Device for grouping risks after NSCLC thoracoscope lobectomy based on SNII and readable storage medium thereof

Abstract

The invention discloses a device for grouping risks after NSCLC thoracoscope lobectomy based on SNII and a readable storage medium of the device, and belongs to the technical field of medical care informatics. The technical problem to be solved by the invention is how to group the postoperative risks of non-small cell lung cancer patients to be subjected to thoracoscope lobectomy on the basis of nutrition and inflammation states. The data processing device comprises a storage, a processor and a computer program stored in the storage, and the processor executes the computer program to achieve the following steps of obtaining a postoperative risk judgment threshold value, inputting data, comparing the data and outputting a result. And outputting the postoperative risk of the subject according to a comparison result. According to the method, the judgment threshold value and the judgment standard of the SNII are developed and verified by designing the research and development queue and the verification array, and compared with the existing nutrition and inflammation indexes, the SNII has the advantages of high comprehensiveness, accuracy, usability and guidance.

Description

Technical Field

[0001] This application belongs to the technical field of healthcare informatics, and particularly relates to a device for clustering the postoperative risks of NSCLC thoracoscopic lobectomy based on SNII and its readable storage medium. Background Art

[0002] In the treatment of non-small cell lung cancer (NSCLC), perioperative management is crucial for the prognosis of patients. Currently, although surgical techniques and perioperative care have advanced, the incidence of postoperative complications in NSCLC patients undergoing thoracoscopic surgery (VATS) lobectomy remains relatively high, which not only increases the pain and medical costs of patients, but may also affect the long-term survival rate of patients. In the prior art, although there are some indicators based on routine blood tests used to evaluate the nutritional and inflammatory status of patients, such as albumin / globulin ratio (AGR), lymphocyte / monocyte ratio (LMR), etc., the effectiveness of these indicators in predicting postoperative complications and postoperative recovery is limited. They often can only reflect a single nutritional or inflammatory indicator and cannot comprehensively evaluate the nutritional and inflammatory status of patients, so there are certain limitations in practical applications.

[0003] When the existing nutritional and inflammatory risk assessment methods predict postoperative complications and recovery, the independent predictive value is relatively weak, and they cannot accurately identify high-risk patients, thus unable to provide effective guidance for clinicians to develop personalized postoperative management plans. In clinical practice, there is an urgent need for a more accurate, comprehensive, and easy-to-use tool to evaluate the nutritional and inflammatory status of NSCLC patients in order to take intervention measures in advance and improve the postoperative outcomes of patients. Summary of the Invention

[0004] The technical problem to be solved by this application is how to cluster the postoperative risks of non-small cell lung cancer patients who are to undergo thoracoscopic lobectomy based on their nutritional and inflammatory status.

[0005] To solve the above technical problem, this application provides a data processing device, including a memory, a processor, and a computer program stored on the memory. The processor executes the computer program to implement the following steps:

[0006] (A1) Obtaining the determination threshold of postoperative risk: Receiving the systemic nutrition-inflammation index (SNII) and postoperative risk data of the research cohort, and obtaining the determination threshold (cut-off) of the postoperative risk of NSCLC patients undergoing thoracoscopic lobectomy based on the systemic nutrition-inflammation index and the postoperative risk data; the systemic nutrition-inflammation index = total cholesterol content × total lymphocyte count / total monocyte count;

[0007] (A2) Data input: Input sample data, and the sample data may be the systemic nutrition-inflammation index of a subject.

[0008] (A3) Data comparison and result output: Compare the sample data with the determination threshold, and output the postoperative risk of the subject according to the comparison result.

[0009] Further, in the data processing device, outputting the postoperative risk of the subject according to the comparison result may be: The postoperative risk of a subject with a systemic nutrition-inflammation index lower than the determination threshold is higher than that of a subject with a systemic nutrition-inflammation index higher than the determination threshold.

[0010] That is: The postoperative risk prediction of a subject with a systemic nutrition-inflammation index lower than the determination threshold is a high risk. And / or the postoperative risk prediction of a subject with a systemic nutrition-inflammation index higher than the determination threshold is a medium to low risk.

[0011] In this application, the English name corresponding to the systemic nutrition-inflammation index is systemic nutrition-inflammation index, abbreviated as SNII. In this application, the terms "systemic nutrition-inflammation index", "systemic nutrition-inflammation index" and "SNII" have the same meaning.

[0012] This application also provides a device for postoperative risk grouping or assisting in grouping of a subject, and the device may include the following modules:

[0013] (B1) Data receiving and analysis processing module: Used to receive the systemic nutrition-inflammation index and postoperative risk data of a research cohort, and obtain the determination threshold of the postoperative risk of non-small cell lung cancer patients treated by thoracoscopic lobectomy according to the systemic nutrition-inflammation index and postoperative risk data; the systemic nutrition-inflammation index = total cholesterol content × total lymphocyte count / total monocyte count;

[0014] (B2) Data input module: Used to input sample data, and the sample data may be the systemic nutrition-inflammation index of a subject.

[0015] (B3) Data comparison and result output module: Used to compare the sample data with the determination threshold, and group the postoperative risk of the subject according to the comparison result.

