CN110634569B - Uremia patient nutrition and calcium and phosphorus metabolism condition comprehensive evaluation device - Google Patents
Uremia patient nutrition and calcium and phosphorus metabolism condition comprehensive evaluation device Download PDFInfo
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Abstract
The invention discloses a comprehensive evaluation device for nutrition and calcium and phosphorus metabolism conditions of uremia patients. A comprehensive evaluation device for nutrition and calcium and phosphorus metabolism conditions of uremia patients is provided, which comprises: the computer programmed to perform the steps of: step 1: height, body mass index BMI, bioelectrical impedance and serum composition assay data are obtained for a plurality of end-stage renal failure dialysis patients, each with an accurate measurement time. The invention has the following beneficial effects: the comprehensive evaluation method for nutrition and calcium-phosphorus metabolism conditions of the final-stage renal failure patient fully utilizes the original data obtained by bioelectrical impedance measurement, and overcomes the inaccuracy of a fixed formula method in bioelectrical impedance measurement of human body components.
Description
Technical Field
The invention relates to the field of treatment and health evaluation of patients with end-stage renal failure, in particular to a comprehensive evaluation device for nutrition and calcium and phosphorus metabolism conditions of uremia patients.
Background
Malnutrition and disturbances of calcium and phosphorus metabolism are major complications in patients with end-stage renal failure. The treatment of renal failure is a comprehensive medical procedure, mainly including hemodialysis, peritoneal dialysis and other medication, and the treatment plan is made to depend on the detection of serum components. However, all of the existing methods for measuring serum components are based on chemical measurement, which are expensive and require frequent blood drawing although the measurement is accurate. Therefore, the serum composition of the patient in the end-stage renal failure needs to be evaluated in an accurate, convenient and quick way, so that the physical condition of the patient can be effectively evaluated, and the treatment is convenient for medical staff.
The traditional technology has the following technical problems:
the bioelectrical impedance technology is a new human body component measuring technology and is widely applied to the aspect of measuring the moisture and fat of the human body at present. However, in practice, it has been found that the results of the evaluation of the physical condition of patients with end-stage renal failure by the existing body composition measuring instruments based on the bioelectrical impedance technology do not correspond well to the symptoms of the patients. Therefore, it is important to improve the bioelectrical impedance technology to improve the versatility and accuracy of the bioelectrical impedance technology in the field of the assessment of the physical condition of patients with end-stage renal failure.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a comprehensive evaluation device for nutrition and calcium and phosphorus metabolism conditions of uremia patients, which provides a corresponding relation between a direct bioelectrical impedance measurement result and serum components, so as to evaluate the serum components of the patients by measuring the body electrical impedance of the patients with renal failure at the final stage, and further comprehensively evaluate the nutrition and calcium and phosphorus metabolism conditions of the patients.
In order to solve the above technical problems, the present invention provides a comprehensive evaluation device for nutrition and calcium and phosphorus metabolism of uremia patients, comprising: the computer programmed to perform the steps of:
step 1: obtaining the body height, body mass index BMI, bioelectrical impedance and serum component test data of a plurality of patients with end-stage renal failure dialysis, wherein each data has accurate measuring time;
step 2: for each bioelectrical impedance data, the most recently measured height and BMI data were used for processing: each piece of bioelectrical impedance data is divided by the height to form new data R1-R/H, X1-X/H; each piece of bioelectrical impedance data is divided by the BMI to form new data R2 ═ R/BMI and X2 ═ X/BMI; the four physical quantities together form a new bioelectrical impedance data set (R1, X1, R2 and X2); for each bioelectrical impedance data, connecting all the serum component test data within 48 hours after the data at the measuring time to form a series of bioelectrical impedance-serum component test data pairs (R1, X1, R2, X2, serum component test data);
and 3, carrying out further regression analysis on the data in the previous step, wherein the further regression analysis is divided into the following substeps:
a substep (a) of performing a univariate regression analysis, calculating the correlation and regression equation of each serum component and each of R1, X1, R2, and X2, and calculating the F-test significance (P value) of the correlation coefficient thereof; for each serum component, this step will form 4 regression equations and corresponding F test results;
and a substep (b) of performing a binary regression analysis, calculating binary correlations and regression equations of each serum component assay data and (R1, X1) and (R2, X2), and calculating F-test significance (P value) thereof. For each chemical quantity, 2 regression equations and corresponding F test results are formed in the step;
a substep (c) of performing a quaternary regression analysis, calculating a quaternary correlation and a regression equation of each of the serum component assay data and (R1, X1, R2, X2) four electrical impedance quantities, and calculating an F-test significance (P-value) thereof; for each chemical quantity, 1 regression equation and corresponding F test result are formed in the step;
a substep (d) of comparing the results of the three substeps, and selecting a result with the highest significance (P value) of the F test from the 7 equations for each chemical quantity as a final regression expression, so as to be used as a corresponding relation between the bioelectrical impedance and the serum component;
and 4, step 4: and (3) acquiring the height (H), BMI and bioelectrical impedance (bioelectrical resistance R and bioelectrical impedance X) of the patient to be detected according to the same process and method as in the step 1, and converting the height (H), BMI and bioelectrical impedance into bioelectrical impedance data (R1, X1, R2 and X2) according to the method in the step 2.
