TWI888054B - Dengue fever progression assessment method and system thereof - Google Patents

Abstract

The present invention relates to a dengue fever progression assessment method and system thereof. The dengue fever progression assessment method comprises the following steps: inputting a plurality of cytokine data and a plurality of blood biomarker data obtained from dengue patients through an input device, and storing the plurality of cytokine data and the plurality of blood biomarker data in a storage device; performs biostatistical analysis on the plurality of cytokine data and the plurality of blood biomarker data, finds out cytokine data and blood biomarker data with significant differences, then applies the cytokine data and the blood biomarker data with significant differences to establish a dengue fever progression discriminant model with principal component analysis(PCA) algorithm; obtain cytokine data and blood biomarker data to be discriminated through the input device, and use the processor to perform an interpretation program to obtain an interpretation result of dengue fever progression; the interpretation result of dengue fever progression is output through a output device. The system includes an input device, a storage device, a processor, and an output device.

Description

Dengue fever progress assessment method and system

The present invention relates to a dengue fever progression assessment method and system, and in particular to an assessment method and system that utilizes biostatistics analysis combined with principal component analysis (PCA) algorithm to analyze cytokine data and blood marker data, thereby accurately predicting the progression of dengue fever.

Dengue fever is an acute viral infection caused by dengue virus (DENV). It is transmitted by the bites of mosquitoes carrying the dengue virus, mainly Aedes aegypti and Aedes albopictus. The disease is common in tropical and subtropical regions and is a major public health issue in these regions. Symptoms of dengue fever range from mild fever and joint pain to severe bleeding and shock, and severe cases may lead to death.

Symptoms of dengue fever usually appear within 4 to 10 days after infection, and include sudden high fever, headache, muscle and joint pain, rash and bleeding tendency. In some cases, dengue fever may progress to severe hemorrhagic tropical disease, in which case patients may experience life-threatening complications such as thrombocytopenia, organ failure and shock.

Dengue fever is graded according to severity, usually mild, moderate, and severe, based on the patient's symptoms and clinical manifestations. Mild cases may present with no symptoms or fever, headache, muscle and joint pain, which may be accompanied by a rash, but the patient usually remains awake and has no signs of organ damage; moderate to severe cases present with severe headache and fatigue, thrombocytopenia, and organ damage. If the disease is a little more severe, there may be bleeding tendency, low blood pressure, and organ failure, requiring hospitalization; and patients with severe cases may have shock and severe organ failure, requiring further treatment with blood transfusions or other supportive therapies while in hospital.

The current detection methods for grading the severity of dengue fever mainly include clinical symptoms and laboratory tests. Doctors will evaluate the patient's symptoms and assess the severity of the disease by testing blood markers such as white blood cells (WBC, unit is 10 ⁶ /L), hemoglobin concentration (hemoglobin, unit is g/dL), hematocrit (hematocrit, unit is %), platelet count (platelet, unit is 10 ⁹ /L), alanine aminotransferase concentration (GPT, unit is U/L) and serum creatinine concentration (creatinine, unit is mg/dL).

However, even if a patient is found to be infected with dengue virus, it is impossible to know whether the patient will further develop into moderate, severe or extremely severe illness. Therefore, when a patient is found to be infected with dengue virus, it is generally recommended that the patient be hospitalized for observation or further treatment, which will increase the burden on the medical system. If the patient does not develop into moderate, severe or extremely severe illness after hospitalization, in addition to wasting medical manpower and resources, patients who develop into moderate, severe or extremely severe illness will not be able to receive complete care and treatment.

In summary, there is an urgent need for effective assessment intervention in the clinical practice of dengue fever, providing doctors with additional patient cytokine-related data supplemented by blood marker data as a reference, so as to speed up the judgment of whether dengue patients need to be hospitalized or only need home treatment, so that medical manpower and resources can be properly used to reduce the burden on the medical system.

In view of the above-mentioned problem of assessing the progress of dengue fever, the purpose of the present invention is to provide a method and system for assessing the progress of dengue fever, so that medical staff can make quick medical decisions on whether dengue patients need to be hospitalized, improve the work efficiency of the medical system, reduce the burden of medical manpower, and enable patients to receive timely and appropriate treatment.

