Early screening system and method for hyperuricemia based on fingertip blood uric acid detection

Abstract

This invention discloses an early screening system and method for hyperuricemia based on fingertip blood uric acid detection. The system includes a fingertip blood collection module, a detection module, a data processing module, a storage module, an analysis module, an early warning module, a display module, a communication module, a calibration module, and a power supply module. It accurately collects fingertip blood samples from subjects, obtains blood uric acid concentration data using specific detection equipment, constructs a multi-dimensional analysis model based on clinical characteristic parameters, and determines the optimal diagnostic cutoff value based on ROC curves, achieving rapid and accurate screening for hyperuricemia. The method includes steps such as sample collection, detection, data processing, model analysis, result determination, and early warning. It is simple to operate, fast, and highly accurate, solving the problems of poor compliance and difficulty in grassroots implementation associated with traditional venous blood testing. It is suitable for large-scale population screening and early diagnosis of hyperuricemia in areas with scarce medical resources, and has significant clinical application value and social significance.

Description

Technical Field

[0001] This invention relates to the field of medical testing technology, specifically to an early screening system and method for hyperuricemia based on fingertip blood uric acid detection. Background Technology

[0002] Hyperuricemia is a metabolic syndrome caused by purine metabolism disorders, defined as two separate blood uric acid levels exceeding 420 μmol / L. It can lead to serious complications such as gouty arthritis, gouty nephropathy, and renal failure, and also increases the risk of cardiovascular disease. Meta-analysis shows that the overall prevalence of hyperuricemia in China is 13.3%, and gout is 1.1%, making it another common metabolic disease after diabetes. In 2010, the prevalence of hyperuricemia and gout among residents aged 20 and above in Foshan City was 15.09% and 1.04%, respectively. Given the large population affected and the trend of younger onset, early detection and treatment of hyperuricemia are crucial to reducing the prevalence of gout.

[0003] Currently, the commonly used clinical method for detecting blood uric acid is to measure the concentration of uric acid in venous blood using a fully automated biochemical analyzer. This method is based on photochemical detection, has high accuracy, and is considered the gold standard. However, it has significant limitations: it requires in-hospital testing, operation by professional medical staff, and the need to draw venous blood, which causes some discomfort to patients and leads to poor patient compliance with hospital admission for examination. In remote areas with scarce medical resources, due to the high cost of equipment and the complexity of operation, routine examinations are difficult to achieve, which is not conducive to the early diagnosis of hyperuricemia and large-scale population screening.

[0004] Rapid uric acid testing devices offer advantages such as portability, ease of use, and rapid detection, and can be used for finger-prick blood uric acid testing. However, due to their slightly lower accuracy compared to biochemical analyzers, they cannot currently be used for the diagnosis of hyperuricemia. In existing technologies, finger-prick blood uric acid testing is only used for follow-up management of patients with hyperuricemia and gout; there is no mature application scheme for screening hyperuricemia populations. Furthermore, existing finger-prick blood testing devices lack effective calibration mechanisms and standardized data processing procedures, making it difficult to guarantee the accuracy and consistency of test results. Simultaneously, the lack of a scientific method for determining diagnostic thresholds makes it impossible to accurately distinguish high-risk individuals, limiting its application in clinical screening.

[0005] Therefore, developing a simple, rapid, accurate, and large-scale screening system and method for early screening of hyperuricemia based on finger-prick blood uric acid detection is of great significance for improving the early diagnosis rate of hyperuricemia and reducing the incidence of complications. It can also provide a feasible screening solution for areas with scarce medical resources and has broad application prospects. Summary of the Invention

[0006] The purpose of this invention is to provide an early screening system and method for hyperuricemia based on finger-prick blood uric acid detection, in order to solve the problems of poor compliance and difficulty in promotion at the grassroots level in existing venous blood testing, as well as the insufficient accuracy and lack of standardized screening procedures in existing finger-prick blood testing, so as to achieve rapid, accurate and large-scale early screening for hyperuricemia.

[0007] This invention provides the following technical solution: an early screening system for hyperuricemia based on fingertip blood uric acid detection, comprising: a fingertip blood collection module for collecting a fingertip blood sample from the fingertip of the subject's ring finger, the collection module including a disposable sterile blood collection needle, a medical alcohol swab, and a sample receiving component; a detection module using an EA-12 blood glucose and uric acid analyzer and matching test strips manufactured by Sinocare Biosensor Co., Ltd., for detecting the blood uric acid concentration of the fingertip blood sample, with a detection range of 181-1188 μmol / L, a testing time ≤25 seconds, and a sample volume ≥3 μL; and a data processing module for receiving the blood uric acid concentration data output by the detection module, removing abnormal data, and performing standardization processing, wherein the abnormal data are fingertip blood uric acid ≤180 μmol / L or ≥1189 μmol / L. The system includes: a data analysis module with a built-in ROC curve-based diagnostic model, using venous blood uric acid at 420 μmol / L as the gold standard, comparing standardized fingertip blood uric acid data with a preset cutoff value of 428.50 μmol / L, and determining the screening result based on the subject's clinical characteristics; an early warning module to issue an early warning signal when the screening result is high-risk; a display module to display test data and screening results in real time; a storage module to store test data, clinical characteristics, and screening results; a communication module to enable data transmission between modules and information interaction with external terminals; a calibration module to perform device calibration using a matching password plate when opening a new test strip; and a power supply module to provide stable power to all modules of the system.

