Method and system for evaluating onset risk of chest pain disease based on low-temperature event

Abstract

The invention relates to a low-temperature event-based chest pain disease onset risk assessment method and system. The method comprises the following steps of S1, acquiring onset data of STEMI, NSTEMI, UA, AD and PE of a certain region in at least five natural years; acquiring meteorological data including atmospheric temperature of the region in at least five natural years; s2, calculating the percentile of the daily average temperature of the region, and defining the condition that the daily average temperature is lower than the first percentile as a high-strength cold-wave day; the condition that the daily average temperature is lower than the 5th percentile is defined as moderate-intensity cold waves; defining that the daily average temperature is lower than the 10th percentile as a low-intensity cold-wave day; and S3, estimating the relationship between the attack risk of the chest pain disease and cold waves through a time stratification case cross design model. Effective evidences are provided for intervention measures for specific diseases under different cold wave levels.

Description

Technical Field

[0001] The present disclosure relates to the technical field of health assessment, and in particular to a method and system for assessing the risk of chest pain disease based on hypothermia events. Background Art

[0002] The joint clinical guidelines of the American College of Cardiology / American Heart Association state that ST-Elevation Myocardial Infarction (STEMI), Non-ST Elevation Myocardial Infarction (NSTEMI), Unstable Angina (UA), Aortic Dissection (AD) and Pulmonary Embolism (PE) are a group of life-threatening acute cardiovascular diseases characterized by chest pain. These diseases are characterized by rapid onset and high mortality.

[0003] Globally, STEMI, NSTEMI, and UA account for approximately 50% of all cardiovascular disease-related deaths. One-third of STEMI patients die within 24 hours of onset, and approximately 15% of UA / NSTEMI patients die or have reinfarction within 30 days. Acute aortic dissection is a complex cardiovascular disease with an incidence of 7.7 cases per 100,000 person-years. The mortality rate caused by acute aortic dissection after out-of-hospital cardiac arrest is 100%. The incidence of PE ranges from 39 cases per 100,000 to 115 cases per 100,000 people and is on the rise, ranking first among the causes of cardiovascular disease deaths.

[0004] In this context, exploring the risk factors that induce acute exacerbations of these life-threatening acute chest pain diseases and implementing targeted intervention and prevention and control measures as early as possible are urgent tasks to reduce the mortality and morbidity of these life-threatening acute chest pain diseases.

[0005] Existing research evidence shows that cold is a triggering factor for acute exacerbation of cardiovascular disease, which can lead to increased cardiovascular disease mortality and hospitalization rates. 9.1‰ of cardiovascular disease deaths are attributed to cold days (less than 2.5%). A large number of related mechanism studies have reported that hypothermia can lead to increased sympathetic nerve activity, causing vascular smooth muscle contraction, increased blood viscosity, impaired endothelial function, and decreased dilation capacity. However, the existing reliable evidence is all for cold and a wide range of cardiovascular diseases, which is insufficient to provide the necessary research evidence for preventive interventions for these life-threatening acute chest pain diseases. Specifically, some studies have pointed out that these highly life-threatening acute chest pain diseases are more common in winter.

[0006] Currently, against the backdrop of a changing climate, the world is experiencing increasingly severe cold events. In the winter of 2020-2024, multiple rounds of super cold waves swept across most of the Northern Hemisphere. Cold wave records in Asia, Europe, and North America continue to be broken. However, due to the lack of large-sample incidence monitoring data, there has been a lack of conclusive epidemiological evidence for specific life-threatening acute chest pain diseases associated with cold. In addition, existing studies have confirmed that cold is a risk factor for the onset of cardiovascular disease. However, there are still gaps in the existing evidence on key quantitative target intensity values ​​for preventing cold waves, especially the lack of research evidence on intervention measures to address specific diseases at different cold wave levels. Summary of the invention

[0007] Based on this, the purpose of the present disclosure is to provide a chest pain disease incidence risk assessment program that can provide effective evidence for intervention measures to deal with specific diseases under different cold wave levels and provide strong support for customized intervention measures to prevent chest pain diseases in winter.

