Risk prediction model for excretion delay of large-dose methotrexate of patient with central nervous system lymphoma and construction method of risk prediction model

Abstract

The invention belongs to the technical field of adverse drug reaction prediction, and particularly relates to a risk prediction model for large-dose methotrexate excretion delay of central nervous system lymphoma patients and a construction method thereof, and the method comprises the steps: collecting clinical data of the central nervous system lymphoma patients which are treated by large-dose methotrexate and are subjected to plasma concentration monitoring; screening independent influence factors of excretion delay of methotrexate through single factor analysis and an AIC principle; the screened factors are incorporated into the Nomogram model, and the Nomogram model is constructed; and analyzing and evaluating the prediction capability and clinical effectiveness of the model through an ROC curve, a calibration curve and a decision curve, and performing internal verification of the model by adopting a Bootstrap (n = 1000) method. According to the risk prediction model for the large-dose methotrexate excretion delay of the patient with the central nervous system lymphoma, the probability of methotrexate excretion delay of the patient is evaluated through two independent influence factors, namely, the age is larger than 60 years old and ALB, assistance is provided for reducing adverse reactions of large-dose methotrexate, and support is provided for achieving clinical individualized drug delivery.

Description

Technical Field

[0001] The present invention belongs to the technical field of adverse drug reaction prediction, and specifically relates to a risk prediction model for delayed excretion of high-dose methotrexate in patients with central nervous system lymphoma and a construction method thereof. Background Art

[0002] High-dose methotrexate (defined as methotrexate dose > 0.5 g / m 2 ) is an important part of the treatment of acute lymphoblastic leukemia, osteosarcoma and lymphoma. Because it can penetrate the blood-brain barrier, it is also a basic drug for the treatment of central nervous system lymphoma. However, methotrexate has problems such as a narrow therapeutic window and large adverse reactions in clinical use. Therefore, hydration, urine alkalinization, and calcium folinate rescue are usually used in clinical practice to accelerate its clearance and reduce the occurrence of toxic side effects. However, the clearance of methotrexate varies from person to person, and some patients will experience delayed excretion, resulting in increased methotrexate concentrations in the body, which in turn induces serious adverse reactions and even endangers life. Therefore, there is an urgent need for clinical practice to accurately identify patients who may be at risk of delayed excretion in order to reduce the occurrence of adverse reactions and ensure the safety of patient medication.

[0003] Several studies have shown that age, 24-hour methotrexate blood concentration, alanine aminotransferase, hemoglobin, white blood cell count, etc. are associated with delayed excretion of high-dose methotrexate. Therefore, if a prediction model integrating multiple factors can be established to effectively identify such patients and implement personalized dosing regimens before chemotherapy, the occurrence of adverse reactions in these patients can be reduced, providing strong support for the realization of clinical individualized dosing. However, current research on the prediction model for delayed excretion of high-dose methotrexate mainly focuses on osteosarcoma, leukemia, and hematological malignancies, and there is a lack of prediction models for patients with central nervous system lymphoma. Therefore, there is an urgent need for a reliable prediction model to predict the risk of delayed excretion of high-dose methotrexate in patients with central nervous system lymphoma. Summary of the invention

[0004] In view of this, in order to solve the problems existing in the prior art, the first object of the present invention is to provide a method for constructing a risk prediction model for delayed excretion of high-dose methotrexate in patients with central nervous system lymphoma.

[0005] In order to achieve the above object, the present invention adopts the following technical solution:

[0006] A method for constructing a risk prediction model for delayed excretion of high-dose methotrexate in patients with central nervous system lymphoma comprises: collecting clinical data of patients with central nervous system lymphoma treated with high-dose methotrexate and monitoring blood drug concentration; screening independent influencing factors of delayed excretion of methotrexate by univariate analysis and AIC principle; incorporating the screened independent influencing factors into a nomogram model; evaluating the prediction ability and clinical utility of the model by ROC curve, calibration curve and decision curve analysis, and internally validating the model by using Bootstrap (n=1000) method.

