KR102574100B1 - System of Suppling the Data for Congenital Metabolic Disease Examination for a newborn infant - Google Patents

Abstract

According to an embodiment of the present invention, the system for providing findings generating data for the examination of congenital metabolic abnormalities of newborns using Tandem Mass Spectrometry is a system for providing findings generating data for the examination of congenital metabolic abnormalities of newborns using Tandem Mass Spectrometry, which is set in advance. A database that establishes a matching relationship between a plurality of analysis items and a plurality of pre-set disease names, an analysis module that analyzes analysis items for 80 types using a tandem mass spectrometer for blood primarily collected from newborns, and analyzed A detection module that determines whether each analysis item is detected, a matching module that derives a matched disease name using a database for the detected analysis item, and a data providing module that provides data for generating findings on the derived disease name. Including, according to the system for providing findings generating data for the test for congenital metabolic abnormalities of newborns according to an embodiment of the present invention, there is an effect of quantifying and automating the tests for congenital metabolic abnormalities of newborns.

Description

System of Suppling the Data for Congenital Metabolic Disease Examination for a newborn infant}

The present invention relates to a system for providing data for generating findings for the examination of congenital metabolic abnormalities in newborns, and more particularly, to tandem mass spectrometry data related to the quantification of metabolites used in the examination of congenital metabolic abnormalities in newborns. It relates to a system that analyzes and provides findings generating data.

A mass spectrometer is an instrument used to identify the components to be analyzed. A mass spectrometer measures the mass of a substance as a ratio of mass to charge, and ions are formed by various ionization methods.

It is known that diseases are often caused by genetic changes and environmental factors. In particular, environmental factors are gradually being identified as major factors in diseases, and are known to have an absolute effect on changes in genes. As such, in a series of processes in which diseases occur due to interaction of genetic changes with the environment, proteins, which are products of genes, serve as major markers of diseases and are used as substances that provide guidelines for diagnosis, treatment, and treatment of diseases.

A stumbling block in protein analysis related to disease is that there are various types of proteins present in bodily fluids and they have a wide concentration range. In particular, in the case of blood, the need for analysis of the types and concentration ranges of proteins present still remains.

Korean Patent Registration No. 10-0572619 is a method for analyzing data obtained by scanning amino acids and acylcarnitine derivatized from blood points collected from newborns with a tandem mass spectrometer. In the present invention, after derivatization, data expressed as concentrations of each metabolite directly proportional to butyl ester fragments are obtained, compared with phylogenetic tree threshold flags, and used to determine the significance of deviations.

In Korean Patent Registration No. 10-1795662, the apparatus for examining abnormal metabolic diseases receives metabolite concentration values for each metabolite in a sample, compares the received metabolite concentration with the reference concentration for each metabolite, and determines the metabolite concentration. A metabolite concentration abnormal determination unit for determining whether there is an abnormality in the metabolite concentration, a metabolite-related disease screening unit for selecting a metabolite-related disease corresponding to the metabolite determined to have an abnormality in the metabolite concentration, A genetic status classification unit that receives genetic mutation data including information on genetic mutation and classifies the genetic mutation as one of a plurality of predetermined genetic states, and a gene that selects a gene-related disease corresponding to the classified genetic state A related disease screening unit and a disease included in the selected metabolite-related disease or gene-related disease are set as candidate diseases, and the candidate disease is determined as a metabolic abnormality of the specimen according to the type of the gene state corresponding to the candidate disease. It may include an integrated analysis unit that determines information on whether or not it corresponds to a disease.

However, there is still a lack of automated and database-formed methods for interpreting data used for the purpose of generating findings for congenital metabolic abnormality tests in newborns.

Republic of Korea Patent No. 10-0572619 Republic of Korea Patent No. 10-1795662

An object of the present invention to solve the above problems is to analyze the results using Tandem Mass Spectrometry to derive a matching relationship between a plurality of analysis items and a plurality of disease names. It is to provide a system for providing findings generating data for examination.