[0008] To achieve the above objectives, the present disclosure adopts the following technical solutions:

[0009] A method for assessing the risk of chest pain based on hypothermia events comprises the following steps:

[0010] S1. Obtain the incidence data of STEMI, NSTEMI, UA, AD and PE in a certain region within at least five natural years; and obtain the meteorological data including atmospheric temperature in the region within the at least five natural years;

[0011] S2. Calculate the percentiles of the daily average temperature in the region, and define a high-intensity cold wave day as a day when the daily average temperature is lower than the 1st percentile; define a moderate-intensity cold wave day as a day when the daily average temperature is lower than the 5th percentile; and define a low-intensity cold wave day as a day when the daily average temperature is lower than the 10th percentile;

[0012] S3. The relationship between the risk of chest pain and cold waves was estimated using the following model using a time-stratified case-crossover design:

[0013] E(Logistic(Y i ))=α+βCS i +γStratumt i +ns(RHmean,3)+δHoliday i

[0014] Among them, Y i Indicates whether STEMI, NSTEMI, UA, AD, and PE occur in case i, CS irepresents the intensity of the cold wave experienced by case i on the case day and the control day, Stratumt i represents the time stratification of case i defined by the time stratified case-control matching method, RHmean represents the daily average relative humidity, Holiday i Indicates whether it is a holiday;

[0015] Among them, CS i The values ​​of are: 0 for non-cold wave days, 1, 2 and 3 for low-intensity cold wave days, moderate-intensity cold wave days and high-intensity cold wave days respectively.

[0016] Preferably, the method further comprises a step S11 of cleaning the onset data obtained in step S1: deleting data lacking any information of disease diagnosis, gender, age, residential address and onset time.

[0017] Preferably, in step S3, a subgroup analysis is performed on the relationship between the incidence of chest pain and cold waves according to gender, age and north-south region, and the north-south region is divided by the Qinling Mountains and Huaihe River heating dividing lines.

[0018] Preferably, in step S1, the meteorological data includes daily average temperature, daily maximum temperature, daily minimum temperature, daily average relative humidity, daily average wind speed, daily average surface pressure, and daily total precipitation;

[0019] In step S1, it also includes collecting the daily average PM of the region in the at least five natural years. 2.5 Air pollution data including concentration and daily average O3 concentration.

[0020] Preferably, step S3 also includes estimating the relationship between the incidence of chest pain diseases and the intensity of the cold wave from the first day of the cold wave to the 14th day after the cold wave, so as to analyze the lag effect.

[0021] To achieve the above objectives, the present disclosure also adopts the following technical solutions:

[0022] A system for assessing the risk of chest pain based on hypothermia events, the system being used to implement any of the aforementioned methods, comprising:

[0023] A data collection unit, used to obtain the incidence data of STEMI, NSTEMI, UA, AD and PE in a certain region within at least five natural years, and obtain the meteorological data including the atmospheric temperature of the region within the at least five natural years;

[0024] Cold wave definition unit, used to define high intensity cold waves, moderate intensity cold waves and low intensity cold waves;

[0025] The data analysis unit is used to estimate the relationship between the incidence of chest pain diseases and cold waves using the following model using a time-stratified case-crossover design:

[0026] E(Logistic(Y i ))=α+βCS i +γStratumt i +ns(RHmean,3)+δHoliday i

[0027] Among them, Y i Indicates whether STEMI, NSTEMI, UA, AD, and PE occur in case i, CS i represents the intensity of the cold wave experienced by case i on the case day and the control day, Stratumt i represents the time stratification of case i defined by the time stratified case-control matching method, RHmean represents the daily average relative humidity, Holiday i Indicates whether it is a holiday;

[0028] Among them, CS i The values ​​of are: 0 for non-cold wave days, 1, 2 and 3 for low-intensity cold wave days, moderate-intensity cold wave days and high-intensity cold wave days respectively.