[0007] It is worth noting that the technical task of the present invention is to provide a method that can effectively predict the probability of delayed excretion of high-dose methotrexate before administering high-dose methotrexate to patients with central nervous system lymphoma, thereby reducing the occurrence of adverse reactions, in response to the current lack of a prediction model for delayed excretion of high-dose methotrexate in patients with central nervous system lymphoma. The prediction model constructed by the nomogram can predict the risk of delayed excretion of methotrexate before patients take the medicine, thereby achieving early detection of patients with delayed excretion, thereby reducing the risk of clinical medication and ensuring the safety of patient medication.

[0008] Furthermore, the specific steps of the method for constructing a risk prediction model for delayed excretion of high-dose methotrexate in patients with central nervous system lymphoma include:

[0009] S1: Systematic collection of patient data, including demographics and laboratory test and treatment details;

[0010] S2: According to the established inclusion and exclusion criteria, the collected patient data were screened one by one, and a data set was generated for patients who met the criteria. The data set was divided into a delayed excretion group and a normal group according to whether the patient had delayed methotrexate excretion;

[0011] S3: Univariate analysis and AIC principle were used in both groups to screen out independent influencing factors affecting the delayed excretion of high-dose methotrexate;

[0012] S4: using the nomogram to establish a risk prediction model for predicting delayed excretion of high-dose methotrexate based on the information obtained in step S3;

[0013] S5: Evaluate and internally verify the prediction model established in step S4.

[0014] In step S1, the patient data includes: age>60 years old, gender, BMI, methotrexate dosage, WBC, LYM, RBC, HGB, PLT, AST, ALT, AST / ALT, ALB, GLB, A / G, UREA, CREA, UA, GLU.

[0015] It is worth noting that, in the present invention, BMI: body mass index; WBC: white blood cell count; LYM: absolute lymphocyte count; RBC: red blood cell; HGB: hemoglobin; PLT: platelet count; ALT: alanine aminotransferase; AST: aspartate transferase; AST / ALT: aspart / alanine; ALB: albumin; GLB: globulin; A / G: albumin / globulin; UREA: urea; CREA: serum creatinine; UA: uric acid; GLU: glucose; *P<0.05.

[0016] Furthermore, in step S2, the standard is defined as: methotrexate C24h≤10μmol / L, C48h≤1μmol / L, C72h≤0.1μmol / L.

[0017] Furthermore, in step S2, the inclusion criteria established include:

[0018] (1) Patients diagnosed with primary central nervous system lymphoma, lymphoma involving the central nervous system, or lymphoma that may involve the central nervous system;

[0019] (2) Age ≥18 years;

[0020] (3) Hospitalized patients;

[0021] (4) patients were treated with HD-MTX and the blood concentration of MTX was monitored during treatment;

[0022] (5) The clinical and demographic data of the patients were complete 7 days before MTX treatment.

[0023] Furthermore, in step S2, the exclusion criteria established include:

[0024] (1) Outpatients;

[0025] (2) The data on MTX blood concentrations during hospitalization were incomplete, and it was impossible to determine whether the patients experienced delayed excretion;

[0026] (3) There is a lack of clinical information and demographic data related to the 7 days before methotrexate administration.

[0027] Furthermore, in step S3, IBM SPSS Statistics 29.0.1.0 was used for statistical analysis, and the normally distributed quantitative data were expressed as mean ± standard deviation. The t test was used; the non-normally distributed quantitative data were expressed as median (quartile) [M (P25, P75)] and the Mann-Whitney U test was used; the categorical variables were analyzed using the χ 2The results were expressed as numbers and percentages. After screening using the AIC principle, it was determined that age >60 years and ALB were independent influencing factors for delayed excretion of high-dose methotrexate.

[0028] Furthermore, in step S4, the independent influencing factors are used to construct a risk prediction model using RStudio4.4.1; the sum of the scores corresponding to each variable at different values ​​is calculated as the probability of delayed methotrexate excretion in each patient.

