CN113077905B - Multi-kind cardiotoxicity assessment method for tumor patients - Google Patents
Multi-kind cardiotoxicity assessment method for tumor patients Download PDFInfo
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- CN113077905B CN113077905B CN202110306821.8A CN202110306821A CN113077905B CN 113077905 B CN113077905 B CN 113077905B CN 202110306821 A CN202110306821 A CN 202110306821A CN 113077905 B CN113077905 B CN 113077905B
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Abstract
The invention provides a method for evaluating various cardiotoxicity of tumor patients, which is characterized in that the existing Meaocardiotoxicity score of the tumor patients is limited in related evaluation factors, and the evaluation advice is general. The method provided by the invention comprises the steps of incorporating risk factors which are proved to have influence on the cardiotoxicity by the current research, assigning values to the risk factors according to the correlation degree of the risk factors and various cardiotoxicity risks, establishing an evaluation model by referring to a Meio scoring system, classifying risk grades of six cardiotoxicity and giving corresponding evaluation suggestions; the method expands the existing evaluation system, fills the blank of domestic heart toxicity evaluation, and provides reliable basis and advice for clinicians to treat and follow-up the tumor patients.
Description
Technical Field
The invention relates to a medical scale evaluation method, in particular to a method for evaluating various cardiotoxicity types of tumor patients.
Background
Cardiotoxicity in a oncological patient refers to the occurrence of a cardiac disorder caused by toxic effects of oncological drugs or radiation therapy on the patient's cardiac muscle and/or the cardiac electrical conduction system after the oncological patient has been treated. In recent years, with the continuous updating and progress of early diagnosis and treatment strategies of tumors, the survival time of tumor patients is prolonged, but the incidence of cardiotoxicity and mortality rate caused by anti-cancer treatment are increased year by year, and cardiovascular diseases become one of the most common diseases in adverse reactions caused by anti-tumor treatment, and have great negative effects on the health recovery and quality of life of patients in the prognosis stage. ( Reference is made to: xia Yunlong, liu Jiwei, manual for tumour cardiology treatment [ M ]. Beijing: people health publishers.2018. )
In published studies, various anticancer drugs and patient own risk factors may lead to cardiotoxicity, (reference ：Barros-Gomes,S.,Herrmann,J.,Mulvagh,S.L.et al.Rationale for settingup acardio-oncologyunit:ourexperience atMayo Clinic.Cardio-Oncology2,5(2016).), for example:
Traditional chemotherapeutics including anthracyclines, alkylating agents, antimicrotubules, antimetabolites and other chemotherapeutics may cause cardiotoxicity. ( Reference is made to: ni Chenxu, shen Fuming clinical manifestations of cardiovascular toxicity of antitumor drugs and prevention and treatment of [ EB/OL ] https:// mp.weixin.qq.com/s/OCx77CLmht7AWDxpjYcomw,2020-05-29 )
Anthracyclines include doxorubicin, epirubicin, daunorubicin, idarubicin, pirarubicin, mitoxantrone, and the like. The heart toxicity caused by anthracyclines is mainly I type, can directly influence myocardial tissues of patients, causes irreversible and permanent myocardial damage, and is dose-dependent.
The 2017 edition of "guidelines for prevention and treatment of cardiac toxicity of anthracyclines" indicates that the occurrence of cardiac toxicity is 8% when anthracyclines are combined with trastuzumab, and that cardiac toxicity for anthracyclines combined with paclitaxel and trastuzumab can rise to 13%, whereas cardiac toxicity for anthracyclines combined with cyclophosphamide and trastuzumab is as high as 27%.
Alkylating agents include cyclophosphamide, ifosfamide, and the like. Cyclophosphamide (CTX) is often expressed as: QRS complex amplitude decreases, nonspecific T wave or ST segment abnormalities, tachyarrhythmia, atrial fibrillation, and total atrioventricular block. Ifosfamide causes LVD at less than 1% incidence, and single bolus administration (> 12.5g/m 2) significantly increases the risk of cardiotoxicity.
