CN116072259B - Neonatal beta lactam medicine optimal dose selection method and system - Google Patents
Neonatal beta lactam medicine optimal dose selection method and system Download PDFInfo
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Abstract
The application discloses a method and a system for selecting optimal dosage of a neonatal beta-lactam drug, wherein the method comprises the following steps: acquiring characteristic information of newborns, and selecting medicines and medicine administration dosage and frequency information; determining whether the drug administration dosage is the recommended optimal dosage according to the characteristic information, the selected drug, the drug administration dosage and the frequency information of the neonate and the trained optimal dosage selection model of the drug, wherein the optimal dosage selection model of the drug is obtained by constructing a Catboost model by taking the characteristic information, the selected drug, the drug administration dosage and the frequency information of the neonate as input and taking the optimal dosage of the drug administration dosage as output; when the drug administration dose is not the recommended optimal dose, the drug administration dose is readjusted so that the adjusted drug administration dose is judged to be the recommended optimal dose by the drug optimal dose selection model. Accurate prediction of the optimal dosage of the medicine is realized.
Description
Technical Field
The application relates to the technical field of optimal dosage prediction of medicines, in particular to a method and a system for selecting optimal dosage of a beta-lactam medicine for a newborn.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
Neonatal sepsis is a systemic disease, with clinical manifestations ranging from subclinical infections to severe local or systemic infections, with high morbidity and mortality, and β -lactam drugs are one of the most commonly used antibiotics for the treatment of neonatal sepsis. How to accurately predict the optimal dosage of beta-lactams is a problem that needs to be solved at present.
The optimal dosage of the beta-lactam drug is determined by a number of factors, including: (1) Rapid physiological changes and special pathophysiology of newborns lead to wide inter-and intra-individual variability in drug treatment and clinical response; (2) The infectious entity and its corresponding Minimum Inhibitory Concentration (MICs) are important bases for antibiotic treatment, however, it is clinically difficult to obtain positive culture results and antibiotic susceptibility results of neonatal bacteria; (3) The Pharmacodynamic (PD) goal of time-dependent β -lactams is the fraction of time that the free antibiotic concentration remains above the target pathogen minimum inhibitory concentration (% fT > MIC), however, the specific PD target for newborns is controversial, with no commonly accepted PD targets, as PD targets vary from 40% fT > MIC to 100% fT >4-5xMIC in recent newborns studies; (4) The choice of specific beta-lactam drugs and the dosage regimen chosen for neonatal sepsis vary from center to center and from region to region.
At present, the optimal dosage of the beta-lactam medicine is mainly predicted by a group pharmacokinetics model, but when the optimal dosage of the beta-lactam medicine is predicted, PD indexes of the medicine are not considered, so that the predicted result is inaccurate.
Disclosure of Invention
In order to solve the problems, the application provides a method and a system for selecting the optimal dosage of a beta-lactam drug in a newborn, which can accurately predict the optimal dosage of the beta-lactam drug.
In order to achieve the above purpose, the application adopts the following technical scheme:
in a first aspect, a method for optimal dosage selection of a neonatal β -lactam drug is provided, comprising:
acquiring characteristic information of newborns, and selecting medicines and medicine administration dosage and frequency information;
determining whether the drug administration dosage is the recommended optimal dosage according to the characteristic information, the selected drug, the drug administration dosage and the frequency information of the neonate and the trained optimal dosage selection model of the drug, wherein the optimal dosage selection model of the drug is obtained by constructing a Catboost model by taking the characteristic information, the selected drug, the drug administration dosage and the frequency information of the neonate as input and taking the optimal dosage of the drug administration dosage as output;
when the drug administration dose is not the recommended optimal dose, the drug administration dose is readjusted so that the adjusted drug administration dose is judged to be the recommended optimal dose by the drug optimal dose selection model.
In a second aspect, a neonatal beta lactam drug optimal dose selection system is provided, comprising:
the data acquisition module is used for acquiring characteristic information of newborns, and selecting medicines and medicine administration doses and frequency information;
the recommended optimal dose determining module is used for determining whether the drug administration dose is an optimal dose according to the characteristic information, the selected drug, the drug administration dose and frequency information of the neonate and the trained optimal drug dose selecting model, wherein the optimal drug dose selecting model takes the characteristic information, the selected drug, the drug administration dose and frequency information of the neonate as input, takes the optimal drug administration dose as output and is obtained by constructing a Catboost model; when the drug administration dose is not the recommended optimal dose, the drug administration dose is readjusted so that the adjusted drug administration dose is judged to be the recommended optimal dose by the drug optimal dose selection model.
