AIDS infection prediction model and establishment method thereof

Abstract

The invention discloses an AIDS infection course prediction model and an establishment method thereof, and relates to the technical field of AIDS diagnosis, the prediction model comprises an AIDS combined fungal infection prediction model and an AIDS combined multi-infection prediction model; the formula of the AIDS combined fungal infection prediction model is as follows: logistic (p) =-5.838 to 0.348 * CD4 + T + 0.128 * CD8 + T + 7.329 * CD4 / CD8 + 8.236 * CD64; the formula of the AIDS combined fungal infection prediction model is as follows: logistic (p) =-0.824 to 0.182 * CD4 + T + 0.054 * CD8 + T-0. 065 * L / CD45. The established prediction model for the disease progress of the AIDS patient is helpful for further improving the definite diagnosis rate of the AIDS combined fungal infection patient and the AIDS combined multi-infection patient so as to promote the early treatment of the AIDS combined fungal infection patient and the AIDS combined multi-infection patient; meanwhile, the casualty rate of the AIDS patient caused by complications such as opportunity infection is reduced as much as possible, and a theoretical basis and a reference basis are provided for early diagnosis and precise treatment of the AIDS patient clinically.

Description

Technical Field

[0001] The present invention relates to the technical field of AIDS diagnosis, and more specifically, to an AIDS infection prediction model and an establishment method thereof. Background Art

[0002] AIDS (acquired immuno-deficiency syndrome, AIDS) is an infectious disease caused by human immunodediency virus (HIV) infection. The disease has a very high mortality rate and there is currently no cure. The epidemic pattern of AIDS is mainly blood transmission and sexual transmission, and the infection rate among male homosexual transmission has been rising in recent years.

[0003] In the process of antiviral treatment for AIDS patients, highly active antiretroviral therapy (HARRT) is mainly used. Since HARRT was widely used in the 1990s, the disease progression of AIDS patients has been effectively delayed, and HARRT has significantly reduced the mortality rate of AIDS patients. HARRT treatment can inhibit HIV replication, restore the patient's immunity, and increase CD4 + The number of T cells can reduce the incidence of opportunistic infections in patients. However, during the treatment of HARRT, the patient's immune status should be tested regularly, and the side effects caused by the interaction between different antiviral drugs and antifungal drugs should be noted. The medication regimen should be adjusted in time according to the patient's own immune status and the nature of the drug. At present, the main antiretroviral drugs in my country include nucleoside reverse transcriptase inhibitors (zidovudine, lamivudine, tenofovir disoproxil fumarate, etc.), non-nucleoside reverse transcriptase inhibitors (nevirapine, efavirenz, rilpivirine, etc.), protease inhibitors (ritonavir, cobicistat, etc.), integrase inhibitors, fusion enzyme inhibitors, etc.

[0004] The clinical stages of AIDS mainly include the following three stages: (1) Acute stage: The acute stage mainly occurs 2 to 4 weeks after the patient is initially infected. During this stage, the patient may experience symptoms similar to respiratory infections, including fever, fatigue, nausea, muscle and joint pain, etc. During this period, a physical examination of the patient may show enlarged lymph nodes in the neck and armpits. (2) Asymptomatic stage: The period from when the patient is infected with HIV to when they develop AIDS is called the asymptomatic infection period, which is an average of 7 to 8 years. Some patients have a shorter asymptomatic infection period of about 1 to 2 years. During the asymptomatic period, the patient's HIV antibody is positive, and persistent superficial lymphadenopathy may occur. (3) AIDS stage: This stage is the final stage of HIV infection, characterized by severe immunodeficiency, opportunistic infections, and malignant tumors. HIV is mainly expressed in CD4 +T lymphocytes are the target of attack, causing CD4 + The number of T cells continues to decline, causing the patient's cellular immune function to be deficient, and secondary to the deficiency of humoral immune function, eventually entering the AIDS stage, complicated by various opportunistic infections and tumors.

