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TOPIC: Chest Infections TYPE: Original Investigations PURPOSE: Certain risk factors and prognostic indicators have been identified in patients with coronavirus disease 2019 (COVID-19). However, no study has addressed the risk factor distribution pattern among patients who die from COVID-19. In this study, we used latent class analysis (LCA) to identify phenotypes and risk factor distribution patterns in hospitalized patients who died from COVID-19. METHODS: We reviewed the charts of patients who died from COVID-19 at Greenwich Hospital from February 1 to May 30, 2020. We performed LCA based on well-documented prognostic factors of COVID-19. We also compared the in-hospital laboratory results, and treatment information among the different clusters identified by LCA. To validate of the clustering results, we conducted a robust LCA of the entire COVID-19 cohort. RESULTS: 483 patients who were admitted for COVID-19 infection from February 1 to May 30, 2020, 81 patients died. Using latent cluster analysis, we identified two risk factor clusters among COVID-19 death: C1 (n=58) and C2 (n=23). In C1, patients were older (p<0.001) with a higher proportion of comorbidities such as hypertension (82.8% vs. 39.1%, p<0.001), CAD (43.1% vs. 0%, p<0.001), CHF (22.4% vs. 0%, p=0.015), and pre-existing respiratory disease (32.8% vs. 0%, p=0.004) than in C2. In C2, patients were significantly younger and were more likely to be obese (BMI ≥30 kg/m2;56.5% vs. 24.1%, p=0.012), male (87.0% vs. 58.6%, p=0.018), and non-white (60.9% vs. 15.8%, p<0.001) than in C1. Compared with patients in C1, patients in C2 showed a pattern of increased expression of inflammatory and hypercoagulable markers, including C-reactive protein (84.4±117 vs. 12.7±10.7 mg/L, p=0.008) and D-dimer (17.8±13.6 vs. 8.0±9.9 mg/L, p=0.004). The robust-test by using the entire cohort of patients hospitalized with COVID-19 had identified two clusters of risk factor patterns similar to those identified in the cohort of patients who died from COVID-19. CONCLUSIONS: Our study suggests that there are two patterns of risk factors that contributed to death in patients with COVID-19. These results indicate that different pathophysiologic processes lead to COVID-19 death and may be useful in identifying treatment targets and selecting patients with severe COVID-19 disease for future clinical trials. CLINICAL IMPLICATIONS: The results indicate that each phenotype of patients who died from COVID-19 has its distinct feature and underlying pathophysiology. Research focused on targeted therapy for each phenotype may help decrease the mortality rate among patients with severe COVID-19. DISCLOSURES: No relevant relationships by Deepa Jansen, source=Web Response No relevant relationships by Pengyang Li, source=Web Response No relevant relationships by Catherine Teng, source=Web Response