Risk stratification model of HIV (Human Immunodeficiency Virus) combined hepatocellular carcinoma and construction method thereof

Abstract

The invention discloses an HIV (human immunodeficiency virus) combined hepatocellular carcinoma risk stratification model and a construction method thereof, the construction method comprises the following steps: obtaining clinical data of an HIC-HCC patient, the clinical data comprising demographic information, clinical feature information and treatment and outcome information; taking the total lifetime of the patient as an outcome, performing single-factor analysis on the clinical data, and screening out a plurality of risk factors influencing the total lifetime of the patient; performing multi-factor analysis on the plurality of risk factors to obtain independent prognosis factors of the total lifetime of the patient; the independent prognosis factors comprise organ fatigue and multiple tumors; and establishing a risk layering model based on the independent prognosis factors.

Description

Technical Field

[0001] The present invention relates to the field of biomedical technology, and in particular to a risk stratification model for HIV combined with hepatocellular carcinoma and a construction method thereof. Background Art

[0002] The number of patients infected with human immunodeficiency virus (HIV) is increasing worldwide. Such patients often have concurrent hepatitis virus infection or other chronic liver diseases, and may even develop hepatocellular carcinoma (HCC). Despite the favorable effects of combined antiretroviral therapy (cART) on tumor immune surveillance, the incidence of HIV-positive HCC (HIV-HCC) continues to increase, as confirmed by association studies conducted in 14 regional HIV / AIDS and cancer registries in the United States. HIV-positive patients have prolonged survival, and many concurrent chronic liver diseases progress over time, resulting in a higher mortality rate in HIV-positive HCC patients.

[0003] The Barcelona Clinic Liver Cancer (BCLC) system and the Chinese Liver Cancer Staging (CNLC) system have been widely validated and are currently the most commonly used HCC staging systems. They determine cancer stage and patient prognosis based on tumor burden, liver disease severity, and patient performance status. The above two models have been applied to the overall HCC patient population and can predict good prognostic efficacy in non-HIV patients.

[0004] The poor prognosis of HIV-HCC patients indicates that it is crucial to actively explore the risk factors for poor prognosis in HIV-HCC patients. However, in the cART era, data on prognostic factors and outcomes of treated HIV-HCC patients are still scarce, and specific prognostic models for these patients are still missing. Therefore, a novel and practical model should be constructed to guide the risk stratification and clinical management of HIV-HCC patients. Summary of the invention

[0005] In order to overcome the above technical defects, the present invention provides a risk stratification model for HIV combined with hepatocellular carcinoma and a method for constructing the model.

[0006] In order to solve the above problems, the present invention is implemented according to the following technical solutions:

[0007] In a first aspect, the present invention provides a method for constructing a risk stratification model for HIV combined with hepatocellular carcinoma, the method comprising the following steps:

[0008] Obtain clinical data of HIC-HCC patients, including demographic information, clinical characteristics information, and treatment and outcome information;

[0009] Taking the overall survival of patients as the outcome, univariate analysis was performed on the clinical data to screen out several risk factors that affect the overall survival of patients;

[0010] Multivariate analysis of several risk factors was performed to obtain independent prognostic factors for the patient's overall survival; the independent prognostic factors included organ involvement and multiple tumors;

[0011] A risk stratification model was established based on independent prognostic factors.

[0012] In combination with the first aspect, the present invention further provides a first specific implementation manner of the first aspect, specifically, the risk stratification model includes a prognostic indicator, a risk score calculated based on the prognostic indicator, risk groups corresponding to different risk scores, and a prognostic state corresponding to each risk group;

[0013] Among them, the prognostic indicators include organ involvement and tumor multiplicity.

[0014] In combination with the first aspect, the present invention further provides a second specific implementation of the first aspect, specifically, the risk score calculation formula of the risk stratification model is: risk score = (1.124×organ involvement) + (0.832×tumor multiplicity);

[0015] Among them, the index value of organ involvement is 0 or 1, and the index value of tumor multiplex is 0 or 1.

[0016] In combination with the first aspect, the present invention further provides a third specific implementation of the first aspect, specifically, the risk stratification model includes a low-risk group, a medium-risk group and a high-risk group;

[0017] The value of the risk score corresponding to the low-risk group is 0;

[0018] The value of the risk score corresponding to the medium risk group is 0.832 or 1.124;

[0019] The risk score value corresponding to the high-risk group is 1.956.

[0020] In combination with the first aspect, the present invention also provides a fourth specific implementation manner of the first aspect, specifically, the prognosis status of the low-risk group is better, the prognosis status of the medium-risk group is average, and the prognosis status of the high-risk group is poor.

[0021] In a second aspect, the present invention further provides a risk stratification model for HIV combined with hepatocellular carcinoma, characterized in that the risk stratification model is obtained based on the construction method described in the first aspect.

[0022] In combination with the second aspect, the present invention further provides a first specific implementation manner of the second aspect, specifically, the risk stratification model includes:

[0023] Prognostic indicators, wherein the prognostic indicators are composed of organ involvement and tumor multiplicity;

[0024] Risk score, the risk score is calculated based on the prognostic index, and the calculation formula of the risk score is: risk score = (1.124 × organ involvement) + (0.832 × tumor multiplicity);

[0025] In the formula, the index value of organ involvement is 0 or 1, and the index value of tumor multiplicity is 0 or 1;

[0026] The risk group includes a low-risk group, a medium-risk group and a high-risk group corresponding to different risk scores; the prognosis of the low-risk group is better, the prognosis of the medium-risk group is average, and the prognosis of the high-risk group is worse.

[0027] In combination with the second aspect, the present invention further provides a second specific implementation manner of the second aspect, specifically, the value of the risk score corresponding to the low-risk group is 0;

[0028] The value of the risk score corresponding to the medium risk group is 0.832 or 1.124;

[0029] The risk score value corresponding to the high-risk group is 1.956.

[0030] Compared with the prior art, the present invention has the following beneficial effects:

[0031] The present invention provides a method for constructing a risk stratification model for HIV combined with hepatocellular carcinoma, the method comprising the following steps: obtaining clinical data of HIC-HCC patients, the clinical data comprising demographic information, clinical characteristic information, and treatment and outcome information; taking the overall survival of the patients as the outcome, performing univariate analysis on the clinical data to screen out a number of risk factors that affect the overall survival of the patients; performing multivariate analysis on the several risk factors to obtain independent prognostic factors for the overall survival of the patients; the independent prognostic factors comprising organ involvement and multiple tumors; and establishing a risk stratification model based on the independent prognostic factors.

[0032] The purpose of the present invention is to provide a clinical prognosis model for HIV-HCC patients. The main idea of ​​the present invention is to collect clinical data of HIV-HCC patients and use statistical methods for retrospective analysis to obtain independent prognostic factors and construct a prognostic model for risk stratification of patients, and to improve prognosis by providing guidance and early intervention for risk factors of poor prognosis of patients. The risk stratification model has good clinical practicality, conveniently, accurately and efficiently evaluates the clinical outcomes of HIV-HCC patients, thereby assisting clinicians in judging the clinical development and disease progression of patients, and is more conducive to doctors formulating reasonable treatment plans and timely and effective doctor-patient communication. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The specific embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings, wherein:

[0034] Figure 1 It is the X-tile grouping of the OS of the HIHP model;

[0035] Figure 2 is the Kaplan-Meier curve of the HIHP model for OS in HIV-HCC patients in the experimental cohort and validation cohort;

[0036] Figure 3 It is a comparison of the prognostic evaluation of different models of OS;

[0037] Figure 4 The (A) AUC and (B) C-index of different prognostic risk stratification models for OS of the HIHP model were compared;

[0038] Figure 5 To compare the 1-, 3-, and 5-year AUCs of OS in the HIHP model with those of different prognostic models;

[0039] Figure 6 The AUC of OS at 1, 3, and 5 years in the HIV-positive internal validation cohort are (A) HIHP model, (B) BCLC, and (C) CNLC;

[0040] Figure 7 It is a schematic flow chart of a method for constructing a risk stratification model for HIV combined with hepatocellular carcinoma according to the present invention. DETAILED DESCRIPTION

[0041] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0042] Most of the existing clinical analysis data are evaluated in HIV-negative HCC patients, which is difficult to objectively represent the HIV population. The applicant's research found that compared with HIV-negative patients, HIV-HCC patients are usually in the late stage of the disease, with high tumor burden, poor liver function, and higher risk of infection, and the overall prognosis is still unsatisfactory.

