CN115376649B - Dose prediction method and device for intrathecal opioid analgesic - Google Patents
Dose prediction method and device for intrathecal opioid analgesic Download PDFInfo
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Abstract
The invention relates to a dose prediction method and a dose prediction device for intrathecal opioid analgesics, wherein the method comprises the steps of acquiring historical data through hospitalization records, and generating training samples according to the historical data; determining a factor variable, constructing a multiple linear regression analysis equation based on the factor variable, and inputting the training sample into the multiple linear regression analysis equation for training to obtain a intrathecal dose prediction model; and inputting variable information of the patient to be detected into the intrathecal dose prediction model to obtain a prediction result. The invention predicts the needed intrathecal analgesic dose of the patient based on the clinical characteristics of the patient and the gene detection result, and the individual dose is used for each patient, so that the dosage actually needed by the patient can be maximally approximate, and the time for reaching the actual needed intrathecal dose of the patient is reduced.
Description
Technical Field
The invention belongs to the technical field of intelligent models, and particularly relates to a dose prediction method and device for intrathecal opioid analgesics.
Background
Intrathecal analgesia refers to implantation of a catheter into cerebrospinal fluid through surgery, bypassing the blood brain barrier, and direct injection of small doses of opioid analgesics such as morphine into the cerebrospinal fluid to exert potent analgesic effects. The method is suitable for severe pain patients with poor analgesic effect or intolerable adverse reaction of opium after administration by systemic route, is one of novel treatment means for cancer pain, and has the characteristics of small administration dosage, strong analgesic effect, light adverse reaction and the like.
Although intrathecal analgesia has the advantage of strong analgesic effect, the method has the disadvantage that the administration dosage is difficult to determine. In the related art, the current clinical calculation of intrathecal analgesic doses is 1/300 of the current oral dose of the patient, and on the basis, the dose is increased or decreased according to the therapeutic response of the patient. However, clinical practice has shown that there is a large difference in the ratio of intrathecal to oral doses of morphine between patients, with a random distribution around 1/300. Thus, the patient needs a long time to increase or decrease the dose after intrathecal administration of the dose calculated in this proportion, which is liable to cause serious consequences such as insufficient analgesia and drug poisoning, and the current situation that the dose of intrathecal analgesia is difficult to determine also results in that the average hospitalization day (13 days) of the intrathecal analgesic patient is far higher than the overall average hospitalization day (6.7 days) of the pain department.
Disclosure of Invention
In view of the above, the present invention aims to overcome the shortcomings of the prior art, and provide a method and a device for predicting the dose of an intrathecal opioid analgesic, so as to solve the problem that the dose of the intrathecal analgesic is difficult to determine in the prior art.
In order to achieve the above purpose, the invention adopts the following technical scheme: a method of dose prediction for intrathecal opioid analgesic comprising:
acquiring historical data through hospitalization records, and generating training samples according to the historical data;
determining a factor variable, constructing a multiple linear regression analysis equation based on the factor variable, and inputting the training sample into the multiple linear regression analysis equation for training to obtain a intrathecal dose prediction model;
and inputting variable information of the patient to be detected into the intrathecal dose prediction model to obtain a prediction result.
Further, the factor variable includes:
basic information of human body, pain characteristics, pre-intrathecal analgesic drug use information, biochemical indexes, intrathecal analgesic drug use information and gene SNP locus genotype affecting dosage of the intrathecal analgesic drug.
Further, the human body basic information comprises gender, age, body mass index and Carlsberg function state score;
the pain characteristics include pain duration, pain score prior to intrathecal analgesia, and primary tumor system;
the intrathecal pre-analgesic drug use information includes an oral morphine equivalent dose and an oral morphine equivalent dose to be converted into an intrathecal dose;
the biochemical indexes comprise liver function Child-pugh score and creatinine clearance rate;
the intrathecal analgesic drug use information comprises the position of the intrathecal catheter, the type of the intrathecal opioid analgesic drug and the dosage of the intrathecal analgesic drug;
the gene SNP locus genotypes affecting the dosage of the intrathecal analgesic drug comprise ABCB1rs1045642, rs1128503, rs2032582 and rs9282564.
Further, the method further comprises the following steps:
and preprocessing the construction factor data.
Further, preprocessing the construction factor data, including:
converting the dose of the opioid analgesic prior to intrathecal analgesia in the patient to an oral morphine equivalent dose;
setting a preset part of the factor variables as dummy variables;
wherein the preset part of the factor variables comprise gender, primary tumor system, oral morphine equivalent dose, liver function Child-pugh score, creatinine clearance rate, intrathecal catheter position, intrathecal opioid analgesic type, ABCB1rs1045642, rs1128503, rs2032582 and rs9282564.
