KR20210101441A - Method and system for predicting prognosis in patients Community-Acquired Pneumonia(CAP) - Google Patents

Abstract

Provided is a method for quickly and accurately predicting the prognosis of the risk, mortality or the like of pneumonia of a patient from the blood test results of a patient with community-acquired pneumonia. The method for predicting the prognosis of pneumonia patients is based on the blood test results and can predict the patient as a patient at high risk of pneumonia, when the average platelet volume of the patient is relatively large compared to other patients or the number of platelets is relatively small compared to other patients.

Description

Method and system for predicting prognosis in patients Community-Acquired Pneumonia (CAP)}

The present invention relates to a method for predicting the mortality of a patient with community-acquired pneumonia (CAP), and in particular, a method for predicting the prognosis for pneumonia in a patient from platelet information extracted from a patient with community-acquired pneumonia, and the method It is about a prediction system for

Pneumonia is defined as an infection of the lung parenchyma. These pneumonias are largely classified into community-acquired pneumonia (CAP) and hospital-acquired pneumonia (HAP).

Community-acquired pneumonia is mainly caused by Streptococcus pneumoniae, Mycoplasma pneumoniae, and Hemophilus influenzae. In addition, as symptoms of its onset, respiratory symptoms such as cough, sputum, and shortness of breath, and systemic symptoms such as headache, fatigue, fever, and chills appear.

Community-acquired pneumonia is the most common cause of death among infectious diseases. For example, in Korea, the death rate from pneumonia was 9.4 per 100,000 population as of 2006, showing the highest mortality rate among deaths from other infectious diseases.

In particular, the death rate due to pneumonia is high in the elderly with weakened immunity compared to young people, and in Korea, which has recently entered a rapidly aging society, the mortality rate due to pneumonia among the elderly is expected to increase.

Accordingly, various methods for predicting the prognosis of pneumonia have been proposed in order to reduce the mortality and medical costs caused by community-acquired pneumonia, and among them, the Pneumonia Severity Index (PSI) is widely used.

The pneumonia severity index calculates a score based on gender, age, comorbidity, examination and physical examination results for patients with community-acquired pneumonia, and classifies it into 5 stages for each stage, such as the prognosis of pneumonia, such as mortality a way to predict.

However, the conventional prognosis prediction method of community-acquired pneumonia requires a large number of predictors and takes a lot of time due to the increase in the complexity of prediction. In particular, in the case of the conventional pneumonia severity index, it is relatively There was a problem that the accuracy of prediction was lowered due to a large score deviation.

The present invention is to solve the above problems, by predicting the prognosis for pneumonia of the patient from platelet information extracted from the patient with community-acquired pneumonia, a prediction method that can accurately predict the prognosis of pneumonia in a simplified process in a short time and It is intended to provide a prediction system for this purpose.

A method for predicting the prognosis of a patient with community-acquired pneumonia according to an embodiment of the present invention comprises: extracting platelet information from the patient's blood test results; calculating a platelet index from the platelet information; classifying the platelet index into one of the set first to fourth classes; and predicting the prognosis for pneumonia of the patient corresponding to the platelet index of the class by analyzing each of the classified classes.

Here, the platelet information is characterized in that it includes data of each of the average platelet volume and platelet count.

A prediction system for predicting the prognosis of a patient with community-acquired pneumonia according to an embodiment of the present invention includes: a test unit for performing a blood test for the patient; a calculation unit for calculating a platelet index by extracting platelet information of a patient from a blood test result by the test unit; a classification unit for classifying the platelet index into one of preset first to fourth classes; and a prediction unit that analyzes each class to predict the prognosis of pneumonia for a patient of the corresponding class.

The present invention can predict the prognosis of pneumonia using a minimum number of predictive variables, so that the prediction system can be configured simply in comparison with the prior art, and accordingly, the prognosis of pneumonia for a patient can be predicted in a short time.