The microtubule-resisting medicine includes taxol, catharanthine, camptothecine, etc. The representative drug is paclitaxel (TAX) which can cause a series of heart changes such as asymptomatic reversible bradycardia, blood pressure changes, arrhythmia, myocarditis, pericarditis, pericardial tamponade, acute myocardial infarction and the like, wherein the bradycardia is most common, most of which are asymptomatic and self-limiting, so that conventional heart monitoring is not needed. Paclitaxel cardiotoxicity incidence is 0.5% -5%, docetaxel is 1.7%, and TAX may exacerbate cardiac injury caused by anthracyclines.
Antimetabolites include fluorouracil, pemetrexed, and the like. Fluorouracil is currently considered as the most common chemotherapeutic agent with cardiotoxicity except anthracyclines, and the occurrence rate of cardiotoxicity is 1-4.3%. Most commonly manifested as myocardial ischemia, chest pain, angina, asymptomatic electrocardiographic changes (ST segment changes, T wave abnormalities). Pemetrexed has an adverse effect on the cardiovascular system of about 6.2% and is mainly manifested by increased heart rate, arrhythmia and blood pressure drop.
Other chemotherapeutics such as platinum, arsenical, hormonal, etc. can cause cardiotoxicity.
The novel targeted antitumor drug can also cause cardiotoxicity. (ref. Hu Zhijiang, yu Wentao, yao Wenxiu, et al. Research progress on anti-tumor drug-induced cardiotoxicity and its control measures [ J ]. Chinese pharmacy 2020.) include anti-Her-2 targeting drugs (trastuzumab), anti-VEGF targeting drugs (bevacizumab), multi-target VEGF-TKI (sunitinib, sorafenib, etc.), and immune checkpoint inhibitors.
To assist clinicians in understanding the cardiac toxicity that may be induced by various treatment regimens and the risk of potentially cardiac disease in the patient itself, methods of assessing cardiac toxicity in some oncological patients have emerged internationally, intended to guide clinicians in classifying patients prior to treatment, in prioritizing and focusing on high risk patients, in choosing treatment regimens to minimize the impact of cardiac toxicity on patients, and in conducting scientific cardiac health follow-up and follow-up during the patient prognosis phase, to maximize the prevention and reduction of cardiac toxicity.
The currently more widely used evaluation method is the american meo cardiotoxicity (cardiac insufficiency) score (hereinafter referred to as "meo score"). The method comprises the steps of endowing corresponding scores with 7 cardiotoxicity risk factors (sex, age, hypertension, diabetes, coronary heart disease, cardiomyopathy/heart failure, anthracycline medical history and chest radiotherapy history) and 16 antitumor medical uses (anthracycline, trastuzumab, cyclophosphamide, 5-fluorouracil, pertuzumab, vinblastine, capecitabine, panatinib, bevacizumab, imatinib, carboplatin, fludarabine, paclitaxel and rituximab), then picking out items of a patient, accumulating the corresponding scores, classifying the cardiotoxicity (cardiac insufficiency) of the patient into 5 dangerous grades according to the scores, and giving corresponding diagnosis and treatment suggestions.
The meo score has a certain guiding effect on clinic, but more and more researches show that most of antitumor drugs can have negative effects on the heart, different treatment schemes can generate different kinds of cardiovascular toxicity, and the toxicity degrees can be differentiated. The evaluation factor related to the Meio cardiotoxicity score is limited, the conclusion is that the risk classification is carried out on the cardiotoxicity of cardiac insufficiency, and the evaluation suggestion is also more general, so that the practical diagnosis and treatment guidance of patients is greatly limited.
Disclosure of Invention
In order to solve the problems, the invention provides a method for evaluating various health risk factors and treatment schemes and evaluating various cardiotoxicity.
The invention provides a method for evaluating the toxicity of various hearts of tumor patients, which comprises the following specific steps:
S1, inducing the type of cardiotoxicity;
s2, arranging patient information and risk factors associated with cardiotoxicity;
s3, assigning a value for the risk factor weight, and establishing a corresponding model;
S4, calculating risk scores of each cardiac toxicity model of the patient by an accumulation method for each cardiac toxicity model;
s5, judging corresponding risk grades according to the risk scores of each type of cardiotoxicity of the patient, and acquiring corresponding medical advice according to the highest risk grade;
s6, applying a clinical data training algorithm of a past patient to calibrate and optimize the risk score and the evaluation suggestion; the clinical data training algorithm is as follows:
for any cardiotoxicity type, the patient data includes X i＝[x1,x2,x3,...xn ] and y, X i represents a baseline dataset of the patient, X 1,x2,x3,...xn includes baseline data in patient personal information, test information, examination information, cancer diagnosis and treatment information, cardiac disease information, n dimensions in total; y represents the result label data of the patient's cardiotoxicity assessment, corresponding to one of the cardiotoxicity assessment results, including mild, moderate and severe;
The data set formed by the m cancer patient data is:
X＝[X1,X2,X3,...Xm]T;Y＝[y1,y2,y3,...ym]T;
And carrying out data processing and feature extraction on each type of data in the baseline data, and forming a feature vector X '=f feature (X) by the features extracted by each type of data, wherein the f feature function is a data processing and feature extraction function and is used for processing a feature set X' which is suitable for training an analysis model from an original X dataset by applying a corresponding algorithm.
