Heart Failure Clinical Trial
Official title:
China Heart Failure Registry Study: A Multicenter, Prospective Investigation of Clinical Characteristics, Treatment, and Outcome of Patients Hospitalized With Heart Failure
The purpose of this study is to assess clinical characteristics, treatment, and outcome of patients hospitalized with heart failure in China and compared those with other registries
- All sites were monitored randomly every year. Trained monitors were designated to
monitor the process, compliance of enrollment criteria and accuracy of original data by
visiting every site.
- In order to make sure the accuracy of data collected, we make some restrictions (range
or format) and reminding functions in the electronic Case Report Form (eCRF).
- Detail information of each variable has been pre-defined. Staffs were trained before
data entry.
- Standard Operating Procedures: Case data were collected by printed Case Report Form
(CRF) and entered into a web-based data center (http://103.31.202.60/subFW/). Hospitals
that did not complete eCRF would send printed CRF to data center. Trained clinicians or
staffs were asked to enter these data into the web-based eCRF. Following variables were
included in the database: (i) demography and characteristics of patients: age, gender,
medical history, life style, precipitating factors of HF, laboratory tests, ECG, chest
X-ray and comorbidities diagnosed at discharge. Physical examination, echocardiography,
laboratory tests were performed at both admission and discharge; (ii) Heart failure
management included pharmacological treatments (before admission, during
hospitalization and at discharge), and nonpharmacological treatments (including
coronary revascularization, implantable cardioverter-defibrillator, cardiac
resynchronization therapy, pacemakers, and surgical or transcatheter valvular
therapies, electrical conversion, intra aortic balloon pump, continuous renal
replacement therapy, and invasive mechanical ventilation).The causes of in-hospital
death included sudden death, cardiac death and non-cardiac death. All patients were
followed up after discharge in outpatient department, or via electronic hospital
records, or conversations with patients or patients' families by telephone. Outcomes
for post-discharge included death (sudden death, cardiac death and non-cardiac death),
and rehospitalization for heart failure deterioration, acute coronary syndrome, cardiac
shock, syncope, and cardiac transplantation were recorded and entered into the
web-based database. Information about physical examination, laboratory tests, ECG,
chest X-ray, echocardiography, and costs and use of medications were collected at each
follow-up visit.
- Sample size: A total of 15000 patients with heart failure were planned to be recruited
from hospitals based on classification of economic-geographic regions in China, and a
minimum number of patients were expected to enroll according to the number of
hospitalized cases for each hospital. By September 2015, data from 13687 patients have
been collected.
- Plan for missing data: Validated data was used to describe the clinical characteristics
and treatment of patients without considering those missing. As for September 2015, the
missing data on most of variables accounts for no more than 10% of all patients (n =
13687). Furthermore, variables obtained in multiple Logistic regression models (10318)
for death were from over three quarters of (75.4%) of all patients. Simple and multiple
imputation methods were not used in this study because of a large number of patients
has been validated for analysis.
- Statistical analysis plan: Clinical characteristics, treatments, and outcomes were
described by using means ± standard deviation or interquartile range (IQR) for
continuous variables, and absolute numbers and percentages for categorical variables.
The comparison of subgroups were performed by Student t-test or ANOVA for symmetrical
continuous, Mann-Whitney U or Kruskal-Wallis H test for nonsymmetric continuous, and χ2
tests for categorical variables. Logistic regression was used to select potential risk
factors for outcomes. Candidate variables associated with mortality in univariable
Logistic regression analysis (P ≤ 0.20) were proceeded with multivariable analysis.
Variables with significant P values (P < 0.05) were retained in the final multivariable
model. Logarithmic transformation was performed to normalize the distribution of
variables with skewed distribution. All P values of less than 0.05 from two-sided tests
were accepted as statistically significant.
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Observational Model: Cohort, Time Perspective: Prospective
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