CN110322963B - Neonatal genetic metabolic disease detection and analysis method, device and system - Google Patents
Neonatal genetic metabolic disease detection and analysis method, device and system Download PDFInfo
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Abstract
The invention provides a method, a device and a system for detecting and analyzing a neonatal genetic metabolic disease, which originally create an interpretation mode of combining a computer system, a disease database and a doctor, can more quickly and accurately interpret the detection result of the neonatal genetic metabolic disease tandem mass spectrum and give out an accurate conclusion, realize comprehensive datamation of the whole working flow, enable the interpretation flow to be easier to trace to the source and to repeatedly compare and research in the later stage, greatly avoid incapability of retrospective analysis due to forgetting in the later stage of manual interpretation, realize higher degree of automation in a man-machine cooperation mode, greatly reduce the investment of labor cost, and realize computer artificial intelligence interpretation even possible with the increase of data quantity and continuous optimization of the system.
Description
Technical Field
The invention relates to the field of medical equipment, in particular to a method, a device and a system for detecting and analyzing neonatal genetic metabolic diseases.
Background
Genetic metabolic disorders are diseases caused by genetic defects in the biosynthesis of certain enzymes, receptors, vectors and membrane pumps consisting of polypeptides and/or proteins, which are necessary for maintaining the normal metabolism of the body, i.e. by mutations in the genes encoding such polypeptides (proteins), also known as abnormal or congenital metabolism.
During the course of many years of popularization, the detection of genetic metabolic diseases is also generally: the detection department gives a detection report according to the measured value of the detection index, and a clinician judges the disease according to the detection report. However, the variety of genetic metabolic diseases is large, and the variety of genetic metabolic diseases which have been found so far exceeds 3000. Meanwhile, the genetic metabolic disease indexes detected by adopting the tandem mass spectrometry are dozens, and hundreds of different detection result combinations can appear due to individual differences, so that the genetic metabolic disease indexes represent different types of genetic metabolic diseases or different risks possibly existing. Different clinicians not only have different degrees of understanding the genetic metabolic diseases, but also have different types of the learned genetic metabolic diseases, if the clinical laboratory doctor or the clinical doctor only relies on the experience to obtain correct conclusions from hundreds of conclusions through repeated comparison analysis, the time and the effort are consumed, the limitation is very strong, and the accurate interpretation of the results cannot be completed by the doctor with the experience.
The comparison document CN201710170285.7 discloses a screening method for the genetic disease and metabolic disease of the newborn, which comprises the steps of comparing a detection sample with index data in a medical database, judging whether the detection sample is matched with the index data in the medical database, if so, outputting the screening data of the genetic metabolic disease corresponding to the matched index data, and if not, outputting the disease. This comparison document has two problems, problem one: although the judgment strength of the clinician is reduced to a certain extent, the comparison document CN201710170285.7 outputs the results as two: the illness or the disease is not, the output result is too armed, and the disease condition cannot be accurately described. For example, a genetic metabolic disease is commonly composed of a plurality of indexes with different functions, but only one of the indexes is abnormal or a plurality of auxiliary indexes with no determining function are abnormal, which cannot represent that the infant is ill. And a second problem: there is no specific way to build this database.
Thus, in the prior art, although some institutions are equipped with a simple tandem mass spectrometry detection report system for the genetic metabolic diseases, the detection indexes are simply interpreted under the condition of comparison and analysis without a large database, and the interpretation result is shallow and unprofitable and is very easy to make mistakes.
Disclosure of Invention
In order to solve the problems, the invention provides a method for detecting and analyzing neonatal genetic metabolic diseases, which comprises the following steps: s1: acquiring sample data; s2: distinguishing according to the sample data, and executing a step S3 of 2/6 page lines if the sample data is abnormal; if the sample data is normal, executing step S6; s3: performing first-step classification according to the action degree of each index in metabolic pathways of various genetic metabolic diseases to obtain a first-step classification result; s4: performing multi-index joint risk assessment classification to obtain a second classification result; s5: combining the first classification result and the second classification result to give a final disease classification result;
s6: and (5) ending.
The step S1 includes analyzing the sample data by a quality control analysis method, if the sample data is unqualified, resampling or rechecking, otherwise, entering a step S2.
The specific implementation mode of the S2 is as follows:
s21: reading indexes affecting the genetic metabolic diseases and factor indexes possibly affecting normal interpretation;
s22: judging whether all indexes in the step S21 are normal, if all indexes are normal, judging that the indexes are normal, or the probability is very low, not prompting diseases, and executing the step S6; otherwise, the genetic metabolism disorder is determined to be suspicious, and the step S3 is executed.