According to one purpose of the present invention, a method for evaluating the progress of dengue fever is provided, which comprises the following steps: inputting a plurality of cytokine data and a plurality of blood marker data of a dengue patient through an input device and storing them in a storage device; accessing the storage device through a processor, performing biostatistical analysis on the plurality of cytokine data and the plurality of blood marker data, and finding out the cytokine data and the blood marker data with significant differences. Blood marker data, and then the cytokine data and blood marker data with significant differences are used to establish a dengue fever progression assessment model using a principal component analysis (PCA) algorithm; the cytokine data and blood marker data to be evaluated are obtained through an input device, and a dengue fever progression interpretation result is obtained by a processor through a reading process; the dengue fever progression interpretation result is output by accessing a storage device through an output device.

Among them, the multiple cytokine data are the expression levels (pg/ml) of various cytokines, and the multiple blood marker data include white blood cell count, hemoglobin concentration, hematocrit, platelet count, alanine transaminase concentration and serum creatinine concentration; the multiple cytokine data and the multiple blood marker data are all obtained from the patient's blood sample.

There are 24 cytokines, and their types are shown in Table 1:

Table 1 Cytokines Chinese name IL-10 Interleukin-10 IFN-γ Interferon-γ IL-6 Interleukin-6 TNF-α Tissue necrosis factor alpha IL-8 Interleukin-8 IP-10 Interferon gamma-induced 10-kilodalton protein MCP-1 Monocyte tropism factor protein 1 IL-4 Interleukin-4 MIP-1β Macrophage inflammatory protein-1β IFN-α Interferon-α RANTES TF-L5 GM-CSF Granular mononuclear cell colony stimulating growth factor IL-15 Interleukin-15 VEGF Vascular endothelial growth factor IL-13 Interleukin-13 IL-18 Interleukin-18 IL-1Rα IL-1Rα IL-12 Interleukin-12 MIP-1α Macrophage inflammatory protein-1α MIG Interferon gamma-induced mononuclear factor IL-17 Interleukin-17 IL-1β Interleukin-1β IL-7 Interleukin-7 IL-2 Interleukin-2

Since there are a large number of cytokines and blood markers, the efficiency and accuracy of directly training a large amount of cytokine data and blood marker data by machine learning are low. Therefore, a large amount of cytokine data and blood marker data are first subjected to biostatistical analysis to find out cytokines and blood markers with significant differences for machine learning, which can greatly improve the training efficiency and accuracy of machine learning.

The biostatistical analysis is based on classification into mild (classified as Category A), moderate to severe (classified as Category B) and extremely severe (classified as Category C), and cytokine data and blood marker data with significant differences are found out from a large amount of cytokine data and blood marker data.

Then, the cytokine data and blood marker data with significant differences were trained using the principal component analysis (PCA) algorithm to obtain a dengue fever progression assessment model; the principal component analysis can find the maximum variance after weighted averaging of the values, and can transform many highly correlated variables into independent variables, and select fewer new variables that can explain most of the variance in the data, which are the so-called principal components. The selected principal components are then used to explain the comprehensive indicators of the original data.

Finally, the cytokine data and blood marker data to be evaluated are evaluated using the dengue fever progression assessment model to obtain the dengue fever progression interpretation results; the dengue fever progression interpretation results can tell whether the dengue patients to be evaluated will further develop from mild symptoms to moderate to severe or extremely severe symptoms, and provide medical staff with the ability to judge whether the dengue patients to be evaluated need to be hospitalized or only need home treatment, so as to reduce the burden on the medical system.

According to another object of the present invention, a dengue fever progression assessment system is provided, which includes an input device, a storage device, a processor, and an output device. The input device is used to input a plurality of cytokine data and a plurality of blood marker data of a dengue patient, as well as the cytokine data and blood marker data to be assessed; the storage device is connected to the input device, and is used to store a plurality of cytokine data and a plurality of blood marker data, as well as the cytokine data and blood marker data to be assessed; the output device is connected to the storage device, and is used to output the dengue fever progression interpretation result; the processor is connected to the storage device, and executes a plurality of instructions to perform the following steps: The method comprises the steps of: performing biostatistical analysis on the cytokine data and a plurality of blood marker data to find out the cytokine data and blood marker data with significant differences; calculating the cytokine data and blood marker data with significant differences using a principal component analysis (PCA) algorithm to establish a dengue fever progression assessment model; performing a judgment procedure on the cytokine data and blood marker data to be assessed based on the dengue fever progression assessment model to obtain a dengue fever progression judgment result; and outputting the dengue fever progression judgment result by accessing a storage device through an output device.