In order to solve the above problems, a system for providing findings generating data for a test for congenital metabolic abnormalities in newborns according to an embodiment of the present invention provides findings generating data for tests for congenital abnormal metabolic abnormalities in newborns using Tandem Mass Spectrometry. A providing system comprising: a database in which a matching relationship between a plurality of preset analysis items and a plurality of preset disease names is set; An analysis module for analyzing 80 types of analysis items using the tandem mass spectrometer for blood primarily collected from a newborn baby; a detection module for determining whether to detect each of the analyzed analysis items; a matching module for deriving a matched disease name by using the database with respect to the detected analysis item; and a data providing module providing data for generating findings on the derived disease names.

Here, if the matched disease name is not derived by using the database in the matching module, the data providing module does not provide the finding generation data, and the matching module uses the database to derive the matched disease name. Only when it is done, the data providing module may provide data for opinion generation.

Here, the blood collected primarily from the newborn is taken within 48 hours after birth when the newborn's weight is 2.5 kg or more, and between 5 and 7 days after birth when the newborn's weight is less than 2.5 kg. The second blood collected from the newborn was collected within 1 day after determining the need for secondary collection if the newborn's weight was 2.5 kg or more, and from 29 days to 31 days after birth if the newborn's weight was less than 2.5 kg. It may have been collected in between.

Here, as shown in FIGS. 2 and 3 , the database setting the matching relationship between the analysis item and the disease name establishes a matching relationship for each disease name in response to the detection of the at least one analysis item. it could be

Here, in the detection module, the plurality of analysis items are classified as either a first analysis item or a second analysis item as shown in Table 1 below, and the first analysis item includes eighth analysis items out of a total of 80 analysis items. No. (C0), No. 20 (C16), and No. 24 (C18) are applicable, and the detection of the first analysis item is determined as detection only when it is less than 0.01% or more than 99.99% in the concentration normal distribution, The second analysis item may include the rest of the analysis items, and detection of the second analysis item may be determined as detection only when the concentration is less than 0.25% or greater than 99.75% in the concentration normal distribution.

number Measurement details (analysis items) division One 5-OXO_PRO 2nd analysis item 2 ADO 2nd analysis item 3 Ala 2nd analysis item 4 Arg 2nd analysis item 5 ASA1 2nd analysis item 6 ASA2 2nd analysis item 7 ASATOTAL 2nd analysis item 8 C0 1st analysis item 9 C10 2nd analysis item 10 C10:1 2nd analysis item 11 C10:2 2nd analysis item 12 C12 2nd analysis item 13 C12:1 2nd analysis item 14 C12:1-OH 2nd analysis item 15 C12-OH 2nd analysis item 16 C14 2nd analysis item 17 C14:1 2nd analysis item 18 C14:2 2nd analysis item 19 C14-OH 2nd analysis item 20 C16 1st analysis item 21 C16:1 2nd analysis item 22 C16:1-OH 2nd analysis item 23 C16-OH 2nd analysis item 24 C18 1st analysis item 25 C18:1 2nd analysis item 26 C18:1-OH 2nd analysis item 27 C18:2 2nd analysis item 28 C18:2-OH 2nd analysis item 29 C18-OH 2nd analysis item 30 C2 2nd analysis item 31 C20 2nd analysis item 32 C20:0-LPC 2nd analysis item 33 C22 2nd analysis item 34 C22:0-LPC 2nd analysis item 35 C24 2nd analysis item 36 C24:0-LPC 2nd analysis item 37 C26 2nd analysis item 38 C26:0-LPC 2nd analysis item 39 C3 2nd analysis item 40 C3-DC\C4-OH 2nd analysis item 41 C4 2nd analysis item 42 C4-DC\C5-OH 2nd analysis item 43 C5 2nd analysis item 44 C5:1 2nd analysis item 45 C5-DC\C6-OH 2nd analysis item 46 C6 2nd analysis item 47 C6-DC 2nd analysis item 48 C8 2nd analysis item 49 C8:1 2nd analysis item 50 Cit 2nd analysis item 51 D-ADO 2nd analysis item 52 Glu 2nd analysis item 53 Gly 2nd analysis item 54 His 2nd analysis item 55 Leu 2nd analysis item 56 Lys 2nd analysis item 57 Met 2nd analysis item 58 Orn 2nd analysis item 59 Phe 2nd analysis item 60 Pro 2nd analysis item 61 Ser 2nd analysis item 62 SUAC 2nd analysis item 63 Tyr 2nd analysis item 64 Val 2nd analysis item 65 Leu+Val 2nd analysis item 66 Leu+5-OXO_PRO 2nd analysis item 67 (C0+C2+C3+C16+C18:1+C18)/Cit ratio 2nd analysis item 68 C0/(C16+C18) ratio 2nd analysis item 69 C14:1/C16 ratio 2nd analysis item 70 C16-OH/C16 ratio 2nd analysis item 71 C26/C22 ratio 2nd analysis item 72 C3-DC\C4-OH/C2 ratio 2nd analysis item 73 C8/C2 ratio 2nd analysis item 74 Cit/Arg ratio 2nd analysis item 75 Gly/Ala ratio 2nd analysis item 76 Leu/Ala ratio 2nd analysis item 77 Leu/Phe ratio 2nd analysis item 78 Met/Phe ratio 2nd analysis item 79 Phe/Tyr ratio 2nd analysis item 80 Val/Phe ratio 2nd analysis item