[0016] Further, in the described device, the subjects can be grouped according to the following criteria based on the postoperative risk: The postoperative risk of subjects with a systemic nutritional inflammation index lower than the determination threshold is higher than that of subjects with a systemic nutritional inflammation index higher than the determination threshold.

[0017] That is, if the systemic nutritional inflammation index of a subject is lower than the determination threshold, the subject is or is a candidate for a high postoperative risk individual; if the systemic nutritional inflammation index of a subject is higher than the determination threshold, the subject is or is a candidate for a medium to low postoperative risk individual.

[0018] Further, in the described data processing device and / or the described device, the determination threshold can be determined based on the distribution characteristics of the systemic nutritional inflammation index in the research cohort.

[0019] Further, the determination threshold can be the tertile of the systemic nutritional inflammation index. Further, the determination threshold can be the lower tertile of the systemic nutritional inflammation index.

[0020] The research cohort can be a group composed of patients with known postoperative risks. The patients can be non-small cell lung cancer patients who have undergone thoracoscopic lobectomy. The research cohort can include at least 500 patients.

[0021] In this application, the data processing device can be a computer device.

[0022] In this application, the postoperative period can be after thoracoscopic lobectomy. The thoracoscopic lobectomy can be performed in accordance with the "Clinical Practice Guidelines for Thoracoscopic Lobectomy in China". The "Clinical Practice Guidelines for Thoracoscopic Lobectomy in China" was published in the Chinese Medical Journal, 2018, 98(47): 3832 - 3841. DOI: 10.3760 / cma.j.issn.0376 - 2491.2018.47.005.

[0023] The postoperative risk can be reflected by the occurrence of complications and / or the postoperative hospital stay. The result of the occurrence of complications can be the occurrence of complications or no complications. The unit of measurement for the postoperative hospital stay is days.

[0024] The systemic nutritional inflammation index can be the systemic nutritional inflammation index within one week before surgery.

[0025] In this application, the total cholesterol content can be the total cholesterol content in peripheral blood. The total lymphocyte count and the total monocyte count can both be the total lymphocyte count and the total monocyte count in peripheral blood.

[0026] In this application, the unit of the total cholesterol content can be: mmol / L. The counting units of the total lymphocyte count and the total monocyte count can be ×109 per / L.

[0027] In some embodiments of the present application, the determination threshold is 15.6.

[0028] In the present application, the subject may be a non-small cell lung cancer patient who is to undergo thoracoscopic lobectomy.

[0029] The present application also provides a computer program product, including a computer program, which when executed by a processor implements the above steps (A1) to (A3) and / or, which when executed by a processor implements the above steps (B1) to (B3).

[0030] The present application also provides a computer-readable storage medium, on which a computer program is stored, which when executed by a processor implements the steps (A1) to (A3) and / or, which when executed by a processor implements the steps (B1) to (B3).

[0031] The present application also provides the application of the above systemic nutritional inflammation index in clustering or assisting in clustering subjects. The application may not include the step of obtaining a biological sample from an animal body. The application may not take a living human body or animal body as an object, but only take data as an object. The application may be an information processing method in which all steps are implemented by a device such as a computer.

[0032] The present application also provides the application of a substance for detecting the above systemic nutritional inflammation index in the preparation of a product for clustering or assisting in clustering subjects. The subject may be a non-small cell lung cancer patient who plans to undergo thoracoscopic lobectomy. The product may be a reagent, a kit, an instrument, a device, and / or an apparatus.

[0033] The present application also provides the application of the above systemic nutritional inflammation index in predicting or assisting in predicting the risk after thoracoscopic lobectomy of non-small cell lung cancer patients. The application may not include the step of obtaining a biological sample from an animal body. The application may not take a living human body or animal body as an object, but only take data as an object. The application may be an information processing method in which all steps are implemented by a device such as a computer.

[0034] The present application also provides the application of a substance for detecting the above systemic nutritional inflammation index in the preparation of a product for predicting or assisting in predicting the risk after thoracoscopic lobectomy of non-small cell lung cancer patients. The product may be a reagent, a kit, an instrument, a device, and / or an apparatus.

[0035] The present application also provides a method for postoperative risk grouping or auxiliary grouping of a subject. The method may include the following steps: comparing sample data with a determination threshold, and determining the grouping of the subject according to the comparison result; the sample data may be the systemic nutritional inflammation index of the subject, and the systemic nutritional inflammation index = total cholesterol content × total lymphocyte count / total monocyte count. The method may not include the step of obtaining a biological sample from an animal body. The method may not take a living human body or animal body as an object, but only take data as an object. The method may be an information processing method in which all steps are implemented by a device such as a computer.

[0036] Further, the determination threshold may be obtained according to the following method: receiving the systemic nutritional inflammation index and postoperative risk data of a research cohort, analyzing the systemic nutritional inflammation index and postoperative risk data, and obtaining a determination threshold for the postoperative risk of non-small cell lung cancer patients treated by thoracoscopic lobectomy.

[0037] Further, the postoperative risk of the subject output according to the comparison result may be determined according to the following criteria: the postoperative risk of a subject with a systemic nutritional inflammation index lower than the determination threshold is higher than that of a subject with a systemic nutritional inflammation index higher than the determination threshold.

