ABSTRACT
OBJECTIVE: To determine the association between angiotensin-converting enzyme inhibitors (ACEIs) or angiotensin II receptor blockers (ARBs) use and coronavirus disease 2019 (COVID-19) severity and outcomes in US veterans. PATIENTS AND METHODS: We retrospectively examined 27,556 adult US veterans who tested positive for COVID-19 between March to November 2020. Logistic regression and Cox proportional hazards models using propensity score (PS) for weight, adjustment, and matching were used to examine the odds of an event within 60 days following a COVID-19-positive case date and time to death, respectively, according to ACEI and/or ARB prescription within 6 months prior to the COVID-19-positive case date. RESULTS: The overlap PS weighted logistic regression model showed lower odds of an intensive care unit (ICU) admission (odds ratio [OR] 95% CI 0.77, 0.61-0.98) and death within 60 days (0.87, 0.79-0.97) with an ACEI or ARB prescription. Veterans with an ARB-only prescription also had lower odds of an ICU admission (0.64, 0.44-0.92). The overlap PS weighted model similarly showed a lower risk of time to all-cause mortality in veterans with an ACEI or ARB prescription (HR [95% CI]: 0.87, 0.79-0.97) and an ARB only prescription (0.78, 0.67-0.91). Veterans with an ACEI prescription had higher odds of experiencing a septic event within 60 days after the COVID-19-positive case date (1.22, 1.02-1.46). CONCLUSION: In this study of a national cohort of US veterans, we found that the use of an ACEI/ARB in patients with COVID-19 was not associated with increased mortality and other worse outcomes. Future studies should examine underlying pathways and further confirm the relationship of ACEI prescription with sepsis.
Subject(s)
Angiotensin Receptor Antagonists/therapeutic use , Angiotensin-Converting Enzyme Inhibitors/therapeutic use , COVID-19/epidemiology , Hypertension/drug therapy , Hypertension/epidemiology , Adult , Aged , Aged, 80 and over , Angiotensin Receptor Antagonists/administration & dosage , Angiotensin-Converting Enzyme Inhibitors/administration & dosage , COVID-19/mortality , Female , Humans , Intensive Care Units/statistics & numerical data , Logistic Models , Male , Middle Aged , Patient Acuity , Proportional Hazards Models , Retrospective Studies , SARS-CoV-2 , Sepsis/epidemiology , Sociodemographic Factors , VeteransABSTRACT
This research presents an integrated approach combining Coloured Petri Nets and Interpretive Structural Modelling, called hybrid ISM-CPN model, to risk assessment of wind farms development. The dynamic nature of the component elements of wind farms and considering the risks interdependencies motivate this combination. A questionnaire survey targeting experts is conducted for calculating the modified Risk Priority Numbers (RPNs). Thirty-four factors are ranked, and the nine CSFs identified as “Political instability”, “Sanctions”, “Economic insecurity”, “Interest rate fluctuations”, “Exchange rate fluctuations”, “Inflation rate fluctuations”, “Feasibility risk”, “Shortage of capital risk”, and “Supplier risk”. The values of RPNT = 260.26, RPNC = 251.31, and RPNQ = 238.77 indicate that “Exchange rate fluctuations” is the most important critical risk. The second one, is “Political instability” with RPNT = 255.35, RPNC = 247.28, and RPNQ = 230.56. The simulation results of a 50 MW wind farm reveal, with a 90% confidence level, “Sanctions” would cause 43.9% increase in project execution time and 28% decrease in project quality, and “Shortage of capital risk” has the greatest impact on project cost, with a 25.79% increase. This work further proposes several strategies to respond to the CRs and concludes that investments in REs can support post-COVID-19 economic recovery.