Here, in the matching module, the plurality of disease names are classified into any one of the primary disease names in Table 2 and the secondary disease names in Table 3 below, and the primary disease names are first determined, and sequentially The secondary disease name is determined, but the primary disease name may be selected according to the order of the frequency of occurrence among a plurality of disease names.

number disease name (Korean) disease name (English) 01 Algininosuccinic aciduria Argininosuccinic aciduria 02 Citrullinemia type I Citrullinemia type I 03 homocystinuria Classic Homocystinuria with Hypermethioninemia 04 maple monouria Maple syrup urine disease 05 phenylketonuria Phenylketonuria 06 Tyrosinemia type I Tyrosinemia type I 07 3-hydroxy-3-methylglutaric aciduria 3-Hydroxy-3-methylglutaric aciduria 08 3-methylcrotonylcoate decarboxylase deficiency 3-Methylcrotonyl-CoA carboxylase deficiency 09 biotinidase deficiency Biotinidase Deficiency 10 Glutaric aciduria type I Glutaric acidemia type I 11 Isovaleric acidemia Isovaleric acidemia 12 Vitamin B12-responsive methylmalonic acidemia Methylmalonic acidemia, Vitamin B12-responsive 13 Methylmalonylcoeimutase Enzyme Deficiency Methylmalonyl-CoA Mutase Deficiency 14 complex decarboxylase deficiency Multiple carboxylase deficiency 15 propionic acidemia Propionic acidemia 16 Beta Ketothiolase Deficiency β-Ketothiolase Deficiency 17 Carnitine Receptor Deficiency Carnitine uptake defect 18 Long-Chain Hydroxyacyl CoA Dehydrogenase Deficiency Long-chain L-3-Hydroxy acyl-CoA dehydrogenase deficiency 19 Heavy chain acyl-coate dehydrogenase deficiency Medium-chain acyl-CoA dehydrogenase deficiency 20 trifunctional protein deficiency Trifunctional protein deficiency 21 long-chain acyl-CoA dehydrogenase deficiency Very long-chain acyl-CoA dehydrogenase deficiency 22 Severe combined immunodeficiency syndrome due to adenosine deaminase deficiency Severe combined immunodeficiency due to adenosine deaminase deficiency 23 X-related adrenoleukodystrophy X-linked Adrenoleukodystrophy

