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DOI 10.1007/s12561-023-09375-9

Медицинская библиотека · Statistics in biosciences · 2023

Statistical Learning of Large-Scale Genetic Data: How to Run a Genome-Wide Association Study of Gene-Expression Data Using the 1000 Genomes Project Data.

Akhter Zohaib, Rind Irfan Ali, Aijaz Saba, Sattar Saadia, Malik Rehan, Pathan Asad

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Аннотация

OBJECTIVE

To assess clinical characteristics, management strategies and in-hospital outcome among high-risk patients of non-ST elevation myocardial infarction.

METHODS

The retrospective cross-sectional study was conducted at Tabba Heart Institute, Karachi, and comprised data from July 2013 to December 2016 of adult non-ST elevation myocardial infarction patients who had first cardiac event having Global Registry of Acute Coronary Eventsrisk score>140. Subcategories were formed on the basis of score range 140-159, 160-189 and ?190.Stata 12.1 was used for data analysis.

RESULTS

Of the 817 patients, 567(69.4%) were men. Overall, mean age was 66.3}9.3 years. Coronary angiography was performed in 692(84.4%). With higher risk score categories, there was less frequent use of guideline directed medical therapy, coronary angiography and percutaneous or surgical revascularisation (p<0.05 each). Overall mortality was 59(7.2%). Mortality rates increased with increase in risk score subcategory (p<0.05). Multivariable model identified higher risk score category, no revascularisation and lack of guideline directed medical therapy as significant independent predictors of mortality (p<0.05 each).

CONCLUSIONS

Mortality increased with higher risk score category. Paradoxically, high-risk patients were less likely to receive guideline directed medical therapy, to undergo coronary angiography and revascularisation, possibly suggesting a risk aversion approach by the treating physicians.

Для цитирования: Akhter Zohaib, Rind Irfan Ali, Aijaz Saba, Sattar Saadia, Malik Rehan, Pathan Asad. Statistical Learning of Large-Scale Genetic Data: How to Run a Genome-Wide Association Study of Gene-Expression Data Using the 1000 Genomes Project Data.. Statistics in biosciences. 2023. DOI: 10.1007/s12561-023-09375-9.
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