[0029] Preferably, it also includes a data cleaning unit for cleaning the onset data: deleting data that lacks any information of disease diagnosis, gender, age, residential address and onset time.

[0030] The technical solution claimed in the present disclosure has achieved the following beneficial effects:

[0031] A time-stratified case-crossover design was used to provide effective evidence for the key quantitative target intensity values ​​for preventing cold waves, especially to provide effective research evidence for intervention measures to deal with specific diseases under different cold wave levels, which can provide strong support for customized intervention measures to prevent chest pain diseases in winter. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are merely embodiments of the present disclosure, and a person of ordinary skill in the art can obtain other drawings based on the provided drawings without paying any creative work.

[0033] Figure 1 Schematic diagram of the process of data cleaning for the collected incidence data.

[0034] Figure 2The exposure-response relationship between different cold wave intensities (low, moderate, and high) and the risk of STEMI (lag 6), NSTEMI (lag 5), UA (lag 5), AD (lag 1), and PE (lag 1).

[0035] Figure 3 The exposure-response relationship between different cold wave intensities (low, moderate, and high) and the risk of STEMI, NSTEMI, UA, AD, and PE from lag 0 to lag 14 days.

[0036] Figure 4 The exposure-response relationship between cold waves (lasting 1 day or longer) of different intensities (low, moderate, and high) and the risk of STEMI, NSTEMI, UA, AD, and PE from lag 0 to lag 14 days.

[0037] Figure 5 Schematic diagram of the evaluation system module. DETAILED DESCRIPTION

[0038] In order to make the purpose, technical solution and beneficial effects of the embodiments of the present disclosure clearer, the technical solution in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments of the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure.

[0039] Example 1

[0040] This embodiment uses the following process to assess the risk of chest pain based on hypothermia events.

[0041] Data collection:

[0042] The chest pain disease incidence data collected by the data collection unit of this embodiment comes from the China Cardiovascular Health Alliance-Chest Pain Center Database, including STEMI (International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10: I21.0-I21.3)), (ICD-10: I21.4), UA (ICD-10: I20.0), AD (ICD-10: I71.0) and PE (ICD-10: I26.0 and I26.9) from January 1, 2015 to December 31, 2020, including STEMI, NSTEMI, UA, AD, and PE cases reported in 1,766 counties and districts in China from November to March 2020 from 2015. This embodiment also deletes cases that lack any of the following personal information through the data cleaning unit: disease diagnosis, gender, age, residential address, and onset time. Please refer to the attached data cleaning process. Figure 1 .

[0043] This example uses 607,883 cases from 2,680 chest pain center hospitals in 1,766 districts and counties in China. The research sample size is large, the internal consistency is good, and the data quality is controllable. And the incidence data is representative: the distribution of the research sites is consistent with the population distribution in China, and the number of chest pain centers in each province is relatively balanced with the proportion of all secondary and above medical institutions. Secondly, the data has a large north-south span, with a latitude difference of 49°, covering 5 different climate zones in China, so the exposure is highly representative, the statistical validity is high, and the results are more reliable.

[0044] The data acquisition unit of this embodiment also obtains hourly atmospheric temperature, relative humidity, wind speed, surface air pressure and precipitation data at a 0.1°×0.1° grid level from the European Centre for Medium-Range Weather Forecasts (www.ecmwf.int / en). 2.5 The O3 concentration data are from the China High-resolution and High-quality Near-surface Air Pollutant Dataset (https: / / weijing-rs.github.io / product.html), which has been widely used before. The daily average temperature, daily maximum temperature, daily minimum temperature, daily average relative humidity, daily average wind speed, daily average surface pressure, daily total precipitation, and daily average PM are calculated based on the hourly value data at the grid level. 2.5 The grid data were then matched to the grid closest to the district and county geospatial center, and the corresponding county-level data were calculated. For each case and control day, the district and county were matched with the corresponding meteorological and air pollution data, and the corresponding hospital grid data were calculated for sensitivity analysis.