[0005] Opportunistic infections (OIs) refer to some opportunistic pathogens that do not cause a response when the human immune system is normal. When the human immune function is impaired and the resistance is low, the susceptibility to these opportunistic pathogens increases, thus causing infectious diseases. There are many types of pathogens that cause opportunistic infections in AIDS patients. The main pathogenic microorganisms that cause opportunistic infections in HIV / AIDS patients include bacteria (Mycobacterium tuberculosis, Shigella dysenteriae, Klebsiella pneumoniae, Pseudomonas aeruginosa, Escherichia coli, etc.), fungi (Candida albicans, Cryptococcus neoformans, Pneumocystis jiroveci, Talaromyces marneffei, etc.), viruses (herpes simplex virus, cytomegalovirus, hepatitis B virus, Epstein-Barr virus, etc.) and protozoa (Toxoplasma gondii, Cryptosporidium, etc.). The clinical manifestations of opportunistic infections are often complex and atypical, with the characteristics of difficult diagnosis and treatment, poor treatment effect and poor prognosis, which seriously affect the physical and mental health and quality of life of patients. Opportunistic infections are one of the main causes of treatment and death in AIDS patients. Therefore, it is very important to diagnose and treat opportunistic infections in AIDS patients early and accurately, and to adjust medication regimens in a timely manner.

[0006] CD4 + T lymphocyte levels can be used as an indicator to monitor the incidence of opportunistic infections in HIV / AIDS patients. + The level of T cells reflects the immune level of HIV / AIDS patients. + The sharp decline in T cell levels indicates the progressive development of HIV / AIDS and is closely related to opportunistic infections. + T lymphocytes can be activated and differentiated into cytotoxic T lymphocytes (CTL) by HIV virus stimulation. CTL has a strong killing effect, that is, CTL response. CTL is essential for controlling certain cancers, HIV and other related viral infections. Therefore, CD8 + The increase in T cell levels and the decrease in the CD4 / CD8 ratio have a certain predictive value for AIDS combined with opportunistic infections. Summary of the invention

[0007] The purpose of this invention is to achieve early prediction of the disease process of AIDS patients, so as to carry out accurate treatment for related complications and reduce the mortality rate of AIDS patients in clinic. + T lymphocyte level, CD8 + To investigate the correlation between T lymphocyte levels, CD4 / CD8, CD14 / CD45, L / CD45, HLA-DR / CD14, and CD64 indexes and the occurrence of opportunistic infections, and to explore the differences in the levels of different indicators in patients without opportunistic infections, patients with AIDS complicated by fungal infections, and patients with AIDS complicated by multiple infections, so as to provide a reference and theoretical basis for the preliminary clinical judgment of opportunistic infections in AIDS patients.

[0008] The above technical objectives of the present invention are achieved through the following technical solutions: a prediction model for the course of AIDS infection, characterized in that: the prediction model includes a prediction model for distinguishing AIDS and AIDS combined with fungal infection, and a prediction model for distinguishing AIDS and AIDS combined with multiple infections;

[0009] The prediction model for distinguishing fungal infection between AIDS and AIDS is based on the combination of CD4 + T lymphocytes, CD8 + T lymphocytes, CD4 / CD8 and CD64 indicators were constructed, and the prediction model formula was:

[0010] logistic(p)=-5.838-0.348*CD4 + T+0.128*CD8 + T+7.329×CD4 / CD8+8.236*CD64, the diagnostic efficacy of this prediction model is 0.937 (0.882-0.992), the sensitivity is 82.9%, and the specificity is 96.5%. In actual clinical situations, if the results of relevant indicators of AIDS patients after flow cytometry examination are as follows: -0.348*CD4+T+0.128*CD8+T+7.329*CD4 / CD8+8.236*CD64≥6.261, it is recommended that doctors consider that the patient may have concurrent fungal infection, and can perform additional mycological examinations to achieve early diagnosis and early treatment of AIDS-related opportunistic infections.

[0011] The prediction model for identifying AIDS and AIDS co-infection is based on the combination of CD4 + T lymphocytes, CD8 + T lymphocyte and L / CD45 indicators were constructed, and the prediction model formula was:

[0012] logistic(p)=-0.826-0.181*CD4 + T+0.054*CD8 + T-0.065*L / CD45, the diagnostic efficacy of the prediction model is 0.930 (0.877-0.983), the sensitivity is 90.7%, and the specificity is 90.2%. In the actual clinical situation, for example, after the flow cytometry examination of AIDS patients, the results of their related indicators are as follows: -0.181*CD4 + T+0.054*CD8 + T-0.065*L / CD45≥1.273, which means that the doctor is advised to consider that the patient may have multiple infections at the same time, and additional bacteriological and mycological examinations can be performed to reduce the missed diagnosis rate of opportunistic infections caused by AIDS.