Further, the constructing a multiple linear regression analysis equation based on the factor variable includes:
and inputting a factor variable by using SPSS statistical analysis software, taking the dosage of the intrathecal analgesic as the dependent variable, taking other factor variables except for the intrathecal analgesic as independent variables, and performing multiple linear stepwise regression analysis on the correlation between the independent variables and the dependent variable by using a back-off method.
Furthermore, the gene affecting the dosage of the intrathecal analgesic drug is SNP locus genotype, and is obtained by adopting gene sequencing.
Further, the method further comprises the following steps:
using mean absolute error, root mean square error and R 2 The score algorithm evaluates the predictive capabilities of the predictive model.
Embodiments of the present application provide a dose prediction device for intrathecal opioid analgesics comprising:
the acquisition module is used for acquiring historical data through the hospitalization record and generating a training sample according to the historical data;
the training module is used for determining factor variables, constructing a multiple linear regression analysis equation based on the factor variables, inputting the training sample into the multiple linear regression analysis equation for training, and obtaining a intrathecal dose prediction model;
and the prediction module is used for inputting variable information of the patient to be detected into the intrathecal dose prediction model to obtain a prediction result.
An embodiment of the present application provides a computer device, including: a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the steps of any of the methods of dose prediction for an intrathecal opioid analgesic described above.
By adopting the technical scheme, the invention has the following beneficial effects:
the invention provides a dose prediction method and a dose prediction device for intrathecal opioid analgesics, which predict the required intrathecal analgesic dose of a patient based on clinical characteristics and gene detection results of the patient, and use individual doses for each patient, so that the dose actually required by the patient can be maximally approached, and the time for reaching the actual required intrathecal dose of the patient is reduced. In addition, the required dose of each patient is calculated through the intrathecal dose prediction model, so that the dose determination efficiency can be improved, and the pain insufficiency and serious adverse reaction can be avoided.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic step diagram of a method of dose prediction for intrathecal opioid analgesics according to the invention;
FIG. 2 is a schematic diagram of the structure of a dose prediction device for intrathecal opioid analgesic of the present invention;
fig. 3 is a schematic hardware architecture of the environment in which the method of dose prediction for intrathecal opioid analgesics of the present invention is implemented.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be described in detail below. It will be apparent that the described embodiments are only some, but not all, embodiments of the invention. All other embodiments, based on the examples herein, which are within the scope of the invention as defined by the claims, will be within the scope of the invention as defined by the claims.
Traditional methods employing the same dosage strategy for all patients may allow patients requiring higher doses to spend a significant amount of time increasing the dosage, during which time patients suffering from inadequate analgesic doses may suffer from poor pain control, and may also cause patients requiring lower doses to experience serious adverse effects such as nausea and vomiting, excessive sedation, respiratory depression, etc. after normal dose administration, affecting patient outcome. Thus, there is a need for a method of rapidly determining the required dose for a patient to increase the dose determination efficiency and avoid analgesia inadequacies and serious adverse effects.
A specific method and apparatus for dose prediction for intrathecal opioid analgesic provided in embodiments of the present application is described below with reference to the accompanying drawings.
As shown in fig. 1, the method for dose prediction for intrathecal opioid analgesic provided in the embodiments of the present application includes:
s101, acquiring historical data through hospitalization records, and generating training samples according to the historical data;
patients treated for intrathecal analgesia in the pain department of hospitals were prospective for 68 patients in this application. It will be appreciated that the number of patients may be determined or varied according to the actual circumstances and is not limited herein.
S102, determining a factor variable, constructing a multiple linear regression analysis equation based on the factor variable, and inputting the training sample into the multiple linear regression analysis equation for training to obtain a intrathecal dose prediction model;
s103, inputting variable information of the patient to be detected into the intrathecal dose prediction model to obtain a prediction result.
The working principle of the dose prediction method for the intrathecal opioid analgesic is as follows: firstly, acquiring historical data through hospitalization records, and generating training samples according to the historical data; then determining factor variables for constructing a multiple linear regression analysis equation, thereby constructing the multiple linear regression analysis equation based on the factor variables, and inputting a training sample into the multiple linear regression analysis equation for training to obtain a intrathecal dose prediction model; and finally, inputting variable information of the patient to be detected into the intrathecal dose prediction model to obtain a prediction result.
By establishing the intrathecal dose prediction model for predicting the dosage of the intrathecal opioid analgesic required by the patient, the method can provide a rapid, effective and safe dosage scheme for the intrathecal analgesic patient, and can accelerate the determination of the optimal intrathecal dose compared with the traditional method for carrying out dose titration according to the treatment response of the patient.
