# Data-driven modelling of IRCU patient flow during the COVID-19 pandemic

**Authors:** Ana Carmen Navas-Ortega, José Antonio Sánchez-Martínez, Paula García-Flores, Concepción Morales-García, Rene Fabregas

PMC · DOI: 10.1016/j.csbj.2025.10.017 · Computational and Structural Biotechnology Journal · 2025-10-17

## TL;DR

This study evaluates how a specialist-staffed Intermediate Respiratory Care Unit (IRCU) in Spain managed severe COVID-19 patients, showing high recovery rates and reduced ICU pressure.

## Contribution

The paper introduces dynamic modeling approaches to simulate patient flow in an IRCU during a pandemic, highlighting the impact of efficient care and admission surges.

## Key findings

- Patients managed without NIV had no ICU transfers or deaths, indicating effective care in the IRCU.
- 68% of NIV-treated patients recovered in the IRCU without needing ICU escalation.
- ODE and LOS-based models showed that admission surges strain the system but efficient care can mitigate some impacts.

## Abstract

Intermediate Respiratory Care Units (IRCUs) function as vital intermediaries between general wards and Intensive Care Units (ICUs), particularly during crises such as the COVID-19 pandemic. A unit’s effectiveness depends on its structure, protocols, and clinical expertise. In this study, we assessed the clinical outcomes and operational dynamics of a new IRCU that implemented a specialist staffing model during the pandemic in Spain.

We conducted a prospective cohort study at the UHVN IRCU (Granada, Spain) from April to August 2021, enrolling 249 adult patients with COVID-19-associated respiratory failure. We collected data on patient demographics, Non-Invasive Ventilation (NIV) use, length of stay (LOS), and outcomes, including ICU transfer, mortality, and recovery. We then analysed these outcomes stratified by NIV status. Furthermore, we developed and calibrated a compartmental Ordinary Differential Equation (ODE) model and an empirical LOS-based convolution model to simulate patient flow dynamics under scenarios of admission surges and varying care efficiency.

The cohort’s median age was 51 years, and 31 % (n=77) required NIV. Patients requiring NIV were significantly older than those who did not (median 61 vs 42 years, p<0.001). Overall, 8 % of patients (n=20) were subsequently transferred to the ICU, and 3 % (n=7) died within the IRCU. Notably, no patients managed without NIV required ICU transfer or died. Among the 77 high-risk patients who received NIV, 68 % recovered within the IRCU without needing ICU escalation. Our ODE modelling accurately reproduced aggregate outcomes and demonstrated that simulated admission surges placed the system under significant strain, which enhanced recovery efficiency partially mitigated. The LOS-based modelling yielded consistent peak occupancy estimates.

This IRCU, characterised by specialist clinical staffing, demonstrated effective management of severe COVID-19 respiratory failure. We observed high recovery rates, particularly among NIV patients, which eased pressure on ICU resources. Our dynamic modelling confirmed the unit’s vulnerability to admission surges but also quantified the positive impact of efficient care. These findings underscore the importance of well-structured and expertly staffed IRCUs in pandemic response and the broader provision of respiratory care.

## Linked entities

- **Diseases:** COVID-19 (MONDO:0100096)
- **Species:** Homo sapiens (taxon 9606)

## Full-text entities

- **Diseases:** COVID-19 (MESH:D000086382), respiratory failure (MESH:D012131)
- **Species:** Homo sapiens (human, species) [taxon 9606]

## Full text

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## Figures

8 figures with captions in the complete paper: https://tomesphere.com/paper/PMC12613035/full.md

## References

76 references — full list in the complete paper: https://tomesphere.com/paper/PMC12613035/full.md

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Source: https://tomesphere.com/paper/PMC12613035