As mentioned above, the dengue fever progression assessment method and system of the present invention can be used to quickly and accurately determine whether a dengue patient will develop from a mild case to a moderately severe or extremely severe case by combining cytokine data with blood markers known to detect dengue fever in a short period of time after diagnosis, so that subsequent medical staff can quickly choose the corresponding medical decision on whether the patient needs hospitalization. In addition to improving the utilization of medical resources, it can also effectively provide corresponding treatment to dengue patients who may develop into moderately severe or extremely severe cases, thereby increasing the survival rate of patients.

In order to help the review committee understand the technical features, contents and advantages of the present invention and the effects that can be achieved, the present invention is described in detail as follows with the accompanying drawings and in the form of embodiments. The drawings used therein are only for illustration and auxiliary description, and may not be the true proportions and precise configurations after the implementation of the present invention. Therefore, it should not be interpreted based on the proportions and configurations of the attached drawings to limit the scope of rights of the present invention in actual implementation.

Unless otherwise defined, all terms (including technical and scientific terms) used herein have the meanings commonly understood by those of ordinary skill in the art to which the present invention belongs. It will be further understood that those terms as defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meanings in the context of the relevant art and the present invention, and will not be interpreted as idealized or overly formal meanings unless expressly so defined herein.

Please refer to FIG. 1, which is a flow chart of the dengue fever progress assessment method according to an embodiment of the present invention. As shown in FIG. 1, the dengue fever progress assessment method comprises the following steps (S1-S4):

Step S1: Input a plurality of cytokine data and a plurality of blood marker data of a dengue patient through an input device and store them in a storage device.

The cytokine data and multiple blood marker data of dengue patients that can be collected are input into the storage device of the system through the input device. The input device described here is not limited to a flow cytometer for obtaining cytokine data and a hematology analyzer for obtaining blood marker data. The cytokine data and blood marker data stored in the database of a medical institution can also be input into the system database through physical lines, files in a storage device, or files of cytokine data and blood marker data can be transmitted through a wired or wireless network to serve as training data for model construction.

Among them, the plurality of cytokine data and the plurality of blood marker data are all obtained from blood samples of dengue patients, and the data are all detected from blood samples obtained within 7 days when the patient has mild clinical manifestations (i.e., the patient is tested when visiting the hospital); therefore, the blood sample of each dengue patient has a plurality of cytokine data and a plurality of blood marker data, the plurality of cytokine data are specifically 24 cytokine data (as shown in the aforementioned Table 1), and the plurality of blood marker data are white blood cell count, hemoglobin concentration, hematocrit, platelet count, alanine transaminase concentration and serum creatinine concentration.

Step S2: The processor accesses the storage device to perform biostatistical analysis on a plurality of cytokine data and a plurality of blood marker data to find out the cytokine data and blood marker data with significant differences, and then establishes a dengue fever progression assessment model using the principal component analysis (PCA) algorithm for the cytokine data and blood marker data with significant differences.

Since there are a large number of cytokines and blood markers, the efficiency and accuracy of directly training a large amount of cytokine data and blood marker data by machine learning are low; therefore, a large amount of cytokine data and blood marker data are first subjected to biostatistical analysis to find out cytokines and blood markers with significant differences for machine learning, which can greatly improve the training efficiency and accuracy of machine learning.

Please refer to Table 2, which shows the expression of 24 cytokines (concentration, unit is pg/ml) in patients with different severity of dengue, the changes in mild (category A), moderately severe (category B) and extremely severe (category C), and the changes in the expression of these cytokines in the development of mild to moderately severe or extremely severe (A to B/C), mild to moderately severe (A to B), mild to extremely severe (A to C) and moderately severe to extremely severe (B to C). The data were calculated using mean ± standard deviation, and the Spearman correlation method was used for data analysis. The Mann Whitney test was used to determine the differences between the groups; where n represents the number of dengue patients, and the effective p value is <0.05.