[0038] That is, if the systemic nutritional inflammation index of the subject is lower than the determination threshold, the subject is or is a candidate for a postoperative high-risk individual; and / or

[0039] If the systemic nutritional inflammation index of the subject is higher than the determination threshold, the subject is or is a candidate for a postoperative medium-low risk individual.

[0040] In the present application, the subject may be a non-small cell lung cancer patient scheduled for thoracoscopic lobectomy.

[0041] In the present application, the terms "grouping" and "stratification" have the same meaning. Grouping or stratification means differentiating subjects through "grouping characteristics" or "stratification characteristics". In the present application, the "grouping characteristics" or "stratification characteristics" may be the systemic nutritional inflammation index.

[0042] The beneficial technical effects achieved by the present application are as follows:

[0043] The present application designs a research cohort and a validation cohort to develop and validate the determination threshold and determination criteria of SNII. Compared with the existing nutrition and inflammation indexes, SNII has the following advantages:

[0044] (1) Comprehensiveness: SNII comprehensively considers multiple indicators closely related to nutrition and inflammation, such as total cholesterol content, total lymphocyte count, and total monocyte count, and can more comprehensively reflect the nutritional and inflammatory status of patients. Compared with existing single indicators or simple ratio indicators, it can more accurately evaluate the postoperative risk of patients.

[0045] (2) Accuracy: Verified by a large amount of clinical data, SNII shows stronger predictive ability in predicting the incidence, severity, and length of hospital stay of postoperative complications, can more accurately identify high-risk patients, and provide more reliable decision-making basis for clinicians.

[0046] (3) Ease of use: The calculation method of SNII is simple, based on routine preoperative blood test indicators, without additional detection means or complex calculation processes, and is easy to promote and apply in clinical practice.

[0047] (4) Strong guidance: According to the risk classification of SNII, it can provide clear postoperative management suggestions for clinicians, help formulate personalized treatment plans, improve the utilization efficiency of medical resources, and improve the prognosis of patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 Flow chart of the development and application of the systemic nutrition inflammation index in this application.

[0049] Figure 2 Is the comparison result of SNII and existing nutrition and inflammation indicators.

[0050] Figure 3 Is the result of the comprehensive subgroup analysis of the SNII classification system. DETAILED DESCRIPTION OF THE INVENTION

[0051] Terms in this application:

[0052] In this application, when used in a list of two or more items, the term "and / or" means that any one of the listed items can be used alone or in combination with any one or more of the listed items. For example, the expression "A and / or B" is intended to mean either A or B or both, i.e., A alone, B alone, or a combination of A and B. The expression "A, B and / or C" means A alone, B alone, C alone, a combination of A and B, a combination of A and C, a combination of B and C, or a combination of A, B and C.

[0053] SNII: Systemic Nutrition-Inflammation Index. The calculation formula is: SNII = total cholesterol content × total lymphocyte count / total monocyte count; where the unit of total cholesterol content is mmol / L, and the units of total lymphocyte count and total monocyte count are ×10 9 / L.

[0054] PNI: Prognostic Nutritional Index; The prognostic nutritional index is calculated based on the serum albumin level and lymphocyte count, and the calculation formula is: PNI = serum albumin (g / L) + 5 × total number of peripheral blood lymphocytes (×10 9 / L).

[0055] CONUT: Controlling Nutritional Status Score; The CONUT score is calculated from the patient's serum albumin level, peripheral blood lymphocyte count, and total cholesterol level, and values are assigned to each indicator according to laboratory test results. ① Serum albumin level ≥ 35 g / L is 0 points, 30 to 34 g / L is 2 points, 25 to 29 g / L is 4 points, < 25 g / L is 6 points; ② Lymphocytes > 1.6 × 10 9 / L is 0 points, (1.2 to 1.6) × 10 9 / L is 1 point, (0.8 to 1.1) × 10 9 / L is 2 points, < 0.8 × 10 9 / L is 3 points; ③ Total cholesterol ≥ 180 mg / dl is 0 points, 140 to 179 mg / dl is 1 point, 100 to 139 mg / dl is 2 points, < 100 mg / dl is 3 points. The scores of the above 3 indicators are added to get the total score (range 0 to 12 points). The higher the total score, the worse the nutritional status. 0 to 1 point indicates normal nutrition, 2 to 4 points indicates mild malnutrition, 5 to 8 points indicates moderate malnutrition, and 9 to 12 points indicates severe malnutrition.

[0056] AGR: Albumin-to-Globulin Ratio.

[0057] NLR: Neutrophil-to-Lymphocyte Ratio.

[0058] PLR: Platelet-to-Lymphocyte Ratio.

[0059] NPR: Neutrophil-to-Platelet Ratio.

[0060] LMR: Lymphocyte-to-Monocyte Ratio.

[0061] SIRI: Systemic Inflammation Response Index; SIRI is an indicator reflecting the systemic inflammatory response, which is obtained by calculating the values of white blood cell count, hematocrit, and plasma C-reactive protein. The calculation formula of SIRI is: SIRI = (WBC × HCT) / CRP, where WBC represents white blood cell count, HCT represents hematocrit, and CRP represents plasma C-reactive protein.

