Predictive Modeling of COVID-19 Intensive Care Unit Patient Flows and Nursing Complexity

Abstract

This study aimed to develop a Monte Carlo simulation model to forecast the number of ICU beds needed for COVID-19 patients and the subsequent nursing complexity in a French teaching hospital during the first and second pandemic outbreaks. The model used patient data from March 2020 to September 2021, including age, sex, ICU length of stay, and number of patients on mechanical ventilation or extracorporeal membrane oxygenation. Nursing complexity was assessed using a simple scale with three levels based on patient status. The simulation was performed 1000 times to generate a scenario, and the mean outcome was compared with the observed outcome. The model also allowed for a 7-day forecast of ICU occupancy. The simulation output had a good fit with the actual data, with an R 2 of 0.998 and a root mean square error of 0.22. The study demonstrated the usefulness of the Monte Carlo simulation model for predicting the demand for ICU beds and could help optimize resource allocation during a pandemic. The model's extrinsic validity was confirmed using open data from the French Public Health Authority. This study provides a valuable tool for healthcare systems to anticipate and manage surges in ICU demand during pandemics.