Early in the course of the pandemic in Colorado, researchers wished to fit a sparse predictive model to intubation status for newly admitted patients. Unfortunately, the training data had considerable missingness which complicated the modeling process. I developed a quick solution to this problem: Median Aggregation of penaLized Coefficients after Multiple imputation (MALCoM). This fast, simple solution proved successful on a prospective validation set. In this manuscript, I show how MALCoM performs comparably to a popular alternative (MI-lasso), and can be implemented in more general penalized regression settings. A simulation study and application to local COVID-19 data is included.