OBJECTIVE: Rapidly phenotyping patients can inform public health action plans in new pandemics. This study aimed to derive meaningful SARS-CoV-2 reinfected patients' phenotypes based on easily-available patient data and explore key epidemiological factors of reinfections. METHODS: We conducted a retrospective study of a cohort of SARS-CoV-2 reinfected adults from the Basque Country between January 1, 2021 and January 9, 2022. Phenotypes were defined in an unsupervised manner with clustering algorithms, incorporating variables like age, Charlson score, vaccination status and pre-existing treatments and comorbidities. Subsequently, clinical characteristics of phenotypes were compared, and their behavioral differences were evaluated through generalized additive models. Finally, their association with clinical outcomes was assessed. RESULTS: Four phenotypes were identified, which subsequently had a direct relationship with the risk levels for severe COVID-19 outcomes. The highest-risk group, phenotype 4, consisted of older adults -76 years, [62-85] (Median, [Interquartile range])- with multiple comorbidities and extensive baseline medication use. Phenotype 3 was slightly younger -64 years, [58-77]- but presented very low Charlson scores and few comorbidities, representing an intermediate-risk group. Phenotypes 1 and 2 were younger and healthier adults with similar clinical profiles. However, phenotype 1 showed a less protective attitude, with a higher rate of unvaccinated patients and shorter time intervals between infections. CONCLUSIONS: We were able to classify reinfected patients into four distinct groups based on easily available variables, and these phenotypes had a direct relationship with COVID-19 clinical outcomes. Thus, rapidly phenotyping infected individuals can serve as a preventive public health strategy during new pandemics.