To develop and validate a nomogram using on admission data to predict in-hospital survival probabilities of coronavirus disease 2019 (COVID-19) patients. We analyzed 855 COVID-19 patients with 52 variables. The least absolute shrinkage and selection operator regression and multivariate Cox analyses were used to screen significant factors associated with in-hospital mortality. A nomogram was established based on the variables identified by Cox regression. The performance of the model was evaluated by C-index and calibration plots. Decision curve analysis was conducted to determine the clinical utility of the nomogram. Six variables, including neutrophil (hazard ratio [HR], 1.088; 95% confidence interval [CI], [1.0004-1.147]; p < .001), C-reactive protein (HR, 1.007; 95% CI, [1.0026-1.011]; p = .002), IL-6 (HR, 1.001; 95% CI, [1.0003-1.002]; p = .005), d-dimer (HR, 1.034; 95% CI, [1.0111-1.057]; p = .003), prothrombin time (HR 1.086, 95% CI [1.0369-1.139], p < .001), and myoglobin (HR, 1.001; 95% CI, [1.0007-1.002]; p < .001), were identified and applied to develop a nomogram. The nomogram predicted 14-day and 28-day survival probabilities with reasonable accuracy, as assessed by the C-index (0.912) and calibration plots. Decision curve analysis showed relatively wide ranges of threshold probability, suggesting a high clinical value of the nomogram. Neutrophil, C-reactive protein, IL-6, d-dimer, prothrombin time, and myoglobin levels were significantly correlated with in-hospital mortality of COVID-19 patients. Demonstrating satisfactory discrimination and calibration, this model could predict patient outcomes as early as on admission and might serve as a useful triage tool for clinical decision making.