Papers › Data-Driven Modeling of IRCU Patient Flow in the COVID-19 Pandemic
Data-Driven Modeling of IRCU Patient Flow in the COVID-19 Pandemic
Ana Carmen Navas-Ortega, José Antonio Sánchez-Martínez, Paula García-Flores, Concepción Morales-García, Rene Fabregas
Intermediate Respiratory Care Units (IRCUs) are vital during crises like COVID-19. This study evaluated clinical outcomes and operational dynamics of a new Spanish IRCU with specialized staffing. A prospective cohort study (April-August 2021) included 249 adult patients with COVID-19 respiratory failure (UHVN IRCU, Granada). Data on demographics, Non-Invasive Ventilation (NIV), length of stay (LOS), and outcomes (ICU transfer, exitus, recovery) were analyzed. Patient flow was simulated using a data-calibrated deterministic compartmental model (Ordinary Differential Equations, ODEs) that represented state transitions, and an empirical LOS-based stochastic convolution model that incorporated admission variability. The median age was 51; 31% of patients required NIV. NIV patients were older (median 61 vs 42, p<0.001). Overall, 8% needed ICU transfer; 3% experienced in-IRCU exitus. Notably, no ICU transfers or deaths occurred among 172 non-NIV patients. Of 77 high-risk NIV patients, 68% recovered in IRCU without ICU escalation. The ODE model, based on transition rates between patient states, reflected aggregate outcomes. Both modeling approaches demonstrated system strain during admission surges (partially mitigated by simulated care efficiency improvements via parameter modulation) and yielded consistent peak occupancy estimates. This IRCU, with specialized staffing, effectively managed severe COVID-19. High recovery rates, especially for NIV patients, potentially eased ICU pressure. Dynamic modeling confirmed surge vulnerability but highlighted the benefits of care efficiency from modulated transition parameters. Findings underscore positive outcomes in this IRCU model and support such units in pandemic response.
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