And 5: calculating an estimated value of each serum component index by substituting the bioelectrical impedance obtained in the step 3 into a corresponding relational expression between the bioelectrical impedance and each serum component based on the bioelectrical impedance data (R1, X1, R2, X2) calculated in the step 4;
step 6: and (5) comprehensively evaluating the nutrition and calcium and phosphorus metabolism conditions of the patient through the estimated value of the chemical index obtained in the step 5.
In one embodiment, regression analysis is performed on the data set obtained in step 2 using the SciPy statistical Algorithm library of Python.
In one embodiment, if the bioelectrical impedance and the serum component data have no or less simultaneously measured data, the data of the serum component data measured at a time after the bioelectrical impedance data is selected for each bioelectrical impedance data to be connected, the time being determined as the case may be.
In one embodiment, all data in the step 1 is recorded by one MySQL database system.
A computer-readable storage medium, on which a computer program is stored which, when executed by a processor, carries out the steps of:
step 1: obtaining the body height, body mass index BMI, bioelectrical impedance and serum component test data of a plurality of patients with end-stage renal failure dialysis, wherein each data has accurate measuring time;
step 2: for each bioelectrical impedance data, the most recently measured height and BMI data were used for processing: each piece of bioelectrical impedance data is divided by the height to form new data R1-R/H, X1-X/H; each piece of bioelectrical impedance data is divided by the BMI to form new data R2 ═ R/BMI and X2 ═ X/BMI; the four physical quantities together form a new bioelectrical impedance data set (R1, X1, R2 and X2); for each bioelectrical impedance data, connecting all the serum component test data within 48 hours after the data at the measuring time to form a series of bioelectrical impedance-serum component test data pairs (R1, X1, R2, X2, serum component test data);
and 3, carrying out further regression analysis on the data in the previous step, wherein the further regression analysis is divided into the following substeps:
a substep (a) of performing a univariate regression analysis, calculating the correlation and regression equation of each serum component and each of R1, X1, R2, and X2, and calculating the F-test significance (P value) of the correlation coefficient thereof; for each serum component, this step will form 4 regression equations and corresponding F test results;
and a substep (b) of performing a binary regression analysis, calculating binary correlations and regression equations of each serum component assay data and (R1, X1) and (R2, X2), and calculating F-test significance (P value) thereof. For each chemical quantity, 2 regression equations and corresponding F test results are formed in the step;
a substep (c) of performing a quaternary regression analysis, calculating a quaternary correlation and a regression equation of each of the serum component assay data and (R1, X1, R2, X2) four electrical impedance quantities, and calculating an F-test significance (P-value) thereof; for each chemical quantity, 1 regression equation and corresponding F test result are formed in the step;
a substep (d) of comparing the results of the three substeps, and selecting a result with the highest significance (P value) of the F test from the 7 equations for each chemical quantity as a final regression expression, so as to be used as a corresponding relation between the bioelectrical impedance and the serum component;
and 4, step 4: and (3) acquiring the height (H), BMI and bioelectrical impedance (bioelectrical resistance R and bioelectrical impedance X) of the patient to be detected according to the same process and method as in the step 1, and converting the height (H), BMI and bioelectrical impedance into bioelectrical impedance data (R1, X1, R2 and X2) according to the method in the step 2.
And 5: calculating an estimated value of each serum component index by substituting the bioelectrical impedance obtained in the step 3 into a corresponding relational expression between the bioelectrical impedance and each serum component based on the bioelectrical impedance data (R1, X1, R2, X2) calculated in the step 4;
step 6: and (5) comprehensively evaluating the nutrition and calcium and phosphorus metabolism conditions of the patient through the estimated value of the chemical index obtained in the step 5.