The specific implementation mode of the S3 is as follows:
s31, screening indexes related to the genetic metabolic disease according to what index abnormality is mainly caused by the genetic metabolic disease confirmed by clinical research;
s32: the degree of the effect of the index in the metabolic process is distinguished, and the first classification of the data is performed according to the classification.
If there is a major increase or decrease in the index, the outcome is considered to be very relevant to the occurrence of the disease. Determining a first classification result by combining the index abnormality degree and the clinical characterization; otherwise, step S4 is performed.
In particular, the genetic metabolic diseases refer to a type of genetic diseases with metabolic function defects, and most of the genetic diseases are monogenic genetic diseases, including metabolic macromolecular diseases (such as lysosomal storage diseases (thirty diseases), mitochondrial diseases and the like) and metabolic small molecular diseases (such as diseases related to metabolic abnormality of amino acids, organic acids, fatty acids and the like). Part of the etiology of the disease is caused by genetic inheritance, and part is caused by acquired genetic mutation. According to clinical research, the indexes highly related to the occurrence of the genetic metabolic diseases are confirmed, and are input into a database for interpretation, and detection data is imported, namely, the risk degree of the diseases is interpreted moderately and severely.
The specific implementation manner of the step S4 is as follows:
in the judgment of whether the disease is the genetic metabolic disease, the other indexes except the highly relevant index in the step S3 are continuously compared with a system disease database for analysis and judgment, and meanwhile, the accurate judgment is carried out by combining the prenatal and prenatal health and nutrition states of the mother and the disease and nutrition states of the newborn when blood is taken. If the abnormality index is related to the prenatal and prenatal health and nutrition state of the mother and the nutrition state of the newborn when blood is taken, the abnormality index is not related to the genetic metabolic disease, and the abnormality index is judged to have low probability of prompting the disease; if the condition of the mother and the neonate is excluded from blood sampling, and the disease index is abnormal and the abnormal change is not large, the condition is judged to be possible to be mild genetic metabolic disease, and attention or intervention can be carried out in advance.
Preferably, in the step S1, the sample data of the cord blood or the sole blood of the newborn is passed.
Preferably, in step S6, before the end, a corresponding suggestion is given according to the interpretation result and the clinical information of the sender, and a report is generated.
Preferably, the neonatal genetic metabolic disease detection and analysis apparatus performs neonatal genetic metabolic disease detection and analysis by the method described above.
Preferably, the system for detecting and analyzing the neonatal inherited metabolic disease comprises a data acquisition terminal and a neonatal inherited metabolic disease detection and analysis device; the neonatal genetic metabolic disease detection and analysis device comprises a data server, an analysis terminal and a display terminal; the data acquisition terminal, the data server, the analysis terminal and the display terminal are sequentially connected, and the data acquisition terminal comprises an information input module and a sample detection module; the data server stores a neonate information base and a disease data 3/6 page base; the analysis terminal is used for acquiring detection data from the data server and generating a report according to the detection data; the display terminal is used for displaying the generated detection report; the device for detecting and analyzing the genetic metabolic disease of the newborn uses the method as described above. Preferably, the neonatal genetic metabolic disease detection and analysis system comprises a feedback terminal, wherein the analysis terminal is in electrical signal connection with the feedback terminal; the feedback terminal is used for obtaining the prediction result of the genetic metabolic disease and feeding back the actual result of the genetic metabolic disease.
The invention has the beneficial effects that: 1. the method creates an interpretation mode combining a computer system, a disease database (big data) and a doctor, and can interpret the tandem mass spectrum detection result of the neonatal genetic metabolic disease more rapidly and accurately and give an accurate conclusion. 2. The disease database covers more than hundred thousand cases of detection results, and the confirmed cases are more and more progressive along with time, so that the interpretation of the results is more and more accurate, and finally, the accurate interpretation is achieved; has very large supplement and perfection corresponding to the traditional experience interpretation. 3. The conclusion proposal is that digital authoritative experts agree on drafting, the professional authority is higher, the proposal of five flowers and eight doors given by artificial subjective factors of different people can be avoided to the greatest extent, the trouble is brought to subsequent interpretation,
4. the system has high automation degree, is completed in a man-machine cooperation mode, can greatly reduce the investment of labor cost, and can complete the work load by 5-10 doctors through a 2-3 personal cooperation system. 5. The comprehensive datamation of the flow enables the interpretation flow to be more easy to trace to the source and to be repeatedly compared and researched in the later period, and the problem that retrospective analysis cannot be carried out due to forgetting in the later period of manual interpretation can be greatly avoided. 6. With the increase of data volume and the continuous optimization of the system, the interpretation of the tandem mass spectrum detection result of the neonatal genetic metabolic disease can even realize the interpretation of computer artificial intelligence.