[0013] The present invention further discloses a method for establishing a prediction model for the course of AIDS infection, comprising the following steps:

[0014] S1: Enroll patients according to their examination results, clinical manifestations, and admission and discharge diagnoses, and collect basic information of all enrolled patients, including name, age, and gender;

[0015] S2: The patients were divided into groups according to their admission and discharge diagnoses and related examination results, including AIDS alone group, AIDS complicated with fungal infection group, and AIDS complicated with multiple infections group. The immune cell subset detection data of the patients in each group during the admission examination were collected.

[0016] S3: The basic information of each group of patients and the data of immune cell subsets were tested for normality; if the data distribution conformed to normality, the Ordinary one-way ANOVA test was used for comparison among the three groups, and the Unpaired t test was used for comparison between the two groups; if the data distribution did not conform to normality, the Kruskal-Wallis test was used for comparison among the three groups, and the Mann-Whitney test was used for comparison between the two groups;

[0017] S4: Screen out the immune cell subsets with significant statistical differences between the AIDS group and the AIDS complicated with fungal infection group, and between the AIDS group and the AIDS complicated with multiple infections group as key predictive factors;

[0018] S5: ROC curves were used to evaluate the differential diagnostic efficiency of key predictive factors between the AIDS group and the AIDS complicated with fungal infection group, and between the AIDS group and the AIDS complicated with multiple infections group;

[0019] S6: In order to obtain higher diagnostic efficacy, binary logistic regression analysis method was further used in combination with key predictive factors to construct joint prediction models that can be used for differential diagnosis between AIDS group and AIDS complicated with fungal infection group, and between AIDS group and AIDS complicated with multiple infections group.

[0020] The present invention is further configured as follows: the immune cell subset detection data collected in S2 includes T lymphocytes, CD4 + T cells, CD8 + T cells, B lymphocytes, NK cells, CD4 / CD8, L / CD45, HLA-DR / CD14 and CD64 indexes.

[0021] The present invention is further configured as follows: the immune cell subpopulation detection data to be collected in S2 is uniformly detected using a FACSCalibur flow cytometer.

[0022] The present invention is further configured as follows: the normal distribution test method in S3 is: statistical analysis of the gender, age and immune cell subset indicators of the clinical samples is performed using GraphPad Prism, and a normal distribution test is performed on each group of data using D'Agostino & Pearson test.

[0023] The present invention is further configured as follows: the method for determining whether there is a statistical difference in S5 is: P<0.05 indicates a significant difference, and P<0.01 indicates a very significant difference.

[0024] The present invention is further configured as follows: the diagnostic efficacy analysis of S6 is analyzed using SPSS software, specifically: the immune cell subpopulation indicators with significant statistical differences between the two groups are imported into the SPSS software, and the ROC curve method is used to analyze the differential diagnostic efficacy of the corresponding indicators.

[0025] The present invention is further configured as follows: the model construction method of S7 is: using a binary logistic regression method to analyze and obtain a joint model with the highest diagnostic efficacy, and derive a prediction formula, a cutoff value, sensitivity and specificity, etc.

[0026] In summary, the present invention has the following beneficial effects:

[0027] 1. The present invention analyzes the level differences of multiple lymphocyte indicators among three groups of patients, and constructs a disease progression prediction model for AIDS patients using key prediction factors. The prediction model will help clinically differentiate AIDS patients, AIDS patients with fungal infections, and AIDS patients with multiple infections, thereby improving the diagnosis rate of AIDS patients with opportunistic infections, timely adjusting medication treatment plans, and providing a reference basis and theoretical basis for preliminary clinical diagnosis and treatment;

[0028] 2. The present invention adopts binary logistic regression method combined with CD4 + T lymphocytes, CD8 + T lymphocytes, CD4 / CD8 and CD64 established a prediction model for whether AIDS patients have fungal infections. The combined index has a high prediction accuracy (93.7%), sensitivity (83%) and specificity (96%). In actual clinical situations, such as flow cytometry examination of AIDS patients, the results of their related indicators are as follows: -0.348*CD4 + T+0.128*CD8 + T+7.329*CD4 / CD8+8.236*CD64≥6.261, which means that the doctor is advised to consider that the patient may have concurrent fungal infection and to perform additional mycological examinations to achieve early diagnosis and early treatment of opportunistic infections associated with AIDS.