[0045] Definition of cold wave:

[0046] According to previous studies, the definition of cold waves includes thresholds and durations, while the percentile of temperature is mainly used as a threshold in environmental health research. In previous studies, the thresholds used to define cold waves ranged from the 1st percentile to the 10th percentile. Not only do cold waves defined as thresholds with the 1st percentile have health effects, but cold waves defined as thresholds with the 10th percentile also have health effects. Therefore, this embodiment defines cold waves of the 1st, 5th, and 10th percentiles as three intensity stratification levels: low, medium, and high.

[0047] The cold wave intensity stratification is obtained in three steps.

[0048] Step 1: Determine the threshold and calculate percentiles using the daily average temperature data for the entire year from 2015 to 2020, including the 1st, 5th, and 10th percentiles.

[0049] Step 2: Define cold waves. Three definitions of cold waves are defined: 1) The daily average temperature is below the 1st percentile. 2) The daily average temperature is below the 5th percentile. 3) The daily average temperature is below the 10th percentile. In this step, only November to March of each year are marked.

[0050] Step 3: Cold wave intensity stratification: 1) High intensity cold wave days, the daily average temperature is lower than the 1st percentile. 2) Moderate intensity cold wave days, the daily average temperature is lower than the 5th percentile, excluding high intensity cold wave days. 3) Low intensity cold wave days, the daily average temperature is lower than the 10th percentile, excluding high intensity cold wave days and moderate intensity cold wave days. Non-cold wave days are days from November to mid-March that do not include the above three types of cold waves. The identification of cold waves is based on the temperature level of each county.

[0051] In order to verify the rationality of the above-mentioned main cold wave definitions, this embodiment adjusts a series of definitions from three aspects: temperature index, threshold and duration. Temperature index: Different temperature indexes are used, including daily average temperature, daily maximum temperature and daily minimum temperature. For the regional resolution based on temperature index, the temperature of the grid where the district and county center is located and the temperature of the grid where the hospital is located are used. Threshold: The 2nd and 3rd percentiles are used as high intensity thresholds, the 4th and 6th percentiles are used as medium intensity thresholds, and the 8th and 9th percentiles are used as low intensity thresholds. Duration: 1, 2, 3, 4, 5, 6 and 7 days or longer are used. All definitions are shown in Table 1.

[0052] Table 1

[0053]

[0054]

[0055] Statistical analysis:

[0056] The data analysis unit used a time-stratified case-crossover design to estimate the relationship between short-term cold wave exposure and the risk of STEMI, NSTEMI, UA, AD, and PE in winter. For each patient, cold wave exposure on case days was compared with that on control days. Case days were defined as the day of onset. Control days were matched with case days in the same week, month, and year. The number of controls for each case was 3 to 4. This time-stratified case-control matching method has been used in many previous studies and is an effective optimal matching strategy.

[0057] The odds ratio (OR) of the incidence of each disease associated with short-term cold wave exposure was estimated by fitting a conditional logistic regression for all case days and control days. The value of case days was 1, and the value of matched control days was 0. The regression model used the type of cold wave day as the independent variable, with the value of non-cold wave days as 0 and the intensity values ​​of low-intensity, moderate-intensity, and high-intensity cold wave days as 1, 2, and 3, respectively. The daily average relative humidity was used as a covariate and controlled using natural splines with three degrees of freedom. Holidays were controlled as categorical covariates. We also considered a single-day lag of cold waves of 0 to 14 days to analyze the lag effect.

[0058] In the preferred scheme, subgroup analysis was also performed according to gender (male and female), age (<65 years and ≥65 years) and region (north and south). Among them, the Qinling Mountains and Huaihe River heating dividing line are used to distinguish the north and south. There is municipal central heating to the north of the boundary (north). There is no municipal central heating in the south. Therefore, this embodiment performs a two-sample test to evaluate the statistically significant difference between the estimates of the north and the south.

[0059] The present example uses the "survival" package of R software (version 3.6.3) for statistical analysis. The morbidity results associated with cold waves are reported in the form of OR and 95% confidence interval.

[0060] In the data collected in this embodiment, the number of STEMI cases was 217,377, the number of NSTEMI cases was 115,648, the number of UA cases was 248,215, the number of AD cases was 18,715, and the number of PE cases was 7,878 (Table 3). A total of 486,740 cases occurred on non-cold wave days, 62,826 cases occurred on low-intensity cold wave days, 47,986 cases occurred on moderate-intensity cold wave days, and 10,281 cases occurred on high-intensity cold wave days. The average daily temperature in each county from November to March from 2015 to 2020 was 5.8°C. The average daily relative humidity was 66.1% (Table 4).

[0061] Table 3. Number of cases included in this example (number, percentage)

[0062]

[0063] The percentages in the table are the proportion of the number of cases in each group to the total number of cases.

[0064] Table 4. Descriptive analysis of meteorological and air pollution data from November to March 2015 to 2020 in 1766 counties and districts in China

[0065]

[0066] Figure 2 The highest single-day effect OR estimates for high-intensity cold waves within the 14-day evaluation period are shown. The OR value of STEMI is highest 6 days after a high-intensity cold wave. The OR values ​​of NSTEMI and UA are highest 5 days after a high-intensity cold wave. The OR values ​​of AD and PE are highest 1 day after a high-intensity cold wave. The risk of cold-wave-related STEMI, NSTEMI, UA, AD, and PE gradually increases from low-intensity cold waves to moderate-intensity cold waves, and then to high-intensity cold waves. The method in this embodiment adopts a time-stratified case crossover design to provide effective evidence for the key quantitative target intensity values ​​for preventing cold waves, especially to provide effective research evidence for interventions to deal with specific diseases at different cold wave levels, which can provide strong support for customized interventions to prevent chest pain diseases in winter.

[0067] Example 2

[0068] Reference Figure 5 This embodiment provides a system for assessing the risk of chest pain based on hypothermia events. The system is used to implement the method described in Embodiment 1, and includes:

[0069] The data collection unit is used to obtain the incidence data of STEMI, NSTEMI, UA, AD and PE in a certain area within at least five natural years, and obtain the meteorological data including atmospheric temperature and the daily average PM in the area within at least five natural years. 2.5 Air pollution data including concentration and daily average O3 concentration;

[0070] A cold wave definition unit, used to define a high intensity cold wave, a medium intensity cold wave and a low intensity cold wave in Example 1;

[0071] The data analysis unit is used to estimate the relationship between the incidence of chest pain diseases and cold waves using the following model using a time-stratified case-crossover design:

[0072] E(Logistic(Y i ))=α+βCS i +γStratumt i +ns(RHmean,3)+δHoliday i

[0073] Among them, Y i Indicates whether STEMI, NSTEMI, UA, AD, and PE occur in case i, CS i represents the intensity of the cold wave experienced by case i on the case day and the control day, Stratumt i represents the time stratification of case i defined by the time stratified case-control matching method, RHmean represents the daily average relative humidity, Holiday i Indicates whether it is a holiday;

[0074] Among them, CS on non-cold wave days i The value is 0, the CS of low intensity cold wave day, moderate intensity cold wave day and high intensity cold wave day i The intensity values ​​are 1, 2, and 3.

[0075] Data cleaning unit, used to Figure 1 The method shown cleans the incidence data by deleting data that lacks any information on disease diagnosis, gender, age, residential address, and onset time.

[0076] The embodiments described above are merely illustrative descriptions of the present disclosure and are not intended to limit the scope of the present disclosure. Without departing from the design spirit of the present disclosure, various modifications and improvements made to the technical solutions of the present disclosure by ordinary technicians in this field should all fall within the protection scope determined by the present disclosure.

Claims

1. A method for assessing the risk of chest pain based on hypothermia events, characterized in that: The following steps are involved: S1. Obtain the incidence data of STEMI, NSTEMI, UA, AD and PE in a certain region within at least five natural years; and obtain the meteorological data including atmospheric temperature in the region within the at least five natural years; S2. Calculate the percentiles of the daily average temperature in the region, and define a high-intensity cold wave day as a day when the daily average temperature is lower than the 1st percentile; define a moderate-intensity cold wave day as a day when the daily average temperature is lower than the 5th percentile; and define a low-intensity cold wave day as a day when the daily average temperature is lower than the 10th percentile; S3. The relationship between the risk of chest pain and cold waves was estimated using the following model using a time-stratified case-crossover design: E(Logistic(Y i ))=a+βCS i +γStratumt i +ns(RHmean,3)+δHolliday i Among them, Y i Indicates whether STEMI, NSTEMI, UA, AD, and PE occur in case i, CS i represents the intensity of the cold wave experienced by case i on the case day and the control day, Stratumt i represents the time stratification of case i defined by the time stratified case-control matching method, RHmean represents the daily average relative humidity, Holiday i Indicates whether it is a holiday; Among them, CS i The values ​​of are: 0 for non-cold wave days, 1, 2 and 3 for low-intensity cold wave days, moderate-intensity cold wave days and high-intensity cold wave days respectively.

2. The method according to claim 1, characterized in that: The method also includes a step S11 of cleaning the onset data obtained in step S1: deleting data that lacks any information on disease diagnosis, gender, age, residential address and onset time.

3. The method according to claim 1, characterized in that In step S3, a subgroup analysis is also performed on the relationship between the incidence of chest pain and the intensity of cold waves according to gender, age, and north-south regions, where the north-south regions are divided by the Qinling Mountains and the Huaihe River heating dividing line.

4. The method according to claim 1, characterized in that In step S1, the meteorological data includes daily average temperature, daily maximum temperature, daily minimum temperature, daily average relative humidity, daily average wind speed, daily average surface pressure, and daily total precipitation; In step S1, it also includes collecting the daily average PM of the region in the at least five natural years. 2.5 Air pollution data including concentration and daily average O3 concentration.

5. The method according to claim 1, characterized in that In step S3, it also includes estimating the relationship between the incidence of chest pain diseases and the intensity of the cold wave from the first day of the cold wave to the 14th day after the cold wave, so as to analyze the lag effect.

6. A system for assessing the risk of chest pain based on hypothermia events, characterized in that: The system is used to implement the method according to any one of claims 1 to 5, comprising: A data collection unit, used to obtain the incidence data of STEMI, NSTEMI, UA, AD and PE in a certain region within at least five natural years, and obtain the meteorological data including the atmospheric temperature of the region within the at least five natural years; Cold wave definition unit, used to define high intensity cold waves, moderate intensity cold waves and low intensity cold waves; The data analysis unit is used to estimate the relationship between the incidence of chest pain diseases and cold waves using the following model using a time-stratified case-crossover design: E(Logistic(Y i )=α+βCS i +γStratumt i +ns(RHmean, 3)+δHoliday i Among them, Y i Indicates whether STEMI, NSTEMI, UA, AD, and PE occur in case i, CS i represents the intensity of the cold wave experienced by case i on the case day and the control day, Stratumt i represents the time stratification of case i defined by the time stratified case-control matching method, RHmean represents the daily average relative humidity, Holiday i Indicates whether it is a holiday; Among them, CS i The values ​​of are: 0 for non-cold wave days, 1, 2 and 3 for low-intensity cold wave days, moderate-intensity cold wave days and high-intensity cold wave days respectively.

7. The system according to claim 6, characterized in that It also includes a data cleaning unit for cleaning the onset data: deleting data that lacks any information on disease diagnosis, gender, age, residential address and onset time.

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