number disease name (Korean) disease name (English) 24 alginemia Argininemia 25 Carbamoyl phosphate synthase deficiency Carbamoyl-phosphate synthase deficiency 26 Citrullinemia type II Citrullinemia type II 27 Dihydrolipoamide dehydrogenase deficiency Dihydrolipoamide dehydrogenase deficiency 28 Biopterin cofactor biosynthesis deficiency Disorders of biopterin biosynthesis 29 Biopterin cofactor regeneration deficiency Disorders of biopterin regeneration 30 Forminoglutamic aciduria Formiminoglutamic acidemia 31 histidinaemia Histidinemia 32 Homocystinuria without methylmalonic aciduria and hypermethioninemia Homocystinuria without Hypermethioninemia & Methylmalonic Aciduria 33 hydroxyprolinemia Hydroxyprolinemia 34 Highleucine-isoleucinemia Hyperleucine-isoleucinemia 35 hyperlysinemia Hyperlysinemia 36 Hypermethioninemia Hypermethioninemia 37 High ornithine-high ammonia-homocitrulline syndrome Hyperornithinemia-Hyperammonemia-Homocitrullinuria syndrome 38 hyperphenylalanemia Hyperphenylalaninemia, variant, benign 39 hyperprolinemia type I Hyperprolinemia type I 40 Hyperprolinemia type II Hyperprolinemia type II 41 Lysinuric protein intolerance Lysinuric protein intolerance 42 N-acetylglutamate synthetase deficiency N-acetylglutamate synthase deficiency 43 Non-ketotic hyperglycinemia Nonketotic hyperglycinemia, glycine encephalopathy 44 ornithine amino deficiency Ornithine Amiotransferase deficiency 45 Ornithine transcarbamylase deficiency Ornithine transcarbamylase deficiency 46 pyruvate carboxylase deficiency Pyruvate carboxylase deficiency, type A/B/C 47 serine deficiency Serine deficiency 48 Neonatal hypertyrosinemia Transient Neonatal Tyosinemia 49 Tyrosinemia type II Tyrosinemia type II 50 Tyrosinemia type III Tyrosinemia type III 51 balinemia Valinemia 52 2-methyl-3-hydroxybutylicuria 2-Methyl-3-hydroxybutyric aciduria 53 2-methylbutylylglycinuria 2-Methylbutyrylglycinuria 54 Methylglutaconic aciduria 3-Methylglutaconic aciduria, type I/II/III/IV 55 ethylmalonic encephalopathy Ethylmalonic encephalopathy 56 isobutylylglycinuria Isobutyrylglycinuria 57 malonic aciduria Malonic acidemia 58 Methylcobalamin deficiency Methylcobalamin deficiency 59 Methylenetetrahydrofolate reductase deficiency Methylene tetrahydrofolate reductase deficiency 60 Methylmalonic acid and homocystinuria Methylmalonic acidemia with Homocystinuria 61 Pyroglutamic aciduria Pyroglutamic acidemia 62 Succinic Acid CoA Linking Enzyme Deficiency Succinyl-CoA ligase deficiency 63 2,4-Dienoyl Koei Reductase Deficiency 2,4-Dienoyl-CoA reductase deficiency 64 Carnitine palmitoyltransferase deficiency type I Carnitine palmitoyltransferase I deficiency 65 Carnitine palmitoyltransferase deficiency type II Carnitine palmitoyltransferase II deficiency 66 acylcarnitine converting enzyme deficiency Carnitine-acylcarnitine translocase deficiency 67 dienoyl reductase deficiency Dienoyl reductase deficiency 68 Glutaric aciduria type II Glutaric acidemia type II 69 short-chain acyl-coate dehydrogenase deficiency Short-chain acyl-CoA dehydrogenase deficiency 70 short-chain hydroxyacyl-coate dehydrogenase deficiency Short-chain L-3-hydroxy acyl-CoA dehydrogenase deficiency 71 Heavy chain ketoacyl co-thiolase deficiency Medium-chain 3-ketoacyl-CoA thiolase deficiency 72 Medium/Short Chain Hydroxyacyl COA Dehydrogenase Deficiency Medium/Short-chain 3-OH acyl-CoA dehydrogenase deficiency 73 Transcobalamin II deficiency Transcobalamin II deficiency 74 D-positive protein deficiency D-bifunctional protein deficiency 75 Refsum disease in infants Infantile Refsum disease 76 liver disease Liver disease 77 Peroxisome acyl-CoA oxidase type I deficiency Peroxisomal acyl-CoA oxidase I deficiency 78 Primary hyperlactatemia Primary lactic acidemia, various types 79 vitamin B12 deficiency Vitamin B12 deficiency 80 Zellweger Syndrome Zellweger syndrome

Here, when there is a detection decision for any one of the first disease names, the first comprehensive decision flag is detected and determined, and when there is a detection decision for any one of the second disease names. may be to detect and determine the second comprehensive decision flag.