In one embodiment, regression analysis is performed on the data set obtained in step 2 using the SciPy statistical Algorithm library of Python.
In one embodiment, if the bioelectrical impedance and the serum component data have no or less simultaneously measured data, the data of the serum component data measured at a time after the bioelectrical impedance data is selected for each bioelectrical impedance data to be connected, the time being determined as the case may be.
In one embodiment, all data in step 1 is recorded by one MySQL database system.
A processor for running a program, wherein the program when run performs the steps of:
step 1: obtaining the body height, body mass index BMI, bioelectrical impedance and serum component test data of a plurality of patients with end-stage renal failure dialysis, wherein each data has accurate measuring time;
step 2: for each bioelectrical impedance data, the most recently measured height and BMI data were used for processing: each piece of bioelectrical impedance data is divided by the height to form new data R1-R/H, X1-X/H; each piece of bioelectrical impedance data is divided by the BMI to form new data R2 ═ R/BMI and X2 ═ X/BMI; the four physical quantities together form a new bioelectrical impedance data set (R1, X1, R2 and X2); for each bioelectrical impedance data, connecting all the serum component test data within 48 hours after the data at the measuring time to form a series of bioelectrical impedance-serum component test data pairs (R1, X1, R2, X2, serum component test data);
and 3, carrying out further regression analysis on the data in the previous step, wherein the further regression analysis is divided into the following substeps:
a substep (a) of performing a univariate regression analysis, calculating the correlation and regression equation of each serum component and each of R1, X1, R2, and X2, and calculating the F-test significance (P value) of the correlation coefficient thereof; for each serum component, this step will form 4 regression equations and corresponding F test results;
and a substep (b) of performing a binary regression analysis, calculating binary correlations and regression equations of each serum component assay data and (R1, X1) and (R2, X2), and calculating F-test significance (P value) thereof. For each chemical quantity, 2 regression equations and corresponding F test results are formed in the step;
a substep (c) of performing a quaternary regression analysis, calculating a quaternary correlation and a regression equation of each of the serum component assay data and (R1, X1, R2, X2) four electrical impedance quantities, and calculating an F-test significance (P-value) thereof; for each chemical quantity, 1 regression equation and corresponding F test result are formed in the step;
a substep (d) of comparing the results of the three substeps, and selecting a result with the highest significance (P value) of the F test from the 7 equations for each chemical quantity as a final regression expression, so as to be used as a corresponding relation between the bioelectrical impedance and the serum component;
and 4, step 4: and (3) acquiring the height (H), BMI and bioelectrical impedance (bioelectrical resistance R and bioelectrical impedance X) of the patient to be detected according to the same process and method as in the step 1, and converting the height (H), BMI and bioelectrical impedance into bioelectrical impedance data (R1, X1, R2 and X2) according to the method in the step 2.
And 5: calculating an estimated value of each serum component index by substituting the bioelectrical impedance obtained in the step 3 into a corresponding relational expression between the bioelectrical impedance and each serum component based on the bioelectrical impedance data (R1, X1, R2, X2) calculated in the step 4;
step 6: and (5) comprehensively evaluating the nutrition and calcium and phosphorus metabolism conditions of the patient through the estimated value of the chemical index obtained in the step 5.
In one embodiment, if the bioelectrical impedance and the serum component data have no or less simultaneously measured data, the data of the serum component data measured at a time after the bioelectrical impedance data is selected for each bioelectrical impedance data to be connected, the time being determined as the case may be.
In one embodiment, if the bioelectrical impedance and the serum component data have no or less simultaneously measured data, the data of the serum component data measured at a time after the bioelectrical impedance data is selected for each bioelectrical impedance data to be connected, the time being determined as the case may be.
The invention has the beneficial effects that:
according to the comprehensive evaluation method for the nutrition and calcium-phosphorus metabolism conditions of the terminal renal failure patient, the original data obtained by bioelectrical impedance measurement is fully utilized, and the inaccuracy of a fixed formula method in bioelectrical impedance human body component measurement is overcome; meanwhile, the bioelectrical impedance technology is applied to serum component measurement, and only enough early-stage data accumulation is needed, the method can realize the rapid measurement of the content of various serum components such as serum urea nitrogen, calcium, phosphorus, creatinine, albumin, hemoglobin and the like by using one bioelectrical impedance spectrometer, greatly reduces the cost and the time consumption of serum component measurement in comprehensive evaluation of nutrition and calcium-phosphorus metabolism conditions, is beneficial to medical staff to follow up the physical condition of a patient in time, and can prevent abnormal conditions possibly occurring to the patient in time.
