Tracing In-Hospital COVID-19 Outcomes: A Multistate Model Exploration (TRACE)

Area of Science:

• Epidemiology
• Health Informatics
• Biostatistics

Background:

• The COVID-19 pandemic significantly impacted hospital resource allocation and patient management.
• Accurate forecasting of hospital length of stay (LOS) is crucial for effective healthcare planning.
• Existing models may not adequately capture the dynamic transitions of COVID-19 patients through different care states.

Purpose of the Study:

• To develop and apply a novel multistate model for estimating and forecasting hospital length of stay (LOS) for COVID-19 patients.
• To analyze factors influencing patient trajectories and resource utilization without external software packages.
• To provide insights for managing patient flow and predicting intensive care unit (ICU) demands.

Main Methods:

• A multistate model was developed and applied to data from 2285 hospitalized COVID-19 patients.
• Transition probabilities and risk rates between states (admission, ICU transfer, discharge, death) were calculated.
• Key factors including age, gender, comorbidities, and COVID-19 waves were analyzed.

Main Results:

• Average LOS for hospitalized patients: 11.90 days (discharge), 2.84 days (ICU transfer), 34.21 days (death).
• Average LOS for ICU patients: 24.08 days (initial ICU stay), 124.30 days (discharge), 35.44 days (death).
• The model identified significant factors affecting patient trajectories and provided subgroup-specific ICU load predictions.

Conclusions:

• The developed multistate model offers critical insights into COVID-19 patient flow and healthcare resource utilization.
• Findings highlight varying hospital stay durations and patient trajectories, aiding in preparedness for future health crises.
• Future work includes integrating the model into a Health Information System (HIS) and enhancing its efficiency with recursive methods.