[0029] Furthermore, in step S5, the established nomogram model draws the ROC curve to evaluate the discrimination of the prediction model, draws the calibration curve to evaluate the calibration of the prediction model, draws the decision analysis curve to evaluate the clinical net benefit of the prediction model, and the Bootstrap method is used for internal validation; the area under the ROC curve (AUC) of the nomogram model is 0.717 (95% CI: 0.605-0.830), and the HL deviation test χ 2 =6.229, P=0.622>0.05, the threshold probability of the decision analysis curve was 11%-62%, and the area under the ROC curve of internal validation (Bootstrap=1000 times) was 0.717 (95%CI: 0.602-0.832).

[0030] It is worth noting that step S5 shows that the prediction model has good accuracy and discrimination, can accurately predict the occurrence of delayed excretion of large-dose methotrexate, and has good internal verification results.

[0031] The second object of the present invention is to provide a risk prediction model for delayed excretion of high-dose methotrexate in patients with central nervous system lymphoma by providing a method for constructing the model as described above.

[0032] A risk prediction model for delayed excretion of high-dose methotrexate in patients with central nervous system lymphoma, with age > 60 years and ALB as independent influencing factors for delayed excretion of high-dose methotrexate, was developed to predict the risk of delayed methotrexate excretion in patients before medication using a nomogram.

[0033] When used, the risk prediction model for delayed excretion of high-dose methotrexate in patients with central nervous system lymphoma is used to enter the patient information into the nomogram to predict the risk probability of delayed excretion of methotrexate in patients with central nervous system lymphoma and to predict the occurrence of delayed excretion of methotrexate.

[0034] It is worth noting that the risk prediction model for delayed excretion of high-dose methotrexate in patients with central nervous system lymphoma disclosed in the present invention evaluates the probability of delayed methotrexate excretion in patients through two independent influencing factors: age > 60 years and ALB, thereby helping to reduce the occurrence of adverse reactions to high-dose methotrexate and providing strong support for the realization of clinical individualized drug administration.

[0035] Compared with the prior art, the risk prediction model for delayed excretion of high-dose methotrexate in patients with central nervous system lymphoma disclosed in the present invention and the construction method thereof collect patient case information retrospectively, and compare the difference between patients with delayed methotrexate excretion and patients with normal excretion according to the patient's methotrexate blood concentration. The independent influencing factors of delayed methotrexate excretion in patients with central nervous system lymphoma are determined by single factor and AIC principle, and a risk prediction model is established to provide a reference for clinical intervention measures to be taken as early as possible to reduce the occurrence of adverse reactions. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0037] Figure 1 The present invention is a flow chart of a method for constructing a risk prediction model for delayed excretion of high-dose methotrexate in patients with central nervous system lymphoma.

[0038] Figure 2 This is a risk prediction model diagram for delayed excretion of high-dose methotrexate in patients with central nervous system lymphoma of the present invention.

[0039] Figure 3 Schematic diagram of the ROC curve of the model in Example 1 of the present invention.

[0040] Figure 4 Schematic diagram of the model calibration curve of Example 1 of the present invention.

[0041] Figure 5 This is a schematic diagram of the decision curve of the model in Example 1 of the present invention.

[0042] Figure 6 Schematic diagram of ROC curve for internal validation of the model in Example 1 of the present invention. DETAILED DESCRIPTION

[0043] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] The word "embodiment" used here as an "exemplary" does not necessarily mean that any embodiment described is superior to or better than other embodiments. Unless otherwise specified, the performance index tests in the embodiments of this application are performed using conventional test methods in the art. It should be understood that the terms described in this application are only used to describe specific implementation methods and are not used to limit the content disclosed in this application.

[0045] Unless otherwise specified, the technical and scientific terms used in this document have the same meanings as commonly understood by ordinary technicians in the technical field to which this application belongs; other experimental methods and technical means not specifically specified in this application refer to experimental methods and technical means commonly used by ordinary technicians in this field.

[0046] In order to better illustrate the content of the present application, numerous specific details are provided in the specific examples below. It should be understood by those skilled in the art that the present application can also be implemented without certain specific details. In the embodiments, some methods, means, instruments, equipment, etc. well known to those skilled in the art are not described in detail in order to highlight the main purpose of the present application.