Drawings
FIG. 1 is a flow chart of a computer in the comprehensive evaluation device for nutrition and calcium and phosphorus metabolism of uremic patients according to the invention.
FIG. 2 is a diagram showing the results of regression analysis of the albumin index and the impedance index in the comprehensive evaluation of the nutritional and calcium-phosphorus metabolism status of uremic patients according to the present invention.
FIG. 3 is a second diagram of the results of regression analysis of the albumin index and the electrical impedance index in the comprehensive evaluation of the nutritional and calcium-phosphorus metabolism status of uremic patients according to the present invention.
Detailed Description
The present invention is further described below in conjunction with the following figures and specific examples so that those skilled in the art may better understand the present invention and practice it, but the examples are not intended to limit the present invention.
The method mainly uses the bioelectrical resistance, the bioelectrical reactance, the height and the Body Mass Index (BMI) of a patient as measured values, and calculates the content of serum components such as serum urea nitrogen, calcium, phosphorus, creatinine, albumin, hemoglobin and the like in the serum of the patient through a corresponding formula between the estimated bioelectrical impedance and the serum chemical quantity, so as to help medical staff to evaluate the nutritional status and the calcium-phosphorus metabolic status of a patient with end-stage renal failure.
Specifically, the object of the present invention can be achieved by the following technical measures: the comprehensive evaluation method for the nutrition and calcium-phosphorus metabolism condition of the patient with end-stage renal failure comprises the following steps: step 1, in step 1, obtaining bioelectrical impedance and serum test data of a terminal renal failure dialysis patient group as much as possible by long-term tracking type height (H), BMI, bioelectrical impedance (bioelectrical resistance R, bioelectrical reactance X) measurement and serum component test, and each data has accurate measurement time. (ii) a Step 2, preprocessing and connecting the measured data; step 3, establishing a corresponding relation among the height, the BMI, the bioelectrical impedance and the serum components through regression analysis; step 4, measuring the body geometric parameters and the bioelectrical impedance of the patient to obtain electrical impedance data; step 5, estimating the value of the serum component of the patient according to the corresponding relation between the electrical impedance and the serum component; and 6, comprehensively evaluating the nutrition and calcium and phosphorus metabolism conditions of the patient through the estimation value of the serum component of the patient.
The data collected in step 1 were from 688 patients with end stage renal failure in nephrology department in Cinese-free Hospital, 1565 bioelectrical impedance data in data point form (measurement time, name, sex, height H, weight W, impedance amplitude Z, impedance phase angle alpha). Wherein the height and the weight are respectively measured by a height measuring instrument (Seca, Hamburg, Germany) with a division value of 1mm and a non-automatic weight measuring instrument (Seca, Hamburg, Germany) with a division value of 0.1 kg; the impedance values were measured by a multifrequency impedance measuring instrument (Fresenius, Shanghai, China). The serum composition assay data was 249586 pieces in total, spanning a 5 year period from 2014 to 2019. Wherein the serum assay data comprises a total of 11 chemical quantities, each of which is as follows: 36821 pieces of albumin, 46937 pieces of blood calcium, 36935 pieces of blood phosphorus, 32035 pieces of hemoglobin, 15707 pieces of parathyroid hormone, 38855 pieces of serum urea nitrogen, 41938 pieces of creatinine, and 25-hydroxyvitamin D358 pieces. Wherein serum urea nitrogen, calcium, creatinine, albumin, total cholesterol, low density lipoprotein cholesterol, phosphorus are measured by an automated chemical analyzer (BeckmanCoulter AU5800) and hemoglobin is measured by an automated blood analyzer (SysmexN-9000); parathyroid hormone was measured by an automated immunofluorescence analyzer (BeckmanCoulter DxI 800). N-terminal-B type natriuretic peptide precursor and 25-hydroxyvitamin D immunoassay (COBASE-602). All data is recorded by a MySQL (Communnityversion 8.0.14, Oracle corporation) database system.
In step 2, since the data collected in step 1 do not provide direct BMI, resistance value R, and reactance value X, it is necessary to calculate the values according to the formulas BMI ═ height/weight square, R ═ Z · cos (a), and X ═ R ═ Z · sin (a). Using Python (version3.7.2, Python software Foundation) to take out the height and weight in the bioelectrical impedance data from the database one by one for calculation, and correspondingly putting the calculated BMI data back to the database one by one so as to change the bioelectrical impedance data point form from (measuring time, name, sex, height H, weight W, impedance amplitude Z and impedance phase angle A) to (measuring time, name, sex, height H, BMI, resistance R and reactance X).