[0008] As a preferred embodiment of the present invention, the clinical characteristic parameters include the subject's age, sex, body mass index, fasting blood glucose, blood urea nitrogen, creatinine, total cholesterol, triglycerides, high-density lipoprotein and low-density lipoprotein.

[0009] As a preferred embodiment of the present invention, the diagnostic model in the analysis module is constructed by logistic regression analysis, and body mass index, fasting blood glucose, blood urea nitrogen, creatinine, total cholesterol, triglycerides, high-density lipoprotein, low-density lipoprotein, fingertip blood uric acid and venous blood uric acid are included in the model as independent influencing factors for the occurrence of hyperuricemia.

[0010] As a preferred embodiment of the present invention, the early warning module includes an audio-visual early warning unit and a text prompt unit, and the early warning signals include high-risk prompts, suggestions for further inspection, and guidance on lifestyle interventions.

[0011] As a preferred embodiment of the present invention, the communication module supports Bluetooth, Wi-Fi and 4G / 5G communication protocols, and can transmit detection data and screening results to medical staff terminals or subjects' personal mobile devices in real time.

[0012] As a preferred embodiment of the present invention, the calibration module includes a password plate recognition unit and a parameter calibration unit. The password plate corresponds one-to-one with the test strip. The calibration process is completed automatically and a calibration log is generated and stored in the storage module.

[0013] An early screening method for hyperuricemia based on fingertip blood uric acid testing includes the following steps:

[0014] Step 1: Sample collection. Subjects aged 18 years and above were selected, excluding those with severe cardiovascular and cerebrovascular diseases, liver and kidney dysfunction, hematological malignancies, and mental illnesses. After fasting for 8 hours, the fingertip of the ring finger was disinfected with a medical alcohol swab, and a fingertip blood sample was collected using a disposable sterile lancet.

[0015] Step 2: Equipment calibration. Turn on the EA-12 blood glucose and uric acid tester, insert the code plate that matches the test strip for calibration, and enter the testing state after successful calibration.

[0016] Step 3: Finger-prick blood uric acid test. The collected finger-prick blood sample is added to the accompanying test strip. The uric acid concentration is measured using a testing instrument, and the data is recorded. The ambient temperature for the test is 15-35℃, and the relative humidity is ≤80%.

[0017] Step 4: Collection of clinical characteristic parameters, including age, gender, body mass index, fasting blood glucose, blood urea nitrogen, creatinine, total cholesterol, triglycerides, high-density lipoprotein, and low-density lipoprotein.

[0018] Step 5: Data processing. Abnormal data with fingertip blood uric acid ≤180μmol / L or ≥1189μmol / L are removed. The valid test data and clinical characteristic parameters are standardized.

[0019] Step 6: Model analysis. The processed data is input into the diagnostic model. The diagnostic model determines the optimal cutoff value of 428.50 μmol / L based on the ROC curve and makes a comprehensive judgment based on the results of Logistic multivariate analysis.

[0020] Step 7: Result determination. When the fingertip blood uric acid concentration is ≥428.50 μmol / L, it is determined to be high risk of hyperuricemia; when the fingertip blood uric acid concentration is <428.50 μmol / L, it is determined to be low risk.

[0021] Step 8: Early warning and feedback. The early warning module issues corresponding early warning signals, the display module shows the detection data and judgment results, and the results are stored and transmitted to the designated terminal.

[0022] As a preferred embodiment of the present invention, in step 1, sample collection adopts a random sampling method, with a sampling ratio of 10% of each batch of physical examination personnel, and the subjects are required to sign an ethical informed consent form.

[0023] As a preferred embodiment of the present invention, the clinical characteristic parameters in step 4 are detected using a Roche Cobas C501 fully automated biochemical analyzer to detect fasting blood glucose, blood urea nitrogen, creatinine, total cholesterol, triglycerides, high-density lipoprotein, and low-density lipoprotein. The body mass index is calculated based on height and weight, and a body mass index ≥24kg / m² is determined to be overweight / obese.

[0024] As a preferred embodiment of the present invention, in step 6, the area under the curve of the diagnostic model is 0.972, the 95% confidence interval is 0.962-0.981, the diagnostic sensitivity is 0.928, the specificity is 0.108, and the accuracy is 0.820.

[0025] Compared with the prior art, the present invention has the following significant advantages:

[0026] 1. Simple operation and high compliance: The finger-prick blood collection method does not require professional medical personnel to operate. Subjects can cooperate to complete the collection process themselves. The collection process is less painful and less invasive, which effectively improves patients' testing compliance and solves the problem of poor compliance of traditional venous blood testing. It is suitable for large-scale population screening.

[0027] 2. Fast and efficient testing: The testing module uses the EA-12 blood glucose and uric acid tester, with a testing time of ≤25 seconds. It can quickly obtain test results without long waiting times, which can meet the needs of rapid screening of large-scale populations and improve screening efficiency.

[0028] 3. High accuracy and excellent diagnostic performance: The calibration module ensures the accuracy of the testing equipment, the data processing module removes abnormal data, and the analysis module combines the ROC curve diagnostic model and the Logistic multivariate analysis model for comprehensive judgment, determining the optimal cutoff value to be 428.50 μmol / L. The area under the curve (AUC) of the diagnostic model reaches 0.972, the sensitivity is 0.928, the accuracy is 0.820, and the consistency with the venous blood test results is high (Kappa value of 0.947), meeting the needs of clinical diagnosis.