In addition, the present invention provides the medical staff with the prediction result of the patient's pneumonia prognosis, thereby enabling customized treatment and treatment of the patient by the medical staff, thereby lowering the mortality rate of the patient due to pneumonia, thereby reducing the medical cost. .

1 is a diagram illustrating a prediction system for predicting the prognosis of a patient with community-acquired pneumonia according to an embodiment of the present invention.
2 is a diagram illustrating a method for predicting the prognosis of a patient with community-acquired pneumonia according to an embodiment of the present invention.
FIG. 3 is a diagram specifically illustrating a step of predicting the prognosis of FIG. 2 .
4 to 6 are embodiments by a method for predicting the prognosis of a patient with community-acquired pneumonia of the present invention.

Hereinafter, the configuration and operation of the embodiment of the present invention will be described with reference to the accompanying drawings.

It should be noted that the same components in the drawings are indicated by the same reference numbers and symbols as much as possible even though they are indicated in different drawings. In the following description of the present invention, if it is determined that a detailed description of a related well-known function or configuration may unnecessarily obscure the gist of the present invention, the detailed description thereof will be omitted. In addition, when a part "includes" a certain component, it means that other components may be further included, rather than excluding other components, unless otherwise stated.

In addition, the terms or words used in the present specification and claims should not be construed in the ordinary and dictionary meaning, and the inventors can properly define the concept of the term to describe their invention in the best way. Based on the principle, it should be interpreted as meaning and concept consistent with the technical idea of the present invention. Accordingly, the embodiments described in this specification and the configurations shown in the drawings are only preferred embodiments of the present invention, and do not represent all of the technical spirit of the present invention, so various equivalents that can be substituted for them at the time of the present application and modifications, and the scope of the present invention is not limited to the embodiments described below.

1 is a diagram illustrating a prediction system for predicting the prognosis of a patient with community-acquired pneumonia according to an embodiment of the present invention.

Referring to FIG. 1, the prediction system 100 of the present embodiment uses blood test information, such as platelet information, for a patient suspected of being infected with community-acquired pneumonia (CAP, hereinafter, pneumonia) who visited the hospital for pneumonia. It can be predicted by analyzing the prognosis for Here, the prognostic prediction for pneumonia may include prediction of the risk of pneumonia for the patient, the possibility of death due to pneumonia, the possibility of being admitted to the intensive care unit due to pneumonia, and the like.

To this end, the prediction system 100 of the present embodiment may include an inspection unit 110 , a calculation unit 120 , a classification unit 130 , and a prediction unit 140 . In addition, the prediction system 100 includes a first database 150 in which the inspection results of the inspection unit 110 are stored, and a first database 150 in which the results from the calculation unit 120 , the classification unit 130 and the prediction unit 140 are stored. 2 It may further include a database 160 .

The test unit 110 may perform various tests on a patient, and store the test results in the first DB 150 for each patient.

The examination performed in the examination unit 110 may include a questionnaire examination of the patient's age, gender, residence, etc., a physical examination such as measurement of the patient's body index, a blood examination of the patient, a radiological examination such as X-rays, and the like.

The calculation unit 120 may extract the patient's platelet information from the blood test result among the test results of the patient stored in the first DB 150 .

The platelet information may include data on a mean platelet volume (MPV) in blood and data on a platelet count (PC). Here, the average platelet volume may be extracted in a femto-liter (fL) unit, and the platelet count may be extracted in a micro-liter (μL) unit.

In addition, the calculation unit 120 may calculate the platelet index (MPR) of the patient from the extracted platelet information. The platelet index for each patient calculated by the calculation unit 120 may be stored in the second DB 160 .

The platelet index can be calculated as the ratio of the average platelet volume to the platelet count. For example, the platelet index may be calculated as a value obtained by dividing the average platelet volume by the number of platelets. Accordingly, the platelet index may be calculated as a value proportional to the average platelet volume and inversely proportional to the platelet count.