[0029] 3. The present invention adopts binary logistic regression method combined with CD4 + T lymphocytes, CD8 + T lymphocytes and L / CD45 were used to construct a prediction model for AIDS combined with multiple infections. The combined index had a high prediction accuracy (93%), a sensitivity (90.7%), and a specificity of 90.2%. In actual clinical situations, for example, after flow cytometry examination of AIDS patients, the results of their related indicators were as follows: -0.181*CD4 + T+0.054*CD8 + T-0.065*L / CD45≥1.273, which means that the doctor is advised to consider that the patient may have multiple infections at the same time, and additional bacteriological and mycological examinations can be performed to reduce the missed diagnosis rate of opportunistic infections caused by AIDS. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 is a flow chart of the steps for establishing the prediction model of the present invention;

[0031] Figure 2 The basic information of the three groups of patients and the results of flow cytometry statistical analysis [age (year), T lymphocytes, CD4 + T cells (%), CD8+ T cell (%), B lymphocyte (%), NK cell (%) indicators];

[0032] Figure 3 It is the statistical analysis results of three groups of flow cytometry of the present invention (CD4 / CD8, L / CD45, CD14 / CD45, HLA-DR / CD14, CD64 indicators);

[0033] Figure 4 The present invention CD4 + T lymphocytes, CD8 + ROC curves of T lymphocytes, CD4 / CD8, and CD64 indexes for differential diagnosis of AIDS patients with concurrent fungal infection;

[0034] Figure 5 The present invention CD4 + T lymphocytes, CD8 + ROC curve of the combined prediction model of T lymphocytes, CD4 / CD8, and CD64 index for differential diagnosis of patients with AIDS and fungal infection;

[0035] Figure 6 The present invention CD4 + T lymphocytes, CD8 + ROC curve of T lymphocyte and L / CD45 indicators for differential diagnosis of AIDS patients with multiple infections;

[0036] Figure 7 The present invention CD4 + T lymphocytes, CD8 + ROC curve of the combined prediction model of T lymphocytes and L / CD45 indicators for differential diagnosis of AIDS patients with multiple infections. DETAILED DESCRIPTION

[0037] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solution of the present invention will be further described in detail below in conjunction with the embodiments of the present invention and the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0038] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present invention can be combined with each other. The present invention will be described in detail below in conjunction with the embodiments.

[0039] Example:

[0040] 1. Materials and methods

[0041] 1.1 General Information

[0042] This example collected 143 AIDS patients hospitalized in our hospital from January 2020 to December 2021, including 61 simple AIDS patients, 38 AIDS patients with fungal infection, and 44 AIDS patients with multiple infections. Among the patients with multiple infections, 25 had AIDS + Pneumocystis / bacterial infection + oral fungal infection + herpes virus infection, 6 had concurrent syphilis, 3 had concurrent tuberculosis (TB), 2 had concurrent nontuberculous mycobacteria (NTM), 2 had concurrent TB + syphilis, and 1 had concurrent chronic hepatitis B (CHB). In addition, there were 1 case of HIV+TB+herpes virusemia, 1 case of HIV+TB+CHB+syphilis, 1 case of HIV+TB+CHB infection, 1 case of HIV+herpes virus infection+oral fungal infection, and 1 case of HIV+TB+herpes virus infection+oral fungal infection). Basic diseases such as hypertension, diabetes, cardiovascular system diseases, and tumor diseases were excluded, as well as pregnant and lactating women. The disease diagnosis of patients was based on a comprehensive diagnosis of the patients' clinical manifestations, signs, laboratory tests, imaging tests, and etiological tests.

[0043] 1.2 Instruments and reagents

[0044] FACS Calibur flow cytometer from BD Company, USA; four-color lymphocyte subset analysis kit, CD64-PE fluorescent monoclonal antibody, CD14-FITC, HLA-DR-APC fluorescent monoclonal antibody, and hemolytic agent for blood cell analysis from Tongsheng Times Company, China.