[0062] SII: Systemic Immune-Inflammation Index; SII is an inflammation-related index calculated from blood test indicators, usually including neutrophil, lymphocyte, and platelet counts. Its general calculation formula is: SII = (neutrophil count × platelet count) / lymphocyte count. This index is applied in evaluating the systemic inflammatory state and predicting the prognosis of certain diseases, such as cancer, cardiovascular diseases, and infectious diseases.

[0063] GNRI: Geriatric Nutritional Risk Index; The Geriatric Nutritional Risk Index (GNRI) is an indicator used to evaluate the nutritional status of the elderly, which was proposed by Japanese scholar Takashi Yamada in 2002. The calculation formula of GNRI is as follows: GNRI = [1.489 × serum albumin (g / L)] + [41.7 × (body weight (kg) / ideal body weight (kg))];

[0064] Among them, the calculation formula of ideal body weight is:

[0065] Ideal body weight for men (kg) = 22 × height (m) × height (m);

[0066] Ideal body weight for women (kg) = 21 × height (m) × height (m).

[0067] ALI: Advanced Lung Cancer Inflammation Index; The advanced lung cancer inflammatory response index is calculated based on NLR (neutrophil-to-lymphocyte ratio) according to body mass index (BMI) × albumin (ALB) / NLR. Among them, BMI = body weight ÷ square of height. (Body weight unit: kilogram; height unit: meter).

[0068] CCI: Comprehensive Complication Index.

[0069] FEV1: Forced Expiratory Volume in 1 second.

[0070] FVC: Forced Vital Capacity.

[0071] DLCO: Diffusing Capacity for Carbon Monoxide.

[0072] In this application, the counting unit of various types of cells, "×10 9 / L", has the same meaning as "×10 9 cells / L".

[0073] II. Technical Solutions Provided by This Application

[0074] In the prior art, there is a lack of a tool that can comprehensively and accurately evaluate the postoperative risks of patients undergoing thoracoscopic lobectomy for non-small cell lung cancer, resulting in the inability to effectively identify high-risk patients, and thus affecting the accuracy and effectiveness of postoperative management. This application aims to solve this problem by providing a systematic nutritional inflammation index for predicting short-term postoperative outcomes of lung cancer based on blood biochemical indicators, which is used to accurately evaluate the postoperative risks of NSCLC patients, so as to provide more powerful decision-making support for clinicians and improve the postoperative outcomes of patients.

[0075] To overcome the defects of the prior art, this project developed and applied a systematic nutritional inflammation index through a multi-center research cohort and an external independent validation cohort ( Figure 1 ).

[0076] In the research cohort, data on blood biochemical indicators within one week before routine surgery were collected, including total protein, serum albumin, serum globulin, total cholesterol content, hemoglobin, total neutrophils, total lymphocytes, total monocytes, and total platelets in peripheral blood. The predictive value of these basic nutritional and inflammatory items for the primary endpoint was evaluated through ROC curve and multivariate regression analysis. These analyses identified total cholesterol content, total lymphocyte count, and total monocyte count as key predictive parameters. Among them, total lymphocyte count and total cholesterol content were negatively correlated with the total complication rate and the prolongation of postoperative hospital stay, while total monocyte count was correlated with increased complications and prolonged postoperative hospital stay. Therefore, by combining these three key predictive parameters, we developed a new nutritional and inflammatory index - SNII: total cholesterol content (mmol / L) × total lymphocyte count (×10 9 / L) / total monocyte count (×10 9 / L). The median SNII in the development cohort was 19.1 (IQR: 13.9 - 25.6). By convention, we used the tertiles of SNII to classify the postoperative risk as low (>23.1), medium (15.6 - 23.1), and high (<15.6), thus establishing a new classification system).

[0077] Subsequently, we validated the predictive effect of the SNII classification system on postoperative outcomes in an external validation cohort.

[0078] Subsequently, after combining the development and validation cohorts, the predictive value of SNII for postoperative endpoints was compared with established nutritional and inflammatory indicators known to have moderate predictive value.

[0079] To further validate the applicability of SNII in different patient populations, subgroup analyses were performed on the patients in the development and validation cohorts according to clinical characteristics such as age, sex, smoking history, comorbidity index, body mass index (BMI), cancer histology, and pathological stage.

[0080] In summary, in the specific application implementation, first, preoperative routine blood biochemical tests are performed on patients to obtain the values of total cholesterol content, total lymphocyte count, and total monocyte count. Then, the SNII value of the patient is calculated according to the above formula, and corresponding postoperative management strategies are formulated according to the level it belongs to. For high-risk patients, more aggressive intervention measures are recommended, such as optimizing the surgical plan, strengthening postoperative monitoring and care, etc., to reduce the incidence and severity of complications, shorten the hospital stay, and improve the quality of postoperative recovery of patients.

[0081] Beneficial effects compared with the prior art:

[0082] The development cohort included 1497 patients. The predictive value of previously established nutritional and inflammatory indicators for postoperative complications and hospital stay was determined by ROC curve and multivariable regression analysis (see Tables 1, 2, and 3 for details). Among them, LMR, SIRI, COUNT, NLR, and AGR - especially LMR and SIRI - showed moderate predictive value for these endpoints in the ROC curve and univariable regression analysis. However, after adjusting for demographic and surgical parameters, none of these established nutritional and inflammatory indicators were found to be independently associated with the primary endpoint. The SNII we established showed the best predictive AUC value for postoperative complications and postoperative hospital stay in the development cohort (Table 1), and still had independent predictive value for postoperative outcomes after adjusting for demographic and surgical parameters (Table 4).