[0043] Therefore, early diagnosis and treatment of HIV-HCC are crucial. Unlike most malignancies, the prognosis of HCC actually depends not only on the anatomical spread of the tumor, but also on the degree of functional impairment associated with cirrhosis, performance status score, and the patient's overall health status reflected by elevated tumor biomarker alpha-fetoprotein (AFP).

[0044] In addition, there are relatively few studies on HIV-HCC in the field, and there is still little data on the prognostic factors and outcomes of HIV-HCC patients treated in the cART era, and specific prognostic models for these patients are still missing. To this end, the purpose of the present invention is to provide a simple and easy-to-use clinical prognostic model for HIV-HCC for risk stratification of patients, and to improve prognosis by providing guidance on risk factors for poor prognosis of patients and early intervention.

[0045] refer to Figure 7 , the present invention provides a flow chart of a method for constructing a risk stratification model for HIV combined with hepatocellular carcinoma. Figures 1 to 6 , the construction method comprises the following steps:

[0046] S100: Acquire clinical data of HIC-HCC patients, wherein the clinical data includes demographic information, clinical characteristic information, and treatment and outcome information.

[0047] In the present invention, the clinical data of HIV-HCC patients include demographic information, clinical characteristic information, and treatment and outcome information.

[0048] In specific implementations, the demographic information of HIV-HCC patients includes: gender, age, ethnicity, height, weight, family history of cancer, body mass index (BMI), time of HIV diagnosis, time of HCC diagnosis, and route of HIV infection.

[0049] In specific implementation, the clinical characteristics of HIV-HCC patients include: Eastern Cooperative Oncology Group performance status score (ECOG PS), history of hepatitis (HBV / HCV), family history of cancer, history of cirrhosis, route of HIV infection, co-infection, maximum tumor diameter, number of tumors, imaging, microvascular invasion (MVI), organ involvement, lymph node invasion, Child-Pugh score, alpha-fetoprotein (AFP), HIV RNA, and CD4+T cell count.

[0050] In specific implementation, the treatment and outcome information of HIV-HCC patients includes: treatment method (surgery, ablation, symptomatic support; presence or absence of antiviral treatment), treatment plan (surgery, ablation, symptomatic support; antiviral drugs), and outcome events.

[0051] In a specific implementation, the present invention is based on the clinical data of HIV-HCC patients in the applicant's medical unit from August 2012 to November 2023.

[0052] S200: Taking the overall survival of HIV-HCC patients as the outcome, a univariate analysis was performed on the clinical data to screen out several risk factors that affect the overall survival of patients.

[0053] In the specific implementation process, the present invention takes the patient's OS as the outcome and first uses the Cox risk model for univariate analysis, and P<0.05 indicates that the difference is statistically significant.

[0054] In this field, factors significantly associated with overall survival are screened out based on the results of univariate analysis. For continuous variables: Kaplan-Meier survival analysis can be used, and the Log-rank test can be used to compare the survival curves between different groups. For categorical variables: Cox proportional hazard regression model can be used to calculate the hazard ratio (Hazard Ratio, HR) and its 95% confidence interval for each independent variable. Statistical test: Determine the P value of each independent variable, usually P value <0.05 is considered statistically significant.

[0055] In the present invention, based on the clinical data of step S100, the risk factors screened out in step S200 include: organ involvement, multiple tumors, elevated AFP, Child-Pugh C grade, BCLCC-D stage and CNLC stage IV are indicators that affect prognosis, and these risk factors are associated with a shorter OS.

[0056] S300: Perform multivariate analysis on several risk factors to obtain independent prognostic factors for the patient's overall survival; the independent prognostic factors include organ involvement and multiple tumors.

[0057] Specifically, through multivariate analysis, the mutual interference between various factors is eliminated to determine which factors still have an independent and significant impact on the patient's overall survival when other factors are considered at the same time. Among them, the present invention selects the Cox Proportional Hazards Model, which can calculate the adjusted hazard ratio (Adjusted Hazard Ratio, aHR) and its 95% confidence interval for each variable, thereby determining which factors are independent prognostic factors for overall survival. The Cox regression model is used to fit the data, and the final independent prognostic factors are screened out by stepwise regression (such as forward stepwise regression or backward stepwise regression). The stepwise regression method helps to eliminate variables that have no significant effect on survival.

[0058] In the specific implementation of the present invention, the patient's OS was taken as the outcome, and the significant variables in the univariate Cox analysis were included in the multivariate Cox proportional hazard regression analysis, and two independent prognostic factors for OS were obtained, namely, organ involvement and tumor multiplexity, so as to construct a model.

[0059] S400: Establish a risk stratification model based on independent prognostic factors.

[0060] In the present invention, a forward stepwise (conditional LR) method is used to identify independent prognostic factors. The forward stepwise method is to gradually add predictive variables to the model and determine which variables should be retained in the model based on certain criteria (such as improvement in goodness of fit indicators). The multivariate Cox regression formula is used to calculate the risk score for each patient.

[0061] In a specific implementation, the risk stratification model includes:

[0062] Prognostic indicators, wherein the prognostic indicators are composed of organ involvement and tumor multiplicity;

[0063] Risk score, the risk score is calculated based on the prognostic index, and the calculation formula of the risk score is: risk score = (1.124 × organ involvement) + (0.832 × tumor multiplicity);

[0064] In the formula, the index value of organ involvement is 0 or 1, and the index value of tumor multiplex is 0 or 1.

[0065] Based on this, the values ​​obtained by substituting them into the calculation formula of the risk score are: the assessment score of the low-risk group is 0; the assessment score of the medium-risk group is 0.832 or 1.124; and the assessment score of the high-risk group is 1.956.

[0066] The risk group includes a low-risk group, a medium-risk group and a high-risk group corresponding to different risk scores; the prognosis of the low-risk group is better, the prognosis of the medium-risk group is average, and the prognosis of the high-risk group is worse.

[0067] In the present invention, the value of the risk score corresponding to the low-risk group is 0; the value of the risk score corresponding to the medium-risk group is 0.832 or 1.124; and the value of the risk score corresponding to the high-risk group is 1.956.

[0068] In a specific implementation, the present invention further provides a method for obtaining a risk group. Specifically, step S400 further includes:

[0069] S410: Based on the clinical data of HIV-HCC patients, the data are input into the risk stratification model to calculate the risk score of each HIV-HCC patient.

[0070] S420: The clinical data and risk score of each HIV-HCC patient were input into the X-tile software, and HIV-HCC patients were divided into low-risk, intermediate-risk, and high-risk groups for OS.

[0071] In the present invention, the low-risk group is without organ involvement and multiple tumors; the medium-risk group is with organ involvement or multiple tumors; and the high-risk group is evaluated as having both organ involvement and multiple tumors.

[0072] Specifically, the low-risk group has a good prognosis, the medium-risk group has a fair prognosis, and the high-risk group has a poor prognosis. The experiment found that compared with the existing BCLC and CNLC scores, its AUC and C-index showed good superiority, indicating that the constructed new model has better prognostic stratification ability.

[0073] S500: Validation of the constructed model.

[0074] In the specific implementation, the risk stratification model obtained from the above experimental group (a total of 60 cases) was verified in the validation group (a total of 25 cases), and the same results were obtained.

[0075] In the present invention, the main core concept of the present invention is to adopt a method of retrospective analysis of clinical data.

