Epidemiological Study of Out-of-hospital Cardiac Arrest in Guangzhou
- Conditions
- Sudden Cardiac DeathCardiac Arrest
- Interventions
- Diagnostic Test: "cardiac arrest" and "sudden death"
- Registration Number
- NCT06448156
- Brief Summary
Aim This was a population-based retrospective cohort study of OHCA. This study intends to retrospectively analyze the data of pre-hospital emergency system in Guangzhou for 10 years, explore the incidence trend of OHCA in Guangzhou for 10 years; Through further analysis, we try to explore the time distribution characteristics of OHCA in order to understand the epidemiological characteristics and rules of OHCA in super large cities in southern China.
Methods The pre-hospital traffic data in the main urban area of Guangzhou Emergency Medical Command Center database from 2011 to 2020 were collected. The cases diagnosed as "cardiac arrest" and "sudden death" were screened, and the cases with non-cardiac causes in the diagnosis were deleted. The crude incidence rate and age-standardized incidence rate of OHCA were calculated. Joinpoint software was used to calculate the changing nodes in the OHCA incidence trend, and the AnnualPercent Change (APC) and Average AnnualPercent Change (Average AnnualPercent Change, APC) of OHCA incidence were calculated. AAPC). The OHCA data were grouped according to the six main urban areas, and the crude incidence rate, ASIR and changing trend of the six main urban areas were calculated. The data of OHCA were grouped by age, and the crude incidence rate, ASIR and changing trend of each age group were calculated. The data information was divided into groups according to 24 hours a day, 7 days a week, and four seasons. The number of OHCA cases in different time periods was statistically described. The data were imported into SPSS 26.0 for analysis, and Mann-Kendall test was used to evaluate the statistical significance of the time trend. Time rhythm variability was tested for mean distribution using chi-square goodness of fit test.
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- ACTIVE_NOT_RECRUITING
- Sex
- All
- Target Recruitment
- 44375
Cases in the database with a secondary diagnosis containing the diagnostic keywords "cardiac arrest" and "sudden death"
- cases where the diagnosis of "cardiac arrest" and "sudden death" includes a diagnosis of a non-cardiac cause such as asphyxiation, suicide, drowning, advanced cancer, trauma, shock, poisoning, cerebral vascular accident, etc;
- cases with duplicate records of sex, age, time of call, pick-up address and initial diagnosis
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Arm && Interventions
Group Intervention Description 20-29 years age group "cardiac arrest" and "sudden death" - 30-39 years age group "cardiac arrest" and "sudden death" - 50 to 59 age group "cardiac arrest" and "sudden death" - 70 to 79 age group "cardiac arrest" and "sudden death" - 40 to 49 age group "cardiac arrest" and "sudden death" - 80+age group "cardiac arrest" and "sudden death" Age group greater than or equal to 80 years 60 to 69 age group "cardiac arrest" and "sudden death" - 0-19 years age group "cardiac arrest" and "sudden death" -
- Primary Outcome Measures
Name Time Method Age standardized incidence rate 2011-01-01 to 2020-12-31 Incidence rates after removing the influence of age, and incidence rates normalised by age. The rationale is that age is an important influence on cancer incidence, with higher incidence rates occurring at older ages, so that if the age structure of the population in two regions is very different, it is not possible to determine whether the high incidence of a disease in a particular region is due to a different age composition or to other influences if incidence rate comparisons are applied.
Average annual percentage change 2011-01-01 to 2020-12-31 Calculated using the weighted average of the APC, it is an overall measure of trend.
Crude incidence rate 2011-01-01 to 2020-12-31 The frequency of new cases of a disease in a given population over a given period of time.
Annual percentage change 2011-01-01 to 2020-12-31 Indicates the change from one year to the next within a segment at a constant percentage on a log-linear model for evaluating trends within segments.
- Secondary Outcome Measures
Name Time Method
Trial Locations
- Locations (1)
Yu Tao
🇨🇳Guangdong, 广东省, China