Development of Clinical Prediction Rules and Health Services Research in Patients With Heart Failure
NCT ID: NCT03512704
Last Updated: 2019-02-05
Study Results
Results pending
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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Recruitment Status
COMPLETED
Total Enrollment
2218 participants
Study Classification
OBSERVATIONAL
Study Start Date
2013-10-31
Study Completion Date
2018-12-31
Brief Summary
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Objectives: Objective of this project are to create several clinical prediction rules (CPR) to stratify patients into different prognostic levels: on arrival at the emergency room, at hospital discharge, in the evolution at 90 days, and up to 1 year; for clinical parameters (mortality, complications, readmissions) and the evolution of the patient health related quality of life. Additional objectives include the analysis of equity in access, continuity of care after discharge, costs, psychosocial support received, and variability in clinical decisions and in the results obtained from inclusion in the study after the visit to the emergency room until 1 year of follow-up. Methods: Prospective observational cohort study with a one year follow-up. Multicenter and coordinated study with 9 hospital in Spain. This project will include around 1000 patients diagnosed of heart failure who come to emergency services of these hospitals, whether discharge home or admitted to the hospital. Multiple parameters (about the process of care, clinical outcomes, and quality of life), will be retrieved in the emergency room visits, admission, discharge and up to one year follow up after discharge. This project bases its work in the large number of variables to be collected and would not be viable with few centers, so centers from other communities will collaborate providing more cases.
Statistical analysis using multivariate logistic regression models or Cox or general linear models or multilevel analysis will derive the CPR in a subsample of the original sample which will be validated in another different subsample.
Statistical analysis using multivariate logistic regression models or Cox or general linear models or multilevel analysis will derive the CPR in a subsample of the original sample which will be validated in another different subsample.
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Detailed Description
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DESIGN: multicenter prospective observational cohort study with one year follow-up after inclusion in the study.
SCOPE: Nine hospitals participating in this project: Hospital Universitario Basurto, Santa Marina, Donostia, and Galdakao-Usansolo, Hospital de Antequera, Costa del Sol, Universitario de Canarias, Universitario Parc Taulí y Bellvitge.
SUBJECTS: patients with known chronic heart failure (or de novo diagnosed in the emergency department visit) attended in the emergency services of the participating hospitals prospectively diagnosed with acute or decompensated heart failure and recruited during the first year of the study, including patients admitted or given discharge in the hospital emergency room. The first admission of each patient during the recruitment period will be taken as a reference, and after this episode the follow-up will be carried out during a whole year, collecting all the events that happen related to their illness.
Missing patients: In all patients who meet the selection criteria the investigators will collect data on essential sociodemographic and clinical variables in order to be able to compare the patients lost in the follow-up with the patients who finally participate in the entire study.
Sample size calculation: Predictive model development studies establish that it is necessary to have at least 10 events of the dependent variable of interest (in our case: mortality, major complications, recurrence or re-admissions, separately) for each independent variable included in the multivariate logistic regression model . Given that our intention is to include in the multivariate model a limited but exhaustive number of variables (predictably, no less than 10), the investigators estimate that it will be necessary to have at least 100 events of the dependent variable in the derivation sample (of 1000 patients ) to make sure that the regression model converges properly. Data from our centers indicate that the number of events of the dependent mortality variable would be\> 15% of the patients admitted, with the percentages expected from the other parameters with higher results. With all the participating centers and 1 year of recruitment, the investigators hope to recruit around 2000 valid patients (50% for the derivation and 50% in the validation sample) sufficient to meet the stated objectives.
Sample size: Based on data from the year 2012 of our centers for this pathology and according to the expected exclusions (80% will meet the selection criteria with their acceptance to participate in the study ) and losses (20% of losses in the follow-up of those that meet the selection criteria), the recruitment of this number of patients is guaranteed for the majority of the participating centers and, therefore, the response to this and other hypotheses of the study.
Sampling: non-probabilistic sampling of convenience of consecutively recruited patients in each of the participating centers for 12 months.
VARIABLES. Sources for the collection of information: it will be done through the medical record (of emergencies, hospital admission or primary care), information systems of the participating centers (medical/electronic records), hospital and primary care, and directly from patients through a survey. Summary of the variables to be collected:
1. Socio-demographic data: age, sex, level of studies, place of residence, distance to the hospital, family situation.
2. Antecedents: symptoms; time of evolution; risk factors and health habits; previous pharmacological / non-pharmacological treatments, previous vaccines; comorbidities and their respective treatments; previous income by HF.