In some embodiments, the factor variable comprises:
basic information of human body, pain characteristics, pre-intrathecal analgesic drug use information, biochemical indexes, intrathecal analgesic drug use information and gene SNP locus genotype affecting dosage of the intrathecal analgesic drug.
The human basic information includes sex (gender), age (year), body Mass Index (BMI) and Carlsberg function status score (KPS);
the pain characteristics include pain duration (pain), pain score before intrathecal analgesia (primary tumor system);
the intrathecal pre-analgesic drug use information includes an Oral Morphine Equivalent Dose (OMED) and an oral morphine equivalent dose (transdose) to be converted to an intrathecal dose;
the biochemical indicators include liver function Child-pugh score (Child pugh) and creatinine clearance (Ccr);
the intrathecal analgesic drug use information includes a position of the intrathecal catheter, a type of the intrathecal opioid analgesic (ITdrug), and a dose of the intrathecal analgesic drug (ITdose);
the gene SNP locus genotypes affecting the dosage of the intrathecal analgesic drug comprise ABCB1rs1045642, rs1128503, rs2032582 and rs9282564.
It will be appreciated that the basic information of the human body, the pain characteristics, the pre-intrathecal analgesic drug usage information, the biochemical index, the intrathecal analgesic drug usage information, and the genotype of the gene SNP locus affecting the dosage of the intrathecal analgesic drug may also include other factors, which are not limited herein.
In some embodiments, further comprising:
and preprocessing the construction factor data.
As a preferred embodiment, preprocessing the construction factor data includes:
converting the dose of the opioid analgesic prior to intrathecal analgesia in the patient to an oral morphine equivalent dose;
setting a preset part of the factor variables as dummy variables;
wherein the preset part of the factor variables comprise gender, primary tumor system, oral morphine equivalent dose, liver function Child-pugh score, creatinine clearance rate, intrathecal catheter position, intrathecal opioid analgesic type, ABCB1rs1045642, rs1128503, rs2032582 and rs9282564.
Specifically, the dose of opioid analgesic prior to intrathecal analgesia in a patient is converted to an Oral Morphine Equivalent Dose (OMED) using conversion ratios such as shown in Table 1.
TABLE 1 opioid dosage conversion ratio
| Medicament | Oral administration (mg) | Parenteral administration (mg) |
| Morphine | 30 | 10 |
| Hydromorphone | - | 2 |
| Oxycodone | 15 | - |
| Fentanyl | - | 15ug/h (percutaneous absorption) |
Some factor variables were set to dummy variables including gender (gender), primary tumor system (organ), oral morphine equivalent dose (ome), child-pugh score (Child pugh), creatinine clearance (Ccr), position of intrathecal catheter (position), type of intrathecal opioid analgesic (ITdrug), ABCB1rs1045642, rs1128503, rs2032582, rs9282564 locus genotype, etc. The specific variable assignment method is shown in table 2.
Table 2 dummy variable assignment form
In some embodiments, the constructing a multiple linear regression analysis equation based on the factor variable includes:
and inputting a factor variable by using SPSS statistical analysis software, taking the dosage of the intrathecal analgesic as the dependent variable, taking other factor variables except for the intrathecal analgesic as independent variables, and performing multiple linear stepwise regression analysis on the correlation between the independent variables and the dependent variable by using a back-off method.
Specifically, the intrathecal dose prediction model is a multiple linear regression equation of the form y=kx 1 +kx 2 ...kx n +b, where x is an independent variable, i.e., gender, age, body mass index, carlsberg function status score, pain duration, intrathecalPain score before analgesia, primary tumor system, oral morphine equivalent dose to be converted into intrathecal dose, liver function Child-pugh score, creatinine clearance, intrathecal catheter position, variety of intrathecal opioid analgesics, ABCB1rs1045642, rs1128503, rs2032582 and rs9282564, taking the dose of intrathecal analgesic drug as dependent variable, i.e. y; and selecting data of a plurality of independent variables for multiple linear regression analysis to obtain an optimal multiple regression equation, namely the intrathecal dose prediction model.
The results of the multiple linear regression analysis showed that the age (year), BMI, pain duration (pain), oral morphine equivalent dose (trans), karst status score (KPS), oral morphine equivalent dose (ome) > 600mg (ome=3) and other factors of the patient had a correlation with the intrathecal opioid analgesic dose as shown in table 3. Based on the multiple linear regression analysis result, an intrathecal dose prediction model is established, namely, the intrathecal morphine dose (mg) = 19.752-0.134 age-0.305 bmi+1.141 pain duration (year) +0.005 to be converted into the oral morphine equivalent dose of the intrathecal dose-0.0378 kps value-4.035 oral morphine equivalent dose is more than or equal to 600mg.