[0083] A total of 505 NSCLC patients were included in the external validation cohort. The overall complication rate of patients with high postoperative risk identified by SNII was significantly higher, with an increase in Clavien–Dindo grade and CCI. This difference remained after propensity score matching. High-risk patients also had an extended duration of chest tube drainage, increased drainage volume, and prolonged hospital stay, and the differences in chest tube drainage duration and hospital stay remained significant after matching. In addition, patients with high postoperative risk identified by SNII also showed a trend of increased unplanned readmission within 30 days and 90-day mortality. See Table 5 for details.

[0084] After combining the development and validation cohorts, we compared the predictive value of SNII for postoperative endpoints with established nutritional and inflammatory indicators (such as LMR, SIRI, COUNT, NLR, and AGR) that are known to have moderate predictive value ( Figure 2 ). The ROC curve indicated that compared with these established indicators, SNII had stronger predictive ability for the occurrence of overall complications, major complications, cardiac complications, pulmonary complications, unplanned readmission within 30 days, and 90-day mortality. In addition, the time-dependent ROC curve showed that the association between SNII and postoperative hospital stay (at the 8-day time point) and chest tube drainage duration (at the 6-day time point) was stronger than that of other nutritional and inflammatory indicators.

[0085] After combining the development and validation cohorts, high postoperative risk identified by SNII was associated with an increased risk of complications and prolonged hospital stay in most subgroups (defined by age, gender, smoking history, comorbidity index, specific comorbidities, body mass index (BMI), cancer histology, and pathological stage) ( Figure 3 ).

[0086] In summary, the technical solution provided by this application has the following beneficial effects:

[0087] (1) Comprehensiveness: SNII comprehensively considers multiple indicators closely related to nutrition and inflammation, such as total cholesterol content, total lymphocyte count, and total monocyte count, and can more comprehensively reflect the patient's systemic nutritional and inflammatory status. Compared with existing single indicators or simple ratio indicators, it can more accurately evaluate the patient's postoperative risk.

[0088] (2) Accuracy: Verified by a large amount of clinical data, SNII shows stronger predictive ability in predicting the incidence, severity of postoperative complications, and hospital stay, etc., and can more accurately identify high-risk patients, providing a more reliable decision-making basis for clinicians.

[0089] (3) Ease of use: The calculation method of SNII is simple, based on routine preoperative blood test indicators, without the need for additional detection means or complex calculation processes, and is easy to promote and apply in clinical practice.

[0090] (4) Strong guidance: According to the risk classification of SNII, it can provide clear postoperative management suggestions for clinicians, help formulate personalized treatment plans, improve the utilization efficiency of medical resources, and improve the prognosis of patients.

[0091] The following further describes the present application in combination with specific implementation manners. The provided embodiments are only for clarifying the present application, rather than limiting the scope of the present application. The following provided embodiments can be used as a guide for those of ordinary skill in the art to make further improvements, and do not constitute any limitation to the present application in any way.

[0092] The experimental methods in the following embodiments, unless otherwise specified, are all conventional methods, carried out according to the techniques or conditions described in the literature in this field or according to the product instructions. The materials, reagents, etc. used in the following embodiments, unless otherwise specified, can all be obtained from commercial channels.

[0093] In the quantitative tests in the following embodiments, unless otherwise specified, three replicates are set, and the results are averaged.

[0094] Example 1. Development, determination of threshold and clustering of systemic nutrition-inflammation index

[0095] The present application develops and applies the systemic nutrition-inflammation index (SNII) through a multi-center research cohort and an external independent validation cohort (the process is shown in Figure 1 ).

[0096] As Figure 1 shown, a total of 1497 patients were collected as the research cohort after denoising the patients in Cohort 1 and Cohort 2 according to the exclusion criteria; a total of 505 patients were collected as the validation cohort after denoising the patients in Cohort 3 according to the exclusion criteria. Specifically as follows:

[0097] Cohort 1: Non-small cell lung cancer patients (N = 1005) who underwent thoracoscopic lobectomy at the First Affiliated Hospital of Zhengzhou University from 2018 to 2020;

[0098] Cohort 2: Non-small cell lung cancer patients (N = 845) who underwent thoracoscopic lobectomy at Peking University People's Hospital from 2014 to 2016;

[0099] Cohort 3: Non-small cell lung cancer patients (N = 665) who underwent thoracoscopic lobectomy at Henan Cancer Hospital from 2017 to 2018;

[0100] If a patient meets any of the following exclusion criteria, they will be excluded. The exclusion criteria are as follows:

[0101] 1. Bilobectomy or sleeve resection;

[0102] 2. Received anti-cancer treatment before surgery;

[0103] 3. Non-radical surgery;

[0104] 4. Active infection;

[0105] 5. Had thoracoabdominal surgery within 1 year;

[0106] 6. Had a history of cancer within 5 years;

[0107] 7. Special comorbidities;

[0108] 8. Lack of required data.

