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

Abstract

This study aims to develop and apply multistate models to estimate, forecast, and manage hospital length of stay during the COVID-19 epidemic without using any external packages. Data from Bellvitge University Hospital in Barcelona, Spain, were analyzed, involving 2285 hospitalized COVID-19 patients with moderate to severe conditions. The implemented multistate model includes transition probabilities and risk rates calculated from transitions between defined states, such as admission, ICU transfer, discharge, and death. In addition to examining key factors like age and gender, diabetes, lymphocyte count, comorbidity burden, symptom duration, and different COVID-19 waves were analyzed. Based on the model, patients hospitalized stay an average of 11.90 days before discharge, 2.84 days before moving to the ICU, or 34.21 days before death. ICU patients remain for about 24.08 days, with subsequent stays of 124.30 days before discharge and 35.44 days before death. These results highlight hospital stays' varying durations and trajectories, providing critical insights into patient flow and healthcare resource utilization. Additionally, it can predict ICU peak loads for specific subgroups, aiding in preparedness. Future work will integrate the developed code into the hospital's Health Information System (HIS) following ISO 13606 EHR standards and implement recursive methods to enhance the model's efficiency and accuracy.

Keywords: COVID-19; comorbidity; diabetes mellitus; hospital length of stay; lymphocytes; mortality; multistate model; prognosis; risk factors.

Figure 1

A 4-state model based on transitions between different states.

Figure 2

Flowchart related to programming code.

Figure 3

This figure shows the output of the 4-state model based on symptom duration: (a) symptom duration > 7; (b) symptom duration < 7. In the stacked probability plot, yellow, red, gray, and blue correspond to admission (Ward), ICU, death, and discharge.

Figure 4

This figure shows the output of the 4-state model. In the stacked probability plot, yellow, red, gray, and blue correspond with admission (Ward), ICU, death, and discharge.

Figure 5

This figure shows the output of the 4-state model based on sex: (a) Female; (b) Male. In the stacked probability plot, yellow, red, gray, and blue correspond with admission (Ward), ICU, death, and discharge.

Figure 6

This figure shows the output of the 4-state model with age grouping. This grouping includes the following items: (a) Age group less than 45 years (B0); (b) B1 is age group between 45 and 54 years (45–54); (c) B2 is age group between 55 and 64 years (55–64); (d) B3 is age group between 65 and 74 years (65–74); (e) B4 is Age group more than 75 years. In the stacked probability plot, yellow, red, gray, and blue correspond with admission (Ward), ICU, death, and discharge.

Figure 7

This figure shows the output of the 4-state model based on people with diabetes and without diabetes: (a) non-diabetic people; (b) diabetic people. In the stacked probability plot, yellow, red, gray, and blue correspond with admission (Ward), ICU, death, and discharge.

Figure 8

This figure shows the output of the 4-state model with the grouping of lymphocytes: (a) With lymphocytes > 910 × 10⁶/L (b) With lymphocytes < 910 × 10⁶/L. In the stacked probability plot, yellow, red, gray, and blue correspond with admission (Ward), ICU, death, and discharge.

Figure 9

This figure shows the output of the 4-state model with the grouping of CCI: (a) With CCI = 0 (b) With CCI > 0. In the stacked probability plot, yellow, red, gray, and blue correspond with admission (Ward), ICU, death, and discharge.

Figure 10

Output of the 4-state model grouped by COVID-19 waves: (a) First wave, (b) Second wave, (c) Third wave. In the stacked probability plot, yellow represents admission (Ward), red represents ICU, gray represents death, and blue represents discharge.