[0045] 1.3 Methods

[0046] like Figure 1 The basic information of all enrolled patients, including name, age and gender, was collected. The AIDS-only group was regarded as the stage of the disease without opportunistic infection, and the AIDS combined with fungal infection group and AIDS combined with multiple infections were regarded as different stages of AIDS with opportunistic infection. Flow cytometry or T cell subset detection data were collected when the patients were first hospitalized, including T lymphocytes, CD4+T cells (%), CD8+T cells (%), B lymphocytes, NK cells, CD4 / CD8, CD14 / CD45, L / CD45, HLA-DR / CD14, and CD64 indexes. FACS Calibur (BD, USA) flow cytometer was used for unified detection.

[0047] 1.4 Statistical analysis

[0048] The collected quantitative data were collated and entered using Microsoft Office; GraphPadPrism (version 9.5) and SPSS (version 26) were used for statistical analysis of the data, and the age of the three groups of patients and flow cytometry indicators were tested for normal distribution (KS normality test). One-way analysis of variance was used to compare the data that met the normal distribution among each three groups

[0049] (One-way ANOVA), nonparametric tests were used for data that did not conform to normal distribution

[0050] (Kruskal-Wallis test). Unpaired t test was used to compare the data between two groups if they were in accordance with normal distribution, and non-parametric test was used to compare the data if they were not in accordance with normal distribution.

[0051] (Mann-Whitney test). P<0.05 indicated a significant difference, and P<0.01 indicated a very significant difference.

[0052] 2. Results

[0053] In this example, a simple AIDS group (61 cases), an AIDS complicated with fungal infection group (38 cases), and an AIDS complicated with multiple infections group (44 cases) were set up as research subjects. The statistical data showed that there was no significant difference in age among the three groups of samples, and there was no significant difference in gender between each two groups of samples, indicating that the data of the three samples were comparable (P>0.05). The flow cytometry indicators of the three groups of patients, including T lymphocytes (%), CD4 + T lymphocytes (%), CD8 + For each indicator, including T lymphocytes (%), B lymphocytes (%), NK cells (%), CD4 / CD8 ratio, L / CD45, CD14 / CD45, HLA-DR / CD14, and CD64 index, there were groups in the three data sets that did not conform to the normal distribution. Therefore, the non-parametric test (Mann-Whitney test) was used for statistical analysis of the data between each two groups.

[0054] 2.1 Statistical analysis of flow cytometric indicators in the AIDS-only group and the AIDS-combined fungal infection group

[0055] Through statistical comparison of clinical data, it was found that in the AIDS group alone and the AIDS combined with fungal infection group, CD4 + T cells (P<0.0001), CD8 +There were statistical differences in T cells (P<0.0001), B cells (P=0.0383), CD4 / CD8 (P<0.0001), L / CD45 (P=0.0009), and HLA-DR / CD14 (P=0.0183) between the two groups. In addition, there were no statistical differences in T lymphocytes (P=0.0536), NK cells (P=0.1452), CD14 / CD45 (P=0.1326), and CD64 index (P=0.0569) between the two groups. + T lymphocytes were significantly higher than those in the AIDS combined with fungal infection group, CD8 + The T lymphocyte count was significantly lower than that in the AIDS combined with fungal infection group, and the CD4 / CD8 ratio was significantly higher than that in the AIDS combined with fungal infection group (see Figure 2 , Figure 3 and Table 1).

[0056] Table 1. Summary of basic information of clinical samples and flow cytometry data

[0057]

[0058]

[0059] Data presentation: median (25% digit - 75% digit). D'Agostino & Pearson test was used to test the normal distribution of each group of data. If the three groups of data for each indicator were all in accordance with the normal distribution, the Ordinary one-way ANOVA test was used. If there were groups that did not conform to the normal distribution, the Kruskal-Wallis test was used. P represents the comparison between the simple AIDS group, the AIDS combined with fungal infection group, and the AIDS combined with multiple infection group. #Chi-square test (Chi-square or Chi-square with Yates' correction) was used to compare the gender differences between each two groups.

[0060] 2.2 Statistical analysis of flow cytometric indicators in the AIDS-only group and the AIDS-combined multi-infection group

[0061] Through statistical comparison of clinical data, it was found that the CD4 + T cells (P<0.0001), CD8 +There were statistically significant differences in T cells (P<0.0001), B cells (P=0.0402), CD4 / CD8 (P<0.0001), L / CD45 (P<0.0001), CD14 / CD45 (P=0.0066), and HLA-DR / CD14 (P=0.0038) between the two groups, while there were no statistically significant differences in T lymphocytes (P=0.7662), NK cells (P=0.2499), and CD64 index (P=0.6136) between the two groups (see for details). Figure 2 , Figure 3 and Table 1).