[0109] 1.1. Development of the systemic nutritional inflammation index

[0110] In the R & D cohort, data on routine preoperative blood (peripheral blood) biochemical indexes, overall complications, and postoperative hospital stay of patients were collected. The correlations between preoperative nutritional and inflammatory indexes and postoperative complications and recovery endpoints were analyzed. Based on the results of the correlation analysis, evaluation and grading criteria for the systemic nutritional inflammation index were established. The routine preoperative blood biochemical index data within one week included total protein content (g / L), serum albumin content (g / L), serum globulin content (g / L), total cholesterol content (mmol / L), hemoglobin content (g / L), total number of neutrophils in peripheral blood (×10 9 / L), total number of lymphocytes (×10 9 / L), total number of monocytes (×10 9 / L), and total number of platelets (×10 9 / L).

[0111] The research cohort included 1,497 NSCLC patients. The predictive values of baseline nutritional and inflammatory indicators for the primary endpoints (overall complications and postoperative hospital stay) were evaluated by ROC curve and multivariable regression analysis, and the results are shown in Tables 1 and 3. These analyses identified total cholesterol content, total lymphocyte count, and total monocyte count as key predictive parameters. Notably, total lymphocyte count and total monocyte count are parameters widely used for established nutritional and inflammatory indicators, while total cholesterol content has not been fully recognized or utilized. In addition, total lymphocyte count and total cholesterol content were negatively correlated with the incidence of total complications and the prolongation of postoperative hospital stay, while total monocyte count was associated with increased complications and prolonged postoperative hospital stay. Therefore, we developed a new systemic nutritional inflammation index, SNII = total cholesterol content × total lymphocyte count / total monocyte count, by combining these three key predictive parameters using variable division; where the unit of total cholesterol content is mmol / L, and the unit of total lymphocyte count and / or total monocyte count is: ×10 9 / L.

[0112] The results of overall complications included the occurrence and non-occurrence of complications. The result of overall complications was considered to be the occurrence of complications if any of the following complications occurred after surgery. If none of the following complications occurred after surgery, the result of overall complications was the non-occurrence of complications. Surgical complications were mainly divided into cardiovascular system, respiratory system, gastrointestinal system, and / or other complications. Cardiovascular system complications included arrhythmias (such as supraventricular arrhythmias), myocardial ischemia / infarction, and / or heart failure. Respiratory system complications included pneumonia, acute respiratory distress syndrome (ARDS), respiratory failure, bronchopleural fistula, bronchoesophageal fistula, and / or atelectasis. Gastrointestinal system complications included peptic ulcer, duodenal perforation, gastroparesis, intestinal obstruction, and / or liver function injury. Other complications included hypoalbuminemia, chylothorax, wound infection, and / or thrombosis, etc.

[0113] The upper and lower tertiles of SNII in the research cohort were 23.1 and 15.6, respectively. By convention, we used the tertiles of SNII to divide the postoperative risk into low (>23.1), medium (15.6 - 23.1), and high (<15.6), thus establishing a new systemic nutritional inflammation classification system.

[0114] The effectiveness of SNII was verified by comparing postoperative endpoint indicators such as the incidence of postoperative complications, severity of complications, duration of chest drainage tube, and hospital stay in patients in different risk groups. The results showed that the incidence and severity of complications in the high-risk group were significantly higher than those in the low-risk and medium-risk groups, and the hospital stay was longer, indicating that this index could effectively predict the postoperative risk of patients. The results are shown in Table 4.

[0115] Table 1: Predictive value of nutrition and inflammation indicators for perioperative outcomes in the R & D cohort (n = 1497).

[0116]

[0117] 1 The receiver operating characteristic (ROC) curve was used to evaluate the predictive value of nutrition and inflammation indicators for the incidence of postoperative complications.

[0118] 2 The time-dependent ROC curve was used to evaluate the predictive value of nutrition and inflammation indicators for the length of postoperative hospital stay. These analyses reported the area under the curve (AUC) values. The specific time points for the analysis were 6 days, 8 days, and 12 days after surgery.

[0119] Logistic regression analyses and Cox’s proportional hazards regression analyses were used to evaluate the correlation between clinicopathological characteristics and the length of postoperative hospital stay. To evaluate and determine the predictive value of previous nutrition and inflammation indicators for postoperative complications and hospital stay (see Table 2 for details).

[0120] Table 2: Predictive value of clinicopathological data of non-small cell lung cancer patients in the R & D cohort for perioperative outcomes (n = 1497).

[0121]

[0122]

[0123]

[0124] Variables with a P value less than 0.10 in the univariate analysis were selected into the multivariate regression model and analyzed using the backward method. The results of the multivariate analysis included demographic and surgical parameters, and the systemic nutrition and inflammation index (SNII) was included in the multivariate regression model in the analysis.

[0125] 1 Logistic regression analyses were used to evaluate the correlation between clinicopathological characteristics and the overall incidence of complications. The results were expressed as odds ratios (ORs) and their 95% confidence intervals (CIs).

[0126] 2Cox's proportional hazards regression analyses were used to evaluate the correlation between clinicopathological features and postoperative hospital stay. The results were expressed as hazard ratios (HRs) and their 95% confidence intervals (CIs).

[0127] 3 All systemic nutrition and inflammation indicators were separately included in the multivariate analysis.