Here, the detection criterion range for each analysis item may be determined by comparing with a standard value provided by the Centers for Disease Control and Prevention (CDC) every 6 months.

Here, the detection criterion range for each analysis item may be to perform an adequacy review based on whether the detection rate is within 0.2% to 0.3% on a yearly basis.

According to the system for providing findings for congenital metabolic abnormality examination of newborns according to an embodiment of the present invention, the results using Tandem Mass Spectrometry are analyzed to determine a plurality of analysis items and a plurality of disease names. Since a matching relationship is derived, it is possible to provide more accurate findings generation data.

In addition, since a database in which matching relationships are established for multiple analysis items and multiple disease names is used, an automated system can be established, and results for congenital metabolic abnormality tests in newborns can be generated more quickly, accurately, and conveniently. There is an effect that can provide data.

1 is a configuration diagram for explaining a finding generating data system for congenital metabolic abnormality examination of newborns according to an embodiment of the present invention.
Figure 2 shows a database in which a matching relationship is set for a plurality of analysis items and a plurality of disease names in a system for providing findings generation data for a test for congenital metabolic abnormality of a newborn according to an embodiment of the present invention, and analysis items 1 to 2 are shown. It is a drawing for explaining number 40.
Figure 3 shows a database in which a matching relationship is set for a plurality of analysis items and a plurality of disease names in the finding generation data providing system for the congenital metabolic abnormality test of a newborn according to an embodiment of the present invention, and analysis items 41 to 41 are shown. It is a drawing for explaining number 80.
4 is a view for explaining detection criteria for analysis items in the system for providing findings generating data for a test for congenital metabolic abnormalities of newborns according to an embodiment of the present invention.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings so that those skilled in the art can easily carry out the present invention. The present invention may be embodied in many different forms and is not limited to the embodiments described herein.

In order to clearly describe the present invention, parts irrelevant to the description are omitted, and the same reference numerals are assigned to the same or similar components throughout the specification.

Throughout the specification, when a part is said to be "connected" to another part, this includes not only the case where it is "directly connected" but also the case where it is "electrically connected" with another element interposed therebetween. . In addition, when a certain component is said to "include", this means that it may further include other components without excluding other components unless otherwise stated.

When a part is referred to as being “on” another part, it may be directly on top of the other part or may have other parts in between. In contrast, when a part is said to be “directly on” another part, there are no other parts in between.

Terms such as first, second and third are used to describe, but are not limited to, various parts, components, regions, layers and/or sections. These terms are only used to distinguish one part, component, region, layer or section from another part, component, region, layer or section. Accordingly, a first part, component, region, layer or section described below may be referred to as a second part, component, region, layer or section without departing from the scope of the present invention.

The terminology used herein is only for referring to specific embodiments and is not intended to limit the present invention. As used herein, the singular forms also include the plural forms unless the phrases clearly indicate the opposite. The meaning of "comprising" as used herein specifies particular characteristics, regions, integers, steps, operations, elements and/or components, and the presence or absence of other characteristics, regions, integers, steps, operations, elements and/or components. Additions are not excluded.

Terms indicating relative space, such as “below” and “above,” may be used to more easily describe the relationship of one part to another shown in the drawings. These terms are intended to include other meanings or operations of the device in use with the meaning intended in the drawings. For example, if the device in the figures is turned over, certain parts described as being “below” other parts will be described as being “above” the other parts. Thus, the exemplary term "below" includes both directions above and below. The device may be rotated 90 degrees or other angles, and terms denoting relative space are interpreted accordingly.

Although not defined differently, all terms including technical terms and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the art to which the present invention belongs. Terms defined in commonly used dictionaries are additionally interpreted as having meanings consistent with related technical literature and currently disclosed content, and are not interpreted in ideal or very formal meanings unless defined.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings so that those skilled in the art can easily carry out the present invention. However, the present invention may be implemented in many different forms and is not limited to the embodiments described herein.