Table 2 Cytokine (mean ± SD) A (n = 27) B (n = 31) C (n = 34) P-value P-value A to B/C A to B A to C B to C IL-10 17.641 ± 19.122 51.999 ± 54.483 83.179 ± 89.928 0.005 0.006 0.033 0.006 0.319 IFN-γ 142.715 ± 123.331 66.076 ± 63.509 49.301 ± 57.801 0.000 0.001 0.017 0.001 0.166 IL-6 18.025 ± 14.548 28.480 ± 31.251 101.491 ± 99.561 0.000 0.015 0.551 0.000 0.005 TNF-α 28.257 ± 18.564 36.306 ± 26.116 49.526 ± 38.030 0.023 0.060 0.272 0.030 0.189 IL-8 38.766 ± 37.487 23.082 ± 15.394 61.411 ± 52.328 0.100 0.591 0.469 0.114 0.017 IP-10 48074 ± 59916 44534 ± 46697 51794 ± 51394 0.690 0.690 0.708 0.740 0.840 MCP-1 2818 ± 2431 1229 ± 934 2475 ±1965 0.989 0.149 0.021 0.729 0.019 IL-4 0.327 ± 0.312 0.421 ± 0.353 0.339 ± 0.274 0.913 0.520 0.368 0.815 0.453 MIP-1β 48.429 ± 32.901 39.314 ± 21.524 50.561 ± 40.776 0.891 0.639 0.522 0.860 0.578 IFN-α 248.738 ± 213.070 89.336 ± 78.236 76.111 ± 87.247 0.000 0.000 0.001 0.000 0.337 RANTES 12500 ± 7424 9210 ± 7126 4654 ± 4119 0.000 0.000 0.046 0.000 0.004 GM-CSF 0.000 0.000 0.001 ± 0.004 0.090 0.341 1.000 0.181 0.162 IL-15 34.244 ± 19.227 30.610 ± 18.082 46.228 ± 29.628 0.083 0.602 0.441 0.117 0.016 VEGF 21.540 ± 19.983 22.464 ± 23.011 20.175 ± 25.760 0.495 0.607 0.842 0.488 0.631 IL-13 7.287 ± 5.064 7.682 ± 7.562 11.609 ± 12.146 0.862 0.811 0.540 0.889 0.531 IL-18 45.046 ± 48.702 121.876 ± 77.975 143.978 ± 127.205 0.000 0.000 0.000 0.000 0.920 IL-1Rα 13.984 ± 13.357 14.210 ± 14.244 38.180 ± 36.862 0.047 0.170 0.937 0.029 0.043 IL-12 2.296 ± 2.048 1.337 ± 0.429 1.245 ± 0.508 0.099 0.174 0.446 0.111 0.336 MIP-1α 28.178 ± 20.190 18.110 ± 12.052 28.023 ± 23.088 0.791 0.234 0.079 0.749 0.127 MIG 8479 ± 5282 8061 ± 4620 11865 ± 6764 0.020 0.169 0.838 0.027 0.023 IL-17 1.790 ± 1.503 1.370 ± 1.251 0.755 ± 0.415 0.002 0.009 0.133 0.002 0.134 IL-1β 4.498 ± 4.877 3.698 ± 3.239 2.627 ± 3.110 0.059 0.249 0.833 0.077 0.088 IL-7 4.305 ± 3.312 1.884 ± 1.888 2.823 ± 2.659 0.135 0.009 0.004 0.090 0.228 IL-2 0.458 ± 0.351 0.418 ± 0.273 0.337 ± 0.226 0.146 0.433 0.953 0.173 0.155

As can be seen from Table 2, the expression levels of different cytokines are different in mild, moderate, severe and extremely severe cases, and as mild cases develop into moderate, severe or extremely severe cases, the expression levels of some cytokines are upregulated or downregulated.

Please refer to Figure 2, Figure 3A to Figure 3C. Figure 2 is a hot map of different cytokine expression levels in mild, moderate, severe and extremely severe cases; the concentrations of each cytokine in the figure have been normalized and expressed as a percentage (%). Figures 3A to 3C are schematic diagrams of cytokine expression levels that are upregulated, downregulated, and have no significant changes when mild cases progress to moderate, severe or extremely severe cases, respectively; in Figures 3A to 3C, * indicates p value < 0.05, ** indicates p value < 0.01, and *** indicates p value < 0.001.

As can be seen from Figure 2, the expression levels of different cytokines are different in mild, moderate, severe and extremely severe cases. For example, when the disease is mild (category A), the expression level of IL-18 tends to be downregulated compared to healthy subjects who are not infected with dengue fever (green part), while when it progresses to moderate, severe (category B) or extremely severe (category C), the expression level of IL-18 tends to be upregulated compared to healthy subjects who are not infected with dengue fever (red part). Next, different cytokines were classified according to whether their expression levels were up-regulated, down-regulated, or had no significant changes, as shown in Figures 3A to 3C, respectively. It can be seen from these figures that the expression levels of eight cytokines, including IL-18, IL-6, IL-10, IL-1Rα, MIG, IL-15, TNF-α, and IL-8, were up-regulated, the expression levels of six cytokines, including RANTES, IFN-α, IFN-γ, IL-17, IL-7, and MCP-1, were down-regulated, and the expression levels of ten cytokines, including MIP-1α, IP-10, VEGF, IL-4, IL-13, GM-CSF, MIP-1β, IL-1β, IL-12, and IL-2, had no significant changes.

