The Evidence Speaks Series is a recurring feature highlighting the latest in Advancing Health research. This series features summaries of select publications and is designed to keep media and the research community up to date with the Centre’s current research results in the health outcomes field.
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Ivermectin unlikely to improve COVID-19 hospital survival rates or health outcomes
Ivermectin for Critically and Noncritically Ill Hospitalized Patients With COVID-19: Randomized, Embedded, Multifactorial Adaptive Platform Trial for Community-Acquired Pneumonia (REMAP-CAP). Hashmi M., Haniffa R., Jayakumar D., Murthy S., Arabi Y, Nichol A., et al. Crit Care Med. 2026.
A small study early in the pandemic suggested that the drug ivermectin, primarily used for de-worming horses, might hold potential for the treatment of COVID-19. Due to global interest in this potential treatment, a team of scientists, including Advancing Health scientist Dr. Srinivas Murthy, ran a clinical trial assessing the drug in hospitals in Pakistan, India, and Ireland. From June 2021 to September 2022, 150 critically and non-critically ill hospitalized COVID-19 patients were enrolled, with half of the patients receiving ivermectin and half not.
The main outcome measures were how many days patients could manage without machine support for their lungs and heart, as well as in-hospital death rates. Among the 89 non-critically ill patients, the average number of days without organ support were both 22, suggesting similar performance. Among critically ill patients, however, death was such a common occurrence that it became the main measurement. 35.1 per cent of critically ill patients receiving ivermectin survived, compared to 37.5 per centin the control group. Among non-critically ill patients, the survival rate was 84.1 per cent for the ivermectin group and 77.8% for the control group. Ivermectin therapy did not shorten the time spent in the ICU or survival over 90 days after discharge. Altogether, this suggests that ivermectin therapy is an ineffective treatment for hospitalized COVID-19 patients.
Donor age shows little to no direct impact on recognition, rejection, or survival with a transplanted kidney
Impact of older donor age in kidney transplants in a biopsy-based observational study. Madill-Thomsen K, Mackova M, Chang J, Akalin E, Alhamad T, Anand S, Arnol M, Baliga R, Banasik M, et al. CI Insight. 2026.
Donor age is a considerable barrier when it comes to organ transplantation, with the fear of adverse outcomes leading many potentially viable kidneys from donors older than 50 years to be discarded, even as thousands of people are on waitlists. To understand the actual impact of donor age on the transplanted kidneys, a team of researchers, including Advancing Health scientists Drs. John Gill and Jagbir Gill, studied the correlations of donor age with microscopic, chemical, or genetic damage (“molecular injury”) to the transplanted kidney. They also looked at rejection score (a measure of the immune system’s attack on a transplanted kidney that it may reject) and survival outcomes in 4,502 kidney transplant biopsies spanning from day 1 to year 45 post-transplant.
The mean ages of donors and recipients were 43 and 50, respectively. The team found that older donor age was associated with the kidney being less able to filter waste and fluid from the blood (a lower eGFR score), as well as higher chronic injury scores. Molecular injuries, which usually occur immediately after transplant and resolve over the first 4 to 6 weeks, didn’t increase significantly with older donor age, but after the injury resolved, older kidneys showed a greater tendency towards failed repair — where some damaged cells in the kidney don’t fully recover. However, donor age did not affect rejection rates and deceased donor kidneys from older donors also showed no more rejection than living donor kidneys.
They also found that when tracking whether kidneys were still functioning 3-years post biopsy, neither donor age nor recipient age had any impact on survival rates. These findings offer hope that aging and molecular injury do not affect allorecognition (the body’s ability to recognize a foreign organ), rejection, and survival, and urge reconsideration of the importance given to donor age in transplant decisions. Future studies should focus on building long-term data beyond three years post-biopsy to see if age becomes a factor in determining health outcomes for transplanted kidneys beyond that period.
Machine-learning can help create dietary communities and establish associations with health outcomes for CVD
Cardiovascular Disease Events and Life Expectancy Lost Attributable to Machine Learning-Derived Dietary Networks: Evidence from Canadian National Nutrition Survey Linked to Routinely Collected Administrative Databases. Wang Y, Sutherland JM, Jessri M. J Nutr. 2026
Diet quality plays an important role in determining health outcomes, especially for cardiovascular disease (CVD), which is the leading cause of death and disability globally. In Canada, CVD is the second leading cause of death. A group of researchers, including Advancing Health scientist Dr. Jason Sutherland, explored how advanced machine learning techniques can identify dietary patterns and their associations with cardiovascular disease and mortality in Canadian adults.
Using analytical samples from the Canadian National Nutrition Surveys, which provided information on food and nutrient intake, health status, and sociodemographic characteristics of respondents, the team studied dietary patterns and their correlation with cardiovascular disease. Using machine learning and network analysis methods they isolated three dietary communities (sorted by clusters of food that are consumed together) from the samples: i) vegetable-rich (included vegetables, legumes, soy, pasta, and rice), ii) high-sugary beverage and low fruit (included sugar-sweetened beverages, refined grains and salty snacks), and iii) high-fat breakfast (solid fats, processed meats, sugar, eggs, waffles and pancakes). They established a comparison between the individuals who strongly follow a dietary pattern leading to higher scores versus those who don’t and have lower scores. Higher vegetable-rich diet scores were linked to 51 per cent lower mortality and 45 per cent lower cardiovascular disease; higher high-sugar beverages and low fruit diet scores were linked to 31 per cent higher mortality (especially in males); and high-fat breakfast scores were not associated with any significant mortality or CVD. Higher vegetable-rich diet scores were also associated with higher life expectancy of 8.3 years for females and 6.1 years among males at the age of 45. The findings of this study allow scoring of dietary behaviours and establishing patterns using machine learning in ways that can be linked with health outcomes, which can then be translated into actionable advice in clinical practice and public health policy. Future studies should look at validating the findings of this study in more diverse audiences and settings to improve population health.
