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Objectives: To efficiently assess the disease-modifying potential of new osteoarthritis treatments, clinical trials need progression-enriched patient populations. To assess whether the application of machine learning results in patient selection enrichment, we developed a machine learning recruitment strategy targeting progressive patients and validated it in the IMI-APPROACH knee osteoarthritis prospective study. Design: We designed a two-stage recruitment process supported by machine learning models trained to rank candidates by the likelihood of progression. First stage models used data from pre-existing cohorts to select patients for a screening visit. The second stage model used screening data to inform the final inclusion. The effectiveness of this process was evaluated using the actual 24-month progression. Results: From 3500 candidate patients, 433 with knee osteoarthritis were screened, 297 were enrolled, and 247 completed the 2-year follow-up visit. We observed progression related to pain (P, 30%), structure (S, 13%), and combined pain and structure (P ​+ ​S, 5%), and a proportion of non-progressors (N, 52%) ∼15% lower vs an unenriched population. Our model predicted these outcomes with AUC of 0.86 [95% CI, 0.81–0.90] for pain-related progression and AUC of 0.61 [95% CI, 0.52–0.70] for structure-related progression. Progressors were ranked higher than non-progressors for P ​+ ​S (median rank 65 vs 143, AUC = 0.75), P (median rank 77 vs 143, AUC = 0.71), and S patients (median rank 107 vs 143, AUC = 0.57). Conclusions: The machine learning-supported recruitment resulted in enriched selection of progressive patients. Further research is needed to improve structural progression prediction and assess this strategy in an interventional trial.

Long-term sequelae of coronavirus disease (COVID)-19 are frequent and of major concern. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection affects the host gut microbiota, which is linked to disease severity in patients with COVID-19. Here, we report that the gut microbiota of post-COVID subjects had a remarkable predominance of Enterobacteriaceae strains with an antibiotic-resistant phenotype compared to healthy controls. Additionally, short-chain fatty acid (SCFA) levels were reduced in feces. Fecal transplantation from post-COVID subjects to germ-free mice led to lung inflammation and worse outcomes during pulmonary infection by multidrug-resistant Klebsiella pneumoniae. transplanted mice also exhibited poor cognitive performance. Overall, we show prolonged impacts of SARS-CoV-2 infection on the gut microbiota that persist after subjects have cleared the virus. Together, these data demonstrate that the gut microbiota can directly contribute to post-COVID sequelae, suggesting that it may be a potential therapeutic target.