Long Term Outcome in ICU Treated COVID-19: Risk Factors for 1-year Mortality

NCT05063747 | Completed

• 1 other identifier: U1111-1269-5925
• Study Type: observational
• Target: 13,537
• Locations: 1 country
• Sites: 1

Brief Summary

Mortality within one year after intensive care unit (ICU) admission with Coronavirus disease 2019 (COVID-19) will be assessed. Risk and risk factors for one year mortality in ICU patients will be compared to patients admitted to hospital with COVID-19 and general population controls. The ICU population comprises all Swedish ICU patients with COVID-19 with at lease one year of follow up. The hospital admitted cohort comprises four hospital admitted patients with COVID-19 per ICU patient, matched on age, legal gender and region. The general population controls are matched to the ICU patients in a one to four fashion on age, legal gender and region. ICU patients are identified in the Swedish intensive care registry. The hospital admitted patients are identified in the national patient registry and the population controls are identified in the population registry. Data on socioeconomics and income are provided by the Statistics Sweden. Data on comorbidity, medications and death are provided from the National board of health and welfare.

Conditions

Covid19

Keywords

Long term outcome; Mortality; COVID-19; Risk factors; Critical care

Outcome Measures

Primary Outcomes (2)

• What factors have significant odds ratios in a logistic model of the risk of one year mortality?

  Variables in a binary logistic model on mortality one year after ICU admission: age, legal gender, highest education, immigrant background, income previous year, martial status, ischemic heart disease, chronic renal failure, stroke, type 2 diabetes melitus, chronic obstructive pulmonary disease, asthma, hypertension, malignacy, treatment with renin angiotensin angiotensinogen inhibitors, treatment with statins

  One year

• Is cohort an independent risk factor in a logistic model of one year mortality?

  Binary logistic model, interaction with a variable denoting cohort (ICU, Hospital or General population). A significant interaction denotes a differential effect of a risk factor between cohorts. Variables in a binary logistic model on mortality one year after ICU admission: age, legal gender, highest education, immigrant background, income previous year, martial status, ischemic heart disease, chronic renal failure, stroke, type 2 diabetes melitus, chronic obstructive pulmonary disease, asthma, hypertension, malignancy, treatment with renin-angiotensin-angiotensinogen inhibitors and treatment with statins.

  One year

Secondary Outcomes (1)

• Does acute disease severity, hospital length of stay or ICU length of stay affect the significance of variables when added to the risk model in Outcome 1 for the ICU cohort?

  One year

Study Arms (3)

COVID-19 ICU cohort

All patients admitted to a Swedish ICU with COVID-19 with at least one year of follow up. COVID-19 is defined by the International Statistical Classification of Diseases and Related Health Problems 10th Revision (ICD-10) diagnosis U07.1 in the nationwide Swedish intensive care registry.

Other: No intervention.

COVID-19 hospital admission control cohort

Four random control patients per ICU patient matched on age legal gender and region. Controls selected from all patients admitted to a Swedish hospital with COVID-19 with at least one year of follow up not including patients in the COVID-19 ICU cohort. COVID-19 is defined by the ICD-10 diagnosis U07.1 in the nationwide Swedish national patient registry.

Other: No intervention.

General population control cohort

Four general population controls per ICU patient, matched on age, legal gender and region drawn from the total population register of Sweden. ICU and hospital admitted COVID-19 patients are not included.

Other: No intervention.

Interventions

No intervention. OTHER

Observational study. No intervention.

COVID-19 ICU cohort; COVID-19 hospital admission control cohort; General population control cohort

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

The study population consists of all patients a fulfilling the eligibility criteria.

You may qualify if:

• Admitted to a Swedish ICU and registered in the Swedish intensive care registry with the ICD 10 diagnosis U07.1 before 1 July 2020. ICU-cohort.
• or randomly selected from all patients admitted to hospital but not ICU with the ICD 10 diagnosis U07.1 in the national patient registry, matched on age, legal gender and region (four per ICU patient) before 1 July 2020. Hospital cohort.
• or randomly selected from the general population (and not included in the ICU or hospital admitted cohorts), matched on age, legal gender and region (four per ICU patient)

You may not qualify if:

• Missing a Swedish personal identification number

Sponsors & Collaborators

• Uppsala University: lead
• Dalarna County Council, Sweden: collaborator

Study Sites (1)

Uppsala University

Uppsala, 79182, Sweden

Location

MeSH Terms

Conditions

COVID-19

Condition Hierarchy (Ancestors)

Pneumonia, Viral; Pneumonia; Respiratory Tract Infections; Infections; Virus Diseases; Coronavirus Infections; Coronaviridae Infections; Nidovirales Infections; RNA Virus Infections; Lung Diseases; Respiratory Tract Diseases

Study Officials

• Miklos Lipcsey, Professor

  Uppsala University

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: CASE CONTROL
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: September 29, 2021
• First Posted: October 1, 2021
• Study Start: October 15, 2021
• Primary Completion: November 15, 2021
• Study Completion: November 15, 2021
• Last Updated: May 24, 2022

Record last verified: 2022-05

Data Sharing

• IPD Sharing: Will not share

Locations

SE (1)