CN111261300A - Method for acquiring normal predicted value of lung function parameter - Google Patents
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- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
Abstract
The invention relates to a method for acquiring a normal predicted value of a lung function parameter. The invention adopts a large cross section sample research data of multilayer random sampling, aims to establish a method for acquiring the normal predicted value of lung function of adults in Chinese healthy Han nationality and provides a reliable basis for interpretation of clinical lung function measurement results and evaluation of patient conditions. The expected lung function value obtained by the invention can judge whether the measured lung function value of the subject is abnormal or not, is beneficial to clinical evaluation of lung function conditions of healthy people and patients with various respiratory system diseases, and assists diagnosis, severity evaluation and curative effect evaluation of diseases.
Description
Technical Field
The invention relates to a method for calculating a normal predicted value of lung function parameters of adults in Chinese healthy Han nationality.
Background
The lung function test can be widely applied to diagnosis and evaluation of respiratory system diseases, and the result of the lung function test contains a plurality of parameters which are compared with reference values of normal individuals to judge whether lung function damage exists. The expected value of the individual lung function is influenced by factors such as age, sex, height, race, etc., and the definition of "normal lung function" also needs to consider individual, socioeconomic and environmental factors, including nutritional status, occupation, air quality and motion condition. In recent years, with the remarkable improvement of the socioeconomic level of China, the factors change along with time and have geographical differences, so that accurate clinical assessment and decision are dependent on the continuous updating of regional lung function reference values.
At present, the normal reference value of the lung function is mainly obtained by formula calculation, the calculation formula is from the analysis modeling of large-scale population research data, and variables comprise age, sex, height, race and the like. The currently adopted normal reference value data mainly comes from caucasians, such as GLI2012 formula, NHANES III formula, etc., and some researches attempt to adapt these methods to be applied to chinese population. Most lung function instruments and lung function laboratories in China adopt the methods to obtain normal predicted values, but the applicability of the methods in Chinese population is not supported by sufficient evidence.
The national normal reference value calculation method for lung function of China, which is published for the first time in 1988, establishes calculation formulas for six different regions in the country respectively, but the sample size researched at that time is small, the difference between a measuring instrument and a measuring method and the currently used instrument and method is large, social, economic and environmental factors, the nutritional status of Chinese people and the exercise condition are changed more than 30 years ago, and the clinical application value of the method is limited. Another study reports a calculation method of normal reference values of lung functions of children and adults in multiple provinces and cities in the country in 2007-2010, and the grouped objects are physical examination people, so that the representativeness of the data has limitations; the method for evaluating the normal value of the lung function of the local region is updated in 1998 and 2011 in Shanghai, but the data sample size is small, and more data are needed to evaluate the applicability of the method.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: at present, a method for acquiring the normal expected value of the lung function, which is suitable for healthy adults in China, from data with large sample size, strong representativeness, accuracy and reliability is lacked.
In order to solve the above technical problem, an aspect of the present invention provides a method for obtaining a normal expected value of a lung function parameter, including:
step 1, 3372 healthy volunteers of Han nationalities in a plurality of nationalities in a Chinese target area are extracted, wherein the healthy volunteers are defined as the volunteers without history of COPD, chronic bronchitis, emphysema, asthma, tuberculosis, interstitial lung disease and lung cancer, chronic cough, expectoration, wheezing or dyspnea, no smoking history or smoking history for less than 10 years, no chest operation history, no history of myocardial infarction in the last 3 months and no hospitalization for heart diseases in the last 1 month, no abnormality in chest X-ray examination, no overweight, pregnancy or use of β receptor blocker;
step 2, obtaining the sex, age and height of each healthy volunteer, and performing lung function test on each healthy volunteer to obtain lung function parameters of each healthy volunteer, wherein the lung function parameters comprise: a forced expiratory volume FEV1 of 1 second, a forced vital capacity FVC, a one-second rate FEV1/FVC, a vital capacity VC, a deep inspiratory capacity IC, a peak expiratory flow rate PEF, an expiratory flow rate FEF25 when the forced expiratory is 25% of the vital capacity, an expiratory flow rate FEF50 when the forced expiratory is 50% of the vital capacity, an expiratory flow rate FEF75 when the forced expiratory is 75% of the vital capacity, an average flow FEF25-75 when the forced expiratory is 3% -75% of the vital capacity, a forced expiratory volume FEV3 of 3 seconds, and a forced expiratory volume FEV6 of 6 seconds;
