Clinical trial NCT04398563: Heart Failure in Norway: Clinical Characteristics, Mortality and Health Care Resource Use
Official title: Heart Failure in Norway: Combining Artificial Intelligence and a Database of 15000 Patients to Understand Clinical Characteristics, Mortality and Health Care Resource Use
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Heart Failure
Heart Failure With Preserved Ejection Fraction
Heart Failure With Reduced Ejection Fraction
Lead sponsor: University Hospital, Akershus
Sponsor class: OTHER
Collaborators: name: Novartis
First posted: 2020-05-21
Enrollment: 14998
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: All patients admitted to a University Hospital serving a population of approx. 600 000 inhabitants with heart failure diagnosis in the period 2007 to 2019. In total 14998 patients. Mortality according to The Norwegian Death Registry for all up until 31st of December 2019. In addition all patients with a diagnosis of COPD in the same period as well as all subjects with NT-proBNP above age specific normal range

Brief summary: The prevalence of Heart failure above 70 years of age is 10% and 5 year mortality rate above 60%, higher than for cancer. The readmission rate first after hospitalisation is 44% despite the availability of life prolonging and life quality enhancing treatment. There is a lack of resources for adequate diagnostic workup necessary for implementing evidence-based treatment. This projects aims at assessing the impact of guidelines based diagnostic workup and guidelines based treatment of heart failure on mortality and readmission rates. As the symptoms defining the degree of heart failure and the discharge medication only is available in the electronic patient files, artificial intelligence is used to retrieve this information to assess if treatment is according to guidelines.

The project is using first a rule based text processing approach using IBM Watson, then advancing to a machine learning approach using readmission and mortality as endpoints.

The project has access to digitally stored echocardiographic measurements as well as digital ECG's and lab data on 15 000 patients admitted with a diagnosis of Heart failure. If the retrieval of symptoms and function by artificial intelligence is successful, the next step is to assess if those benefitting the most from echocardiography can be identified using information from the ECG's, lab data or symptoms and functional capacity as described in the Electronic Health Records.


Eligibility criteria: Inclusion Criteria: Diagnosis with ICD10 codes as follows; I50.\*, I42.3, I11.\* and COPD J44.\* from 2007 through 2019.

In addition all subjects with NT-proBNP above age specific normal range in same period.

Exclusion Criteria:

none

Primary outcomes:
measure: Mortality
timeFrame: Change over 12 years
measure: Readmission
timeFrame: Change over 12 years
Secondary outcomes:
measure: Diagnostic work up according to guidelines
timeFrame: Within 6 months before or after first diagnosis
measure: Treatment according to guidelines
timeFrame: within first year after first diagnosis
Other outcomes:
Locations:
city: Lørenskog
country: Norway
facility: Akershus University Hospital
Study officials:
affiliation: University Hospital, Akershus
name: Henrik Schirmer, MD, PhD
role: PRINCIPAL_INVESTIGATOR
References: