WO2021102776A1 - 一种用于早期体腔内感染性并发症诊断的标记物及方法 - Google Patents

一种用于早期体腔内感染性并发症诊断的标记物及方法 Download PDF

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WO2021102776A1
WO2021102776A1 PCT/CN2019/121489 CN2019121489W WO2021102776A1 WO 2021102776 A1 WO2021102776 A1 WO 2021102776A1 CN 2019121489 W CN2019121489 W CN 2019121489W WO 2021102776 A1 WO2021102776 A1 WO 2021102776A1
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body cavity
infectious complications
cavity
subject
patient
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PCT/CN2019/121489
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English (en)
French (fr)
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季加孚
李子禹
吴舟桥
石晋瑶
陕飞
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北京肿瘤医院(北京大学肿瘤医院)
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/96—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving blood or serum control standard

Definitions

  • This application relates to the field of biomedicine, in particular to a marker, method and device for early diagnosis of infectious complications in a body cavity.
  • anastomotic leakage is one of the most serious postoperative complications of intra-abdominal surgery such as gastrointestinal surgery. Its occurrence is related to the failure of the anastomotic healing after the reconstruction of the digestive tract. Once anastomotic leakage occurs, it can cause abdominal and pelvic infections, abscesses, pan-peritonitis, and even sepsis, leading to perioperative death. According to literature reports, the incidence of anastomotic leakage after gastric surgery is between 1-6%, and the incidence of colorectal anastomotic leakage is between 4-33%.
  • Anastomotic leakage seriously affects the safety of patients after surgery and places a serious burden on the medical and health system. Regardless of whether from foreign literature or domestic summary data, about one-third of perioperative deaths are directly related to anastomotic leakage, which is currently the leading cause of perioperative death in patients undergoing gastrointestinal surgery. Anastomotic leakage prolongs the patient’s hospital stay and financial expenditure during hospitalization. Although there is still a lack of health economic analysis on anastomotic leakage in China, from the analysis of relevant data in the United States, anastomotic leakage has greatly increased the hospitalization costs of patients. Gastric anastomotic leakage is about 46,000 U.S. dollars, and colorectal leakage is about 34,000 U.S.
  • PNI prognostic nutritional index
  • Other risk factors for anastomotic leakage after gastrointestinal surgery include smoking and alcohol abuse, low anastomosis, advanced cancer surgery, emergency surgery, massive blood loss, long operation time, and preoperative hormone use.
  • the prevention of anastomotic leakage clinically includes three aspects: preoperative, intraoperative and postoperative.
  • Preoperative prevention means that patients with high-risk factors are selected for targeted treatment through preoperative evaluation, to ensure that patients receive adequate preoperative preparation, including preoperative smoking cessation, correction of water and electrolyte disorders and hypoproteinemia, and treatment for patients with diabetes or anemia Give adequate treatment, oral antibiotics 3 days before surgery, etc.
  • Intraoperative prevention includes fine operations during the operation to ensure good blood supply to the anastomotic stoma without tension, etc. It is essentially the quality control and optimization of the operation level.
  • there are clinical teams that use protein glue to reinforce the anastomotic stoma to reduce the incidence of anastomotic leakage.
  • Postoperative prevention mainly includes postoperative preventive use of antibiotics, routine gastrointestinal imaging and other imaging examinations.
  • the early diagnosis of anastomotic leakage is also one of the preventive strategies for postoperative anastomotic leakage.
  • the existing diagnostic methods are mostly routine laboratory examinations, among which white blood cell count and CRP are widely used as routine laboratory examination items for clinical diagnosis of postoperative infectious complications.
  • white blood cell count and CRP are widely used as routine laboratory examination items for clinical diagnosis of postoperative infectious complications.
  • CRP routine laboratory examination items
  • a large number of studies have tried to find whether there is a certain indicative relationship between laboratory test items and the occurrence of anastomotic leakage, so as to be used for early diagnosis of anastomotic leakage or abdominal abscess.
  • most of the current research results do not show satisfactory results.
  • Commonly used clinical laboratory indicators such as white blood cell count, C-reactive protein, troponin, etc. have limited clinical value in predicting anastomotic leakage.
  • the present invention provides a reliable, convenient and timely new method for the early clinical diagnosis of infectious complications in the body cavity, as well as the diagnostic markers involved in the method, Diagnostic kits, diagnostic devices and methods, etc.
  • a diagnostic marker in a first aspect, includes a series of inflammatory factors, the detection of the marker in a test sample or a test site can be used to diagnose infectious complications in the body cavity, and the inflammatory factor is selected from cytokines , Matrix metalloproteinase, reactive oxygen species, vascular endothelial growth factor, tissue inhibitor of metalloproteinase, C-reactive protein, white blood cell count, etc. or a combination of two or more factors.
  • kits for early diagnosis of infectious complications in a body cavity which is used to quickly, conveniently and promptly diagnose whether a patient is at risk of developing infectious complications in a body cavity.
  • the kit includes Detection reagents and/or detection devices for detecting inflammatory factors.
  • a use of a diagnostic marker in the preparation of a detection reagent or detection kit for early diagnosis of infectious complications in a body cavity includes a detection reagent or detection device for detecting inflammatory factors.
  • a scoring method for detecting infectious complications in the body cavity of a subject for scoring the risk of infective complications in the body cavity of the detected subject, the method includes the following steps:
  • Y is the score of infectious complications in the body cavity
  • X refers to the content (such as concentration) of a certain marker at a certain time point
  • ⁇ is the coefficient, which is the value of the corresponding variable X in the score of infectious complications in the body cavity
  • the weight, ⁇ is a constant.
  • the values of ⁇ and ⁇ are determined by a statistical method, and the statistical method is preferably LASSO regression.
  • LASSO regression Least absolute shrinkage and selection operator regression was used to select the best indicators from inflammatory factors at different time points after surgery to construct a scoring system for infectious complications in the body cavity. The related calculation is done through the R language "glmnet" package. LASSO regression is commonly used to construct high-latitude forecasting models. This method uses L1 regularization penalty correction to accurately shrink some regression coefficients to zero. LASSO regression selects the best parameters from high-dimensional data, which can avoid overfitting while taking into account the accuracy of the prediction model.
  • a screening method for markers for diagnosing infectious complications in the body cavity of a subject which is used to screen and obtain diagnostic markers that have more diagnostic value for the occurrence of infectious complications in the body cavity of the subject.
  • the method includes the following steps:
  • the statistical analysis method is preferably LASSO regression, and the X1...Xn retained in the model 1 obtained by the regression are the diagnostic markers obtained by screening.
  • a method for detecting the risk level of infective complications in the body cavity in a subject for evaluating the risk level of infective complications in the body cavity of the detected subject. The method includes the following steps:
  • Y is the score of infectious complications in the body cavity
  • X refers to the content (such as concentration) of a certain marker at a certain time point
  • ⁇ is the coefficient, which is the value of the corresponding variable X in the score of infectious complications in the body cavity Weight, ⁇ is a constant
  • the values of ⁇ and ⁇ are determined by a statistical method, and the statistical method is preferably LASSO regression;
  • the corresponding reference value is calculated by statistical methods, preferably the Cut-Off value, that is, obtained by ROC analysis.
  • Receiver operating characteristic curve (receiver operating characteristic curve, ROC curve) is used to measure the diagnostic efficacy of diagnostic indicators, and the maximum Youden Index (Youden Index) is set as the best Cut-Off value of the ROC curve, Youden Index
  • a method for detecting the risk probability of infective complications in a body cavity in a subject for evaluating the risk probability of infective complications in a body cavity in a subject. The method includes the following steps:
  • Y is the score of infectious complications in the body cavity
  • X refers to the content (such as concentration) of a certain marker at a certain time point
  • ⁇ is the coefficient, which is the value of the corresponding variable X in the score of infectious complications in the body cavity Weight, ⁇ is a constant
  • the values of ⁇ and ⁇ are determined by a statistical method, and the statistical method is preferably LASSO regression;
  • model 2 is:
  • a device for early diagnosis of infectious complications in a body cavity includes an analysis unit 1, an analysis unit 2, and an analysis unit 3, wherein:
  • the analysis unit 1 is used to detect diagnostic markers and their corresponding content (such as concentration);
  • the analysis unit 2 is used to obtain the analysis calculation result Y by passing one or more measured quantities obtained in the analysis unit 1 through the model 1;
  • the analysis unit 3 is used to compare the calculation result Y in the analysis unit 2 with the corresponding reference value to obtain the risk level of infectious complications in the body cavity;
  • it further comprises an analysis unit 4 and/or an analysis unit 5, wherein:
  • the analysis unit 4 is used to record and analyze relevant clinical factors
  • the analysis unit 5 is used to combine the risk level obtained by the analysis unit 3 with the clinical factors in the analysis unit 4, and obtain the risk probability p of infectious complications in the body cavity through analysis and calculation of the model 2.
  • Each of the analysis units includes a corresponding computer-executed algorithm.
  • the content (such as concentration) of the diagnostic marker can be input; or one or more detection reagents or kits for detecting the content (such as concentration) of the marker can be used to determine the sample.
  • ⁇ is the coefficient
  • the weight of the corresponding variable X in the score of infectious complications in the body cavity, and ⁇ is a constant;
  • the corresponding reference value is calculated by a statistical method, preferably the Cut-Off value, that is, the maximum value of the Youden index obtained by ROC analysis; the corresponding reference value is obtained by the analysis unit 2
  • the calculated result Y is compared with the corresponding reference value.
  • model 2 is:
  • the independent variables x 1 , x 2 ...x n refer to various clinical factors and the scores of infectious complications in the body cavity and other indicators, where w is the coefficient or weight, which is obtained through Logistic regression; x is the independent variable and is included in the formula Calculate the value of g(x) to obtain the risk probability p of infectious complications in the body cavity.
  • a diagnostic method for early diagnosis of infectious complications in a body cavity which is used for early diagnosis of infectious complications in vivo, including the following steps:
  • Y is the score of infectious complications in the body cavity
  • X is the content of a certain marker at a certain time point
  • ⁇ is the coefficient
  • ⁇ is constant
  • the values of ⁇ and ⁇ are determined by a statistical method, and the statistical method is preferably LASSO regression;
  • the beneficial effect of the present invention is that the product and method of the present invention can predict the occurrence of infectious complications in the body cavity in advance, guide the clinic to take more effective intervention measures for the infectious complications in the body cavity, and can be used to reduce the perioperative period of surgical patients.
  • Mortality rate Doctors can effectively distinguish low-risk and high-risk patients with infectious complications in the body cavity based on the scores of infectious complications in the body cavity, and decide whether the patient can resume eating and discharge from the hospital, or whether further imaging examinations and antibiotic treatment are needed in time.
  • the further combination of clinical features or clinical risk factors can predict the risk of infectious complications in the body cavity, and specify the risk of infectious complications in the body cavity in postoperative patients.
  • the product and method of the invention have extremely high clinical application value for the early diagnosis of infectious complications in the body cavity.
  • Figure 1 The content of inflammatory factors in peritoneal drainage
  • the concentration is logarithmically converted with a base of 10.
  • IL-1 ⁇ level in abdominal drainage fluid after gastric tumor surgery IL-6 level in abdominal drainage fluid after gastric tumor surgery
  • IL-10 level in abdominal drainage fluid after gastric tumor surgery IL-10 level in abdominal drainage fluid after gastric tumor surgery
  • TNF- ⁇ level in abdominal drainage after gastric tumor operation IL-6 level in abdominal drainage fluid after gastric tumor operation
  • MMP-2 level in abdominal drainage after gastric tumor operation MMP-9 level in abdominal drainage after gastric tumor operation.
  • Figure 3.1 ROC curve of the diagnosis of anastomotic leakage by inflammatory factors in the abdominal drainage fluid on the day of surgery
  • Figure 3.2 ROC curve of inflammatory factors in the abdominal drainage fluid for diagnosis of anastomotic leakage on the first postoperative day
  • Figure 3.3 ROC curve of inflammatory factors in the peritoneal drainage fluid on the second day after operation for the diagnosis of anastomotic leakage
  • Figure 3.4 ROC curve of the diagnosis of anastomotic leakage by inflammatory factors in the abdominal drainage fluid on the 3rd day after operation
  • the black vertical lines in the cross-validation graph respectively indicate the ⁇ value (Minimum Criteria) corresponding to the mean value of the minimum target parameter and the best ⁇ value (1-SE Criteria) corresponding to the least independent variable equation obtained within one of its variance ranges.
  • the red vertical line in the LASSO regression graph represents the best lambda value corresponding to the best equation.
  • Figure 5 The ROC curve of anastomotic leakage scoring at each time node for diagnosis of anastomotic leakage
  • Figure 6 Specific distribution of anastomotic leakage scores in patients undergoing gastric tumor surgery on the 3rd day after surgery
  • Figure 9 ROC curve of postoperative anastomotic leakage risk probability model and clinical risk factor assessment model
  • Figure 10 Analysis of clinical benefit and usability of anastomotic leakage risk probability model and clinical risk factor assessment model
  • Figure 11 Dynamic changes of routine laboratory examination indexes in patients with postoperative infectious complications
  • Figure 12 Dynamic changes of routine laboratory examination indexes in patients with postoperative anastomotic leakage
  • the inflammatory factor in the present invention is selected from one or a combination of two or more factors selected from cytokines, matrix metalloproteinases, reactive oxygen species, vascular endothelial growth factor, tissue inhibitor of metalloproteinases, C-reactive protein, white blood cell count and the like.
  • the cytokine is selected from: Interleukin (IL), Colony-Stimulating Factor (CSF), Interferon (IFN), Tumor-Necrosis Factor (TNF) , Chemokine (CK), or Growth Factor (GF), one or a combination of two or more factors.
  • IL Interleukin
  • CSF Colony-Stimulating Factor
  • IFN Interferon
  • TNF Tumor-Necrosis Factor
  • CK Chemokine
  • GF Growth Factor
  • the interleukin is selected from: IL-1 ⁇ , IL-1 ⁇ , IL10, IL11, IL12A, IL12B, IL13, IL15, IL16, IL17A, IL17B, IL17C, IL17D, IL17F, IL18Aa, IL18Ba, IL18Ca, IL19 , IL1A, IL1B, IL1F10, IL1RN, IL2, IL20, IL21, IL22, IL22F1a, IL22F2a, IL22F3a, IL22F4a, IL22F5a, IL23A, IL24, IL25, IL26, IL27, IL31, IL33, IL36L1a, IL36L2a, IL36 , IL6, IL7, IL8, or IL9, etc.; the colony stimulating factor is selected from: CNTF, CSF1, CSF2,
  • the matrix metalloproteinase is selected from: MMP-1, MMP-2, MMP-3, MMP-4, MMP-5, MMP-6, MMP-7, MMP-8, MMP-9 or MMP-10, etc.
  • MMP-1 MMP-1, MMP-2, MMP-3, MMP-4, MMP-5, MMP-6, MMP-7, MMP-8, MMP-9 or MMP-10, etc.
  • MMP-2, MMP-3, MMP-6 or MMP-9 is preferred.
  • the inflammatory factor is selected from one or more of IL-1 ⁇ , IL-6, IL-10, TNF- ⁇ , MMP-2 and MMP-9;
  • the inflammatory factor is selected from one or more of IL-1 ⁇ , IL-6, IL-10 and MMP-9;
  • IL-1 ⁇ IL-1 ⁇
  • IL-10 IL-10
  • MMP-9 inflammatory factor
  • the detection sample of the inflammatory factor is a fluid in a body cavity
  • the body cavity is selected from the abdominal cavity, pelvic cavity, thoracic cavity environment and/or brain cavity, etc.
  • the fluid in the body cavity is selected from the abdominal cavity, pelvic cavity, and thoracic cavity environment
  • the liquid and/or cerebrospinal fluid in the liquid further, the liquid is a cavity fluid or a fluid obtained by drainage; further, the liquid is preferably an abdominal cavity fluid or an abdominal cavity drainage fluid.
  • the detection site of the inflammatory factor is the infected area in or around the body cavity; further, for patients undergoing surgery, such as patients undergoing body cavity surgery, the inflammatory factors are selected from the 0-15 days after surgery Inflammatory factors; preferably inflammatory factors on day 0-7 after surgery, preferably inflammatory factors on day 0-5 after surgery, preferably inflammatory molecules on day 0-3 after surgery; more preferably on day 0 and day 1 after surgery Inflammatory factors on day, day 2, or day 3; more preferably, inflammatory factors on day 3 after surgery.
  • the subject in the present invention is mammals, including humans, domestic animals, pets, laboratory animals, etc., among which humans include patients of various ages and gender characteristics, and further are patients who have undergone a first operation or have undergone multiple operations.
  • the subject is a patient with a body cavity organ or tissue related disease, further is a patient with a abdominal cavity, pelvic cavity, thoracic environment or a cranial cavity related disease, further is a postoperative patient with a abdominal cavity, pelvic cavity, thoracic environment, or cranial cavity related disease, and further is a digestive System, urinary system, reproductive system, respiratory system, or cardiovascular system and other abdominal, pelvic and thoracic organs after surgery; further for digestive tract, liver, gallbladder, pancreas, spleen, kidney, ureter or bladder and other organ-related diseases after surgery Patients, or patients after respiratory tract or cardiac surgery; further patients after gastrointestinal surgery, further patients undergoing transperitoneal surgery, and further, said patients are patients who have clinically collected abdominal drainage fluid.
  • digestive system diseases include: digestive tract tumors, digestive tract inflammation, digestive ulcers and other digestive tract diseases; such as chronic active gastritis, chronic atrophic gastritis, gastric ulcer, duodenal ulcer, ulcerative colitis, inflammatory Bowel disease, ulcerative colitis, Crohn's disease (, collagenous colitis, lymphocytic colitis, ischemic colitis, diversion colitis, Behcet's syndrome, infectious colitis, unspecified Colitis, ulcerative colitis, familial adenomatous polyposis, Hirschsprung disease, intestinal stenosis, proctitis, rectal mucositis, colon cancer, rectal cancer, fistula, intestinal obstruction, mechanical intestinal obstruction, paralytic Intestinal obstruction, gastrointestinal fistula, pancreatic fistula, other unnatural fistulas (including rectovaginal fistula, rectal bladder fistula, intestinal fistula, etc.), ischemic intestinal necrosis, cecal cancer, rectal and sig
  • Hepatobiliary diseases include: cholelithiasis, cholecystitis, cholangitis, chronic hepatitis or liver cancer, etc.;
  • Pancreatic diseases include: pancreatitis, pancreatic cancer, pancreatic fistula; kidneys: kidney cancer, nephritis, kidney stones, pyelonephritis or renal pelvis cancer, etc.;
  • Splenic diseases include: splenic infarction, etc.;
  • Bladder diseases include: bladder cancer, etc.;
  • Reproductive system diseases include: uterine and ovarian diseases, such as endometrial cancer, ovarian cancer, uterine fibroids, endometriosis, adenomyosis or chocolate cysts, etc.; vaginal diseases, such as rectovaginal fistulas, etc.;
  • Celiac disease includes: infectious peritonitis, spontaneous peritonitis, tuberculous peritonitis or ascites without clear cause, etc.;
  • Thoracic diseases include: bronchial fistula, pneumonia, atelectasis or pleural effusion, etc.;
  • Nervous system diseases include: nervous system infections, such as intracranial infections, and so on.
  • the infectious complications of the body cavity in the present invention are selected from the infectious complications of abdominal cavity, pelvic cavity, thoracic cavity or brain cavity, and further involve abdominal cavity infection, abdominal effusion, peritonitis, abdominal abscess, sepsis, anastomosis Oral leakage, pancreatic fistula, duodenal stump fistula, other gastrointestinal fistulas, lymphatic fistula, chyle fistula, etc.; further, anastomotic leakage is preferred.
  • the clinical factors described in the present invention include: patient's birthday, surgery date, age, gender, height, weight, BMI, ASA score, diabetes, smoking, alcoholism, medication status (antihypertensive drugs, hypolipidemic drugs, corticosteroids, antihypertensive drugs) Coagulants, non-steroidal anti-inflammatory drugs), history of heart disease, cardiovascular symptoms, peripheral vascular disease, history of respiratory system diseases, respiratory system symptoms, preoperative intestinal obstruction, neoadjuvant radiotherapy, neoadjuvant radiotherapy and chemotherapy, prophylactic antibiotic use.
  • Patient's surgical method (laparoscopic/laparoscopic resection), surgical method change, reason for change, elective/emergency surgery, resection scope, anastomosis (type, initial, placement type, location, manual suture), surgical indications, surgical time, Anesthesia, intraoperative complications, blood loss, admission time, discharge time, hospitalization period, drainage, stoma (location, type), air leak test (including test results), surgeons (number and professionalism), etc.
  • statistical methods include methods commonly used in the field.
  • continuous variable data conforming to a normal distribution can be recorded as mean ⁇ standard deviation
  • categorical variable data can be recorded in the form of quantity and percentage
  • normal distribution The t test can be used for the comparison between continuous variables
  • the nonparametric test can be used for the comparison between the non-normally distributed continuous variables
  • the Mann-Whitney test is used for the comparison between the two groups
  • the row ⁇ column ⁇ 2 test is used for the comparison between categorical variables or the single factor analysis
  • Fisher's exact test single factor factors with predictive value (P>0.1) were included in the multivariate analysis.
  • Least Absolute Shrinkage and Selection Operator can be used to screen and analyze the inflammatory factors in the postoperative abdominal drainage fluid.
  • Logistic regression analysis can be used for multivariate analysis of categorical variables, and the results can be represented by Nomogram.
  • the calibration curve can be used to evaluate the reliability of the nomogram, and the decision curve and clinical influence curve can be used to evaluate the clinical application value of the nomogram.
  • Receiver operating characteristic curve (receiver operating characteristic curve, ROC curve) can be used to measure the diagnostic efficacy of diagnostic indicators.
  • the maximum Youden Index Youden Index
  • the above analysis and calculation can be done using SPSS 20.0 statistical software, R language "rms" and "rmda” program packages.
  • Example 1 Quantitative analysis of inflammatory factors in peritoneal drainage
  • Patient's condition underwent gastric tumor surgery, with abdominal drainage fluid.
  • Clinical data patient's birthday, surgery date, age, gender, height, weight, BMI, ASA score, diabetes, smoking, alcoholism, medication status (antihypertensive drugs, hypolipidemic drugs, corticosteroids, anticoagulants, non-steroids) Anti-inflammatory drugs), history of heart disease, cardiovascular symptoms, peripheral vascular disease, history of respiratory disease, respiratory symptoms, preoperative intestinal obstruction, neoadjuvant radiotherapy, neoadjuvant chemoradiation, prophylactic antibiotic use.
  • Patient's surgical method (laparoscopic/laparoscopic resection), surgical method change, reason for change, elective/emergency surgery, resection scope, anastomosis (type, initial, placement type, location, manual suture), surgical indications, surgical time, Anesthesia, intraoperative complications, blood loss, admission time, discharge time, hospitalization period, drainage, stoma (location, type), air leak test (including test results), surgeons (number and professionalism), etc.
  • Intraoperative information collection surgical method (laparoscopic/laparoscopic resection), surgical method change, reason for change, elective/emergency surgery, resection scope, anastomosis (type, initial, placement type, location, manual suture), surgical indications , Operation time, anesthesia, intraoperative complications, blood loss, admission time, discharge time, hospitalization period, drainage, stoma (location, type), air leak test (including test results), surgeon (number and professionalism) .
  • Postoperative information collection routine laboratory test results (white blood cells, CRP, etc.), drainage fluid shape, color, drainage, survival within 30 days after surgery, re-admission status, second surgery status.
  • Registration of postoperative complications of the enrolled patients prospective registration of postoperative complications of the enrolled patients, independent registration by the investigator and clinician, and entry into various databases, and regular verification and summary.
  • Registration content and standards 21 complications were included in the registration category, and the severity of complications was graded according to the Clavin-Dindo classification (CD classification), as shown in Table 1.
  • CD classification Clavin-Dindo classification
  • the abdominal drainage fluid was collected three days after the operation (including the day after the operation to the third day after the operation), and collected at a fixed time point every day, 20ml each time. After sampling the peritoneal drainage fluid samples, centrifuge them at 4°C and 2800g for 10 minutes. Separate the supernatant and the precipitate and store them in a refrigerator at -80°C.
  • IL-1 ⁇ , IL-6, IL-10 and TNF- ⁇ were detected using HSTCMAG-28SK (EMD Millipore, USA) kit; the amounts of MMP2 and MMP9 were HMMP2MAG-55K (EMD Millipore, USA) The kit is tested.
  • the concentration of inflammatory factors is represented by the median (quartile); the difference is statistically significant and marked in bold (P ⁇ 0.05).
  • the levels of inflammatory factors in the abdominal drainage fluid of patients with gastric tumors showed dynamic changes (Figure 2).
  • the level of IL-1 ⁇ in the abdominal drainage fluid of patients with anastomotic leakage continued to increase from the first day after surgery ( Figure 2a).
  • the -10 level showed a downward trend in the early postoperative period, and increased again on the second day after surgery ( Figure 2c).
  • the content of MMP-9 in the abdominal drainage fluid decreased every other day, but the concentration of patients with anastomotic leakage was higher than that of other patients within 3 days after surgery (Figure 2f).
  • Example 2 Diagnostic value of inflammatory factors in abdominal drainage fluid for anastomotic leakage
  • the ROC curve was used to evaluate the diagnostic efficacy of a single inflammatory factor in the abdominal drainage fluid for the diagnosis of anastomotic leakage.
  • the ROC analysis results of each inflammatory factor in the diagnosis of anastomotic leakage within 3 days after gastric tumor surgery are shown in Figure 3.1-3.4 and Table 3-6. .
  • AUC The larger the AUC value of the area under the curve, the higher the diagnostic power. Usually AUC greater than 0.8 has clinical guiding significance.
  • Analysis of the diagnostic efficacy of a single inflammatory factor in the abdominal drainage fluid after gastric tumor surgery shows that IL-1 ⁇ , IL-10 and MMP-9 have the highest diagnostic efficacy for anastomotic leakage on the third day after gastric tumor surgery. AUC respectively They are 0.76 (p ⁇ 0.01), 0.77 (p ⁇ 0.01) and 0.75 (p ⁇ 0.01).
  • the diagnostic efficacy of TNF- ⁇ on anastomotic leakage reached the highest on the second day after gastric tumor operation, and its AUC was 0.73 (p ⁇ 0.01).
  • IL-6 and MMP-2 are less effective in diagnosing anastomotic leakage.
  • the above results also show that for patients after gastric tumor surgery, the diagnostic efficacy of a single inflammatory factor for anastomotic leakage does not meet the requirements of clinical application.
  • Example 3 Parameter selection and construction of anastomotic leakage scoring system
  • LASSO Least Absolute Shrinkage and Selection Operator
  • the ROC curve was used to analyze the effectiveness of the anastomotic leakage score for the diagnosis of anastomotic leakage after gastric tumor surgery.
  • the ROC analysis results of the anastomotic leakage score at different time nodes for the diagnosis of anastomotic leakage are shown in Figure 5 and Table 8.
  • the anastomotic leakage score on the 3rd postoperative day was the most effective in diagnosing anastomotic leakage, and its AUC was 0.87 (p ⁇ 0.01).
  • the score value -2.801 corresponding to the maximum Youden index in the ROC analysis was selected as the Cut-Off value of the anastomotic leakage score on the third day after gastric tumor surgery, and the patients after gastric tumor surgery were divided into high group and low group.
  • the distribution of anastomotic leakage scores on the 3rd day after gastric tumor surgery is shown in Figure 6 and Figure 7, and the effect of Cut-Off value on patients after gastric tumor surgery is shown in Table 9.
  • Enrollment conditions and diagnostic criteria are the same as before.
  • a total of 66 patients undergoing gastric tumor surgery were enrolled, including 48 males and 18 females; the median age was 57 years; a total of 7 patients were diagnosed with anastomotic leakage.
  • Example 3 Using the third-day anastomotic leakage scoring model in Example 3: -3.10+0.000241 ⁇ IL-1 ⁇ (D3)+0.00183 ⁇ IL-10(D3)+0.000000853 ⁇ MMP-9(D3), 66 patients were scored , And according to the score of -2.801 corresponding to the maximum Youden index in the ROC analysis as the Cut-Off value of the anastomotic leakage score on the third day after gastric tumor surgery, the diagnosed AUC was 0.83 (p ⁇ 0.01), using the training cohort The cut-off value obtained divides the patients after gastric tumor surgery into high group and low group.
  • the inventors further combined the clinical characteristics of patients undergoing gastric tumor surgery with the anastomotic leakage score results, and then used Logistic regression to construct the anastomotic leakage score results and clinical results.
  • Anastomotic leakage risk probability model of risk factors By constructing an anastomotic leakage risk probability model, with the help of a nomogram, the probability of an anastomotic leakage in the patient is calculated, and the decision curve is compared to decide whether to take the corresponding clinical intervention.
  • Preoperative information collection birthday, surgery date, age, gender, height, weight, BMI, ASA score, diabetes, smoking, alcoholism, medication status (antihypertensive drugs, hypolipidemic drugs, corticosteroids, anticoagulants, non-steroids) Anti-inflammatory drugs), history of heart disease, cardiovascular symptoms, peripheral vascular disease, history of respiratory disease, respiratory symptoms, preoperative intestinal obstruction, neoadjuvant radiotherapy, neoadjuvant chemotherapy and radiotherapy, and prophylactic antibiotic use.
  • Intraoperative information collection surgical method (laparoscopic/laparoscopic resection), surgical method change, reason for change, elective/emergency surgery, resection scope, anastomosis (type, initial, placement type, location, manual suture), surgical indications , Operation time, anesthesia, intraoperative complications, blood loss, admission time, discharge time, hospitalization period, drainage, stoma (location, type), air leak test (including test results), surgeon (number and professionalism) .
  • Postoperative information collection routine laboratory test results (white blood cells, CRP, etc.), drainage fluid shape, color, drainage, survival within 30 days after surgery, re-admission status, second surgery status.
  • BMI Body Mass Index
  • Patient specific baseline information, ASA score, surgical information, etc. are shown in Table 11.
  • 24 patients were diagnosed with infectious complications, including 17 patients with anastomotic leakage, the incidence of anastomotic leakage was 6.46%; in addition, a total of 5 patients had more than two infectious complications (Table 12); One patient died within 30 days after surgery.
  • Age and BMI are represented by the median (quartile); differences that are statistically significant are marked in bold (P ⁇ 0.05); *Mann-Whitney U test. Including Billroth-II and Uncut Roux-en-Y
  • Logistic regression was used to establish a postoperative anastomotic leakage risk probability model for gastric tumors.
  • the included independent variables included anastomotic method, surgical method, tumor location, and anastomotic leakage score on the third day after surgery.
  • the model prediction results are presented through the nomogram.
  • the AUC of the anastomotic leakage risk probability model was 0.93 (p ⁇ 0.01)
  • the AUC of the clinical risk factor assessment model was 0.86 (p ⁇ 0.01).
  • the decision curve was used to compare the clinical benefits of the anastomotic leakage risk probability model and the clinical risk factor evaluation model. The results showed that the net benefits of the anastomotic leakage risk probability model were higher than the clinical risk factor evaluation model when the risk threshold was less than 0.7. , As shown in Figure 10a, Figure 10b, Figure 10c.