step 3, the age, age 2, height and height of each healthy volunteer obtained in the step 22Taking the corresponding lung function parameters as an example of a training sample, and dividing all the training samples into a male training sample set and a female training sample set according to the gender;
and 4, utilizing the male training sample set and the female training sample set obtained in the step 3, taking the age, the age 2, the height and the height 2 as independent variables, and performing modeling and training on each lung function parameter y serving as a dependent variable by using a least square function-based multiple linear regression method through R language software to obtain the following formula (1):
y-b 0+ b1 × age + b2 × age2+ b3 × height + b4 × height2(1)
In the case of male, the values of the different constant terms b0 and partial regression coefficients b1 to b4 according to the lung function parameters y to be calculated are shown in the following table 1, and in the case of female, the values of the different constant terms b0 and partial regression coefficients b1 to b4 according to the lung function parameters y to be calculated are shown in the following table 2:
TABLE 1
TABLE 2
And 5, acquiring the age and height of the current patient in real time, selecting corresponding constant term b0 and values of partial regression coefficients b1 to b4 from the table 1 or the table 2 according to the formula (1) in the step 4 according to the specific lung function parameters and the gender of the current patient which need to be estimated, and calculating to obtain the normal predicted value of the lung function parameters of the current patient.
Preferably, in the step 1, the healthy volunteers are extracted by adopting a multi-layer random sampling method.
Preferably, in step 1, the overweight refers to: body weight>136kg，BMI>35kg/m2。
The invention adopts a large cross section sample research data of multilayer random sampling, aims to establish a method for acquiring the normal predicted value of lung function of adults in Chinese healthy Han nationality and provides a reliable basis for interpretation of clinical lung function measurement results and evaluation of patient conditions. The expected lung function value obtained by the invention can judge whether the measured lung function value of the subject is abnormal or not, is beneficial to clinical evaluation of lung function conditions of healthy people and patients with various respiratory system diseases, and assists diagnosis, severity evaluation and curative effect evaluation of diseases.
The invention has the following advantages:
(1) the invention is from the latest cross section research data, and the research sample amount is large, the sample representativeness is strong, and the data is accurate and reliable.
(2) The model and the parameters contained in the invention are obtained by screening and calculating through a statistical method, and the method is rigorous and scientific and has accurate calculation results.
(3) Compared with foreign NHANES, GLI formulas and the like, the formula contained in the invention adopts a polynomial form, only contains three parameters of sex, age and height, is convenient to calculate and is suitable for various pulmonary function instruments.
Detailed Description
The invention will be further illustrated with reference to the following specific examples. It should be understood that these examples are for illustrative purposes only and are not intended to limit the scope of the present invention. Further, it should be understood that various changes or modifications of the present invention may be made by those skilled in the art after reading the teaching of the present invention, and such equivalents may fall within the scope of the present invention as defined in the appended claims.
The invention provides a method for acquiring a normal predicted value of a lung function parameter, which starts from high-quality large-sample cross section research data, takes the lung function parameter as a dependent variable and takes age and age2Height, height2And the gender is independent variable, and a model is trained by adopting a multivariate linear regression method, so that a method for acquiring the normal predicted value of the lung function parameter of the Chinese Han nationality adult is provided. The invention specifically comprises the following steps:
step 1, obtaining a sample
The sample data obtained by the method comes from latest epidemiological investigation, and compared with the source of the previously used calculation formula, the sample size is larger, the sampling method is more scientific, the lung function detection quality is higher, and the novelty and the scientificity of the method for acquiring the normal value of the lung function in the patent are ensured. The sample data contained 5281 total adult volunteer data from Shanghai City of the Chinese Lung health (CPH) study. Volunteer recruitment takes a multi-level random sampling method, and randomly selects 4 districts (counties) from 18 districts (counties) in Shanghai city, wherein 2 are located in urban areas and 2 are located in suburban areas. A community is randomly selected from each district (county), residents of 20-89 years old residents participate in the research at random, and only one person participates in each family.