In performing data processing and extracting features, different data processing is used for different data types. For discrete data, for example, the missing values and outliers may be replaced with the same type of mean, median, or other statistic. In performing feature extraction, different feature extraction functions are also used for different data types. For example, for a portion of continuous data, it is converted into unordered or ordered variables according to common sense or medical knowledge.
Training an analysis model by utilizing the feature vector corresponding to the baseline data and the treatment result label data:
Y'＝Fθ(X')；
F＝argmin∑(log(Y')-log(Y))2；
Wherein F θ represents a model function, and Y' is a judgment result obtained by training an analysis model according to a historical dataset; f is F θ constraints (i.e., constraints that select and train the analytical model); θ is a vector value that minimizes the function Σ (log (Y') -log (Y)) 2;
Updating an analytical model using the accumulated baseline data and cardiotoxicity results, the updated model for calibrating and optimizing the risk score and the assessment recommendation of the step S5, and for application to the remaining patients:
Y'＝Fθ(X')；
F＝argmin∑(log(Y')-log(Y))2；
The rewriter f=argmin Σ (log (Y') -log (Y)) 2 is: j (θ) = Σ (log (F (X')) -log (Y)); min θ J (θ) biased against function J (θ):
Wherein θ i represents a value before update, represents an amount decreasing in the gradient direction, and α represents a step size, which is a change amount in the gradient decreasing direction each time; f θ corresponding to the vector θ of min θ J (θ) at the time of change is the classification model F with minimum root mean square error.
Preferably, the cardiotoxic types are classified into six categories, including cardiac insufficiency, coronary artery disease, arrhythmia, hypertension, thrombosis and stroke, QT interval prolongation.
Preferably, the risk factor associated with cardiotoxicity comprises drug-induced cardiotoxicity.
Preferably, the patient information and the risk factors associated with cardiotoxicity may be obtained from a tumor heart disease registration platform data framework, including patient profile, patient test, cancer diagnosis, cancer treatment, cardiac assessment, cardiac treatment, and the like.
Preferably, the patient profile includes personal information, contact information, past history, family history, lifestyle, physical examination.
The personal information comprises a name, an identity card number, gender, age, a mobile phone number and blood type; the contact information comprises names, relations and mobile phone numbers. The prior history comprises heart disease history, other disease history and allergy history, wherein the allergy history comprises medicament names; the heart history includes age and disease names including heart failure, arrhythmia, coronary heart disease, hypertension, and the like. The family history includes a heart disease history including a disease name and a cancer history.
The living habit comprises smoking history, drinking history and exercise condition, the smoking chamber comprises smoking, stopping smoking and no smoking, and the smoking comprises smoking index. The history of drinking includes drinking, abstaining from drinking, and not drinking; the drinking includes drinking index and drinking type; the abstinence includes drinking index and drinking type
The physical examination includes BMI index and blood pressure.
The test examination includes blood examination, image examination, pathology examination, etc.
The blood test includes test items, test results, test time. The test items include sugar chain antigen, NSE, troponin I (TnI), high-sensitivity troponin (hs-TnI), blood potassium, total cholesterol, etc.; the test results include values, units, and whether or not there is an abnormality.
The image examination includes electrocardiogram, echocardiogram, nuclear magnetic resonance, nuclear medicine examination, CT, endoscopic examination, etc.
The electrocardiogram comprises data indexes, abnormal manifestations, images and examination time; the data index includes PR interval, QT interval, RR interval, etc. The abnormal manifestations include sinus tachycardia, ST segment abnormalities, T wave abnormalities, and the like.