3. Clinical data:
A.-Presentation in the emergency room: a.1.-symptoms; a.2.-signs. a.3.- Complementary tests and their result: ECG, chest x-ray, BNP test, laboratory parameters; urine analysis. a.4.- Pharmacological treatment of HF; Other treatments; Specific treatments and associated comorbidities. a.5.- Destination at discharge.
B.-Patients discharge from the emergency room: data at discharge (medication, symptoms and signs, laboratory data and complementary examinations, diagnosis, destination at discharge, prescribed controls) C.-Patients hospital admitted. Evolution of: c.1.- symptoms; c.2.-signs. c.3.- Complementary tests performed: ECG, echocardiography, . c.4.- Use of other complementary tests. c.5.- Diagnosis of the HF and type. c.6.-Pharmacological treatment of HF; Other treatments; c.7.- Interventional procedures. c.8.- Need for more intensive treatment. c.9.-Specific treatments and associated comorbidities.
D.-Outcomes: death; complications during admission; Intensive treatments; Other reperfusion treatments; Other complications; days of stay.
E.-Data at hospital discharge of patients admitted: symptoms, signs, laboratory data at discharge, diagnosis at discharge, prescribed treatment, care and established controls.
4. Alternatives to classic hospital admission: home hospitalization, palliative care, medium-stay units-centers.
5.-Other health care / interventions. 6-Utilization of health services after the emergency visit / discharge from hospitalization.
7-Availability and use of psycho-social support services. 8.- Other results in the follow-up until the year: Evolution of symptoms, signs and basic analytics, the latter included in the readmissions and the primary care databases.
9-Quality of life questionnaires: Minnesota Living with Heart Failure Questionnaire (MLWHFQ; EuroQol-5D and Barthel Index) Questionnaires to pass in the first contact and in the 1st year, moments in which transitional questions on evolution will be included of symptoms and general condition.
10.-Clinical results to be measured during follow-up: death, re-admissions, complications, visits to the emergency department and surgical interventions, both cardiological and other, evolution of dyspnea.
DATA COLLECTION: the above parameters will be collected from the arrival of the patient to the emergency room until discharge (from the emergency room or after hospital admission) with the patient being followed until one year after the index visit, collecting information on the care received in other services (hospitalization), at home, primary care, for social services, at the hospital level, (including possible new readmissions) through the clinical history (of emergencies, hospital admission or primary care) and information systems of the participating centers (medical/electronic records) and evolution of the quality of life (PROm) and symptoms, for which the same questionnaires will be passed on again at the baseline time per year.
ETHICAL AND CONFIDENTIALITY ASPECTS. The project has been evaluated by the research commissions of the participating centers and the Clinical Research Ethics Committee accredited (CEIC autonomic of the Basque Country in this case) receiving their approval. The laws on personal data management will be followed, ensuring that the processing of personal data will be carried out in such a way that the information obtained can not be associated with identified or identifiable persons (Organic Law 15/1999, 13-12, Protection of Data of Character Personal).
SCOPE: Nine hospitals participating in this project: Hospital Universitario Basurto, Santa Marina, Donostia, and Galdakao-Usansolo, Hospital de Antequera, Costa del Sol, Universitario de Canarias, Universitario Parc Taulí y Bellvitge.
SUBJECTS: patients with known chronic heart failure (or de novo diagnosed in the emergency department visit) attended in the emergency services of the participating hospitals prospectively diagnosed with acute or decompensated heart failure and recruited during the first year of the study, including patients admitted or given discharge in the hospital emergency room. The first admission of each patient during the recruitment period will be taken as a reference, and after this episode the follow-up will be carried out during a whole year, collecting all the events that happen related to their illness.
Missing patients: In all patients who meet the selection criteria the investigators will collect data on essential sociodemographic and clinical variables in order to be able to compare the patients lost in the follow-up with the patients who finally participate in the entire study.