Step 2, sample screening
From the population in step 1, only healthy volunteers with the nation of Han were selected. Healthy volunteers were defined as: history of past chronic obstructive pulmonary disease (chronic obstructive pulmonary disease) COPD, chronic bronchitis, emphysema, asthma, pulmonary tuberculosis, interstitial lung disease and lung cancer; no symptoms of chronic cough, expectoration, wheezing or dyspnea; no smoking history or smoking history<10 years; no history of thoracic surgery; no history of myocardial infarction in about 3 months and no hospitalization for heart disease in about 1 month; no abnormality in chest X-ray examination; without overweight (body weight)>136kg，BMI>35kg/m2) The final lung function normality prediction model was analyzed using a total of 3372 healthy volunteers, excluding 1909 (36.1% of the total population in step 1) incongruent volunteers, the final cohort was between 20 and 88 years old, the average age was 54.9 (quartering distance IQR 46-62) years old, the average height was 1.60(IQR 1.55-1.65) m, and the average weight was 61.4(IQR 54.8-68) kg.
Step 3, carrying out lung function test method and quality control on the sample
And (3) carrying out a lung function test on each sample obtained in the step (2), wherein the lung function test is completed by technicians with experience and qualification, and the test process is strictly carried out according to a lung function detection standard. And the quality control of the lung function test data is carried out according to the American thoracic society and European respiratory syndrome standards, and data with the detection result quality lower than C grade are excluded. Obtaining lung function parameters for each sample by a lung function test, the lung function parameters comprising: volume of forced expiration of 1 second FEV1, forced vital capacity FVC, rate of one second FEV1/FVC, vital capacity VC, deep inspiration IC, peak expiratory flow rate PEF, expiratory flow rate FEF25 for forced expiration of 25% vital capacity, expiratory flow rate FEF50 for forced expiration of 50% vital capacity, expiratory flow rate FEF75 for forced expiration of 75% vital capacity, average flow FEF25-75 for forced expiration of 3% -75% vital capacity, volume of forced expiration of 3 second FEV3, and volume of forced expiration of 6 second FEV 6.
And 4, performing statistical modeling, comprising the following steps:
step 401, selecting a modeling method
In order to obtain the normal expected value of the lung function indexes such as FEV1, FVC, FEV1/FVC, VC, IC, PEF and FEF 25-75%, the factors related to the lung function parameters include age, sex, height, race, etc. according to the prior literature reports. And modeling the relationship between the lung function parameters (dependent variables) and the independent variables by using the influence factors as independent variables and utilizing a least square function to obtain a multiple linear regression equation of the normal predicted value.
Step 402, selection of an argument
In order to make the final expected value formula easy to use, regression models are respectively established for different sexes. The study population included only the Han nationality, and thus the "race" variables were not included in the model. Meanwhile, through a large amount of literature investigation, adding the quadratic power of age and height to the model can improve the fitting degree of the model, so the age and the age are selected to realize the best fitting2Height, height2As an independent variable.
Step 403, modeling
The age and age of each healthy volunteer obtained in the previous step2Height and height2And taking the corresponding lung function parameters as an example of a training sample, and dividing all the training samples into a male training sample set and a female training sample set according to the gender. Using the male training sample set and the female training sample set to determine the age and age2Height and height2As independent variables, R language software is adopted to carry out modeling and training on each lung function parameter y as dependent variables respectively by a least square function-based multiple linear regression method to obtain the following formula (1),
y-b 0+ b1 × age + b2 × age2+ b3 × height + b4 × height2(1)
In the case of male, the values of the different constant terms b0 and partial regression coefficients b1 to b4 according to the lung function parameters y to be calculated are shown in the following table 1, and in the case of female, the values of the different constant terms b0 and partial regression coefficients b1 to b4 according to the lung function parameters y to be calculated are shown in the following table 2:
| index of lung function | b0 | b1 | b2 | b3 | b4 |
| FEV1(L) | 1.1259 | -0.0141 | -0.0001 | 0.0000 | 1.1108 |
| FEV1/FVC(％) | -188.1673 | -0.3331 | 0.0000 | 349.9744 | -107.2381 |
| FVC(L) | 0.1157 | 0.0000 | -0.0002 | 0.0000 | 1.5509 |
| IC(L) | 15.7974 | 0.0428 | -0.0005 | -20.4883 | 7.3397 |
| VC(L) | -0.2156 | 0.0114 | -0.0003 | 0.0000 | 1.6196 |
| FEF25(L) | -2.6416 | 0.0574 | -0.0011 | 5.6758 | 0.0000 |
| FEF25-75％(L) | 3.2896 | -0.0872 | 0.0003 | 1.8502 | 0.0000 |
| FEF50％(L) | 1.8142 | -0.0258 | -0.0003 | 2.4592 | 0.0000 |
| FEF75％(L) | 2.5468 | -0.0763 | 0.0004 | 0.8825 | 0.0000 |
| FEV3(L) | 0.4548 | 0.0000 | -0.0003 | 0.0000 | 1.4099 |
| FEV6(L) | 0.3470 | 0.0000 | -0.0002 | 0.0000 | 1.4971 |
| PEF(L/s) | -4.4912 | 0.0696 | -0.0012 | 7.0816 | 0.0000 |
TABLE 1
TABLE 2
In the calculation process, the type III square sum and the R of each lung function parameter model are obtained simultaneously2。R2The larger the number, the higher the model fit to the sample. The above-mentioned model pairThe key indexes FEV1 have goodness of fit R2 of 0.43 (male), 0.43 (female) and FVC2The degree of fitting is 0.58 (male) and 0.57 (female).