In a third aspect, an electronic device is provided comprising a memory and a processor and computer instructions stored on the memory and running on the processor, which when executed by the processor, perform the steps of a method for optimal dosage selection of a beta lactam drug in a neonate.
In a fourth aspect, a computer readable storage medium is provided for storing computer instructions which, when executed by a processor, perform the steps of a method for optimal dosage selection of a beta lactam drug in a neonate.
Compared with the prior art, the application has the beneficial effects that:
1. according to the application, when the optimal dosage of the medicine is predicted, the characteristic information of the neonate and the selected medicine are comprehensively considered, and whether the dosage of the medicine is the recommended optimal dosage is determined by setting the relation between the PD index of the medicine under the dosage and the PD target, so that the minimum value is selected from the recommended optimal dosage to be used as the optimal dosage of the medicine, and the accurate prediction of the optimal dosage of the medicine is realized.
2. The present application takes the MIC of all pathogens of sepsis into account when selecting PD targets so that the optimal dose selected can be effective on all pathogens of sepsis.
3. According to the application, the optimal dosage recommended by the drug is predicted only through the optimal dosage selection model of the drug, and the prediction efficiency is improved on the basis of ensuring the optimal dosage prediction accuracy.
Additional aspects of the application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the application.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application.
FIG. 1 is a flow chart of the method disclosed in example 1;
fig. 2 is a flow chart of the application of the drug optimal dose selection model disclosed in example 1.
Detailed Description
The application will be further described with reference to the drawings and examples.
It should be noted that the following detailed description is illustrative and is intended to provide further explanation of the application. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
Example 1
In this example, a method of optimal dosage selection of a neonatal beta lactam drug is disclosed, as shown in fig. 1 and 2, comprising:
s1: the characteristic information of the neonate, the selected medicine and the medicine administration dosage and frequency information are obtained.
The characteristic information of the neonate includes demographic information and diagnostic information.
Demographic information includes gender, height, birth weight, current weight, BMI, gestational age, post-partum day age, corrected gestational age, combined disease, surgical history, and biochemical information.
The biochemical information includes information such as renal function information, liver function information, blood routine, etc.
Renal function information includes creatinine, uric acid, urea nitrogen, cystatin-C and carbon dioxide information; liver function information includes aspartate aminotransferase, alanine aminotransferase, total bilirubin, direct bilirubin, albumin and globulin information; blood routine information includes platelet count, white blood cell count, red blood cell count, neutrophils, C-reactive protein, and blood sedimentation information.
According to the diagnostic information of the neonate, the doctor selects the medicine for the diagnostic information, and the embodiment aims at the neonatal septicemia, so that the doctor can select the commonly used beta-lactam medicines such as cefotaxime, ceftazidime, meropenem, moxidectin and the like for the neonatal septicemia.
And preliminarily setting the administration dosage and frequency information of the selected medicines according to the selected medicines.
S2: determining whether the drug administration dosage is the recommended optimal dosage according to the characteristic information, the selected drug, the drug administration dosage and the frequency information of the newborn and the trained optimal dosage selection model of the drug, wherein the optimal dosage selection model of the drug is obtained by taking the characteristic information, the selected drug, the drug administration dosage and the frequency information of the newborn as input, taking the drug administration dosage as output and taking the recommended optimal dosage as output and constructing a Catboost model.
The process of obtaining the trained optimal dosage selection model of the medicine is as follows:
acquiring the existing neonatal characteristic information, selected drug information, drug administration dosage and frequency information and adverse reaction, taking the acquired neonatal characteristic information, selected drug information, drug administration dosage and frequency information and adverse reaction as initial training data, and determining PD indexes of drugs under different drug administration dosages;
labeling initial training data according to the size relation between the PD index and the PD target, and taking the initial training data as training data, wherein when the PD index is larger than or equal to the PD target, the labeling label of the initial training data is the recommended optimal dose, and when the PD index is smaller than the PD target, the labeling label of the initial training data is not the recommended optimal dose;
and training the constructed optimal drug dosage selection model through training data, wherein the trained optimal drug dosage selection model is the trained optimal drug dosage selection model.
In practice, the initial training data are all relevant data obtained by written consent of parents of the newborn and the corrected gestational age of the newborn is less than 48 weeks, while the main study data such as the newborn ID, demographic information, diagnostic information, administration dosage and frequency information which are treated by the systemic test drugs are seriously missing or other data of the newborn unsuitable for selection are deleted.
And deleting the data type with the loss rate larger than the set value in the acquired neonatal data as final initial training data when the type of the initial training data is determined.
In the embodiment, initial training data are simulated through a group pharmacokinetics model, and PD indexes of each drug under different administration doses are obtained.