TABLE 3 multiple linear regression analysis results
As shown in table 4, the analysis results showed that the intrathecal dose prediction model had significant statistical significance.
Table 4 statistical analysis results of predictive model
In some embodiments, the gene affecting intrathecal analgesic drug dose is obtained using genetic sequencing for SNP locus genotype. The primer sequences of the detection sites are as follows:
in some embodiments, further comprising:
using mean absolute error, root mean square error and R 2 The score algorithm evaluates the predictive capabilities of the predictive model.
The present application also enables performance verification of intrathecal dose prediction models, in particular using mean absolute error (Mean Absolute Error, MAE), root mean square error (Root Mean Square Error, RMSE) and R 2 The score evaluates the predictive power of the predictive model. MAE, RMSE and R of the model 2 Scores were 2.24,3.22 and 64.9%, respectively, indicating that the model had good predictive performance.
As shown in fig. 2, an embodiment of the present application provides a dose prediction device for intrathecal opioid analgesic, comprising:
an acquisition module 201, configured to acquire historical data through a hospitalization record, and generate a training sample according to the historical data;
the training module 202 is configured to determine a factor variable, construct a multiple linear regression analysis equation based on the factor variable, and input the training sample into the multiple linear regression analysis equation for training to obtain a intrathecal dose prediction model;
and the prediction module 203 is used for inputting variable information of the patient to be detected into the intrathecal dose prediction model to obtain a prediction result.
The working principle of the dose prediction device for intrathecal opioid analgesic provided by the embodiment of the application is that the acquisition module 201 acquires historical data through hospitalization records, and generates a training sample according to the historical data; the training module 202 determines factor variables, constructs a multiple linear regression analysis equation based on the factor variables, and inputs the training sample into the multiple linear regression analysis equation for training to obtain a intrathecal dose prediction model; the prediction module 203 inputs the variable information of the patient to be detected to the intrathecal dose prediction model to obtain a prediction result.
The application provides a computer device comprising: the memory and processor may also include a network interface, the memory storing a computer program, the memory may include volatile memory in a computer readable medium, random Access Memory (RAM) and/or nonvolatile memory, etc., such as Read Only Memory (ROM) or flash memory (flash RAM). The computer device stores an operating system, with memory being an example of a computer-readable medium. The computer program, when executed by the processor, causes the processor to perform a dose prediction method for an intrathecal opioid analgesic, the structure shown in fig. 3 is merely a block diagram of a portion of the structure relevant to the present application and does not constitute a limitation of the computer device to which the present application is applied, a particular computer device may include more or fewer components than shown in the figures, or may combine certain components, or have a different arrangement of components.
In one embodiment, the dose prediction method for intrathecal opioid analgesic provided herein may be implemented in the form of a computer program executable on a computer device as shown in fig. 3.
In some embodiments, the computer program, when executed by the processor, causes the processor to perform the steps of: acquiring historical data through hospitalization records, and generating training samples according to the historical data; determining a factor variable, constructing a multiple linear regression analysis equation based on the factor variable, and inputting the training sample into the multiple linear regression analysis equation for training to obtain a intrathecal dose prediction model; and inputting variable information of the patient to be detected into the intrathecal dose prediction model to obtain a prediction result.
The present application also provides a computer storage medium, examples of which include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassette storage or other magnetic storage devices, or any other non-transmission medium, that can be used to store information that can be accessed by a computing device.
In some embodiments, the present invention also provides a computer readable storage medium storing a computer program which, when executed by a processor, obtains historical data through a hospitalization record, and generates training samples according to the historical data; determining a factor variable, constructing a multiple linear regression analysis equation based on the factor variable, and inputting the training sample into the multiple linear regression analysis equation for training to obtain a intrathecal dose prediction model; and inputting variable information of the patient to be detected into the intrathecal dose prediction model to obtain a prediction result.
In summary, the present invention provides a method and apparatus for dose prediction of intrathecal opioid analgesic, which predicts the required intrathecal analgesic dose of a patient based on clinical characteristics of the patient and gene detection results, and uses an individualized dose for each patient, so that the dose actually required by the patient can be maximally approximated, and the time to reach the actual required intrathecal dose of the patient can be reduced. In addition, the required dose of each patient is calculated through the intrathecal dose prediction model, so that the dose determination efficiency can be improved, and the pain insufficiency and serious adverse reaction can be avoided.