And 5, acquiring the age and height of the current patient in real time, selecting corresponding constant term b0 and values of partial regression coefficients b1 to b4 from the table 1 or the table 2 according to the formula (1) in the step 4 according to the specific lung function parameters and the gender of the current patient which need to be estimated, and calculating to obtain the normal predicted value of the lung function parameters of the current patient.
Example (c): 31 year old male, Han nationality, height 1.70m, calculate FEV1 expected value. Looking up the calculation parameters of FEV1 in the male formula parameter table, substituting the parameters, age and height, and predicting the formula according to the lung function index: y-b 0+ b1 × age + b2 × age2+ b3 × height + b4 × height2The calculation is as follows:
FEV1 expects (L) 1.1259-0.0141 × 31-0.0001 × 312+0 × 1.70+1.1108 × 1.702 to 3.8029.
The method for calculating the lung function normal predicted value of the Chinese healthy adult is reliable and wide in application range, obtains three indexes of age, sex and height of a testee, and can calculate the normal predicted value of each lung function parameter. The method can be set as a program for calculating the expected value of the lung function instrument in each level of hospitals, and can be used for evaluating the lung function required by clinic and scientific research. Determining whether obstructive or restrictive ventilation dysfunction is present based on the patient's lung function measured/expected percentage, e.g., FVC measured/expected percentage < 80%, indicating restrictive ventilation dysfunction, and a lower value indicating greater severity of ventilation dysfunction.
Claims (3)
1. A method for obtaining a normal predicted value of a lung function parameter is characterized by comprising the following steps:
step 1, extracting 3372 healthy volunteers of Han nationalities in a plurality of nationalities in a Chinese target area, and defining the healthy volunteers as no history of chronic obstructive pulmonary disease COPD, chronic bronchitis, emphysema, asthma, tuberculosis, interstitial lung disease and lung cancer, no symptoms of chronic cough, expectoration, wheezing or dyspnea, no history of smoking or smoking history of less than 10 years, no history of chest surgery, no history of myocardial infarction in last 3 months and no hospitalization for heart disease in last 1 month, no abnormality in chest X-ray examination, no overweight, pregnancy or use of β receptor blocker;
step 2, obtaining the sex, age and height of each healthy volunteer, and performing lung function test on each healthy volunteer to obtain lung function parameters of each healthy volunteer, wherein the lung function parameters comprise: a forced expiratory volume FEV1 of 1 second, a forced vital capacity FVC, a one-second rate FEV1/FVC, a vital capacity VC, a deep inspiratory capacity IC, a peak expiratory flow rate PEF, an expiratory flow rate FEF25 when the forced expiratory is 25% of the vital capacity, an expiratory flow rate FEF50 when the forced expiratory is 50% of the vital capacity, an expiratory flow rate FEF75 when the forced expiratory is 75% of the vital capacity, an average flow FEF25-75 when the forced expiratory is 3% -75% of the vital capacity, a forced expiratory volume FEV3 of 3 seconds, and a forced expiratory volume FEV6 of 6 seconds;
step 3, the age and age of each healthy volunteer obtained in the step 22Height and height2Taking the corresponding lung function parameters as an example of a training sample, and dividing all the training samples into a male training sample set and a female training sample set according to the gender;
step 4, utilizing the male training sample set and the female training sample set obtained in the step 3 to determine the age and the age2Height and height2As independent variables, modeling each lung function parameter y as dependent variable by adopting R language software to perform a least square function-based multiple linear regression method to obtain the following formula (1):
y-b 0+ b1 × age + b2 × age2+ b3 × height + b4 × height2(1)
In the case of male, the values of the different constant terms b0 and partial regression coefficients b1 to b4 according to the lung function parameters y to be calculated are shown in the following table 1, and in the case of female, the values of the different constant terms b0 and partial regression coefficients b1 to b4 according to the lung function parameters y to be calculated are shown in the following table 2:
TABLE 1
TABLE 2
And 5, acquiring the age and height of the current patient in real time, selecting corresponding constant term b0 and values of partial regression coefficients b1 to b4 from the table 1 or the table 2 according to the formula (1) in the step 4 according to the specific lung function parameters and the gender of the current patient which need to be estimated, and calculating to obtain the normal predicted value of the lung function parameters of the current patient.