Drawings
FIG. 1 is a system block diagram;
FIG. 2 is a flow chart of a first sort interpretation;
fig. 3 is a flow chart of a second sort interpretation.
Detailed Description
For a clearer understanding of technical features, objects, and effects of the present invention, a specific embodiment of the present invention will be described with reference to the accompanying drawings.
As shown in figure 1, the system for detecting and analyzing the genetic metabolic disease of the newborn comprises a data acquisition terminal and a device for detecting and analyzing the genetic metabolic disease of the newborn; the neonatal genetic metabolic disease detection and analysis device comprises a data server, an analysis terminal and a display terminal; the data acquisition terminal, the data server, the analysis terminal and the display terminal are sequentially connected, and the data acquisition terminal comprises an information input module and a sample detection module; the data server stores a neonate information base and a disease database; the analysis terminal is used for acquiring detection data from the data server and generating a report according to the detection data; the display terminal is used for displaying the generated detection report. The neonatal genetic metabolic disease detection and analysis system comprises a feedback terminal, wherein the analysis terminal is in electrical signal connection with the feedback terminal; the feedback terminal is used for obtaining the prediction result of the genetic metabolic disease and feeding back the actual result of the genetic metabolic disease.
The data acquisition terminal comprises an information input module and a sample detection module. The information input module is used for inputting the basic information and the health information of the neonate into the system and perfecting the information base. The sample detection module is used for acquiring detection data according to umbilical blood or plantar blood of the neonate, completing an online process according to a tandem mass spectrometry detection process (sample receiving, experiment batch generation, sample detection and experiment completion) of the neonate genetic metabolic disease, and guiding the acquired detection data into the data server. The Mass Spectrum (Mass Spectrum) gasifies the sample molecule to be tested, bombards the gaseous molecule with electron beam with certain energy to make it lose one electron and become positively charged ion, the ion can be broken into various fragment ions, all positive ions are orderly arranged according to the Mass-to-charge ratio (m/z) under the combined action of 4/6 pages of electric field and magnetic field to obtain spectrogram, and the spectrogram is used for detecting the structure (qualitative) and the mixture composition (quantitative), and two or more Mass spectra are connected together to be tandem Mass Spectrum. The tandem mass spectrometry technology has certain requirements on blood samples, and the required blood sampling time is preferably carried out after infants are born for 72 hours and eat more than 6 times of milk, mainly because if the infants do not eat or do not ingest enough milk, the phenylalanine concentration in the blood is low, false negative is easy to cause in detection, meanwhile, the time of physiological thyrotropin rising can be avoided when blood sampling is carried out after 72 hours of birth, the false positive for screening hypothyroidism is reduced, the false negative of infants can be prevented from being delayed by TSH rising, and the blood samples can be ensured to meet the requirements through the blood sampling condition recorded by an information recording module.
The data server stores a neonatal information base and a disease database. The neonate information stores basic information and health information of the neonate, and detection data related to the basic information and the health information of the neonate, wherein the basic information and the health information of the neonate specifically comprise birth date, gender, delivery mode, weight, disease treatment condition, maternal pregnancy condition, blood sampling condition and the like. The disease database stores index information and diagnosis case information related to genetic metabolism diseases, wherein the index comprises alanine Ala and leucine
Leu\Ile\Pro-OH, immune cell C4, oleic acid C18:1, etc., leucine Leu\Ile\Pro-OH is related to maple syrup urine disease and hydroxyproline blood disease, immune cell C4 is related to isobutyryl glyciuria disease, ethyl malonate encephalopathy, short chain acyl-CoA dehydrogenase deficiency, oleic acid C18:1 is related to carnitine palmitoyl transferase deficiency II type, carnitine-acyl carnitine translocase deficiency, said index information includes the corresponding relation of specific index and genetic metabolic disease. The disease database currently covers more than hundred thousand detection results, and the number of confirmed cases is increased gradually, so that the interpretation of the results is more and more accurate, and the accurate interpretation is finally realized, so that the method has great supplement and perfection compared with the traditional experience interpretation.
