COVID-19 is a disease with vast impact, yet much remains unclear about patient outcomes . Most approaches to risk prediction of COVID-19 focus on binary or tertiary severity outcomes, despite the heterogeneity of the disease . In this work, we identify heterogeneous subtypes of COVID-19 outcomes by considering 'axes' of prognosis . We propose two innovative clustering approaches - 'Layered Axes' and 'Prognosis Space' - to apply on patients' outcome data . We then show how these clusters can help predict a patient's deterioration pathway on their hospital admission, using random forest classification . We illustrate this methodology on a cohort from Wuhan in early 2020 . We discover interesting subgroups of poor prognosis, particularly within respiratory patients, and predict respiratory subgroup membership with high accuracy . This work could assist clinicians in identifying appropriate treatments at patients' hospital admission . Moreover, our method could be used to explore subtypes of 'long COVID' and other diseases with heterogeneous outcomes.