[0047] Under the premise of no conflict, the technical features disclosed in the embodiments of the present application can be combined arbitrarily, and the resulting technical solutions belong to the contents disclosed in the embodiments of the present application.

[0048] The present invention belongs to the technical field of drug adverse reaction prediction, and specifically relates to a risk prediction model for delayed excretion of high-dose methotrexate in patients with central nervous system lymphoma and a method for constructing the same, including: collecting clinical data of patients with central nervous system lymphoma treated with high-dose methotrexate and monitoring blood drug concentration; screening independent influencing factors of delayed excretion of methotrexate by univariate analysis and AIC principle; incorporating the screened factors into and constructing a Nomogram model; evaluating the model prediction ability and clinical utility by ROC curve, calibration curve and decision curve analysis, and using the Bootstrap (n=1000) method for internal validation of the model. The risk prediction model for delayed excretion of high-dose methotrexate in patients with central nervous system lymphoma evaluates the probability of delayed excretion of methotrexate in patients by two independent influencing factors of age>60 years and ALB, providing help for reducing the occurrence of adverse reactions of high-dose methotrexate, and providing strong support for realizing clinical individualized drug administration.

[0049] In order to better understand the present invention, the present invention is further specifically described below through the following examples, but it should not be understood as a limitation of the present invention. Some non-essential improvements and adjustments made by technicians in this field based on the above invention content are also considered to fall within the protection scope of the present invention.

[0050] Example 1

[0051] A risk prediction model for delayed excretion of high-dose methotrexate in patients with central nervous system lymphoma and its construction method Figure 1 )

[0052] S1: Retrospective collection of patients receiving HD-MTX (>0.5 g / m 2 Data on demographics, laboratory tests, and treatment details were included for adult patients with CNS lymphoma treated with leukemia / negative lymphoma (CEL) treated with leukemia / negative lymphoma (CEL). Patient data included: age > 60 years, sex, BMI, methotrexate dose, WBC, LYM, RBC, HGB, PLT, AST, ALT, AST / ALT, ALB, GLB, A / G, UREA, CREA, UA, GLU.

[0053] S2: The collected patients were screened according to the established inclusion and exclusion criteria, and divided into delayed excretion group and normal group according to whether the patient's methotrexate blood concentration was C24h≤10μmol / L, C48h≤1μmol / L, and C72h≤0.1μmol / L.

[0054] Inclusion criteria: (1) patients diagnosed with primary central nervous system lymphoma, lymphoma involving the central nervous system, or possible involvement of the central nervous system; (2) aged ≥18 years; (3) hospitalized patients; (4) receiving HD-MTX treatment and monitoring of MTX blood concentration during treatment; (5) complete clinical and demographic data of patients within 7 days before MTX treatment.

[0055] Exclusion criteria: (1) outpatients; (2) incomplete data on MTX blood concentrations during hospitalization, making it impossible to determine whether the patient had delayed excretion; and (3) lack of clinical information and demographic data related to the 7 days before methotrexate administration.

[0056] In total, 34 patients with 104 treatment cycles were screened, and each treatment cycle was treated as an independent data set, including 27 patients in the delayed excretion group and 77 patients in the normal group.

[0057] S3: The excretion delay group and the normal group were screened out using the single factor and AIC principle to identify independent influencing factors affecting the delayed excretion of high-dose methotrexate.

[0058] Specifically, the S3 steps are as follows:

[0059] S3.1: Univariate analysis: IBM SPSS Statistics 29.0.1.0 was used for statistical analysis. Normally distributed quantitative data were expressed as mean ± standard deviation. The t test was used; the non-normally distributed quantitative data were expressed as median (quartile) [M (P25, P75)] and the Mann-Whitney U test was used; the categorical variables were analyzed using the χ 2 The results were expressed as numbers and percentages. P < 0.05 was considered to be a significant difference between the two groups.