Sample size calculation: Predictive model development studies establish that it is necessary to have at least 10 events of the dependent variable of interest (in our case: mortality, major complications, recurrence or re-admissions, separately) for each independent variable included in the multivariate logistic regression model . Given that our intention is to include in the multivariate model a limited but exhaustive number of variables (predictably, no less than 10), the investigators estimate that it will be necessary to have at least 100 events of the dependent variable in the derivation sample (of 1000 patients ) to make sure that the regression model converges properly. Data from our centers indicate that the number of events of the dependent mortality variable would be\> 15% of the patients admitted, with the percentages expected from the other parameters with higher results. With all the participating centers and 1 year of recruitment, the investigators hope to recruit around 2000 valid patients (50% for the derivation and 50% in the validation sample) sufficient to meet the stated objectives.
Sample size: Based on data from the year 2012 of our centers for this pathology and according to the expected exclusions (80% will meet the selection criteria with their acceptance to participate in the study ) and losses (20% of losses in the follow-up of those that meet the selection criteria), the recruitment of this number of patients is guaranteed for the majority of the participating centers and, therefore, the response to this and other hypotheses of the study.
Sampling: non-probabilistic sampling of convenience of consecutively recruited patients in each of the participating centers for 12 months.
VARIABLES. Sources for the collection of information: it will be done through the medical record (of emergencies, hospital admission or primary care), information systems of the participating centers (medical/electronic records), hospital and primary care, and directly from patients through a survey. Summary of the variables to be collected:
1. Socio-demographic data: age, sex, level of studies, place of residence, distance to the hospital, family situation.
2. Antecedents: symptoms; time of evolution; risk factors and health habits; previous pharmacological / non-pharmacological treatments, previous vaccines; comorbidities and their respective treatments; previous income by HF.
3. Clinical data:
A.-Presentation in the emergency room: a.1.-symptoms; a.2.-signs. a.3.- Complementary tests and their result: ECG, chest x-ray, BNP test, laboratory parameters; urine analysis. a.4.- Pharmacological treatment of HF; Other treatments; Specific treatments and associated comorbidities. a.5.- Destination at discharge.
B.-Patients discharge from the emergency room: data at discharge (medication, symptoms and signs, laboratory data and complementary examinations, diagnosis, destination at discharge, prescribed controls) C.-Patients hospital admitted. Evolution of: c.1.- symptoms; c.2.-signs. c.3.- Complementary tests performed: ECG, echocardiography, . c.4.- Use of other complementary tests. c.5.- Diagnosis of the HF and type. c.6.-Pharmacological treatment of HF; Other treatments; c.7.- Interventional procedures. c.8.- Need for more intensive treatment. c.9.-Specific treatments and associated comorbidities.
D.-Outcomes: death; complications during admission; Intensive treatments; Other reperfusion treatments; Other complications; days of stay.
E.-Data at hospital discharge of patients admitted: symptoms, signs, laboratory data at discharge, diagnosis at discharge, prescribed treatment, care and established controls.
4. Alternatives to classic hospital admission: home hospitalization, palliative care, medium-stay units-centers.
5.-Other health care / interventions. 6-Utilization of health services after the emergency visit / discharge from hospitalization.
7-Availability and use of psycho-social support services. 8.- Other results in the follow-up until the year: Evolution of symptoms, signs and basic analytics, the latter included in the readmissions and the primary care databases.
9-Quality of life questionnaires: Minnesota Living with Heart Failure Questionnaire (MLWHFQ; EuroQol-5D and Barthel Index) Questionnaires to pass in the first contact and in the 1st year, moments in which transitional questions on evolution will be included of symptoms and general condition.
10.-Clinical results to be measured during follow-up: death, re-admissions, complications, visits to the emergency department and surgical interventions, both cardiological and other, evolution of dyspnea.
DATA COLLECTION: the above parameters will be collected from the arrival of the patient to the emergency room until discharge (from the emergency room or after hospital admission) with the patient being followed until one year after the index visit, collecting information on the care received in other services (hospitalization), at home, primary care, for social services, at the hospital level, (including possible new readmissions) through the clinical history (of emergencies, hospital admission or primary care) and information systems of the participating centers (medical/electronic records) and evolution of the quality of life (PROm) and symptoms, for which the same questionnaires will be passed on again at the baseline time per year.
ETHICAL AND CONFIDENTIALITY ASPECTS. The project has been evaluated by the research commissions of the participating centers and the Clinical Research Ethics Committee accredited (CEIC autonomic of the Basque Country in this case) receiving their approval. The laws on personal data management will be followed, ensuring that the processing of personal data will be carried out in such a way that the information obtained can not be associated with identified or identifiable persons (Organic Law 15/1999, 13-12, Protection of Data of Character Personal).