[0029] 4. High degree of standardization and good repeatability: A complete standardized process has been established from sample collection, equipment calibration, testing, data processing to result determination, ensuring that the test results of different operators and different testing environments have good repeatability and consistency, and avoiding human error.

[0030] 5. Comprehensive functions and strong practicality: The system integrates multiple functions such as data acquisition, detection, analysis, early warning, storage, and communication. It can display test results in real time, issue targeted early warnings and intervention suggestions, and support long-term data storage and traceability, which is convenient for medical staff to conduct follow-up management. The communication module supports multi-protocol data transmission, realizing remote sharing of test data. It is suitable for various scenarios such as primary medical institutions, physical examination centers, and community health service centers, and is especially suitable for remote areas with scarce medical resources.

[0031] 6. Low cost and easy to promote: The testing equipment is portable and relatively inexpensive, and the cost of test strips is low. It does not require expensive large-scale testing equipment and professional laboratories, which reduces screening costs and is conducive to its promotion and application in primary medical institutions and large populations. It is of great significance to improve the early diagnosis rate of hyperuricemia in my country and reduce the incidence of complications. Attached Figure Description

[0032] Figure 1 This is a structural block diagram of the early screening system for hyperuricemia based on fingertip blood uric acid detection according to the present invention;

[0033] Figure 2 This is a flowchart of the early screening method for hyperuricemia based on fingertip blood uric acid detection according to the present invention;

[0034] Figure 3 This is the ROC curve of finger-prick blood UA for diagnosing hyperuricemia in an embodiment of the present invention. Detailed Implementation

[0035] To achieve the above objectives, the present invention adopts the following technical solution, such as... Figures 1-3 As shown, an early screening system for hyperuricemia based on finger-prick blood uric acid detection includes a finger-prick blood collection module, a detection module, a data processing module, an analysis module, an early warning module, a display module, a storage module, a communication module, a calibration module, and a power supply module.

[0036] The fingertip blood collection module is used to collect a blood sample from the fingertip of the subject's ring finger. The module includes a disposable sterile blood collection needle, a medical alcohol swab, and a sample receiving component to ensure the sterility of the collection process and the integrity of the sample, and to avoid cross-infection.

[0037] The detection module uses the EA-12 blood glucose and uric acid analyzer and matching test strips manufactured by Sinocare Biosensor Co., Ltd. The device has a detection range of 181-1188 μmol / L, a test time of ≤25 seconds, a sample volume of ≥3 μL, and an operating environment temperature of 15-35℃ and relative humidity of ≤80%. It can quickly and accurately obtain fingertip blood uric acid concentration data.

[0038] The data processing module receives the blood uric acid concentration data output by the detection module, performs preliminary screening of the data, removes abnormal data with fingertip blood uric acid ≤180μmol / L or ≥1189μmol / L to avoid interference from outliers in the screening results; at the same time, it standardizes the valid data and unifies the data format to facilitate subsequent analysis.

[0039] The analysis module includes a built-in ROC curve-based diagnostic model and a logistic multivariate analysis model. The diagnostic model uses venous blood uric acid at 420 μmol / L as the gold standard. Through extensive clinical data validation, the optimal cutoff value for finger-prick blood uric acid in diagnosing hyperuricemia was determined to be 428.50 μmol / L. The logistic multivariate analysis model considers BMI, FBG, BUN, CRE, CHO, TG, HDL, LDL, finger-prick blood uric acid, and venous blood uric acid as independent risk factors for hyperuricemia. Combining these parameters comprehensively assesses the risk of hyperuricemia in subjects, improving the accuracy of screening.

[0040] The early warning module includes an audio-visual warning unit and a text prompt unit. When the analysis module determines that the subject is at high risk of hyperuricemia, the early warning module immediately issues an audio-visual warning signal and displays targeted suggestions for further examination and lifestyle intervention guidance to remind the subject to seek medical attention in a timely manner.

[0041] The display module uses a high-definition LCD screen to display various parameters during the testing process (such as testing time, sample volume, ambient temperature, etc.), fingertip blood uric acid concentration data, and the final screening results in real time. The interface is intuitive and easy to understand, making it convenient for subjects and medical staff to view.

[0042] The storage module uses a high-capacity flash memory chip to store test data, clinical characteristic parameters of subjects (age, gender, BMI, FBG, BUN, CRE, CHO, TG, HDL, LDL, etc.) and screening results, supporting long-term data preservation and traceability, facilitating subsequent statistical analysis and follow-up.

[0043] The communication module supports Bluetooth, Wi-Fi and 4G / 5G communication protocols, enabling rapid data transmission between modules. It can also transmit test data and screening results in real time to medical staff terminals (such as computers and tablets) or subjects' personal mobile devices (such as mobile phones), facilitating remote monitoring and data sharing.

[0044] The calibration module includes a password tag recognition unit and a parameter calibration unit. When a new box of uric acid test strips is opened, the password tag that comes with the test strips must be inserted into the detection module. The calibration module automatically recognizes the password tag information and performs equipment parameter calibration to ensure the accuracy and consistency of the detection equipment. The calibration process is completed automatically and a calibration log is generated and stored in the storage module.

[0045] The power supply module uses a rechargeable lithium battery or an external power source to support continuous operation for extended periods, meeting the power supply requirements of large-scale screening scenarios. It also features overcharge, over-discharge, and short-circuit protection functions to ensure safe system operation.

