Our study undertook an innovative approach using a clustering algorithm to identify different clinical phenotypes of patients hospitalized with COVID-19 with varying infection viral load to assess the role of remdesivir use in terms of mortality. We documented significant differences in this important outcome amongst clusters. These results are of great importance as they confirm that those patients with higher viral load benefitted more, even in terms of reduced mortality, from remdesivir use. Moreover, our results highlight the potential use of computers, especially with their ability to cluster patients, to improve the decision-making process in relation to antiviral use.

Remdesivir received definitive approval by health authorities as COVID-19 treatment in October 2020. However, two trials assessing the impact of remdesivir use on outcomes in patients with COVID-19 did not find any benefits in clinical improvement or mortality. Amongst the 237 randomized patients in the first study, the median time from symptom onset to remdesivir use was 11 days; 19% of patients had undetectable viral RNA in rRT-PCR at trial inclusion; and 32% of patients had a lymphocyte count of more than 1000 when remdesivir was started [7]. The second study had no data reporting patient characteristics in relation to any of these variables [8]. Data on Ct values in rRT-PCR at COVID-19 diagnosis were lacking in both studies.

Conversely, other trials demonstrated clinical benefits from the use of remdesivir [2, 5, 7, 8, 11, 12]. This benefit was more pronounced in patients who had shorter pre-admission duration of symptoms. Data on lymphocyte count or Ct in rRT-PCR at diagnosis were not reported.

In our study the clustering K-means method optimally classifies patients depending on the viral load and a worse response to viral infection—as defined by lower Ct of rRT-PCR and lymphocyte count at COVID-19 diagnosis and shorter pre-test duration of symptoms—and it helps to draw a clear clinical picture of different mortality rates. These findings are consistent with those previously reported in clinical studies [17, 23,24,25] and strengthen the link between viral load and mortality. Furthermore, these observations reinforce the idea that administering early antiviral treatment should be important in improving mortality of specific patients hospitalized with COVID-19.

Studies on the impact of new antiviral strategies in patients with COVID-19 have been recently conducted. All these therapeutic approaches have mainly been tested on patients within the initial days following a COVID-19 diagnosis; results are encouraging [26,27,28,29,30]. Our study shows that controversial results may be explained by the heterogeneity of patient characteristics at inclusion. Thus, researchers evaluating the impact of treatments on COVID-19 prognosis should describe those phenotypes more precisely to better explain why those treatments are beneficial in some cases while not in others. In this context, providing clustering methods that assist physicians with objectively classifying patients and/or analyzing a large number of variables will be breakthrough in medicine. This methodology makes it possible to have objective, reproducible classifications, available with a small, 24/7 computer support tool for all physicians, irrespective of their expertise. The use of clustering algorithms in medical research remains scarce, and some authors have expressed concern about the black box that some algorithms or clustering performed by a computer may represent [21]. However, studies such as ours, in which computers perform clustering of patients based on variables determined by our team, and confirm results in an external validation cohort, strengthen our confidence in these techniques. To our knowledge, this is the first study that demonstrated the impact of a specific antiviral treatment in patients classified with an unsupervised clustering algorithm and confirmed the results using an external validation cohort.

The main limitations of our study include the following. First, we did not have any information on the specific SARS-CoV-2 variants in our patients. The most frequent circulating variants throughout the study period included SARS-CoV-2 Wuhan-1 and B.1.1.7 (alpha). Further studies are necessary to analyze whether our results could be extrapolated to other new viral variants, as susceptibility of SARS-CoV-2 to the antiviral may potentially change according to the appearance of mutations in the antiviral target. Second, we have no information on patients’ vaccine status. As a result of the study period, most patients in this cohort were not vaccinated. The results in the vaccinated population might be different. Indeed, in our study, viral load and the potential host response to SARS-CoV-2 were analyzed directly by Ct of rRT-PCR and indirectly by lymphocyte count and pre-test duration of symptoms. It would be plausible to consider modifications in these variables due to vaccination and SARS-CoV-2 new variants’ susceptibility to current vaccination regimen. That stated, there is a need for future studies to investigate the impact of these values, particularly among vaccinated patients, on the relationship between prognosis and the use of remdesivir. Finally, we focus on mortality, and other potential benefits of remdesivir in outcomes are not analyzed. Another important aspect to be further investigated in the future is the evolution of the clinical phenotype of COVID-19 during pandemic waves. Examining the evolution of viral phenotypes throughout the pandemic, as well as their relationship with secondary inflammation due to COVID-19, would be of interest. This knowledge could guide us in understanding the importance of antiviral strategies compared to other COVID-19 treatments, with a more focused approach on controlling inflammation.