[0062] 2.3 Statistical analysis of flow cytometry indicators in the AIDS-combined fungal infection group and the AIDS-combined multiple infection group

[0063] The statistical data showed that there was a statistically significant difference in NK cells (P=0.0213) between the AIDS complicated with fungal infection group and the AIDS complicated with multiple infection group. T lymphocytes (P=0.0917), CD4 + T cells (P = 0.2137), CD8 + There was no statistically significant difference between the two groups in T cells (P=0.4043), B lymphocytes (P=0.7867), CD4 / CD8 ratio (P=0.3001), L / CD45 (P=0.3495), CD14 / CD45 (P=0.2834), HLA-DR / CD14 (P=0.8120), and CD64 index (P=0.1716) (see for details). Figure 2 , Figure 3 and Table 1).

[0064] 2.4 Construction of a joint prediction model for differential diagnosis of AIDS alone and AIDS combined with fungal infection

[0065] In order to further improve the early diagnosis of AIDS patients with opportunistic infections, this example combines the above indicators and uses SPSS software ROC curve method and binary logistic regression to analyze the clinical value of constructing a differential diagnosis model for AIDS and AIDS combined with fungal infection and AIDS combined with multiple infections. + T cells, CD8 + The clinical data of T cells, B cells, CD4 / CD8, L / CD45, HLA-DR / CD14 and CD64 index were imported into SPSS software, and ROC curve analysis was performed on the included indicators. The predictive efficacy of the above flow cytometry indicators in the differential diagnosis of simple AIDS and AIDS combined with fungal infection is shown in Table 2.+ T lymphocytes, CD8 + T lymphocytes and CD4 / CD8 have high diagnostic efficacy and are statistically significant, but the sensitivity and specificity of each indicator alone are difficult to achieve high levels at the same time.

[0066] In order to obtain higher differential diagnosis efficiency, this example further uses binary logistic regression method combined with multiple flow indicators to construct a prediction model for differential diagnosis of simple AIDS patients and AIDS patients with fungal infection. + T lymphocytes, CD8 + The prediction model constructed by T lymphocytes, CD4 / CD8, and CD64 can significantly improve the ability to differentiate AIDS and fungal infections. The prediction formula is as follows: logistic (p) = -5.838-0.348*CD4 + T+0.128*CD8 + T+7.329*CD4 / CD8+8.236*CD64, the diagnostic efficacy of this prediction model was 0.937 (0.882-0.992), the sensitivity was 82.9%, and the specificity was 96.5% (see the results Figure 4 , Figure 5 ). In actual clinical situations, for example, after AIDS patients were examined by flow cytometry, the results of their related indicators were as follows: -0.348*CD4 + T+0.128*CD8 + T+7.329*CD4 / CD8+8.236*CD64≥6.261, which means that the doctor is advised to consider that the patient may have concurrent fungal infection and to perform additional mycological examinations to achieve early diagnosis and early treatment of opportunistic infections associated with AIDS.

[0067] Table 2. Summary of the accuracy of flow cytometry indicators in predicting AIDS patients with concurrent fungal infection

[0068]

[0069] AUC = Area Under Curve, ie, diagnostic efficacy.

[0070] 2.5 Construction of a joint prediction model for differential diagnosis of simple AIDS and AIDS with multiple infections

[0071] CD4 in the AIDS-only group and the AIDS-combined multi-infection group + T lymphocytes, CD8 +The clinical data of T lymphocytes, B lymphocytes, CD4 / CD8, L / CD45, CD14 / CD45, and HLA-DR / CD14 were imported into SPSS software, and ROC curve analysis was performed on the included indicators. + T lymphocytes, CD8 + As individual predictive factors, T lymphocytes, CD4 / CD8 and L / CD45 have good diagnostic efficacy in the differential diagnosis of AIDS-only group and AIDS combined with multiple infections group, but it is difficult to achieve high levels of sensitivity and specificity at the same time.