The classification unit 130 may classify the platelet index of the patient stored in the second DB 160 into a predetermined number of classes. The platelet index class classified by the classification unit 130 may be stored in the second DB 160 to match the previously stored platelet index for each patient.

The classification unit 130 sets four classes, for example, the first class (class 1) to the fourth class (class 4), based on the size range of the total platelet index stored in the second DB 160, and each Depending on the size of the patient's platelet count, it can be classified into a corresponding class. The size range of the total platelet index may be determined by a platelet index having a minimum value and a platelet index having a maximum value.

Here, the classification unit 130 may set each class so that each of the first class (class1) to the fourth class (class4) has the same size range within the size range of the total platelet index.

In addition, the platelet index classified into each class by the classification unit 130 may increase as the class increases, that is, from the first class (class1) to the fourth class (class4).

The prediction unit 140 may analyze each of the platelet index classes for each patient stored in the second DB 160 , and predict the prognosis of the patient's pneumonia based on the analysis result. The prediction unit 140 may predict the prognosis of pneumonia for each class of patients through logistic regression analysis for the entire platelet index class.

The pneumonia prognosis predicted by the prediction unit 140 may be stored by matching each patient in at least one of the first DB 150 and the second DB 160 . In addition, the predicted pneumonia prognosis may be transmitted to an external device, for example, a terminal possessed by medical personnel such as a doctor, and may be referred to for patient treatment by them.

As such, the prediction system 100 of the present embodiment can predict the prognosis, such as the risk of pneumonia patient, the possibility of survival, the possibility of admission to the intensive care unit based on the blood test result, that is, the platelet information of the patient among various test results of the patient.

Accordingly, the prediction system 100 of the present invention can predict the prognosis of pneumonia using the minimum predictive variables, and thus the prediction system can be configured simply compared to the prior art, and accordingly, the accurate prognosis of pneumonia for the patient in a short time can be predicted

In addition, the prediction system 100 of the present invention provides the medical staff with the prediction result of the patient's pneumonia prognosis, thereby enabling customized treatment and treatment of the patient by the medical staff, thereby lowering the mortality rate of the patient due to pneumonia, cost can be reduced.

2 is a diagram illustrating a method for predicting the prognosis of a patient with community-acquired pneumonia according to an embodiment of the present invention, and FIG. 3 is a diagram specifically illustrating the step of predicting the prognosis of FIG. 2 . In addition, Figures 4 to 6 are embodiments by the method of predicting the prognosis of the community-acquired pneumonia patient of the present invention.

Hereinafter, the prognosis prediction method of this embodiment will be described in detail with reference to the previously described prediction system 100 of FIG. 1 for convenience of explanation.

Referring to FIG. 2 , first, the test unit 110 of the prediction system 100 may perform various tests on a patient suspected of having pneumonia. Then, the calculation unit 120 may extract platelet information about the patient from the blood test during the test performed by the test unit 110 (S10).

The examination of the patient may include a questionnaire examination of the patient's age, gender, residence, etc., a physical examination such as measurement of the patient's body index, a blood test of the patient, a radiological examination such as X-rays, and the like. In addition, the platelet information may include data on the average platelet volume in blood and data on the number of platelets.

Next, the calculation unit 120 may calculate a platelet index from the extracted platelet information of the patient (S20). The calculated platelet index may be matched and stored for each patient.

The calculation unit 120 may calculate the platelet index according to the ratio of the average platelet volume to the platelet count. For example, the platelet index may be calculated according to a value obtained by dividing the average platelet volume by the platelet count. Accordingly, the platelet index may be calculated as a value proportional to the patient's average platelet volume and inversely proportional to the patient's platelet count.

Subsequently, the classification unit 130 may classify the calculated patient's platelet index into one of the first class (class1) to the fourth class (class4) (S30).