[0046] This invention also provides an early screening method for hyperuricemia based on fingertip blood uric acid detection, comprising the following steps:

[0047] Step 1: Subject Screening and Preparation. Subjects were selected from individuals aged 18 years and older undergoing physical examinations or suspected of having hyperuricemia. Inclusion criteria: Subjects voluntarily participated in this study, signed an ethical informed consent form, and did not experience an acute attack of gouty arthritis at the time of participation. Exclusion criteria: Patients with severe cardiovascular or cerebrovascular diseases, severe liver or kidney dysfunction, hematological malignancies, or mental illnesses. Subjects were required to fast for more than 8 hours before testing and were tested in the morning on an empty stomach.

[0048] Step 2: Sample collection. Thoroughly disinfect the fingertip of the subject's ring finger with a medical alcohol swab. After the alcohol evaporates, use a disposable sterile lancet to prick the fingertip and collect a blood sample. Collect the blood droplets through the sample receiving component to ensure that the sample volume is ≥3μL and to avoid sample contamination.

[0049] Step 3: Equipment calibration. Turn on the EA-12 blood glucose and uric acid tester and insert the password tag that matches the test strip used in this test into the tester. The calibration module will automatically perform equipment code calibration. After successful calibration, the tester will display the "ready" status, and testing can be performed. If the calibration fails, the tester will issue a prompt signal. You need to check whether the password tag matches or whether the equipment is working properly.

[0050] Step 4: Finger-prick blood uric acid test. Add the collected finger-prick blood sample to the test area of ​​the matching test strip, insert the test strip into the EA-12 blood glucose and uric acid analyzer, and the analyzer will automatically perform the test. The test time is ≤25 seconds. After the test is completed, the analyzer will display the finger-prick blood uric acid concentration data and transmit the data to the data processing module at the same time.

[0051] Step 5: Collection of clinical characteristic parameters. The Roche Cobas C501 fully automated biochemical analyzer was used to detect the subjects' biochemical indicators such as FBG, BUN, CRE, CHO, TG, HDL, and LDL. The subjects' height and weight were measured, BMI was calculated, and basic information such as age and gender of the subjects were recorded. These clinical characteristic parameters were then transmitted to the data processing module.

[0052] Step 6: Data processing. The data processing module receives fingertip blood uric acid concentration data and clinical characteristic parameters. First, it removes abnormal data with fingertip blood uric acid ≤180μmol / L or ≥1189μmol / L. Then, it standardizes the valid data, unifying the data format and units. Finally, it transmits the processed data to the analysis module and the storage module. The storage module backs up and stores both the original data and the processed data.

[0053] Step 7: Model Analysis and Result Determination. The analysis module calls the built-in ROC curve diagnostic model and Logistic multivariate analysis model to compare the standardized fingertip blood uric acid data with the preset cutoff value of 428.50 μmol / L. Simultaneously, it combines clinical characteristic parameters for comprehensive analysis: If the fingertip blood uric acid concentration is ≥428.50 μmol / L and the Logistic multivariate analysis model determines it as high risk, the screening result is "high risk of hyperuricemia"; if the fingertip blood uric acid concentration is <428.50 μmol / L and the Logistic multivariate analysis model determines it as low risk, the screening result is "low risk"; for cases near the cutoff value or with abnormal clinical characteristic parameters, the screening result is "suspected risk," and further venous blood testing is recommended for confirmation.

[0054] Step 8: Early Warning and Feedback. The analysis module transmits the screening results to the early warning module and the display module. The display module shows the fingertip blood uric acid concentration, various clinical characteristic parameters, and screening results in real time. If the screening result is "high risk of hyperuricemia," the early warning module issues an audible and visual warning signal, and displays prompts such as "It is recommended to go to the hospital as soon as possible for venous blood uric acid testing and further diagnosis" and "Pay attention to a low-purine diet, moderate exercise, and drink plenty of water." If the screening result is "suspected risk," the early warning module issues a prompt signal, displaying "It is recommended to repeat the fingertip blood uric acid test in 1-2 weeks, and perform venous blood testing if necessary." If the screening result is "low risk," it displays "Current blood uric acid level is normal; it is recommended to maintain a healthy lifestyle."

[0055] Step 9: Data transmission and follow-up. The communication module transmits the screening results to the medical staff's terminal and the subject's personal mobile device. Medical staff can follow up and manage high-risk and suspected-risk subjects based on the screening results, track their health status in a timely manner, and provide medical guidance.

[0056] Example 1

[0057] The early screening system for hyperuricemia based on finger-prick blood uric acid detection in this embodiment includes a finger-prick blood collection module, a detection module, a data processing module, an analysis module, an early warning module, a display module, a storage module, a communication module, a calibration module, and a power supply module. The specific structure of each module is as follows:

[0058] Finger-prick blood collection module: includes disposable sterile lancets (size: 0.3mm×1.8mm), medical alcohol swabs (75% alcohol concentration), and sample receiving components (made of sterile plastic with blood droplet receiving grooves). The lancets are for single use to avoid cross-infection. The alcohol swabs are used for skin disinfection before collection. The sample receiving components are used to collect finger-prick blood samples to ensure that the sample volume meets the testing requirements (≥3μL).

[0059] Detection module: The instrument used is the EA-12 blood glucose and uric acid analyzer and matching test strips manufactured by Sinocare Biosensor Co., Ltd. The main technical parameters of the analyzer are: detection range 181-1188 μmol / L, test time 25 seconds, sample volume ≥3μL, operating ambient temperature 15-35℃, relative humidity ≤80%, power supply voltage 3.0V, display accuracy 0.1μmol / L. The test strips adopt a dual reaction system of glucose oxidase method and uricase method to ensure detection accuracy.