The echocardiography includes ultrasound type, data index, abnormal performance, image, and examination time. The ultrasound types include two-dimensional ultrasound, three-dimensional ultrasound, doppler ultrasound, and the like. The data indexes comprise LVEF, E/A, E', and the like; the abnormal manifestations include mitral valve moderate regurgitation and the like.
The nuclear magnetic resonance comprises an inspection part, an inspection result and an inspection time.
The pathological examination comprises an examination name, an examination part, an examination result and an examination time.
The cancer diagnosis comprises cancer name, clinical stage, pathological typing, diagnosis time and the like.
The cancer names include breast cancer, lung cancer, stomach cancer, etc.
The cancer treatment includes surgery, medicine, radiotherapy. The operation comprises operation date, operation mode, preoperative diagnosis, intraoperative view, postoperative diagnosis and the like; the medicine comprises a starting time, a medicine type, a medicine name, a medicine administration mode, a medicine carrier, a medicine administration frequency, a medicine administration day, a medicine administration dosage and the like. The drug types include chemotherapeutic drugs, targeted drugs, immune checkpoint inhibitors, hormones and endocrine drugs; the administration modes include oral administration, intravenous drip, intramuscular injection, local tumor injection, arterial infusion, intracavity injection and the like. The radiotherapy includes treatment time, radiation source, irradiation mode (long distance, short distance), dose, etc.
The cardiac assessment includes cardiac diagnosis/scoring, diagnostic basis, cancer treatment advice, cardiac treatment advice, current assessment time, next assessment time, and the like. Such cardiac diagnosis/scoring, heart failure, coronary heart disease, cardiac arrhythmias, and the like. The cancer treatment advice includes maintaining an existing treatment regimen, altering an existing treatment regimen (drug prescription, radiation therapy prescription), ceasing an existing treatment regimen; the cardiac treatment advice includes medication and surgical treatment.
The cardiac treatment includes surgery and medications. The surgery comprises a surgery date, a surgery name, a surgery result and the like; the medicine comprises a starting time, a medicine name, a medicine administration mode, a medicine administration frequency, a medicine administration day, a medicine administration dosage and the like.
Preferably, the model in step S3 includes a cardiac insufficiency model, and specific risk factors and weight assignment are shown in fig. 1, including daily health risk factors and the like.
Preferably, the model in step S3 further includes a coronary artery disease model, and specific risk factors and weight assignment are shown in fig. 2, including basic risk factors and the like.
Preferably, the model in step S3 further includes a hypertension model, and specific risk factors and weight assignment are shown in fig. 3, including anthracyclines and the like.
Preferably, the model in step S3 further includes arrhythmia model, and specific risk factors and weight assignment are shown in fig. 4, including antimetabolites and the like.
Preferably, the model in step S3 further includes a thrombus and stroke model, and specific risk factors and weight assignment are shown in fig. 5, including basic risk factors, drug factors, and the like.
Preferably, the model in step S3 further includes a QT interval prolongation model, and specific risk factors and weight assignment are shown in fig. 6, including adjustable factors and non-adjustable factors, and the like.
Preferably, for the cardiac insufficiency model, the corresponding risk level and advice are specifically as follows:
preferably, the risk level and advice corresponding to the coronary artery disease model, arrhythmia model, hypertension model, thrombus and stroke model, QT interval prolongation model are specifically as follows:
Preferably, when a patient has diagnosed with a cardiac disorder corresponding to any of the models, such as cardiac insufficiency, coronary artery disease, arrhythmia, hypertension, thrombosis and stroke, QT interval prolongation, the cardiac disorder plays a decisive role in the risk assessment of the corresponding cardiotoxicity model, the risk level of which is directly assessed as high risk.
The invention incorporates the risk factors which are proved to have influence on the cardiotoxicity by the current research, assigns values for a period according to the correlation degree of the risk factors and various cardiotoxicity risks, establishes an evaluation model by referring to a Meio scoring system, classifies risk grades of six cardiotoxicity and gives corresponding evaluation suggestions; the method expands the existing evaluation system, fills the blank of domestic heart toxicity evaluation, and provides reliable basis and advice for clinicians to treat and follow-up the tumor patients.