[0076] (I) Statistical methods used in the present invention:

[0077] The present invention uses t test (normal distribution) or Mann-Whitney U test (non-normal distribution) for comparison of continuous variables, and chi-square analysis or Fisher's exact test for comparison of categorical variables. Kaplan-Meier method and log-rank test are used for comparison of survival curves. Univariate and multivariate analysis are performed using Cox risk model to calculate hazard ratio (HR) and its 95% confidence interval (CI).

[0078] In specific implementations, X-tile 3.6.1 software, SPSS25.0, Adobe Illustrator, GraphPad Prism 9 and R software can be used to determine the optimal cutoff value for statistical analysis. p ≤ 0.05 indicates that the difference is statistically significant.

[0079] (II) Development and verification of prediction models:

[0080] In the present invention, a prognostic model was established by analyzing the potential predictive factors of overall survival (OS). The significant variables in the univariate Cox analysis were included in the multivariate Cox proportional hazard regression analysis, and the forward stepwise method (Forward Stepwise, conditional LR) was used. The analysis of the present invention aims to determine independent prognostic factors, incorporate the screened factors into the logistic regression equation, calculate the predicted probability and draw the receiver operating characteristic curve. The performance of the model was evaluated by comparing the area under the curve (AUC) and the Harrell consistency index (C-index).

[0081] In the specific implementation, the Hosmer-Lemeshow test was used to evaluate the goodness of fit of the model, and the decision curve analysis (DCA) was used to evaluate the net benefit of the model.

[0082] The present invention provides a risk stratification model for HIV combined with hepatocellular carcinoma, the risk stratification model comprising:

[0083] Prognostic indicators, wherein the prognostic indicators are composed of organ involvement and tumor multiplicity;

[0084] Risk score, the risk score is calculated based on the prognostic index, and the calculation formula of the risk score is: risk score = (1.124 × organ involvement) + (0.832 × tumor multiplicity);

[0085] In the formula, the index value of organ involvement is 0 or 1, and the index value of tumor multiplex is 0 or 1.

[0086] It is understandable that when the clinical data of HIV-HCC patients show organ involvement, the index value of organ involvement is 1; when the clinical data of HIV-HCC patients do not show organ involvement, the index value of organ involvement is 0. When the clinical data of HIV-HCC patients show multiple tumors, the index value of multiple tumors is 1; when the clinical data of HIV-HCC patients do not show multiple tumors, the index value of multiple tumors is 0.

[0087] The risk group includes a low-risk group, a medium-risk group and a high-risk group corresponding to different risk scores; the prognosis of the low-risk group is better, the prognosis of the medium-risk group is average, and the prognosis of the high-risk group is worse.

[0088] Among them, the value of the risk score corresponding to the low-risk group is 0; the value of the risk score corresponding to the medium-risk group is 0.832 or 1.124; the value of the risk score corresponding to the high-risk group is 1.956.

[0089] In the specific example, the current survival and health status of HIV-HCC patients were followed up by searching the hospitalization medical record system and conducting telephone follow-up until November 2023. The recurrence time and cause of HIV-HCC patients and the time and cause of death were inquired in detail. OS is defined as the time interval from the diagnosis of the disease to the last follow-up or death due to any reason, and OS is calculated in months.

[0090] ① Male HIV-HCC patient, 50 years old, with organ involvement and multiple tumors. According to the risk stratification model of the present invention, the patient was determined to be in the high-risk group for OS. The results showed that as of the last follow-up date of November 2023, the patient had died, with an OS of 7 months, and a poor prognosis.

[0091] ② Male HIV-HCC patient, 56 years old, without organ involvement and without multiple tumors. According to the risk stratification model of the present invention, the patient was determined to be in the low-risk group for OS. The results showed that as of the last follow-up date of November 2023, the patient was still alive with an OS of 48 months, and the prognosis was good.

[0092] ③ Male HIV-HCC patient, 36 years old, without organ involvement and multiple tumors. According to the risk stratification model of the present invention, the patient was determined to be in the medium-risk group for OS. The results showed that as of the last follow-up date of November 2023, the patient was still alive, with an OS of 35 months and a general prognosis.