1 is a configuration diagram for explaining a finding generating data system for congenital metabolic abnormality examination of newborns according to an embodiment of the present invention. Figure 2 shows a database in which a matching relationship is set for a plurality of analysis items and a plurality of disease names in a system for providing findings generation data for a test for congenital metabolic abnormality of a newborn according to an embodiment of the present invention, and analysis items 1 to 2 are shown. It is a drawing for explaining number 40. Figure 3 shows a database in which a matching relationship is set for a plurality of analysis items and a plurality of disease names in the finding generation data providing system for the congenital metabolic abnormality test of a newborn according to an embodiment of the present invention, and analysis items 41 to 41 are shown. It is a drawing for explaining number 80. 4 is a diagram for explaining detection criteria for analysis items in the system for providing findings generating data for a test for congenital metabolic abnormalities of newborns according to an embodiment of the present invention.

1 to 4 together, the finding generating data system 100 for the congenital metabolic abnormality test of a newborn according to an embodiment of the present invention is a congenital metabolic abnormality test of a newborn using a tandem mass spectrometry. In the finding generation data providing system 100 for, Database 110 that sets a matching relationship for a plurality of pre-set analysis items and a plurality of pre-set disease names; An analysis module 120 for analyzing 80 types of analysis items using the tandem mass spectrometer for blood primarily collected from a newborn baby; a detection module 130 for determining whether to detect each of the analyzed analysis items; A matching module 140 for deriving a matched disease name by using the database with respect to the detected analysis item; and a data providing module 150 providing data for generating findings on the derived disease names.

The database 110 in which a matching relationship between the analysis item and the disease name is established may be one in which a matching relationship is established for each disease name in response to detection of the at least one analysis item.

On the other hand, if the matching module 140 utilizes the database 110 and the matched disease name is not derived, the data providing module 150 does not provide the data for generating the findings,

When the matched disease name is derived by using the database 110 in the matching module 140, the analysis module 120 uses the tandem mass spectrometer to determine the 80 It may be to re-analyze the assay for species.

In addition, when the matched disease name is not derived using the database 110 in the matching module 140 for the second blood collected from the newborn, the finding generating data in the data providing module 150 It may be that the data providing module 150 provides data for generating findings only when the matched disease name is derived by utilizing the database 110 in the matching module 140 without providing .

In the end, if the matching module 140 does not derive a matching disease name by using the database 110, the process ends with the data providing module 150 not providing the data for generating the findings. You will be able to.

On the other hand, when the matched disease name is derived by using the database 110 in the matching module 140, the tandem mass spectrometer is used for the blood secondarily collected from the newborn in the analysis module 120. So, the analysis items for 80 species will be analyzed again,

In this case, when the matched disease name is not derived by using the database 110 in the matching module 140 for the blood secondarily collected from the newborn, the data providing module 150 also The procedure may be terminated by not providing the data for generating the findings.

However, only when the matched disease name is derived using the database 110 in the matching module 140 for the blood secondarily collected from the newborn, the data for generating findings in the data providing module 150 may be providing

That is, data for generating findings may be provided only when the same disease name is derived in both the first and second cases according to the results of a rigorous analysis.

On the other hand, the blood primarily collected from the newborn is taken within 48 hours after birth when the newborn's weight is 2.5 kg or more, and is collected between 5 and 7 days after birth when the newborn's weight is less than 2.5 kg. ,

The second blood collected from the newborn was collected within 1 day after determining the need for secondary collection if the newborn's weight was 2.5 kg or more, and between 29 and 31 days after birth if the newborn's weight was less than 2.5 kg. may have been harvested.

Again, primary and secondary blood will be collected from newborns under strict conditions, and through strict analysis, it may be provided only when the same disease name is derived in both primary and secondary cases.