Drawings
FIG. 1 is a list of models of cardiac insufficiency;
FIG. 2 is a list of models of coronary artery disease;
FIG. 3 is a list of hypertension models;
FIG. 4 is a list of arrhythmia models;
FIG. 5 is a list of thrombus and stroke models;
FIG. 6 is a list of QT interval prolongation models;
FIG. 7 is a diagram of the design concept of the present invention;
FIG. 8 is an evaluation flow chart of the present invention;
FIG. 9 is a flow chart of a model for evaluating cardiac toxicity of a patient in example 3 of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The design concept of the invention is shown in fig. 7, and the steps are as follows:
1. Patient data is acquired. Daily health data and treatment information of the patient are collected.
2. The risk degree of six types of cardiotoxicity, including cardiac insufficiency, coronary artery disease, hypertension, arrhythmia, stroke and thrombus, and QT interval prolongation, was calculated by different models.
3. Assessment suggestions are given according to the type of cardiotoxicity and the risk level, and the assessment suggestions are classified into low-risk, medium-risk and high-risk grades.
As shown in fig. 8, the present invention provides a method for evaluating the toxicity of various hearts of a tumor patient, comprising the steps of: summarizing the type of the cardiotoxicity to be evaluated, sorting risk factors associated with various cardiotoxicities, assigning values for the risk factors, calculating the risk factors associated with various cardiotoxicities, assigning values for the risk factors, calculating various cardiotoxicity scores, giving evaluation suggestions according to the type of the cardiotoxicity and the risk level, and optimizing an evaluation scheme according to actual clinical data and the latest research.
In practical application, the invention can collect relevant data of a patient in a mode of questionnaire or directly grabbing data in the patient, calculates risk levels of cardiac insufficiency, coronary artery disease, arrhythmia, hypertension, thrombus, apoplexy and QT interval prolongation of the patient by using various cardiotoxicity evaluation methods of the tumor patient, and gives corresponding treatment and follow-up advice.
The questionnaire used in the embodiments of the present invention is as follows:
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the invention is explained below using three examples.
Example 1
One female 75 year old breast cancer tumor patient, who had completed four cycles of AC-T regimen, was in the stage of single-dose docetaxel chemotherapy. Risk factors and scores associated with each cardiac toxicity may be listed based on their clinical data, and the total score and risk level calculated. The method comprises the following steps:
1. Cardiac insufficiency
2. Coronary artery disease
3. Hypertension of the type
4. Arrhythmia of heart
5. Stroke and thrombus
6. QT interval prolongation
In summary, the patients in example 1 were rated as high risk because they had a high risk rating. The advice corresponding to the high-risk rating is given according to the overall rating, and the advice is specifically as follows: transthoracic echocardiography with strain before chemotherapy, every 3 cycles of chemotherapy, at the end, 3-6 months after the end, and 1 year; ECG, troponin, BNP/NT-proBNP and transthoracic echocardiography may be monitored during chemotherapy.
Example 2
A male patient with 57 years old colon cancer, 3 years after colon cancer operation, found that the abdominal cavity was widely transferred for 2 years, and was admitted for treatment. Risk factors and scores associated with each cardiac toxicity may be listed based on their clinical data, and the total score and risk level calculated. The method comprises the following steps:
1. Cardiac insufficiency
2. Coronary artery disease
3. Hypertension of the type
4. Arrhythmia of heart
5. Stroke and thrombus
6. QT interval prolongation
In summary, the patients in example 2 were rated as high risk because they had a high risk rating. The advice corresponding to the high-risk rating is given according to the overall rating, and the advice is specifically as follows: transthoracic echocardiography with strain before chemotherapy, every 3 cycles of chemotherapy, at the end, 3-6 months after the end, and 1 year; ECG, troponin, BNP/NT-proBNP and transthoracic echocardiography may be monitored during chemotherapy.
Example 3
The present invention fully mines and analyzes the medical data of cancer patients, trains an analysis model, and in this embodiment, uses the analysis model to evaluate the cardiac toxicity of cancer patients to prevent and reduce the cardiac toxicity caused by cancer treatment. The analysis model can be updated regularly along with the accumulation of medical data of cancer patients, so that the evaluation result is more and more accurate, and the heart toxicity of the current cancer diagnosis patients can be evaluated and predicted, so that the medical staff can be assisted to formulate an optimal treatment scheme on the premise of reducing the heart toxicity to the maximum extent.