The classification unit 130 divides the platelet index of all patients stored in the second DB 160 into a size range, for example, a size range according to a minimum value and a maximum value into the same range, and divides the first class (class1) to the fourth class (class4) ) can be set.

And, the classification unit 130 may classify the corresponding platelet index into one of the first class (class1) to the fourth class (class4) based on the size of the platelet index of each patient. In this case, the size of the platelet index classified in the corresponding class may be relatively large as it goes from the first class (class1) to the fourth class (class4).

In addition, the platelet index class classified by the classification unit 130 may be stored for each patient in the second DB 160 to match the platelet index.

Next, the prediction unit 140 may predict the prognosis for the pneumonia patient through the analysis of the platelet index for each class (S40).

The prediction unit 140 may predict the prognosis, such as the risk of pneumonia patients, the possibility of survival, the possibility of being admitted to the intensive care unit through class-by-class analysis. Also, the prediction unit 140 may store the prediction result for each patient or transmit it to an external medical staff.

Referring to FIG. 3 , the prediction unit 140 may calculate an odds ratio for each platelet index class ( S110 ).

The odds ratio can be expressed as a ratio value between the total platelet index classes. For convenience, this embodiment will be described as an example calculated as a ratio of the remaining classes based on the first class (class 1).

In more detail with reference to FIG. 4 , first, the odds ratio for the first class (class 1) may be defined as 1. Then, the odds ratio for the second class may be calculated by dividing the odds ratio of the first class (class1) by the mortality ratio (mortality/survival) value for the second class (class 2) patient. In this case, in an unadjusted state of the odds ratio, the odds ratio for the second class (class2) may be calculated as 1.16.

In addition, the odds ratio for the third class (class 3) may be calculated by dividing the odds ratio of the first class (class1) by the death ratio value for the third class (class 3) patient. In this case, in an unadjusted state of the odds ratio, the odds ratio for the third class (class3) may be calculated as 1.48.

Then, the odds ratio for the fourth class (class 4) may be calculated by dividing the odds ratio of the first class (class1) by the death ratio value for the fourth class (class 4) patient. In this case, in an unadjusted state of the odds ratio, the odds ratio for the fourth class (class4) may be calculated as 2.93.

Next, the prediction unit 140 may adjust the pre-calculated odds ratio for each class according to the first variable (S120). Here, the prediction unit 140 may adjust the odds ratio for each class more than once according to the first variable.

The first variable may be a value corresponding to the patient's examination information stored in the first DB 150, for example, the patient's age or medical history. For example, the first variable may be at least one of values corresponding to the patient's age and an existing medical history such as hypertension. In addition, the first variable may be at least one of a value corresponding to a patient's white blood cell index (WBC), delta neutrophil index (DNI), erythrocyte sedimentation index (ESR), and procalcitonin.

Accordingly, as shown in FIG. 4 , when the odds ratio is adjusted by the first variable, the odds ratio of the second class (class2) is adjusted to 1.20 and calculated, and the odds ratio of the third class (class3) is calculated. It is calculated by adjusting it to 1.49, and the odds ratio of the fourth class (class4) can be calculated by adjusting it to 3.06.

At this time, the adjusted odds ratios of the second class (class2) and the third class (class3) are not significantly different from the unadjusted odds ratio, but the adjusted odds ratio of the fourth class (class4) is the unadjusted odds ratio. It can be calculated by greatly increasing in comparison with the rain.

On the other hand, p shown in FIG. 4 means statistical significance, and is a value indicating statistical significance when p<0.05.

Subsequently, the prediction unit 140 may predict the prognosis of pneumonia of the patient, ie, the prognosis according to the probability of survival and death due to pneumonia, based on the pre-calculated odds ratio for each class ( S130 ).

As described above, the odds ratio of each of the second to fourth classes may be a value indicating that the risk of death due to pneumonia is higher than that of the control class, that is, the first class. And, the odds ratio of each class may be calculated as a relatively large value as the class increases, that is, as the size of the platelet index increases.