On the other hand, please refer to Table 3, which shows the changes of blood markers of dengue patients with different severity in mild (category A), moderately severe (category B) and extremely severe (category C). The data were calculated using mean ± standard deviation, and the Spearman correlation method was used for data analysis; the n value represents the number of dengue patients, and the effective p value is <0.05; it can be seen from Table 3 that the changes of different blood markers in mild, moderately severe and extremely severe cases are different.

Table 3 Blood markers (mean ± SD) A (n = 27) B (n = 31) C (n = 34) P-value White blood cell count (WBC) 4741 ± 1784 4757 ± 2969 6542 ± 3400 0.043 Hemoglobin concentration 14 ± 2 13 ± 2 13 ± 3 0.252 Hematocrit 41 ± 7 39 ± 6 40 ± 8 0.576 Platelet count 139 ± 49 91 ± 77 64 ± 52 0.000 Alanine transaminase concentration (GPT) 25 ± 23 39 ± 30 46 ± 28 0.002 Serum creatinine concentration 0.98 ± 0.45 0.90 ± 0.29 1.10 ± 0.44 0.302

From the above changes in the expression of cytokines and blood markers, it can be seen that the expression of different cytokines and the changes in different blood markers are different in mild, moderate, severe and extremely severe cases; then, the cytokines and blood markers were further analyzed by ROC analysis, as shown in Tables 4 and 5 respectively; in Tables 4 and 5, the values in bold indicate that after ROC analysis, the area under the curve (AUC) obtained has clinical significance, and the values with an upward arrow indicate that the higher the value, the higher the positive rate, and conversely, the values with a downward arrow indicate that the lower the value, the higher the positive rate.

Table 4 Cytokines Area Under the Curve (AUC) A to B/C A to B A to C B to C GM-CSF 0.546↑ 0.527↑ 0.563↑ 0.538↓ IFN-⍺ 0.699↓ 0.708↓ 0.691↓ 0.518↓ IFN-𝛾 0.669↓ 0.664↓ 0.673↓ 0.541↑ IL-1β 0.572↓ 0.525↓ 0.615↓ 0.596↑ IL1-R⍺ 0.557↑ 0.505↑ 0.605↑ 0.592↓ IL-2 0.583↓ 0.588↓ 0.578↓ 0.506↑ IL-4 0.502↑ 0.503↑ 0.501↑ 0.506↑ IL-6 0.701↑ 0.586↑ 0.805↑ 0.712↑ IL-7 0.662↓ 0.721↓ 0.608↓ 0.600↑ IL-8 0.597↑ 0.515↑ 0.671↑ 0.659↓ IL-10 0.641↑ 0.608↑ 0.671↑ 0.580↓ IL-12 0.608↓ 0.582↓ 0.631↓ 0.565↑ IL-13 0.517↓ 0.524↓ 0.510↓ 0.506↓ IL-15 0.556↑ 0.524↓ 0.630↑ 0.649↓ IL-17 0.636↓ 0.611↓ 0.658↓ 0.546↑ IL-18 0.765↑ 0.760↑ 0.770↑ 0.534↑ IP-10 0.525↓ 0.516↓ 0.533↑ 0.502↑ MCP-1 0.583↓ 0.640↓ 0.531↑ 0.622↓ MIG 0.643↑ 0.547↑ 0.731↑ 0.697↓ MIP-1⍺ 0.508↑ 0.558↓ 0.568↑ 0.621↓ MIP-1β 0.550↑ 0.520↑ 0.577↑ 0.568↓ RANTES 0.740↓ 0.653↓ 0.820↓ 0.693↓ TNF-⍺ 0.637↑ 0.584↑ 0.685↑ 0.620↓ VEGF 0.564↑ 0.551↑ 0.575↑ 0.545↓