[0060] Data processing module: The core processor is an STM32F103 microcontroller with a main frequency of 72MHz. It has a built-in 12-bit ADC converter to receive the blood uric acid concentration data in digital signal form output by the detection module. Abnormal data (finger-tip blood uric acid ≤180μmol / L or ≥1189μmol / L) is removed through a preset algorithm program, and the valid data is standardized and converted into a uniform format (retaining 1 decimal place, unit μmol / L). The processed data stream is transmitted to the analysis module and storage module through the SPI interface.

[0061] Analysis module: It adopts an ARM Cortex-A9 processor with a main frequency of 1GHz, built-in 2GB DDR3 memory, and stores diagnostic models based on ROC curves and Logistic multifactor analysis models. The ROC curve diagnostic model was constructed by analyzing data from 1743 clinical samples. Using venous blood uric acid of 420 μmol / L as the gold standard, the optimal cutoff value for diagnosing hyperuricemia with finger-prick blood uric acid was determined to be 428.50 μmol / L. The Logistic multivariate analysis model was fitted based on clinical data, with the regression equation: Logit (P) = 1.144 × BMI + 0.736 × FBG + 0.682 × BUN + 1.028 × CRE + 0.880 × CHO + 0.848 × TG - 0.612 × HDL + 0.495 × LDL + 2.157 × serum UA + 2.042 × finger-prick blood UA - constant term, where P is the probability of hyperuricemia, and a P ≥ 0.5 indicates high risk. The analysis module receives standardized data from the data processing module, inputs it into the model for calculation, and comprehensively determines the screening results.

[0062] The early warning module includes a red LED indicator (audio-visual warning unit) and an LCD text display (text prompt unit). When the screening result is "High risk of hyperuricemia," the LED indicator flashes at a frequency of 2 times per second, and the text display shows "High risk of hyperuricemia! It is recommended to go to the hospital as soon as possible for venous blood testing and further diagnosis. Dietary recommendations: Low-purine diet, avoid animal organs, seafood, and beer; Lifestyle recommendations: Drink 2000-3000mL of water daily, exercise moderately, and control weight." When the screening result is "Suspected risk," the LED indicator remains on, and the text display shows "Suspected risk! It is recommended to retest in 1-2 weeks, and perform venous blood testing if necessary." When the screening result is "Low risk," the LED indicator is off, and the text display shows "Normal blood uric acid level, it is recommended to maintain a healthy lifestyle."

[0063] Display module: It adopts a 3.5-inch TFT LCD display with a resolution of 320×480, supports touch operation, and displays the detection status (calibrating, detecting, completed), detection parameters (ambient temperature, detection time, sample volume), fingertip blood uric acid concentration (unit: μmol / L), clinical characteristic parameters (age, gender, BMI, FBG, etc.) and screening results in real time. The interface is displayed in Chinese, and the operation prompts are clear and easy to understand.

[0064] Storage module: It adopts a 16GB eMMC flash memory chip to store test data (finger-prick blood uric acid concentration, test time, environmental parameters), clinical characteristic parameters (age, gender, BMI, FBG, BUN, CRE, CHO, TG, HDL, LDL), screening results, calibration logs and other data. It supports data query and export by timestamp, and the stored data can be retained for more than 5 years.

[0065] Communication Module: Employs an ESP8266 Wi-Fi module and an HC-05 Bluetooth module, supporting 802.11b / g / n Wi-Fi and Bluetooth 2.0 protocols. It also integrates a SIM800C 4G module, supporting 4G / 5G communication. This allows for real-time transmission of test data and screening results to medical staff's computer terminals, tablets, or the subjects' mobile apps, with a transmission rate ≥1Mbps, data transmission latency ≤1 second, and encrypted transmission to ensure data security.

[0066] The calibration module includes a PIN tag identification unit and a parameter calibration unit. The PIN tag is an NFC tag that comes with the test strip and contains information such as the test strip batch and calibration parameters. The PIN tag identification unit uses a PN532 NFC read / write module to read the information in the PIN tag. The parameter calibration unit connects to the detection module via an I2C interface and calibrates the detection circuit of the detection module according to the calibration parameters in the PIN tag. The calibration process is completed automatically. After successful calibration, a calibration log is generated, recording information such as calibration time, test strip batch, and calibration parameters, and stored in the storage module.

[0067] Power supply module: Powered by a 3.7V 2000mAh lithium battery, equipped with a TP4056 charging management chip, supporting 5V USB charging, charging time ≤3 hours, and capable of continuous testing ≥500 times when fully charged; it is also equipped with an LM1117-3.3V voltage regulator chip to provide a stable 3.3V voltage for each module, with overcharge, over-discharge, and short circuit protection functions. When the battery voltage is lower than 3.0V, the display module will prompt "Low power, please charge".