As shown in FIGS. 2 and 3 , the database 110 setting a matching relationship between the analysis item and the disease name sets a matching relationship for each disease name in response to detection of the at least one analysis item. it could be

Figure 2 shows a database in which a matching relationship is set for a plurality of analysis items and a plurality of disease names in a system for providing findings generation data for a test for congenital metabolic abnormality of a newborn according to an embodiment of the present invention, and analysis items 1 to 2 are shown. It is a drawing for explaining number 40. Figure 3 shows a database in which a matching relationship is set for a plurality of analysis items and a plurality of disease names in the finding generation data providing system for the congenital metabolic abnormality test of a newborn according to an embodiment of the present invention, and analysis items 41 to 41 are shown. It is a drawing for explaining number 80.

In particular, each disease name must be matched with at least one analysis item to provide findings generation data. Some disease names will satisfy the condition even if only one analyte is detected, while other disease names will satisfy the condition only if all five analytes are detected.

For example, in the case of disease number 20, analysis items 22, 23, 26, 29, and 70 must all be detected to provide findings generation data for disease number 20, and disease number 10. In the case of the disease, even if only the 45th analysis item is detected, it will be possible to provide the findings generation data for the 10th disease name.

Meanwhile, in the detection module 130, the plurality of analysis items are classified into either a first analysis item or a second analysis item as shown in Table 1 below, and the first analysis item includes a total of 80 analysis items. No. 8 (C0), No. 20 (C16), and No. 24 (C18) are applicable, and detection of the first analysis item is determined as detection only when it is less than 0.01% or greater than 99.99% in the concentration normal distribution. do,

The second analysis item may include the rest of the analysis items, and detection of the second analysis item may be determined as detection only when the concentration is less than 0.25% or greater than 99.75% in the concentration normal distribution.

The plurality of analysis items are classified into one of the first analysis item and the second analysis item, which is classified because of the difference in the concentration range of the detection decision, and the first analysis item includes the 8th analysis item out of a total of 80 analysis items. No. (C0), No. 20 (C16), and No. 24 (C18) are applicable, and the second analysis item will include the rest of the analysis items.

On the other hand, referring to FIG. 4 together, the detection decision of the first analysis item is determined as detection only when the concentration is less than 0.01% or greater than 99.99% in the concentration normal distribution, and the detection decision of the second analysis item is the concentration normal distribution. It may be determined as detection only when less than 0.25% or greater than 99.75% in . That is, according to the detection concentration range, it is divided into a first analysis item and a second analysis item.

In the matching module 140, the plurality of disease names are classified into any one of the primary disease names in Table 2 and the secondary disease names in Table 3 below, and the primary disease names are first determined, and sequentially It may be to determine the name of the secondary disease.

The first disease name is determined according to the order of the frequency of occurrence among the plurality of disease names, that is, after the disease name determination for the first disease name is first performed, the second disease name It may be to perform disease name determination for.

The matching module 140 may detect and determine a first comprehensive decision flag when there is a detection decision for any one of the first disease names. In order to minimize the user's judgment error when there is a detection decision of any one disease name among all the primary disease names, a first comprehensive decision flag is used to inform the user of any one of the primary disease names. may indicate whether there is a detection decision.

In addition, if there is any detection decision in all the secondary disease names, the secondary comprehensive determination flag may be detected and determined, and this is also any one disease in the entire secondary disease names. In order to minimize the user's decision error when there is a detection decision for a name, it may be to inform the user whether one of the secondary disease names has a detection decision by using a secondary comprehensive decision flag (Flag). .

Meanwhile, the detection criterion range for each analysis item may be determined by comparing with a standard value provided by the Centers for Disease Control and Prevention (CDC) every 6 months.

Since the detection criteria range for each analysis item requires a high level of accuracy, it is intended to periodically reflect changes over time and is provided by the Centers for Disease Control and Prevention (CDC) every 6 months. It may be to determine the detection determination standard range for each analysis item by comparing with a reference value.

In addition, the detection criterion range for each analysis item may be to perform an adequacy review based on whether the detection rate is included within 0.2% to 0.3% on a yearly basis. It may be to determine whether the detection rate for each analysis item is within the general detection rate of 0.2% to 0.3% on a one-year cycle, and to conduct a review of the adequacy of the detection criteria range for each analysis item. Through this, it will be possible to impart reliability to the detection criteria range for each analysis item.