[0060] Table 1 Patient demographic characteristics and univariate analysis results

[0061]

[0062] Note: BMI: body mass index; WBC: white blood cell count; LYM: absolute lymphocyte count; RBC: red blood cell; HGB: hemoglobin; PLT: platelet count; ALT: alanine aminotransferase; AST: aspartate aminotransferase; AST / ALT: aspartate / alanine; ALB: albumin; GLB: globulin; A / G: albumin / globulin; UREA: urea; CREA: serum creatinine; UA: uric acid; GLU: glucose; *P<0.05.

[0063] S3.2: Univariate analysis found that age>60 years, WBC, and ALB were statistically significant (P<0.05). The above three factors were constructed using the minimum AIC method, and the lowest AIC value (112.35) was obtained using the backward stepwise method. Age>60 and ALB were independent influencing factors for delayed excretion of high-dose methotrexate.

[0064] S4: Based on the information obtained in step S3, a prediction model for predicting the risk of delayed excretion of high-dose methotrexate is established. The results are shown in Figure 2 ; Specifically: RStudio 4.4.1 was used to construct a risk prediction model; in the constructed risk model, age > 60 years old and ALB were used as independent influencing factors as variables, and the probability of each patient experiencing the risk of delayed excretion of high-dose methotrexate was predicted by calculating the scores corresponding to each variable under different values ​​and the total score of all variables added together.

[0065] S5: Evaluate the Nomogram model established in step S4.

[0066] Specifically, the steps of S5 are as follows:

[0067] S5.1: Use the receiver operating characteristic curve (ROC curve) to evaluate the discrimination of the prediction model: The ROC curve is used to evaluate the effect of certain indicators in distinguishing two different categories of samples. The curve is drawn with the true positive rate as the vertical axis and the false positive rate as the horizontal axis. By drawing the ROC curve and calculating the area under the curve (AUC), the closer the AUC is to 1, the better the classification or diagnosis effect is. The discrimination evaluation results are shown in Figure 3 , the AUC of this model was 0.717 (95% CI: 0.605-0.830), indicating that the prediction model had good discrimination.

[0068] S5.2: Use calibration curve to evaluate the calibration of prediction model: The calibration curve is used to evaluate the difference between the model prediction results and the actual results. The curve is drawn with the predicted probability as the horizontal axis and the actual probability as the vertical axis. The diagonal dotted line in the figure represents the ideal reference line (the model prediction probability and the actual probability are completely consistent), and the solid line is the curve calculated by multiple sampling (Bootstrap = 1000 times). The calibration evaluation results are shown in Figure 4 , HL deviation test χ 2 =6.229, P = 0.622>0.05, indicating that the model can accurately predict the occurrence of delayed excretion of high-dose methotrexate.

[0069] S5.3: Use the decision curve (DCA curve) to evaluate the clinical benefit of the Nomogram model: The DCA curve is a method to evaluate the application value of the prediction model in actual clinical decision-making. The curve is drawn with the threshold probability as the horizontal axis and the net benefit as the vertical axis. The black solid line in the figure indicates no intervention for anyone, the gray solid line indicates intervention for anyone, and the red solid line indicates the overall net benefit of the Nomogram model within the threshold range. Therefore, the larger the area from the red solid line to the gray solid line and the black solid line, and the wider the range, the better the clinical benefit of the model. See the clinical benefit results. Figure 5 , the threshold probability of the decision analysis curve of this model is from 11% to 62%.

[0070] S5.4: The nomogram model was internally validated using the Bootstrap method; the Bootstrap method generates a large number of virtual "resampled" data sets by repeatedly extracting from the original data set (with replacement), and uses these data sets to simulate possible situations. ROC curve of internal validation (Bootstrap = 1000 times) ( Figure 6 ) was 0.717 (95% CI: 0.602-0.832), and the internal validation result was good.

[0071] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for constructing a risk prediction model for delayed excretion of high-dose methotrexate in patients with central nervous system lymphoma, characterized in that: include: Clinical data of patients with central nervous system lymphoma treated with high-dose methotrexate and monitored for blood drug concentration were collected; independent influencing factors of delayed methotrexate excretion were screened by univariate analysis and AIC principle; the screened factors were incorporated into the Nomogram model; the predictive ability and clinical utility of the model were evaluated by ROC curve, calibration curve and decision curve analysis, and the Bootstrap (n=1000) method was used for internal validation of the model.