FIG. 9 is a method for modeling a cardiac toxicity assessment of a cancer patient according to an embodiment of the present application, the method comprising the following steps.
S1, acquiring baseline data and cardiotoxicity results of historical cancer patients.
Historical cancer patients refer to patients who were first diagnosed with cancer at home and treated over a period of time, including patients who are in hospital, have been discharged from home and die, and the medical data of these historical patients is used as sample data for a training analysis model.
The baseline data reflects various physiological indicators of the patient prior to cancer treatment, including, but not limited to, a large amount of data in various formats, such as personal information (e.g., age, sex, height, weight, etc.), test information (e.g., blood routine, cardiac markers, electrolytes, etc.), examination information (e.g., blood pressure measurements, electrocardiography, ultrasound, etc.), cancer diagnosis and treatment information (e.g., cancer diagnosis, chemotherapy, radiation therapy, etc.), heart disease information (e.g., heart basal disease, family history, related symptoms, etc.).
The cardiotoxicity result is a label data reflecting whether the patient has adverse consequences in six aspects of cardiac insufficiency, coronary artery disease, hypertension, arrhythmia, stroke and thrombus, QT interval prolongation after a period of cancer treatment, and the consequences are classified into mild, moderate, severe and the like.
Baseline data and cardiotoxicity results for historical cancer patients are obtained from the database as sample data for a training analysis model.
S2, extracting features of the baseline data to obtain corresponding feature vectors, and training an analysis model between the feature vectors and a cardiotoxicity result according to the feature vectors; the analytical model is used to evaluate the patient's current cardiotoxicity therapy outcome based on baseline data of historical cancer patients.
The baseline data may be analyzed using a feature extraction algorithm to obtain its corresponding feature vector. And training an analysis model according to the feature vector and the corresponding cardiotoxicity result.
In one embodiment of the application, an analytical model of cardiac insufficiency can be obtained by the following method (similar to the other five methods of cardiotoxicity). The data for a patient is assumed to include: x i＝[x1,x2,x3,...xn ] and y.
Wherein, X i represents a baseline dataset of the patient, and X 1,x2,x3,...xn corresponds to baseline data of each dimension of the height, weight, blood pressure, cancer medication and the like of the patient, and the total number of the baseline data is n; y represents the patient's cardiac insufficiency outcome tag data, corresponding to one of cardiac insufficiency outcomes (e.g., mild, moderate, severe, etc.).
Accordingly, the data set formed by the m historical cancer patient data is:
X＝[X1,X2,X3,...Xm]T;Y＝[y1,y2,y3,...ym]T.
And carrying out data processing and feature extraction on different types of data in the baseline data, and forming a feature vector X '=f feature (X) by the features extracted from the different types of data, wherein the f feature function is a data processing and feature extraction function and is used for processing a feature set X' which is suitable for training an analysis model from an original X dataset by applying a corresponding algorithm.
In performing data processing, different data processing functions are used for different data. For example, for discrete data, the missing values and outliers may be replaced with the same type of mean, median, or other statistic. In performing feature extraction, different feature extraction functions are also used for different data. For example, for a portion of data, the data is converted into ordered or unordered classification variables and then used to train the model.
Training an analysis model by utilizing the feature vector corresponding to the baseline data and the treatment result label data:
Y'＝Fθ(X')；
F＝arg min∑(log(Y')-log(Y))2；
Wherein F θ represents a model function, and Y' is a judgment result obtained by training an analysis model according to a historical dataset; f is F θ constraints (i.e., constraints that select and train the analytical model); θ is a vector value that minimizes the error function Σ (log (Y') -log (Y)) 2, and belongs to a vector of the error function solution space. That is, the total error between the cardiac insufficiency result Y' obtained by the evaluation of the above-described analysis model and the actual cardiac insufficiency result Y of the cancer patient diagnosed in the history data is minimum for all patients. With the continuous accumulation of historical data, the more accurate the evaluation result of the analysis model is, the smaller the total error is.
In performing data processing, different data processing functions are used for different data. For example, for discrete data, the missing values and outliers may be replaced with the same type of mean, median, or other statistic. In performing feature extraction, different feature extraction functions are also used for different data. For example, for a portion of data, the data is converted into ordered or unordered classification variables and then used to train the model.