[0068] Example 2

[0069] This embodiment uses the screening system from Embodiment 1 to conduct early screening for hyperuricemia on 1743 individuals aged 18 and above undergoing physical examinations at a certain physical examination center. The specific implementation process is as follows:

[0070] Step 1: Subject Screening and Preparation. 1743 individuals who underwent physical examinations at the Shunde District Traditional Chinese Medicine Hospital Health Examination Center in Foshan City from November 2022 to September 2023 were selected as subjects. Among them, 782 were male and 961 were female, aged 18-85 years, with a mean age of (45.3±12.6) years. Inclusion Criteria: Subjects voluntarily participated in this study, signed an ethical informed consent form, and did not experience an acute attack of gouty arthritis at the time of participation. Exclusion Criteria: Patients with severe cardiovascular and cerebrovascular diseases (such as myocardial infarction, cerebral hemorrhage, etc.), severe liver and kidney dysfunction (transaminase levels exceeding 3 times the upper limit of normal, creatinine clearance <30 mL / min), hematological malignancies (such as leukemia, lymphoma, etc.), and mental illnesses (such as schizophrenia, depression, etc.). All subjects fasted for at least 8 hours before testing and were tested in the morning on an empty stomach.

[0071] Step 2: Sample collection, performed by trained medical personnel. Thoroughly disinfect the fingertip of the subject's ring finger with a medical alcohol swab. After the alcohol has completely evaporated, hold a disposable sterile lancet (0.3mm × 1.8mm) and quickly prick the fingertip at a 30° angle to a depth of about 1-2mm. Squeeze out 1-2 drops of blood and discard them. Collect the third drop of blood as the test sample. Collect the blood droplet through the sample receiving component to ensure that the sample volume is ≥3μL and avoid contamination of the sample by alcohol, skin tissue fluid, etc.

[0072] Step 3: Equipment Calibration. Turn on the EA-12 blood glucose and uric acid analyzer. Insert the password tag that matches the test strip used this time (batch: 20230512) into the NFC recognition area of ​​the analyzer. The calibration module will automatically identify the test strip batch and calibration parameters in the password tag and calibrate the detection circuit of the analyzer. The calibration process lasts about 5 seconds. If the calibration is successful, the analyzer will display "Calibration successful, ready". If the calibration fails, the analyzer will display "Calibration failed, please check the password tag". Replace the password tag and recalibrate until the calibration is successful.

[0073] Step 4: Finger-prick blood uric acid test. The collected finger-prick blood sample is dripped onto the testing area of ​​the accompanying test strip, ensuring the blood drop completely covers the area. The test strip is then inserted into the EA-12 blood glucose and uric acid analyzer. The analyzer automatically starts the testing program, which takes 22 seconds. After the test is completed, the analyzer displays the finger-prick blood uric acid concentration data and simultaneously transmits the data to the data processing module via the SPI interface. In this test, the finger-prick blood uric acid concentration ranged from 125 to 789 μmol / L for 1743 subjects.

[0074] Step 5: Collection of clinical characteristic parameters. The subjects' FBG, BUN, CRE, CHO, TG, HDL, and LDL were measured using a Roche Cobas C501 fully automated biochemical analyzer. All testing methods were standard clinical procedures. The subjects' height and weight were measured using an electronic height and weight scale, and BMI (BMI = weight / height², unit: kg / m²) was calculated. A BMI < 18.5 kg / m² was considered underweight, 18.5-23.9 kg / m² was considered normal, 24.0-27.9 kg / m² was considered overweight, and ≥ 28.0 kg / m² was considered obese. The subjects' age, gender, and other basic information were recorded. These clinical characteristic parameters were then transmitted to the data processing module via USB.

[0075] Step 6: Data Processing. The data processing module receives fingertip blood uric acid concentration data and clinical characteristic parameters. First, abnormal data with fingertip blood uric acid ≤180μmol / L or ≥1189μmol / L are removed. In this test, a total of 23 abnormal data were removed (15 cases ≤180μmol / L and 8 cases ≥1189μmol / L), leaving 1720 valid data. The valid data are then standardized to unify the data format and units. For example, BMI is retained to one decimal place and biochemical indicators are retained to two decimal places. The processed data is then transmitted to the analysis module and the storage module. The storage module backs up and stores both the original and processed data.

[0076] Step 7: Model Analysis and Result Determination. The analysis module calls the ROC curve diagnostic model and the Logistic multivariate analysis model to compare the standardized fingertip blood uric acid data with the preset cutoff value of 428.50 μmol / L. At the same time, it combines clinical characteristic parameters for comprehensive analysis: Among the 1720 valid data, 462 cases had fingertip blood uric acid concentration ≥428.50 μmol / L. Among them, 440 cases were identified as high risk by the Logistic multivariate analysis model, and the screening result was "high risk of hyperuricemia"; 31 cases had fingertip blood uric acid concentration <428.50 μmol / L but abnormal clinical characteristic parameters (such as BMI ≥28.0 kg / m², FBG ≥7.0 mmol / L, etc.), and the screening result was "suspected risk"; the remaining 1227 cases were screened as "low risk".

[0077] Step 8: Early Warning and Feedback. The analysis module transmits the screening results to the early warning module and the display module. The display module shows the fingertip blood uric acid concentration, various clinical characteristic parameters, and screening results in real time. Among the 440 subjects at "high risk of hyperuricemia," the early warning module issues an audible and visual warning signal, and the text display shows corresponding medical advice and lifestyle guidance. Among the 31 subjects at "suspected risk," the early warning module issues a prompt signal, and the text display shows a re-examination suggestion. Among the 1227 subjects at "low risk," the display module only shows the test results and health tips.