[0072] In order to achieve better differential diagnosis, this example further uses binary logistic regression method combined with multiple flow indicators to build a prediction model for differential diagnosis of simple AIDS patients and AIDS patients with multiple infections. + T lymphocytes, CD8 + The prediction model constructed by T lymphocytes and L / CD45 can significantly improve the ability to differentiate and diagnose AIDS with multiple infections. The prediction formula is as follows: logistic (p) = -0.826-0.181*CD4 + T+0.054*CD8 + T-0.065*L / CD45, the diagnostic efficacy of this prediction model was 0.930 (0.877-0.983), the sensitivity was 90.7%, and the specificity was 90.2% ( Figure 6 , Figure 7 ). In actual clinical situations, for example, after AIDS patients were examined by flow cytometry, the results of their related indicators were as follows: -0.181*CD4 + T+0.054*CD8 + T-0.065*L / CD45≥1.273, which means that the doctor is advised to consider that the patient may have multiple infections at the same time, and additional bacteriological and mycological examinations can be performed to reduce the missed diagnosis rate of opportunistic infections caused by AIDS.

[0073] Table 3. Summary of the accuracy of flow cytometry indicators in predicting AIDS patients with multiple infections

[0074]

[0075] AUC = Area Under Curve, ie, diagnostic efficacy.

[0076] IV. Conclusion

[0077] CD4 + T cells, CD8 +There were statistically significant differences in T cells, B cells, CD4 / CD8, L / CD45, and HLA-DR / CD14 between the simple AIDS group and the AIDS combined with fungal infection group. + T lymphocytes, CD8 + T lymphocytes, CD4 / CD8 and CD64 can establish the optimal prediction model for the differential diagnosis of simple AIDS and AIDS combined with fungal infection. The diagnostic efficacy of the combined prediction model is 0.937, the sensitivity is 82.9%, and the specificity is 96.5%. + T cells, CD8 + There were statistical differences in T cells, CD4 / CD8, L / CD45, CD14 / CD45, and HLA-DR / CD14. + T cells, CD8 + The prediction model constructed by T cells and L / CD45 can significantly improve the differential diagnosis ability of AIDS with multiple infections, with a diagnostic efficacy of 0.930, a sensitivity of 90.7%, and a specificity of 90.2%.

[0078] The present invention selected 143 AIDS patients and divided them into a simple AIDS group, an AIDS complicated with fungal infection group, and an AIDS complicated with multiple infections group. These three groups were used as three stages in the course of AIDS for comparative study. GraphPad and SPSS were used to perform statistical analysis on all data. In this experiment, a total of 10 related indicators were compared and studied, among which CD4 + T lymphocytes, CD8 + There were statistically significant differences in T lymphocytes, CD4 / CD8, and CD64 indexes between the simple AIDS group and the AIDS combined with fungal infection group. + T lymphocytes, CD8 + T lymphocytes, CD4 / CD8 and CD64 can be used to establish a prediction model to jointly predict whether AIDS patients have fungal infections. The combined index has a high diagnostic efficacy (0.937), sensitivity (83%) and specificity (96%). + T lymphocytes, CD8 + There were statistically significant differences in T lymphocytes and L / CD45 between the AIDS group and the AIDS combined with fungal infection group. + T lymphocytes, CD8 +The prediction model constructed by T lymphocytes and L / CD45 can significantly improve the diagnostic ability of AIDS combined with multiple infections, with a diagnostic efficacy of (0.929), a sensitivity of (91%), and a specificity of 90%. There is no statistical difference in the indicators between the AIDS combined fungal infection group and the AIDS combined multiple infection group, and it is impossible to distinguish between the two groups. This embodiment analyzes the statistical differences of the above 10 lymphocyte indicators among the three groups of patients, establishes a prediction model that can be used for the differential diagnosis of simple AIDS and AIDS combined with fungal infection, and establishes a joint model for the differential diagnosis of simple AIDS and AIDS combined with multiple infections. The prediction accuracy of these two joint prediction models can reach more than 92%, and they have high prediction sensitivity and specificity, which can significantly improve the diagnosis rate of patients with AIDS combined with fungal infection and patients with AIDS combined with multiple infections, and timely adjust the medication treatment plan, which not only reduces the adverse drug reactions that may be caused by multiple drug treatments, but also helps to achieve early treatment of fungal infections and multiple infections, and achieves therapeutic effects as soon as possible, aiming to provide a certain reference value for clinical early diagnosis and effective treatment.

