Clinical Trial Details
— Status: Active, not recruiting
Administrative data
| NCT number |
NCT04009798 |
| Other study ID # |
17-6063 |
| Secondary ID |
|
| Status |
Active, not recruiting |
| Phase |
|
| First received |
|
| Last updated |
|
| Start date |
May 18, 2018 |
| Est. completion date |
December 31, 2021 |
Study information
| Verified date |
November 2020 |
| Source |
University Health Network, Toronto |
| Contact |
n/a |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Observational
|
Clinical Trial Summary
The purpose of the project is to conduct a physician-initiated Canadian multicentre
observational research study that compares physician judgement and model prediction to
estimate one-year survival in ambulatory heart failure (HF) patients and evaluate the use of
resources according to physician intuitive risk. This study will evaluate the accuracy and
impact of physician intuition and predictive models in the assessment of prognosis in
ambulatory HF patients by: comparing 1-year physician predicted survival and 1-year observed
survival to 1-year model predicted survival; evaluating whether model performance could be
enhanced by incorporating physician intuition; evaluating the accuracy of physician intuition
according to level of confidence in physician intuition (very low, low, moderate, high or
very high); evaluating whether physician expertise impacts accuracy of physician intuition;
and evaluating patient management and use of resources according to physician estimated
survival.
Description:
Heart failure (HF) is a large and growing medical and economic problem, with over 26 million
people suffering worldwide. The prognosis associated with HF is poor. Optimal management of
patients with HF, crucial to improve outcomes and minimize costs, depends on adequate
assessment of prognosis to decide on appropriate testing and intervention.
There are predictive models, with satisfactory discrimination and calibration, that can
accurately estimate mortality in HF patients. Despite their availability, physicians seldom
use these models, instead relying on their informed intuition, which has proven to be
limited. No studies have compared physician intuition (standard practice) and model predicted
survival in patients with HF. The investigators therefore propose a Canadian multicentre
study comparing physician intuition and model prediction to estimate one-year survival in
ambulatory HF patients and secondly to assess the possible impact of intuition versus model
prediction on the use of resources. Evaluating whether predictive models are more accurate
than physician intuition will inform the best strategy to assess patient prognosis in HF.
More accurate prognostic estimates will facilitate patient management by matching the need
for further therapy or testing to patient risk thus offering greater clinical benefit and
improving utilization of resources.
This study will evaluate the accuracy and impact of physician intuition and predictive models
in the assessment of prognosis in ambulatory HF patients by: i. comparing the accuracy of
1-year physician predicted survival and 1-year model predicted survival to the true
(observed) 1-year survival; ii. evaluating the accuracy of physician intuition according to
physician's level of confidence in their intuition (very low, low, moderate, high or very
high); iii. evaluating whether physician expertise impacts accuracy of physician intuition;
iv. evaluating whether physician gender, patient gender and physician-patient gender
concordance impact accuracy of physician intuition; and v. studying the association between
physician estimated survival and resource use and related cost.
The investigators hypothesize that predictive models will more accurately predict mortality
than physicians, with more marked differences in less experienced physicians or when
physician confidence in estimate is low. Physicians will use more resources when they
consider patients to be high risk. If these hypotheses prove correct, incorporation of
user-friendly systems to estimate prognosis into clinical practice can offer clinical
benefit, facilitate patient management and improve utilization of resources.
This is a Canadian multicentre prospective cohort study of consecutive consenting ambulatory
adult heart failure (HF) patients followed in a HF clinic. Participating centers include
tertiary care hospitals with dedicated HF clinics in British Columbia (St. Paul's Hospital,
Providence Health Care Centre); Manitoba (St. Boniface General Hospital); Ontario (Toronto
General Hospital, St. Michael's Hospital, Ottawa Heart Institute, Sunnybrook Hospital, Mount
Sinai Hospital, Hamilton Health Science, Southlake Regional Health Centre); Quebec (McGill
University Health Centre); and Nova Scotia (Nova Scotia Health Authority). These clinics
attend to different HF populations permitting a wide representation of patient profiles.
After obtaining written informed consent, research assistants will collect clinical and
laboratory data from electronic records and paper charts necessary to describe the patient
population and to calculate predictive model survival. These constitute part of the routine
clinical assessment and will include demographic characteristics (age, sex, race),
co-morbidities (diabetes, hypertension, smoking, peripheral vascular disease, chronic lung
disease), HF characteristics and history (underlying cause, LVEF by echocardiogram, last HF
hospital admission, medications and use of ICD and/or cardiac resynchronization therapy) and
physical examination (body mass index (BMI), current NYHA class, heart rate, and blood
pressure at rest). Laboratory values will include hemoglobin, leucocytes, lymphocytes,
electrolytes, BUN (blood urea nitrogen), serum creatinine, total cholesterol and uric acid.
Brain natriuretic peptide (BNP) or N-terminal pro-BNP and peak oxygen consumption (peak VO2)
will be collected when available. Cardiac rhythm and QRS duration will be collected by
electrocardiography and peak oxygen consumption by cardiopulmonary exercise study.