It can be understood that the above-provided method embodiments correspond to the above-described apparatus embodiments, and corresponding specific details may be referred to each other and will not be described herein.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, magnetic disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (8)
1. A method of dose prediction for intrathecal opioid analgesic comprising:
acquiring historical data through hospitalization records, and generating training samples according to the historical data;
determining a factor variable, constructing a multiple linear regression analysis equation based on the factor variable, and inputting the training sample into the multiple linear regression analysis equation for training to obtain a intrathecal dose prediction model;
inputting variable information of a patient to be detected into the intrathecal dose prediction model to obtain a prediction result;
the factor variables include:
basic information of human body, pain characteristics, pre-intrathecal analgesic drug use information, biochemical indexes, intrathecal analgesic drug use information and gene SNP locus genotype affecting dosage of the intrathecal analgesic drug;
the human body basic information comprises gender, age, body mass index and Carlsberg function state scores;
the pain characteristics include pain duration, pain score prior to intrathecal analgesia, and primary tumor system;
the intrathecal pre-analgesic drug use information includes an oral morphine equivalent dose and an oral morphine equivalent dose to be converted into an intrathecal dose;
the biochemical indexes comprise liver function Child-pugh score and creatinine clearance rate;
the intrathecal analgesic drug use information comprises the position of the intrathecal catheter, the type of the intrathecal opioid analgesic drug and the dosage of the intrathecal analgesic drug;
the gene SNP locus genotypes affecting the dosage of the intrathecal analgesic drug comprise ABCB1rs1045642, rs1128503, rs2032582 and rs9282564.
2. The method as recited in claim 1, further comprising:
preprocessing the construction factor data.
3. The method of claim 2, wherein preprocessing the build factor data comprises:
converting the dose of the opioid analgesic prior to intrathecal analgesia in the patient to an oral morphine equivalent dose;
setting a preset part of the factor variables as dummy variables;
wherein the predetermined portion of the factor variables include gender, primary tumor system, oral morphine equivalent dose, liver function Child-pugh score, creatinine clearance, intrathecal catheter location, intrathecal opioid analgesic type, ABCB1rs1045642, rs1128503, rs2032582, and rs9282564.
4. The method of claim 1, wherein said constructing a multiple linear regression analysis equation based on said factor variable comprises:
and inputting a factor variable by using SPSS statistical analysis software, taking the dosage of the intrathecal analgesic as the dependent variable, taking other factor variables except for the intrathecal analgesic as independent variables, and performing multiple linear stepwise regression analysis on the correlation between the independent variables and the dependent variable by using a back-off method.
5. The method of claim 1, wherein the step of determining the position of the substrate comprises,
the gene affecting the dosage of the intrathecal analgesic drug is SNP locus genotype, and is obtained by adopting gene sequencing.
6. The method as recited in claim 1, further comprising:
using mean absolute error, root mean square error and R 2 The score algorithm evaluates the predictive capabilities of the predictive model.
7. A dose prediction device for intrathecal opioid analgesic, comprising:
the acquisition module is used for acquiring historical data through the hospitalization record and generating a training sample according to the historical data;
the training module is used for determining factor variables, constructing a multiple linear regression analysis equation based on the factor variables, inputting the training sample into the multiple linear regression analysis equation for training, and obtaining a intrathecal dose prediction model; the factor variables include:
basic information of human body, pain characteristics, pre-intrathecal analgesic drug use information, biochemical indexes, intrathecal analgesic drug use information and gene SNP locus genotype affecting dosage of the intrathecal analgesic drug;
the human body basic information comprises gender, age, body mass index and Carlsberg function state scores;
the pain characteristics include pain duration, pain score prior to intrathecal analgesia, and primary tumor system;
the intrathecal pre-analgesic drug use information includes an oral morphine equivalent dose and an oral morphine equivalent dose to be converted into an intrathecal dose;
the biochemical indexes comprise liver function Child-pugh score and creatinine clearance rate;
the intrathecal analgesic drug use information comprises the position of the intrathecal catheter, the type of the intrathecal opioid analgesic drug and the dosage of the intrathecal analgesic drug;
the gene SNP locus genotypes affecting the dosage of the intrathecal analgesic drug comprise ABCB1rs1045642, rs1128503, rs2032582 and rs9282564;
and the prediction module is used for inputting variable information of the patient to be detected into the intrathecal dose prediction model to obtain a prediction result.
8. A computer device, comprising: a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the dose prediction method for an intrathecal opioid analgesic as claimed in any one of claims 1 to 6.
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