The analysis model is trained by using the feature vector corresponding to the baseline data and the treatment result label data:
Y'＝Fθ(X')；
F＝arg min∑(log(Y')-log(Y))2；
Wherein F θ represents a model function, and Y' is a judgment result obtained by training an analysis model according to a historical dataset; f is F θ constraints (i.e., constraints that select and train the analytical model); θ is a vector value that minimizes the error function Σ (log (Y') -log (Y)) 2, and belongs to a vector of the error function solution space. That is, the total error between the cardiac insufficiency result Y' obtained by the evaluation of the above-described analysis model and the actual cardiac insufficiency result Y of the cancer patient diagnosed in the history data is minimum for all patients. With the continuous accumulation of historical data, the more accurate the evaluation result of the analysis model is, the smaller the total error is.
The steps are to train an analysis model between the baseline data and the cardiac toxicity result by using the medical data of the historical cancer patient, and the cardiac toxicity result can be estimated by using the analysis model and combining the baseline data of the current cancer patient, so that reliable prediction can be generated on the cardiac toxicity result.
The process of updating the analytical model is further described below, and in this embodiment, the method further includes the steps of:
and S3, updating an analysis model according to the baseline data and the cardiotoxicity result of the historical cancer patients accumulated in the last statistical time.
Updating the analytical model using the accumulated baseline data and cardiotoxicity results:
Y'＝Fθ(X')；
F＝argmin∑(log(Y')-log(Y))2。
The formula f=argmin Σ (log (Y') -log (Y)) 2 is rewritten as: j (θ) = Σ (log (F (X')) -log (Y)); min θ J (θ).
For the function J (θ), the bias derivative J:
where θ i represents a value before update, represents an amount decreasing in the gradient direction, and α represents a step size, that is, a change amount in the gradient decreasing direction each time.
For vector θ, each dimension of component θ i can determine a gradient direction, so that an overall direction can be determined, and when the direction of greatest decrease is changed, a minimum point can be reached, and no matter whether the vector θ is local or global, the minimum point corresponds to F θ which is the classification model F with the least root mean square error.
In this embodiment, when the analysis model is updated, the direction in which the reduction amount in each gradient direction of the error function is greatest is changed, so that the root mean square error result is as small as possible, and the error of the analysis model is smaller.
After training to obtain an analysis model, the treatment result of the current cancer patient can be predicted by using the analysis model. In this embodiment, as shown in fig. 3, the method further includes the steps of:
s4, acquiring baseline data of the current cancer patient, and extracting features to obtain feature vectors corresponding to the baseline data.
The current cancer patient refers to a patient who has been diagnosed. The baseline data generated by the current cancer patient is used to construct a feature vector describing the current patient baseline data using the same data processing and feature extraction algorithms used in generating the assessment model.
S5, inputting the feature vector corresponding to the current patient baseline data into an analysis model, and evaluating the heart toxicity result of the current patient.
For example, the baseline data of the current patient is X new＝[x1,x2,x3,...xn ], the feature vector X 'new corresponding to the baseline data of the current patient is obtained after the data processing and feature extraction algorithm, and the treatment result Y' new＝F(X'new of the current patient is obtained by evaluating the analysis model.
Y 'new represents the current patient's cardiotoxicity results from the analysis model evaluation, and may be one of the label data representing "mild, moderate, severe" etc. Based on the different treatment results obtained by the evaluation, if the current cardiac toxicity risk of the patient is high or the given cancer treatment scheme is predicted to bring high cardiac toxicity risk, the system gives an early warning to medical staff so as to prompt the team of the tumor heart disease doctor to intervene in time.
In the embodiment, the analysis model is utilized to predict the heart toxicity result of the current cancer patient, which is helpful for medical staff to discover the heart health risk of the patient in time, so as to adjust the cancer treatment scheme in time and reduce the myocardial damage of chemotherapy and radiotherapy to the patient. And when the medium-high risk cardiotoxicity risk is predicted, the team of the tumor cardiologist is prompted to intervene in time so as to take countermeasures.
The above description is only of the preferred embodiments of the present application and is not intended to limit the present application, and various modifications and variations may be made to the present application by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.
