽  
Blood Levels ◽  
Clinical Risk ◽  
Atrial Ablation ◽  
And Gender

Abstract Objective Inflammation of the atria is an important factor in the pathogenesis of atrial fibrillation (AF). Whether the extent of atrial inflammation relates with clinical risk factors of AF, however, is largely unknown. This we have studied comparing patients with paroxysmal and long-standing persistent/permanent AF. Methods Left atrial tissue was obtained from 50 AF patients (paroxysmal = 20, long-standing persistent/permanent = 30) that underwent a left atrial ablation procedure either or not in combination with coronary artery bypass grafting and/or valve surgery. Herein, the numbers of CD45+ and CD3+ inflammatory cells were quantified and correlated with the AF risk factors age, gender, diabetes, and blood CRP levels. Results The numbers of CD45+ and CD3+ cells were significantly higher in the adipose tissue of the atria compared with the myocardium in all AF patients but did not differ between AF subtypes. The numbers of CD45+ and CD3+ cells did not relate significantly to gender or diabetes in any of the AF subtypes. However, the inflammatory infiltrates as well as CK-MB and CRP blood levels increased significantly with increasing age in long-standing persistent/permanent AF and a moderate positive correlation was found between the extent of atrial inflammation and the CRP blood levels in both AF subtypes. Conclusion The extent of left atrial inflammation in AF patients was not related to the AF risk factors, diabetes and gender, but was associated with increasing age in patients with long-standing persistent/permanent AF. This may be indicative for a role of inflammation in the progression to long-standing persistent/permanent AF with increasing age. Graphic abstract

scholarly journals Clinical Application of a Novel Genetic Risk Score for Ischemic Stroke in Patients with Cardiometabolic Disease

Circulation ◽  
2020 ◽  
Author(s):  
Nicholas A. Marston ◽  
Parth N. Patel ◽  
Frederick K. Kamanu ◽  
Francesco Nordio ◽  
Giorgio M. Melloni ◽  
...  
Keyword(s):  
Risk Factors ◽  
Atrial Fibrillation ◽  
Ischemic Stroke ◽  
Genetic Risk ◽  
Genetic Risk Score ◽  
Clinical Risk Factors ◽  
Cardiometabolic Disease ◽  
Clinical Risk ◽  
Hazard Ratios ◽  
Increased Risk

Background: Genome-wide association studies have identified single nucleotide polymorphisms (SNPs) that are associated with an increased risk of stroke. We sought to determine whether a genetic risk score (GRS) could identify subjects at higher risk for ischemic stroke after accounting for traditional clinical risk factors in five trials across the spectrum of cardiometabolic disease. Methods: Subjects who had consented for genetic testing and who were of European ancestry from the ENGAGE AF-TIMI 48, SOLID-TIMI 52, SAVOR-TIMI 53, PEGASUS-TIMI 54, and FOURIER trials were included in this analysis. A set of 32 SNPs associated with ischemic stroke was used to calculate a GRS in each patient and identify tertiles of genetic risk. A Cox model was used to calculate hazard ratios for ischemic stroke across genetic risk groups, adjusted for clinical risk factors. Results: In 51,288 subjects across the five trials, a total of 960 subjects had an ischemic stroke over a median follow-up period of 2.5 years. After adjusting for clinical risk factors, increasing genetic risk was strongly and independently associated with increased risk for ischemic stroke (p-trend=0.009). When compared to individuals in the lowest third of genetic risk, individuals in the middle and top tertiles of genetic risk had adjusted hazard ratios of 1.15 (95% CI 0.98-1.36) and 1.24 (95% CI 1.05-1.45) for ischemic stroke, respectively. Stratification into subgroups revealed the performance of the GRS appeared stronger in the primary prevention cohort with an adjusted HR for the top versus lowest tertile of 1.27 (95% CI 1.04-1.53), compared with an adjusted HR of 1.06 (95% CI 0.81-1.41) in subjects with prior stroke. In an exploratory analysis of patients with atrial fibrillation and CHA 2 DS 2 -VASc of 2, high genetic risk conferred a 4-fold higher risk of stroke and an absolute risk equivalent to those with CHA 2 DS 2 -VASc of 3. Conclusions: Across a broad spectrum of subjects with cardiometabolic disease, a 32-SNP GRS was a strong, independent predictor of ischemic stroke. In patients with atrial fibrillation but lower CHA 2 DS 2 -VASc scores, the GRS identified patients with risk comparable to those with higher CHA 2 DS 2 -VASc scores.

scholarly journals Associations Between Bone Impact Microindentation and Clinical Risk Factors for Fracture

Endocrinology ◽  
2019 ◽  
Vol 160 (9) ◽  
pp. 2143-2150 ◽  
Author(s):  
Pamela Rufus-Membere ◽  
Kara L Holloway-Kew ◽  
Adolfo Diez-Perez ◽  
Mark A Kotowicz ◽  
Julie A Pasco
Keyword(s):  
Risk Factors ◽  
Hip Fracture ◽  
Clinical Risk Factors ◽  
Material Strength ◽  
Mineral Density ◽  
Clinical Risk ◽  
Increased Risk ◽  
Vitamin D Levels ◽  
Prior Fracture ◽  
Impact Microindentation

Abstract Impact microindentation (IMI) measures bone material strength index (BMSi) in vivo. However, clinical risk factors that affect BMSi are largely unknown. This study investigated associations between BMSi and clinical risk factors for fracture in men. BMSi was measured using the OsteoProbe in 357 men (ages 33 to 96 years) from the Geelong Osteoporosis Study. Risk factors included age, weight, height, body mass index (BMI), femoral neck bone mineral density (BMD), parental hip fracture, prior fracture, type 2 diabetes mellitus (T2DM), secondary osteoporosis, smoking, alcohol consumption, sedentary lifestyle, medications, diseases, and low serum vitamin D levels. BMSi was negatively associated with age (r = −0.131, P = 0.014), weight (r = −0.109, P = 0.040), and BMI (r = −0.083, P = 0.001); no correlations were detected with BMD (r = 0.000, P = 0.998) or height (r = 0.087, P = 0.10). Mean BMSi values for men with and without prior fracture were 80.2 ± 6.9 vs 82.8 ± 6.1 (P = 0.024); parental hip fracture, 80.1 ± 6.1 vs 82.8 ± 6.9 (P = 0.029); and T2DM, 80.3 ± 8.5 vs 82.9 ± 6.6 (P = 0.059). BMSi did not differ in the presence vs absence of other risk factors. In multivariable models, mean (± SD) BMSi remained associated with prior fracture and parental hip fracture after adjusting for age and BMI: prior fracture (80.5 ± 1.1 vs 82.8 ± 0.4, P = 0.044); parental fracture (79.9 ± 1.2 vs 82.9 ± 0.4, P = 0.015). No other confounders were identified. We conclude that in men, BMSi discriminates prior fracture and parental hip fracture, which are both known to increase the risk for incident fracture. These findings suggest that IMI may be useful for identifying men who have an increased risk for fracture.

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