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Abstract

一种体腔内感染性并发症早期诊断的标记物，以及相关的试剂盒和装置，同时构建出一种对腔内感染性并发症的评分方法、对其危险等级的检测方法，以及提供一种筛选体腔内感染性并发症的标记物的方法。实现了对体腔内感染的早期诊断，能够有效地指导临床干预，降低患者围手术期死亡率，具有极高的临床应用价值。

Description

一种用于早期体腔内感染性并发症诊断的标记物及方法 技术领域
本申请涉及生物医疗领域，具体涉及一种体腔内感染性并发症早期诊断的标记物、方法和装置。
背景技术
体腔脏器组织术后局部感染性并发症是制约患者术后康复的重要因素。例如吻合口漏，它是腹腔内手术如胃肠外科最严重的术后并发症之一,其发生与消化道重建后吻合口的愈合障碍有关。一旦发生吻合口漏，可引起腹盆腔感染、脓肿、全腹膜炎，甚至发生脓毒症导致患者围术期死亡。从文献报道来看，胃部手术后吻合口漏发生率介于1-6％，结直肠吻合口漏的发生率介于4-33％。吻合口漏严重影响患者术后安全并给医疗卫生系统造成严重负担。无论从国外文献还是国内汇总数据来看，约三分之一的围术期死亡与吻合口漏直接相关，是目前胃肠道手术患者围术期死亡的最主要原因。吻合口漏延长了患者的住院时间和住院期间经济开支。尽管国内仍然缺乏对于吻合口漏的卫生经济学分析，但从美国相关数据分析来看吻合口漏大大增加了患者住院费用，胃吻合口漏约4.6万美元，结直肠约为3.4万美元，约为其他患者的3倍之高，而其住院时间为其他患者的2倍。即使治疗后病情缓解，吻合口漏患者的术后生活质量也显著差于其他患者。吻合口漏还是肿瘤局部复发的高危因素，严重影响着肿瘤患者的治疗效果乃至生存时限。
对于体腔局部感染性并发症，临床常规实验室检查的诊断效力有限。白细胞计数和CRP作为常规实验室检查项目被广泛应用于临床诊断外科术后感染性并发症。近年来有大量研究试图寻找实验室检查项目与吻合口漏的发生之间是否存在某种指示关系，以便用于吻合口漏或腹腔脓肿等体腔感染性并发症的早期诊断。然而目前相关研究结果多未呈现令人满意的结果，临床常用的实验室指标诸如白细胞计数、C反应蛋白、肌钙蛋白等对预测吻合口漏的临床价值有限，似乎难以仅通过全身“系统性”感染表现来诊断一个由“局部”病变渐变为全身感染的疾病。
近年来有大量研究试图探究胃肠术后吻合口漏的危险因素。两个来自荷兰的多中心回顾性研究分别分析了36900多例结直肠术后患者以及600多例因炎症性肠病而行手术 治疗的患者临床数据，发现肥胖以及高美国麻醉协会(American Society of Anesthesiologists,ASA)评分是吻合口漏的独立危险因素。一个来自国内多中心的研究收集了300多例直肠中低位切除患者的数据得出男性、糖尿病、术前放化疗和肿瘤位置是吻合口漏的独立危险因素。来自日本的一项研究分析了胃癌腔镜辅助下全胃切除术后吻合口漏的独立危险因素，认为预后营养指数(Prognostic Nutritional Index,PNI)较低的患者更容易出现吻合口漏。一项国内研究报道称高龄(年龄≥65岁)、贫血(血红蛋白≤8.0g/dL)以及营养不良是胃癌术后吻合口漏的独立高危因素。其他胃肠外科术后吻合口漏的危险因素还包括抽烟酗酒、低位吻合、晚期癌症手术、急诊手术、大量失血、手术时间长以及术前使用激素等。然而稍加分析就能发现，实际上绝大多数患者均会有一个或几个上述的危险因素，因此仅仅罗列危险因素未必能够给临床工作带来实际效用。针对这类型的研究，存在着一个棘手的问题：这些危险因素以及相关研究并没有真正给出相应的应对方法。在临床上当遇到有多个危险因素的患者时，是否继续手术或者是否改变手术方案，依然没有足够的临床证据予以直接支持，给出解决方案。
目前，临床上吻合口漏的预防有术前、术中及术后三个方面。术前预防即通过术前评估，选择出具有高危因素的患者进行针对性处理，保证患者接受充分的术前准备，包括术前戒烟、纠正水电解质紊乱和低蛋白血症、对糖尿病或贫血患者给予充分治疗、术前3天口服抗生素等。术中预防包括手术中精细操作，保证吻合口血运良好、无张力等，其实质上是手术操作层面的质控和优化。此外，临床上还有团队使用蛋白胶加固吻合口来降低吻合口漏的发生率，但综合分析后发现，蛋白胶对于预防吻合口漏未显示确切效果，同时由于高昂的价格，其在临床中的应用还较为局限。术后预防主要包括术后预防性使用抗生素、常规进行消化道造影等影像学检查等。
对此，吻合口漏的早期诊断也是术后吻合口漏的预防策略之一。现有的诊断方法多为常规实验室检查，其中白细胞计数和CRP作为常规实验室检查项目被广泛应用于临床诊断外科术后感染性并发症。近年来有大量研究试图寻找实验室检查项目与吻合口漏的发生之间是否存在某种指示关系，以便用于吻合口漏或腹腔脓肿的早期诊断。然而目前相关研究结果多未呈现令人满意的结果，临床常用的实验室指标诸如白细胞计数、C反应蛋白、肌钙蛋白等对预测吻合口漏的临床价值有限，似乎难以仅通过全身“系统性”感染表现来诊断一个由“局部”病变渐变为全身感染的疾病。同时临床上对于吻合口漏的诊治效果并不令人满意，医生只能等到吻合口漏严重到一定程度，出现明显的实验室指标异常，并结合影像学或内镜检查才能确诊。因此目前大多数吻合口漏的诊断时间为 术后5-8天甚至更晚，且约有一半被诊断为漏的患者需要再次通过手术来治疗。由此可见，吻合口漏诊断的最大障碍在于缺乏“早期”且“局部”的监测手段。
发明内容
针对体腔内感染性并发症早期诊断的必要性和迫切性，本发明提供了一套可靠、便捷、及时的体腔内感染性并发症临床早期诊断新方法，以及该方法所涉及的诊断标记物、诊断试剂盒、诊断装置即方法等。
本发明采用如下技术方案：
第一方面，提供一种诊断标记物，所述诊断标记物包括一系列炎症因子，在检测样品或检测部位中检测标记物可用于诊断体腔内感染性并发症，所述炎症因子选自细胞因子、基质金属蛋白酶、活性氧簇、血管内皮生长因子、组织金属蛋白酶抑制因子、C反应蛋白、白细胞计数等中的一种或两种以上因子的组合。
第二方面，提供一种用于早期诊断体腔内感染性并发症的试剂盒，用于快速、便捷、及时地诊断患者是否有发生体腔内感染性并发症的风险，所述试剂盒包含用于检测炎症因子的检测试剂和/或检测装置。
第三方面，提供一种诊断标记物在用于制备早期诊断体腔内感染性并发症的检测试剂或检测试剂盒中的用途；所述试剂盒包含用于检测炎症因子的检测试剂或检测装置。
第四方面，提供一种用于检测对象体腔内感染性并发症的评分方法，用于对所检测对象发生体腔内感染性并发症的发生风险进行评分，所述方法包括如下步骤：
1)检测来自对象样品的标记物及其含量，和
2)将步骤1测定标记物的含量经模型1进行计算，获得一数值Y，即体腔内感染性并发症评分；
其中所述统计学模型1为：Y＝X1β1+X2β2+………+Xnβn+ε，
其中Y是体腔内感染性并发症评分分值，X是指某个时间点的某个标记物的含量(如浓度)，β是系数，为相应变量X在体腔内感染性并发症评分中的权重，ε是常数。
其中，通过统计学方法确定β和ε的值，所述统计学方法优选LASSO回归。
采用Least absolute shrinkage and selection operator(LASSO)回归从术后不同时间点的炎症因子中挑选出最佳指标用以构建体腔内感染性并发症的评分系统。相关计算通过R语言“glmnet”包完成。LASSO回归普遍应用于构建高纬度预测模型。该方法使用L1正则化惩罚校正将一些回归系数精确地收缩为零。LASSO回归从高维度数据中选出最佳参数，在兼顾 预测模型准确性的同时还能避免过拟合。
第五方面，提供一种用于诊断对象体腔内感染性并发症的标记物的筛选方法，用于筛选得出对所检测对象发生体腔内感染性并发症更具有诊断价值的诊断标记物，所述方法包括如下步骤：
1)检测来自对象样品的标记物及其含量，和
2)将步骤1测定标记物的含量经模型1进行统计学分析；
其中，所述统计学模型1为：Y＝X1β1+X2β2+……+Xnβn+ε，Y是体腔内感染性并发症评分分值，X是指某个时间点的某个标记物的含量(如浓度)，β是系数，为相应变量X在体腔内感染性并发症评分中的权重，ε是常数；
其中，所述统计学分析方法优选LASSO回归，回归得到的模型1中保留的X1…Xn即为筛选得出的所述诊断标记物。
第六方面，提供一种用于检测对象中发生体腔内感染性并发症危险等级的方法，用于对所检测对象发生体腔内感染性并发症的危险等级进行评价，所述方法包括如下步骤：
1)检测来自对象样品的标记物及其含量，和
2)将步骤1测定标记物的含量经模型1进行计算，获得一数值Y；
其中所述统计学模型1为：Y＝X1β1+X2β2+………+Xnβn+ε，
其中Y是体腔内感染性并发症评分分值，X是指某个时间点的某个标记物的含量(如浓度)，β是系数，为相应变量X在体腔内感染性并发症评分中的权重，ε是常数；
其中，通过统计学方法确定β和ε的值，所述统计学方法优选LASSO回归；
3)将所述计算得到的数值Y与相应的参考值进行比较，当Y大于参考值，则体腔内感染性并发症危险等级相对较高；当Y小于等于参考值，则体腔内感染性并发症危险等级相对较低。
其中，所述相应的参考值是经统计学方法计算得到的，优选为Cut-Off值，即是经ROC分析得到的。受试者工作特征曲线(receiver operating characteristic curve，ROC曲线)用以衡量诊断指标的诊断效力，并将约登指数(Youden Index)最大值设为ROC曲线的最佳Cut-Off值，约登指数计算公式为：约登指数＝敏感性+特异性-1。
第七方面，提供一种用于检测对象中发生体腔内感染性并发症风险概率的方法，用于对所检测对象发生体腔内感染性并发症的风险概率进行评价，所述方法包括如下步骤：
1)检测来自对象样品的标记物及其含量，和
2)将步骤1测定标记物的含量经模型1进行计算，获得一数值Y；
其中所述统计学模型1为：Y＝X1β1+X2β2+………+Xnβn+ε，
其中Y是体腔内感染性并发症评分分值，X是指某个时间点的某个标记物的含量(如浓度)，β是系数，为相应变量X在体腔内感染性并发症评分中的权重，ε是常数；
其中，通过统计学方法确定β和ε的值，所述统计学方法优选LASSO回归；
3)记录、分析相关临床因素；
4)通过模型2分析计算，获得体腔内感染性并发症发生风险概率p；
其中，所述模型2为：
Figure PCTCN2019121489-appb-000001
采用Logistic回归建立，其中，自变量x 1，x 2…x n指各种临床因素以及体腔内感染性并发症评分分值等指标，其中，w为系数或权重，x为自变量即纳入公式的指标，计算得出g(x)的值，从而得出体腔内感染性并发症发生风险概率p。
其中，由于吻合口漏发生与否是分类变量，0表示“无”，1表示“有”，则所述模型所得体腔内感染性并发症发生风险概率在0～1之间。上述公式中g(x)为连续变量，0～1的取值范围不适用，因此使用Logistic变换，将g(x)转换为介于0～1的风险概率。分析计算运用SPSS统计软件完成。
第八方面，提供一种用于早期诊断体腔内感染性并发症的装置，所述装置包括分析单元1、分析单元2和分析单元3，其中：
所述分析单元1用于检测诊断标记物及其相应含量(如浓度)；
所述分析单元2用于将分析单元1中得到的一个或多个测定量通过模型1获得分析计算结果Y；
所述分析单元3用于将分析单元2中的计算结果Y与相应的参考量值进行比较，得到体腔内感染性并发症的危险等级；
优选地，进一步包括分析单元4和/或分析单元5，其中：
分析单元4用于记录、分析相关临床因素；
分析单元5用于将分析单元3得到的危险等级与分析单元4中临床因素结合，通过模型2分析计算，获得体腔内感染性并发症发生风险概率p。
所述各分析单元均包含相应的计算机执行的算法。
进一步的，分析单元1中，可输入所述诊断标记物的含量(如浓度)；或包含检测所述标记物含量(如浓度)的一种或多种检测试剂或试剂盒，用于测定样品中相应标记物的量；
进一步的，分析单元2中，所述统计学模型1为：Y＝X1β1+X2β2+………+Xnβn+ε，其中Y是体腔内感染性并发症评分分值，X是指某个时间点的某个标记物的含量(如浓度)，β是系数，为相应变量X在体腔内感染性并发症评分中的权重，ε是常数；
进一步的，分析单元3中，所述相应的参考值是经统计学方法计算得到的，优选为Cut-Off值，即是经ROC分析得到的约登指数的最大值；将分析单元2得到的计算结果Y与相应的参考值进行比较，当Y＞参考值，则体腔内感染性并发症危险等级相对较高；当Y小于等于参考值，则体腔内感染性并发症危险等级相对较低；
进一步的，分析单元5中，所述模型2为：
Figure PCTCN2019121489-appb-000002
其中，自变量x 1，x 2…x n指各种临床因素以及体腔内感染性并发症评分分值等指标，其中，w为系数或权重，通过Logistic回归得到；x为自变量即纳入公式的指标，计算得出g(x)的值，从而得出体腔内感染性并发症发生风险概率p。
其中，由于吻合口漏发生与否是分类变量，0表示“无”，1表示“有”，则所述模型所得体腔内感染性并发症发生风险概率在0～1之间。上述公式中g(x)为连续变量，0～1的取值范围不适用，因此使用Logistic变换，将g(x)转换为介于0～1的风险概率。分析计算运用SPSS统计软件完成。
第九方面，提供一种用于早期诊断体腔内感染性并发症的诊断方法，用于早期诊断体内感染性并发症，包括如下步骤：
1)检测来自对象样品的标记物及其含量，和
2)将步骤1测定标记物的含量经模型1进行计算，获得一数值Y；
其中所述统计学模型1为：Y＝X1β1+X2β2+………+Xnβn+ε，
其中Y是体腔内感染性并发症评分分值，X是指某个时间点的某个标记物的含量，β是系数，为相应变量X在体腔内感染性并发症评分中的权重，ε是常数；
其中，通过统计学方法确定β和ε的值，所述统计学方法优选LASSO回归；
3)将所述计算得到的数值Y与相应的参考值进行比较，当Y大于参考值，则为体腔内感染性并发症高危组；当Y小于等于参考值，则为体腔内感染性并发症低危组。
本发明的有益效果在于，本发明中的产品和方法可提早预测体腔内感染性并发症是否发生，指导临床对体腔内感染性并发症采取更为有效的干预措施，可用于降低手术患者围术期 死亡率。医生可以根据体腔内感染性并发症的评分来有效区分体腔内感染性并发症低危和高危患者，决定患者是否可以恢复进食以及出院，或是否需要及时给予进一步影像学检查和抗生素治疗。在此基础上，进一步结合临床特征或临床危险因素可预测体腔内感染性并发症的风险概率，将术后患者发生体腔内感染性并发症的风险具体化。本发明产品和方法对于体腔内感染性并发症早期诊断具有极高的临床应用价值。
附图说明
图1：腹腔引流液中炎症因子的含量
浓度用以10为底作对数转换。(a)胃肿瘤术后腹腔引流液中IL-1β水平；(b)胃肿瘤术后腹腔引流液中IL-6水平；(c)胃肿瘤术后腹腔引流液中IL-10水平；(d)胃肿瘤术后腹腔引流液中TNF-α水平；(e)胃肿瘤术后腹腔引流液中MMP-2水平；(f)胃肿瘤术后腹腔引流液中MMP-9水平。
图2：腹腔引流液中炎症因子含量的动态变化
(a)胃肿瘤术后腹腔引流液中IL-1β的动态变化；(b)胃肿瘤术后腹腔引流液中IL-6的动态变化；(c)胃肿瘤术后腹腔引流液中IL-10的动态变化；(d)胃肿瘤术后腹腔引流液中TNF-α的动态变化；(e)胃肿瘤术后腹腔引流液中MMP-2的动态变化；(f)胃肿瘤术后腹腔引流液中MMP-9的动态变化。*P<0.05；**P<0.01；***P<0.001。
图3.1：手术当天腹腔引流液中炎症因子诊断吻合口漏的ROC曲线
图3.2：术后第1天腹腔引流液中炎症因子诊断吻合口漏的ROC曲线
图3.3：术后第2天腹腔引流液中炎症因子诊断吻合口漏的ROC曲线
图3.4：术后第3天腹腔引流液中炎症因子诊断吻合口漏的ROC曲线
图4：采用LASSO回归对炎症因子指标进行筛选
(a)胃肿瘤术后3天内所有时间节点的炎症因子指标筛选；(b)胃肿瘤术后第1天的炎症因子指标筛选；(c)胃肿瘤术后第2天的炎症因子指标筛选；(d)胃肿瘤术后第3天的炎症因子指标筛选；(e)胃肿瘤术后第3天的炎症因子指标筛选。
交叉验证图中黑色竖线分别表示最小目标参量均值对应的λ值(Minimum Criteria)和其一个方差范围内获得最少自变量方程所对应的最佳λ值(1-SE Criteria)。LASSO回归图中红色竖线表示获得最佳方程所对应的最佳λ值。
图5：各时间节点吻合口漏评分诊断吻合口漏的ROC曲线
图6：术后第3天吻合口漏评分在胃肿瘤手术患者中的具体分布
图7：术后第3天吻合口漏评分的Cut-Off值
图8：吻合口漏风险概率模型的校准曲线
图9：术后吻合口漏风险概率模型和临床危险因素评估模型的ROC曲线
图10：吻合口漏风险概率模型和临床危险因素评估模型的临床效益与可用性分析
(a)胃肿瘤术后吻合口漏风险概率模型和临床危险因素评估模型的决策曲线；(b)吻合口漏风险概率模型的临床预测曲线；(c)临床危险因素评估模型的临床影响曲线。
图11：术后感染性并发症患者常规实验室检查指标动态变化
(a)胃肿瘤术后感染性并发症患者白细胞计数动态变化；(b)胃肿瘤术后感染性并发症患者CRP水平动态变化。*p<0.05
图12：术后吻合口漏患者常规实验室检查指标动态变化
(a)胃肿瘤术后吻合口漏患者白细胞计数动态变化；(b)胃肿瘤术后吻合口漏患者CRP水平动态变化。*p<0.05
具体实施方式：
本发明中所述炎症因子选自细胞因子、基质金属蛋白酶、活性氧簇、血管内皮生长因子、组织金属蛋白酶抑制因子、C反应蛋白、白细胞计数等中的一种或两种以上因子的组合。
进一步的，所述细胞因子选自：白细胞介素(Interleukin,IL)、集落刺激因子(Colony-Stimulating Factor,CSF)、干扰素(Interferon,IFN)、肿瘤坏死因子(Tumor-Necrosis Factor,TNF)、趋化因子(Chemokine,CK)、或生长因子(Growth Factor,GF)等中的一种或两种以上因子的组合。
进一步的，所述白细胞介素选自：IL-1α、IL-1β、IL10、IL11、IL12A、IL12B、IL13、IL15、IL16、IL17A、IL17B、IL17C、IL17D、IL17F、IL18Aa、IL18Ba、IL18Ca、IL19、IL1A、IL1B、IL1F10、IL1RN、IL2、IL20、IL21、IL22、IL22F1a、IL22F2a、IL22F3a、IL22F4a、IL22F5a、IL23A、IL24、IL25、IL26、IL27、IL31、IL33、IL36L1a、IL36L2a、IL36RN、IL4、IL5、IL6、IL7、IL8或IL9等；所述集落刺激因子选自：CNTF、CSF1、CSF2、CSF3或CTF1等；所述干扰素选自：IFNA1、IFNA2、IFNA3、IFNA4、IFNB、IFNG、IFNL1或IFNL2等；所述肿瘤坏死因子选自：TNF、TNFα、TNFβ、LTA、LTB、TNFSF4、CD40LG、FASLG、CD70、TNFSF8、TNFSF9、TNFSF10、TNFSF10L、TNFSF11、TNFSF13La、TNFSF13B、TNFSF14、TNFSF15、TNFSF18或EDA等；所述趋化因子选自：CC family、CCL26、CCLD1a、CCLD2a、CCLD3a、CCLD4a、CCLD5a、CCLD6a、CCLD7a、CCLD8a、CCLD9a、CCLD10a、CCLD11a、CCLD12a、CCLD13a、CCLD14a、CCLD15a、CCLD16a、CCL17、CCL19、 CCL20、CCL21、CCL22、CCL25、CCL27、CCL28、CCL24、CXC family、CXCLD1a、CXCLD2a、CXCL8、CXCL9、CXCL10LAa、CXCL10LBa、CXCL11、CXCL12、CXCL13La、CXCL14、CXCL16、CXCL17、XC family、XCLAa、XCLBa、CX3C family或CX3CL1等；所述生长因子选自TGFB1、TGFB2、TGFB3或VEGFA等。
进一步的，基质金属蛋白酶选自：MMP-1、MMP-2、MMP-3、MMP-4、MMP-5、MMP-6、MMP-7、MMP-8、MMP-9或MMP-10等中的一种或两种以上；优选MMP-2、MMP-3、MMP-6或MMP-9中的一种或两种以上。
进一步的，所述炎症因子选自IL-1β、IL-6、IL-10、TNF-α、MMP-2和MMP-9中的一种或两种以上；
进一步的，所述炎症因子选自IL-1β、IL-6、IL-10和MMP-9中的一种或两种以上；
进一步优选炎症因子IL-1β、IL-10和MMP-9中的一种或两种以上。
进一步的，所述炎症因子的检测样品为体腔中的液体，所述体腔选自腹腔、盆腔、胸腔环境和/或脑部腔隙等；所述体腔中的液体选自腹腔、盆腔、胸腔环境中的液体和/或脑脊液；进一步的，所述液体为腔隙积液或引流得到的液体；进一步的，所述液体优选腹腔积液或腹腔引流液。
进一步的，所述炎症因子的检测部位是体腔内感染区域或其周围；进一步的，对于进行了手术的患者，例如进行了体腔手术的患者，所述炎症因子选自术后第0-15天的炎症因子；优选术后第0-7天的炎症因子，优选术后第0-5天的炎症因子，优选术后第0-3天的炎症分子；进一步优选术后第0天、第1天、第2天或第3天的炎症因子；进一步优选术后第3天的炎症因子。
本发明中所述对象为哺乳动物，包括人类、家畜、宠物、实验动物、等，其中人类包括各年龄段、各种性别特征的患者，进一步为首次手术或经多次手术的患者。进一步的，所述对象为体腔器官或组织相关疾病的患者，进一步为腹腔、盆腔、胸腔环境或颅腔相关疾病患者，进一步为腹腔、盆腔、胸腔环境或颅腔相关疾病的术后患者，进一步为消化系统、泌尿系统、生殖系统、呼吸系统或心血管系统等腹盆腔、胸腔脏器术后患者；进一步为消化道、肝、胆、胰、脾、肾、输尿管或膀胱等脏器相关疾病术后患者，或呼吸道、心脏术后患者；进一步为胃肠术后患者，进一步为经腹腔手术的患者，进一步的，所述患者为临床采集腹腔引流液的患者。
其中，消化系统疾病包括：消化道肿瘤、消化道炎症、消化道溃疡等消化道疾病；如慢性活动性胃炎、慢性萎缩性胃炎、胃溃疡、十二指肠溃疡、溃疡性结肠炎、炎性肠病、溃疡 性结肠炎、克隆恩氏病(、胶原性结肠炎、淋巴细胞性结肠炎、缺血性结肠炎、改道性结肠炎、贝塞特氏综合症、感染性结肠炎、未定型结肠炎、溃疡性结肠炎、家族性腺瘤性息肉病、先天性巨结肠症、肠道狭窄、直肠炎、直肠粘膜炎、结肠癌、直肠癌、瘘管、肠梗阻、机械性肠梗阻、麻痹性肠梗阻、消化道瘘、胰瘘、其他的非自然瘘道(包括直肠阴道瘘、直肠膀胱瘘、肠内瘘等)、缺血性肠坏死、盲肠癌、直肠乙状结肠癌、或胃癌等。
肝胆疾病包括：胆石症、胆囊炎、胆管炎、慢性肝炎或肝癌等；
胰腺疾病包括：胰腺炎、胰腺癌、胰瘘；肾脏：肾癌、肾炎、肾结石、肾盂肾炎或肾盂癌等；
脾脏疾病包括：脾梗死等；
膀胱疾病包括：膀胱癌等；
生殖系统疾病包括：子宫、卵巢疾病，如子宫内膜癌、卵巢癌、子宫肌瘤、子宫内膜异位症、腺肌症或巧克力囊肿等；阴道疾病，如直肠阴道瘘等；
腹腔疾病包括：感染性腹膜炎、自发性腹膜炎、结核性腹膜炎或无明确原因腹水等；
胸腔疾病包括：支气管瘘、肺炎、肺不张或胸腔积液等；
神经系统疾病包括：神经系统感染，如颅脑内感染，等。
本发明中所述体腔内感染性并发症选自腹腔、盆腔、胸腔或脑部腔隙感染性并发症，进一步的，涉及腹腔感染、腹腔积液、腹膜炎、腹腔脓肿、脓毒血症、吻合口漏、胰瘘、十二指肠残端瘘、其他消化道瘘、淋巴瘘、乳糜瘘等；进一步的，优选吻合口漏。
本发明中所述的临床因素有：患者生日、手术日期、年龄、性别、身高、体重、BMI、ASA评分、糖尿病、吸烟、酗酒、用药情况(降压药、降血脂药、皮质类固醇、抗凝药、非甾体类消炎药)、心脏病史、心血管症状、周围血管疾病、呼吸系统疾病史、呼吸系统症状、术前肠梗阻、新辅助放疗、新辅助放化疗、预防性抗生素使用。患者手术方式(腹腔镜/腹腔镜切除)、手术方式变更、变更原因、择期/急症手术、切除范围、吻合口(类型、初次，放置类型、部位、手工缝合)、手术适应症、手术时间、麻醉、术中并发症、失血量、入院时间、出院时间、住院周期、引流、造口(位置、类型)，漏气试验(包括试验结果)，手术医师(人数和专业度)，等。
本发明中，统计学方法包括本领域通常使用的方法，例如：符合正态分布的连续变量资 料可采用平均值±标准差记录，分类变量资料可采用数量及百分比的形式进行记录，正态分布连续变量组间比较可采用t检验，非正态分布连续变量组间比较可采用非参数检验，两组间比较采用Mann-Whitney检验，分类变量组间比较或单因素分析采用行×列χ2检验及Fisher精确检验，单因素有预测价值的因素(P＞0.1)纳入多因素分析。可采用Least Absolute Shrinkage and Selection Operator(LASSO)回归对术后腹腔引流液中炎症因子进行筛选、分析。分类变量的多因素分析可采用Logistic回归分析，结果用可列线图(Nomogram)表示。可用校准曲线评估列线图可靠性，可用决策曲线和临床影响曲线评估列线图临床应用价值。受试者工作特征曲线(receiver operating characteristic curve，ROC曲线)可用以衡量诊断指标的诊断效力。可将约登指数(Youden Index)最大值设为ROC曲线的最佳Cut-Off值，所有统计结果双侧P＜0.05认为有统计学意义。以上分析计算可运用SPSS 20.0统计软件、R语言“rms”和“rmda”程序包等完成。
为了进一步说明本发明早期诊断提前内感染性并发症的方法及其效果，特给出如下实施例，所述实施例仅是本发明方法的一种示例，并不费本发明保护主题及保护范围造成约束，在本发明思路范围内的其他等同技术也属于本发明的范畴。
实施例1：腹腔引流液中炎症因子的定量分析
1.材料
1.1患者条件及临床资料采集
患者条件：接受胃肿瘤手术，有腹腔引流液。
临床资料：患者生日、手术日期、年龄、性别、身高、体重、BMI、ASA评分、糖尿病、吸烟、酗酒、用药情况(降压药、降血脂药、皮质类固醇、抗凝药、非甾体类消炎药)、心脏病史、心血管症状、周围血管疾病、呼吸系统疾病史、呼吸系统症状、术前肠梗阻、新辅助放疗、新辅助放化疗、预防性抗生素使用。患者手术方式(腹腔镜/腹腔镜切除)、手术方式变更、变更原因、择期/急症手术、切除范围、吻合口(类型、初次，放置类型、部位、手工缝合)、手术适应症、手术时间、麻醉、术中并发症、失血量、入院时间、出院时间、住院周期、引流、造口(位置、类型)，漏气试验(包括试验结果)，手术医师(人数和专业度)，等。
术中信息采集：手术方式(腹腔镜/腹腔镜切除)、手术方式变更、变更原因、择期/急症手术、切除范围、吻合口(类型、初次，放置类型、部位、手工缝合)、手术适应症、手 术时间、麻醉、术中并发症、失血量、入院时间、出院时间、住院周期、引流、造口(位置、类型)，漏气试验(包括试验结果)，手术医师(人数和专业度)。
术后信息采集：常规实验室检验结果(白细胞，CRP等)、引流液形状、颜色、引流量、术后30日内生存情况，再入院情况、二次手术情况。
入组患者术后并发症登记：对入组患者术后并发症进行前瞻性登记，由研究者与临床医师独立登记并录入各种数据库，定期进行核对汇总。
登记内容及标准：将21项并发症纳入登记范畴，以Clavien-Dindo分级(CD分级)为标准对并发症严重程度进行分级，具体如表1所示。
表1胃肿瘤术后并发症诊断要点
Figure PCTCN2019121489-appb-000003
Figure PCTCN2019121489-appb-000004
1.2腹腔引流液标本采集与预处理
分别收集患者术后三天的腹腔引流液(包括术后当天至术后第3天)，每天固定时间点收集一次，每次收集20ml。腹腔引流液标本取样完成后于4℃、2800g条件下离心10分钟，将上清液和沉淀单独分装并保存于-80℃冰箱。
2.方法及步骤
选择腹腔引流液中具有代表性的炎症因子IL-1β、IL-6、IL-10、TNF-α、MMP2、MMP9的定量分析进行定量分析
使用多因子酶联免疫检测法，定量分析各因子的含量。其中IL-1β、IL-6、IL-10和TNF-α的量使用HSTCMAG-28SK(EMD Millipore公司，美国)试剂盒进行检测；MMP2和MMP9的量使用HMMP2MAG-55K(EMD Millipore公司，美国)试剂盒进行检测。
3.结果
3.1临床结果：胃肿瘤术后患者263例，其中男性209例，女性54例；中位年龄62(54-67)岁；其中，手术后30天内共81例患者被诊断有术后并发症，并发症发生率为30.8％。其中24例患者被诊断为感染性并发症，包括17例患者出现了吻合口漏，吻合口漏发生率为6.46％；此外共有5例患者存在两种以上感染性并发症；有1例患者在术后30天内死亡。
3.2胃肿瘤术后腹腔引流液中炎症因子的水平
胃肿瘤患者术后腹腔引流液中各炎症因子的浓度如图1所示，发生吻合口漏的患者术后第3天腹腔引流液中IL-1β、IL-10和MMP-9的水平明显高于其他患者，其差异具有统计学意义(表2)。
表2胃肿瘤术后腹腔引流液炎症因子定量分析结果
Figure PCTCN2019121489-appb-000005
Figure PCTCN2019121489-appb-000006
炎症因子浓度用中位数(四份位数)表示；差异有统计学意义用粗体标明(P<0.05)。
3.3胃肿瘤术后腹腔引流液中炎症因子水平的动态变化
胃肿瘤患者术后腹腔引流液中炎症因子水平呈现动态变化(图2)，其中吻合口漏患者腹腔引流液中IL-1β水平自术后第1天起持续升高(图2a)，而IL-10水平在术后早期阶段呈下降趋势，至术后第2天起再次升高(图2c)。腹腔引流液中MMP-9的含量隔天递减，但发生吻合口漏的患者其浓度在术后3天内均高于其他患者(图2f)。
实施例2：腹腔引流液中炎症因子对于吻合口漏的诊断价值
1.胃肿瘤术后腹腔引流液中单个炎症因子对于吻合口漏的诊断效能
采用ROC曲线评估腹腔引流液中单个炎症因子对于吻合口漏的诊断效力，各炎症因子在胃肿瘤术后3天内对于吻合口漏诊断的ROC分析结果如图3.1-3.4、表3-6所示。