Accordingly, the prediction unit 140 may analyze the risk of death of the patient due to pneumonia based on the odds ratio for each class, and predict the pneumonia prognosis for each patient accordingly.

More specifically, based on the unadjusted odds ratio shown in FIG. 4 , the prediction unit 140 determines that the risk of death of the patient classified into the second class (class2) is classified as the first class (class1). It can be analyzed to be approximately 1.16 times higher than that of the patient. In addition, it can be analyzed that the risk of death of the patient classified into the third class (class3) is approximately 1.48 times higher than that of the patient classified as the first class (class1). In addition, it can be analyzed that the risk of death of the patient classified into the fourth class (class4) is approximately 2.93 times higher than that of the patient classified as the first class (class1).

In addition, the prediction unit 140 is analyzed based on the adjusted odds ratio, the risk of death of the patient classified into the second class (class2) is approximately 1.20 times higher than that of the first class (class1), and the third class (class3) ), the risk of death of patients classified as 1.49 times higher, and the risk of death of patients classified as class 4 can be analyzed as being about 3.06 times higher.

Accordingly, the prediction unit 140 may predict the survival rate and mortality rate due to pneumonia of the patient for each class based on the analyzed odds ratio for each class. And, according to the prediction result, it is possible to output the cumulative short-term survival rate for each class for all patients. Here, the short-term survival rate may mean a 60-day survival rate of pneumonia patients.

That is, as shown in FIG. 5 , the prediction unit 140 has a survival rate of approximately 85.1% of patients classified into each of the first class (class1) to the third class (class3) out of 150 total patients, and the mortality rate is approximately It can be predicted at 14.9%. However, for patients classified into class 4, the survival rate is approximately 62.5%, and the mortality rate can be predicted to be approximately 37.5%. A high mortality rate can be expected.

Also, as shown in FIG. 6 , the prediction unit 140 outputs the cumulative short-term survival rate for the patient of the first class (class1) as approximately 0.89, and the cumulative short-term survival rate for the patient of the second class (class2) The output is approximately 0.87, and the cumulative short-term survival rate for the third class (class3) patient can be output as approximately 0.84. However, the cumulative short-term survival rate for the patient of the fourth class (class4) is output to be about 0.76, so it can be seen that the patient classified as the fourth class (class4) has a lower survival rate compared to the patients of other classes.

As such, the pneumonia prognosis prediction method of the present invention classifies the classes according to the size of the patient's platelet index, and can predict the pneumonia prognosis of the patient based on the results of analyzing the risk of pneumonia for each class.

At this time, the risk of pneumonia in patients whose platelet index is calculated with a relatively large value, that is, patients classified as class 4, is analyzed to be higher than that of patients in other classes. It can be predicted that the mortality rate by As shown in FIG. 5, the prediction method of the present invention shows similar results to the conventional Pneumonia Severity Prediction (PSI).

That is, the pneumonia prognosis prediction method of the present invention can predict the risk of pneumonia and mortality due to the pneumonia according to the size of the platelet index calculated according to the patient's blood test information.

In this case, the platelet index is calculated as the ratio of the patient's average platelet volume to the number of platelets, and it can be predicted that the higher the platelet index is, the greater the risk of pneumonia. From this, the present invention is a patient with a high risk of pneumonia when the average platelet volume is relatively large compared to other patients or the platelet count is relatively small compared to other patients during blood tests for patients suspected of having pneumonia. can be predicted that

Therefore, the method for predicting the prognosis of pneumonia of the present invention can show high predictive accuracy for the prognosis of pneumonia even using the minimum predictive variable, that is, the patient's blood test result, compared to the prior art, and the present invention is simple compared to the prior art. With the configuration, it is possible to predict the patient's accurate pneumonia prognosis in a short time, and through this, the optimal pneumonia treatment and treatment can be made to the patient.

