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 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 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.

Long-Term Follow-Up of Patients With Isolated Side Branch Coronary Artery Disease

Angiology ◽  
2021 ◽  
pp. 000331972110280
Author(s):  
Sukru Arslan ◽  
Ahmet Yildiz ◽  
Okay Abaci ◽  
Urfan Jafarov ◽  
Servet Batit ◽  
...  
Keyword(s):  
Risk Factors ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Vessel Diameter ◽  
Side Branch ◽  
Clinical Risk Factors ◽  
Clinical Risk ◽  
Artery Disease

The data with respect to stable coronary artery disease (SCAD) are mainly confined to main vessel disease. However, there is a lack of information and long-term outcomes regarding isolated side branch disease. This study aimed to evaluate long-term major adverse cardiac and cerebrovascular events (MACCEs) in patients with isolated side branch coronary artery disease (CAD). A total of 437 patients with isolated side branch SCAD were included. After a median follow-up of 38 months, the overall MACCE and all-cause mortality rates were 14.6% and 5.9%, respectively. Among angiographic features, 68.2% of patients had diagonal artery and 82.2% had ostial lesions. In 28.8% of patients, the vessel diameter was ≥2.75 mm. According to the American College of Cardiology lesion classification, 84.2% of patients had either class B or C lesions. Age, ostial lesions, glycated hemoglobin A1c, and neutrophil levels were independent predictors of MACCE. On the other hand, side branch location, vessel diameter, and lesion complexity did not affect outcomes. Clinical risk factors seem to have a greater impact on MACCE rather than lesion morphology. Therefore, the treatment of clinical risk factors is of paramount importance in these patients.

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