The analysis terminal is used for acquiring detection data from the data server, generating a report according to the detection data, and the report is related to the genetic metabolic disease prediction result. As shown in fig. 3, the specific workflow of the analysis terminal is divided into:
1) And (3) data inspection: carrying out mass control analysis on the detection data in a statistical way, wherein the quality control is qualified, entering the next analysis link, and the unqualified data is required to be resampled or rechecked so as to ensure that the data entering the disease analysis link is accurate and reliable;
2) Data screening: classifying and screening whether the quality qualified data is abnormal or not according to the genetic metabolic disease related indexes stored in the system disease database and other factor indexes possibly influencing normal interpretation by taking the sample as a unit;
3) And (5) a trial interpretation: the system matches the detection result with the basic information of the patient, and carries out preliminary interpretation on the detection result, if all indexes are in the normal value range, the detection result is preliminarily interpreted as A and the indexes are normal, the neonate health is indicated, the output reporting step is directly carried out, otherwise, the detection result is preliminarily interpreted as B and the indexes are abnormal, and the next node is interpreted according to the flow;
4) And (3) second-examination interpretation: and judging the result of judging the B index abnormality by a first-pass node through the comparison and judgment of the index and the comparison analysis and judgment of a system disease database, and performing further judgment. As shown in fig. 2, if more than 1 associated index is not in the normal reference value range, judging as C, and entering the next node for judgment if the associated index is suspicious about the disease; if more than 1 disease key index is not in the normal reference value range, judging as D, and judging by entering the next node, wherein the D is very relevant to the disease;
5) Three-review interpretation a: and C, further accurately judging the secondary node judgment, namely accurately judging by comparing and judging indexes and comparing and analyzing with a system disease database and combining the health and nutrition states of the mother before and during birth and the diseases and nutrition states of the newborn when blood is taken. If the abnormality index is related to the prenatal and prenatal health and nutrition state of the mother and the nutrition state of the newborn when blood is taken, judging that E is not great in relation to the genetic metabolic disease, and judging that the abnormality prompt disease probability is low; if the condition of the mother and the neonate is excluded from blood sampling, and the disease index is abnormal and the abnormal change is not large, the patient is judged to be F, the patient is possibly a mild genetic metabolic disease, and attention or intervention can be carried out in advance.
6) Three-review interpretation b: and classifying the secondary node interpretation into a result of D, comparing and judging the index and comparing and analyzing and judging the index with a system disease database, and accurately interpreting according to the increasing or decreasing degree of the index and combining clinical characterization. The degree of the increase or decrease of the index is not very high, and the index is interpreted as G, which indicates moderate genetic metabolic diseases; the index is increased or reduced to a larger degree, and the index is interpreted as H, which indicates severe genetic metabolic diseases.
8) Report output: and combining the basic information, the detection data and the final disease analysis advice of the patient, and generating a detection report display terminal for displaying the generated detection report after the system is integrated.
Claims (1)
1. The detecting and analyzing method for the genetic metabolic disease of the newborn is applied to a detecting and analyzing system for the genetic metabolic disease of the newborn and is characterized in that the detecting and analyzing system for the genetic metabolic disease of the newborn comprises a data acquisition terminal and a detecting and analyzing device for the genetic metabolic disease of the newborn;
the neonatal genetic metabolic disease detection and analysis device comprises a data server, an analysis terminal and a display terminal; the data acquisition terminal, the data server, the analysis terminal and the display terminal are sequentially connected, and the data acquisition terminal comprises an information input module and a sample detection module; the data server stores a neonate information base and a disease database; the analysis terminal is used for acquiring detection data from the data server and generating a report according to the detection data; the display terminal is used for displaying the generated detection report;
the neonatal genetic metabolic disease detection and analysis system comprises a feedback terminal, wherein the analysis terminal is in electrical signal connection with the feedback terminal; the feedback terminal is used for obtaining the genetic metabolic disease prediction result and feeding back the actual result of the genetic metabolic disease;
the method comprises the following steps:
s1: acquiring sample data;
s2: distinguishing according to the sample data, and executing step S3 if the sample data is abnormal; if the sample data is normal, executing step S6;
s3: performing first-step classification according to the action degree of each index in metabolic pathways of various genetic metabolic diseases to obtain a first-step classification result;
s4: performing multi-index joint risk assessment classification to obtain a second classification result;
s5: combining the first classification result and the second classification result to give a final disease classification result;
s6: ending;
the step S1 comprises the steps of analyzing sample data through a quality control analysis method, and if the sample data is unqualified, resampling or rechecking is carried out, otherwise, the step S2 is carried out;
the specific implementation mode of the S2 is as follows:
s21: reading indexes affecting the genetic metabolic diseases and factor indexes possibly affecting normal interpretation;