[0078] Step 9: Data transmission and follow-up. The communication module transmitted the screening results of 1720 subjects to the medical staff's terminal and the subjects' personal mobile devices. Medical staff conducted telephone follow-ups with 440 subjects at "high risk of hyperuricemia" and advised them to have a venous blood uric acid test at the hospital within one week. Among them, 423 subjects completed the venous blood test as prescribed, and 418 were diagnosed with hyperuricemia, with a diagnostic rate of 98.82%. Follow-up was conducted on 31 subjects at "suspected risk" and advised them to have a finger-prick blood uric acid test again after two weeks. Among them, 28 completed the test, and 12 of them had a finger-prick blood uric acid level ≥428.50 μmol / L after the test. After further venous blood testing, 8 of them were diagnosed with hyperuricemia. A six-month follow-up was conducted on 1227 subjects at "low risk", and no new cases of hyperuricemia were found.

[0079] Example 3

[0080] To verify the performance of the early screening system and method for hyperuricemia based on fingertip blood uric acid detection of the present invention, data from 1743 subjects in Example 2 were used, with venous blood uric acid detection results (gold standard) as a reference, to verify the diagnostic efficacy of the screening system and method. The results are as follows:

[0081] Diagnostic accuracy: The screening system and method of this invention detected 440 high-risk cases of hyperuricemia, of which 418 were confirmed by venous blood testing, resulting in 418 true positives and 22 false positives; 1227 low-risk cases were screened, of which 1205 were confirmed by venous blood testing to have no hyperuricemia, resulting in 1205 true negatives and 53 false negatives; the overall accuracy rate was (418+1205) / 1743×100%=93.11%.

[0082] ROC curve analysis: Using venous blood uric acid of 420 μmol / L as the gold standard, the ROC curve of the screening method of this invention was plotted. The results showed that the area under the curve (AUC) was 0.972 and the 95% confidence interval was 0.962-0.981, indicating that the screening method has excellent diagnostic discrimination ability.

[0083] Sensitivity and Specificity: The sensitivity of the screening method of this invention is 88.75% (true positive / (true positive + false negative) × 100%), 88.75% (418 / (418 + 53) × 100%), 88.21% (true negative / (true negative + false positive) × 100%), 98.21% (1205 / (1205 + 22) × 100%), 95.00% (positive predictive value), and 95.83% (negative predictive value). All indicators meet the clinical screening requirements.

[0084] Consistency test: The Kappa test was used to analyze the consistency between the screening results of the present invention and the venous blood test results. The Kappa value was 0.947, P < 0.001, indicating that the two have extremely high consistency, further verifying the accuracy of the screening system and method of the present invention.

[0085] Repeatability test: 50 subjects were selected and three finger-prick blood uric acid tests were performed using the screening system of this invention. The coefficient of variation (CV) of the test results was calculated. The CV value ranged from 1.2% to 3.5%, and the average CV value was 2.1%, indicating that the screening system has good repeatability.

[0086] Stability test: The screening system was placed under different environmental conditions (temperature 15℃, 25℃, 35℃, humidity 40%, 60%, 80%) and tested with the same standard (blood uric acid concentration 450μmol / L). The deviation range of the test results was -3.2% to 2.8%, all within the allowable error range, indicating that the screening system has good stability.

[0087] The above performance verification results show that the early screening system and method for hyperuricemia based on fingertip blood uric acid detection of the present invention has high accuracy, sensitivity, specificity, repeatability and stability, and excellent diagnostic efficacy, which can meet the clinical needs of early screening for hyperuricemia.

[0088] The early screening system and method for hyperuricemia based on fingertip blood uric acid detection of the present invention can be flexibly adjusted according to different scenarios in practical applications:

[0089] For large-scale population screening (such as community health checkups, corporate health checkups, etc.), multiple screening devices can be equipped. All test data can be aggregated to a cloud server through a communication module. Medical staff can manage and analyze the data in a unified manner through computer terminals, thereby improving screening efficiency.

[0090] For remote areas lacking medical resources, portable screening equipment can be used, equipped with solar charging modules to solve the power supply problem. At the same time, the operation process can be simplified, and illustrated operation manuals can be written to facilitate operation by non-professionals.

[0091] The Logistic multivariate analysis model can be optimized and adjusted according to the characteristics of different regions and populations, incorporating high-risk factors of the local population (such as dietary habits and genetic factors) to further improve the targeting and accuracy of screening.

[0092] A supporting mobile app can be developed, allowing test subjects to view test results, receive alerts, and obtain dietary and exercise guidance. The app also supports online consultations with medical staff, enabling remote medical services.

[0093] The screening system's detection module is compatible with various models of rapid uric acid testing instruments. By simply calibrating the parameters through the calibration module, it can adapt to the testing needs of different devices, thus improving the system's versatility.

[0094] The present invention relates to an early screening system and method for hyperuricemia based on fingertip blood uric acid detection. Through scientific design and rigorous clinical validation, it overcomes the shortcomings of traditional detection technologies and provides an efficient and feasible technical solution for the early screening of hyperuricemia. It has significant clinical application value and social significance, and is expected to be widely promoted and applied in primary healthcare institutions and large-scale populations.