表3胃肿瘤手术当天腹腔引流液中炎症因子诊断吻合口漏的ROC分析
Figure PCTCN2019121489-appb-000007
AUC曲线下面积；差异有统计学意义用粗体标明(P<0.05)。
表4胃肿瘤术后第1天腹腔引流液中炎症因子诊断吻合口漏的ROC分析
Figure PCTCN2019121489-appb-000008
Figure PCTCN2019121489-appb-000009
AUC曲线下面积；差异有统计学意义用粗体标明(P<0.05)。
表5胃肿瘤术后第2天腹腔引流液中炎症因子诊断吻合口漏的ROC分析
Figure PCTCN2019121489-appb-000010
AUC曲线下面积；差异有统计学意义用粗体标明(P<0.05)。
表6胃肿瘤术后第3天腹腔引流液中炎症因子诊断吻合口漏的ROC分析
Figure PCTCN2019121489-appb-000011
AUC曲线下面积；差异有统计学意义用粗体标明(P<0.05)。
曲线下面积AUC值越大，诊断效力越高，通常AUC大于0.8在临床上具有指导意义。通过对胃肿瘤术后腹腔引流液中单个炎症因子的诊断效能分析可见，其中IL-1β、IL-10和MMP-9在胃肿瘤术后第3天对吻合口漏的诊断效力最高，AUC分别为0.76(p<0.01)、0.77(p<0.01)和0.75(p<0.01)。TNF-α对吻合口漏的诊断效力在胃肿瘤术后第2天达到最高，其AUC为0.73(p<0.01)。IL-6和MMP-2对吻合口漏的诊断效力较低，在胃肿瘤术后3天内对吻合口漏诊断的最大AUC分别为0.62(p＝0.11)和0.59(p＝0.22)。以上结果也显示，对于胃肿瘤术后患者，单个炎症因子对吻合口漏的诊断效力未达到临床应用要求。
实施例3：吻合口漏评分系统的参数选择和构建
1.胃肿瘤术后吻合口漏评分系统的参数选择
为进一步优化胃肿瘤术后腹腔引流液中炎症因子对吻合口漏的诊断效力，我们采用Least Absolute Shrinkage and Selection Operator(LASSO)回归从胃肿瘤术后3天不同时间点的腹腔引流液中炎症因子进行筛选，挑选出最佳指标用以构建吻合口漏预测模型。从术后3天内不同时间节点共24项炎症因子指标中筛选出6项指标(图4a)，从手术当天6项炎症因子指标中筛选出2项指标(图4b)，从术后第1天6项炎症因子指标中筛选出2项指标(图4c)，从术后第2天6项炎症因子指标中筛选出2项指标(图4d)，从术后第3天6项炎症因子指标中筛选出3项指标(图4e)，分别以上述指标作为参数建立吻合口漏评分系统，胃肿瘤术后不同时间节点的吻合口漏评分计算模型如表7所示。
表7胃肿瘤术后各时间节点吻合口漏评分模型
Figure PCTCN2019121489-appb-000012
D0手术当天；D1术后1天；D2术后第2天；D3术后第3天。
2.吻合口漏评分系统对于胃肿瘤术后吻合口漏的诊断效能
采用ROC曲线分析吻合口漏评分对于胃肿瘤术后吻合口漏的诊断效力，不同时间节点的吻合口漏评分对于吻合口漏诊断的ROC分析结果如图5、表8所示。术后第3天吻合口漏评分对于吻合口漏的诊断效力最高，其AUC为0.87(p<0.01)。
表8胃肿瘤术后各时间节点吻合口漏评分诊断吻合口漏的ROC分析
Figure PCTCN2019121489-appb-000013
AUC曲线下面积；差异有统计学意义用粗体标明(P<0.05)。
3.术后第3天吻合口漏评分Cut-Off值的设定和对吻合口漏诊断的效力
选取ROC分析中约登指数最大值时所对应分值-2.801作为胃肿瘤术后第3天吻合口漏评分的Cut-Off值，将胃肿瘤术后患者分为高分组和低分组。胃肿瘤术后第3天吻合口漏评分的分布如图6、图7所示，Cut-Off值对于胃肿瘤术后患者的区分作用如表9所示。
由此，以-2.801作为术后第3天吻合口漏评分的Cut-Off值，将所有患者分为吻合口漏高危组和低危组，其中低危组181例患者中共2人发生了吻合口漏，而高危组43例患者中共11人发生了吻合口漏，另外有39例患者因缺少术后第3天吻合口漏评分计算所需参数而未被划入高危组或低危组，其中包含4例吻合口漏患者。与临床实际结果比较，本发明评分方法的敏感性和特异性都超过了80％，阴性预测值更是高达98.5％。
表9术后第3天吻合口漏评分对吻合口漏的诊断效能
Figure PCTCN2019121489-appb-000014
实施例4：术后吻合口漏评分系统的临床验证
通过临床时间检验吻合口漏评分系统和危险等级评价系统的可靠性和应用价值。
入组条件和诊断标准同前。共入组66例胃肿瘤手术病人，其中男性48人，女性18人；中位数年龄57岁；共有7人被诊断有吻合口漏。
采用实例3中的第三天吻合口漏评分模型：-3.10+0.000241×IL-1β(D3)+0.00183×IL-10(D3)+0.000000853×MMP-9(D3)，对66例患者进行评分，并根据ROC分析中约登指数最大值时所对应分值-2.801作为胃肿瘤术后第3天吻合口漏评分的Cut-Off值，诊断的AUC为0.83(p<0.01)，使用训练队列所得Cut-Off值将胃肿瘤术后病人分为高分组和低分组。
66人中共有17人被评为吻合口漏高分组，其中有6人发生吻合口漏，有49人被评为吻合口漏低分组，其中有48人未发生吻合口漏。诊断的准确率、特异性、敏感性等如表10所示。
表10吻合口漏评分系统的诊断效力分析结果
Figure PCTCN2019121489-appb-000015
实施例5：构建吻合口漏风险概率模型
为了使炎症因子对吻合口漏的诊断效果更加贴近临床实际，发明人进一步将胃肿瘤手术患者的临床特征与吻合口漏评分结果相结合，继而采用Logistic回归构建了包含吻合口漏评分结果和临床危险因素的吻合口漏风险概率模型。通过构建吻合口漏风险概率模型，借助列线图，计算出患者出现吻合口漏的概率值，并比照决策曲线决定是否采取相应临床干预。
1.收集患者临床基本资料
术前信息采集：生日、手术日期、年龄、性别、身高、体重、BMI、ASA评分、糖尿病、吸烟、酗酒、用药情况(降压药、降血脂药、皮质类固醇、抗凝药、非甾体类消炎药)、心脏病史、心血管症状、周围血管疾病、呼吸系统疾病史、呼吸系统症状、术前肠梗阻、新辅助放疗、新辅助放化疗、预防性抗生素使用。
术中信息采集：手术方式(腹腔镜/腹腔镜切除)、手术方式变更、变更原因、择期/急症手术、切除范围、吻合口(类型、初次，放置类型、部位、手工缝合)、手术适应症、手术时间、麻醉、术中并发症、失血量、入院时间、出院时间、住院周期、引流、造口(位置、类型)，漏气试验(包括试验结果)，手术医师(人数和专业度)。
术后信息采集：常规实验室检验结果(白细胞，CRP等)、引流液形状、颜色、引流量、术后30日内生存情况，再入院情况、二次手术情况。
2.临床危险因素探究
共入组胃肿瘤术后患者263例，其中男性209例，女性54例；中位年龄62(54-67)岁；中位体质指数(Body Mass Index,BMI)为24(22-26)kg/m2；患者具体基线信息、ASA评分、手术信息等如表11所示。其中，手术后30天内共81例患者被诊断有术后并发症，并发症发生率为30.8％。其中24例患者被诊断为感染性并发症，包括17例患者出现了吻合口漏，吻合口漏发生率为6.46％；此外共有5例患者存在两种以上感染性并发症(表12)；有1例患者在术后30天内死亡。
表11胃肿瘤术后患者临床资料
Figure PCTCN2019121489-appb-000016
Figure PCTCN2019121489-appb-000017
年龄及BMI用中位数(四份位数)表示；差异有统计学意义用粗体标明(P<0.05)；*Mann-Whitney U检验。
Figure PCTCN2019121489-appb-000018
包括Billroth-II式以及Uncut Roux-en-Y式
表12胃肿瘤术后感染性并发症登记结果
Figure PCTCN2019121489-appb-000019
在此基础上，通过单因素分析发现年龄(p＝0.02)、肿瘤位置(p<0.01)、切除范围(p<0.01) 以及吻合方式(p<0.01)与胃肿瘤术后并发症的发生有关；对于感染性并发症，其与年龄(p＝0.01)、肿瘤位置(p<0.01)、切除范围(p＝0.04)以及吻合方式(p＝0.05)有关；而吻合口漏则与年龄(p＝0.03)、肿瘤位置(p<0.01)、手术方式(p＝0.01)、切除范围(p<0.01)和吻合方式(p<0.01)有关。将上述因素纳入多因素分析，结果表明年龄(p＝0.03)和吻合方式(p＝0.03)是胃肿瘤术后并发症的独立危险因素；年龄(p＝0.03)肿瘤位置(p＝0.01)以及切除范围(p＝0.04)是感染并发症的独立危险因素；肿瘤位置(p＝0.02)和手术方式(p<0.01)是吻合口漏的独立危险因素，具体如表13所示。
表13胃肿瘤术后并发症独立危险因素分析
Figure PCTCN2019121489-appb-000020
差异有统计学意义用粗体标明(P<0.05)；“-”不适用；
Figure PCTCN2019121489-appb-000021
包括Billroth-II式以及Uncut Roux-en-Y式。
3.构建吻合口漏风险概率模型
通过Logistic回归建立胃肿瘤术后吻合口漏风险概率模型，纳入的自变量包括吻合方式、 手术方式、肿瘤部位以及术后第3天吻合口漏评分。通过列线图呈现模型预测结果。
Figure PCTCN2019121489-appb-000022
胃肿瘤术后吻合口漏风险预测列线图
4.吻合口漏风险概率模型的可靠性评估
通过校正曲线对吻合口漏风险概率模型可靠性进行评估，平均绝对误差(Mean Absolute Error)为0.016(图8)，可见模型的预测风险概率和实际观测概率有较好的一致性。
5.吻合口漏风险概率模型的临床应用价值评估