[0128] Table 3: Association analysis of basic nutrition, inflammation parameters and perioperative outcomes in patients with non-small cell lung cancer in the R & D cohort (n = 1497).

[0129]

[0130] 1 In the multivariate analysis, all nutrition and inflammation parameters were simultaneously included in the regression model, and the backward conditional method was used for variable selection.

[0131] 2 Logistic regression analyses were performed to evaluate the correlation between nutrition and inflammation parameters and the overall complication rate. The results were expressed as odds ratios (ORs) and their 95% confidence intervals (CIs).

[0132] 3 Cox's proportional hazards regression analyses were performed to evaluate the correlation between nutrition and inflammation parameters and postoperative hospital stay. The results were expressed as hazard ratios (HRs) and their 95% confidence intervals (CIs).

[0133] Table 4: Multivariate analysis of systemic nutrition inflammation indicators and postoperative outcomes in the R & D cohort (n = 1497).

[0134]

[0135] Multivariate analysis was performed to determine the independent association between the systemic nutritional inflammation index and postoperative endpoints while controlling for confounding clinicopathological parameters. The models varied according to the different clinicopathological characteristics included, as follows: Model 1 adjusted for demographic data (age, gender, smoking history, Charlson comorbidity index, and lung function); Model 2 further incorporated adjustments for surgical data (surgical time, estimated blood loss, and lymph node dissection); and Model 3 encompassed adjustments for demographic and surgical data as well as cancer characteristics (tumor location, histological type, and cancer stage).

[0136] 1 Multivariate logistic regression analysis was performed using the backward conditional method, and the results were reported as odds ratios (ORs) and 95% confidence intervals (CIs).

[0137] 2 Multivariate Cox proportional hazards regression analysis was performed using the backward conditional method, and the results were reported as hazard ratios (HRs) and 95% confidence intervals (CIs).

[0138] 1.2, Validation of the systemic nutritional inflammation index

[0139] The validation cohort consisted of 505 NSCLC patients. The median preoperative SNII was 18.4 (IQR: 12.0 - 25.4). Using the established SNII classification, the systemic nutritional inflammation index was divided into low, medium, and high grades, with 163 (32.3%), 163 (32.3%), and 179 (35.4%) patients, respectively (Table 5). After 114 pairs of propensity score matching, no significant differences in clinicopathological characteristics were observed between the high-risk group and the low- and medium-risk groups. Compared with low- and medium-risk patients, the high postoperative risk patients identified by SNII had increased surgical time and estimated blood loss, but there were no differences after propensity score matching. The overall complication rate in the high-risk group was significantly higher, with an increase in Clavien–Dindo grade and CCI, and this difference remained after propensity score matching. High-risk patients also had an extended duration of chest tube drainage, increased drainage volume, and prolonged hospital stay, and the differences in chest tube drainage duration and hospital stay remained significant after matching, but the difference in drainage volume was not significant. In addition, patients at high SNII risk also showed a trend towards increased unplanned readmission within 30 days and 90-day mortality. The results are shown in Table 5.

[0140] Based on the above development and validation process, this application combines the medium risk (15.6 - 23.1) and low risk (>23.1) in the determination thresholds initially determined by the tertiles of SNII into a medium - low risk (>15.6). Taking SNII = 15.6 as the determination threshold, a standard and method for clustering non - small cell lung cancer patients who are to undergo thoracoscopic lobectomy based on the postoperative risk evaluated by SNII are established. Specifically: The postoperative risk of subjects with SNII lower than the determination threshold is higher than that of subjects with SNII higher than the determination threshold. That is, if the subject's SNII ≥ 15.6, then the subject has a medium - low risk of postoperative thoracoscopic lobectomy; if the subject's SNII < 15.6, then the subject has a high risk of postoperative thoracoscopic lobectomy.

[0141] Table 5: Characteristics of clinicopathological data grouped by systemic nutritional inflammation index in the validation cohort (n = 505)..

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[0145] Data are presented as mean ± standard deviation, number (percentage), or median (interquartile range). Inter - group differences were evaluated by analysis of variance (ANOVA), Pearson chi - square test, Fisher's exact test, Mann–Whitney U test, or Kruskal–Wallis test. In propensity score matching, the postoperative risk classified according to the systemic nutritional inflammation index was used as the intervention index, and the confounding covariates included age, sex, smoking history, comorbidity index, lung function, tumor location, cancer histological type, and pathological stage. Pairing of low - to - medium risk and high risk was performed in a 1:1 ratio by the nearest propensity score, with a caliper width of 0.2 standard deviations.

