Using artificial intelligence as an aid to predict the risk of hospital readmission in patients with COVID-19

Predicting unplanned hospital readmission prior to discharge in patients with COVID-19: development, validation, and implementation of a machine-learning-based risk prediction model

Status

Recruiting

Phases

Unknown

Study type

Observational

Source

ISRCTN

Registry ID

ISRCTN85858267

Enrollment

5261

Registered

2022-03-14

Start date

2022-02-14

Completion date

Unknown

Last updated

2022-10-31

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

COVID-19 (SARS-CoV-2 infection) Infections and Infestations

Interventions

Trajectory modelling: understanding readmission and its consequences: First, the researchers will examine the factors associated with readmission with time-to-event models, accounting

Eligibility

Sex/Gender

All

Inclusion criteria

Inclusion criteria: Consecutive patients (ISARIC4C/CO-CIN) aged 18 years and older with a completed index admission for COVID-19 in England and Scotland

Exclusion criteria

Exclusion criteria: Age <18 years

Design outcomes

Primary

Measure	Time frame
Hospital readmission measured using NHS data at 30 and 90 days	—

Secondary

Measure	Time frame
Mortality measured using NHS data at 30 and 90 days	—

Countries

England, Scotland, United Kingdom

Outcome results

None listed

Source: ISRCTN (via WHO ICTRP) · Data processed: Feb 4, 2026