将吻合方式、手术方式和肿瘤部位3项临床指标纳入Logistic回归建立胃肿瘤术后吻合口漏临床危险因素评估模型。
通过ROC分析，比较吻合口漏风险概率模型与临床危险因素评估模型的诊断效能(图9)。
其中，吻合口漏风险概率模型的AUC为0.93(p<0.01)，临床危险因素评估模型AUC为0.86(p<0.01)。采用决策曲线比较吻合口漏风险概率模型与临床危险因素评估模型的临床获益情况，结果表明，吻合口漏风险概率模型的净获益在风险阈值<0.7时，均高于临床危险因素评估模型，如图10a、图10b、图10c。
由此可见，综合相比单由临床危险因素构建的临床危险因素评估模型来说，纳入术后第3天吻合口漏评分可以提高模型的诊断效力。从决策曲线分析结果来看，吻合口漏风险阈值在5％～70％的范围内时，根据吻合口漏风险概率模型决定是否采取临床干预的净获益都要高于临床危险因素评估模型。
以上显示和描述了本发明的基本原理和主要特征，而非用于限制本发明。本领域技术人 员应该了解，在不脱离本发明构思和范围的前提下，举凡所属技术领域中具有通常知识者在未脱离本发明所揭示的精神与技术思想下所完成的一切等效修饰或改变，仍应由本发明的权利要求所涵盖。

Claims (12)

  1. 一种用于早期诊断体腔内感染性并发症的标记物，其特征在于：所述诊断标记物包括一系列炎症因子，所述炎症因子选自细胞因子、基质金属蛋白酶、活性氧簇、血管内皮生长因子、组织金属蛋白酶抑制因子、C反应蛋白、白细胞计数等中的一种或两种以上的组合,优选所述炎症因子为细胞因子、基质金属蛋白酶中的一种或两种以上的组合,优选所述细胞因子选自：白细胞介素、集落刺激因子、干扰素、肿瘤坏死因子、趋化因子或生长因子中的一种或两种以上，进一步优选所述炎症因子选自：白细胞介素、干扰素、肿瘤坏死因子或基质金属蛋白酶中的一种或两种以上，更进一步优选所述炎症因子选自：IL-1β、IL-6、IL-10、TNF-α、MMP-2和MMP-9中的一种或两种以上，优选为IL-1β、IL-6、IL-10和MMP-9中的一种或两种以上，进一步优选IL-1β、IL-10和MMP-9中的一种或两种以上。
  2. 根据权利要求1所述的用于早期诊断体腔内感染性并发症的标记物组合物，其特征在于：所述炎症因子来源于体腔内的液体，如腹腔、盆腔、胸腔环境或脑部腔隙中的液体，优选所述炎症因子来源于腹腔、盆腔、胸腔环境中的液体或脑脊液，进一步优选所述炎症因子来源于腔隙积液或引流得到的液体，优选腹腔积液或腹腔引流液。
  3. 根据权利要求1-2任一项所述的用于早期诊断体腔内感染性并发症的标记物组合物，其特征在于：所述炎症因子为术后第0-15天的炎症因子；优选术后第0-7天的炎症因子；优选术后第0-5天的炎症因子；优选术后第0-3天的炎症因子，进一步优选所述炎症因子为术后第0天、第1天、第2天或第3天的炎症因子。
  4. 根据权利要求1-3所述的用于早期诊断体腔内感染性并发症的标记物组合物，其特征在于：所述体腔内感染性并发症选自腹腔、盆腔、胸腔或脑部腔隙感染性并发症，进一步优选所述体腔内感染性并发症选自腹腔感染、腹腔积液、腹膜炎、腹腔脓肿、脓毒血症、吻合口漏、胰瘘、十二指肠残端瘘、其他消化道瘘、淋巴瘘上帝上帝或乳糜瘘，进一步优选所述体腔内感染性并发症为吻合口漏。
  5. 一种用于早期诊断体腔内感染性并发症的试剂盒，其特征在于：包含用于检测权利要求1-4中任一项所述标记物组合物的检测试剂。
  6. 一种权利要求1-4任一项所述标记物在用于制备早期诊断体腔内感染性并发症的检测试剂或检测试剂盒中的用途，优选所述试剂盒包含用于检测所述标记物的检测试剂或检测装置。
  7. 一种用于检测对象体腔内感染性并发症的评分方法，其特征在于，包括如下步骤：
    1)检测来自对象样品的权利要求1-4所述标记物及其含量，和
    2)将步骤1测定标记物的含量经模型1进行计算，获得一数值Y，即体腔内感染性并发症 评分；
    其中所述统计学模型1为：Y＝X1β1+X2β2+……+Xnβn+ε，
    其中Y是体腔内感染性并发症评分分值，X是指某个时间点的某个标记物的含量，β是系数，为相应变量X在体腔内感染性并发症评分中的权重，ε是常数，优选所述统计学方法优选LASSO回归，进一步优选所述对象为体腔器官或组织相关疾病的患者，优选所述对象为腹腔、盆腔、胸腔环境或颅腔相关疾病的术后患者，进一步优选所述对象为胃肠术后患者，如经腹腔手术的患者，更进一步优选所述对象为临床采集腹腔引流液的患者。
  8. 一种用于诊断对象体腔内感染性并发症的标记物的筛选方法，其特征在于，包括如下步骤：
    1)检测来自对象样品的权利要求1-4所述标记物及其含量，和
    2)将步骤1测定标记物的含量经模型1进行统计学分析；
    其中，所述统计学模型1为：Y＝X1β1+X2β2+……+Xnβn+ε，Y是体腔内感染性并发症评分分值，X是指某个时间点的某个标记物的含量，β是系数，为相应变量X在体腔内感染性并发症评分中的权重，ε是常数；
    其中，所述统计学分析方法优选LASSO回归，回归得到的模型1中保留的X1…Xn即为筛选得出的所述诊断标记物，优选所述对象为体腔器官或组织相关疾病的患者，进一步优选所述对象为腹腔、盆腔、胸腔环境或颅腔相关疾病的术后患者，更进一步优选所述对象为胃肠术后患者，如经腹腔手术的患者，最优选所述对象为临床采集腹腔引流液的患者。
  9. 一种用于检测对象中发生体腔内感染性并发症危险等级的方法，其特征在于，包括如下步骤：
    1)检测来自对象样品的权利要求1-4所述标记物及其含量，和
    2)将步骤1测定标记物的含量经模型1进行计算，获得一数值Y；
    其中所述统计学模型1为：Y＝X1β1+X2β2+………+Xnβn+ε，
    其中Y是体腔内感染性并发症评分分值，X是指某个时间点的某个标记物的含量，β是系数，为相应变量X在体腔内感染性并发症评分中的权重，ε是常数；
    其中，通过统计学方法确定β和ε的值，所述统计学方法优选LASSO回归；
    3)将所述计算得到的数值Y与相应的参考值进行比较，当Y大于参考值，则体腔内感染性并发症危险等级相对较高；当Y小于等于参考值，则体腔内感染性并发症危险等级相对较低，优选所述对象为体腔器官或组织相关疾病的患者，进一步优选所述对象为腹腔、盆腔、胸腔环境或颅腔相关疾病的术后患者，更进一步优选所述对象为胃肠术后患者，如经腹腔手术的 患者，最优选所述对象为临床采集腹腔引流液的患者。
  10. 一种用于检测对象中发生体腔内感染性并发症风险概率的方法，其特征在于，包括如下步骤：
    1)检测来自对象样品的权利要求1-4所述标记物及其含量，和
    2)将步骤1测定标记物的含量经模型1进行计算，获得一数值Y；
    其中所述统计学模型1为：Y＝X1β1+X2β2+………+Xnβn+ε，
    其中Y是体腔内感染性并发症评分分值，X是指某个时间点的某个标记物的含量，β是系数，为相应变量X在体腔内感染性并发症评分中的权重，ε是常数；
    其中，通过统计学方法确定β和ε的值，所述统计学方法优选LASSO回归；
    3)记录、分析相关临床因素；
    4)通过模型2分析计算，获得体腔内感染性并发症发生风险概率p；
    其中，所述模型2为：
    Figure PCTCN2019121489-appb-100001
    采用Logistic回归建立，其中，自变量x 1，x 2…x n指各种临床因素以及体腔内感染性并发症评分分值等指标，其中，w为系数或权重，x为自变量即纳入公式的指标，计算得出g(x)的值，从而得出体腔内感染性并发症发生风险概率p，优选所述对象为体腔器官或组织相关疾病的患者，进一步优选所述对象为腹腔、盆腔、胸腔环境或颅腔相关疾病的术后患者，更进一步优选所述对象为胃肠术后患者，如经腹腔手术的患者，最优选所述对象为临床采集腹腔引流液的患者。
  11. 一种用于早期诊断体腔内感染性并发症的装置，其特征在于：所述装置包括分析单元1、分析单元2和分析单元3，其中
    所述分析单元1用于检测权利要求1-4所述标记物及其相应含量；
    所述分析单元2用于将分析单元1中得到的一个或多个测定量通过模型1获得分析计算结果Y；
    所述分析单元3用于将分析单元2中的计算结果Y与相应的参考量值进行比较，得到体腔内感染性并发症的危险等级；
    优选地，进一步包括分析单元4和/或分析单元5，其中：
    分析单元4用于记录、分析相关临床因素；
    分析单元5用于将分析单元3得到的危险等级与分析单元4中临床因素结合，通过模型2 分析计算，获得体腔内感染性并发症发生风险概率p；
    所述各分析单元均包含相应的计算机执行的算法，优选所述对象为体腔器官或组织相关疾病的患者，进一步优选所述对象为腹腔、盆腔、胸腔环境或颅腔相关疾病的术后患者，更进一步优选所述对象为胃肠术后患者，如经腹腔手术的患者，最优选所述对象为临床采集腹腔引流液的患者。
  12. 一种用于早期诊断体腔内感染性并发症的诊断方法，其特征在于，包括如下步骤：
    1)检测来自对象样品的权利要求1-4所述标记物及其含量，和
    2)将步骤1测定标记物的含量经模型1进行计算，获得一数值Y；
    其中所述统计学模型1为：Y＝X1β1+X2β2+………+Xnβn+ε，
    其中Y是体腔内感染性并发症评分分值，X是指某个时间点的某个标记物的含量，β是系数，为相应变量X在体腔内感染性并发症评分中的权重，ε是常数；
    其中，通过统计学方法确定β和ε的值，所述统计学方法优选LASSO回归；
    3)将所述计算得到的数值Y与相应的参考值进行比较，当Y大于参考值，则为体腔内感染性并发症高危组；当Y小于等于参考值，则为体腔内感染性并发症低危组，优选所述对象为体腔器官或组织相关疾病的患者，进一步优选所述对象为腹腔、盆腔、胸腔环境或颅腔相关疾病的术后患者，更进一步优选所述对象为胃肠术后患者，如经腹腔手术的患者，最优选所述对象为临床采集腹腔引流液的患者。
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