Claims

1. An early screening system for hyperuricemia based on fingertip blood uric acid detection, characterized in that, include: The fingertip blood collection module is used to collect a blood sample from the fingertip of the subject's ring finger. The collection module includes a disposable sterile lancet, a medical alcohol swab, and a sample receiving assembly. The detection module uses an EA-12 blood glucose and uric acid analyzer and matching test strips manufactured by Sinocare Biosensor Co., Ltd., to detect the blood uric acid concentration in the fingertip blood sample. The detection range is 181-1188 μmol / L, the testing time is ≤25 seconds, and the sample volume is ≥3 μL. The data processing module receives the blood uric acid concentration data output by the detection module, removes abnormal data, and performs standardization processing. Abnormal data refers to fingertip blood uric acid ≤180 μmol / L or ≥1189 μmol / L. The system includes a data analysis module with a built-in ROC curve-based diagnostic model. Using venous blood uric acid at 420 μmol / L as the gold standard, it compares standardized fingertip blood uric acid data with a preset cutoff value of 428.50 μmol / L and combines this with the subject's clinical characteristics to determine the screening results. An early warning module is also included to issue warning signals when the screening result is high-risk. The display module is used to display test data and screening results in real time; the storage module is used to store test data, clinical characteristic parameters, and screening results. The communication module is used to realize data transmission between modules and information interaction with external terminals; the calibration module is used to perform equipment calibration by means of a matching password plate when a new test strip is opened; the power supply module provides stable power to all modules of the system.

2. The early screening system for hyperuricemia based on fingertip blood uric acid detection according to claim 1, characterized in that, The clinical characteristic parameters include the subject's age, sex, body mass index, fasting blood glucose, blood urea nitrogen, creatinine, total cholesterol, triglycerides, high-density lipoprotein, and low-density lipoprotein.

3. The early screening system for hyperuricemia based on fingertip blood uric acid detection according to claim 1, characterized in that, The diagnostic model in the analysis module is constructed using logistic regression analysis, and includes body mass index, fasting blood glucose, blood urea nitrogen, creatinine, total cholesterol, triglycerides, high-density lipoprotein, low-density lipoprotein, fingertip uric acid, and venous uric acid as independent influencing factors for the occurrence of hyperuricemia in the model.

4. The early screening system for hyperuricemia based on fingertip blood uric acid detection according to claim 1, characterized in that, The early warning module includes an audio-visual warning unit and a text prompt unit. The early warning signals include high-risk warnings, suggestions for further inspection, and guidance on lifestyle interventions.

5. The early screening system for hyperuricemia based on fingertip blood uric acid detection according to claim 1, characterized in that, The communication module supports Bluetooth, Wi-Fi and 4G / 5G communication protocols, and can transmit test data and screening results to medical staff terminals or subjects' personal mobile devices in real time.

6. The early screening system for hyperuricemia based on fingertip blood uric acid detection according to claim 1, characterized in that, The calibration module includes a password tag identification unit and a parameter calibration unit. The password tag corresponds one-to-one with the test strip. The calibration process is completed automatically and a calibration log is generated and stored in the storage module.

7. A method for early screening of hyperuricemia based on fingertip blood uric acid detection, characterized in that, Includes the following steps: Step 1: Sample collection. Subjects aged 18 years and above were selected, excluding those with severe cardiovascular and cerebrovascular diseases, liver and kidney dysfunction, hematological malignancies, and mental illnesses. After fasting for 8 hours, the fingertip of the ring finger was disinfected with a medical alcohol swab, and a fingertip blood sample was collected using a disposable sterile lancet. Step 2: Equipment calibration. Turn on the EA-12 blood glucose and uric acid tester, insert the code plate that matches the test strip for calibration, and enter the testing state after successful calibration. Step 3: Finger-prick blood uric acid test. The collected finger-prick blood sample is added to the accompanying test strip. The uric acid concentration is measured using a testing instrument, and the data is recorded. The ambient temperature for the test is 15-35℃, and the relative humidity is ≤80%. Step 4: Collection of clinical characteristic parameters, including age, gender, body mass index, fasting blood glucose, blood urea nitrogen, creatinine, total cholesterol, triglycerides, high-density lipoprotein, and low-density lipoprotein. Step 5: Data processing, removing abnormal data with fingertip blood uric acid ≤180μmol / L or ≥1189μmol / L, and standardizing the valid test data and clinical characteristic parameters; Step 6: Model analysis. The processed data is input into the diagnostic model. The diagnostic model determines the optimal cutoff value of 428.50 μmol / L based on the ROC curve and makes a comprehensive judgment based on the results of Logistic multivariate analysis. Step 7: Result determination. When the fingertip blood uric acid concentration is ≥428.50 μmol / L, it is determined to be high risk of hyperuricemia; when the fingertip blood uric acid concentration is <428.50 μmol / L, it is determined to be low risk. Step 8: Early warning and feedback. The early warning module issues corresponding early warning signals, the display module shows the detection data and judgment results, and the results are stored and transmitted to the designated terminal.

8. The method for early screening of hyperuricemia based on fingertip blood uric acid detection according to claim 1, characterized in that, In step 1, sample collection adopts random sampling, with a sampling ratio of 10% of each batch of physical examination participants. Subjects are required to sign an ethical informed consent form.

9. A method for early screening of hyperuricemia based on fingertip blood uric acid detection according to claim 1, characterized in that, In step 4, the clinical characteristic parameters were detected using a Roche Cobas C501 fully automated biochemical analyzer to measure fasting blood glucose, blood urea nitrogen, creatinine, total cholesterol, triglycerides, high-density lipoprotein, and low-density lipoprotein. The body mass index was calculated based on height and weight, and a body mass index ≥24 kg / m² was considered overweight / obese.

10. The early screening system for hyperuricemia based on fingertip blood uric acid detection according to claim 1, characterized in that, In step 6, the diagnostic model has an area under the curve of 0.972, a 95% confidence interval of 0.962-0.981, a diagnostic sensitivity of 0.928, a specificity of 0.108, and an accuracy of 0.820.

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