[0146] Table 6: Data after propensity score matching of Table 5 in the validation cohort (228 cases)

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[0154] Example 2. Comparison of SNII with Existing Nutritional and Inflammatory Markers

[0155] After combining the development and validation cohorts, we compared the predictive value of SNII with previous nutritional and inflammatory markers (LMR, SIRI, COUNT, NLR, and AGR) for postoperative endpoints. The ROC curves showed that SNII had a stronger predictive ability for the occurrence of total complications, major complications, cardiac complications, pulmonary complications, unplanned readmission within 30 days, and 90-day mortality compared with these previous markers. In addition, the time-dependent ROC curves showed that SNII had a stronger association with postoperative length of stay (at the 8-day time point) and duration of chest tube drainage (at the 6-day time point) than other nutritional and inflammatory markers. The results are shown in Figure 2 。

[0156] Example 3. Comprehensive Subgroup Analysis of the SNII Classification System

[0157] After combining the development and validation cohorts, the high postoperative risk identified by SNII was associated with an increased risk of complications and a prolonged length of stay in most subgroups (defined by age, sex, smoking history, comorbidity index, specific comorbidities, body mass index (BMI), cancer histology, and pathological stage). The results are shown in Figure 3 。

[0158] The above has described this application in detail. For those skilled in the art, without departing from the purpose and scope of this application and without unnecessary experiments, this application can be implemented within a relatively wide range under equivalent parameters, concentrations, and conditions. Although specific examples of this application are given, it should be understood that this application can be further improved. In short, according to the principle of this application, this application is intended to include any changes, uses, or improvements to this application, including changes made using conventional techniques known in the art that are outside the scope disclosed in this application.

Claims

1. A data processing device comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the following steps: (A1) Obtaining the threshold value for determining postoperative risk: receiving the systemic nutritional inflammatory index and postoperative risk data of the research and development cohort, and obtaining the threshold value for determining postoperative risk of thoracoscopic lobectomy in patients with non-small cell lung cancer based on the systemic nutritional inflammatory index and postoperative risk data; the systemic nutritional inflammatory index = total cholesterol content × total lymphocyte count / total monocyte count; (A2) Data input: inputting sample data, wherein the sample data is the systemic nutritional inflammation index of the subject; (A3) Data comparison and result output: The sample data is compared with the determination threshold, and the postoperative risk of the subject is output according to the comparison result.

2. The data processing device according to claim 1, characterized in that: The postoperative risk of the subject is output according to the comparison result: the postoperative risk of the subject whose systemic nutritional inflammatory index is lower than the determination threshold is higher than that of the subject whose systemic nutritional inflammatory index is higher than the determination threshold.

3. A device for performing postoperative risk grouping or auxiliary grouping of subjects, characterized in that: The device comprises the following modules: (B1) Data receiving and analyzing module: used to receive the systemic nutritional inflammation index and postoperative risk data of the research and development cohort, and obtain the postoperative risk determination threshold of non-small cell lung cancer patients treated with thoracoscopic lobectomy according to the systemic nutritional inflammation index and postoperative risk data; the systemic nutritional inflammation index = total cholesterol content × total lymphocyte count / total monocyte count; (B2) Data input module: used to input sample data, wherein the sample data is the systemic nutritional inflammation index of the subject; (B3) Data comparison and result output module: used to compare the sample data with the determination threshold, and group the subjects' postoperative risks according to the comparison results.

4. The device according to claim 3, characterized in that: The subjects are grouped according to the postoperative risk according to the following criteria: the subjects whose systemic nutritional inflammatory index is lower than the determination threshold have a higher postoperative risk than the subjects whose systemic nutritional inflammatory index is higher than the determination threshold.

5. A computer program product, comprising a computer program, characterized in that: When the computer program is executed by a processor, it implements steps (A1) to (A3) described in claim 1 or 2 and / or, when the computer program is executed by a processor, it implements steps (B1) to (B3) described in claim 3 or 4.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, it implements steps (A1) to (A3) described in claim 1 or 2 and / or, when the computer program is executed by a processor, it implements steps (B1) to (B3) described in claim 3 or 4.

7. Any of the following applications: (C1) Use of the systemic nutritional inflammatory index described in claim 1 in grouping or assisting in grouping of subjects; (C2) Use of a substance for detecting the systemic nutritional inflammatory index described in claim 1 in the preparation of a product for grouping or assisting in grouping of subjects; The subjects are patients with non-small cell lung cancer who are scheduled to undergo thoracoscopic lobectomy.

8. Any of the following applications: (D1) Use of the systemic nutritional inflammatory index described in claim 1 in predicting or assisting in predicting the risk after thoracoscopic lobectomy in patients with non-small cell lung cancer; (D2) Use of a substance for detecting the systemic nutritional inflammatory index described in claim 1 in the preparation of a product for predicting or assisting in predicting the risk of postoperative thoracoscopic lobectomy in patients with non-small cell lung cancer.

9. A method for performing postoperative risk grouping or auxiliary grouping of subjects, the method comprising the following steps: comparing sample data with a determination threshold, and determining the grouping of the subjects based on the comparison result; the sample data is a systemic nutritional inflammatory index of the subjects, and the systemic nutritional inflammatory index = total cholesterol content × total lymphocyte count / total monocytes.

10. The method according to claim 9, characterized in that: The determination threshold is obtained according to the following method: receiving the systemic nutritional inflammatory index and postoperative risk data of the research and development cohort, analyzing the systemic nutritional inflammatory index and postoperative risk data, and obtaining a determination threshold for postoperative risk of patients with non-small cell lung cancer treated with thoracoscopic lobectomy; Furthermore, based on the comparison result output, the postoperative risk of the subject is determined according to the following criteria: the postoperative risk of the subject whose systemic nutritional inflammatory index is lower than the determination threshold is higher than that of the subject whose systemic nutritional inflammatory index is higher than the determination threshold.

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