100: prediction system 110: inspection unit
120: calculation unit 130: sorting unit
140: prediction unit 150: first database
160: second database

Claims (12)

In the prognosis prediction method using a prediction system to predict the prognosis of a patient with community-acquired pneumonia,
extracting platelet information from the patient's blood test results;
calculating a platelet index from the platelet information;
classifying the platelet index into one of the set first to fourth classes; and
Analyzing each of the classified classes and predicting the prognosis for pneumonia of the patient corresponding to the platelet index of the class,
The platelet information is a prognosis prediction method of a patient with community-acquired pneumonia, characterized in that it includes data of each of the average platelet volume and platelet count. According to claim 1,
The platelet index is a prognosis prediction method of a patient with community-acquired pneumonia, characterized in that calculated as a ratio of the average platelet volume to the platelet count. According to claim 1,
The platelet index is proportional to the average platelet volume, the prognosis prediction method of a patient with community-acquired pneumonia, characterized in that calculated as a value inversely proportional to the number of platelets. According to claim 1,
The step of classifying the platelet index,
setting the platelet index size range of all patients to the first to fourth classes having the same range, respectively; and
Comprising the step of classifying into one of the first class to the fourth class according to the size of the platelet index for each patient,
A method for predicting the prognosis of a patient with community-acquired pneumonia, characterized in that the size of the platelet index classified from the first class to the fourth class is relatively increased. According to claim 1,
Predicting the prognosis for pneumonia in the patient comprises:
calculating an odds ratio of each of the first to fourth classes;
adjusting the odds ratio based on a first variable; and
Predicting the prognosis by analyzing the risk of death for pneumonia for each patient of the first to fourth classes according to the adjusted odds ratio,
The prognosis is a method for predicting the prognosis of a patient with community-acquired pneumonia, characterized in that it includes the survival rate and mortality of the patient due to pneumonia. 6. The method of claim 5,
The step of calculating the odds ratio is
defining an odds ratio of the first class as 1; and
and calculating the odds ratio of each of the second class to the fourth class by dividing the odds ratio of the first class by the mortality ratio value for each patient of the second class to the fourth class. A method for predicting the prognosis of patients with acquired pneumonia. 7. The method of claim 6,
The prognosis prediction method of the community-acquired pneumonia patient, characterized in that the odds ratio has a relatively large value as it goes from the first class to the fourth class. 6. The method of claim 5,
The first variable is
A community characterized in that it is at least one of a value corresponding to the patient's age, a value corresponding to a previous medical history, a value corresponding to a leukocyte index (WBC), a delta neutrophil index (DNI), an erythrocyte sedimentation index (ESR), and a procalcitonin (procalcitonin) A method for predicting the prognosis of patients with acquired pneumonia. As a prediction system for a method for predicting the prognosis of a patient with community-acquired pneumonia according to any one of claims 1 to 8,
The prediction system is
a test unit that conducts a blood test for a patient;
a calculation unit for calculating a platelet index by extracting platelet information of a patient from a blood test result by the test unit;
a classification unit for classifying the platelet index into one of preset first to fourth classes; and
A prediction system comprising a prediction unit that analyzes each class to predict the prognosis of pneumonia for a patient of the corresponding class. 10. The method of claim 9,
The platelet information includes data of each of the average platelet volume and platelet count,
The platelet index is a prognosis prediction system for patients with community-acquired pneumonia, characterized in that calculated as a ratio of the average platelet volume to the platelet count. 10. The method of claim 9,
Further comprising a database for storing the platelet count of all patients,
The first class to the fourth class is a prognosis prediction system for patients with community-acquired pneumonia, characterized in that it is set to have the same range according to the size range of the platelet index of the entire patient. 10. The method of claim 9,
The prediction unit is
The prognosis of pneumonia for the patient of the corresponding class is predicted by calculating the odds ratio of each of the first to fourth classes,
The prognosis prediction system for community-acquired pneumonia patients, characterized in that the odds ratio is adjusted according to the first variable.

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