Table 5 Blood markers Area Under the Curve (AUC) A to B/C A to B A to C B to C White blood cell count (WBC) 0.585↑ 0.510↓ 0.672↑ 0.607↓ Hemoglobin concentration 0.588↓ 0.591↓ 0.585↓ 0.504↑ Hematocrit 0.644↑ 0.613↑ 0.682↑ 0.589↓ Platelet count 0.776↓ 0.723↓ 0.825↓ 0.578↓ Alanine transaminase concentration (GPT) 0.719↑ 0.619↑ 0.811↑ 0.704↑ Serum creatinine concentration 0.629↑ 0.516↑ 0.731↑ 0.708↑

It can be seen from Tables 4 and 5 that after ROC analysis, among the cytokines, IFN-⍺, IL-6, IL-7, IL-18 and RANTES have significant differences, and among the blood markers, platelets, alanine aminotransferase and serum creatinine have significant differences; therefore, the data of the 5 cytokines and 3 blood markers with significant differences were used as parameters of the subsequent principal component analysis (PCA) algorithm to establish a dengue fever progression assessment model.

Next, from the data of the above-mentioned 5 cytokines and 3 blood markers with significant differences, 3 data, 4 data, and all 8 data were selected individually based on their significant changes in mild (category A), moderate to severe (category B), and extremely severe (category C) cases, and used as parameters of the principal component analysis algorithm in sequence to establish a dengue fever progression assessment model; the types of parameters selected for different severity of the disease are shown in Table 6.

Table 6 Types of PCA algorithm parameters A to B/C A to B A to C B to C 3 parameters GPT, IL-6, IL-18 Platelet, IFN-⍺, IL-18 GPT, IL-6, RANTES GPT, IL-6, RANTES 4 parameters GPT, IL-6, IL-18, RANTES Platelet, IFN-⍺, IL-18, IL-7 GPT, IL-6, RANTES, IL-18 GPT, IL-6, RANTES, creatinine 8 parameters Platelet, GPT, creatinine, IFN-⍺, IL-6, IL-7, IL-18, RANTES

Step S3: Obtain the cytokine data and blood marker data to be evaluated through the input device, and use the processor to perform an interpretation process to obtain the dengue fever progression interpretation result.

Through the above step S2, according to different cytokines with significant differential expression, combined with blood markers with significant differential expression as parameters (see Table 6), a dengue fever progression evaluation model is established by the principal component analysis algorithm. The cytokine data and blood marker data to be evaluated are evaluated using the model to obtain the dengue fever progression interpretation results, and the results are shown in Table 7.

Table 7 Prediction Accuracy A to B/C A to B A to C B to C 3 parameters 79.4% 81.5% 85.7% 63.3% 4 parameters 75.0% 76.3% 81.6% 79.6% 8 parameters 89.7% 84.2% 89.8% 65.3%

From the results in Table 7, it can be seen that the prediction accuracy of mild cases developing into moderate or severe cases or extremely severe cases (A to B/C), mild cases developing into moderate or severe cases (A to B), and mild cases developing into extremely severe cases (A to C) are all the highest when using 8 parameters, which are 89.7%, 84.2% and 89.8% respectively, all of which are more than 80% accurate; and the prediction rate of moderate and severe cases developing into extremely severe cases (B to C) is the highest when using 4 parameters, with a prediction accuracy of 79.6%, which is also nearly 80% accurate.

Therefore, when it is desired to predict whether a dengue patient will develop from a mild case to a moderate or severe case or an extremely severe case, a mild case to a moderate or severe case, or a mild case to an extremely severe case, three blood markers, namely platelets, alanine aminotransferase, and serum creatinine, and five cytokines, namely IFN-⍺, IL-6, IL-7, IL-18, and RANTES, are used as parameters to establish a dengue fever progression assessment model for prediction; if it is desired to predict whether a dengue patient will develop from a moderate or severe case to an extremely severe case, two blood markers, namely alanine aminotransferase and serum creatinine, and two cytokines, namely IL-6 and RANTES, are used as parameters to establish a dengue fever progression assessment model for prediction.

Step S4: The dengue fever progression interpretation result is outputted by accessing the storage device through the output device.

The dengue fever progression judgment result obtained in step S3 can be further outputted through an output device. The output device disclosed in this embodiment can include various display interfaces, such as a computer screen, a display, or a handheld device display.

Finally, please refer to FIG. 4 , which is a schematic diagram of a dengue fever progress assessment system according to an embodiment of the present invention. As shown in FIG. 4 , the dengue fever progress assessment system 20 may include an input device 21 , a storage device 22 , a processor 23 and an output device 24 .