s22: judging whether all indexes in the step S21 are normal, if all indexes are normal, judging that the non-genetic metabolic disease is abnormal, and executing the step S6; otherwise, determining that the genetic metabolism disease is abnormal, and executing the step S3;
the specific implementation mode of the S3 is as follows:
s31, screening indexes related to the genetic metabolic disease according to abnormal indexes which are confirmed by clinical researches and cause the genetic metabolic disease;
s32: dividing the index into primary and secondary indexes, wherein the index highly related to a certain genetic metabolic disease is used as a primary index, and the index lightly related to a certain genetic metabolic disease is used as a secondary index;
s33: distinguishing the action degree of the index in the metabolic process, and classifying the data for the first time according to the classification; if the main index is increased or decreased, the abnormal prompt is considered to be very relevant to the disease, and the first classification result is determined according to the index abnormality degree and the clinical characterization; otherwise, executing the step S4;
the specific implementation manner of the step S4 is as follows:
the second classification result is obtained through the comparison and interpretation of indexes and the comparison analysis and judgment of a system disease database, and the accurate interpretation is carried out by combining the health and nutrition states of the mother before and during birth and the disease and nutrition states of the newborn when blood is taken; if the abnormality index is related to the prenatal and prenatal health and nutrition state of the mother and the nutrition state of the newborn when blood is taken, the abnormality index is not related to the genetic metabolic disease, and the abnormality index is judged to have low probability of prompting the disease; if the condition of the mother and the neonate is excluded from blood sampling, and the disease index is abnormal and the abnormal change is in the change range, judging that the maternal and neonate is a mild genetic metabolic disease, and paying attention or intervening in advance;
in the step S1, sample data of umbilical cord blood or plantar blood of the newborn is passed; the blood sampling time is 72 hours after the birth of the infant and after the infant is saturated with more than 6 times of milk;
in the step S6, before ending, corresponding suggestions are given according to the etiology, and a report is generated;
the specific workflow of the neonatal genetic metabolic disease detection and analysis device is divided into:
and (3) data inspection: carrying out mass control analysis on the detection data in a statistical way, wherein the quality control is qualified, entering the next analysis link, and the unqualified data is required to be resampled or rechecked so as to ensure that the data entering the disease analysis link is accurate and reliable;
data screening: classifying and screening whether the quality qualified data is abnormal or not according to the genetic metabolic disease related indexes stored in the system disease database and other factor indexes possibly influencing normal interpretation by taking the sample as a unit;
and (5) a trial interpretation: the system matches the detection result with the basic information of the patient, and carries out preliminary interpretation on the detection result, if all indexes are in the normal value range, the detection result is preliminarily interpreted as A and the indexes are normal, the neonate health is indicated, the output reporting step is directly carried out, otherwise, the detection result is preliminarily interpreted as B and the indexes are abnormal, and the next node is interpreted according to the flow;
and (3) second-examination interpretation: judging a result of B index abnormality by a check node through comparing and judging indexes and comparing, analyzing and judging the result with a system disease database, and further judging; if more than 1 secondary indexes are not in the normal reference value range, judging as C, and entering the next node for judgment if the secondary indexes are suspicious related to the disease; if more than 1 main indexes of the diseases are not in the normal reference value range, judging that the main indexes are D, are very relevant to the diseases, and entering a next node for judging;
three-review interpretation a: the secondary node is interpreted as C to carry out further accurate interpretation, and accurate interpretation is carried out by comparing and judging indexes and comparing and analyzing and judging the indexes with a system disease database and combining the health and nutrition states of the mother before and during birth and the diseases and nutrition states of the newborn when blood is taken; if the abnormality index is related to the prenatal and prenatal health and nutrition state of the mother and the nutrition state of the newborn when blood is taken, judging that E is not great in relation to the genetic metabolic disease, and judging that the abnormality prompt disease probability is low; if the condition of the mother and the neonate is excluded from blood sampling, and the disease index is abnormal, and the abnormal change is in the change range, judging that F is a mild genetic metabolic disease, and paying attention or intervening in advance;
three-review interpretation b: the secondary node interpretation is classified as a result of D, and accurate interpretation is performed according to the increasing or decreasing degree of the index and the clinical characterization through the comparison interpretation of the index and the comparison analysis and judgment of the system disease database; the degree of the increase or decrease of the index is within a set range, and the index is interpreted as G, so that moderate genetic metabolic diseases are prompted; the degree of the increase or decrease of the index is outside the set range, and the index is judged to be H, so that severe genetic metabolic diseases are prompted;
report output: and combining the basic information, the detection data and the final disease analysis advice of the patient, and generating a detection report display terminal for displaying the generated detection report after the system is integrated.
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