This study will include physicians with different level of expertise in HF (HF cardiologists
and Family Physicians). Physicians will be asked to provide their intuitive estimates of the
likelihood of survival at one year following the patient baseline clinic visit. Physicians
will be blinded to the predicted model survival. In the survey, the physician will: (1)
estimate patient 1-year survival in absolute terms (from 0% to 100%); (2) rate the confidence
in their prediction (from 1 - no confident at all to 5- very confident); (3) collect their
impression about the possibility of initiating assessment to evaluate candidacy for advanced
heart failure therapies including heart transplant or mechanical circulatory support in the
next year (1- not a candidate, 2- patient is already listed, 3- too early, and 4-patient is a
candidate); and (4) record status of optimization of medical management (1- beginning
optimization, 2- early in the process, 3- late in the process, and 4- completed). Patient
candidacy to advanced HF therapies and status of optimization can influence the use of
resources. If a patient is not a candidate for advanced HF therapies or is already under
optimal medical therapy, the use of resources will be lower compared to candidate patients or
patients under medical therapy optimization. This will be considered in the analysis of the
impact of physician intuitive risk on resource utilization.
There are many predictive models in HF. Of these, the investigators have chosen three models
based on comparably acceptable performance: the Seattle Heart Failure Model (SHFM), the HF
Meta-Score and the Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC) score. These
models include a set of different variables, they have been validated in contemporary cohorts
of HF patients and have demonstrated excellent calibration and discrimination with a
c-statistic >0.70.
Patients will be followed until the last recruited patient is followed for a minimum of
1-year and the following outcomes will be collected: i. Death, urgent VAD implant and urgent
heart transplant. Urgent VAD implant or heart transplant will be defined by the use of
intravenous inotropic support at the time of the surgery. The investigators will collect this
information from electronic records and by linking with the administrative databases housed
at the Canadian Institute for Health Information (CIHI) via OHIP number or name and date of
birth (retrieved from the patients' medical records) using deterministic/probabilistic
linkage. CIHI is an independent, not-for-profit organization that provides essential
information on Canada's health systems. This entity houses 28 pan-Canadian databases across
various health sectors. CIHI holds ISO/IEC 27001:2005 certification for information security
management. ii. Utilization of health resources and related costs will be collected by
linking the project database to the administrative databases, administered by the CIHI
Discharge Abstract Database (CIHI DAD) and CIHI National Ambulatory Case Reporting System
(CIHI NACRS). Cost information will be obtained and estimated from the HF clinic at Toronto
General Hospital, which is representative of other HF clinics. The cost associated to
resources in other provinces will be adjusted by a calculated coefficient using publicly
provincial health cost information. The investigators will collect inpatient and outpatient
costs and resources from clinic visits, imaging and laboratory tests, cardiac rehabilitation,
hospitalization and visits to an emergency department.
Continuous variables will be expressed as a mean and standard deviation (SD) or median and
interquartile ranges for variables with non-Gaussian distributions. All discrete variables
will be expressed as counts (n) and percentages (%) of the study population. The statistical
analysis will be performed using SAS 9.4 (North Carolina, USA).
The investigators will assess physician intuition in comparison to the performance of
predictive models by comparing their discrimination, calibration and risk reclassification.
Physician intuition and model prediction accuracy will be evaluated separately by physician
expertise: HF cardiologists and family physicians. Kaplan-Meier analysis and Cox proportional
hazards model will be used to predict 1-year event-free survival with the score from each
model and physician intuition to assess calibration and discrimination, respectively. HF
clinics may see a set of different HF patients and HF cardiologists practicing in the same HF
clinic will potentially have similar practice and intuition. In order to consider this
potential confounding effect, the analysis will be adjusted for HF clinic region (Greater
Toronto Area, Ontario, Quebec, Manitoba, Nova Scotia and British Columbia). Follow up will be
censored as alive at the time of non-urgent VAD or heart transplant or last clinic visit
after a year follow up. Observed versus predicted survival will be used to assess calibration
illustrating the relationship in a scatter plot and will assess and compare intuition and
predictive models' discrimination using Harrell's c-statistic.
The investigators will then use risk reclassification analysis (reclassification tables and
reclassification calibration test) and absolute net reclassification improvement (NRI) to
assess global model performance of physician intuition in comparison to the predictive
models. Risk reclassification analysis will be used to show how patients classified by
physician intuition are reclassified by the predictive models and will compare the observed
and predicted survival in each cross-classified category. The absolute NRI represents the net
proportion of patients correctly or incorrectly classified assessing if patients were
reclassified in the correct direction, i.e. if survivors are reclassified as having better
survival and deceased patients are reclassified as having lower survival. For this analysis,
patients will be classified based on deciles of 1-year predicted survival (100-90%, 90-80%,
80-70% and <70%).
The association between physician intuition and utilization of health resources will be
evaluated by categorizing patients in the pre-defined risk categories (low, medium, high or
very high risk). Multivariate logistic regression will be used to evaluate the association
between physician intuition and resources measured as binary variables (i.e. referral to
specialists or palliative care) and multivariable Poisson regression to evaluate the
association between physician intuition and resources measured as count variables (i.e.
clinic visits) adjusted for region. Cost analysis will be used to evaluate the association
between total annual cost and physician intuition. Cost will be described using median and
interquartile range due to expected positively skewed distribution and evaluate its
association with physician intuition using generalized linear models with a gamma
distribution adding region as a fixed effect. The investigators will evaluate the impact of
model predictive survival using the results from the risk reclassification analysis (e.g. if
the predictive model better reclassified 10% of high-risk patients, 10% of the increased cost
of treating high-risk patients may be saved by using the model).