[0079] This specific embodiment is merely an explanation of the present invention and is not a limitation of the present invention. After reading this specification, those skilled in the art may make non-creative modifications to the present embodiment as needed. However, as long as they are within the scope of the claims of the present invention, they are protected by the patent law.

Claims

1. A model for predicting the course of AIDS infection, characterized by: The prediction models include a prediction model for distinguishing AIDS and AIDS combined with fungal infection, and a prediction model for distinguishing AIDS and AIDS combined with multiple infections; The prediction model for distinguishing fungal infection between AIDS and AIDS is based on the combination of CD4 + T lymphocytes, CD8 + T lymphocytes, CD4 / CD8 and CD64 indicators were constructed, and the prediction model formula was: logistic(p)=-5.838-0.348*CD4 + T+0.128*CD8 + T+7.329×CD4 / CD8+8.236*CD64; the identification prediction model of AIDS and AIDS combined with multiple infections is based on the combination of CD4 + T lymphocytes, CD8 + T lymphocyte and L / CD45 indicators were constructed, and the prediction model formula was: logistic(p)=-0.826-0.181*CD4 + T+0.054*CD8 + T-0.065*L / CD45。 2. The method for establishing a prediction model for the course of AIDS infection according to claim 1, characterized in that: The following steps are involved: S1: Enroll patients according to their examination results, clinical manifestations, and admission and discharge diagnoses, and collect basic information of all enrolled patients, including name, age, and gender; S2: The patients were divided into groups according to their admission and discharge diagnoses and related examination results, including AIDS alone group, AIDS complicated with fungal infection group, and AIDS complicated with multiple infections group. The immune cell subset detection data of the patients in each group during the admission examination were collected. S3: The basic information of each group of patients and the data of immune cell subsets were tested for normality; if the data distribution conformed to normality, the Ordinary one-way ANOVA test was used for comparison among the three groups, and the Unpaired t test was used for comparison between the two groups; if the data distribution did not conform to normality, the Kruskal-Wallis test was used for comparison among the three groups, and the Mann-Whitney test was used for comparison between the two groups; S4: Screen out the immune cell subsets with significant statistical differences between the AIDS group and the AIDS complicated with fungal infection group, and between the AIDS group and the AIDS complicated with multiple infections group as key predictive factors; S5: ROC curves were used to evaluate the differential diagnostic efficiency of key predictive factors between the AIDS group and the AIDS complicated with fungal infection group, and between the AIDS group and the AIDS complicated with multiple infections group; S6: In order to obtain higher diagnostic efficacy, binary logistic regression analysis method was further used in combination with key predictive factors to construct joint prediction models that can be used for differential diagnosis between AIDS group and AIDS complicated with fungal infection group, and between AIDS group and AIDS complicated with multiple infections group.

3. The method for establishing an AIDS infection prediction model according to claim 2, characterized in that: The immune cell subset detection data collected in S2 include T lymphocytes, CD4 + T cells, CD8 + T cells, B lymphocytes, NK cells, CD4 / CD8, L / CD45, HLA-DR / CD14 and CD64 indexes.

4. The method for establishing an AIDS infection prediction model according to claim 2, characterized in that: The immune cell subset detection data that need to be collected in S2 are uniformly detected using a FACS Calibur flow cytometer.

5. The method for establishing an AIDS infection prediction model according to claim 2, characterized in that: The normal distribution test method in S3 is: GraphPadPrism 9 is used to perform statistical analysis on the gender, age and immune cell subset indicators of the clinical samples included in the group, and D'Agostino & Pearson test is used to perform a normal distribution test on each group of data.

6. The method for establishing an AIDS infection prediction model according to claim 2, characterized in that: The method for judging whether there is a statistical difference in S5 is: P<0.05 indicates a significant difference, and P<0.01 indicates a very significant difference.

7. The method for establishing an AIDS infection prediction model according to claim 2, characterized in that: The diagnostic efficacy analysis of S6 was performed using SPSS software, specifically: the immune cell subpopulation indicators with significant statistical differences between the two groups were imported into SPSS software, and the ROC curve method was used to analyze the differential diagnostic efficacy of the corresponding indicators.

8. The method for establishing an AIDS infection prediction model according to claim 2, characterized in that: The S7 model construction method is to use binary logistic regression analysis to obtain a joint model with the highest diagnostic efficacy, and to derive a prediction formula, cutoff value, sensitivity and specificity, etc.

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