Although the embodiments of the present invention have been described with reference to the accompanying drawings, those skilled in the art can realize that the present invention can be implemented in other specific forms without changing the technical spirit or essential features. you will be able to understand For example, those skilled in the art may change the material, size, etc. of each component according to the field of application, or combine or substitute the disclosed embodiments to practice in a form not clearly disclosed in the embodiments of the present invention, but this is also the present invention. It does not go beyond the scope of the invention. Therefore, the embodiments described above should not be understood as illustrative in all respects and limiting, and these modified embodiments should be included in the technical idea described in the claims of the present invention.

100: System for providing data for generating findings for congenital metabolic abnormality tests in newborns
110: database
120: analysis module
130: detection module
140: matching module
150: data provision module

Claims (11)

In the system for providing findings generation data for congenital metabolic abnormality examination of newborns using Tandem Mass Spectrometry,
A database in which a matching relationship between a plurality of preset analysis items and a plurality of preset disease names is set;
An analysis module for analyzing 80 types of analysis items using the tandem mass spectrometer for blood primarily collected from a newborn baby;
a detection module for determining whether to detect each of the analyzed analysis items;
a matching module for deriving a matched disease name by using the database with respect to the detected analysis item; and
A data providing module for providing data for generating findings for the derived disease name; including,
The database setting the matching relationship between the analysis item and the disease name establishes a matching relationship for each disease name in response to the detection of the at least one analysis item,
If the matching disease name is not derived by using the database in the matching module, the data for generating the findings is not provided in the data providing module,
When the matching disease name is derived by using the database in the matching module, the analysis module analyzes the analysis items for 80 types again using the tandem mass spectrometer for the second blood collected from the newborn in the analysis module would,
Regarding the blood collected secondly from the newborn,
If the matching disease name is not derived by using the database in the matching module, the finding generating data is not provided in the data providing module,
Only when the matching disease name is derived by using the database in the matching module, the data providing module provides data for generating findings,
The first blood collected from the newborn is
If the newborn's weight is 2.5 kg or more, it is collected within 48 hours after birth, and if the newborn's weight is less than 2.5 kg, it is collected between 5 and 7 days after birth,
Secondary blood collected from the newborn
If the newborn's weight is 2.5 kg or more, it is collected within 1 day after determining the need for secondary collection, and if the newborn's weight is less than 2.5 kg, it is collected between 29 and 31 days after birth,
As shown in FIGS. 2 and 3 below, the database in which the matching relationship between the analysis item and the disease name is established corresponds to the detection of the at least one analysis item, and a matching relationship is established for each disease name,


In the detection module,
The plurality of analysis items are classified as either a first analysis item or a second analysis item as shown in Table 1 below,
The first analysis item corresponds to No. 8 (C0), No. 20 (C16), and No. 24 (C18) from a total of 80 analysis items, and the detection decision of the first analysis item is determined by the concentration normal distribution. It is judged as detection only when it is less than 0.01% or greater than 99.99%,
The second analysis item includes the rest of the analysis items, and detection of the second analysis item is determined as detection only when the concentration is less than 0.25% or greater than 99.75% in the concentration normal distribution,

In the matching module,
The plurality of disease names are classified into either one of the primary disease names in Table 2 below and the secondary disease names in Table 3 below, and the primary disease names are determined first, and the secondary disease names are sequentially determined. judge,
The first disease name is to select a disease name according to the order of the highest frequency of occurrence among a plurality of disease names,


If there is a detection decision in any of the first disease names, a first comprehensive decision flag is detected and determined;
If any of the secondary disease names have a detection determination, a secondary comprehensive determination flag is detected and determined,
The detection criterion range for each analysis item is determined by comparing with the standard value provided by the Centers for Disease Control and Prevention (CDC) every 6 months,
The detection criterion range for each analysis item is a system for providing findings generation data for the congenital metabolic abnormality test of newborns, characterized in that the adequacy review is performed on the basis of whether the detection rate is within 0.2% to 0.3% on a yearly basis. .
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