2. The construction method according to claim 1, characterized in that: The specific steps include: S1: Systematic collection of patient data, including demographics and laboratory test and treatment details; S2: According to the established inclusion and exclusion criteria, the collected patient data were screened one by one, and a data set was generated for patients who met the criteria. The data set was divided into a delayed excretion group and a normal group according to whether the patient had delayed methotrexate excretion; S3: Univariate analysis and AIC principle were used in both groups to screen out independent influencing factors affecting the delayed excretion of high-dose methotrexate; S4: using the nomogram to establish a risk prediction model for predicting delayed excretion of high-dose methotrexate based on the information obtained in step S3; S5: Evaluate and internally verify the prediction model established in step S4.

3. The construction method according to claim 2, characterized in that: In step S1, the patient data includes: age>60 years old, gender, BMI, methotrexate dosage, WBC, LYM, RBC, HGB, PLT, AST, ALT, AST / ALT, ALB, GLB, A / G, UREA, CREA, UA, GLU.

4. The construction method according to claim 2, characterized in that: In step S2, reaching the standard is defined as: methotrexate C24h≤10 μmol / L, C48h≤1 μmol / L, C72h≤0.1 μmol / L.

5. The construction method according to claim 2, characterized in that: In step S2, the inclusion criteria established include: (1) Patients diagnosed with primary central nervous system lymphoma, lymphoma involving the central nervous system, or lymphoma that may involve the central nervous system; (2) Age ≥18 years; (3) Hospitalized patients; (4) patients were treated with HD-MTX and the blood concentration of MTX was monitored during treatment; (5) The clinical and demographic data of the patients were complete 7 days before MTX treatment.

6. The construction method according to claim 2, characterized in that: In step S2, the exclusion criteria established include: (1) Outpatients; (2) The data on MTX blood concentrations during hospitalization were incomplete, and it was impossible to determine whether the patients experienced delayed excretion; (3) There is a lack of clinical information and demographic data related to the 7 days before methotrexate administration.

7. The construction method according to claim 2, characterized in that: In step S3, IBM SPSS Statistics 29.0.1.0 was used for statistical analysis, and the normally distributed quantitative data were expressed as mean ± standard deviation. The t test was used; the non-normally distributed quantitative data were expressed as median (quartile) [M (P25, P75)] and the Mann-Whitney U test was used; Categorical variables were analyzed using χ 2 The results were expressed as numbers and percentages. After screening using the AIC principle, it was determined that age >60 years and ALB were independent influencing factors for delayed excretion of high-dose methotrexate.

8. The construction method according to claim 2, characterized in that: In step S4, the independent influencing factors are used to construct a risk prediction model using RStudio 4.4.1; the sum of the scores corresponding to each variable at different values ​​is calculated as the probability of delayed methotrexate excretion in each patient.

9. The construction method according to claim 2, characterized in that: In step S5, the established nomogram model draws an ROC curve to evaluate the discrimination of the prediction model, draws a calibration curve to evaluate the calibration of the prediction model, draws a decision analysis curve to evaluate the clinical net benefit of the prediction model, and the Bootstrap method is used for internal validation; the area under the ROC curve (AUC) of the nomogram model is 0.717 (95% CI: 0.605-0.830), and the HL deviation test χ 2 =6.229, P=0.622>0.05, the threshold probability of the decision analysis curve was 11%-62%, and the area under the ROC curve of internal validation (Bootstrap=1000 times) was 0.717 (95%CI: 0.602-0.832).

10. The risk prediction model for delayed excretion of high-dose methotrexate in patients with central nervous system lymphoma obtained by the construction method according to any one of claims 1 to 9, characterized in that: Age > 60 years and ALB were considered as independent influencing factors for delayed excretion of high-dose methotrexate. A nomogram was used to predict the probability of delayed excretion of methotrexate before patients took the drug.

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