In step 2, based on the previous processing, new bioelectrical impedance data (measurement time, name, sex, R1, X1, R2, and X2) can be continuously constructed according to the formula of R1, R/H, X1, X/HR2, R/BMI, and X2, X/BMI, and the processing method.
In step 2, the new bioelectrical impedance data and the serum composition assay data are further connected. Specifically, the bioelectrical impedance data is taken out one by one from the database using Python (version3.7.2, Python software foundation), and according to the time value marked by the data point and the name thereof, all data under the name within 48 hours after the marked time value are searched in the serum component assay data to constitute a new data set, and the data points in the data set are changed in form (sex, R1, X1, R2, X2, serum component assay data). The total amount of data after treatment was reduced to 3339, among which 479 albumin, 571 blood calcium, 460 blood phosphorus, 433 hemoglobin, 230 parathyroid hormone, 559 serum urea nitrogen, 577 creatinine and 25-hydroxyvitamin D30.
In step 3, regression analysis was performed on the dataset obtained in step 2 using the SciPy (v1.4.0.dev0+65733d0, SciPydevelopers) statistical algorithm library from Python. Following the one-, two-, and four-way regression analysis described above, 7 regression equations and 7F test results were obtained for all of the 11 serum assay indices, for a total of 77 equations and test results. In consideration of sex differences between men and women, the data set is divided according to sex, and regression analysis is performed on the data of each sex. The results of 77F tests on 11 serum assay markers in each sex population are shown in tables 1 and 2:
TABLE 1 Male F test results (. about.P < 0.01;. about.P <0.05)
TABLE 2 female F test results (. about.P < 0.01;. about.P <0.05)
According to the method in the step 3, the criterion that the result can be trusted with the significance index P <0.01 is selected, and the regression result with the maximum F value is selected as the final regression relation. The final relationships are shown in tables 3 and 4, which are the results obtained in step 3. Fig. 2 and 3 are representations of the albumin regression results in tables 3 and 4 on a coordinate system.
TABLE 3 relationship between Male bioelectrical impedance and serum stoichiometry
| Amount of serum assayed | Regression equation |
| Urea (mmol/L) | The significance does not reach the standard |
| Calcium (mmol/L) | y＝+0.009935X1+1.9927 |
| Creatinine (umol/L) | y＝-2.9590R1+19.1081X1+1217.4232 |
| Albumin (g/L) | y＝+0.0114X2+23.4329 |
| Phosphorus (mmol/L) | y＝-0.000111R2+0.000965X2+2.0756 |
| Hemoglobin (g/L) | y＝+0.0265X2+71.2241 |
| Parathyroid hormone (Pg/ml) | The significance does not reach the standard |
| 25-Hydroxyvitamin D (ng/mL) | The significance does not reach the standard |
TABLE 4 relationship between female bioelectrical impedance and serum stoichiometry
| Amount of serum assayed | Regression equation |
| Urea (mmol/L) | y＝-0.001186R2+0.0116X2+23.5187 |
| Calcium (mmol/L) | y＝+0.003565R1-0.0561X1-0.000106R2+0.001650X2+2.2446 |
| Creatinine (umol/L) | y＝-2.9150R1+24.5576X1+1166.0831 |
| Albumin (g/L) | y＝+0.3180X1+23.8384 |
| Phosphorus (mmol/L) | y＝+0.000756X2+0.8370 |
| Hemoglobin (g/L) | y＝+0.0203X2+73.3635 |
| Parathyroid hormone (Pg/ml) | y＝+0.0406R2-312.5617 |
| 25-Hydroxyvitamin D (ng/mL) | y＝+0.0745R1+0.5537X1-31.7926 |
In step 4, the electrical impedance data of the patient to be evaluated is measured, the process and the used instruments are completely the same as in step 1, and the obtained electrical impedance data (measurement time, name, sex, height H, weight W, impedance amplitude Z, impedance phase angle a) are also converted into bioelectrical impedance data (measurement time, name, sex, R1, X1, R2, X2) in the same way as in step 2. For example, there are three patients whose electrical impedance measurements and transformed bioelectrical impedance data are shown in table 5.
