Study of China Heart Failure Registry to Investigate Characteristics of Patients in China

Overview

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.

Conditions

Eligibility

Locations (1)

Outcomes

Central Contacts