In this embodiment, the input device 21 can be a flow cytometer for obtaining cytokine data and a hematology analyzer for obtaining blood marker data, both of which are obtained from a patient's blood sample. In another embodiment, the input device 21 is not limited to flow cytometers and hematology analyzers. The input device 21 may include input interfaces of electronic devices such as personal computers, smart phones, servers, etc., including touch screens, keyboards, mice, etc., to transmit cytokine data and blood marker data in file form; or upload historical data to the memory storage in the storage device 22 via wireless network transmission, wireless communication transmission or general wired Internet. The memory may include read-only memory, flash memory, disk or cloud database, etc.

Secondly, the dengue fever progress assessment system 20 accesses the storage device 22 through the processor 23. The processor 23 may include a central processing unit, an image processor, a microprocessor, etc. in a computer or server, and may include a multi-core processing unit or a combination of multiple processing units. The processor 23 executes instructions to access multiple cytokine data and multiple blood marker data of dengue patients in the storage device 22 for training procedures, and accesses the cytokine data and blood marker data to be evaluated for interpretation procedures. Specifically, the training procedure is to perform biostatistical analysis on a plurality of cytokine data and a plurality of blood marker data of dengue patients originally stored in the storage device 22, find out the cytokine data and blood marker data with significant differences, and calculate the cytokine data and blood marker data with significant differences using the principal component analysis (PCA) algorithm to establish a dengue fever progression assessment model.

Next, the cytokine data and blood marker data to be evaluated are calculated through the established dengue fever progression evaluation model through the interpretation procedure, and are classified into one of the four conditions: mild symptoms develop into moderately severe or extremely severe symptoms (A to B/C), mild symptoms develop into moderately severe symptoms (A to B), mild symptoms develop into extremely severe symptoms (A to C), and moderately severe symptoms develop into extremely severe symptoms (B to C) according to the severity of the progression of dengue fever symptoms, thereby obtaining the dengue fever progression interpretation result; the output device 24 accesses the storage device 22 to output the dengue fever progression interpretation result, and the output device 24 may include various display interfaces, such as a computer screen, a display, or a handheld device display.

Through the above-mentioned dengue fever progression assessment method and system, by combining cytokine data with blood markers known to be used for detecting dengue fever, it is possible to quickly and accurately determine whether a dengue patient will develop from a mild case to a moderately severe or extremely severe case. The assessment accuracy rate can reach nearly 80%, so that subsequent medical staff can quickly choose the corresponding medical decision on whether the patient needs hospitalization. In addition to improving the utilization of medical resources, it can also effectively provide corresponding treatment to dengue patients who may develop into moderately severe or extremely severe cases, thereby increasing the survival rate of patients.

The above description is for illustrative purposes only and is not intended to be limiting. Any equivalent modifications or changes made to the invention without departing from the spirit and scope of the invention shall be included in the scope of the attached patent application.

20: Dengue fever progress assessment system 21: Input device 22: Storage device 23: Processor 24: Output device S1~S4: Steps

In order to make the technical features, contents and advantages of the present invention and the effects that can be achieved more obvious, the present invention is described in detail as follows with reference to the accompanying drawings and in the form of embodiments:

Figure 1 is a flow chart of the dengue fever progression assessment method of the present invention; Figure 2 is a heat map of different cytokine expression levels in mild, moderate, severe and extremely severe cases of the present invention; Figures 3A, 3B and 3C are schematic diagrams of the upregulation, downregulation and no significant change of cytokine expression levels when the mild case progresses to moderate, severe or extremely severe cases of the present invention; Figure 4 is a schematic diagram of the dengue fever progression assessment system of the present invention.

S1~S4: Steps

Claims (9)