Conditions
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Heart Failure
Study Design
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Observational Model Type
COHORT
Study Time Perspective
PROSPECTIVE
Interventions
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Collection of possible predictors from patient data
Collection of sociodemographic and clinical parameters, from medical records, and health related quality of life data, that may predict the outcomes of interest (mortality, complications, readmissions)
Intervention Type
OTHER
Eligibility Criteria
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Inclusion Criteria
* Patients with a diagnosis of heart failure (code ICD-9-CM: 428.x, some of the 402.x) who come to hospital emergencies according to the definition, signs and symptoms used to diagnose the disease according to the clinical practice guide of the European Society of Cardiology (ESC) for the diagnosis and treatment of acute and chronic heart failure, such as, among others, lack of air or fatigue at rest or during exercise; signs of fluid retention, such as pulmonary congestion or swelling of the ankles, and objective evidence of a structural or functional cardiac disturbance at rest .
* Patients over 18 years of age and belonging to the hospital's coverage area.
* Patients who agree to participate and sign the informed consent.
* Patients over 18 years of age and belonging to the hospital's coverage area.
* Patients who agree to participate and sign the informed consent.
Exclusion Criteria
* Patients who develop episode of HF during admission, if they have been admitted for another cause.
* Patients transferred from other health centers, since study variables may be missing.
* Cerebrovascular accident in the 4 weeks before admission.
* Patients who do not wish to participate.
* Terminal status that prevents them from participating in completing the questionnaires.
* Impossibility to complete the questionnaires or with external help (reviewer, family, social) due to neurosensory, dementia or ignorance of the language.
* Patients transferred from other health centers, since study variables may be missing.
* Cerebrovascular accident in the 4 weeks before admission.
* Patients who do not wish to participate.
* Terminal status that prevents them from participating in completing the questionnaires.
* Impossibility to complete the questionnaires or with external help (reviewer, family, social) due to neurosensory, dementia or ignorance of the language.
Minimum Eligible Age
18 Years
Eligible Sex
ALL
Accepts Healthy Volunteers
No
Sponsors
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Hospital de Basurto
OTHER
Sponsor Role
collaborator
Hospital Donostia
OTHER
Sponsor Role
collaborator
Hospital Costa del Sol
OTHER
Sponsor Role
collaborator
Hospital Universitari de Bellvitge
OTHER
Sponsor Role
collaborator
Parc Taulí Hospital Universitari
OTHER
Sponsor Role
collaborator
Hospital Universitario de Canarias
OTHER
Sponsor Role
collaborator
Hospital Santa Marina
UNKNOWN
Sponsor Role
collaborator
Hospital de Antequera
UNKNOWN
Sponsor Role
collaborator
Hospital Galdakao-Usansolo
OTHER_GOV
Sponsor Role
lead
Responsible Party
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JOSE M QUINTANA-LOPEZ, MD PhD
Chief of Research Unit
Responsibility Role
PRINCIPAL_INVESTIGATOR
Principal Investigators
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Jose M Quintana, MD, PhD
Role: PRINCIPAL_INVESTIGATOR
Chief of Research Unit
Locations
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Hospital Comarcal de Antequera
Antequera, , Spain
Site Status
Hospital Santa Marina
Bilbao, , Spain
Site Status
Hospital U Basurto
Bilbao, , Spain
Site Status
Hospital Universitario Donostia
Donostia / San Sebastian, , Spain
Site Status
Hospital Galdakao-Usansolo
Galdakao, , Spain
Site Status
Hospital de Bellvitge
L'Hospitalet de Llobregat, , Spain
Site Status
Hospital Costa del Sol
Marbella, , Spain
Site Status
Hospital Universitari Parc Taulí
Sabadell, , Spain
Site Status
Hospital Universitario de Canarias
Santa Cruz de Tenerife, , Spain
Site Status
Countries
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Spain
Other Identifiers
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2013111071
Identifier Type: OTHER_GRANT
Identifier Source: secondary_id
PI13/02230
Identifier Type: OTHER_GRANT
Identifier Source: secondary_id
PI12/01671
Identifier Type: -
Identifier Source: org_study_id
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