2. The method for obtaining the expected normal lung function parameter value of claim 1, wherein in step 1, the healthy volunteers are sampled by multi-level random sampling.
3. The method according to claim 1, wherein the overweight in step 1 refers to: body weight>136kg，BMI>35kg/m2。
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112071424A (en) * | 2020-08-31 | 2020-12-11 | 吾征智能技术(北京)有限公司 | Construction method and system of disease prediction model based on vital capacity data |
| CN112992337A (en) * | 2021-02-07 | 2021-06-18 | 华南理工大学 | Lung function assessment algorithm, device, medium and equipment for cervical and spinal cord injury patient |
| CN114027824A (en) * | 2021-12-16 | 2022-02-11 | 上海交通大学 | Universal lung ventilation volume and transthoracic electrical impedance linear model construction method and application |
| CN114343610A (en) * | 2021-12-29 | 2022-04-15 | 深圳麦科田生物医疗技术股份有限公司 | Lung detection method and related device |
| CN114550945A (en) * | 2022-02-21 | 2022-05-27 | 湖北省疾病预防控制中心（湖北省预防医学科学院） | Method for repairing missing data in pulmonary function detection |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103153184A (en) * | 2010-08-13 | 2013-06-12 | 呼吸运动公司 | Devices and methods for respiratory variation monitoring by measurement of respiratory volumes, motion and variability |
| CN108109697A (en) * | 2017-12-20 | 2018-06-01 | 中国科学院合肥物质科学研究院 | A kind of pulmonary function test (pft) system and method based on expiration mathematical model |
-
2020
- 2020-01-16 CN CN202010045824.6A patent/CN111261300A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103153184A (en) * | 2010-08-13 | 2013-06-12 | 呼吸运动公司 | Devices and methods for respiratory variation monitoring by measurement of respiratory volumes, motion and variability |
| CN108109697A (en) * | 2017-12-20 | 2018-06-01 | 中国科学院合肥物质科学研究院 | A kind of pulmonary function test (pft) system and method based on expiration mathematical model |
Non-Patent Citations (2)
| Title |
|---|
| 任为英 等: "上海市成人肺功能医学参考值范围的初步研究", 《中国呼吸与危重监护杂志》, vol. 11, no. 3, 31 May 2012 (2012-05-31), pages 253 - 256 * |
| 舒正 等: "台湾地区正常人群通气肺功能指标正常值范围的预测", 《中华疾病控制杂志》, vol. 15, no. 8, 31 December 2011 (2011-12-31), pages 652 - 656 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112071424A (en) * | 2020-08-31 | 2020-12-11 | 吾征智能技术(北京)有限公司 | Construction method and system of disease prediction model based on vital capacity data |
| CN112992337A (en) * | 2021-02-07 | 2021-06-18 | 华南理工大学 | Lung function assessment algorithm, device, medium and equipment for cervical and spinal cord injury patient |
| CN114027824A (en) * | 2021-12-16 | 2022-02-11 | 上海交通大学 | Universal lung ventilation volume and transthoracic electrical impedance linear model construction method and application |
| CN114027824B (en) * | 2021-12-16 | 2023-08-18 | 上海交通大学 | Construction method and application of linear model of universal pulmonary ventilation and transthoracic electrical impedance |
| CN114343610A (en) * | 2021-12-29 | 2022-04-15 | 深圳麦科田生物医疗技术股份有限公司 | Lung detection method and related device |
| CN114550945A (en) * | 2022-02-21 | 2022-05-27 | 湖北省疾病预防控制中心（湖北省预防医学科学院） | Method for repairing missing data in pulmonary function detection |
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