A dengue fever progression assessment method comprises the following steps: Step S1: Input a plurality of cytokine data and a plurality of blood marker data of a dengue patient through an input device and store them in a storage device; Step S2: Access the storage device through a processor, perform a biostatistical analysis on the plurality of cytokine data and the plurality of blood marker data, find out the cytokine data and blood marker data with significant differences, and then establish a dengue fever progression assessment model with the cytokine data and blood marker data with significant differences using a principal component analysis (PCA) algorithm; Step S3: Obtain the cytokine data and blood marker data to be evaluated through the input device, and use the processor to perform a judgment procedure to obtain a dengue fever progression judgment result; and Step S4: Access the storage device through an output device and output the dengue fever progression judgment result, wherein the plurality of cytokine data are selected from IL-10, IFN-γ, IL-6, TNF-α, IL-8, IP-10, MCP-1, IL-4, MIP-1β, IFN-α, RANTES, GM-CSF, IL-15, VEGF, IL-13, IL-18, IL-1Rα, IL-12, MIP-1α, MIG, IL-17, IL-1β, IL-7 and IL-2; The plurality of blood marker data are selected from white blood cells, hemoglobin, hematocrit, platelets, alanine aminotransferase and serum creatinine; The cytokine data with significant differences are selected from IFN-⍺, IL-6, IL-7, IL-18 and RANTES; The blood marker data with significant differences are selected from platelets, alanine aminotransferase and serum creatinine; and Steps S1 to S4 are all completed by computer software. The method for assessing the progression of dengue fever as described in claim 1, wherein the input device comprises a flow cytometer and a hematology analyzer. The dengue fever progression assessment method as described in claim 1, wherein the biostatistical analysis is performed using the Spearman correlation method. The dengue fever progression assessment method as described in claim 1, wherein the dengue fever progression assessment model is divided into the following four dengue fever progression assessment models according to the severity of the dengue fever progression: 1. A dengue fever progression assessment model for a mild case to a moderately severe case or an extremely severe case; 2. A dengue fever progression assessment model for a mild case to a moderately severe case; 3. A dengue fever progression assessment model for a mild case to an extremely severe case; and 4. A dengue fever progression assessment model for a moderately severe case to an extremely severe case. The dengue fever progression assessment method as described in claim 4, wherein the dengue fever progression assessment model for the progression of mild cases to moderately severe or extremely severe cases is established using five cytokine data, namely IFN-⍺, IL-6, IL-7, IL-18 and RANTES, in combination with three blood marker data, namely platelets, alanine transaminase and serum creatinine, as its parameters. The dengue fever progression assessment method as described in claim 4, wherein the dengue fever progression assessment model for the progression from mild to moderate to severe disease is established using five cytokine data, namely IFN-⍺, IL-6, IL-7, IL-18 and RANTES, in combination with three blood marker data, namely platelets, alanine transaminase and serum creatinine, as its parameters. The dengue fever progression assessment method as described in claim 4, wherein the dengue fever progression assessment model for the progression from mild to extremely severe disease is established using five cytokine data, namely IFN-⍺, IL-6, IL-7, IL-18 and RANTES, in combination with three blood marker data, namely platelets, alanine transaminase and serum creatinine, as its parameters. The dengue fever progression assessment method as described in claim 4, wherein the dengue fever progression assessment model for the development of moderate to severe cases into extremely severe cases is established using two cytokine data, IL-6 and RANTES, in combination with two blood marker data, alanine transaminase and serum creatinine, as its parameters. A dengue fever progression assessment system comprises: an input device for inputting a plurality of cytokine data and a plurality of blood marker data of a dengue patient, as well as the cytokine data and blood marker data to be assessed; a storage device connected to the input device for storing the plurality of cytokine data and the plurality of blood marker data, as well as the cytokine data and blood marker data to be assessed; an output device connected to the storage device for outputting a dengue fever progression interpretation result; and a processor connected to the storage device for executing a plurality of instructions to perform the following steps: Perform a biostatistical analysis on multiple cytokine data and multiple blood marker data of dengue patients to find out cytokine data and blood marker data with significant differences; Calculate the cytokine data and blood marker data with significant differences using a principal component analysis (PCA) algorithm to establish a dengue fever progression assessment model; Based on the dengue fever progression assessment model, perform a judgment procedure on the cytokine data and blood marker data to be evaluated to obtain a dengue fever progression judgment result; and Access the storage device through the output device to output the dengue fever progression judgment result, wherein The plurality of cytokine data are selected from IL-10, IFN-γ, IL-6, TNF-α, IL-8, IP-10, MCP-1, IL-4, MIP-1β, IFN-α, RANTES, GM-CSF, IL-15, VEGF, IL-13, IL-18, IL-1Rα, IL-12, MIP-1α, MIG, IL-17, IL-1β, IL-7 and IL-2; The plurality of blood marker data are selected from white blood cells, hemoglobin, hematocrit, platelets, alanine aminotransferase and serum creatinine; The cytokine data with significant differences are selected from IFN-⍺, IL-6, IL-7, IL-18 and RANTES; and The blood marker data with significant differences were selected from platelets, alanine aminotransferase and serum creatinine.