The group pharmacokinetics model takes a one-chamber or two-chamber model based on a atrioventricular structure as a general framework, and carries an inter-individual variability model and a residual model, wherein the inter-individual variability model is in a power exponent mode, and the residual model is in an addition mode, a proportion mode or a mixed mode.
In order to improve the accuracy of the acquired PD index, the existing group pharmacokinetic model is screened, and the optimal group pharmacokinetic model is selected from the current group pharmacokinetic model to serve as the group pharmacokinetic model for data simulation in the embodiment.
When the initial training data is simulated through the group pharmacokinetics model, different PD indexes of each drug under different administration doses are obtained by taking different selected drugs and different drug administration doses as simulation conditions and simulating the initial training data.
The different PD indices were 50% dosing interval, 70% dosing interval and 100% dosing interval, the drug concentration in the blood obtained.
Any one of the three PD indicators may be selected for subsequent comparison with the PD target.
There is currently no clear international consensus as to which of the PD indicators is better. Therefore, the three most common PD indexes of the drug are obtained in this embodiment, and according to actual needs, appropriate PD indexes may be selected and then compared with PD targets to determine the optimal dosage of the drug, so as to improve the adaptability of the method disclosed in this embodiment.
The criteria for PD targeting were selected as: the maximum value of MIC of all pathogens of sepsis was taken as PD target.
And (3) summarizing and counting all pathogenic bacteria of neonatal septicemia at home and abroad, and counting MICs corresponding to all the pathogenic bacteria under the condition of different medicines, wherein each medicine selects one MIC capable of covering all common pathogenic bacteria as a PD target. The optimal dose of drug screened by this PD target can be effective against all pathogens of neonatal sepsis.
And summarizing and integrating the initial training data and PD index data obtained based on simulation and emulation under different conditions to obtain a neonatal virtual database which is used as training data for subsequent machine learning analysis.
The characteristic information of newborns, selected medicines, medicine administration doses and frequency information are taken as input, whether the medicine administration doses are recommended optimal doses or not is taken as output, and a medicine optimal dose selection model is constructed through a Catboost model.
And randomly dividing training data into a training set and a testing set, training the constructed optimal dosage selection model of the constructed medicament, and obtaining the trained optimal dosage selection model of the constructed medicament after the training is completed.
The Catboost model not only excels in processing classification features, but also can process prediction offsets, thereby reducing the likelihood of model overfitting; the built-in model of the Catboost algorithm is a symmetrical tree, the training speed is high, the model is not easy to be fitted too easily, and the model can be used for analyzing and modeling beta-lactam drug data of a neonate patient well.
And (3) inputting the characteristic information, the selected medicine and the medicine administration dosage and frequency information of the newborn obtained in the step (S1) into a trained medicine optimal dosage selection model, and determining whether the medicine administration dosage is the recommended optimal dosage.
When the drug administration dose is not the recommended optimal dose, the drug administration dose is readjusted so that the adjusted drug administration dose is judged to be the recommended optimal dose by the trained drug optimal dose selection model.
In order to obtain the optimal dosage of the medicine, different medicine administration dosages can be set, the recommended optimal dosage is further selected through a trained medicine optimal dosage selection model, and the optimal dosage with the minimum value as the medicine is selected from the recommended optimal dosages.
Although the optimal dosage of the medicine and the corresponding adverse reaction are obtained in the embodiment, the optimal dosage of the medicine is only used for doctors to refer in specific application, and is not directly applied to neonatal treatment.
In the embodiment, when the optimal dosage of the medicine is predicted, the characteristic information of the neonate and the selected medicine are comprehensively considered, and whether the dosage of the medicine is the recommended optimal dosage or not is determined by setting the relation between the PD index of the medicine under the dosage and the PD target, and then the minimum value is selected from the recommended optimal dosage to be used as the optimal dosage of the medicine, so that the accurate prediction of the optimal dosage of the medicine is realized; the MIC of all pathogens of sepsis is taken into account when selecting PD targets so that the optimal dose selected can be effective for all pathogens of sepsis.
According to the method, the optimal dosage recommended by the drug is predicted only through the optimal dosage selection model, and the prediction efficiency is improved on the basis of ensuring the optimal dosage prediction accuracy.
Example 2
In this embodiment, a neonatal beta lactam drug optimal dose selection system is disclosed comprising:
the data acquisition module is used for acquiring characteristic information of newborns, and selecting medicines and medicine administration doses and frequency information;
the recommended optimal dose determining module is used for determining whether the drug administration dose is an optimal dose according to the characteristic information, the selected drug, the drug administration dose and frequency information of the neonate and the trained optimal drug dose selecting model, wherein the optimal drug dose selecting model takes the characteristic information, the selected drug, the drug administration dose and frequency information of the neonate as input, takes the optimal drug administration dose as output and is obtained by constructing a Catboost model; when the drug administration dose is not the recommended optimal dose, the drug administration dose is readjusted so that the adjusted drug administration dose is judged to be the recommended optimal dose by the drug optimal dose selection model.