TABLE 5 Electrical impedance measurements of three patients to be evaluated
| Name (I) | Sex | Height of a person | Body weight | Amplitude value | Phase angle | R1 | X1 | R2 | X2 |
| Patient 1 | For male | 160 | 74.4 | 559.6 | 6.5 | 336.971 | 38.393 | 15194.3 | 1731.17 |
| Patient 2 | Woman | 157 | 41.7 | 709.9 | 4.98 | 450.458 | 39.251 | 11964.4 | 1042.54 |
In step 5, an estimated value of the assay amount of partial serum can be obtained by performing calculation by substituting the data (measurement time, name, sex, R1, X1, R2, X2) in step 4 according to the equations listed in tables 3 and 4, as shown in table 6.
TABLE 6 estimation of the serum assay in three patients
| Name (I) | Patient 1 | Patient 2 |
| Urea (mmol/L) | - | 21.435 |
| Calcium (mmol/L) | 2.3741 | 2.0964 |
| Creatinine (umol/L) | 953.94 | 816.92 |
| Albumin (g/L) | 43.249 | 36.319 |
| Phosphorus (mmol/L) | 2.0556 | 1.6247 |
| Hemoglobin (g/L) | 117.18 | 94.549 |
| Parathyroid hormone (Pg/ml) | - | 173.10 |
| 25-Hydroxyvitamin D (ng/mL) | - | 23.506 |
In step 6, the nutrient calcium and phosphorus metabolism of the patient can be evaluated according to the estimated serum assay value obtained in step 5.
The comprehensive evaluation device for the nutrition and calcium-phosphorus metabolism condition of the end-stage renal failure patient can provide a set of simple and effective method for evaluating the nutrition and calcium-phosphorus metabolism condition of the end-stage renal failure patient for medical staff. The most reliable analysis result is screened out by collecting data to carry out regression analysis, the serum component of the patient to be detected is estimated on the basis, the physical condition of the patient to be detected is analyzed, and the comprehensive evaluation work of the nutrition and calcium and phosphorus metabolism condition of the patient with renal failure at the final stage can be realized.
The above-mentioned embodiments are merely preferred embodiments for fully illustrating the present invention, and the scope of the present invention is not limited thereto. The equivalent substitution or change made by the technical personnel in the technical field on the basis of the invention is all within the protection scope of the invention. The protection scope of the invention is subject to the claims.
Claims (10)
1. A uremia patient nutrition and calcium and phosphorus metabolism comprehensive evaluation device is characterized by comprising: a computer programmed to perform the steps of:
step 1: obtaining the body height, body mass index BMI, bioelectrical impedance and serum component test data of a plurality of patients with end-stage renal failure dialysis, wherein each data has accurate measuring time;
step 2: for each bioelectrical impedance data, the most recently measured height and BMI data were used for processing: each piece of bioelectrical impedance data is divided by the height to form new data R1= R/H, X1= X/H; dividing each piece of bioelectrical impedance data by BMI to form new data R2= R/BMI and X2= X/BMI; the four physical quantities together form a new bioelectrical impedance data set (R1, X1, R2 and X2); for each bioelectrical impedance data, connecting all the serum component test data within 48 hours after the data at the measuring time to form a series of bioelectrical impedance-serum component test data pairs (R1, X1, R2, X2, serum component test data);
and 3, carrying out further regression analysis on the data in the previous step, wherein the further regression analysis is divided into the following substeps:
a substep (a) of performing a univariate regression analysis, calculating the correlation and regression equation of each serum component and each of R1, X1, R2, and X2, and calculating the F-test significance P value of the correlation coefficient thereof; for each serum component, this step will form 4 regression equations and corresponding F test results;
a substep (b) of performing a binary regression analysis, calculating binary correlations and regression equations of each serum component assay data and (R1, X1) and (R2, X2), and calculating F-test significance P values thereof; for each chemical quantity, 2 regression equations and corresponding F test results are formed in the step;
a substep (c) of performing quaternary regression analysis, calculating quaternary correlations and regression equations of each serum component assay data and (R1, X1, R2, X2) four electrical impedance quantities, and calculating F-test significance P values thereof; for each chemical quantity, 1 regression equation and corresponding F test result are formed in the step;
substep (d), comparing the results of the three substeps, and selecting a result with the highest F test significance and the lowest P value from the 7 equations as a final regression expression for each chemical quantity, wherein the result is used as a corresponding relation between the bioelectrical impedance and the serum component;
and 4, step 4: obtaining the height H, BMI and the bioelectrical impedance of the patient to be detected according to the same process and method as the step 1, wherein the bioelectrical impedance comprises a bioelectrical resistance R and a bioelectrical reactance X, and converting the bioelectrical impedance into bioelectrical impedance data according to the method in the step 2 (R1, X1, R2 and X2);
and 5: calculating an estimated value of each serum component index by substituting the bioelectrical impedance obtained in the step 3 into a corresponding relational expression between the bioelectrical impedance and each serum component based on the bioelectrical impedance data (R1, X1, R2, X2) calculated in the step 4;
step 6: and (5) comprehensively evaluating the nutrition and the calcium and phosphorus metabolism of the patient through the estimation value of each serum component index obtained in the step 5.