[0093] Through verification by the validation group, the model can effectively predict the prognosis of HIV-HCC patients. Compared with the existing scoring standards, the present invention determines the patient's risk group based on the presence or absence of organ involvement and the presence or absence of multiple tumors, thereby assisting clinicians in judging the patient's clinical development and disease prognosis, achieving precise stratified treatment, and being more conducive to doctors formulating reasonable treatment plans and timely and effective doctor-patient communication.

[0094] For other technical contents of the risk stratification model for HIV combined with hepatocellular carcinoma and its construction method described in this embodiment, please refer to the prior art.

[0095] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Therefore, any modification, equivalent change and modification made to the above embodiment according to the technical essence of the present invention without departing from the technical solution of the present invention shall still fall within the scope of the technical solution of the present invention.

Claims

1. A method for constructing a risk stratification model for HIV combined with hepatocellular carcinoma, characterized in that: The construction method comprises the following steps: Obtain clinical data of HIC-HCC patients, including demographic information, clinical characteristics information, and treatment and outcome information; Taking the overall survival of patients as the outcome, univariate analysis was performed on the clinical data to screen out several risk factors that affect the overall survival of patients; Multivariate analysis of several risk factors was performed to obtain independent prognostic factors for the patient's overall survival; the independent prognostic factors included organ involvement and multiple tumors; A risk stratification model was established based on independent prognostic factors.

2. The method for constructing a risk stratification model for HIV combined with hepatocellular carcinoma according to claim 1, characterized in that: The risk stratification model includes prognostic indicators, risk scores calculated based on the prognostic indicators, risk groups corresponding to different risk scores, and prognostic status corresponding to each risk group; Among them, the prognostic indicators include organ involvement and tumor multiplicity.

3. The method for constructing a risk stratification model for HIV combined with hepatocellular carcinoma according to claim 2, characterized in that: The risk score calculation formula of the risk stratification model is: risk score = (1.124 × organ involvement) + (0.832 × tumor multiplicity); Among them, the index value of organ involvement is 0 or 1, and the index value of tumor multiplex is 0 or 1.

4. The method for constructing a risk stratification model for HIV combined with hepatocellular carcinoma according to claim 2, characterized in that: The risk stratification model includes a low-risk group, a medium-risk group, and a high-risk group; The value of the risk score corresponding to the low-risk group is 0; The value of the risk score corresponding to the medium risk group is 0.832 or 1.124; The risk score value corresponding to the high-risk group is 1.

956.

5. The method for constructing a risk stratification model for HIV combined with hepatocellular carcinoma according to claim 4, characterized in that: The low-risk group has a better prognosis, the medium-risk group has a fair prognosis, and the high-risk group has a poor prognosis.

6. A risk stratification model for HIV-related hepatocellular carcinoma, characterized in that: The risk stratification model is obtained based on the construction method described in any one of claims 1 to 5.

7. A risk stratification model for HIV combined with hepatocellular carcinoma according to claim 6, characterized in that: The risk stratification model includes: Prognostic indicators, wherein the prognostic indicators are composed of organ involvement and tumor multiplicity; Risk score, the risk score is calculated based on the prognostic index, and the calculation formula of the risk score is: risk score = (1.124 × organ involvement) + (0.832 × tumor multiplicity); In the formula, the index value of organ involvement is 0 or 1, and the index value of tumor multiplicity is 0 or 1; The risk group includes a low-risk group, a medium-risk group and a high-risk group corresponding to different risk scores; the prognosis of the low-risk group is better, the prognosis of the medium-risk group is average, and the prognosis of the high-risk group is worse.

8. A risk stratification model for HIV-associated hepatocellular carcinoma according to claim 7, characterized in that: The value of the risk score corresponding to the low-risk group is 0; The value of the risk score corresponding to the medium risk group is 0.832 or 1.124; The risk score value corresponding to the high-risk group is 1.956.

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