Example 3
In this embodiment, an electronic device is disclosed comprising a memory and a processor and computer instructions stored on the memory and running on the processor that, when executed by the processor, perform the steps of a method for optimal dosage selection of a neonatal beta lactam drug disclosed in embodiment 1.
Example 4
In this embodiment, a computer readable storage medium is disclosed for storing computer instructions that, when executed by a processor, perform the steps of a method for optimal dosage selection of a neonatal beta lactam drug disclosed in embodiment 1.
Finally, it should be noted that: the above embodiments are only for illustrating the technical aspects of the present application and not for limiting the same, and although the present application has been described in detail with reference to the above embodiments, it should be understood by those of ordinary skill in the art that: modifications and equivalents may be made to the specific embodiments of the application without departing from the spirit and scope of the application, which is intended to be covered by the claims.
Claims (8)
1. A method of selecting an optimal dosage of a neonatal β -lactam drug comprising:
acquiring characteristic information of newborns, and selecting medicines and medicine administration dosage and frequency information;
determining whether the drug administration dosage is the recommended optimal dosage according to the characteristic information, the selected drug, the drug administration dosage and the frequency information of the neonate and the trained optimal dosage selection model of the drug, wherein the optimal dosage selection model of the drug is obtained by constructing a Catboost model by taking the characteristic information, the selected drug, the drug administration dosage and the frequency information of the neonate as input and taking the optimal dosage of the drug administration dosage as output;
when the drug administration dose is not the recommended optimal dose, readjusting the drug administration dose so that the adjusted drug administration dose is judged to be the recommended optimal dose by the drug optimal dose selection model;
the process of obtaining the trained optimal dosage selection model of the medicine is as follows:
acquiring the existing neonatal characteristic information, selected drug information, drug administration dosage and frequency information as initial training data, and determining PD indexes of drugs under different drug administration dosages;
labeling initial training data according to the size relation between the PD index and the PD target, and taking the initial training data as training data, wherein when the PD index is larger than or equal to the PD target, the labeling label of the initial training data is the recommended optimal dose, and when the PD index is smaller than the PD target, the labeling label of the initial training data is not the recommended optimal dose;
training the constructed optimal drug dosage selection model through training data, wherein the trained optimal drug dosage selection model is a trained optimal drug dosage selection model;
the maximum value of MIC of all pathogens of sepsis was taken as PD target.
2. A method of selecting an optimal dosage of a neonatal beta lactam drug according to claim 1, wherein the minimum value is selected from the recommended optimal dosages as the optimal dosage of the drug.
3. A method of optimal dosage selection of a beta lactam drug of a neonate according to claim 1, wherein the characteristic information of the neonate includes demographic information and diagnostic information.
4. The method for optimal dose selection of a neonatal β -lactam drug according to claim 1, wherein the initial training data is simulated by a population pharmacokinetic model to obtain PD indicators at different doses of each drug.
5. The method for optimal dose selection of beta-lactam drugs for neonates according to claim 4, wherein the population pharmacokinetic model uses different selected drugs and different drug administration doses as simulation conditions, and the initial training data is simulated to obtain PD indexes under different administration doses of each drug.
6. A neonatal beta lactam drug optimal dose selection system, comprising:
the data acquisition module is used for acquiring characteristic information of newborns, and selecting medicines and medicine administration doses and frequency information;
the recommended optimal dose determining module is used for determining whether the drug administration dose is an optimal dose according to the characteristic information, the selected drug, the drug administration dose and frequency information of the neonate and the trained optimal drug dose selecting model, wherein the optimal drug dose selecting model takes the characteristic information, the selected drug, the drug administration dose and frequency information of the neonate as input, takes the optimal drug administration dose as output and is obtained by constructing a Catboost model; when the drug administration dose is not the recommended optimal dose, the drug administration dose is readjusted so that the adjusted drug administration dose is judged to be the recommended optimal dose by the drug optimal dose selection model.
7. An electronic device comprising a memory and a processor and computer instructions stored on the memory and running on the processor, which when executed by the processor, perform the steps of a method of optimal dose selection of a neonatal β -lactam drug as claimed in any one of claims 1 to 5.
8. A computer readable storage medium storing computer instructions which, when executed by a processor, perform the steps of a method of optimal dosage selection of a neonatal β -lactam drug as claimed in any one of claims 1 to 5.
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