2. The uremic patient comprehensive evaluation device for nutritional and calcium-phosphorus metabolic conditions of patients according to claim 1, wherein the data set obtained in step 2 is subjected to regression analysis using the SciPy statistical algorithm library of Python.
3. The apparatus for comprehensively evaluating the nutritional and calcium-phosphorus metabolism status of uremic patients according to claim 1, wherein if the bioelectrical impedance and the serum component data are not simultaneously measured or are less simultaneously measured, the data of the serum component data measured within 48 hours after the bioelectrical impedance data is selected for each bioelectrical impedance data to be connected.
4. The uremic patient nutrition and calcium and phosphorus metabolism comprehensive evaluation device of claim 1, wherein all data of step 1 are recorded by a MySQL database system.
5. A computer-readable storage medium, on which a computer program is stored, which program, when executed by a processor, performs the steps of:
step 1: obtaining the body height, body mass index BMI, bioelectrical impedance and serum component test data of a plurality of patients with end-stage renal failure dialysis, wherein each data has accurate measuring time;
step 2: for each bioelectrical impedance data, the most recently measured height and BMI data were used for processing: each piece of bioelectrical impedance data is divided by the height to form new data R1= R/H, X1= X/H; dividing each piece of bioelectrical impedance data by BMI to form new data R2= R/BMI and X2= X/BMI; the four physical quantities together form a new bioelectrical impedance data set (R1, X1, R2 and X2); for each bioelectrical impedance data, connecting all the serum component test data within 48 hours after the data at the measuring time to form a series of bioelectrical impedance-serum component test data pairs (R1, X1, R2, X2, serum component test data);
and 3, carrying out further regression analysis on the data in the previous step, wherein the further regression analysis is divided into the following substeps:
a substep (a) of performing a univariate regression analysis, calculating the correlation and regression equation of each serum component and each of R1, X1, R2, and X2, and calculating the F-test significance P value of the correlation coefficient thereof; for each serum component, this step will form 4 regression equations and corresponding F test results;
a substep (b) of performing a binary regression analysis, calculating binary correlations and regression equations of each serum component assay data and (R1, X1) and (R2, X2), and calculating F-test significance P values thereof; for each chemical quantity, 2 regression equations and corresponding F test results are formed in the step;
a substep (c) of performing quaternary regression analysis, calculating quaternary correlations and regression equations of each serum component assay data and (R1, X1, R2, X2) four electrical impedance quantities, and calculating F-test significance P values thereof; for each chemical quantity, 1 regression equation and corresponding F test result are formed in the step;
substep (d), comparing the results of the three substeps, and selecting a result with the highest F test significance and the lowest P value from the 7 equations as a final regression expression for each chemical quantity, wherein the result is used as a corresponding relation between the bioelectrical impedance and the serum component;
and 4, step 4: obtaining the height H, BMI and the bioelectrical impedance of the patient to be detected according to the same process and method as the step 1, wherein the bioelectrical impedance comprises a bioelectrical resistance R and a bioelectrical reactance X, and converting the bioelectrical impedance into bioelectrical impedance data according to the method in the step 2 (R1, X1, R2 and X2);
and 5: calculating an estimated value of each serum component index by substituting the bioelectrical impedance obtained in the step 3 into a corresponding relational expression between the bioelectrical impedance and each serum component based on the bioelectrical impedance data (R1, X1, R2, X2) calculated in the step 4;
step 6: and (5) comprehensively evaluating the nutrition and the calcium and phosphorus metabolism of the patient through the estimation value of each serum component index obtained in the step 5.
6. The computer-readable storage medium of claim 5, wherein the dataset obtained in step 2 is subjected to regression analysis using a SciPy statistical algorithm library by Python.