Claims (7)
1. A method for evaluating the toxicity of a plurality of types of hearts in a patient with a tumor, comprising the steps of:
S1, inducing the type of cardiotoxicity;
s2, arranging patient information and risk factors associated with cardiotoxicity;
s3, assigning a value for the risk factor weight, and establishing a corresponding model;
S4, calculating risk scores of each cardiac toxicity model of the patient by an accumulation method for each cardiac toxicity model;
S5, judging corresponding risk grades according to the risk scores of each type of cardiac toxicity of the patient, and acquiring corresponding evaluation suggestions according to the highest risk grade;
s6, applying a clinical data training algorithm of a past patient to calibrate and optimize the risk score and the evaluation suggestion; the clinical data training algorithm is as follows:
for any cardiotoxicity type, the patient data includes X i＝[x1,x2,x3,...xn ] and y, X i represents a baseline dataset of the patient, X 1,x2,x3,...xn includes baseline data in patient personal information, test information, examination information, cancer diagnosis and treatment information, cardiac disease information, n dimensions in total; y represents the result label data of the patient's cardiotoxicity assessment, corresponding to one of the cardiotoxicity assessment results, including mild, moderate and severe;
The data set formed by the m cancer patient data is:
X＝[X1,X2,X3,...Xm]T;Y＝[y1,y2,y3,...ym]T;
Performing data processing and feature extraction on each type of data in the baseline data, and forming a feature vector X' =f feature (X) by the features extracted by each type of data, wherein the f feature function is a data processing and feature extraction function;
Training an analysis model by utilizing the feature vector corresponding to the baseline data and the treatment result label data:
Y'＝Fθ(X')；
F＝arg min∑(log(Y')-log(Y))2；
Wherein F θ represents a model function, and Y' is a judgment result obtained by training an analysis model according to a historical dataset; f is F θ constraints (i.e., constraints that select and train the analytical model); θ is a vector value that minimizes the function Σ (log (Y') -log (Y)) 2;
Updating an analytical model for calibrating and optimizing the risk score and the assessment recommendation of the S5 step using the accumulated baseline data and cardiotoxicity results, and applying to the remaining patients:
Y'＝Fθ(X')；
F＝arg min∑(log(Y')-log(Y))2；
The rewriter f=argmin Σ (log (Y') -log (Y)) 2 is: j (θ) = Σ (log (F (X')) -log (Y)); min θ J (θ)
For the function J (θ), the bias derivative J:
Wherein θ i represents a value before update, represents an amount decreasing in the gradient direction, and α represents a step size, which is a change amount in the gradient decreasing direction each time; f θ corresponding to the vector θ of min θ J (θ) at the time of change is the classification model F with minimum root mean square error.
2. The method of claim 1, wherein the cardiotoxicity types are classified into six categories, including cardiac insufficiency, coronary artery disease, arrhythmia, hypertension, thrombosis and stroke, QT interval prolongation.
3. The method of claim 1, wherein the risk factors associated with cardiotoxicity comprise drug-induced cardiotoxicity.
4. The method of claim 1, wherein the model of step S3 comprises a cardiac insufficiency model, a coronary artery disease model, an arrhythmia model, a hypertension model, a thrombosis and stroke model, and a QT interval prolongation model, and the specific model is as follows:
specific risk factors and weight assignment of the cardiac insufficiency model are shown in the table;
Specific risk factors and weight assignment of the coronary artery disease model are shown in the table;
specific risk factors and weight assignment of the hypertension model are shown in the table;
Specific risk factors and weight assignment of the arrhythmia assessment model are shown in the table;
Specific risk factors and weight assignment of the thrombus and stroke evaluation model are shown in the table;
The QT interval prolonged evaluation model is characterized in that specific risk factors and weight assignment are shown in the table.
5. The method of claim 4, wherein for the cardiac insufficiency model, the risk level obtained by evaluation comprises high risk, medium risk, low risk:
The risk level and the corresponding specific advice are shown in the table.
6. The method of claim 4, wherein for the coronary artery disease model, arrhythmia model, hypertension model, thrombus and stroke model and QT interval prolongation model, the risk level obtained by evaluation comprises high risk, medium risk, low risk:
The risk level and the corresponding specific advice are shown in the table.
7. The method according to any one of claims 1 to 6, wherein the risk level of any model is assessed as high risk when the patient has confirmed diagnosis of heart disease corresponding to the model, such as cardiac insufficiency, coronary artery disease, arrhythmia, hypertension, thrombosis, stroke, QT interval prolongation.
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