7. The computer-readable storage medium of claim 5, wherein if the bioelectrical impedance and the serum component data have no or less simultaneously measured data, the data in the serum component data having a measurement time within 48 hours after the bioelectrical impedance data is selected for each bioelectrical impedance data to be connected.
8. The computer-readable storage medium of claim 5, wherein all data of step 1 is recorded by one MySQL database system.
9. A processor, configured to execute a program, wherein the program when executed performs the following steps:
step 1: obtaining the body height, body mass index BMI, bioelectrical impedance and serum component test data of a plurality of patients with end-stage renal failure dialysis, wherein each data has accurate measuring time;
step 2: for each bioelectrical impedance data, the most recently measured height and BMI data were used for processing: each piece of bioelectrical impedance data is divided by the height to form new data R1= R/H, X1= X/H; dividing each piece of bioelectrical impedance data by BMI to form new data R2= R/BMI and X2= X/BMI; the four physical quantities together form a new bioelectrical impedance data set (R1, X1, R2 and X2); for each bioelectrical impedance data, connecting all the serum component test data within 48 hours after the data at the measuring time to form a series of bioelectrical impedance-serum component test data pairs (R1, X1, R2, X2, serum component test data);
and 3, carrying out further regression analysis on the data in the previous step, wherein the further regression analysis is divided into the following substeps:
a substep (a) of performing a univariate regression analysis, calculating the correlation and regression equation of each serum component and each of R1, X1, R2, and X2, and calculating the F-test significance P value of the correlation coefficient thereof; for each serum component, this step will form 4 regression equations and corresponding F test results;
a substep (b) of performing a binary regression analysis, calculating binary correlations and regression equations of each serum component assay data and (R1, X1) and (R2, X2), and calculating F-test significance P values thereof; for each chemical quantity, 2 regression equations and corresponding F test results are formed in the step;
a substep (c) of performing quaternary regression analysis, calculating quaternary correlations and regression equations of each serum component assay data and (R1, X1, R2, X2) four electrical impedance quantities, and calculating F-test significance P values thereof; for each chemical quantity, 1 regression equation and corresponding F test result are formed in the step;
substep (d), comparing the results of the three substeps, and selecting a result with the highest F test significance and the lowest P value from the 7 equations as a final regression expression for each chemical quantity, wherein the result is used as a corresponding relation between the bioelectrical impedance and the serum component;
and 4, step 4: obtaining the height (H), BMI and bioelectrical impedance of the patient to be detected according to the same process and method as the step 1, wherein the bioelectrical impedance comprises a bioelectrical resistance and a bioelectrical reactance X, and converting the bioelectrical impedance into bioelectrical impedance data according to the method in the step 2 (R1, X1, R2 and X2);
and 5: calculating an estimated value of each serum component index by substituting the bioelectrical impedance obtained in the step 3 into a corresponding relational expression between the bioelectrical impedance and each serum component based on the bioelectrical impedance data (R1, X1, R2, X2) calculated in the step 4;
step 6: and (5) comprehensively evaluating the nutrition and the calcium and phosphorus metabolism of the patient through the estimation value of each serum component index obtained in the step 5.
10. The processor of claim 9, wherein if the bioelectrical impedance and the serum component data are not simultaneously measured or are less simultaneously measured, the data in the serum component data measured within 48 hours after the bioelectrical impedance data are selected for each bioelectrical impedance data to be connected.
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| CN104361191A (en) * | 2014-04-29 | 2015-02-18 | 奥美之路(北京)技术顾问有限公司 | Group composition evaluation model for normal Chinese |
| CN104434098A (en) * | 2014-11-27 | 2015-03-25 | 北京四海华辰科技有限公司 | Human body energy detection method and device |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN104434098A (en) * | 2014-11-27 | 2015-03-25 | 北京四海华辰科技有限公司 | Human body energy detection method and device |
Non-Patent Citations (3)
| Title |
|---|
| Bioelectrical impedance analysis is a useful bedside technique to assess malnutrition in cirrhotic patients with and without ascites;Pirlich M et al.;《Hepatology》;20001231(第32期);第1208-1215页 * |
| 生物电阻抗技术用于老年尿毒症患者两种透析方式的营养状况评估;王葳;《中国中西医结合肾病杂志》;20161031;第17卷(第10期);第895-897页 * |
| 生物电阻抗技术用于腹膜透析患者营养评估的研究;杜琇;《中国血液净化》;20141031;第13卷(第10期);第703-707页 * |
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